This document summarizes a research paper on age and gender detection using deep learning. It discusses using convolutional neural networks (CNNs) to extract features from images of faces and classify them by age and gender. The methodology section describes using a CNN model trained on labeled image data to learn the features that distinguish ages and genders. It then discusses preprocessing images, detecting faces, extracting features using CNN layers, and classifying images into age and gender categories. The output section notes that testing the trained model on new images in real-time can classify faces by gender and assign them to an age group with results displayed on screen.
Age and Gender Classification using Convolutional Neural NetworkIRJET Journal
This document describes a study that aims to accurately identify the gender and age range of facial images using convolutional neural networks. It begins with an introduction to age and gender classification and some of the challenges. It then discusses the system analysis and design, including the use of convolutional neural networks and machine learning techniques. The implementation section notes that Python, TensorFlow, Keras and OpenCV will be used to build a convolutional neural network model to detect faces in images and predict age and gender through training on available datasets. The overall goal is to develop an accurate system for age and gender detection from facial images.
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
The document summarizes research on facial age estimation using transfer learning and Bayesian optimization based on gender information. Specifically:
1) A convolutional neural network is trained to classify gender from facial images. This gender classification CNN is then used as input for an age estimation model.
2) Bayesian optimization is applied to the pre-trained gender classification CNN to fine-tune it for the age estimation task. This reduces error on validation data.
3) Experiments on the FERET and FG-NET datasets show the proposed approach of using gender information and Bayesian optimization outperforms state-of-the-art methods, achieving a mean absolute error of 1.2 and 2.67 respectively.
IRJET- Age Analysis using Face Recognition with Hybrid AlgorithmIRJET Journal
1) The document presents a study on age analysis of human faces using a hybrid algorithm combining multiple methods including convolutional neural networks.
2) Facial images are collected then preprocessed and trained on a detection model to extract features. A convolutional neural network model is used to classify ages from the extracted features.
3) The results found the proposed hybrid algorithm approach achieved 96.7% accuracy in age prediction from faces, outperforming existing methods. This shows promise for accurate automatic age analysis.
Prediction of Age by utilising Image Dataset utilising Machine LearningIRJET Journal
This document discusses using machine learning and convolutional neural networks to predict a person's age from an image of their face. It begins with an abstract that outlines using CNNs to extract features from facial images in order to predict age. The introduction provides context on age prediction applications and common AI methods used, such as deep learning and image recognition.
The document then reviews related literature on using CNNs and other neural networks for age and gender prediction. It describes the CNN architecture to be used - consisting of 3 convolutional layers and 2 fully connected layers. Software requirements are listed, including TensorFlow, Keras and other Python libraries. The implementation section discusses using OpenCV for face detection followed by a CNN for age prediction within 5 age groups. It outlines
IRJET- Spot Me - A Smart Attendance System based on Face RecognitionIRJET Journal
The article discusses international issues. It mentions that globalization has increased economic interdependence between nations while also raising tensions over immigration and trade. Solutions will require cooperation and compromise and a recognition that isolationism is not a viable strategy in an interconnected world.
A Survey on different techniques used for age and gender classificationIRJET Journal
This document summarizes research on techniques for age and gender classification from facial images. It reviews 6 papers that used various methods including convolutional neural networks (CNN), conditional probability neural networks, and GoogleNet. CNN achieved the highest accuracy of 96% for age and gender classification. Conditional probability neural networks achieved 72% accuracy for age classification and 94% for gender. The document also compares datasets like Kaggle, IMDB-WIKI, and Adience that were used and finds CNN to be the most effective technique for this task.
Vehicle Driver Age Estimation using Neural NetworksIRJET Journal
This document presents research on developing a convolutional neural network model to estimate the age and gender of a vehicle driver from their facial image. The researchers assembled a large dataset of over 60,000 face images from various sources to train their CNN model. They implemented the model using Caffe and tested it on a Raspberry Pi 3B+ for real-time age and gender detection. After training, the CNN model was able to accurately classify age and gender from input images with an accuracy of 98.1%. The document discusses the CNN architecture, preprocessing steps, and algorithms used to develop this age and gender detection system for vehicle drivers.
