1. The document discusses various techniques that have been proposed for face detection and attendance systems, including Haar classifiers, improved support vector machines, and local binary patterns algorithms.
2. It reviews several papers that have implemented different methods for face recognition for attendance systems, such as using HOG features and PCA for dimensionality reduction along with SVM classification.
3. The document also summarizes a paper that proposed a context-aware local binary feature learning method for face recognition that exploits contextual information between adjacent image bits.
IRJET- Automation Software for Student Monitoring SystemIRJET Journal
This document proposes and evaluates an automated student monitoring system using various technologies. The system aims to more efficiently track student attendance by automating the process and eliminating issues like proxy attendance. It explores methods like face recognition using parameters like pose, sharpness and brightness. Other approaches examined include voiceprint recognition, RFID tags, and an Android-based system using barcodes and fingerprint sensors. The proposed system would make attendance tracking faster, more accurate, and paperless by automating the process through electronic sensors. It could prevent cheating but may have issues with lighting conditions or noise affecting biometric systems. An evaluation found such a semi-automated system using smartphone Wi-Fi fingerprinting and a k-NN algorithm could provide an inexpensive and effective
IRJET- Analysis of Face Recognition using Docface+ Selfie MatchingIRJET Journal
1. The document proposes a method called DocFace+ to automatically match ID document photos to selfie images in real-time with high accuracy.
2. It uses transfer learning by fine-tuning a base model trained on unconstrained face images on a private ID-selfie dataset. A pair of sibling networks are used to learn unified face representations.
3. Experimental results on an ID-selfie dataset show the proposed DocFace+ system achieves better generalization performance than publicly available general face matchers.
IRJET - Facial Recognition based Attendance Management SystemIRJET Journal
This document summarizes a facial recognition-based attendance management system. The system uses facial recognition techniques to automatically take attendance by comparing photos of students in class to images stored in a database. It involves taking photos of students to create a training dataset, using those images to train a model to recognize faces, taking photos of classrooms, cropping out faces, running those cropped faces through the trained model to identify students, and recording attendance in a database. The system aims to automate attendance tracking to reduce workload for teachers and prevent issues like duplicate signatures.
Smart Doorbell System Based on Face RecognitionIRJET Journal
1. The document describes a smart doorbell system based on face recognition using a Raspberry Pi board. The system uses OpenCV to perform face detection, feature extraction, and recognition.
2. It compares two face recognition algorithms - Eigenfaces and Independent Component Analysis (ICA). The system is designed for low power consumption, optimized resources, and faster speed.
3. The document outlines the system design, including enrolling faces into a training database, preprocessing images, performing face detection and feature extraction, and recognizing faces by comparing extracted features to the training database. It concludes that ICA provides better recognition accuracy than Eigenfaces.
IRJET- Autonamy of Attendence using Face RecognitionIRJET Journal
This document summarizes an automated attendance system using video-based face recognition. The system works by capturing a video of students in a classroom and using face detection and recognition algorithms to identify and mark the attendance of each student. It first detects faces in each video frame using the Haar cascade classifier, then recognizes the faces by comparing them to a training database of student faces using the Eigenfaces algorithm. Finally, it registers the attendance in an Excel sheet. The system aims to make the attendance process more efficient and accurate compared to traditional manual methods.
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.
IRJET- Face Recognition of Criminals for Security using Principal Component A...IRJET Journal
This document presents a face recognition system using principal component analysis to identify criminals at airports. The system is trained on images of known criminals collected from law enforcement agencies. It uses PCA for dimensionality reduction to generate eigenfaces from the training images. During testing, it generates an eigenface from the input image and calculates the Euclidean distance between this eigenface and the eigenfaces of the training images. It identifies the criminal as the one corresponding to the training image with the minimum distance, alerting authorities. The document outlines the methodology, including preprocessing steps like subtracting the mean face, and reviews prior work applying PCA and other algorithms to face recognition.
IRJET- Automation Software for Student Monitoring SystemIRJET Journal
This document proposes and evaluates an automated student monitoring system using various technologies. The system aims to more efficiently track student attendance by automating the process and eliminating issues like proxy attendance. It explores methods like face recognition using parameters like pose, sharpness and brightness. Other approaches examined include voiceprint recognition, RFID tags, and an Android-based system using barcodes and fingerprint sensors. The proposed system would make attendance tracking faster, more accurate, and paperless by automating the process through electronic sensors. It could prevent cheating but may have issues with lighting conditions or noise affecting biometric systems. An evaluation found such a semi-automated system using smartphone Wi-Fi fingerprinting and a k-NN algorithm could provide an inexpensive and effective
IRJET- Analysis of Face Recognition using Docface+ Selfie MatchingIRJET Journal
1. The document proposes a method called DocFace+ to automatically match ID document photos to selfie images in real-time with high accuracy.
2. It uses transfer learning by fine-tuning a base model trained on unconstrained face images on a private ID-selfie dataset. A pair of sibling networks are used to learn unified face representations.
3. Experimental results on an ID-selfie dataset show the proposed DocFace+ system achieves better generalization performance than publicly available general face matchers.
