Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
Hybrid deep learning model using recurrent neural network and gated recurrent...IJECEIAES
This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.
Development of durian leaf disease detection on Android device IJECEIAES
Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponent’s objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf.
Multipurpose medical assistant robot (Docto-Bot) based on internet of things IJECEIAES
The world's population is growing every day, and so is the number of patients. People's life expectancy is increasing due to technology's welfare, but the problem is that the health sector has always faced a shortage of inadequate doctors. This research main objective was to design and implement a biomedical-based medical assistant robot named "Docto-Bot" to deal with this problem. This research concerns this medical assistant robot's design and development for the disabled and the patients in need. Such a robot's prime utilization is to minimize person-to-person contact and ensure the cleaning, sterilization, and support in hospitals and similar facilities such as quarantine. This prototype robot consists of a medicine reminding and medicine providing system, Automatic hand sanitizer and IoT based physiological monitoring system (body temperature, pulse rate, ECG, Oxygen saturation level). A direct one-to-one server-based communication method and user-end android app maintaining system designed. It also included the controlling part, which control automatically and manually by users. Docto-Bot will play a very significant factor in bio-medical robot applications. Though the achievements described in the paper look fruitful and advanced, shortcomings still exist.
A one decade survey of autonomous mobile robot systems IJECEIAES
Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.
ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE MODELING OF A 3G MOBILE PHONE BASE...ijaia
The principal objective of this work is to be able to use artificial intelligence techniques to be able to
design a predictive model of the performance of a third-generation mobile phone base radio, using the
analysis of KPIs obtained in a statistical data set of the daily behaviour of an RBS. For the realization of
these models, various techniques such as Decision Trees, Neural Networks and Random Forest were used.
which will allow faster progress in the deep analysis of large amounts of data statistics and get better
results. In this part of the work, data was obtained from the behaviour of a third-party mobile phone base
radio generation of the Claro operator in Ecuador, it should be noted that. To specify this practical case,
several models were generated based on in various artificial intelligence technique for the prediction of
performance results of a mobile phone base radio of third generation, the same ones that after several tests
were creation of a predictive model that determines the performance of a mobile phone base radio. As a
conclusion of this work, it was determined that the development of a predictive model based on artificial
intelligence techniques is very useful for the analysis of large amounts of data in order to find or predict
complex results, more quickly and trustworthy. The data are KPIs of the daily and hourly performance of a
radio base of third generation mobile telephony, these data were obtained through the operator's remote
monitoring and management tool Sure call PRS.
IRJET- Design an Approach for Prediction of Human Activity Recognition us...IRJET Journal
The document proposes a framework for human activity recognition using smartphones. It involves collecting data from a smartphone's accelerometer and gyroscope sensors worn on the waist during various activities of daily living. The data is preprocessed and classified using machine learning algorithms like Naive Bayes, logistic regression, and SVM. The proposed framework first loads and preprocesses the sensor data, then generates features before splitting the data into training and test sets. Various classifiers are applied and evaluated to select the best performing one for activity recognition. The authors conclude that implementing tri-axial acceleration from sensors provides different accuracy for different algorithms, with SVM achieving maximum accuracy in previous work.
Finding new framework for resolving problems in various dimensions by the use...Alexander Decker
This document provides an overview of expert systems, including their components, development lifecycle, applications, advantages, and limitations. It describes the basic modules of an expert system including the knowledge acquisition subsystem, knowledge base, inference engine, explanation subsystem, and user interface. It also discusses expert system tools, characteristics, and some examples of expert system applications in domains like monitoring, diagnosis, design, and more. Overall, the document presents a broad introduction to expert systems, their architecture and uses.
Multi-objective NSGA-II based community detection using dynamical evolution s...IJECEIAES
Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.
Hybrid deep learning model using recurrent neural network and gated recurrent...IJECEIAES
This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.
Development of durian leaf disease detection on Android device IJECEIAES
Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponent’s objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf.
Multipurpose medical assistant robot (Docto-Bot) based on internet of things IJECEIAES
The world's population is growing every day, and so is the number of patients. People's life expectancy is increasing due to technology's welfare, but the problem is that the health sector has always faced a shortage of inadequate doctors. This research main objective was to design and implement a biomedical-based medical assistant robot named "Docto-Bot" to deal with this problem. This research concerns this medical assistant robot's design and development for the disabled and the patients in need. Such a robot's prime utilization is to minimize person-to-person contact and ensure the cleaning, sterilization, and support in hospitals and similar facilities such as quarantine. This prototype robot consists of a medicine reminding and medicine providing system, Automatic hand sanitizer and IoT based physiological monitoring system (body temperature, pulse rate, ECG, Oxygen saturation level). A direct one-to-one server-based communication method and user-end android app maintaining system designed. It also included the controlling part, which control automatically and manually by users. Docto-Bot will play a very significant factor in bio-medical robot applications. Though the achievements described in the paper look fruitful and advanced, shortcomings still exist.
A one decade survey of autonomous mobile robot systems IJECEIAES
Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.
ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE MODELING OF A 3G MOBILE PHONE BASE...ijaia
The principal objective of this work is to be able to use artificial intelligence techniques to be able to
design a predictive model of the performance of a third-generation mobile phone base radio, using the
analysis of KPIs obtained in a statistical data set of the daily behaviour of an RBS. For the realization of
these models, various techniques such as Decision Trees, Neural Networks and Random Forest were used.
which will allow faster progress in the deep analysis of large amounts of data statistics and get better
results. In this part of the work, data was obtained from the behaviour of a third-party mobile phone base
radio generation of the Claro operator in Ecuador, it should be noted that. To specify this practical case,
several models were generated based on in various artificial intelligence technique for the prediction of
performance results of a mobile phone base radio of third generation, the same ones that after several tests
were creation of a predictive model that determines the performance of a mobile phone base radio. As a
conclusion of this work, it was determined that the development of a predictive model based on artificial
intelligence techniques is very useful for the analysis of large amounts of data in order to find or predict
complex results, more quickly and trustworthy. The data are KPIs of the daily and hourly performance of a
radio base of third generation mobile telephony, these data were obtained through the operator's remote
monitoring and management tool Sure call PRS.
IRJET- Design an Approach for Prediction of Human Activity Recognition us...IRJET Journal
The document proposes a framework for human activity recognition using smartphones. It involves collecting data from a smartphone's accelerometer and gyroscope sensors worn on the waist during various activities of daily living. The data is preprocessed and classified using machine learning algorithms like Naive Bayes, logistic regression, and SVM. The proposed framework first loads and preprocesses the sensor data, then generates features before splitting the data into training and test sets. Various classifiers are applied and evaluated to select the best performing one for activity recognition. The authors conclude that implementing tri-axial acceleration from sensors provides different accuracy for different algorithms, with SVM achieving maximum accuracy in previous work.
Finding new framework for resolving problems in various dimensions by the use...Alexander Decker
This document provides an overview of expert systems, including their components, development lifecycle, applications, advantages, and limitations. It describes the basic modules of an expert system including the knowledge acquisition subsystem, knowledge base, inference engine, explanation subsystem, and user interface. It also discusses expert system tools, characteristics, and some examples of expert system applications in domains like monitoring, diagnosis, design, and more. Overall, the document presents a broad introduction to expert systems, their architecture and uses.
Multi-objective NSGA-II based community detection using dynamical evolution s...IJECEIAES
Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.
