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.
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.
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.
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.
PREDICTIVE MAINTENANCE AND ENGINEERED PROCESSES IN MECHATRONIC INDUSTRY: AN I...ijaia
The paper proposes the results of a research industry project concerning predictive maintenance process
optimization, applied to a machine cutting polyurethane. A company producing cutting machines, has been
provided with an online control system able to detect blade status of a machine supplied to a customer
producing polyurethane components. A software platform has been developed for the real time monitoring
of the blade status and for the prediction of the break up conditions adopting a multi-parametric data
analysis approach, based on the simultaneous use of unsupervised and supervised machine learning
algorithms. Specifically, the proposed method adopts a k-Means algorithm to classify bidimensional risk
maps, and a Long Short Term Memory (LSTM) one to predict the alerting levels based on the analysis of
the last values for some process variables. The analysed algorithms are applied to an experimental dataset.
REVIEWING PROCESS MINING APPLICATIONS AND TECHNIQUES IN EDUCATIONijaia
Process Mining (PM) emerged from business process management but has recently been applied to
educational data and has been found to facilitate the understanding of the educational process.
Educational Process Mining (EPM) bridges the gap between process analysis and data analysis, based on
the techniques of model discovery, conformance checking and extension of existing process models. We
present a systematic review of the recent and current status of research in the EPM domain, focusing on
application domains, techniques, tools and models, to highlight the use of EPM in comprehending and
improving educational processes.
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
Neural Network. The models stated above were compared on a variety of factors, including their accuracy
on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
that certain characteristics have a greater impact on the likelihood of a film's success than others. For
example, the existence of the genre action may have a significant impact on the forecasts, although another
genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the
IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best
performing model of all the models discussed.
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
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%.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
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.
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.
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.
PREDICTIVE MAINTENANCE AND ENGINEERED PROCESSES IN MECHATRONIC INDUSTRY: AN I...ijaia
The paper proposes the results of a research industry project concerning predictive maintenance process
optimization, applied to a machine cutting polyurethane. A company producing cutting machines, has been
provided with an online control system able to detect blade status of a machine supplied to a customer
producing polyurethane components. A software platform has been developed for the real time monitoring
of the blade status and for the prediction of the break up conditions adopting a multi-parametric data
analysis approach, based on the simultaneous use of unsupervised and supervised machine learning
algorithms. Specifically, the proposed method adopts a k-Means algorithm to classify bidimensional risk
maps, and a Long Short Term Memory (LSTM) one to predict the alerting levels based on the analysis of
the last values for some process variables. The analysed algorithms are applied to an experimental dataset.
REVIEWING PROCESS MINING APPLICATIONS AND TECHNIQUES IN EDUCATIONijaia
Process Mining (PM) emerged from business process management but has recently been applied to
educational data and has been found to facilitate the understanding of the educational process.
Educational Process Mining (EPM) bridges the gap between process analysis and data analysis, based on
the techniques of model discovery, conformance checking and extension of existing process models. We
present a systematic review of the recent and current status of research in the EPM domain, focusing on
application domains, techniques, tools and models, to highlight the use of EPM in comprehending and
improving educational processes.
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
Neural Network. The models stated above were compared on a variety of factors, including their accuracy
on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
that certain characteristics have a greater impact on the likelihood of a film's success than others. For
example, the existence of the genre action may have a significant impact on the forecasts, although another
genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the
IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best
performing model of all the models discussed.
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
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%.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
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.
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.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
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.
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.
Comparative Study on Machine Learning Algorithms for Network Intrusion Detect...ijtsrd
Network has brought convenience to the earth by permitting versatile transformation of information, however it conjointly exposes a high range of vulnerabilities. A Network Intrusion Detection System helps network directors and system to view network security violation in their organizations. Characteristic unknown and new attacks are one of the leading challenges in Intrusion Detection System researches. Deep learning that a subfield of machine learning cares with algorithms that are supported the structure and performance of brain known as artificial neural networks. The improvement in such learning algorithms would increase the probability of IDS and the detection rate of unknown attacks. Throughout, we have a tendency to suggest a deep learning approach to implement increased IDS and associate degree economical. Priya N | Ishita Popli "Comparative Study on Machine Learning Algorithms for Network Intrusion Detection System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38175.pdf Paper URL : https://www.ijtsrd.com/computer-science/computer-network/38175/comparative-study-on-machine-learning-algorithms-for-network-intrusion-detection-system/priya-n
COVID-19 detection from scarce chest X-Ray image data using few-shot deep lea...Shruti Jadon
In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate.
MACHINE LEARNING ALGORITHMS FOR HETEROGENEOUS DATA: A COMPARATIVE STUDYIAEME Publication
In the present digital era massive amount of data is being continuously generated
at exceptional and increasing scales. This data has become an important and
indispensable part of every economy, industry, organization, business and individual.
Further handling of these large datasets due to the heterogeneity in their formats is
one of the major challenge. There is a need for efficient data processing techniques to
handle the heterogeneous data and also to meet the computational requirements to
process this huge volume of data. The objective of this paper is to review, describe
and reflect on heterogeneous data with its complexity in processing, and also the use
of machine learning algorithms which plays a major role in data analytics
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
A novel ensemble modeling for intrusion detection system IJECEIAES
Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs, and hence our system is robust and efficient.
Comparative Analysis: Effective Information Retrieval Using Different Learnin...RSIS International
Information Retrieval is the activity of searching meaningful information from a collection of information resources such as Documents, relational databases and the World Wide Web. Information retrieval system mainly consists of two phases, storing indexed documents and retrieval of relevant result. Retrieving information effectively from huge data storage, it requires Machine Learning for computer systems. Machine learning has objective to instruct computers to use data or past experience to solve a given problem. Machine learning has number of applications, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through supervised learning, unsupervised learning and Reinforcement Learning. The goal of these three learning is to provide an effective way of information retrieval from data warehouse to avoid problems such as ambiguity. This study will compare the effectiveness and impuissance of these learning approaches.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs. Through this system, huge volume of data’s that are generated by the system will also get control.