Age and Gender Prediction and Human countIRJET Journal
This document presents a system for age and gender prediction and human counting using deep convolutional neural networks. The system is trained on the Adience benchmark dataset containing over 26,000 images labeled with age and gender. A caffe model is used to perform the predictions, which are then deployed on a website. The model achieves 88% accuracy for age and gender prediction. The system also counts the number of faces in an image using a simple CNN architecture. The purpose is to demonstrate how machine learning can be used to detect age, gender, and count humans in images with reduced manual effort.
Age and Gender Classification using Convolutional Neural NetworkIRJET Journal
This document describes a study that aims to accurately identify the gender and age range of facial images using convolutional neural networks. It begins with an introduction to age and gender classification and some of the challenges. It then discusses the system analysis and design, including the use of convolutional neural networks and machine learning techniques. The implementation section notes that Python, TensorFlow, Keras and OpenCV will be used to build a convolutional neural network model to detect faces in images and predict age and gender through training on available datasets. The overall goal is to develop an accurate system for age and gender detection from facial images.
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
The document summarizes research on facial age estimation using transfer learning and Bayesian optimization based on gender information. Specifically:
1) A convolutional neural network is trained to classify gender from facial images. This gender classification CNN is then used as input for an age estimation model.
2) Bayesian optimization is applied to the pre-trained gender classification CNN to fine-tune it for the age estimation task. This reduces error on validation data.
3) Experiments on the FERET and FG-NET datasets show the proposed approach of using gender information and Bayesian optimization outperforms state-of-the-art methods, achieving a mean absolute error of 1.2 and 2.67 respectively.
IRJET- Age Analysis using Face Recognition with Hybrid AlgorithmIRJET Journal
1) The document presents a study on age analysis of human faces using a hybrid algorithm combining multiple methods including convolutional neural networks.
2) Facial images are collected then preprocessed and trained on a detection model to extract features. A convolutional neural network model is used to classify ages from the extracted features.
3) The results found the proposed hybrid algorithm approach achieved 96.7% accuracy in age prediction from faces, outperforming existing methods. This shows promise for accurate automatic age analysis.
Prediction of Age by utilising Image Dataset utilising Machine LearningIRJET Journal
This document discusses using machine learning and convolutional neural networks to predict a person's age from an image of their face. It begins with an abstract that outlines using CNNs to extract features from facial images in order to predict age. The introduction provides context on age prediction applications and common AI methods used, such as deep learning and image recognition.
The document then reviews related literature on using CNNs and other neural networks for age and gender prediction. It describes the CNN architecture to be used - consisting of 3 convolutional layers and 2 fully connected layers. Software requirements are listed, including TensorFlow, Keras and other Python libraries. The implementation section discusses using OpenCV for face detection followed by a CNN for age prediction within 5 age groups. It outlines
IRJET- Spot Me - A Smart Attendance System based on Face RecognitionIRJET Journal
The article discusses international issues. It mentions that globalization has increased economic interdependence between nations while also raising tensions over immigration and trade. Solutions will require cooperation and compromise and a recognition that isolationism is not a viable strategy in an interconnected world.
A Survey on different techniques used for age and gender classificationIRJET Journal
This document summarizes research on techniques for age and gender classification from facial images. It reviews 6 papers that used various methods including convolutional neural networks (CNN), conditional probability neural networks, and GoogleNet. CNN achieved the highest accuracy of 96% for age and gender classification. Conditional probability neural networks achieved 72% accuracy for age classification and 94% for gender. The document also compares datasets like Kaggle, IMDB-WIKI, and Adience that were used and finds CNN to be the most effective technique for this task.
