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Enhancing Classroom Attendance Systems with Face recognition through CCTV using Deep learning

Enhancing Classroom Attendance Systems with Face Recognition...

引用

3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Patel, Jaykumar Gandhi, Savita Katheriya, Vishal Pataliya, Parth Majumdar, Ankan Department of Computer Sciencce Gujarat University Ahmedabad India

This paper investigates the effectiveness of face recognition-based attendance systems, focusing on deep learning models applied to automate attendance tracking in classroom settings. Attendance management is critical for educational institutions, but traditional methods are often plagued by inefficiencies and inaccuracies. To address these issues, we explored the use of advanced face recognition technology, particularly leveraging CCTV-based systems combined with deep learning models namely ResNet-50 and VGG-16. We used images collected from actual classroom environments, applied image dataset augmentation methods, used transfer learning for features extraction and Principal Component Analysis (PCA) for dimensionality reduction and then used the data as an input in the fine-tuned pre-trained models. Our experimental analysis using PCA as a feature extractor indicate that the ResNet-50 model achieved precision and recall rate of 96% and 93% respectively and VGG16 achieved precision and recall rate of 92% and 89% respectively. Despite promising results, challenges remain, such as the impact of camera quality and inherent limitations such as viewing geometry in current face recognition technologies. Future improvements may involve upgrading camera systems and employing image enhancement techniques. This study offers insights into the practical application of face recognition for attendance and suggests directions for enhancing system reliability and accuracy. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Deep learning

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A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial Intelligence to Quantum machine learning

A Comprehensive Survey on Breast Cancer Diagnostics: From Ar...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： Manideep Raya V S R S Reddy Sunil Kumar Biswajit Bhowmik Dept. of Computer Science and Engineering Maharshi Kanad QC Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Maharshi Patanjali CPS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial Intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum machine learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis.

关键词： Surveys Accuracy Face recognition Biopsy machine learning Ultrasonography Sensitivity and specificity Breast cancer Artificial intelligence Medical diagnostic imaging

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An Integration of Deep learning Approach for prediction of Consumer Reviews using Sentiment Analysis 3

An Integration of Deep Learning Approach for prediction of C...

引用

3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Krishnan, V. Gokula Sankar, K. Saleem, P. A. Abdul Hemamalini, S. Vijayashanthi, V. Sheryl, S. Annie Simats University Saveetha School of Engineering Cse Department Tamil Nadu Chennai India Cvr College of Engineering Cse Department Telangana Hyderabad India Panimalar Institute of Technology Cse Department Tamil Nadu Chennai India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Cse Department Tamil Nadu Chennai India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

There is a massive increase in consumer reviews posted on social media, both in terms of the volume of evaluations and their relevance. This is why sentiment analysis is becoming increasingly popular in a wide range of contexts, including politics, social issues, and current events. For a number of corporate applications, the analysis of social media user reviews is critical. Because social media makes it so simple to obtain public sentiment from around the world, this method is better suited for sentiment mining. With the hybrid technique of two deep constructions, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTTM), this article examines airline industry service and movie reviews (LSTM). The local feature selection power of CNN has been well documented, whereas the sequential text analysis prowess of LSTM has been well documented as well. In sentiment analysis, the suggested CNN-LSTM model primarily aims to achieve two goals. First, it can easily be scaled up to handle large amounts of social media data, and second, unlike traditional machine learning algorithm's, it is not domain-specific. Two review datasets from different fields have been used to train a perfect that can handle all kinds of dependences in a post. This ensemble model outperforms other deep learning algorithms in accuracy and other limits, as shown by the results in the experiments. © 2022 IEEE.

关键词： Long short-term memory

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Identification of Food Packaging Activity Using MoCap Sensor data 1

引用

3rd international conference on Activity and Behavior Computing (ABC)

作者： Anwar, Adrita Tapotee, Malisha Islam Saha, Purnata Ahad, Md Atiqur Rahman Univ Dhaka Elect & Elect Engn Dhaka Bangladesh Univ East London London England

ISBN: (数字)9789811903618

ISBN: (纸本)9789811903618;9789811903601

The automation system has brought a revolutionary change in our lives. Food packaging activity recognition can add a new dimension to industrial automation systems. However, it is challenging to identify the packaging activities using only skeleton data of the upper body due to the similarities between the activities and subject-dependent results. Bento Packaging Activity recognition Challenge 2021 provides us with a dataset of ten different activities performed during Bento box packaging in a laboratory using MoCap (motion capture) sensors. Bento box is a single-serving packed meal that is very popular in Japanese cuisine. In this paper, we develop methods using the classical machine learning approach, as the given dataset is small compared to other skeleton datasets. After preprocessing, we extract different hand-crafted features and train different models like extremely randomized trees, random forest, and XGBoost classifiers and select the best model based on cross-validation score. Then, we explore different combinations of features and use the best combination of features for prediction. By applying our methodology, we achieve 64% accuracy and 53.66% average accuracy in tenfold cross-validation and leave-one-subject-out cross-validation, respectively.