DATA AUGMENTATION TECHNIQUES AND TRANSFER LEARNING APPROACHES APPLIED TO FACI...ijaia
The face expression is the first thing we pay attention to when we want to understand a person’s state of
mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research
field. In this paper, because the small size of available training datasets, we propose a novel data
augmentation technique that improves the performances in the recognition task. We apply geometrical
transformations and build from scratch GAN models able to generate new synthetic images for each
emotion type. Thus, on the augmented datasets we fine tune pretrained convolutional neural networks with
different architectures. To measure the generalization ability of the models, we apply extra-database
protocol approach, namely we train models on the augmented versions of training dataset and test them on
two different databases. The combination of these techniques allows to reach average accuracy values of
the order of 85% for the InceptionResNetV2 model.
MITIGATION TECHNIQUES TO OVERCOME DATA HARM IN MODEL BUILDING FOR MLijaia
Given the impact of Machine Learning (ML) on individuals and the society, understanding how harm might
be occur throughout the ML life cycle becomes critical more than ever. By offering a framework to
determine distinct potential sources of downstream harm in ML pipeline, the paper demonstrates the
importance of choices throughout distinct phases of data collection, development, and deployment that
extend far beyond just model training. Relevant mitigation techniques are also suggested for being used
instead of merely relying on generic notions of what counts as fairness.
Mobile based Automated Complete Blood Count (Auto-CBC) Analysis System from B...IJECEIAES
Blood cells diagnosis is becoming essential to ensure a proper treatment can be proposed to a blood related disease patient. In current research trending, automated complete blood count analysis system is required for pathologists or researchers to count the blood cells from the blood smeared images. Hence, a portable mobile-based complete blood count (CBC) analysis framework with the aid of microscope is proposed, and the smartphone camera is mounted to the viewing port of the light microscope by adding a smartphone support. Initially, the blood smeared image is acquired from a light microscope with objective zoom of 100X magnifications view the eyepiece zoom of 10X magnification, then captured by the smartphone camera. Next, the areas constitute to the WBC and RBC are extracted using combination of color space analysis, threshold and Otsu procedure. Then, the number of corresponding cells are counted using topological structural analysis, and the cells in clumped region is estimated using Hough Circle Transform (HCT) procedure. After that, the analysis results are saved in the database, and shown in the user interface of the smartphone application. Experimental results show the developed system can gain 92.93% accuracy for counting the RBC whereas 100% for counting the WBC.
Application of VLSI In Artificial IntelligenceIOSR Journals
This document discusses the application of VLSI (Very Large Scale Integrated) circuits in artificial intelligence. It begins with a brief history of the development of microelectronics and integrated circuits. It then provides definitions of artificial intelligence and describes how VLSI technology has enabled more powerful computer architectures for AI. The document focuses on how expert systems, which apply reasoning to knowledge bases, have been important early applications of AI to VLSI chip design. It provides examples of expert systems used for tasks like circuit simulation and assisting with VLSI design. In closing, it emphasizes that knowledge-based approaches using rules have advantages for incremental improvements and explaining reasoning.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
DEEP-LEARNING-BASED HUMAN INTENTION PREDICTION WITH DATA AUGMENTATIONijaia
Data augmentation has been broadly applied in training deep-learning models to increase the diversity of
data. This study ingestigates the effectiveness of different data augmentation methods for deep-learningbased human intention prediction when only limited training data is available. A human participant pitches
a ball to nine potential targets in our experiment. We expect to predict which target the participant pitches
the ball to. Firstly, the effectiveness of 10 data augmentation groups is evaluated on a single-participant
data set using RGB images. Secondly, the best data augmentation method (i.e., random cropping) on the
single-participant data set is further evaluated on a multi-participant data set to assess its generalization
ability. Finally, the effectiveness of random cropping on fusion data of RGB images and optical flow is
evaluated on both single- and multi-participant data sets. Experiment results show that: 1) Data
augmentation methods that crop or deform images can improve the prediction performance; 2) Random
cropping can be generalized to the multi-participant data set (prediction accuracy is improved from 50%
to 57.4%); and 3) Random cropping with fusion data of RGB images and optical flow can further improve
the prediction accuracy from 57.4% to 63.9% on the multi-participant data set.
Role of Advanced Machine Learning Techniques and Deep Learning Approach Based...ijtsrd
Accurate Medical diagnosis is not always possible at every medical center, especially in the Developing Countries where poor healthcare services and lack of advanced diagnostic methods and equipments affects procedures of medical diagnosis .Also, physician intuition and experience are not always sufficient to achieve high quality medical procedures results. Therefore, diagnostic errors and undesirable results are reasons for a need for Machine Learning Techniques based decision support system, which in turns reduce diagnostic errors, increasing the patient safety and save lives. This research focuses on this aspect of Medical diagnosis by learning pattern through the collected dataset of respiratory diseases such as pneumonia and Covid 19, also consist implementation and test of intelligent medical decision support system to assist physicians and radiologists can deliver great assistance by improving their decision making ability. In this Research paper, the proposed System use Neural network Resnet 50 and transfer learning technique to classify these severe diseases and performs evaluation of precision, accuracy, speci city of Decision support system. Patel Smitkumar Hareshbhai "Role of Advanced Machine Learning Techniques and Deep Learning Approach Based Decision Support System for Accurate Diagnosis of Severe Respiratory Diseases" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47655.pdf Paper URL : https://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/47655/role-of-advanced-machine-learning-techniques-and-deep-learning-approach-based-decision-support-system-for-accurate-diagnosis-of-severe-respiratory-diseases/patel-smitkumar-hareshbhai
A new system to detect coronavirus social distance violation IJECEIAES
This document proposes a new system to detect social distance violations using a smartphone. The system uses two Android applications - one uses the phone's camera to detect faces and estimate distances during calls, and one uses voice biometrics to differentiate the user's voice from others. Both applications perform real-time processing without collecting or sharing private user data. The system aims to help prevent the spread of COVID-19 by notifying users if social distancing guidelines are violated.
BEHAVIOR-BASED SECURITY FOR MOBILE DEVICES USING MACHINE LEARNING TECHNIQUESijaia
The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of
mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.
Automatic Selection of Open Source Multimedia Softwares Using Error Back-Prop...IJERA Editor
Open source opens a new era to provide license of the software for the user at free of cost which is advantage over paid licensed software. In Multimedia applications there are many versions of software are available and there is a problem for the user to select compatible software for their own system. Most of the time while surfing for software a huge list of software opens in response. The selection of particular software which is pretty suitable for the system from a real big list is the biggest challenge that is faced by the users. This work has been done that focuses on the existing open source software that are widely used and to design an automatic system for selection of particular open source software according to the compatibility of users own system. In this work, error back-propagation based neural network is designed in MATLAB for automatic selection of open source software. The system provides the open source software name after taking the information from user. Regression coefficient of 0.93877 is obtained and the results shown are up to the mark and can be utilized for the fast and effective software search.
NLP-based personal learning assistant for school education IJECEIAES
Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slackbased UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chatbot’s NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future.
PREDICTIVE MAINTENANCE AND ENGINEERED PROCESSES IN MECHATRONIC INDUSTRY: AN I...ijaia
This document summarizes a case study on implementing predictive maintenance processes in a mechatronic industry using machine learning algorithms. A company installed sensors on a cutting machine to monitor blade status in real-time. A software platform was developed to analyze sensor data using k-Means clustering and LSTM algorithms to predict blade break conditions. The platform classified risk maps and predicted alert levels based on recent variable values. This approach aimed to optimize maintenance and reduce machine downtime for customers.
A comparative analysis of data mining tools for performance mapping of wlan dataIAEME Publication
This document compares the performance of different data mining tools for anomaly detection in wireless network data. It analyzes four tools: Weka, SPSS, Tanagra, and Microsoft SQL Server's Business Intelligence Development Studio. The same wireless network log data with 1000 instances and 13 attributes is clustered into 3 groups (normal activities, suspicious activities, anomalous activities) using different unsupervised learning algorithms in each tool. The results from each tool are different due to using different distance measures and clustering algorithms. The paper aims to interpret the results from each tool and determine which provides the most accurate performance mapping for the wireless network data.