IRJET - Facial Recognition based Attendance Management SystemIRJET Journal
This document summarizes a facial recognition-based attendance management system. The system uses facial recognition techniques to automatically take attendance by comparing photos of students in class to images stored in a database. It involves taking photos of students to create a training dataset, using those images to train a model to recognize faces, taking photos of classrooms, cropping out faces, running those cropped faces through the trained model to identify students, and recording attendance in a database. The system aims to automate attendance tracking to reduce workload for teachers and prevent issues like duplicate signatures.
Smart Doorbell System Based on Face RecognitionIRJET Journal
1. The document describes a smart doorbell system based on face recognition using a Raspberry Pi board. The system uses OpenCV to perform face detection, feature extraction, and recognition.
2. It compares two face recognition algorithms - Eigenfaces and Independent Component Analysis (ICA). The system is designed for low power consumption, optimized resources, and faster speed.
3. The document outlines the system design, including enrolling faces into a training database, preprocessing images, performing face detection and feature extraction, and recognizing faces by comparing extracted features to the training database. It concludes that ICA provides better recognition accuracy than Eigenfaces.
IRJET- Autonamy of Attendence using Face RecognitionIRJET Journal
This document summarizes an automated attendance system using video-based face recognition. The system works by capturing a video of students in a classroom and using face detection and recognition algorithms to identify and mark the attendance of each student. It first detects faces in each video frame using the Haar cascade classifier, then recognizes the faces by comparing them to a training database of student faces using the Eigenfaces algorithm. Finally, it registers the attendance in an Excel sheet. The system aims to make the attendance process more efficient and accurate compared to traditional manual methods.
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.
IRJET- Face Recognition of Criminals for Security using Principal Component A...IRJET Journal
This document presents a face recognition system using principal component analysis to identify criminals at airports. The system is trained on images of known criminals collected from law enforcement agencies. It uses PCA for dimensionality reduction to generate eigenfaces from the training images. During testing, it generates an eigenface from the input image and calculates the Euclidean distance between this eigenface and the eigenfaces of the training images. It identifies the criminal as the one corresponding to the training image with the minimum distance, alerting authorities. The document outlines the methodology, including preprocessing steps like subtracting the mean face, and reviews prior work applying PCA and other algorithms to face recognition.
IRJET - Autonomous Navigation System using Deep LearningIRJET Journal
The document describes a proposed methodology for creating an autonomous navigation system using deep learning. Key aspects of the proposed methodology include:
1. Using the Unity real-time 3D rendering platform to build a simulated virtual environment for training an autonomous vehicle.
2. Employing artificial intelligence techniques like convolutional neural networks to create the "brain" of the autonomous system and train a model using data collected from the simulated environment.
3. Training the model to perform tasks like identifying lane lines, classifying images, and replicating human driving behaviors to enable the autonomous vehicle to navigate the virtual environment.
IRJET- Deep Feature Fusion for Iris Biometrics on Mobile DevicesIRJET Journal
This document proposes a deep neural network-based system for iris recognition and authentication on mobile devices. The system uses feature fusion with multiple feature extraction algorithms to recognize iris patterns. It then uses a backpropagation neural network for classification and authentication. The proposed system aims to more accurately recognize iris patterns, reduce processing time, and provide appropriate authentication outputs compared to existing CNN-based systems. It segments the iris from input images, extracts features using algorithms like FAST, SURF, and Harris, and feeds these into a neural network for classification and authentication. Performance charts are generated to evaluate the system. The proposed system seeks to securely authenticate users for mobile applications and transactions.
Real Time Face Recognition Based on Face Descriptor and Its ApplicationTELKOMNIKA JOURNAL
This paper presents a real time face recognition based on face descriptor and its application for door
locking. The face descriptor is represented by both local and global information. The local information, which
is the dominant frequency content of sub-face, is extracted by zoned discrete cosine transforms (DCT). While
the global information, which also is the dominant frequency content and shape information of the whole face,
is extracted by DCT and by Hu-moment. Therefore, face descriptor has rich information about a face image
which tends to provide good performance for real time face recognition. To decrease the dimensional size of
face descriptor, the predictive linear discriminant analysis (PDLDA) is employed and the face classification
is done by kNN. The experimental results show that the proposed real time face recognition provides good
performances which indicated by 98.30%, 21.99%, and 1.8% of accuracy, FPR, and FNR respectively. In
addition, it also needs short computational time (1 second).
Improved learning through remote desktop mirroring controlConference Papers
The document describes a Wireless Stream Management System (WSMS) that allows a moderator (teacher) to remotely manage and control wireless screen mirroring from student devices to support collaborative learning. Key features of WSMS include allowing the teacher to select any student's laptop screen to project, enabling the teacher to remotely control the student's laptop, and distributing presentation content as images to student devices. The system architecture uses various components like a Wireless Screen Sender, Receiver, Administrator and Controller. Performance tests showed the system using under 2 Mbps of bandwidth and latency under 173ms with no major CPU utilization issues.
IRJET- Automated Detection of Gender from Face ImagesIRJET Journal
1) The document describes a system to automatically detect gender from face images using convolutional neural networks and Python. The system was developed to help address problems like security, fraud, and criminal identification.
2) The system uses a CNN classifier trained on the UTKFace dataset of facial images. The CNN model contains convolutional, activation, max pooling, flatten, dense and dropout layers to analyze image features and predict the gender of an unknown input face image.