With the surge in modern research focus towards Pervasive Computing, lot of techniques and challenges
needs to be addressed so as to effectively create smart spaces and achieve miniaturization. In the process of
scaling down to compact devices, the real things to ponder upon are the Information Retrieval challenges.
In this work, we discuss the aspects of multimedia which makes information access challenging. An
Example Pattern Recognition scenario is presented and the mathematical techniques that can be used to
model uncertainty are also presented for developing a system that can sense, compute and communicate in
a way that can make human life easy with smart objects assisting from around his surroundings.
Internet of things-based photovoltaics parameter monitoring system using Node...IJECEIAES
The use of the internet of things (IoT) in solar photovoltaic (PV) systems is a critical feature for remote monitoring, supervising, and performance evaluation. Furthermore, it improves the long-term viability, consistency, efficiency, and system maintenance of energy production. However, previous researchers' proposed PV monitoring systems are relatively complex and expensive. Furthermore, the existing systems do not have any backup data, which means that the acquired data could be lost if the network connection fails. This paper presents a simple and low-cost IoT-based PV parameter monitoring system, with additional backup data stored on a microSD card. A NodeMCU ESP8266 development board is chosen as the main controller because it is a system-on-chip (SOC) microcontroller with integrated Wi-Fi and low-power support, all in one chip to reduce the cost of the proposed system. The solar irradiance, ambient temperature, PV output voltage and PV output current, are measured with photo-diodes, DHT22, impedance dividers and ACS712. While, the PV output power is a product of the PV voltage and PV current. ThingSpeak, an opensource software, is used as a cloud database and data monitoring tool in the form of interactive graphics. The results showed that the system was designed to be highly accurate, reliable, simple to use, and low-cost.
An efficient approach on spatial big data related to wireless networks and it...eSAT Journals
Abstract
Spatial big data acts as a important key role in wireless networks applications. In that spatial and spatio temporal problems contains the distinct role in big data and it’s compared to common relational problems. If we are solving those problems means describing the three applications for spatial big data. In each applications imposing the specific design and we are developing our work on highly scalable parallel processing for spatial big data in Hadoop frameworks by using map reduce computational model. Our results show that enables highly scalable implementations of algorithms using Hadoop for the purpose of spatial data processing problems. Inspite of developing these implementations requires specialized knowledge and user friendly.
Keywords: Spatial Big Data, Hadoop, Wireless Networks, Map reduce
Optimal Operating Performances of Wireless Protocols for Intelligent Sensors ...chokrio
The systems based on intelligent sensors are currently expanding, due to theirs functions and theirs performances of intelligence: transmitting and receiving data in real-time, computation and processing algorithms, metrology remote, diagnostics, automation and storage measurements…The radio frequency wireless communication with its multitude offers a better solution for data traffic in this kind of systems. The mains objectives of this paper is to present a solution of the problem related to the selection criteria of a better wireless communication technology face up to the constraints imposed by the intended application and the evaluation of its key features. The comparison between the different wire-less technologies (Wi-Fi, Wi-Max, UWB, Bluetooth, ZigBee, ZigBeeIP, GSM/GPRS) focuses on their performance which depends on the areas of utilization. Furthermore, it shows the limits of their characteristics. Study findings can be used by the developers/ engineers to deduce the optimal mode to integrate and to operate a system that guarantees quality of communication, minimizing energy consumption, reducing the implementation cost and avoiding time con-straints.
Comparative Performance Analysis of Wireless Communication Protocols for Inte...chokrio
The systems based on intelligent sensors are currently expanding, due to theirs functions and theirs performances of intelligence: transmitting and receiving data in real-time, computation and processing algorithms, metrology remote, diagnostics, automation and storage measurements…The radio frequency wireless communication with its multitude offers a better solution for data traffic in this kind of systems. The mains objectives of this paper is to present a solution of the problem related to the selection criteria of a better wireless communication technology face up to the constraints imposed by the intended application and the evaluation of its key features. The comparison between the different wireless technologies (Wi-Fi, Wi-Max, UWB, Bluetooth, ZigBee, ZigBeeIP, GSM/GPRS) focuses on their performance which depends on the areas of utilization. Furthermore, it shows the limits of their characteristics. Study findings can be used by the developers/ engineers to deduce the optimal mode to integrate and to operate a system that guarantees quality of communication, minimizing energy consumption, reducing the implementation cost and avoiding time constraints.
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.
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.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
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.
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.
Comparative Study on Machine Learning Algorithms for Network Intrusion Detect...ijtsrd
Network has brought convenience to the earth by permitting versatile transformation of information, however it conjointly exposes a high range of vulnerabilities. A Network Intrusion Detection System helps network directors and system to view network security violation in their organizations. Characteristic unknown and new attacks are one of the leading challenges in Intrusion Detection System researches. Deep learning that a subfield of machine learning cares with algorithms that are supported the structure and performance of brain known as artificial neural networks. The improvement in such learning algorithms would increase the probability of IDS and the detection rate of unknown attacks. Throughout, we have a tendency to suggest a deep learning approach to implement increased IDS and associate degree economical. Priya N | Ishita Popli "Comparative Study on Machine Learning Algorithms for Network Intrusion Detection System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38175.pdf Paper URL : https://www.ijtsrd.com/computer-science/computer-network/38175/comparative-study-on-machine-learning-algorithms-for-network-intrusion-detection-system/priya-n
COVID-19 detection from scarce chest X-Ray image data using few-shot deep lea...Shruti Jadon
In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate.
MACHINE LEARNING ALGORITHMS FOR HETEROGENEOUS DATA: A COMPARATIVE STUDYIAEME Publication
In the present digital era massive amount of data is being continuously generated
at exceptional and increasing scales. This data has become an important and
indispensable part of every economy, industry, organization, business and individual.
