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Attendance Management with Facial Recognition using OpenCV

Attendance Management with Facial Recognition using OpenCV

引用

Emerging Technology (INCET), International Conference for

作者： Sudhakar Atchala Shaik Riyaz M. Chola Chaitanya Chippada Uday dept. Computer Science and Engineering Lakireddy Bali Reddy College of Engineering (Autonomous) Mylavaram India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In this research, we propose a comprehensive system integrating facial recognition technology and attendance management for educational institutions or organizations. Leveraging state-of-the-art face recognition algorithms and machine learning techniques, our system aims to automate the attendance tracking process, enhancing accuracy and efficiency. We conducted extensive experiments, employing a diverse dataset for training and testing. The results demonstrate the robustness and reliability of our facial recognition model in real-world scenarios. Furthermore, we implemented a user-friendly web interface for administrators and instructors to manage attendance records seamlessly. The system incorporates an automated email notification feature, sending alerts to students or relevant personnel upon successful face detection. Our research contributes to the evolving field of automated attendance systems, providing an innovative solution that not only simplifies the tracking process but also ensures data accuracy and accessibility. The implementation and evaluation of our system indicate its potential to significantly streamline attendance management practices in educational institutions and beyond.

关键词： Training Accuracy Systematics Face recognition Organizations Feature extraction Face detection

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SMOTE and Hyperparameter Optimization: A Dual Machine Learning Strategy for Enhancing Coupon Recommendation in Vehicular Contexts

SMOTE and Hyperparameter Optimization: A Dual Machine Learni...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Ravikumar R N Sanjay Jain Manash Sarkar Dept. of Computer Science and Engineering Amity University Rajasthan Jaipur India Dept. of Computer Science and Engineering Atria Institute of Technology Banglore India

This study centers on enhancing the accurateness of coupon recommendation algorithms within the framework of in-vehicle settings. The In-Vehicle Coupon Recommendation dataset from the UCI Repository was utilized in this study. A range of machine learning algorithms, such as Random Forest, Gradient Boosting, Extra Trees, LightGBM, XGBoost, AdaBoost, Stochastic Gradient Descent (SGD), and CatBoost, were employed for analysis. Among the several algorithms considered, CatBoost exhibited notable performance, with a baseline accuracy of 74%. In order to improve the efficacy of the model, we utilized the “Synthetic Minority Over-sampling Technique” (SMOTE) to mitigate the issue of class imbalance. This was subsequently followed by a thorough hyperparameter adjustment of the CatBoost algorithm. The improved model demonstrated a significant upgrade, with an accuracy rate of 77%. Additionally, there were noteworthy improvements in precision, recall, and F1 score. The AUC, which represents the Area Under the Receiver Operating Characteristic Curve, saw a notable improvement to 0.82. This signifies a noteworthy enhancement in the capacity to differentiate between positive and negative cases. The results emphasize the efficacy of CatBoost in the context of recommending coupons within vehicles, while also drawing attention to the influence of preprocessing methods and hyperparameter adjustment in enhancing the model's overall performance. This study is a valuable contribution to the domain of personalized advertising in the context of vehicles, providing valuable insights for the advancement of coupon recommendation systems that are more precise and dependable.

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Design and Implementation of Low Power Circuits Based on 5nm Double Gate MOSFET Technology Using Different Materials

Design and Implementation of Low Power Circuits Based on 5nm...

引用

IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Shrabanti Kundu Jyotsna Kumar. Mandal Dept. of Computer Science and Engineering University of Kalyani Kalyani Nadia WB India

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

This paper presents a comprehensive investigation of 5nm double gate MOSFETs (DG MOSFETs) circuits using different substrate semiconductor materials for low-power devices. A comparative analysis of characteristics of SiGe, InGaAs and GaN as substrate materials is performed. The study reveals that InGaAs offers superior performance due to its higher electron mobility, lower threshold voltage and thermal stability than other materials studied here for low-power applications. The electrical properties of DG MOSFETs on these substrates have been investigated by calculating key parameters such as threshold voltages (Vth), Ion and Ioff currents. The results show that InGaAs-based DG MOSFETs exhibit superior performance with threshold voltage, improved leakage current and Ion/Ioff ratio compared to other substrate materials. Additionally, a lower threshold voltage has been observed for InGaAs-based devices, indicating reduced power consumption. These findings demonstrate the potential of InGaAs as a preferred substrate material for low-power, high-performance DG MOSFET devices. Double gate MOSFET circuits are designed using InGaAs and power dissipation is calculated. The results show a significant reduction in power consumption and improved circuit performance compared to traditional Si-based devices. This research demonstrates the potential of InGaAs-based DG MOSFETs for low-power electronic devices, enabling the development of energy-efficient circuits for various applications.