3) The goal of the system is to identify gender from images faster than traditional criminal identification methods in order to help solve crimes and security issues more efficiently.
Explaining Aluminous Ascientification Of Significance Examples Of Personal St...SubmissionResearchpa
This document describes research on algorithms for recognizing ear tags for biometric identification. It presents three algorithms: 1) using discrete cosine transformation to distinguish ear image characteristics, which achieved 86% accuracy; 2) using principal component analysis of ear images, which achieved 89% accuracy; and 3) segmenting ear images into static marks, which achieved the best result of 92% accuracy with 12 marks. The discrete cosine method was less accurate due to extracting too many characteristics, while the principal component and segmentation methods performed better with fewer extracted characteristics.
IRJET - Automated Fraud Detection Framework in Examination HallsIRJET Journal
This document proposes an automated fraud detection framework to detect impersonation of candidates and possession of electronic gadgets in examination halls. It uses image processing techniques like face detection and recognition along with machine learning algorithms like Random Forest and Histogram of Oriented Gradients (HoG) for detection, classification and training. The framework is trained on datasets of images collected and labeled for anomalies. It detects impersonation and presence of gadgets during examinations by processing images using HoG and recognizing faces using a pre-trained Random Forest model for high accuracy classification.
IRJET- Student Attendance System by Face DetectionIRJET Journal
This document describes a student attendance system using face detection and recognition. The system automatically takes attendance by identifying students' faces using image processing techniques. It stores a database of student faces during a training process. When students enter the classroom, the system detects faces in real-time camera footage and compares them to the stored database to identify and mark present any matching students. The system aims to make the attendance process more efficient and accurate compared to traditional manual methods. It provides automated attendance tracking to help monitor student performance without lengthy paperwork.
Face Annotation using Co-Relation based Matching for Improving Image Mining ...IRJET Journal
This document discusses face annotation techniques for improving image mining in videos. It begins by introducing the need for better image retrieval with the rise of online sharing. It then discusses challenges with face annotation in videos and existing techniques like content-based image retrieval and search-based face annotation. The document analyzes limitations of these existing techniques, such as semantic gaps with manual tagging, decreased accuracy, and poor generalization with new data. It proposes using correlation-based matching to address problems in face recognition techniques.
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
IRJET- Face Recognition by Additive Block based Feature ExtractionIRJET Journal
The document describes a proposed method for face recognition using additive block-based feature extraction. The method uses Chirp Z-Transform (CZT) and Goertzel algorithm for preprocessing to perform illumination normalization. It then divides the preprocessed image into blocks of equal size and superimposes them to extract features from the combined block. Gray Level Co-occurrence Matrix (GLCM) is used to further extract texture features. Euclidean distance classification is used to measure similarity between trained and test images. The proposed approach is tested on benchmark datasets and demonstrates better performance compared to existing methods in handling pose and illumination variations.
Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL
With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
Improvement from proof of concept into the production environment cater for...Conference Papers
This document discusses improvements made to the Trust Engine component of an authentication platform to improve performance and scalability. The Proof of Concept system was found to not meet scalability requirements due to the database architecture requiring multiple connections to retrieve and update user data. The improvements included consolidating configuration data, combining user tables, updating the process to perform analysis in memory without database connections, limiting stored login records, and changing to a JSON data format. Performance testing showed the new system completed processes on average 99% faster.
IRJET - Encoded Polymorphic Aspect of ClusteringIRJET Journal
This document discusses using machine learning techniques for clustering multi-view data. It focuses on an unsupervised learning technique called clustering, which groups similar objects together into clusters while separating dissimilar objects into different clusters. Compared to single-view clustering, multi-view clustering can access more characteristics and structural information hidden in the data by exploiting richer properties to improve clustering performance. It also discusses encoding datasets into binary format for storage, clustering the encoded data, and retrieving desired data through decoding based on user queries. The goal is to efficiently handle large datasets using scalable machine learning algorithms.
IRJET- Computerized Attendance System using Face RecognitionIRJET Journal
1. The document describes a computerized attendance system using face recognition for educational institutions. It uses OpenCV with face recognition and detection algorithms like Viola-Jones, PCA, and Eigenfaces.
2. Faces are detected using Viola-Jones algorithm. PCA is used to train detected faces and create a database of known faces. During attendance, faces are compared to the database to identify individuals and mark attendance automatically in an Excel file.
3. This automated system provides benefits over manual attendance systems by saving time, reducing errors, and preventing forgery. It is a more convenient and accurate way to take attendance.
IRJET- A Study on Automated Attendance System using Facial RecognitionIRJET Journal
The document discusses an automated attendance system using facial recognition. It begins with an introduction to facial recognition and the motivation for developing an automated attendance system. It then reviews previous work on facial recognition algorithms such as PCA, Viola-Jones, and neural networks. The proposed system is described as using SVM on LBP features for facial recognition due to its high accuracy. Key advantages of the proposed system include being cost-efficient, easy to deploy, and preventing time fraud. The document concludes facial recognition can effectively automate attendance tracking in educational or commercial organizations.