June 2020: Top Read Articles in Advanced Computational Intelligenceaciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
This paper proposes a new method for fingerprint classification based on orientation field features extracted using a pixel-wise gradient descent method. The orientation field is used to estimate the percentage of directional block classes in four dimensions, which along with singular point information forms a feature vector for classification. A support vector machine classifier is used and shown to achieve high accuracy compared to other spatial domain classifiers. The method extracts discriminative features from the orientation field to classify fingerprints into one of five classes.
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
Shot-Net: A Convolutional Neural Network for Classifying Different Cricket ShotsMohammad Shakirul islam
This document describes a convolutional neural network called Shot-Net that was developed to classify different types of cricket shots. The document provides an overview of related work on sports activity recognition and cricket shot classification. It then describes the proposed Shot-Net methodology, including the dataset used, data preprocessing steps, model architecture, training process, and evaluation of the model's performance through classification reports and confusion matrices. The document concludes by discussing the model's results and proposing areas for future work, such as enriching the dataset and developing applications.
IRJET- Breast Cancer Prediction using Deep LearningIRJET Journal
This document discusses using deep learning to predict breast cancer based on tumor data. It proposes using a neural network model to classify tumors as malignant or benign. The key steps are:
1. Collecting and preprocessing tumor cell data to remove noise and inconsistencies.
2. Developing a neural network model and training it on labeled training data to learn patterns.
3. Testing the trained model on unlabeled testing data to evaluate its accuracy in classifying tumors.
The goal is to develop an accurate model to help doctors determine the critical condition of patients and classify difficult tumors.
IRJET- Kidney Stone Classification using Deep Neural Networks and Facilitatin...IRJET Journal
The document discusses classifying kidney stone images using deep neural networks and facilitating diagnosis using IoT. Kidney stone images are acquired and preprocessed by converting to grayscale, enhancing, and segmenting the area of interest. Texture features are extracted using active contour segmentation and classified using a deep neural network model. The results, including stone type and treatment recommendations, are sent to the cloud where doctors and patients can access them, allowing automated diagnosis without human intervention.
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL
With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
DATA AUGMENTATION TECHNIQUES AND TRANSFER LEARNING APPROACHES APPLIED TO FACI...ijaia
The face expression is the first thing we pay attention to when we want to understand a person’s state of
mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research
field. In this paper, because the small size of available training datasets, we propose a novel data
augmentation technique that improves the performances in the recognition task. We apply geometrical
transformations and build from scratch GAN models able to generate new synthetic images for each
emotion type. Thus, on the augmented datasets we fine tune pretrained convolutional neural networks with
different architectures. To measure the generalization ability of the models, we apply extra-database
protocol approach, namely we train models on the augmented versions of training dataset and test them on
two different databases. The combination of these techniques allows to reach average accuracy values of
the order of 85% for the InceptionResNetV2 model.
MITIGATION TECHNIQUES TO OVERCOME DATA HARM IN MODEL BUILDING FOR MLijaia
Given the impact of Machine Learning (ML) on individuals and the society, understanding how harm might
be occur throughout the ML life cycle becomes critical more than ever. By offering a framework to
determine distinct potential sources of downstream harm in ML pipeline, the paper demonstrates the
importance of choices throughout distinct phases of data collection, development, and deployment that
extend far beyond just model training. Relevant mitigation techniques are also suggested for being used
instead of merely relying on generic notions of what counts as fairness.
Mobile based Automated Complete Blood Count (Auto-CBC) Analysis System from B...IJECEIAES
Blood cells diagnosis is becoming essential to ensure a proper treatment can be proposed to a blood related disease patient. In current research trending, automated complete blood count analysis system is required for pathologists or researchers to count the blood cells from the blood smeared images. Hence, a portable mobile-based complete blood count (CBC) analysis framework with the aid of microscope is proposed, and the smartphone camera is mounted to the viewing port of the light microscope by adding a smartphone support. Initially, the blood smeared image is acquired from a light microscope with objective zoom of 100X magnifications view the eyepiece zoom of 10X magnification, then captured by the smartphone camera. Next, the areas constitute to the WBC and RBC are extracted using combination of color space analysis, threshold and Otsu procedure. Then, the number of corresponding cells are counted using topological structural analysis, and the cells in clumped region is estimated using Hough Circle Transform (HCT) procedure. After that, the analysis results are saved in the database, and shown in the user interface of the smartphone application. Experimental results show the developed system can gain 92.93% accuracy for counting the RBC whereas 100% for counting the WBC.
Application of VLSI In Artificial IntelligenceIOSR Journals
This document discusses the application of VLSI (Very Large Scale Integrated) circuits in artificial intelligence. It begins with a brief history of the development of microelectronics and integrated circuits. It then provides definitions of artificial intelligence and describes how VLSI technology has enabled more powerful computer architectures for AI. The document focuses on how expert systems, which apply reasoning to knowledge bases, have been important early applications of AI to VLSI chip design. It provides examples of expert systems used for tasks like circuit simulation and assisting with VLSI design. In closing, it emphasizes that knowledge-based approaches using rules have advantages for incremental improvements and explaining reasoning.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
DEEP-LEARNING-BASED HUMAN INTENTION PREDICTION WITH DATA AUGMENTATIONijaia
Data augmentation has been broadly applied in training deep-learning models to increase the diversity of
data. This study ingestigates the effectiveness of different data augmentation methods for deep-learningbased human intention prediction when only limited training data is available. A human participant pitches
a ball to nine potential targets in our experiment. We expect to predict which target the participant pitches
the ball to. Firstly, the effectiveness of 10 data augmentation groups is evaluated on a single-participant
data set using RGB images. Secondly, the best data augmentation method (i.e., random cropping) on the
single-participant data set is further evaluated on a multi-participant data set to assess its generalization
ability. Finally, the effectiveness of random cropping on fusion data of RGB images and optical flow is
evaluated on both single- and multi-participant data sets. Experiment results show that: 1) Data
augmentation methods that crop or deform images can improve the prediction performance; 2) Random
cropping can be generalized to the multi-participant data set (prediction accuracy is improved from 50%
to 57.4%); and 3) Random cropping with fusion data of RGB images and optical flow can further improve
the prediction accuracy from 57.4% to 63.9% on the multi-participant data set.
Role of Advanced Machine Learning Techniques and Deep Learning Approach Based...ijtsrd
Accurate Medical diagnosis is not always possible at every medical center, especially in the Developing Countries where poor healthcare services and lack of advanced diagnostic methods and equipments affects procedures of medical diagnosis .Also, physician intuition and experience are not always sufficient to achieve high quality medical procedures results. Therefore, diagnostic errors and undesirable results are reasons for a need for Machine Learning Techniques based decision support system, which in turns reduce diagnostic errors, increasing the patient safety and save lives. This research focuses on this aspect of Medical diagnosis by learning pattern through the collected dataset of respiratory diseases such as pneumonia and Covid 19, also consist implementation and test of intelligent medical decision support system to assist physicians and radiologists can deliver great assistance by improving their decision making ability. In this Research paper, the proposed System use Neural network Resnet 50 and transfer learning technique to classify these severe diseases and performs evaluation of precision, accuracy, speci city of Decision support system. Patel Smitkumar Hareshbhai "Role of Advanced Machine Learning Techniques and Deep Learning Approach Based Decision Support System for Accurate Diagnosis of Severe Respiratory Diseases" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47655.pdf Paper URL : https://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/47655/role-of-advanced-machine-learning-techniques-and-deep-learning-approach-based-decision-support-system-for-accurate-diagnosis-of-severe-respiratory-diseases/patel-smitkumar-hareshbhai
A new system to detect coronavirus social distance violation IJECEIAES
This document proposes a new system to detect social distance violations using a smartphone. The system uses two Android applications - one uses the phone's camera to detect faces and estimate distances during calls, and one uses voice biometrics to differentiate the user's voice from others. Both applications perform real-time processing without collecting or sharing private user data. The system aims to help prevent the spread of COVID-19 by notifying users if social distancing guidelines are violated.