关键词： Performance evaluation MOSFET Electric potential Power demand Logic gates MOSFET circuits Threshold voltage Indium gallium arsenide Substrates Thermal stability

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Integrating Artificial Intelligence and Educational Technology for Inclusive and Ethical Education Practices

Integrating Artificial Intelligence and Educational Technolo...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Lathika Ilayaraja Yukthika S S Subramani Prabakaran S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

AI and EdTech are transforming education by making it more accessible, personalized, and assessable, even in low and middle income countries (LMICs) facing resource constraints. However, challenges come with unequal access, issues in data privacy, and bias in AI model creations. As AI continues to become more popular, the issue of whether the work made by the students will be original. This paper focuses on the impact of artificial intelligence on learning in both its ethical and practical dimensions while highlighting the themes of inclusivity, fairness, and authenticity. Guidelines and policies for responsible AI in integration will be proposed further, alongside teacher training. Results should depict that this moral framework, policies, and teacher education should aid in ensuring that the implications of AI are positive and fruitful for learning. Ideal AI guidelines would foster an educational system that is inclusive, transparent, and considers uniqueness and innovation.

关键词： Training Ethics Technological innovation Large language models Education Educational technology Learning (artificial intelligence) Internet of Things Artificial intelligence Guidelines

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Yolov9 Based Real Time Culinary Equipment Detection to Automate The Cooking System

Yolov9 Based Real Time Culinary Equipment Detection to Autom...

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International Conference on computer and Information Technology (ICCIT)

作者： Ashik Ahmed Samiha Maisha Jeba Rashedul Islam Mariam Siddika Utpal Kanti Das Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

As technology keeps growing, people are getting busier and relying more on machines, which means they have less time for cooking and other household tasks. The field of computer vision is always changing, aiming to make interactions between people and machines more comfortable. One important part of computer vision is recognizing objects, even if they’re only partly visible or close to something else. This research investigates the use of the YOLOv9 model to enhance object detection in kitchen environments, specifically focusing on the accurate recognition of culinary tools and equipment. Object detection is critical for advancing automation in kitchen technology, allowing machines to identify and interact with tools such as knives, plates, spoons, cups and blender in real-time. Our method employs the "You Only Look Once" (YOLO) approach, enabling fast and precise detection by processing images in a single forward pass. Trained on a comprehensive dataset of kitchen tools, the YOLOv9 model achieved a mean Average Precision (mAP) of 78.1% when tested on unseen data. Additionally, an optimized hardware setup was implemented to ensure efficient processing, making the model well-suited for real-world kitchen automation applications. This research marks a significant contribution to the field of computer vision for culinary tasks, offering a reliable and robust solution for object detection in automated cooking systems.

关键词： YOLO computer vision Automation Accuracy Image recognition Computational modeling Real-time systems Reliability Object recognition Information technology

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Minimization of data centers in survivable dynamic SDM-EONs

Minimization of data centers in survivable dynamic SDM-EONs

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sourabh Chandra Khokan Mondal Dept. of Computer Science & Engineering Calcutta Institute of Technology Howrah India Dept. of Computer Science & Engineering Techno India University Kolkata India

Energy consumption due to massive use of data center (DC) is increasing rapidly. The need to improve the energy efficiency of such DC aims to develop an effective DC-placement algorithm. Again, a sudden disaster in the network can affect both fiber path as well as the DC nodes resulting in fiber cuts and huge data loss. Space division multiplexing-based elastic optical network (SDM-EON), being more efficient than the traditional elastic optical network (EON) due to the inclusion of multiple cores in a single fiber link, tries to maximize the utilization of resources by reducing blocking probability. This paper presents a novel DC-placement algorithm in dynamic DC-based SDM-EONs which focuses on enhancing energy efficiency in the network, thereby ensuring the survivability of the network against any sudden disaster. Rigorous simulation analysis proves the efficacy of the proposed algorithm over other existing algorithms.