FUSION BASED MULTIMODAL AUTHENTICATION IN BIOMETRICS USING CONTEXT-SENSITIVE ...cscpconf
Biometrics is one of the primary key concepts of real application domains such as aadhar card, passport, pan card, etc. In such applications user can provide two to three biometrics patterns
like face, finger, palm, signature, iris data, and so on. We considered face and finger patterns
for encoding and then also for verification. Using this data we proposed a novel model for
authentication in multimodal biometrics often called Context-Sensitive Exponent Associative Memory Model (CSEAM). It provides different stages of security for biometrics patterns. In
stage 1, face and finger patterns can be fusion through Principal Component Analysis (PCA), in stage 2 by applying SVD decomposition to generate keys from the fusion data and preprocessed face pattern and then in stage 3, using CSEAM model the generated keys can be encoded. The final key will be stored in the smart cards. In CSEAM model, exponential
kronecker product plays a critical role for encoding and also for verification to verify the chosen samples from the users. This paper discusses by considering realistic biometric data in
terms of time and space
this is VTU FINAL YEAR PROJECT REPORT full report is attached below.this alone with front pages attached Front pages report follows all the guidelines specified by vtu according to our college.
IRJET- Syllabus and Timetable Generation SystemIRJET Journal
The document describes a proposed system called the Syllabus and Timetable Generation System that aims to automatically generate timetables and syllabi for educational institutions. It uses an algorithm that takes inputs like number of classes, subjects, days in a week, and lectures per day to randomly generate timetables for multiple classes without clashes. The algorithm employs recursion to prevent clashes across class timetables. It also includes a static faculty assignment method. The proposed system was able to automatically generate timetables and syllabi for 4 classes with 10 subjects, demonstrating the effectiveness of the algorithm in solving the complex task of timetable scheduling.
This document presents a facial expression recognition system that identifies and classifies seven basic expressions: happy, surprise, fear, disgust, sad, anger, and a neutral state. The system consists of four main parts: image acquisition, pre-processing, feature extraction, and classification. It was developed using both OpenCV and a web-based JavaScript approach. The system was tested on both real-time and pre-recorded video streams and can identify emotions in images and video input from a webcam in real-time. Evaluation showed the JavaScript implementation using a generalized dataset provided more accurate real-time predictions compared to the OpenCV approach.
A Real Time Advance Automated Attendance System using Face-Net AlgorithmIRJET Journal
This document presents a real-time advanced automated attendance system using the Face-Net algorithm. The system uses facial recognition technology to automate the attendance tracking process. It involves developing facial detection and recognition algorithms, a database to store student information, and interfaces for educators. The system captures images of students' faces and matches them to stored data to record attendance in real-time while maintaining privacy. Testing showed the system could accurately detect and recognize faces in classroom settings. The authors aim to contribute to digitizing education administration and allowing educators to focus on teaching.
IRJET- Automated Student’s Attendance Management using Convolutional Neural N...IRJET Journal
This document describes a proposed system to automate student attendance management using convolutional neural networks and face recognition. The system would take attendance automatically by detecting faces in the classroom and comparing them to a database of student faces. This would make the attendance process more efficient than current manual methods like calling roll numbers or paper sign-ins. The system would use a CNN algorithm and face detection/recognition techniques like PCA to detect and identify student faces during lectures and automatically update attendance records.
IRJET - Autonomous Navigation System using Deep LearningIRJET Journal
The document describes a proposed methodology for creating an autonomous navigation system using deep learning. Key aspects of the proposed methodology include:
1. Using the Unity real-time 3D rendering platform to build a simulated virtual environment for training an autonomous vehicle.
2. Employing artificial intelligence techniques like convolutional neural networks to create the "brain" of the autonomous system and train a model using data collected from the simulated environment.
3. Training the model to perform tasks like identifying lane lines, classifying images, and replicating human driving behaviors to enable the autonomous vehicle to navigate the virtual environment.
IRJET- Deep Feature Fusion for Iris Biometrics on Mobile DevicesIRJET Journal
This document proposes a deep neural network-based system for iris recognition and authentication on mobile devices. The system uses feature fusion with multiple feature extraction algorithms to recognize iris patterns. It then uses a backpropagation neural network for classification and authentication. The proposed system aims to more accurately recognize iris patterns, reduce processing time, and provide appropriate authentication outputs compared to existing CNN-based systems. It segments the iris from input images, extracts features using algorithms like FAST, SURF, and Harris, and feeds these into a neural network for classification and authentication. Performance charts are generated to evaluate the system. The proposed system seeks to securely authenticate users for mobile applications and transactions.
Real Time Face Recognition Based on Face Descriptor and Its ApplicationTELKOMNIKA JOURNAL
This paper presents a real time face recognition based on face descriptor and its application for door
locking. The face descriptor is represented by both local and global information. The local information, which
is the dominant frequency content of sub-face, is extracted by zoned discrete cosine transforms (DCT). While
the global information, which also is the dominant frequency content and shape information of the whole face,
is extracted by DCT and by Hu-moment. Therefore, face descriptor has rich information about a face image
which tends to provide good performance for real time face recognition. To decrease the dimensional size of
face descriptor, the predictive linear discriminant analysis (PDLDA) is employed and the face classification
is done by kNN. The experimental results show that the proposed real time face recognition provides good
performances which indicated by 98.30%, 21.99%, and 1.8% of accuracy, FPR, and FNR respectively. In
addition, it also needs short computational time (1 second).