Further handling of these large datasets due to the heterogeneity in their formats is
one of the major challenge. There is a need for efficient data processing techniques to
handle the heterogeneous data and also to meet the computational requirements to
process this huge volume of data. The objective of this paper is to review, describe
and reflect on heterogeneous data with its complexity in processing, and also the use
of machine learning algorithms which plays a major role in data analytics
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
A novel ensemble modeling for intrusion detection system IJECEIAES
Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs, and hence our system is robust and efficient.
Comparative Analysis: Effective Information Retrieval Using Different Learnin...RSIS International
Information Retrieval is the activity of searching meaningful information from a collection of information resources such as Documents, relational databases and the World Wide Web. Information retrieval system mainly consists of two phases, storing indexed documents and retrieval of relevant result. Retrieving information effectively from huge data storage, it requires Machine Learning for computer systems. Machine learning has objective to instruct computers to use data or past experience to solve a given problem. Machine learning has number of applications, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through supervised learning, unsupervised learning and Reinforcement Learning. The goal of these three learning is to provide an effective way of information retrieval from data warehouse to avoid problems such as ambiguity. This study will compare the effectiveness and impuissance of these learning approaches.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs. Through this system, huge volume of data’s that are generated by the system will also get control.
With the surge in modern research focus towards Pervasive Computing, lot of techniques and challenges
needs to be addressed so as to effectively create smart spaces and achieve miniaturization. In the process of
scaling down to compact devices, the real things to ponder upon are the Information Retrieval challenges.
In this work, we discuss the aspects of multimedia which makes information access challenging. An
Example Pattern Recognition scenario is presented and the mathematical techniques that can be used to
model uncertainty are also presented for developing a system that can sense, compute and communicate in
a way that can make human life easy with smart objects assisting from around his surroundings.
Internet of things-based photovoltaics parameter monitoring system using Node...IJECEIAES
The use of the internet of things (IoT) in solar photovoltaic (PV) systems is a critical feature for remote monitoring, supervising, and performance evaluation. Furthermore, it improves the long-term viability, consistency, efficiency, and system maintenance of energy production. However, previous researchers' proposed PV monitoring systems are relatively complex and expensive. Furthermore, the existing systems do not have any backup data, which means that the acquired data could be lost if the network connection fails. This paper presents a simple and low-cost IoT-based PV parameter monitoring system, with additional backup data stored on a microSD card. A NodeMCU ESP8266 development board is chosen as the main controller because it is a system-on-chip (SOC) microcontroller with integrated Wi-Fi and low-power support, all in one chip to reduce the cost of the proposed system. The solar irradiance, ambient temperature, PV output voltage and PV output current, are measured with photo-diodes, DHT22, impedance dividers and ACS712. While, the PV output power is a product of the PV voltage and PV current. ThingSpeak, an opensource software, is used as a cloud database and data monitoring tool in the form of interactive graphics. The results showed that the system was designed to be highly accurate, reliable, simple to use, and low-cost.
An efficient approach on spatial big data related to wireless networks and it...eSAT Journals
Abstract
Spatial big data acts as a important key role in wireless networks applications. In that spatial and spatio temporal problems contains the distinct role in big data and it’s compared to common relational problems. If we are solving those problems means describing the three applications for spatial big data. In each applications imposing the specific design and we are developing our work on highly scalable parallel processing for spatial big data in Hadoop frameworks by using map reduce computational model. Our results show that enables highly scalable implementations of algorithms using Hadoop for the purpose of spatial data processing problems. Inspite of developing these implementations requires specialized knowledge and user friendly.
Keywords: Spatial Big Data, Hadoop, Wireless Networks, Map reduce
Optimal Operating Performances of Wireless Protocols for Intelligent Sensors ...chokrio
The systems based on intelligent sensors are currently expanding, due to theirs functions and theirs performances of intelligence: transmitting and receiving data in real-time, computation and processing algorithms, metrology remote, diagnostics, automation and storage measurements…The radio frequency wireless communication with its multitude offers a better solution for data traffic in this kind of systems. The mains objectives of this paper is to present a solution of the problem related to the selection criteria of a better wireless communication technology face up to the constraints imposed by the intended application and the evaluation of its key features. The comparison between the different wire-less technologies (Wi-Fi, Wi-Max, UWB, Bluetooth, ZigBee, ZigBeeIP, GSM/GPRS) focuses on their performance which depends on the areas of utilization. Furthermore, it shows the limits of their characteristics. Study findings can be used by the developers/ engineers to deduce the optimal mode to integrate and to operate a system that guarantees quality of communication, minimizing energy consumption, reducing the implementation cost and avoiding time con-straints.
Comparative Performance Analysis of Wireless Communication Protocols for Inte...chokrio
The systems based on intelligent sensors are currently expanding, due to theirs functions and theirs performances of intelligence: transmitting and receiving data in real-time, computation and processing algorithms, metrology remote, diagnostics, automation and storage measurements…The radio frequency wireless communication with its multitude offers a better solution for data traffic in this kind of systems. The mains objectives of this paper is to present a solution of the problem related to the selection criteria of a better wireless communication technology face up to the constraints imposed by the intended application and the evaluation of its key features. The comparison between the different wireless technologies (Wi-Fi, Wi-Max, UWB, Bluetooth, ZigBee, ZigBeeIP, GSM/GPRS) focuses on their performance which depends on the areas of utilization. Furthermore, it shows the limits of their characteristics. Study findings can be used by the developers/ engineers to deduce the optimal mode to integrate and to operate a system that guarantees quality of communication, minimizing energy consumption, reducing the implementation cost and avoiding time constraints.
