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Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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computer Modeling in engineering & sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Skill Mastery Solutions: Your Path to Excellence 10

Skill Mastery Solutions: Your Path to Excellence

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Saranya, E. Ajay, K. Hardika, S. Jayasudha, C. Kishore, R.E. Sri Eshwar College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

'Skillstorm' is an innovative and competitive website that harnesses the power of cutting-edge technologies to create a safe and engaging online environment. Developed primarily using Python and Flask, Skillstorm integrates machine learning algorithms to combat cyberbullying, ensuring a positive user experience. This project leverages HashMaps for efficient data management and employs a dynamic front-end interface built with HTML, CSS, and JavaScript. The backend of Skillstorm is powered by Python and Flask, providing a robust and scalable foundation for handling user data, interactions, and website functionality. Skillstorm utilizes machine learning techniques to automatically detect and mitigate instances of cyberbullying and harassment. This proactive approach ensures a safe and welcoming environment for all users. HashMaps are employed for efficient data storage and retrieval, optimizing the performance of the website and enhancing user experience. 'Skillstorm' represents a forward-thinking and comprehensive project that combines Python, Flask, machine learning, HashMaps, HTML, CSS, and JavaScript to create a competitive website that prioritizes user safety and positive interactions in an increasingly digital world. Powered by cutting-edge Python frameworks, we have created a dynamic and immersive experience that will ignite your passion, inspire excellence, and propel you to new heights of achievement. At SkillStorm, we believe that true talent knows no borders. © 2024 IEEE.

关键词： Python

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A Secure and Transparent Voting System Framework Using Finger Vein and Blockchain Technology 16

A Secure and Transparent Voting System Framework Using Finge...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Padal, Chittapuli Bhupathi Vatsavayi, Valli Kumari Andhra University College of Engineering Department of Computer Science and Systems Engieering Visakhapatnam530003 India

ISBN: (纸本)9798331505264

The traditional voting system faces widespread mistrust due to its vulnerability to exploitation, lack of transparency, and limited reliability, leading to concerns about the security of democratic rights. Attempts to digitize voting have also faced challenges, with current digital systems often failing to ensure the transparency and security necessary for fair elections. Both traditional and current digital voting systems are susceptible to exploitation, and their inherent weaknesses often lead to mistrust and vulnerability. This proposed solution is secure and transparent voting system framework that leverages finger vein recognition technique integrated with block chain technology to ensure the integrity and confidentiality of votes. Blockchain technology to create a more secure, transparent and reliable voting system. By utilizing blockchain, we aim to address the issues in both physical and digital voting systems, ensuring a fair election process that upholds voters' democratic rights. Blockchain technology offers a platform that maximizes transparency and reliability, fostering a trustworthy relationship between voters and election authorities. each Voter will be registered with Finger Vein as the password and this finger vein will be encrypted and secured by using Pepper-Salt algorithm. Our proposed framework allows voting activities to be conducted entirely online through blockchain, removing the need for physical polling stations. The system employs a scalable blockchain supported by flexible consensus algorithms, with a Chain Security Algorithm to enhance transaction security. © 2024 IEEE.

关键词： Voting machines

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Integrated Web-Based Platform for AI-Enhanced Autism Assessment 2

Integrated Web-Based Platform for AI-Enhanced Autism Assessm...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Yasir Abdullah, R. Rk, Kamesh Kavin Prabu, S. Sri Subiksha, T. Venkatraman, P. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798331534950

This research presents a novel web-based platform that leverages facial and vocal tracking to enhance the preliminary screening process for Autism Spectrum Disorder (ASD). By incorporating advanced machine learning algorithms to analyze multimodal data, the platform offers a non-invasive, accessible, and cost-effective solution for initial ASD evaluation. The system demonstrates high diagnostic accuracy, achieving 90% in facial pattern analysis, 85% in vocal pattern analysis, and 95% accuracy when both analyses are combined. Additionally, it includes communication features designed to foster a supportive environment for individuals with ASD. This paper provides an overview of the platform's development, functionality, and future potential, with an emphasis on its capacity to improve existing diagnostic methods and its accessibility for underserved communities. © 2024 IEEE.