Improved learning through remote desktop mirroring controlConference Papers
The document describes a Wireless Stream Management System (WSMS) that allows a moderator (teacher) to remotely manage and control wireless screen mirroring from student devices to support collaborative learning. Key features of WSMS include allowing the teacher to select any student's laptop screen to project, enabling the teacher to remotely control the student's laptop, and distributing presentation content as images to student devices. The system architecture uses various components like a Wireless Screen Sender, Receiver, Administrator and Controller. Performance tests showed the system using under 2 Mbps of bandwidth and latency under 173ms with no major CPU utilization issues.
IRJET- Automated Detection of Gender from Face ImagesIRJET Journal
1) The document describes a system to automatically detect gender from face images using convolutional neural networks and Python. The system was developed to help address problems like security, fraud, and criminal identification.
2) The system uses a CNN classifier trained on the UTKFace dataset of facial images. The CNN model contains convolutional, activation, max pooling, flatten, dense and dropout layers to analyze image features and predict the gender of an unknown input face image.
3) The goal of the system is to identify gender from images faster than traditional criminal identification methods in order to help solve crimes and security issues more efficiently.
Explaining Aluminous Ascientification Of Significance Examples Of Personal St...SubmissionResearchpa
This document describes research on algorithms for recognizing ear tags for biometric identification. It presents three algorithms: 1) using discrete cosine transformation to distinguish ear image characteristics, which achieved 86% accuracy; 2) using principal component analysis of ear images, which achieved 89% accuracy; and 3) segmenting ear images into static marks, which achieved the best result of 92% accuracy with 12 marks. The discrete cosine method was less accurate due to extracting too many characteristics, while the principal component and segmentation methods performed better with fewer extracted characteristics.
IRJET - Automated Fraud Detection Framework in Examination HallsIRJET Journal
This document proposes an automated fraud detection framework to detect impersonation of candidates and possession of electronic gadgets in examination halls. It uses image processing techniques like face detection and recognition along with machine learning algorithms like Random Forest and Histogram of Oriented Gradients (HoG) for detection, classification and training. The framework is trained on datasets of images collected and labeled for anomalies. It detects impersonation and presence of gadgets during examinations by processing images using HoG and recognizing faces using a pre-trained Random Forest model for high accuracy classification.
IRJET- Student Attendance System by Face DetectionIRJET Journal
This document describes a student attendance system using face detection and recognition. The system automatically takes attendance by identifying students' faces using image processing techniques. It stores a database of student faces during a training process. When students enter the classroom, the system detects faces in real-time camera footage and compares them to the stored database to identify and mark present any matching students. The system aims to make the attendance process more efficient and accurate compared to traditional manual methods. It provides automated attendance tracking to help monitor student performance without lengthy paperwork.
Face Annotation using Co-Relation based Matching for Improving Image Mining ...IRJET Journal
This document discusses face annotation techniques for improving image mining in videos. It begins by introducing the need for better image retrieval with the rise of online sharing. It then discusses challenges with face annotation in videos and existing techniques like content-based image retrieval and search-based face annotation. The document analyzes limitations of these existing techniques, such as semantic gaps with manual tagging, decreased accuracy, and poor generalization with new data. It proposes using correlation-based matching to address problems in face recognition techniques.
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
IRJET- Face Recognition by Additive Block based Feature ExtractionIRJET Journal
The document describes a proposed method for face recognition using additive block-based feature extraction. The method uses Chirp Z-Transform (CZT) and Goertzel algorithm for preprocessing to perform illumination normalization. It then divides the preprocessed image into blocks of equal size and superimposes them to extract features from the combined block. Gray Level Co-occurrence Matrix (GLCM) is used to further extract texture features. Euclidean distance classification is used to measure similarity between trained and test images. The proposed approach is tested on benchmark datasets and demonstrates better performance compared to existing methods in handling pose and illumination variations.
Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL
With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
Improvement from proof of concept into the production environment cater for...Conference Papers
This document discusses improvements made to the Trust Engine component of an authentication platform to improve performance and scalability. The Proof of Concept system was found to not meet scalability requirements due to the database architecture requiring multiple connections to retrieve and update user data. The improvements included consolidating configuration data, combining user tables, updating the process to perform analysis in memory without database connections, limiting stored login records, and changing to a JSON data format. Performance testing showed the new system completed processes on average 99% faster.
IRJET - Encoded Polymorphic Aspect of ClusteringIRJET Journal
This document discusses using machine learning techniques for clustering multi-view data. It focuses on an unsupervised learning technique called clustering, which groups similar objects together into clusters while separating dissimilar objects into different clusters. Compared to single-view clustering, multi-view clustering can access more characteristics and structural information hidden in the data by exploiting richer properties to improve clustering performance. It also discusses encoding datasets into binary format for storage, clustering the encoded data, and retrieving desired data through decoding based on user queries. The goal is to efficiently handle large datasets using scalable machine learning algorithms.
IRJET- Computerized Attendance System using Face RecognitionIRJET Journal
1. The document describes a computerized attendance system using face recognition for educational institutions. It uses OpenCV with face recognition and detection algorithms like Viola-Jones, PCA, and Eigenfaces.
2. Faces are detected using Viola-Jones algorithm. PCA is used to train detected faces and create a database of known faces. During attendance, faces are compared to the database to identify individuals and mark attendance automatically in an Excel file.