Intelligent GIS-Based Road Accident Analysis and Real-Time Monitoring Automat...CSCJournals
It has been a big concern for many people and government to reduce the amount of road accident specially in Malaysia since it could be a big threat to this country. Malaysian government has spent millions of money in order to reduce the number of accident occurrence through several modes of campaign. Unfortunately, from years to years the number keeps increasing. The lack of a comprehensive accident recording and analysis system in Malaysia can be effective in these kinds of problems. By making use of IRAS (Intelligent Road Accident System), the police would be control and manage whole accident events as a real-time monitoring system. This system exploits WiMAX and GPRS communications to connect to the server for transfer the specific data to the data center. This system can be used for a comprehensive intelligent GIS-based solution for accident analysis and management. The system is developed based on object and aspect oriented software design such as .NET technology.
Secure and reliable wireless advertising system using intellectual characteri...TELKOMNIKA JOURNAL
Smart cities wireless advertising (smart mobile-AD) filed is one of the well-known area of research where smart devices using mobile ad hoc networks (MANET) platform for advertisement and marketing purposes. Wireless advertising through multiple fusion internet of things (IoT) sensors is one of the important field where the sensors combines multiple sensors information and accomplish the control of self-governing intelligent machines for smart cities advertising framework. With many advantages, this field has suffered with data security. In order to tackle security threats, intrusion detection system (IDS) is adopted. However, the existing IDS system are not able to fulfill the security requirements. This paper proposes an intellectual characteristic selection algorithm (ICSA) integrated with normalized intelligent genetic algorithm-based min-max feature selection (NIGA-MFS). The proposed solution designs for wireless advertising system for business/advertising data security and other transactions using independent reconfigurable architecture. This approach supports the wireless advertising portals to manage the data delivery by using 4G standard. The proposed reconfigurable architecture is validated by using applications specific to microcontrollers with multiple fusion IoT sensors.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
RF Planning and Optimization in GSM and UMTS NetworksApurv Agrawal
The report covers various aspects involved in improving the network coverage as well as the parameters used in planning of new network sites for GSM and UMTS networks.
Hand LightWeightNet: an optimized hand pose estimation for interactive mobile...IJECEIAES
In this paper, a hand pose estimation method is introduced that combines MobileNetV3 and CrossInfoNet into a single pipeline. The proposed approach is tailored for mobile phone processors through optimizations, modifications, and enhancements made to both architectures, resulting in a lightweight solution. MobileNetV3 provides the bottleneck for feature extraction and refinements while CrossInfoNet benefits the proposed system through a multitask information sharing mechanism. In the feature extraction stage, we utilized an inverted residual block that achieves a balance between accuracy and efficiency in limited parameters. Additionally, in the feature refinement stage, we incorporated a new best-performing activation function called “activate or not” ACON, which demonstrated stability and superior performance in learning linearly and non-linearly gates of the whole activation area of the network by setting hyperparameters to switch between active and inactive states. As a result, our network operated with 65% reduced parameters, but improved speed by 39% which is suitable for running in a mobile device processor. During experiment, we conducted test evaluation on three hand pose datasets to assess the generalization capacity of our system. On all the tested datasets, the proposed approach demonstrates consistently higher performance while using significantly fewer parameters than existing methods. This indicates that the proposed system has the potential to enable new hand pose estimation applications such as virtual reality, augmented reality and sign language recognition on mobile devices.
EFFECTIVE BANDWIDTH ANALYSIS OF MIMO BASED MOBILE CLOUD COMPUTINGIJCI JOURNAL
Digital Disruption is all around us. Mobile is overtaking desktop, Social Media is beating search, Messaging Application are challenging e-mails and everything around us is becoming connected. Mobile devices especially the smart phones are fueling the culture of “Anytime, Anywhere, And Anything’’. Smartphone is not only ubiquitous but also the primary computing device for many .These paradigm shifts are fueled by the explosive growth of smart phones which has touched a volume of 1.6 billion units globally. Smartphone growth has also triggered the explosive growth of mobile applications and cloud computing .Together, Mobile cloud computing is now a potential technology for mobile services .MCC overcomes obstacles related to battery life, storage capacity and low bandwidth. Current smart phones uses 2x2 MIMO which gives a speed 300Mbps, by using massive MIMO technology speed can be enhanced up to 1Gbps. This paper gives a BER (Bit Error Ratio) analysis to prove that by increasing number of transmitting and receiving antennas the performance can be enhanced.
Sybian Technologies Pvt Ltd
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No,33/10 Meenakshi Sundaram Building,
Sivaji Street,
(Near T.nagar Bus Terminus)
T.Nagar,
Chennai-600 017
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Mobile No:9790877889,9003254624,7708845605
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Cloud Computing
Networking
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Data Mining
Mobile Computing
Service Computing
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Bio Medical / Medical Imaging
Contact Details:
Sybian Technologies Pvt Ltd,
No,33/10 Meenakshi Sundaram Building,
Sivaji Street,
(Near T.nagar Bus Terminus)
T.Nagar,
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A New Data Offloading Framework Between Mobile Network and Campusijsrd.com
Data offloading is a technique to transfer data between different networks like mobile network to WiFi networks. WiFi or Wi-Max networks are very fast and require no spectrum fees to implement them. Whereas Mobile networks require the spectrum reservations which are highly costly and heavily affect the service charges offered by the cellular service providers. In our proposed scenario, we are using controlled data transfer mechanism to offload data between mobile network and campus wireless network to facilitate the calling facility in the campus for the smart-phone users using Wireless network in the campus.
Wideband Sensing for Cognitive Radio Systems in Heterogeneous Next Generation...CSCJournals
Mobile Next Generation Network (MNGN) is characterized as heterogeneous network where variety of access technologies are meant to coexist. Decisions on choosing an air interface that meets a particular need at a particular time will be shifted from the network’s side to (a more intelligent) user’s side. On top of that network operators and regularities have come to the realization that assigned spectrum bands are not utilized as they should be. Cognitive radio stands out as a candidate technology to address many emerging issues in MNGN such as capacity, quality of service and spectral efficiency. As a transmission strategy, cognitive radio systems depend greatly on sensing the radio environment. In this paper, we present a novel approach for interference characterization in cognitive radio networks based on wideband chirp signal. The results presented show that improved sensing accuracy is maintained at tolerable system complexity.