关键词： Decision trees

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Cardio Vascular Disease Prediction and Classification Report Generation using data mining Technique

Cardio Vascular Disease Prediction and Classification Report...

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Smart data Intelligence (ICSMDI), international conference on

作者： T Jemima Jebaseeli Navin Kumar M Angeleen Subagar Santhosh A Department of CSE Karunya Institute of Technology and Sciences Coimbatore

Cardiovascular disease is one of the primary reasons for death in the world today. It has evolved into one of the most challenging illnesses to identify. By a recent WHO research, heart disorders are on the rise. As a result, 17.9 million people die each year. As the population increased, this became increasingly difficult to diagnose and initiate treatment during the initial stages. When it comes to forecasting coronary heart disease, medical analysis of data encounters a huge challenge. Electronic health record systems are currently used to handle the data of patients in hospitals. The huge amount of information created by the medical industry is being misused. A new approach is required to reduce costs and accurately predict heart disease. Hospitals can use appropriate decision support systems to reduce the cost of clinical tests. Several types of research offer barely a glimpse of optimism for employing machine learning approaches for predicting cardiac disease. The proposed study suggests a unique strategy for finding key characteristics via a machine learning approach throughout this work, which would also improve the precision of cardiovascular risk diagnosis. Diverse characteristic correlations and classification algorithms are used to establish the statistical model. Using the Improved random forest with Hyper - parameters tweaking in the classification algorithm for cardiovascular disease, a better reliability with an acceptable accuracy of 94.5% has been obtained. This approach may be valuable to healthcare professionals in their treatment as a decision assistance system.

关键词： Heart Industries Costs Hospitals Sociology Classification algorithms data mining

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Research on Improving Accuracy of machine learning Power Prediction System Based on Semi-Supervised Strategy

Research on Improving Accuracy of Machine Learning Power Pre...

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Energy, Power and Electrical Engineering (EPEE), international conference on

作者： Desheng Wen Denglong Lu Lifeng Fan Jinghui Zhang Yahua Wang Huabei Branch CGN New Energy Holdings Co. Ltd. Shijiazhuang China

The accuracy of wind and solar renewable energy power forecasting not only impacts the assessment of “dual guidelines” for each wind and solar energy site, but also affects the electricity market-based transactions and power generation revenue of these facilities. Employing deep learning models, we amalgamate meteorological grid characteristics, temporal features, weather pattern scenarios, and refined wind speed and power observation data. Utilizing machine learning techniques, we integrate diverse numerical forecasting outcomes and refine wind speed predictions. Furthermore, transitioning from wind to electricity models, we directly forecast wind power outcomes, either through wind speed adjustments or directly generating wind power predictions. In summation, based on the geographical location, meteorological attributes, node consumption conditions, frequency, and types of extreme weather events in the current subsidiary's renewable energy site distribution area, we undertake the research and development of a high-precision power prediction and electricity generation estimation system rooted in a semi-supervised strategic machine learning framework.

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data mining Techniques for Ensuring Security in Large datasets

Data Mining Techniques for Ensuring Security in Large Datase...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Sukhvinder Singh Dari Laith Hussein Khushbu Kriplani G. Jeyaram S. Ramesh Kumar Symbiosis Law School Nagpur Symbiosis International (Deemed University) Pune India College of technical engineering The Islamic university Najaf Iraq Department of Computer Science & Engineering IES College of Technology Bhopal India Department of Computer Science and Engineering M.E.T Engineering College Nagercoil Department of Computer Science & Engineering New Prince Shri Bhavani College of Engineering and Technology India

In the contemporary digital landscape, the security of large datasets is paramount, given the increasing volume, variety, and velocity of data generation and collection. This paper provides an intricate examination of the application of various data mining techniques to bolster security measures in handling expansive datasets. Through a systematic exploration of methods including clustering, classification, and association, we aim to elucidate the efficacy and adaptability of these techniques in identifying and mitigating potential security threats. We critically analyze real-world scenarios and case studies, drawing comparative insights to underscore the nuanced applications and challenges inherent in diverse data environments. A detailed discussion is presented on the role of machine learning algorithms and artificial intelligence in augmenting data mining processes, enhancing predictive accuracy, and real-time responsiveness to security breaches. The findings indicate a significant positive correlation between advanced data mining applications and enhanced data security. However, they also highlight existing vulnerabilities and areas necessitating further research and innovation. We conclude with recommendations for integrating adaptive, scalable, and robust data mining frameworks to ensure comprehensive security in managing large datasets, paving the way for future research intersections between data mining and cybersecurity.