3. This automated system provides benefits over manual attendance systems by saving time, reducing errors, and preventing forgery. It is a more convenient and accurate way to take attendance.
IRJET- A Study on Automated Attendance System using Facial RecognitionIRJET Journal
The document discusses an automated attendance system using facial recognition. It begins with an introduction to facial recognition and the motivation for developing an automated attendance system. It then reviews previous work on facial recognition algorithms such as PCA, Viola-Jones, and neural networks. The proposed system is described as using SVM on LBP features for facial recognition due to its high accuracy. Key advantages of the proposed system include being cost-efficient, easy to deploy, and preventing time fraud. The document concludes facial recognition can effectively automate attendance tracking in educational or commercial organizations.
FUSION BASED MULTIMODAL AUTHENTICATION IN BIOMETRICS USING CONTEXT-SENSITIVE ...cscpconf
Biometrics is one of the primary key concepts of real application domains such as aadhar card, passport, pan card, etc. In such applications user can provide two to three biometrics patterns
like face, finger, palm, signature, iris data, and so on. We considered face and finger patterns
for encoding and then also for verification. Using this data we proposed a novel model for
authentication in multimodal biometrics often called Context-Sensitive Exponent Associative Memory Model (CSEAM). It provides different stages of security for biometrics patterns. In
stage 1, face and finger patterns can be fusion through Principal Component Analysis (PCA), in stage 2 by applying SVD decomposition to generate keys from the fusion data and preprocessed face pattern and then in stage 3, using CSEAM model the generated keys can be encoded. The final key will be stored in the smart cards. In CSEAM model, exponential
kronecker product plays a critical role for encoding and also for verification to verify the chosen samples from the users. This paper discusses by considering realistic biometric data in
terms of time and space
this is VTU FINAL YEAR PROJECT REPORT full report is attached below.this alone with front pages attached Front pages report follows all the guidelines specified by vtu according to our college.
IRJET- Syllabus and Timetable Generation SystemIRJET Journal
The document describes a proposed system called the Syllabus and Timetable Generation System that aims to automatically generate timetables and syllabi for educational institutions. It uses an algorithm that takes inputs like number of classes, subjects, days in a week, and lectures per day to randomly generate timetables for multiple classes without clashes. The algorithm employs recursion to prevent clashes across class timetables. It also includes a static faculty assignment method. The proposed system was able to automatically generate timetables and syllabi for 4 classes with 10 subjects, demonstrating the effectiveness of the algorithm in solving the complex task of timetable scheduling.
This document presents a facial expression recognition system that identifies and classifies seven basic expressions: happy, surprise, fear, disgust, sad, anger, and a neutral state. The system consists of four main parts: image acquisition, pre-processing, feature extraction, and classification. It was developed using both OpenCV and a web-based JavaScript approach. The system was tested on both real-time and pre-recorded video streams and can identify emotions in images and video input from a webcam in real-time. Evaluation showed the JavaScript implementation using a generalized dataset provided more accurate real-time predictions compared to the OpenCV approach.
A Real Time Advance Automated Attendance System using Face-Net AlgorithmIRJET Journal
This document presents a real-time advanced automated attendance system using the Face-Net algorithm. The system uses facial recognition technology to automate the attendance tracking process. It involves developing facial detection and recognition algorithms, a database to store student information, and interfaces for educators. The system captures images of students' faces and matches them to stored data to record attendance in real-time while maintaining privacy. Testing showed the system could accurately detect and recognize faces in classroom settings. The authors aim to contribute to digitizing education administration and allowing educators to focus on teaching.
IRJET- Automated Student’s Attendance Management using Convolutional Neural N...IRJET Journal
This document describes a proposed system to automate student attendance management using convolutional neural networks and face recognition. The system would take attendance automatically by detecting faces in the classroom and comparing them to a database of student faces. This would make the attendance process more efficient than current manual methods like calling roll numbers or paper sign-ins. The system would use a CNN algorithm and face detection/recognition techniques like PCA to detect and identify student faces during lectures and automatically update attendance records.
IRJET - A Review on Face Recognition using Deep Learning AlgorithmIRJET Journal
This document provides an overview of face recognition using deep learning algorithms. It discusses how deep learning approaches like convolutional neural networks (CNNs) have achieved high accuracy in face recognition tasks compared to earlier methods. CNNs can learn discriminative face features from large datasets during training to generalize to new images, handling variations in pose, illumination and expression. The document reviews popular CNN architectures and training approaches for face recognition. It also discusses other traditional face recognition methods like PCA and LDA, and compares their performance to deep learning methods.
Comparative Study of Enchancement of Automated Student Attendance System Usin...IRJET Journal
This document discusses developing an automated student attendance system using facial recognition and deep learning algorithms. It begins with an overview of how facial recognition can be used to take attendance accurately and efficiently. It then describes the methodology, which involves using a convolutional neural network (CNN) to detect and recognize faces. Dimensionality reduction techniques like principal component analysis (PCA) and linear discriminant analysis (LDA) are also used to improve recognition accuracy. The goal is to build a system that can identify students in real-time with a high degree of accuracy, even in varying lighting conditions. It aims to automate the entire attendance tracking process for both students and teachers.