Vehicle Driver Age Estimation using Neural NetworksIRJET Journal
This document presents research on developing a convolutional neural network model to estimate the age and gender of a vehicle driver from their facial image. The researchers assembled a large dataset of over 60,000 face images from various sources to train their CNN model. They implemented the model using Caffe and tested it on a Raspberry Pi 3B+ for real-time age and gender detection. After training, the CNN model was able to accurately classify age and gender from input images with an accuracy of 98.1%. The document discusses the CNN architecture, preprocessing steps, and algorithms used to develop this age and gender detection system for vehicle drivers.
Age and Gender Prediction and Human countIRJET Journal
This document presents a system for age and gender prediction and human counting using deep convolutional neural networks. The system is trained on the Adience benchmark dataset containing over 26,000 images labeled with age and gender. A caffe model is used to perform the predictions, which are then deployed on a website. The model achieves 88% accuracy for age and gender prediction. The system also counts the number of faces in an image using a simple CNN architecture. The purpose is to demonstrate how machine learning can be used to detect age, gender, and count humans in images with reduced manual effort.
FACE VERIFICATION ACROSS AGE PROGRESSION USING ENHANCED CONVOLUTION NEURAL NE...sipij
This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand to develop robust methods to verify facial images when they age. In this paper, a deep learning method based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG) and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and classification. The experiments are based on the facial images collected from MORPH and FG-Net benchmarked datasets. Euclidean distance has been used to measure the similarity between pairs of feature vectors with the age gap. Experiments results show an improvement in the validation accuracy conducted on the FG-NET database, which it reached 100%, while with MORPH database the validation accuracy is 99.8%. The proposed method has better performance and higher accuracy than current state-of-the-art methods.
Face Verification Across Age Progression using Enhanced Convolution Neural Ne...sipij
This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a
texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand
to develop robust methods to verify facial images when they age. In this paper, a deep learning method
based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG)
and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and
classification
An architectural framework for automatic detection of autism using deep conv...IJECEIAES
The brainchild in any medical image processing lied in how accurately the diseases are diagnosed. Especially in the case of neural disorders such as autism spectrum disorder (ASD), accurate detection was still a challenge. Several noninvasive neuroimaging techniques provided experts information about the functionality and anatomical structure of the brain. As autism is a neural disorder, magnetic resonance imaging (MRI) of the brain gave a complex structure and functionality. Many machine learning techniques were proposed to improve the classification and detection accuracy of autism in MRI images. Our work focused mainly on developing the architecture of convolution neural networks (CNN) combining the genetic algorithm. Such artificial intelligence (AI) techniques were very much needed for training as they gave better accuracy compared to traditional statistical methods.
RETINAL IMAGE CLASSIFICATION USING NEURAL NETWORK BASED ON A CNN METHODSIRJET Journal
This document discusses using a convolutional neural network to classify retinal images. Specifically, it aims to develop a system to distinguish between different retinal diseases using fundus images. The system would extract retinal features from the images like the retina, optic nerve and lesions. It then uses a CNN to detect multiple retinal diseases in fundus photographs from a structured analysis database. The CNN is trained on publicly available retinal image datasets. Neural networks have been found to effectively capture disease-specific color and texture features to enable automated diagnosis similar to human experts. The document also provides background on related work using deep learning and CNNs for tasks like lesion detection and classification of retinal diseases from fundus images.
Survey on Human Behavior Recognition using CNNIRJET Journal
This document discusses human behavior recognition using convolutional neural networks (CNNs). It first introduces the importance of human behavior recognition and some commonly used datasets. It then discusses related works that have used techniques like CNNs, LSTM networks, and R-CNN to recognize behaviors. The document proposes using the YOLOv3 algorithm to recognize behaviors in real-time video data. It describes the YOLOv3 algorithm and how it divides images into grids to predict boundary boxes and confidence scores for object detection. The goal is to automatically recognize human behaviors from video data using a CNN-based approach like YOLOv3 without manual annotation of training data.