Comparative Study on Mobile Switching Center of Mobile Generationsijtsrd
The world of mobile wireless communication is rapidly developing. The last few years have experience a remarkable growth in wireless industry. Just within a decade, an evolution of wireless service people used every day can be completely dumbfounded from the roots of analog based first generation service 1G to today's truly broadband ready fourth generation networks. The ever growing demands for higher data rates, greater capacity and better quality of services triggered operations to come up with new network technologies. There are many improvements of mobile generations in the world of telecommunication. Mobile Switching Center can perform various types of function in mobile communication. It also connects VLR Visitor Location Register , HLR Home Location Register , AuC Authentication Center and EIR Equipment Identity Register and they are essential needing for mobile communication. This paper represents the introduction about evolution from 1G to 4G system and discusses about the main elements in Mobile Switching Center eventually it includes comparison of Mobile Switching Center in every mobile generations. Aye Myat Myat Myo | Zar Chi Soe "Comparative Study on Mobile Switching Center of Mobile Generations" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26643.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26643/comparative-study-on-mobile-switching-center-of-mobile-generations/aye-myat-myat-myo
A Review on Cooperative Communication Protocols in Wireless World ijwmn
Future generations of cellular communications requires higher data rates and a more reliable
transmission link with the growth of multimedia services, while keeping satisfactory quality of service, .
MIMO antenna systems have been considered as an efficient approach to address these demands by
offering significant multiplexing and diversity gains over single antenna systems without increasing
bandwidth and power. Although MIMO systems can unfold their huge benefit in cellular base stations,
but they may face limitations when it comes to their deployment in mobile handsets.
To overcome this drawback, relays (fixed or mobile terminals) can cooperate to improve the overall
system performance in cellular networks. Cooperative communications can efficiently combat the severity
of fading and shadowing through the assistance of relays. It has been found that using relays the capacity
and coverage of cellular networks can be extended without increasing mobile transmit power or
demanding extra bandwidth.
Similar to ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE MODELING OF A 3G MOBILE PHONE BASE RADIO (20)
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE MODELING OF A 3G MOBILE PHONE BASE RADIO
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.13, No.1, January 2022
DOI: 10.5121/ijaia.2022.13107 103
ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE
MODELING OF A 3G MOBILE PHONE BASE RADIO
Eduardo Calo1, 5
, Gabriel Vaca1
, Cristina Sánchez2
, David Jines3
,
Giovanny Amancha3
, Ángel Flores3
, Alex Santana G3
and Fernanda Oñate4
1
Carrera Electricidad, Instituto Superior Tecnológico Tungurahua, Ambato, Ecuador
2
Carrera Mecánica Automotriz,
Instituto Superior Tecnológico Tungurahua, Ambato, Ecuador
3
Carrera Electrónica, Instituto Superior Tecnológico Tungurahua, Ambato, Ecuador
4
Instituto Superior Tecnológico Pelileo-Baños, Baños, Ecuador
5
UNIR, Logroño, La Rioja, España
ABSTRACT
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.
KEYWORDS
Artificial Intelligence, 3G Mobile Phone, Neural Networks, Performance, Radio Base, Random Forest,
Throughput.
1. INTRODUCTION
One of the most evolving techniques in the field of Artificial Intelligence has been deep learning,
thanks to this advance a development has begun to very notorious in various sectors such as
financial, vehicle, telecommunications, understanding of natural language, translators of
languages and many more sectors worldwide, streamlining processes optimization and having a
future vision of them, optimizing resources.
The intelligence artificial techniques in a practical case of real life, such as the analysis of the
performance of an RBS and predict its behaviour or results to be obtained after the analysis of its
operating parameters, the same ones that in the telecommunications area will help us in the
optimization of resources and in the improvement of a telephone network third generation, the
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analysis of this performance was carried out at the level of data traffic in telephone stations,
predicting its behaviour with the change of different variables.
To specify this practical case, several models were generated based on in various artificial
intelligence techniques such as Random Forest, Decision Trees and Neural Networks, 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.
1.1. Origin, evolution, perspectives of cellular mobile telephony
The evolution in mobile telephony has had great events, such is the case that the use of mobile
devices has potentially increased use of mobile phones initially relied on him sending voice
signals and text messages in what corresponds to the first generation of mobile telephony, from
there onwards there have been various types of evolutions that range from the first generation to
the fifth generation, from the third generation the development is very significant that the itself
allows the use of mobile phones not only for transmission determined which is the model that
would give us the best results when working With this type of data, the variables with which we
worked, have relation to data traffic measurements within radio bases, both for data upload and
download traffic.
1.2. Cognitive wireless networks for intelligent techniques of dynamic spectrum
The techniques of artificial intelligence that help provide solutions in the management of
electromagnetic spectrum in wireless networks, taking into account that mobile telephony is
within wireless networks due to the use and management of frequencies found in the spectrum,
these investigated techniques allow to analyse parameters such as spectrum availability, energy
consumption, channel division, necessary requirements for the user, taking into account that all
These parameters have as a final result the existence of a transmission and reception wireless
communication system optimal data. [1].