BEHAVIOR-BASED SECURITY FOR MOBILE DEVICES USING MACHINE LEARNING TECHNIQUESijaia
The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of
mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.
Automatic Selection of Open Source Multimedia Softwares Using Error Back-Prop...IJERA Editor
Open source opens a new era to provide license of the software for the user at free of cost which is advantage over paid licensed software. In Multimedia applications there are many versions of software are available and there is a problem for the user to select compatible software for their own system. Most of the time while surfing for software a huge list of software opens in response. The selection of particular software which is pretty suitable for the system from a real big list is the biggest challenge that is faced by the users. This work has been done that focuses on the existing open source software that are widely used and to design an automatic system for selection of particular open source software according to the compatibility of users own system. In this work, error back-propagation based neural network is designed in MATLAB for automatic selection of open source software. The system provides the open source software name after taking the information from user. Regression coefficient of 0.93877 is obtained and the results shown are up to the mark and can be utilized for the fast and effective software search.
NLP-based personal learning assistant for school education IJECEIAES
Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slackbased UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chatbot’s NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future.
PREDICTIVE MAINTENANCE AND ENGINEERED PROCESSES IN MECHATRONIC INDUSTRY: AN I...ijaia
This document summarizes a case study on implementing predictive maintenance processes in a mechatronic industry using machine learning algorithms. A company installed sensors on a cutting machine to monitor blade status in real-time. A software platform was developed to analyze sensor data using k-Means clustering and LSTM algorithms to predict blade break conditions. The platform classified risk maps and predicted alert levels based on recent variable values. This approach aimed to optimize maintenance and reduce machine downtime for customers.
A comparative analysis of data mining tools for performance mapping of wlan dataIAEME Publication
This document compares the performance of different data mining tools for anomaly detection in wireless network data. It analyzes four tools: Weka, SPSS, Tanagra, and Microsoft SQL Server's Business Intelligence Development Studio. The same wireless network log data with 1000 instances and 13 attributes is clustered into 3 groups (normal activities, suspicious activities, anomalous activities) using different unsupervised learning algorithms in each tool. The results from each tool are different due to using different distance measures and clustering algorithms. The paper aims to interpret the results from each tool and determine which provides the most accurate performance mapping for the wireless network data.
June 2020: Top Read Articles in Advanced Computational Intelligenceaciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
This paper proposes a new method for fingerprint classification based on orientation field features extracted using a pixel-wise gradient descent method. The orientation field is used to estimate the percentage of directional block classes in four dimensions, which along with singular point information forms a feature vector for classification. A support vector machine classifier is used and shown to achieve high accuracy compared to other spatial domain classifiers. The method extracts discriminative features from the orientation field to classify fingerprints into one of five classes.
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
Shot-Net: A Convolutional Neural Network for Classifying Different Cricket ShotsMohammad Shakirul islam
This document describes a convolutional neural network called Shot-Net that was developed to classify different types of cricket shots. The document provides an overview of related work on sports activity recognition and cricket shot classification. It then describes the proposed Shot-Net methodology, including the dataset used, data preprocessing steps, model architecture, training process, and evaluation of the model's performance through classification reports and confusion matrices. The document concludes by discussing the model's results and proposing areas for future work, such as enriching the dataset and developing applications.
IRJET- Breast Cancer Prediction using Deep LearningIRJET Journal
This document discusses using deep learning to predict breast cancer based on tumor data. It proposes using a neural network model to classify tumors as malignant or benign. The key steps are:
1. Collecting and preprocessing tumor cell data to remove noise and inconsistencies.
2. Developing a neural network model and training it on labeled training data to learn patterns.
3. Testing the trained model on unlabeled testing data to evaluate its accuracy in classifying tumors.
The goal is to develop an accurate model to help doctors determine the critical condition of patients and classify difficult tumors.
IRJET- Kidney Stone Classification using Deep Neural Networks and Facilitatin...IRJET Journal
The document discusses classifying kidney stone images using deep neural networks and facilitating diagnosis using IoT. Kidney stone images are acquired and preprocessed by converting to grayscale, enhancing, and segmenting the area of interest. Texture features are extracted using active contour segmentation and classified using a deep neural network model. The results, including stone type and treatment recommendations, are sent to the cloud where doctors and patients can access them, allowing automated diagnosis without human intervention.
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL
With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
IRJET- Automated Detection of Gender from Face ImagesIRJET Journal
1) The document describes a system to automatically detect gender from face images using convolutional neural networks and Python. The system was developed to help address problems like security, fraud, and criminal identification.
2) The system uses a CNN classifier trained on the UTKFace dataset of facial images. The CNN model contains convolutional, activation, max pooling, flatten, dense and dropout layers to analyze image features and predict the gender of an unknown input face image.
3) The goal of the system is to identify gender from images faster than traditional criminal identification methods in order to help solve crimes and security issues more efficiently.
Analysis of student sentiment during video class with multi-layer deep learni...IJECEIAES
The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the online class is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.
REVIEW OF FACE DETECTION SYSTEMS BASED ARTIFICIAL NEURAL NETWORKS ALGORITHMSijma
Face detection is one of the most relevant applications of image processing and biometric systems.
Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition.
There is lack of literature surveys which give overview about the studies and researches related to the using
of ANN in face detection. Therefore, this research includes a general review of face detection studies and
systems which based on different ANN approaches and algorithms. The strengths and limitations of these
literature studies and systems were included also.
Review of face detection systems based artificial neural networks algorithmsijma
This document provides a review of face detection systems that are based on artificial neural network algorithms. It summarizes several studies that have used different types of neural networks for face detection, including:
1) Retinal connected neural networks and rotation invariant neural networks.
2) Principal component analysis combined with neural networks.
3) Convolutional neural networks, multilayer perceptrons, backpropagation neural networks, and polynomial neural networks.
4) Fast neural networks, evolutionary optimization of neural networks, and Gabor wavelet features with neural networks. Strengths and limitations of these different approaches are discussed.
This document presents a facial expression recognition system that identifies and classifies seven basic expressions: happy, surprise, fear, disgust, sad, anger, and a neutral state. The system consists of four main parts: image acquisition, pre-processing, feature extraction, and classification. It was developed using both OpenCV and a web-based JavaScript approach. The system was tested on both real-time and pre-recorded video streams and can identify emotions in images and video input from a webcam in real-time. Evaluation showed the JavaScript implementation using a generalized dataset provided more accurate real-time predictions compared to the OpenCV approach.
IRJET- Spot Me - A Smart Attendance System based on Face RecognitionIRJET Journal
The article discusses international issues. It mentions that globalization has increased economic interdependence between nations while also raising tensions over immigration and trade. Solutions will require cooperation and compromise and a recognition that isolationism is not a viable strategy in an interconnected world.
Person identification based on facial biometrics in different lighting condit...IJECEIAES
Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individual’s identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
The document summarizes research on facial age estimation using transfer learning and Bayesian optimization based on gender information. Specifically:
1) A convolutional neural network is trained to classify gender from facial images. This gender classification CNN is then used as input for an age estimation model.
2) Bayesian optimization is applied to the pre-trained gender classification CNN to fine-tune it for the age estimation task. This reduces error on validation data.