Literature Review on Gender Prediction Model using CNN AlgorithmIRJET Journal
The document discusses a literature review on gender prediction models using CNN algorithms. It summarizes previous research that used techniques like CNNs, K-means clustering, and open-CV to develop models for gender classification from images with over 90% accuracy. The paper also proposes developing an enhanced gender prediction model using a CNN approach for pre-processing images and evaluating the accuracy of the model. It suggests CNNs can effectively extract features from faces and classify gender by training on labeled image datasets. Evaluation of the proposed model showed 98.7% accuracy on open-CV and 94% on CNN datasets.
IRJET - Real Time Facial Analysis using Tensorflowand OpenCVIRJET Journal
This document presents a real-time facial analysis system using TensorFlow and OpenCV. The system can detect facial expressions, age, and gender from images and video in real-time. It uses deep learning models trained on facial datasets to analyze faces. The system is designed for applications like security, attendance tracking, and finding lost children. It works by extracting facial features from images, applying preprocessing techniques, classifying faces, and making predictions about attributes. The document discusses the methodology, existing techniques like PCA and HMM, the proposed system architecture, sample code, and conclusions.
Face recognition across age progression is remains one of the areas most challenging tasks now a days, as the aging process affects both the shape and texture of a face. One possible solution is to apply a probabilistic model to represent a face simultaneously with its identity variable, which is stable through time, and its aging variable, which changes with time. This paper proposes a deep learning and set based approach to the face recognition subject to aging. The images for each subject taken at various times are treated as a single set, which is then compared to the sets of images belonging to other subjects. Facial features are extracted using a convolutional neural network characteristic of the deep learning. Our experimental results show that set based recognition performs better than the singleton based approach for the both face identification and face verification. We also find that by using set based recognition, it is easier to recognize older subjects from younger ones rather than younger subjects from older ones. Prathama V | Thippeswamy G ""Age Invariant Face Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23572.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23572/age-invariant-face-recognition/prathama-v
A Deep Learning Approach for the Detection and Identification of Neovasculari...IRJET Journal
This document proposes a deep learning approach to detect and classify neovascularization in fundus images. Neovascularization is abnormal blood vessel growth in the retina that can cause vision loss if not treated early. The authors develop a convolutional neural network model using transfer learning on pre-trained networks to detect neovascularization in fundus images. They then classify the detected neovascularization into stages of severity from healthy to proliferative. This approach aims to help diagnose neovascularization earlier to improve treatment outcomes for patients.
Person identification based on facial biometrics in different lighting condit...IJECEIAES
Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individual’s identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.
Face detection and recognition has been prevalent with research scholars and diverse approaches have been
incorporated till date to serve purpose. The rampant advent of biometric analysis systems, which may be full body
scanners, or iris detection and recognition systems and the finger print recognition systems, and surveillance systems
deployed for safety and security purposes have contributed to inclination towards same. Advances has been made with
frontal view, lateral view of the face or using facial expressions such as anger, happiness and gloominess, still images
and video image to be used for detection and recognition. This led to newer methods for face detection and recognition
to be introduced in achieving accurate results and economically feasible and extremely secure. Techniques such as
Principal Component analysis (PCA), Independent component analysis (ICA), Linear Discriminant Analysis (LDA),
have been the predominant ones to be used. But with improvements needed in the previous approaches Neural Networks
based recognition was like boon to the industry. It not only enhanced the recognition but also the efficiency of
the process. Choosing Backpropagation as the learning method was clearly out of its efficiency to recognize non linear
faces with an acceptance ratio of more than 90% and execution time of only few seconds.
This document summarizes a research paper that proposes a method for detecting and recognizing faces using the Viola Jones algorithm and Back Propagation Neural Network (BPNN).
The paper first discusses face detection and recognition challenges. It then provides background on Viola Jones algorithm and BPNN. The proposed methodology uses Viola Jones for face detection, converts the image to grayscale and binary, then trains segments or the whole image with BPNN. Results are analyzed using training, testing and validation curves in the MATLAB neural network tool to minimize error. In under 3 sentences, this document outlines the key techniques, proposed method, and analysis approach discussed in the source research paper.