2. STATE OF THE ART
This work evaluates the use of Artificial Intelligence as a means of function of receiving the
signals in an RBS both for frequencies of rise and fall in the two types of technologies. [2]
2.1. Intelligent Systems applied to Data
The emergence of artificial intelligence and the use of all its techniques and tools have resulted in
the emergence of intelligent systems the same ones that are capable of learning and execute
actions automatically based solely on examples of some type of functioning adapting to learning
very easily, as the picture shows, neural networks have been used in various investigations for
data congestion analysis in various types of networks such as ATMs, as well as in the part IT
security, obtaining good results. [3]
The discipline in computer science given by the mathematician Alan Turing began the birth of
Artificial Intelligence that aims to question whether machines have the ability to think and is
currently known as the Turing test. To obtain a good design of the topology of a network with
restrictions using the tools of artificial intelligence, it is necessary to build an algorithm that
analyses each and every one of the possible solutions. [4]
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Figure 1. Intelligent Systems applied to Data [4]
2.2. Third generation cellular networks CDMA2000 and WCDMA
After the emergence of second-generation technology and due to the increase in users and
therefore saturation of the network and need to implement new services in what has to do with
mobile telephony, third generation cellular networks appear with its CDMA 2000 and WCDMA
models, the first of these models is the link to second generation and was in charge of the link of
compatibility and migration between second and third generation, while WCDMA focuses on
increasing the number of users, as the picture shows [5]
In CDMA2000 there are two modulation alternatives for the downlink and these are: multicarrier
modulation (MC) and direct spread (DS) that use 3 carriers of 1.25 MHz or 5 MHz; while in
WCDMA it uses a single 5 MHz carrier.[6]
Figure 2. Third generation cellular networks CDMA2000 and WCDMA [5]
2.3. Prediction of the demand for fixed telephony through artificial neural networks
The high increase in users both for fixed telephony and for mobile telephony has made the
statistics in bases regarding the demand for the acquisition of these services increases, as the
picture shows, the operators that provide this type of service are in need of use tools that provide
estimates of next values growth of users and demand for the service. [7]
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The adoption of a new technology generally follows a growth pattern of a logistics curve in
which low growth with few users is initially identified, followed by high accelerated growth in
moderate time intervals. [8]
Figure 3. Prediction of the demand for fixed telephony through artificial neural networks [8]
2.4. Neural networks and traffic prediction
The use of artificial intelligence tools allows us to predict data traffic, as the picture shows,
classify the types of traffic and recognize the existing variables in the analysis of information
traffic of a network, These AI methods are additional alternatives that allow us to an easy way to
solve this problem, in this case it applies a neural network that based on the weights of neurons
and after executing several iterations gives us predictions with data according to expectations. [9]
The different ways that exist to predict traffic consist of extracting the underlying relationships of
the previous values and they are also used to extrapolate and predict future behavior. [10]
Figure 4. Neural networks and traffic prediction [10]
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3. WORK METHODOLOGY
The project is the generation of various predictive models of the performance of a base radio
third-generation mobile telephony, exactly targeted to traffic of data, based on machine learning
algorithms within artificial intelligence, and after an analysis of the techniques existing within
this branch and the type of data that the PRS tool (RBS management and monitoring).[11]
a) For the development and implementation of said models of Predicting the performance of
a mobile phone base radio is used the Python Programming Language, due to its
variability and the large number of intelligence tools and libraries.
b) Use of the free platform Google Colaboratory for development of the models, this is a
cloud service, which provides us with a Jupyter Notebook that we can access with a web
browser without import if we use Windows, Linux or Mac, has as great advantages since
being an online tool suitable for this type of jobs offers high-performance technical
features such as adequate RAM, possibility of activating a GPU.
c) A general analysis was performed to determine the properties statistics of the data and
especially of the variables to be predicted, when use such information what we determine
is the type of data with those that we are going to work on, in addition, it was determined
that for the variables to predict there is a very large difference between their values
because when compare the maximum, minimum and the mean you can notice that they
exist values that can be very small with respect to others of the same variable.
d) Because the range of the values of the variables to be predicted is extremely broad, it was
necessary to include a stage of data normalization in order to avoid a bias in the
implementation of the model, after testing and analysing the results, the normalization
method known as MinMaxScaler, the same one that generated an optimal normalization
of the data with which we should work.[12]
e) Three models were implemented for each variable and one model final where an estimate
of the three variables was attempted at the once with a single model. For this, an order
was followed, being the first to always make the selection of the variables to use
according to their correlation.
f) After analysing the results of this last model, they determined that the first variable has
no relationship with the other two so when using a single model to estimate all three, the
other Variables impair performance in estimating this.
4. DEVELOPMENT
4.1. Obtaining Data
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.
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. [13]
4.2. Selection of Variables to Predict
The predictor variables that were selected for the elaboration from this job they were:
VS.RAC.DL.EqvUserNum, VS.RAC.UL.EqvUserNum, PS Trafic Volume_GUL, this set of
variables help us to have a general and statistical idea of the behaviour of mobile phone base
radios with respect to the data traffic existing in it.
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Figure 5. Graph of the selection of variables to predict (Google Colaboratory)
4.3. Statistic Analysis
First, a general analysis was carried out to determine the statistical properties of the data and
especially of the variables a predict.
Figure 6. Graph of the data set variables (Google Colaboratory)
Using this information, it was determined that the type of data with those that were worked on, in
this case the three variables to be predicted were of the type floating point and 64-bit meaning
that they contained a decimal format.
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Figure 7. Graph of the data type of the variables to be predicted (Google Colaboratory)
Furthermore, what was found is that for the variables to be predicted there were a very big
difference between their values because when comparing the maximum, minimum and mean it
can be noted that there are values that can be very small with respect to others of the same
variable. [14]
Figure 8. Graph of the existing values in the variables (Google Colaboratory)
Moreover, simple graphics prints were also made of the data to visually analyse how the data are
distributed and observe if there could be outliers or also known data as outliers.
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a)
b)
c)
Figure 11. a) Graph of the data distribution of the variable VS.RAC.DL.EqvUserNum, b) Graph of the data
distribution of the variable PS Trafic, c) Graph of the data distribution of the variable
VS.RAC.UL.EqvUserNum.
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4.4. Correlation
Since in the database with which we worked, there was a large number of variables, around a
hundred, that could be used to estimate the target variables, to determine the ones with the most
information that can contribute, an analysis of the correlation between all the variables with
special emphasis on target variables.