3) Experiments on the FERET and FG-NET datasets show the proposed approach of using gender information and Bayesian optimization outperforms state-of-the-art methods, achieving a mean absolute error of 1.2 and 2.67 respectively.
IRJET- Automated Student’s Attendance Management using Convolutional Neural N...IRJET Journal
This document describes a proposed system to automate student attendance management using convolutional neural networks and face recognition. The system would take attendance automatically by detecting faces in the classroom and comparing them to a database of student faces. This would make the attendance process more efficient than current manual methods like calling roll numbers or paper sign-ins. The system would use a CNN algorithm and face detection/recognition techniques like PCA to detect and identify student faces during lectures and automatically update attendance records.
This document summarizes several research papers on human face recognition using feature extraction and measurements. It discusses using face recognition for applications like surveillance, access control, and banking validation. Key steps in face recognition systems include extracting features from captured images, comparing them to known images in a training database, and identifying errors like false acceptance and false rejection rates. Methods discussed for feature extraction and dimensionality reduction include Linear Discriminant Analysis and Principal Component Analysis. The document also examines factors that affect face recognition performance like illumination changes, aging, and expressions. Quantifying uncertainty in face recognition algorithms is identified as important for evaluating system performance.
MUSIC RECOMMENDATION THROUGH FACE RECOGNITION AND EMOTION DETECTIONIRJET Journal
This document describes a system for music recommendation based on facial emotion recognition using convolutional neural networks. It analyzes facial images using CNN models to detect seven basic emotions. Based on the detected emotion, it recommends songs from predefined playlists that match the user's mood. The system architecture inputs images of the user's face, uses a CNN for emotion detection, and then selects appropriate music from playlists organized by emotion. It was developed to provide personalized music recommendations based on a user's real-time facial expressions and emotional state, unlike other systems that rely only on search queries or prior listening history.
SIGN LANGUAGE INTERFACE SYSTEM FOR HEARING IMPAIRED PEOPLEIRJET Journal
The document describes a proposed sign language interface system for hearing impaired people. The system aims to use machine learning algorithms like convolutional neural networks to classify hand gestures captured by a webcam into corresponding letters or words. The system would preprocess the images, extract features, then use a trained CNN model to predict the sign and output it as text and speech for better understanding by users. The goal is to help bridge communication between deaf/mute and normal people without requiring specialized gloves or sensors.
A Real Time Advance Automated Attendance System using Face-Net AlgorithmIRJET Journal
This document presents a real-time advanced automated attendance system using the Face-Net algorithm. The system uses facial recognition technology to automate the attendance tracking process. It involves developing facial detection and recognition algorithms, a database to store student information, and interfaces for educators. The system captures images of students' faces and matches them to stored data to record attendance in real-time while maintaining privacy. Testing showed the system could accurately detect and recognize faces in classroom settings. The authors aim to contribute to digitizing education administration and allowing educators to focus on teaching.
Real time voting system using face recognition for different expressions and ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Automated attendance system using Face recognitionIRJET Journal
This document describes an automated attendance system using face recognition. The system uses image capture to take photos of students entering the classroom. It then uses the Viola-Jones algorithm for face detection and PCA for feature selection and SVM for classification to recognize students' faces and mark their attendance automatically. When compared to traditional attendance methods, this system saves time and helps monitor students. It discusses related work using RFID, fingerprints, and iris recognition for attendance systems. It outlines the proposed system's modules for image capture, face detection, preprocessing, database development, and postprocessing. Finally, it discusses results, conclusions, and opportunities for future work to improve recognition rates under various conditions.
MTCNN BASED AUTOMATIC ATTENDANCE SYSTEM USING FACE RECOGNITIONIRJET Journal
This document summarizes a research paper that proposes an automated attendance system using facial recognition technology. It begins by outlining the limitations of current manual and RFID card-based attendance systems. It then describes a new system that uses MTCNN for face detection and CNN for facial recognition. The system captures images and identifies recognized students as present by matching faces to a database of stored images. The document provides details on the various stages of the proposed method, including face detection using MTCNN, face alignment, feature extraction with FaceNet, and classification with SVM. It presents the overall algorithm and concludes by discussing modelling and analysis.
The document proposes a method for face recognition using deep learning and data augmentation. It cleans and pre-processes existing face datasets to remove noise and extracts faces. It then uses image processing techniques to add masks to the faces to create a new masked face dataset. An Inception Resnet-v1 model is trained on the new dataset. The method is applied to build a face recognition application for employee timekeeping that achieves high accuracy even when faces are masked.
Semantic-based visual emotion recognition in videos-a transfer learning appr...IJECEIAES
Automatic emotion recognition is active research in analyzing human’s emotional state over the past decades. It is still a challenging task in computer vision and artificial intelligence due to its high intra-class variation. The main advantage of emotion recognition is that a person’s emotion can be recognized even if he is extreme away from the surveillance monitoring since the camera is far away from the human; it is challenging to identify the emotion with facial expression alone. This scenario works better by adding visual body clues (facial actions, hand posture, body gestures). The body posture can powerfully convey the emotional state of a person in this scenario. This paper analyses the frontal view of human body movements, visual expressions, and body gestures to identify the various emotions. Initially, we extract the motion information of the body gesture using dense optical flow models. Later the high-level motion feature frames are transferred to the pre-trained convolutional neural network (CNN) models to recognize the 17 various emotions in Geneva multimodal emotion portrayals (GEMEP) dataset. In the experimental results, AlexNet exhibits the architecture's effectiveness with an overall accuracy rate of 96.63% for the GEMEP dataset is better than raw frames and 94% for visual geometry group-19 VGG-19, and 93.35% for VGG-16 respectively. This shows that the dense optical flow method performs well using transfer learning for recognizing emotions.
Similar to Face recognition for presence system by using residual networks-50 architecture (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Face recognition for presence system by using residual networks-50 architecture
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 6, December 2021, pp. 5488~5496
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i6.pp5488-5496 5488
Journal homepage: http://ijece.iaescore.com
Face recognition for presence system by using residual
networks-50 architecture
Yohanssen Pratama, Lit Malem Ginting, Emma Hannisa Laurencia Nainggolan,
Ade Erispra Rismanda
Faculty of Informatics and Electrical Engineering, Institut Teknologi Del, Laguboti, Indonesia
Article Info ABSTRACT
Article history:
Received Aug 10, 2020
Revised Apr 16, 2021
Accepted Apr 26, 2021
Presence system is a system for recording the individual attendance in the
company, school or institution. There are several types presence system,
including the manually presence system using signatures, presence system
using fingerprints and presence system using face recognition technology.
Presence system using face recognition technology is one of presence system
that implements biometric system in the process of recording attendance. In
this research we used one of the convolutional neural network (CNN)
architectures that won the imagenet large scale visual recognition
competition (ILSVRC) in 2015, namely the Residual Networks-50
architecture (ResNet-50) for face recognition. Our contribution in this
research is to determine effectiveness ResNet architecture with different
configuration of hyperparameters. This hyperparameters includes the number
of hidden layers, the number of units in the hidden layer, batch size, and
learning rate. Because hyperparameter are selected based on how the
experiments performed and the value of each hyperparameter affects the final
result accuracy, so we try 22 configurations (experiments) to get the best
accuracy. We conducted experiments to get the best model with an accuracy
of 99%.
Keywords:
Convolutional neural network
Face detection
Face recognition
Presence system
Residual network
This is an open access article under the CC BY-SA license.