IRJET- A Comprehensive Survey and Detailed Study on Various Face Recognition ...IRJET Journal
This document provides a comprehensive survey and detailed study of various face recognition methods. It begins with an introduction to face recognition and its advantages over other biometric methods. It then categorizes face recognition methods into four types: knowledge-based, feature-based, template matching, and appearance-based. The majority of the document discusses these methods in further detail and provides examples of algorithms that fall under each type, such as Eigenfaces, LDA, ICA, and neural networks. It concludes by stating that reviewing existing face recognition algorithms may lead to improved methods for solving this fundamental problem.
https://ijitce.com/index.php
Our journal maintains rigorous peer review standards. Each submitted article undergoes a thorough evaluation by experts in the respective field. This stringent review process helps ensure that only high-quality and scientifically sound research is accepted for publication. Researchers can trust that the articles they find in IJITCE have been critically assessed for validity, significance, and originality.
The document discusses research on developing machine learning and deep learning models to detect mental health issues in university students. It introduces the CASTLE framework, which uses multi-modal data like social interactions, academic performance, and appearance to detect issues. It also discusses using EEG data and features to recognize multiple anxiety levels in students. The objectives are to improve CASTLE to use more student data and analyze results with causal learning techniques. Coursework focuses on machine learning, deep learning, and related topics to support the research.
Study on Different Human Emotions Using Back Propagation Methodijiert bestjournal
With fast evolving technology,Cognitive Science plays a vital role in our day-to-day life. Cognitive science is summed up as the study of mind based on scientific methods. It is al l about the sum of all interdisciplinary like philosophy,psychology,linguistics,artificial intelligence,robot ics,and neuroscience. In this paper,I focused on the facial expressions or emotions of human being as it has an impor tant role in interpersonal relations. Without verb communication,one can imagine the mood of a person by expressions. In this method,we use back propagation neural network for implementation. It is an information proce ssing system that has been developed as a generalization of the mathematical model of human recognition.
1) The document describes methods for detecting depression using image processing and machine learning techniques applied to facial image analysis. It involves steps like face recognition, feature extraction from images, comparing features to labeled training data, and classifying images as depressed or not depressed.
2) Key algorithms discussed are face recognition through feature extraction and graph matching, as well as using classifiers like multiclass models to analyze facial features and recognize emotional states from images.
3) The goal is to develop an automated system that can help clinicians assess and monitor depression levels based on facial image analysis, in order to address the growing problem of depression diagnosis and treatment. The system aims to classify human depression and recognize facial expressions related to depressed emotional states.
Retinal Image Analysis using Machine Learning and Deep.pptxDeval Bhapkar
This document discusses using machine learning and deep learning for retinal image analysis. It begins with an introduction on how deep learning can help interpret complex medical image features. It then discusses benefits of AI in ophthalmology like increased efficiency, cost savings, and accuracy compared to humans. Next, it describes image analysis tasks like detecting shapes and edges. The objectives are outlined to increase accuracy in predicting eye diseases using deep learning techniques. Finally, it discusses generative adversarial networks (GANs) and how they can be used for tasks like de-noising, augmenting, and segmenting retinal images to detect eye diseases more precisely.
FACEMASK AND PHYSICAL DISTANCING DETECTION USING TRANSFER LEARNING TECHNIQUEIRJET Journal
This document describes a system for detecting whether individuals are wearing face masks and maintaining proper social distancing using deep learning and computer vision techniques. The system uses the YOLOv3 object detection algorithm trained on a dataset of images labeled as containing individuals with and without masks. The trained model can detect faces in images and label them as wearing a mask properly or not. It can also detect multiple individuals, calculate distances between them, and identify violations of social distancing guidelines. The system provides outputs by drawing colored boxes around detected faces and individuals to indicate mask wearing and social distancing compliance in real-time. It aims to help authorities monitor adherence to COVID-19 safety measures without manual efforts.