IRJET- Computerized Attendance System using Face RecognitionIRJET Journal
1) The document proposes an automated attendance system using face recognition for educational institutions to replace traditional manual attendance marking.
2) The system uses OpenCV with face detection algorithms like Viola-Jones and PCA to detect faces, create face databases, and compare faces to identities to automatically mark attendance in an excel file.
3) During use, faces will be detected in images from a webcam, compared to stored databases to identify individuals, and their attendance marked electronically without needing physical interaction like ID cards.
Review Paper on Attendance Capturing System using Face RecognitionIRJET Journal
This document summarizes research on various attendance capturing systems using face recognition. It reviews 9 research papers describing different approaches using techniques like convolutional neural networks, MTCNN algorithm, Haar cascade classifier, and PCA. These systems are able to automate attendance marking by detecting faces in images and videos and recognizing students in real-time with accuracy rates ranging from 56% to 99.86%. The reviewed systems provide benefits over manual attendance methods by saving time while also being more accurate in some cases.
IRJET- Free & Generic Facial Attendance System using AndroidIRJET Journal
This document proposes a free and generic facial attendance system using Android that can automatically detect students' faces and mark attendance. It uses face detection and recognition algorithms to capture images from a camera and identify students by matching faces to a database. If a face is detected, attendance is marked as present. The system then creates a Google Sheet to store and access attendance records. This provides a low-cost alternative to commercial biometric systems for tracking student attendance.
A VISUAL ATTENDANCE SYSTEM USING FACE RECOGNITIONIRJET Journal
This document describes a visual attendance system using face recognition. The system was created to make the traditional paper-based attendance process more efficient and less prone to errors. It uses computer vision and face recognition techniques, including the RetinaFace and Arcnet algorithms, to detect and identify students' faces from video feeds or images taken in the classroom. When taking attendance, the system captures photos of students present and searches its database of student faces to automatically record attendance without disrupting the class. The document discusses the methodology and system structure, including face detection, face recognition modeling, and an overall workflow flowchart. It aims to provide an improved digital solution for tracking attendance at universities and colleges.
MTCNN BASED AUTOMATIC ATTENDANCE SYSTEM USING FACE RECOGNITIONIRJET Journal
This document summarizes a research paper that proposes an automated attendance system using facial recognition technology. It begins by outlining the limitations of current manual and RFID card-based attendance systems. It then describes a new system that uses MTCNN for face detection and CNN for facial recognition. The system captures images and identifies recognized students as present by matching faces to a database of stored images. The document provides details on the various stages of the proposed method, including face detection using MTCNN, face alignment, feature extraction with FaceNet, and classification with SVM. It presents the overall algorithm and concludes by discussing modelling and analysis.
IRJET- Intelligent Automated Attendance System based on Facial RecognitionIRJET Journal
This document presents a proposed intelligent automated attendance system based on facial recognition. The system aims to automate the attendance marking process in educational institutions to make it faster and less error-prone compared to manual methods. It works by using computer vision techniques like haar cascade classification for face detection and local binary pattern histograms for face recognition. The system architecture involves capturing images, detecting faces, recognizing students by matching faces to a training database, and marking the attendance automatically. Algorithms like haar cascade and local binary patterns are used for face detection and recognition. The proposed system aims to solve issues with existing manual and automated attendance systems like time consumption, errors, and lack of accuracy.
IRJET - Facial Recognition based Attendance System with LBPHIRJET Journal
This document presents a facial recognition based attendance system using LBPH (Local Binary Pattern Histograms). It begins with an abstract describing the system which takes student attendance using facial identification from classroom camera images. It then discusses related work in attendance and face recognition systems. The proposed system workflow is described involving face detection, feature extraction using LBPH, template matching, and attendance recording. Experimental results demonstrate the system's ability to detect multiple faces and record attendance accurately in an Excel sheet with date/time. The conclusion discusses how the system reduces human effort for attendance and increases learning time compared to traditional methods.
IRJET-Analysis of Face Recognition System for Different ClassifierIRJET Journal
M.Manimozhi, A. John Dhanaseely "Analysis of Face Recognition System for Different Classifier ", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net .published by Fast Track Publications
Abstract
Face recognition plays vital role for authenticating system. Human Face recognition is a challenging task in computer vision and pattern recognition. Face recognition has attracted much attention due to its potential value in security and law enforcement applications and its theoretical challenges. Different methods are used for feature extraction and classification. Kernel fisher analysis is used for feature extraction. The performance analysis for Euclidean, support vector machine is evaluated. The whole process is done using MATLAB software. A set of 10 person real time images is taken for our work. The classifier recognizes the similar posture as an output.
Classroom Attendance using Face Detection and Raspberry-PiIRJET Journal
The document proposes an automated classroom attendance system using face detection and recognition with Raspberry Pi to minimize time spent on manual attendance and reduce human error. The system uses the Haar cascade classifier with OpenCV for face detection, and local binary patterns (LBP) for face recognition to identify students and update attendance records in real-time. Key advantages of the system include increased productivity, reduced proxies, and automatic alerts sent to guardians about student absences.