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Architectural Design for AdhaarAssist — A Forward-Looking Lock-Free STM Solution for Adhaar

Architectural Design for AdhaarAssist — A Forward-Looking L...

引用

Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Sana Jafar Dept. of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Any parallel program is judged based on its efficiency in doing multiple tasks at the same time without getting stuck due to an underlying problem. The main purpose of a parallel program is to effectively and quickly solve a large and complicated problem at hand. This is why; parallel programs form the basis of many powerful supercomputers where parallel computing is supported through special hardware which is complemented with parallel software achieving hundreds of petaflop/s of performance. The biggest challenge for a parallel programmer is to build a program that has a minimum tradeoff for increased parallelism and good concurrency control among the parallel tasks. One of the biggest applications of parallelism is the huge, ever-increasing database of Adhaar biometric. Its crucial to have a robust, fast and efficient way of accessing and manipulating this data so that along with faster results, consistency of the database is also maintained. This paper proposes a schema AdhaarAssist which is a lock-free STM based algorithm that will work on a shared in-memory data structure. Some of its features are increased concurrency by lazy conflict detection, partial aborts that optimize the algorithm for maximum throughput when complex and lengthy transactions are employed, memory barriers and timestamping. The algorithm proposed in this paper will open the way for leveraging it for distributed-shared in-memory databases like the one used by Adhaar.

关键词： Multicore processing Signal processing algorithms Parallel processing System recovery Signal processing Data structures Throughput Supercomputers Software Testing

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Advancements in Breast Cancer Detection: A Comprehensive Review of Deep Learning Techniques

Advancements in Breast Cancer Detection: A Comprehensive Rev...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Drishti Arora Shruti Gupta Jashekam Singh Chawla Rakesh Garg Dept. of Computer Science and Engineering Amity University Uttar Pradesh India Dept. of Computer Science and Engineering Amity University Uttar Pradesh India

This paper addresses the pressing issue of breast cancer-related mortality by comprehensively analyzing six distinct deep learning models utilized in the past decade. With a focus on timely and precise diagnosis, the study evaluates the performance metrics of each model across diverse datasets, image modalities, and methodologies. The primary goal is to identify the most efficient and precise deep learning architecture tailored to classify breast cancer tumors, with potential implications for optimizing early detection strategies and advancing clinical decision-making processes. The findings hold significant implications for refining clinical decision-making processes, potentially leading to more efficacy in timely detection strategies. This research emphasizes on the broader field of image analysis and its role in combating breast cancer.

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Design and Optimization of High-Performance Health Monitoring Systems Using Advanced Sensor Networks

Design and Optimization of High-Performance Health Monitorin...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Heena Mehta Mukesh Singla Dept. of Computer Science and Engineering Baba Mastnath University Rohtak Haryana India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This research elucidates the development and optimization of advanced sensor network-based health monitoring systems. In response to the increasing need for accurate health data in real-time, advanced gadgets capable of continuously monitoring essential indicators have been created. Contemporary sensor technologies, such as biosensors, IoT devices, and WSN s, enable these systems to facilitate data collection and processing with little disturbance. Optimization techniques like PSO, including machine learning algorithms and energy conservation procedures, are used to enhance the system's performance, reliability, and battery longevity. The study focuses on developing robust and scalable systems suitable for various healthcare applications, while also investigating data processing, communication protocols, network architecture, and sensor selection. Advanced sensor networks will be crucial for future health monitoring, as research indicates substantial improvements in monitoring accuracy, system efficiency, and patient outcomes. Present approach is supposed to provide scalable and efficient solution. Moreover, it would support privacy in order to provide secure solution.

关键词： Wireless sensor networks Accuracy Protocols System performance Medical services Network architecture Real-time systems Biosensors Monitoring Optimization

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Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Identification of Cancer Biomarkers for Multi-class Diagnost...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Toccacieli, Ali Petti, Manuela Sapienza University of Rome MSc Program in Engineering in Computer Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them. © 2022 IEEE.

关键词： Platelets

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