Various cataract detection methods-A surveyIRJET Journal
This document summarizes various methods for detecting cataracts. It discusses five different cataract detection methods proposed in previous research: 1) a mobile system using texture analysis and k-NN classification, 2) fundus image processing using histogram equalization, 3) a tri-training method that generates three classifiers, 4) analysis of automatic detection of nuclear and cortical cataracts using fundus images, and 5) enhanced texture features to classify cataractous and non-cataractous lenses. The document also reviews literature on diabetic retinopathy detection and classification. It concludes that while challenges remain, recent applications have potential for early cataract detection and classification.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
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This document discusses human behavior recognition using convolutional neural networks (CNNs). It first introduces the importance of human behavior recognition and some commonly used datasets. It then discusses related works that have used techniques like CNNs, LSTM networks, and R-CNN to recognize behaviors. The document proposes using the YOLOv3 algorithm to recognize behaviors in real-time video data. It describes the YOLOv3 algorithm and how it divides images into grids to predict boundary boxes and confidence scores for object detection. The goal is to automatically recognize human behaviors from video data using a CNN-based approach like YOLOv3 without manual annotation of training data.
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Face recognition across age progression is remains one of the areas most challenging tasks now a days, as the aging process affects both the shape and texture of a face. One possible solution is to apply a probabilistic model to represent a face simultaneously with its identity variable, which is stable through time, and its aging variable, which changes with time. This paper proposes a deep learning and set based approach to the face recognition subject to aging. The images for each subject taken at various times are treated as a single set, which is then compared to the sets of images belonging to other subjects. Facial features are extracted using a convolutional neural network characteristic of the deep learning. Our experimental results show that set based recognition performs better than the singleton based approach for the both face identification and face verification. We also find that by using set based recognition, it is easier to recognize older subjects from younger ones rather than younger subjects from older ones. Prathama V | Thippeswamy G ""Age Invariant Face Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23572.pdf
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Face detection and recognition has been prevalent with research scholars and diverse approaches have been
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scanners, or iris detection and recognition systems and the finger print recognition systems, and surveillance systems
deployed for safety and security purposes have contributed to inclination towards same. Advances has been made with
frontal view, lateral view of the face or using facial expressions such as anger, happiness and gloominess, still images
and video image to be used for detection and recognition. This led to newer methods for face detection and recognition
to be introduced in achieving accurate results and economically feasible and extremely secure. Techniques such as
Principal Component analysis (PCA), Independent component analysis (ICA), Linear Discriminant Analysis (LDA),
have been the predominant ones to be used. But with improvements needed in the previous approaches Neural Networks
based recognition was like boon to the industry. It not only enhanced the recognition but also the efficiency of
the process. Choosing Backpropagation as the learning method was clearly out of its efficiency to recognize non linear
faces with an acceptance ratio of more than 90% and execution time of only few seconds.
This document summarizes a research paper that proposes a method for detecting and recognizing faces using the Viola Jones algorithm and Back Propagation Neural Network (BPNN).
The paper first discusses face detection and recognition challenges. It then provides background on Viola Jones algorithm and BPNN. The proposed methodology uses Viola Jones for face detection, converts the image to grayscale and binary, then trains segments or the whole image with BPNN. Results are analyzed using training, testing and validation curves in the MATLAB neural network tool to minimize error. In under 3 sentences, this document outlines the key techniques, proposed method, and analysis approach discussed in the source research paper.
IRJET- A Comprehensive Survey and Detailed Study on Various Face Recognition ...IRJET Journal
This document provides a comprehensive survey and detailed study of various face recognition methods. It begins with an introduction to face recognition and its advantages over other biometric methods. It then categorizes face recognition methods into four types: knowledge-based, feature-based, template matching, and appearance-based. The majority of the document discusses these methods in further detail and provides examples of algorithms that fall under each type, such as Eigenfaces, LDA, ICA, and neural networks. It concludes by stating that reviewing existing face recognition algorithms may lead to improved methods for solving this fundamental problem.
https://ijitce.com/index.php
Our journal maintains rigorous peer review standards. Each submitted article undergoes a thorough evaluation by experts in the respective field. This stringent review process helps ensure that only high-quality and scientifically sound research is accepted for publication. Researchers can trust that the articles they find in IJITCE have been critically assessed for validity, significance, and originality.