Virtual Contact Discovery using Facial RecognitionIRJET Journal
The document describes a project that aims to use facial recognition as a means of contact discovery and metadata retrieval. The project seeks to optimize machine learning models for facial detection and verification in order to provide fast and accurate contact matching based on facial encodings. It outlines the objectives, scope, literature review, proposed system architecture and implementation details. The system would take facial landmarks and encodings to compare and rank the top 10 most similar encodings to identify matches from a database. The optimized model aims to reduce latency and improve accuracy for contact matching based on facial scans.
IRJET- Class Attendance using Face Detection and Recognition with OPENCVIRJET Journal
This document describes a system to automate class attendance using face detection and recognition with OpenCV. The system uses the Viola-Jones algorithm for face detection and linear binary pattern histograms for face recognition. Detected faces are converted to grayscale images for better accuracy. The system trains on positive images of faces and negative images without faces to build a classifier. It then detects faces in class and recognizes students by matching features to a stored database, updating attendance and notifying administrators. The proposed system aims to reduce time spent on manual attendance and increase accuracy by automating the process through computer vision techniques.
IRJET- Implementation of Gender Detection with Notice Board using Raspberry PiIRJET Journal
1) The document describes a system that uses a Raspberry Pi device with a camera module to implement gender detection.
2) Images captured by the camera are processed through a convolutional neural network to extract facial features and predict gender.
3) The system is intended to address limitations of existing gender detection technologies and provide a low-cost hardware solution using a Raspberry Pi single-board computer.
IRJET- Design of an Automated Attendance System using Face Recognition AlgorithmIRJET Journal
This document describes the design of an automated attendance system using face recognition for Nigerian universities. It aims to provide an efficient alternative to the problematic paper-based attendance system. The proposed system uses single scale Retinex with bi-histogram equalization to enhance face images captured by a webcam. Then, the Viola-Jones algorithm is used to detect faces, and principal component analysis (PCA) is used to recognize faces by comparing them to template images stored in a database. The system was able to achieve over 90% accuracy in tests. If a captured face matches a stored template, a '1' is recorded to mark the student as present. Otherwise, a '0' is recorded to mark them as absent. The percentage
An Effective Attendance Management System using Face RecognitionIRJET Journal
This document describes a proposed automated attendance management system using face recognition. The system uses Viola-Jones algorithm for face detection, and discriminative robust local binary pattern (DRLBP) and local directional pattern (LDP) techniques for feature extraction and matching. The system aims to overcome issues with manual attendance tracking systems by automatically detecting faces from images, extracting features, and matching with a stored database to mark attendance. Key steps include face detection, feature extraction using DRLBP for texture and LDP for shape, and matching extracted features to stored features using Euclidean distance for identification. The goal is a more efficient and secure automated attendance tracking system.
Face Recognition using PCA and Eigen Face ApproachIRJET Journal
This document discusses a face recognition system based on principal component analysis (PCA) and the eigenface approach. The system consists of two levels of authentication: face recognition and password verification via SMS. In the PCA implementation, training images are used to calculate eigenfaces, which are then used to extract features from input images. Euclidean distance between the input and training image features is calculated to classify faces as known or unknown. The system was implemented in MATLAB and experiments showed it could distinguish individual faces from a database. A GSM modem was also interfaced to send one-time passwords to users' phones for additional authentication.
This document presents a proposed automated attendance management system using face recognition. The system would use machine learning algorithms and deep learning approaches to recognize students' faces from images and track attendance. It discusses how face recognition works, including face detection, alignment, feature extraction, and recognition. It reviews similar existing systems and their limitations. The document tests several machine learning algorithms on their dataset and finds that an SVM classifier achieves the highest accuracy of 99.3%. Results are presented showing the system labeling and recognizing faces to mark attendance. The system aims to automate the attendance process to ease the burden on teachers, especially in online learning settings.
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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
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Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
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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.
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...DharmaBanothu
Natural language processing (NLP) has
recently garnered significant interest for the
computational representation and analysis of human
language. Its applications span multiple domains such
as machine translation, email spam detection,
information extraction, summarization, healthcare,
and question answering. This paper first delineates
four phases by examining various levels of NLP and
components of Natural Language Generation,
followed by a review of the history and progression of
NLP. Subsequently, we delve into the current state of
the art by presenting diverse NLP applications,
contemporary trends, and challenges. Finally, we
discuss some available datasets, models, and
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Covid Management System Project Report.pdfKamal Acharya
CoVID-19 sprang up in Wuhan China in November 2019 and was declared a pandemic by the in January 2020 World Health Organization (WHO). Like the Spanish flu of 1918 that claimed millions of lives, the COVID-19 has caused the demise of thousands with China, Italy, Spain, USA and India having the highest statistics on infection and mortality rates. Regardless of existing sophisticated technologies and medical science, the spread has continued to surge high. With this COVID-19 Management System, organizations can respond virtually to the COVID-19 pandemic and protect, educate and care for citizens in the community in a quick and effective manner. This comprehensive solution not only helps in containing the virus but also proactively empowers both citizens and care providers to minimize the spread of the virus through targeted strategies and education.
Sachpazis_Consolidation Settlement Calculation Program-The Python Code and th...Dr.Costas Sachpazis
Consolidation Settlement Calculation Program-The Python Code
By Professor Dr. Costas Sachpazis, Civil Engineer & Geologist
This program calculates the consolidation settlement for a foundation based on soil layer properties and foundation data. It allows users to input multiple soil layers and foundation characteristics to determine the total settlement.
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