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Target recognition and Tracking Using You Only Look Once Version 5 with DeepSORT in Unmanned Aerial Vehicle Vision Systems

Target Recognition and Tracking Using You Only Look Once Ver...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Shuhan Yu Hainan International College Communication University of China Beijing China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Nowadays, the target recognition and tracking technology in Unmanned Aerial Vehicles (UAV) vision systems has gained considerable interest due to its promising implications for various applications. However, the existing Open Flow based Tracking (OFT) system faced challenges by factors such as varying lighting conditions and occlusions. Hence, this research proposes a You Only Look Once version 5 with Deep learning based Simple Online Real-Time Tracking (YOLOv5-DeepSORT) is proposed to handle the occlusions. The proposed YOLOv5-DeepSORT detect and track objects across frames for accurate target detection and tracking in UAV vision systems. Initially, the input data is collected from Vision-based Drone (VisDrone) dataset and then, the data augmentation is performed to expand the dataset variability. After that, YOLOv5 is employed for object detection by incorporating Transformer based Multi-Head Attention (T-MHA) as backbone to extract features. Then, GhostNet is used as feature fusion technique to minimize the computational overhead of the network. Finally, DeepSORT is introduced to track the objects by predicting object motion and associating object across frames. From the results, the proposed YOLOv5-DeepSORT achieved better results in terms of recognition accuracy (99.5%), recognition speed (33.7 vehicles/sec), and manual time (1.4s) when compared with existing UAV-Vision Based Moving Target Detection (UAV-MTD).

关键词： YOLO Deep learning Target tracking Accuracy Target recognition machine vision Autonomous aerial vehicles Feature extraction Transformers Real-time systems

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Real-time Sign Language recognition using Computer Vision 3

Real-time Sign Language Recognition using Computer Vision

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3rd international conference on Signal Processing and Communication (ICPSC)

作者： Raval, Jinalee Jayeshkumar Gajjar, Ruchi Nirma Univ Inst Technol Elect & Commun Dept Ahmadabad Gujarat India

ISBN: (纸本)9781665428644

Speech impairment is a disability that affects an individual's ability to verbal communication. To overcome this issue sign language is used which is one of the most organised languages. There is definitely a need for a method or an application that can recognize sign language gestures so that communication is possible even if someone does not understand sign language. My paper is an effort towards filling the gap between differently-abled people like deaf and dumb and the other people. Image processing combined with machine learning helped in forming a real-time system. Image processing is used for pre-processing the images and extracting different hand from the background. These images obtained after extracting background were used for forming data that contained 24 alphabets of the English language. The Convolutional Neural Network proposed here is tested on both a custom-made dataset and also with real-time hand gestures performed by people of different skin tones. The accuracy obtained by the proposed algorithm is 83%.

关键词： machine learning Image processing Convolutional Neural Network

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machine learning based human recognition via robust Features from audio signals

Machine Learning based human recognition via robust Features...

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Artificial Intelligence (ICAI), international conference on

作者： Umar Sadique Muhammad Suleman Khan Shahzad Anwar Mehran Ahmad AI in Healthcare Lab National Centre of AI University of Engineering and Technology Peshawar Pakistan Department of Electrical Engineering University of Engineering and Technology Peshawar Pakistan Department of Mechatronics Engineering University of Engineering and Technology Peshawar Pakistan

Biometric verification techniques are commissioned throughout the world in different applications. Human voice recognition is one of the biometric techniques. This technique consists of identifying a human from their voice characteristic. This popular and beneficial biometric technique could be employed for identity human, security purposes, and many different applications. Human audio signal recognition consists of two phases i.e., Features Extraction and Classification. The proposed work consists of extracting features through the Mel Frequency Cepstral-Coefficient (MFCC) from the human audio signal, selecting robust features through Principal Component Analysis PCA, and classifying the selected features by comparing seven machine learning and proposed deep learning algorithms. Finally, compare the performance of different algorithms with different percentages of selected features to evaluate the acceptance rate of the correlated features. Support Vector machine SVM shows the best performance with an Accuracy of 99.27% with F1-score and ROC values of 1.00. In the comparison with other methods, the Random Forest and CNN-ANN are 2 nd top robust models with an accuracy of 98.7%. Some of the algorithm's accuracy decreased with fewer features, including Naïve Bayes accuracy suddenly decreases to 60% on 20% of total features. The experiment concludes the acceptance rate of correlated features in different ML and DL algorithms are different in speech processing data.

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