The document discusses research on developing machine learning and deep learning models to detect mental health issues in university students. It introduces the CASTLE framework, which uses multi-modal data like social interactions, academic performance, and appearance to detect issues. It also discusses using EEG data and features to recognize multiple anxiety levels in students. The objectives are to improve CASTLE to use more student data and analyze results with causal learning techniques. Coursework focuses on machine learning, deep learning, and related topics to support the research.
Study on Different Human Emotions Using Back Propagation Methodijiert bestjournal
With fast evolving technology,Cognitive Science plays a vital role in our day-to-day life. Cognitive science is summed up as the study of mind based on scientific methods. It is al l about the sum of all interdisciplinary like philosophy,psychology,linguistics,artificial intelligence,robot ics,and neuroscience. In this paper,I focused on the facial expressions or emotions of human being as it has an impor tant role in interpersonal relations. Without verb communication,one can imagine the mood of a person by expressions. In this method,we use back propagation neural network for implementation. It is an information proce ssing system that has been developed as a generalization of the mathematical model of human recognition.
1) The document describes methods for detecting depression using image processing and machine learning techniques applied to facial image analysis. It involves steps like face recognition, feature extraction from images, comparing features to labeled training data, and classifying images as depressed or not depressed.
2) Key algorithms discussed are face recognition through feature extraction and graph matching, as well as using classifiers like multiclass models to analyze facial features and recognize emotional states from images.
3) The goal is to develop an automated system that can help clinicians assess and monitor depression levels based on facial image analysis, in order to address the growing problem of depression diagnosis and treatment. The system aims to classify human depression and recognize facial expressions related to depressed emotional states.
Retinal Image Analysis using Machine Learning and Deep.pptxDeval Bhapkar
This document discusses using machine learning and deep learning for retinal image analysis. It begins with an introduction on how deep learning can help interpret complex medical image features. It then discusses benefits of AI in ophthalmology like increased efficiency, cost savings, and accuracy compared to humans. Next, it describes image analysis tasks like detecting shapes and edges. The objectives are outlined to increase accuracy in predicting eye diseases using deep learning techniques. Finally, it discusses generative adversarial networks (GANs) and how they can be used for tasks like de-noising, augmenting, and segmenting retinal images to detect eye diseases more precisely.
FACEMASK AND PHYSICAL DISTANCING DETECTION USING TRANSFER LEARNING TECHNIQUEIRJET Journal
This document describes a system for detecting whether individuals are wearing face masks and maintaining proper social distancing using deep learning and computer vision techniques. The system uses the YOLOv3 object detection algorithm trained on a dataset of images labeled as containing individuals with and without masks. The trained model can detect faces in images and label them as wearing a mask properly or not. It can also detect multiple individuals, calculate distances between them, and identify violations of social distancing guidelines. The system provides outputs by drawing colored boxes around detected faces and individuals to indicate mask wearing and social distancing compliance in real-time. It aims to help authorities monitor adherence to COVID-19 safety measures without manual efforts.
Various cataract detection methods-A surveyIRJET Journal
This document summarizes various methods for detecting cataracts. It discusses five different cataract detection methods proposed in previous research: 1) a mobile system using texture analysis and k-NN classification, 2) fundus image processing using histogram equalization, 3) a tri-training method that generates three classifiers, 4) analysis of automatic detection of nuclear and cortical cataracts using fundus images, and 5) enhanced texture features to classify cataractous and non-cataractous lenses. The document also reviews literature on diabetic retinopathy detection and classification. It concludes that while challenges remain, recent applications have potential for early cataract detection and classification.
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TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.