Corresponding Author:
Yohanssen Pratama
Faculty of Informatics and Electrical Engineering
Institut Teknologi Del
Sisingamangaraja Street, Sitoluama, Laguboti 22381, Indonesia
Email: yohanssen.pratama@del.ac.id
1. INTRODUCTION
Biometrics is a term used to determine the DNA of an individual, hand geometry, face or physical
characteristics, such as signatures, sounds and so on. Biometric systems are generally used to authenticate
and identify individuals by analyzing the individual's physical characteristics, such as fingerprints, irises,
veins and others [1]. Biometric systems use unique physical characteristics of individuals which different
from others to be identified and analyzed to achieve certain goals [2].
One of biometric system that also superior and can be applied to the attendance system based on the
comparison made is using faces [3]. As identity information, human faces have the advantage of being
unique and free of imitation [4]. In the face recognition system, the technology used is face detection which
is the first step in facial recognition process and face recognition [5]. One of the supporting media that can be
relied upon in the attendance system using face detection and face recognition is a real time video camera.
Camera in the face detection system have the advantage of application flexibility, so they do not require users
to make direct contact with the attendance system [6].
2. Int J Elec & Comp Eng ISSN: 2088-8708
Face recognition for presence system by using residual networks-50 architecture (Yohanssen Pratama)
5489
In this research, based on a case study of presence system, we conducted a study of the application
on biometric systems by using deep learning. Deep learning is a type of machine learning method that makes
computers learn from experience and knowledge without explicit programming and extract useful patterns
from raw data [7]. With the presence system using deep learning for face detection and face recognition, it is
expected that the process of recording student attendance is more efficient, as well as reducing fraud that
might occur.
In develop the presence system there will be 2 stages, namely face detection and face recognition. In
the face detection stage, the haar cascade classifier method is used to detect elements on the face, namely the
eyes, nose and mouth [8]. In the face recognition stage, the convolutional neural network (CNN) algorithm is
used for the process of recognizing and matching input data with data on the model. In our research we only
use CNN which the values of the kernel are determined by training, while a haar-feature is manually
determined. While well-trained CNN could learn more parameters (and thus detect a larger variety of faces),
haar based classifiers run faster [9]. Haar cascade detect human faces enclosed by a square and give center
points of face elements (eyes, nose, and mouth) [10]. The haar cascade classifier is also called the Viola-Jones
Method, which is the most widely used method for detecting objects. The application of human faces
detection by using haar cascade classifier can be carried out to get a comprehensive result such as for detect
human faces on thermal image [11].
Among all deep learning structure, CNN and recurrent neural networks (RNN) are the most popular
structures [12]. As state above we use CNN because we want to get the best accuracy and CNN has been
proven to be very effective in areas such as facial recognition and classification compared to another method
[13]. Also, CNN extracts features automatically, so there is no need to select features manually [14]. There
are already some researches in face recognition based on CNN, some of them implemented augmented reality
to compared it with face database and give high accuracy [15], and many of them implementing softmax
architecture for facial recognition which already proven give a good accuracy [16]. In here we propose to use
ResNet-50 architecture for recognition, because has good performance if compared to simple CNN [17].
Residual networks (ResNet) are a convolutional network that is trained on more than 1 million images from
the ImageNet database and for ResNet-50 the total number of weighted layers is 50, with 23534592
parameters that can be trained [18].
Our contribution in this research is to determine effectiveness ResNet architecture with different
configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of
units in the hidden layer, batch size, and learning rate [19]. Because hyperparameter are selected based on
experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22
configurations (experiments) to get the best accuracy [20]. We also want to prove that learning rate has a
large influence on accuracy among hyperparameters in here [21].
2. RESEARCH METHOD
The explanation of the data processing design scheme is as follows as shown in Figure 1:
− Data in the form of face images are used as input to be processed.
− Each person images are capture with 15 different position and expression in RGB color space and JPG
format.
− Pre processing stage is the stage of image data uniformity consisting of uniforming the size of the image
and image augmentation.
− In here we make each image has same specification, such as its color space and resolution.
− Classification, namely the stage of recognizing faces with several stages consisting of convolutional
layers, pooling layers, flatten, fully connected layers and softmax.
− In here we use CNN with a certain number of layers. The number of layers that contribute to a model of
data is called the depth of a model [22]. In every stage we do several hyperparameter configuration for
every experiment that we conduct until we found the best combination and get the best accuracy. The
detail about the hyperparameter configuration could be seen in the explanation subsection about the
model experiment design.
− The output of processed data is information from the data that has been identified.
− In here we try to get the result wether the recognition system could recognize the person correctly or not.
We do the experiment with 9 different persons, each of them will be identified by the system that we
build, from that we could know how robust our system to recognize the face of each person.
2.1. Data
The images collected in this study are using the .jpg file format. The collected images are arranged
in 53 directories, each directory representing one student. The format of the name in each directory is X_Y,
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 6, December 2021 : 5488 - 5496
5490
with the explanation X is the first name of the student and Y is the student's NIM, for example students on
behalf of Emma with NIM 11S15003, then the directory folder naming format is Emma_11S15003. In each
directory contains 15 different images by the same student. The format for naming image files is
NamaMahasiswa_XX.jpg, with the explanation StudentName is the first name of the student and XX is the
number of each picture starting from number 0. For example, for students on behalf of Emma, the image file
format in the directory is Emma_0.jpg. The directory setting and naming of each file aims to make it easier
for researchers to manage the images of each student. In here we use RGB colour space as an input, so in
converting an RGB image into a matrix, it takes 3 matrices that can represent each of the red, green and blues
values for each pixel [23].
Figure 1. Data processing design scheme
2.2. Preprocessing
Preprocess data on a convolutional neural network has several stages, the first stage is Image
Scaling at this stage, the input data will be equal in size. This stage is needed because the available image
size does not always match the image size specified as a dataset. After that we continue with Augmentation
process, at this stage, augmentation consists of 3 stages: Giving a blur effect to the image, giving a random
noise effect (noise) and adding light intensity to the image. The purpose of this augmentation is to uniform
image data in order to simplify the classification process.
We also do some geometrical operation such as flip, shift (translation), rotation, and segmentation.
Flip consists of 3 stages: Data changing the image to horizontal, vertical and rotated horizontally and
vertically. The purpose of this flip is to reproduce data to simplify the classification process. At Shift stage,
we translate/shifts the location of the object from the original object in the data. Then in the rotation stage,
the image will be shifted counterclockwise according to a predetermined angle. Last is the segmentation
stage, segmentation is used to detect the edges of faces to get an image from an image.
2.3. Classification
The explanation of the scheme in Figure 2 is as follows:
− Input data will enter the convolution layer, which is the process of manipulating images to produce a new
image to be entered at a later stage. We use zero padding in convolutional process [24] as shown in
Figure 3.
− Pooling layer at this stage is to do calculations on each pixel of the image feature that has been converted
in here into a matrix. The goal is to divide an image into several features to make it easier to do an image
match.
− Flatten is the stage where the features produced at the pooling layer with a matrix size n x m will be
provided that n> 1 and m> 1 will be converted to the order matrix 1x1.
4. Int J Elec & Comp Eng ISSN: 2088-8708
Face recognition for presence system by using residual networks-50 architecture (Yohanssen Pratama)
5491
− Fully connected layer which is the stage of producing output in the form of the probability of an image
that will be used in the classification process of output data.
− Softmax is the stage of calculating probabilities on all labels in the data.
− The final result of this process is the value of the softmax calculation, which is the probability of each
label in the data.
Figure 2. Design of convolutional neural network model
Figure 3. Zero-padding operation in, (a) 5x5 matrices, (b) filter 3x3
2.4. Model experiment design
We divide the data that has been preprocessed into a data train and data test, with a share of 80% for
the data train and 20% for the test data. 1050 data were divided into 2 parts, 840 data to be data train and 210
data to be test data. Data that has been divided into data train and data test will go through the training model
stage using the convolutional neural network algorithm. In the process of training models, for the first
experiment we use hyperparameter that has learning rate with value 0.1, epoch with value of 10 and step per
epoch with value of 100. In data processing using convolutional neural network, the data goes through
several stages, namely pooling layer, flatten, fully connected layer, softmax calculation to produce a model.