Figure 12. Variable correlation graph (Google Colaboratory)
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4.5. Data Normalization
Because the range of the values of the variables to be predicted is extremely broad, it was
necessary to include a stage of data normalization in order to avoid a bias in the implementation
of the model, after testing and analysing the results, the normalization method known as
MINMAXSCALER,
Figure 13. MinMaxScaler normalization graph (Google Colaboratory)
5. MODELS - VARIABLE PS TRAFIC
5.1. Neural Network
In the same way, for the neural network model, various tests modifying the number of neurons in
the hidden layer and the number of hidden layers, as well as their function of activation, its
learning rate, its optimization algorithm, number of epochs and overtraining control, looking for
optimize the value of your hyperparameters for better performance [13], the optimizer that was
used was an LBFGS optimizer, the r2 Score was 0.6979385979700603
Figure 14. Graph of r2 score for the Neural Network (Google Colaboratory)
5.2. Decision Trees
Using decision trees, the hyperparameters by increasing and decreasing depth, parameters with
respect to the sheets and others, in order to find the best ones. [15] As we can see in the graph the
result that is displayed is the value of r2 score, this value is a metric that tells me the accuracy in
prediction calculations of variables that as we can see for this model is a value of
0.6891097048890115
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Figure 15. R2 score graph for the Decision Tree (Google Colaboratory)
5.3. Random Forest
Finally, with the Random Forest technique what was done was a search using grid search to find
your best hyperparameters, prior to this test were performed modifying the parameters manually
and finding in which range is where better performance has the model similar to the process used
for the other models, the r2 Score was 0.7162649923099472
Figure 16. Graph of r2 score for Random Forest (Google Collaboratory)
Once this was determined, a finer adjustment was made. using grid search and using only
parameters in ranges where better performance was found with an r2 Score 0.7196744018529605
Figure 17. R2 score plot for Random Forest with fine adjustment (Google Colaboratory)
A similar procedure was used for the other two variables. During the training and testing of the
models, the only thing that changed is that in these no atypical data were found as in the case of
the data traffic variable. [16]
5.3.1. Variable VS.RAC.DL.EqvUserNum
a) Neural Network
R2 Score of 0.8883605358098641
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Figure 18. R2 score graph for the Neural Network (Google Colaboratory)
b) Decision trees
As we can see in the graph the result that is displayed is the value of r2 score, this value is a
metric that tells me the accuracy in prediction calculations of variables that as we can see for this
model is a value of 0.8324425441778565
Figure 19. R2 score graph for the Decision Tree (Google Collaboratory)
c) Random Forest
R2 score was 0.8915394414631109
Figure 20. R2 score plot for Random Forest (Google Colaboratory)
Once this was determined, a finer adjustment was made. using grid search and using only
parameters with ranges regulated obtaining an r2 Score of 0.8898856746512142
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Figure 21. Graph of r2 score for Random Forest with fine adjustment (Google Colaboratory)
5.3.2. Variable VS. RAC.UL.EqvUserNum '
a) Neural Network
Obtaining an R2 Score of 0.999414359403538
Figure 22. R2 score graph for the Neural Network (Google Colaboratory)
b) Decision trees
As we can see in the graph the result that is displayed is the value of r2 score, this value is a
metric that tells me the accuracy in prediction calculations of variables that as we can see for this
model is a value of 0.9710786186949242. [16]
Figure 23. R2 score graph for the Decision Tree (Google Colaboratory)
c) Random Forest
R2 score was 0.9898875480445627
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Figure 24. R2 score plot for Random Forest (Google Colaboratory)
Once this was determined, a finer adjustment was made. using grid search and using only
parameters in ranges where a better performance was found, and an r2 Score of
0.9917941067924856
Figure 25. R2 score plot for Random Forest with fine adjustment (Google Colaboratory)
5.4. Model to Estimate Three Variables
Once the experiments have been carried out with each variable separately, it was decided to carry
out an additional experiment using random forest, since it was the one that best estimated all the
variables, to estimate all variables at once and not having an independent model for each variable.
In this case, for the selection of variables was chosen for the variables that have a correlation
greater than 0.8 with respect to the variables to be predicted. [17]
Figure 26. R2 score graph for the three-variable model (Google Colaboratory)
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6. RESULTS
6.1. Result of the Model and Testing Variable PS TRAFIC
Regarding the testing process, to validate the models, we used the test data set and the target
variable was estimated. Then the exit of the model was compared with the real data using the
metric of the r2 score which is widely used in this type of model where regressions are
performed, this metric allows us to identify how efficient is the estimate with respect to all the
test data.
Figure 27. Plot of original and predicted data in each model (Google Colaboratory)
6.2. Outcome of the model and testing variable Vs. RAC.UL.EqvUserNum
Graphs were made of the results obtained and the real data for each model, for said variable, here
it could be noted that the decision trees do not have the ability to fully estimate the objective
variables, which does not happen with the other models. [18]
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Figure 28. Plot of original and predicted data in each model (Google Colaboratory)
6.3. Outcome of the model and testing variable Vs. RAC.DL.EqvUserNum
Graphs were made of the results obtained and the real data for each model, for said variable, here
it could be noted that the decision trees do not have the ability to fully estimate the objective
variables, which does not happen with the other models.