关键词： Adversarial machine learning

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CounterFair: Group Counterfactuals for Bias Detection, Mitigation and Subgroup Identification 24

CounterFair: Group Counterfactuals for Bias Detection, Mitig...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Kuratomi, Alejandro Lee, Zed Tsaparas, Panayiotis Junior, Guilherme Dinis Pitoura, Evaggelia Lindgren, Tony Papapetrou, Panagiotis Stockholm University Department of Computer and Systems Sciences Sweden University of Ioannina and Archimedes/Athena RC Department of Computer Science and Engineering Greece

ISBN: (纸本)9798331506681

Counterfactual explanations can be used as a means to explain a models decision process and to provide recommendations to users on how to improve their current status. The difficulty to apply these counterfactual recommendations from the users perspective, also known as burden, may be used to assess the models algorithmic fairness and to provide fair recommendations among different sensitive feature groups. We propose a novel model-agnostic, mathematical programming-based, group counterfactual algorithm that can: (1) detect biases via group counterfactual burden, (2) produce fair recommendations among sensitive groups and (3) identify relevant subgroups of instances through shared counterfactuals. We analyze these capabilities from the perspective of recourse fairness, and empirically compare our proposed method with the state-of-the-art algorithms for group counterfactual generation in order to assess the bias identification and the capabilities in group counterfactual effectiveness and burden minimization. © 2024 IEEE.

关键词： Algorithmic Fairness Counterfactual explanations Group counterfactuals Local explainability

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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High-Capacity DWDM Transmission System for Free-Space Optical Network 6

High-Capacity DWDM Transmission System for Free-Space Optica...

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6th International Conference on Electrical, Control and Instrumentation engineering, ICECIE 2024

作者： Rahman, M.T. Jahid, M.A. Anha, S.Y. Rahman, Md. Shohanur Azmi Orpy, F. Bakibillah, A.S.M. Department of Computer Science and Engineering Bangladesh Tokyo Institute of Technology Department of Systems and Control Engineering Tokyo152-8552 Japan

ISBN: (纸本)9798350380040

This study presents the architecture and performance evaluation of a high-capacity free-space optical (FSO) communication system that makes use of dense wavelength division multiplexing (DWDM) and a 1.28 Tb/s link. The system under consideration comprises 64 individual channels, each of which is capable of transferring data streams at a speed of 20 Gbps. A return-to-zero (RZ) line coding algorithm is incorporated to maximize performance across 2.0 km of FSO link. Important parameters that affect the system's performance, like beam divergence and atmosphere attenuation, are taken into account in the analysis. The signals' bit-error-rate (BER), quality factor (Q), and eye diagrams are calculated and examined to assess the system's performance. For future optical-wireless networks that require significant bandwidth capabilities, cost-effective implementation, and increased flexibility, this novel high-capacity design offers a promising solution. The results aid in the development of dependable and useful high-speed FSO communication systems. © 2024 IEEE.

关键词： Cost effectiveness

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Comparative Study of Machine Learning Models and Distributed Runoff Models for Predicting Flood Water Level

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IEIE Transactions on Smart Processing and Computing 2023年第3期12卷 215-222页

作者： Kubo, Tasuku Okazaki, Takeo Graduate school of Engineering and Science University of the Ryukyus Senbaru Nishihara Japan Department of Computer Science and Intelligent Systems University of the Ryukyus Senbaru Nishihara Japan

Conventional flood forecasting methods can be roughly classified as physics-based models and data-oriented models, which both require parameter optimization. In parameter optimization, a search is generally done to minimize the magnitude of overall errors. However, under- and overestimation errors are not equivalent in flood forecasting since underestimation of the water level leads to delays in decision making. We propose a risk-aware forecasting method that uses a weighted loss function. We applied the proposed method to both physics-based models and machine learning models and compared the prediction results to clarify the difference in the prediction results according to the base model used. The results show that the model optimized by the weighted loss function reduced the underestimation error while maintaining the overall error. © 2023 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Errors

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions 38

Single-Loop Stochastic Algorithms for Difference of Max-Stru...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Hu, Quanqi Qi, Qi Lu, Zhaosong Yang, Tianbao Department of Computer Science & Engineering Texas A&M University United States Department of Computer Science The University of Iowa United States Department of Industrial and Systems Engineering University of Minnesota United States

In this paper, we study a class of non-smooth non-convex problems in the form of minx[maxy∈Y (x, y) − maxz∈Z ψ(x, z)], where both Φ(x) = maxy∈Y (x, y) and Ψ(x) = maxz∈Z ψ(x, z) are weakly convex functions, and (x, y), ψ(x, z) are strongly concave functions in terms of y and z, respectively. It covers two families of problems that have been studied but are missing single-loop stochastic algorithms, i.e., difference of weakly convex functions and weakly convex strongly-concave min-max problems. We propose a stochastic Moreau envelope approximate gradient method dubbed SMAG, the first single-loop algorithm for solving these problems, and provide a state-of-the-art non-asymptotic convergence rate. The key idea of the design is to compute an approximate gradient of the Moreau envelopes of Φ, Ψ using only one step of stochastic gradient update of the primal and dual variables. Empirically, we conduct experiments on positive-unlabeled (PU) learning and partial area under ROC curve (pAUC) optimization with an adversarial fairness regularizer to validate the effectiveness of our proposed algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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