The resulting model will be evaluated to find the accuracy of a model. The experimental design for this
model can be seen in Figure 4.
Figure 4. Model experiment design
3. RESULTS AND DISCUSSION
In this section, it is explained the results of research and at the same time is given the comprehensive
discussion. The results obtained and discussion of the implementation of attendance system development
using face recognition technology are as follows:
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 6, December 2021 : 5488 - 5496
5492
3.1. Result of data collections
Each person dataset must consist of 15 image data with different conditions, we use this based on
design of smart door system for live face recognition based on image processing [25] and we do not use
augmented reality database for comparison. We collected images of each individual faces by taking each
individualfaces with different variations and then developing image collection techniques with preprocess
data methods to homogenize the whole picture. The dataset sample could be seen in Table 1.
Table 1. Dataset example
Caption Example for dataset
Strapping face, eyes facing the camera and without expression.
Face shape facing left 45 °, eyes facing left and smiling expression.
The face forms tilted to the right 45 °, and smiles.
Face shape facing up, eyes closed and expressionless.
The shape of the face is tilted to the right 45 °, and without expression.
Face shape facing right 45 °, and expressionless.
Face shape facing upwards of 45 °, and smiling expressions.
The shape of the face is tilted to the left 45 °, and expressionless.
Face shape facing up to 45 °, eyes facing up and without expression.
Face shape facing right 45 ° and smiling expression.
Face shape facing right 45 °, and expressionless.
Face shape facing left 45 °, and smiling expressions.
The shape of the face is tilted to the left 45 °, and the expression smiles.
Face shape facing up with eyes staring straight at the camera and smiling expression.
Face up with eyes closed and without expression.
3.2. Result of preprocessing data
In this phase the initial data that already collected has different sizes and not uniform, so the
preprocessing stage was needed in this research. We include the function of image normalization to ensure
the uniformity in image size and augmentation. The tests that carried out in the preprocessing stage use 15
variations of image data for 1 class. Following is an example of the test results for one image data that has
through the preprocessing phase that can be seen in Table 2.
6. Int J Elec & Comp Eng ISSN: 2088-8708
Face recognition for presence system by using residual networks-50 architecture (Yohanssen Pratama)
5493
At the preprocess phase, one image data produces 87 preprocess data. So, for 1 class that contains 15
variations of data, the total data generated after preprocessing is 87x15=1,305 data. From 53 classes
collected, preprocess data will be obtained, that is 53 classes x 1,305 data = 69,165 preprocess data.
Table 2. Preprocessing result example
No Preprocess Result Caption
1 Original data Data collected manually
2 Added blur effect The original data is given blur effect
3 Added random noise effect The original data is given random noise effect
4 Added light intensity The original data is given the effect of light intensity
5 Added random noise and light
intensity
The original data is given random noise and light
intensity effect
6 Added blur and light intensity The original data is given blur and light intensity
effect
7 Added random noise, blur, and
light intensity
The original data is given random noise, blur, and
light intensity effect
8 Change the picture horizontally Picture that already given effect 2-7 rotated
horizontally
9 Change the picture verticallys Picture that already given effect 2-7 rotated
vertically
10 Change the picture vertically
and horizontally
Picture that already given effect 2-7 rotated
horizontally & vertically
11 Gives a translational effect on
the image
Picture that already given effect 2-10 given
translation effect
12 Gives the rotation effect on the
image
Picture that already given effect 2-10 given rotation
effect
13 Perform segmentation (edge
detection)
Segmentation on original data, no 4 & 6 data
3.2.1. Result of model testing
In this phase we conducted an experiment with data sharing which is 80:20, with lots of data 1050
with 840 for data train and 210 for test data. We also conducted an experiment by using 13050 data with
10440 for data train and 2610 for test data. The number of classes modeled is 10 classes, this is due to
limitations on inadequate support resources for modeling 53 classes. The result of model testing with
different configuration of hyperparameter can be seen in Table 3. From the results of the modeling
experiments above, it can be concluded that the 22nd
experiment has the best accuracy with 99% data train
accuracy and 99% data test accuracy. This is because we has experimented with hyperparameters and
obtained the right hyperparameters to build a model with value of data train accuracy and data test accuracy
that reached 99%.
3.2.2. Result of prototype presence system
The results of this implementation are the prototype that built using a graphical user interface (GUI)
provided by Python 3.6 and tkinter which is successfully built to recognize the face from each student by
using previously trained model with 22nd
experiment hyperparameter configuration as shown in Figure 5.
7. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 6, December 2021 : 5488 - 5496
5494
Table 3. Model testing result for 22 hyperparameters configuration
Experiment
Number:
Amount of
Data
Data
train
Data
test
Learning
rate
Epoch
Step per-
epoch
Data Train
Accuracy
Data Test
Accuracy
1 1.050 840 210 0.1 10 100 9% 5%
2 1.050 840 210 0.01 20 100 11% 7.5%
3 1.050 840 210 0.001 10 100 55% 50%
4 1.050 840 210 0.0001 10 100 95% 85%
5 1.050 840 210 0.00001 10 100 84% 62.5%
6 1.050 840 210 0.000001 10 100 22% 37.5%
7 1.050 840 210 0.0001 20 100 98% 86.8%
8 1.050 840 210 0.0001 50 100 98% 87.5%
9 1.050 840 210 0.0001 70 100 98% 88%
10 1.050 840 210 0.0001 100 100 99% 94%
11 1.050 840 210 0.0001 100 150 99% 97.5%
… … … … … … … … …
21 13.050 10.440 2.610 0.0001 100 100 99% 98%
22 13.050 10.440 2.610 0.0001 100 150 99% 99%
Figure 5. Example of a figure caption and classification result
3.2.3. Result of prototype presence system
After implementing data modeling, we tests the results of the models that have been built. To get the
results of evaluations that can be compared and concluded the results, we conducted an object experiment on
9 students. Each student did 5 object experiments. The result of object testing can be seen in Table 4.
Table 4. Result of object testing
Experiment for object no: Amount of successful detection Amount of unsuccessful detection
1 5 0
2 5 0
3 5 0
4 5 0
5 5 0
6 4 1
7 5 0
8 5 0
9 4 1
Total 43 2
4. CONCLUSION
The conclusion obtained from the research that already conducted is the presence system was
develop in the form of a prototype using the convolutional neural network (CNN) algorithm by conducting
trial experiments on hyperparameters such as the learning rate with a value of 0.0001, epoch with a value of
100, and step per epoch with a value of 150 so this hyperparameter configuration give us a model with
accuracy of 99 %. After that we built a presence system prototype by using a graphical user interface (GUI)
which is provided by python named tkinter, then we applied the model that has been obtained into the
prototype so that the presence system prototype can be used to predict the facial image.
8. Int J Elec & Comp Eng ISSN: 2088-8708
Face recognition for presence system by using residual networks-50 architecture (Yohanssen Pratama)
5495
ACKNOWLEDGEMENTS
This work is supported in part by Institut Teknologi Del.
REFERENCES
[1] H. Suad and V. Asaf, “Real Time Face Recognition System (RTFRS),” 2016 4th International Symposium on
Digital Forensic and Security (ISDFS), 2016, pp. 107–111, doi: 10.1109/ISDFS.2016.7473527.
[2] M. Baykara and R. Das, “Real Time Face Recognition and Tracking System,” International Conference on
Electronics, Computer and Computation (ICECCO), 2013, pp. 159–163, doi: 10.1109/ICECCO.2013.6718253.