Figure 29. Plot of original and predicted data in each model (Google Colaboratory)
6.4. Discussion and Analysis of Results
As a final part of the development and implementation of the models applied Artificial
Intelligence, the numerical analysis of values given in each of the tests, this in order to have a
overview and summary of the values obtained, for each of the proposed models and depending on
each of the variables of analysis, as detailed below: [19][20]
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Table 1. Analysis of results for all models (Google Collaboratory)
VARIABLE MODELO NORMALIZACION CORRELACIÓN R2 SCORE
PS TRAFIC MB
Arboles de
Decisión MinMaxScaler >=0.72 0.6891097048890115
PS TRAFIC MB
Redes
Neuronales MinMaxScaler >=0.72 0.6979385979700603
PS TRAFIC MB
Random Forest MinMaxScaler >=0.72 0.7162649923099472
Ajuste fino MinMaxScaler >=0.72 0.7196744018529605
VS.RAC.DL.EqvUserNum
Arboles de
Decisión MinMaxScaler >=0.8 0.8324425441778565
VS.RAC.DL.EqvUserNum
Redes
Neuronales MinMaxScaler >=0.8 0.8883605358098641
VS.RAC.DL.EqvUserNum
Random Forest MinMaxScaler >=0.8 0.8915394414631109
Ajuste fino MinMaxScaler >=0.8 0.8898856746512142
VS.RAC.UL.EqvUserNum
Arboles de
Decisión MinMaxScaler >=0.8 0.9710786186949242
VS.RAC.UL.EqvUserNum
Redes
Neuronales MinMaxScaler >=0.8 0.999414359403538
VS.RAC.UL.EqvUserNum
Random Forest MinMaxScaler >=0.8 0.9898875480445627
Ajuste fino MinMaxScaler >=0.8 0.9917941067924856
Modelo para estimar tres
variables
Random Forest MinMaxScaler >=0.8
PS TRAFIC MB=
0.541594732758471
VS.RAC.DL.EqvUserNum=
0.8881648157651807
VS.RAC.UL.EqvUserNum=
0.8931188204989436
7. CONCLUSIONS
In the results it was determined that the model with the best performance is the model based on
random forest, with respect to the other models studied, with the second best performance
compared to the model based on neural networks with fewer deficiencies and the decision tree
model was the poorer performance compared to the others.
It was determined that random forests and neural networks have had the best results as they have
a structure with a greater capacity for generalizing information and knowledge.
The model developed to represent the three variables at the same time, the performance of the
traffic variable has a lower representation, it was predicted that this behavior occurred because
the other two variables between them share a greater relationship than with that one. of traffic
It was identified that the use of these techniques is relevant since it can be extended to
applications such as virtual variable sensors, where variables that are complex to measure (due to
sensor costs or difficult to access) through variables make them easier to measure. measure.
FUTURE LINES OF WORK
In the future this work may be very helpful in the optimization and monitoring of
communications networks, whether mobile or fixed, determining their behaviour and
performance more quickly.
As part of the improvement of this work, it is planned in the future to implement the ARIMA
method for the prediction of this type of data, taking into account that ARIMA is a tool that
allows working with time series, whether stationary or non-stationary, as we could see at the
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beginning of our work the data set that was used contains countless variables, in which the
variable that relates times or schedules of greater data traffic in an RBS could also be inserted.
ACKNOWLEDGEMENTS
The authors thank the higher education institutions that contributed to the research with their
authors, as place of work such us: Instituto Superior Tecnológico María Natalia Vaca, Instituto
Superior Tecnológico Tungurahua, Ambato, Ecuador, Instituto Superior Tecnológico Pelileo-
Baños, Baños, Ecuador
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AUTHORS
Eduardo Luis Calo Villalva - Master's Degree in Artificial Intelligence (Universidad
Internacional de la Rioja), Electronics and Communications Engineer (Technical
University of Ambato), teacher in the Electricity career at the Instituto Superior
Tecnológico María Natalia Vaca.
Gabriel Alejandro Vaca Ortega - Master's Degree in Systems Management (Universidad
de las Fuerzas Armadas ESPE), Electronics and Communications Engineer (Technical
University of Ambato), coordinator and teacher in the Electricity career at the Instituto
Superior Tecnológico María Natalia Vaca.
Cristina Del Rocio Sanchez Lara (Universidad de Fuerzas Armadas ESPEL -
Latacunga), Mechatronics Engineer. Project execution and automation engineer.
Instructor of Educational Robotics with Mechatronic Legos. Full-time professor at the
Instituto Superior Tecnológico Maria Natalia Vaca in the automotive area – in the city of
Ambato.
David Alejandro Jines Espín. - Master in Technologies for the management and
teaching practice (Pontificia Universidad Católica del Ecuador Ambato Headquarters),
Electronics and Communications Engineer (Technical University of Ambato). SNNA
Leveling Teacher at the Technical University of Ambato. Currently Professor of the
electronics career at the Instituto Tecnologico Superior Maria Natalia Vaca.
Washington Giovanny Amancha Punina. - Master's Degree in Artificial Intelligence
(Universidad Internacional de la Rioja), Electronics and Communications Engineer
(Universidad Técnica de Ambato). IT Analyst at Tata Consultancy Services (TCS).
Professor of Instituto Superior Tecnológico SECAP.I currently work as a coordinator and
teacher in the Electronica career at the Instituto Superior Tecnológico María Natalia Vaca.
Angel Arturo Flores Lescano – Master in Management information systems
(Universidad Autónoma de los Andes – Ambato), Electronic and Communications
Engineer (Universidad Técnica de Ambato), Electronics professor at the Instituto Superior
Tecnológico Maria Natalia Vaca.
Alex Mauricio Santana Gallo. - Master's Degree in Electronic Systems Engineering
(Universidad Politécnica de Madrid), Visiting Researcher HCT-LAB Universidad
Autónoma de Madrid 2016 (Universidad de Fuerzas Armadas ESPE), Electronics and
Instrumentation Engineer (Universidad de Fuerzas Armadas ESPE). CEO of the company
IA-KUNTUR S.A.S. B.I.C. dedicated to the development of artificial intelligence and
data science located in the city of Latacunga- Ecuador. Currently teaching the electronic
career at the Instituto Superior Tecnológico María Natalia Vaca, Instituto Superior
Tecnológico Tungurahua.
María Fernanda Oñate Pico. - Master in Higher Education (Universidad de Fuerzas
Armadas ESPE), Teacher in technical specialty (Instituto Superior Tecnológico Pelileo-
Baños), Mathematics Teacher (La Salle Ambato Unit), Mechatronics
Engineering(Universidad de Fuerzas Armadas ESPE), FINANCIAL MANAGER of the
company IA-KUNTUR S.A.S. B.I.C. dedicated to the development of artificial
intelligence and data science located in the city of Latacunga-Ecuador.