[3] N. Bawany, R. Ahmed, and Q. Zakir, “Common Biometric Authentication Techniques: Comparative Analysis,
Usability and Possible Issues Evaluation,” Research Journal of Computer and Information Technology Sciences,
vol. 1, no. 4, pp. 5–14, 2013.
[4] L. Cuimei, Q. Zhiliang, J. Nan, and W. Jianhua, “Human face detection algorithm via Haar cascade classifier
combined with three additional classifiers,” International Conference on Electronic Measurement and Instruments
(ICEMI), 2017, pp. 483–486, doi: 10.1109/ICEMI.2017.8265863.
[5] M. Belahcene, A. Chouchane, and N. Mokhtari, “2D and 3D Face Recognition Based on IPC Detection and Patch
of Interest Regions,” International Conference on Connected Vehicles and Expo (ICCVE), 2014, pp. 627–628,
doi: 10.1109/ICCVE.2014.7297624.
[6] E. Winarno, W. Hadikurniawati, I. H. Al Amin, and M. Sukur, “Anti-Cheating Presence System Based on 3WPCA
Dual Vision Face Recognition,” International Conference on Electrical Engineering, Computer Science and
Informatics (EECSI), 2017, pp. 228–232, doi: 10.1109/EECSI.2017.8239115.
[7] X. Yuan, P. He, Q. Zhu, and X. Li, “Adversarial Examples: Attacks and Defenses for Deep Learning,”
International Transactions on Neural Networks and Learning Systems, vol. 30, no. 9, pp. 2805–2824, 2019,
doi: 10.1109/TNNLS.2018.2886017.
[8] X. Zhao and C. Wei, “A Real-Time Face Recognition System Based on the Improved LBPH Algorithm,”
International Conference on Signal and Image Processing (ICSIP), 2017, pp. 72–76,
doi: 10.1109/SIPROCESS.2017.8124508.
[9] M. D. Putro, T. B. Adji, and B. Winduratna, “Adult Image Classifiers Based on Face Detection Using Viola-Jones
Method,” International Conference on Wireless and Telematics (ICWT), 2015, pp. 1–6,
doi: 10.1109/ICWT.2015.7449208.
[10] E. K. Shimomoto, A. Kimura, and R. Belem, “A Faster Detection Method combining Bayesian and Haar Cascade
Classifier,” 2005 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication
Technologies (CHILECON), 2015, pp. 7–12, doi: 10.1109/Chilecon.2015.7400344.
[11] C. H. Setjo, B. Achmad, and Faridah, “Thermal Image Human Detection Using Haar Cascade Classifier,”
International Annual Engineering Seminar (InAES), 2017, pp. 1–6, doi: 10.1109/INAES.2017.8068554.
[12] D. Zhang, X. Han, and C. Deng, “Review on the Research and Practice of Deep Learning and Reinforcement
Learning in Smart Grids,” CSEE Journal of Power and Energy Systems, vol. 4, no. 3, pp. 362–370, 2018,
doi: 10.17775/CSEEJPES.2018.00520.
[13] M. Wang, Z. Wang, and J. Li, “Deep Convolutional Neural Network Applies to Face Recognition in Small and
Medium Databases,” International Conference on Systems and Informatics (ICSAI), 2017, pp. 1368–1372,
doi: 10.1109/ICSAI.2017.8248499.
[14] D. Qu, Z. Huang, Z. Gao, Y. Zhao, X. Zhao, and G. Song, “An Automatic System for Smile Recognition Based on
CNN and Face Detection,” International Conference on Robotics and Biomimetics (ROBIO), 2018, pp. 243–247,
doi: 10.1109/ROBIO.2018.8665310.
[15] Y. Kewen, H. Shaohui, S. Yaoxian, L. Wei, and F. Neng, “Face Recognition Based on Convolutional Neural
Network,” National Natural Science Foundation (NNSF), pp. 4077–4081, 2017, doi:
10.23919/ChiCC.2017.8027997.
[16] M. Coskun, A. Ucar, O. Yildirim, and Y. Demir, “Face Recognition Based on Convolutional Neural Network,”
International Conference on Modern Electrical and Energy Systems (MEES), 2017, pp. 376–379,
doi: 10.1109/MEES.2017.8248937.
[17] I. Gruber, M. Hlaváč, M. Železný, and A. Karpov, “Facing Face Recognition with ResNet: Round One,” in A.
Ronzhin, G. Rigoll, and R. Meshcheryakov (eds), Interactive Collaborative Robotics. ICR 2017. Lecture
Notes in Computer Science, vol. 10459, 2017.
[18] E. Rezenda, G. Ruppert, T. Carvalho, F. Ramos, and P. De Geus, “Malicious Software Classification using Transfer
Learning of ResNet-50 Deep Neural Network,” International Conference on Machine Learning and Applications
(ICMLA), 2017, pp. 1011–1014, doi: 10.1109/ICMLA.2017.00-19.
[19] P. Balaprakash, M. Salim, T. D. Uram, V. Vishwanath, and S. M. Wild, “DeepHyper: Asynchronous
Hyperparameter Search for Deep Neural Networks,” International Conference on High Performance Computing
(HiPC), 2018, pp. 42–51, doi: 10.1109/HiPC.2018.00014.
[20] Z. Li, L. Jin, C. Yang, and Z. Zhong, “Hyperparameter search for deep convolutional neural network using effect
factors,” China Summit and International Conference on Signal and Information Processing, 2015, pp. 782–786,
doi: 10.1109/ChinaSIP.2015.7230511.
[21] K. Yamana et al., “Adaptive Learning Rate Adjustment with Short-Term Pre-Training in Data-Parallel Deep
Learning,” International Workshop on Signal Processing Systems (SIPS), 2018, pp. 100–105,
doi: 10.1109/SiPS.2018.8598429.
[22] C. Francois, “Deep Learning with Python,” United States of America: Manning Publications Co., 2018.
9. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 6, December 2021 : 5488 - 5496
5496
[23] C. G. Rafael and E. W. Richard, “Digital Image Processing,” Third Edition. United States of America: Pearson
Education. Inc., 2008.
[24] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, “A Convolutional Neural Network for Modelling Sentences,”
arXiv:1404.2188, 2014.
[25] V. E. Vyanza, C. Setianingsih, and B. Irawan, “Design of Smart Door System for Live Face Recognition Based on
Image Processing using Principal Component Analysis and Template Matching Correlation Methods,” Asia Pacific
Conference on Wireless and Mobile (APWiMob), 2017, pp. 23–29, doi: 10.1109/APWiMob.2017.8283999.
BIOGRAPHIES OF AUTHORS
Yohanssen Pratama, Current Faculty Members & Researcher in Institut Teknologi Del. 4+
years experience specializing in back-end/infrastructure, analytical tools development and
computer programming. Teach academic and vocational subjects to undergraduate also pursue
my own research to contribute to the wider research activities of my department.
Lit Malem Ginting, Current Faculty Members & Researcher in Insitut Teknologi Del since
2014. He is currently a Senior Lecturer at the Faculty of Informatics and Electrical Engineering,
Institut Teknologi Del (IT Del), Laguboti. He dedicates himself to university teaching and
conducting research. Her research fields include artificial intelligence, game development, and
computer programming.
Emma Hannisa Laurencia Nainggolan, Currently, work as a Quality Assurance at Salt, West
Jakarta. Have experience at image processing using neural network, web & desktop developing,
UX Design.
Ade Erispra Rismanda, Currently, work as a Software Engineer at Onoff Insurance, Jakarta.
Have experience at image processing using neural network, web & desktop developing,
microservice system.