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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Kirubakaran, N. Preethi, S.T. Parijatham, R. Pramithi, R. Chennai Institute of Technology Department of Computer Science and Business Systems Chennai India S.A. Engineering College Department of Computer Science and Engineering Chennai India Madha Institute Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

The research emphasizes the creation of a powerful and efficient system for the automaticextraction of contact information from physical calling cards through computer vision and information extraction techniques. This is achieved by integrating Tesseract Optical Character Recognition (OCR) with the YOLO (You Only Look Once) object detection algorithm, which acts as a dynamic combination for text and relevant elements identification and digitization from calling cards. The developed system uses client-server architecture to enable communication and data processing seamlessly. The server handles computationally intensive tasks such as object detection, OCR processing, and data validation while the client side has an interactive user interface that enables easy uploading of images and retrieval of structured contact information. The optimizations include this: YOLO is customized for detecting particular card elements (name, phone number, email) and enhancing the capabilities of Tesseract OCR to improve effectiveness for text extraction from varied card formats or fonts in the system. It is designed to be scalable and efficient, processing large datasets without affecting accuracy. This approach not only eases the digitization of calling card details into databases but also goes a long way in contributing to the domains of computer vision, object detection, and mechanized information extraction. It extends the scope to broad applications in automated data entry, customer relationship management (CRM), and digital archival. It takes forward for AI-driven sustaining technologies the integration of these techniques into practical solutions by overcoming the challenges of data extraction from real-world images. © 2024 IEEE.

关键词： Optical character recognition

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Toward actionable testing of deep learning models

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science China(Information sciences) 2023年第7期66卷 300-302页

作者： Yingfei XIONG Yongqiang TIAN Yepang LIU Shing-Chi CHEUNG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education School of Computer Science Peking University Department of Computer Science and Engineering The Hong Kong University of Science and Technology School of Computer Science University of Waterloo Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology

Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.

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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

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computer Modeling in Engineering & sciences 2024年第8期140卷 1339-1370页

作者： Ali S.Alghamdi Mohamed A.Zohdy Saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and Space Science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer Science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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Optimizing Deepfake Detection with Transfer Learning Methods 1

Optimizing Deepfake Detection with Transfer Learning Methods

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Kingsy Grace, R. Ragavan, M. Karthikeyan, N. Prakalathan, M. Saveetha, V. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science & Business Systems Coimbatore India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

The goal of the proposed system is to identify whether a video has endured software manipulation or not. This system specially deals with identifying deepfake videos from real ones. As new techniques emerged to make deepfake, a handful of photos and fake videos are spreading at an alarmingly increasing rate, which is indistinguishable from the real ones to the naked eye. The proposed system uses CNN with transfer learning to predict the degree of manipulation involved. The user uploads the video that they want to check;the video is saved on the server, and then they experience the process of deepfake detection. Then the user is presented with a plot showing the degree of the fakeness of the video and the accuracy of the prediction. The user can then assume based on the plot, as it shows the frames where manipulation might be involved. © 2024 IEEE.

关键词： Convolutional neural networks

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computer Vision Empowered Assistive Technology for Blind People 2

Computer Vision Empowered Assistive Technology for Blind Peo...

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

作者： Mathivanan, P. Bawa Sakthi Harish, S. Yasar Arapath, P. Abishek, N. KIT - Kalaignar Karunanidhi Institute of Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

A cost-effective mobile application is being developed to empower visually impaired individuals by enhancing indoor navigation. This innovative solution leverages advanced computer vision technology, specifically the YOLO v9 object detection model, to provide an affordable alternative to expensive assistive devices. The application delivers real-time assistance, helping users avoid obstacles and navigate safely. Using the smartphone's camera, the system captures video and processes it through the YOLO v9 model, chosen for its speed and accuracy in real-time object detection. The model is pre-trained to recognize common indoor obstacles such as furniture, walls, doors, and stairs. By identifying these barriers, the app enhances the user's confidence in navigating complex indoor environments. Feedback is provided through dual channels: audio alerts and haptic feedback. Clear audio cues like "Chair on the left"or "Wall ahead"help users adjust their movements, while haptic feedback provides tactile proximity cues through vibrations that intensify as users approach obstacles. This dual-feedback system ensures usability in noisy environments or when audio guidance is impractical. The app prioritizes accessibility and ease of use, featuring a simple interface for quick activation. The integration of YOLO v9 and real-time feedback creates a practical tool for visually impaired individuals, enabling them to navigate with greater confidence and independence. By offering an affordable and effective solution, this application bridges the gap between advanced assistive technology and inclusivity, improving the quality of life for visually impaired individuals worldwide. © 2024 IEEE.

关键词： Assistive technology

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Blockchain Based Secure Federated Learning With Local Differential Privacy and Incentivization

IEEE Transactions on Privacy

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IEEE Transactions on Privacy 2024年第1期1卷 31-44页

作者： Chaudhury, Saptarshi De Reddy, Likhith Varun, Matta Sengupta, Tirthankar Chakraborty, Sandip Sural, Shamik Vaidya, Jaideep Atluri, Vijay Indian Institute of Technology Department of Computer Science and Engineering Kharagpur721302 India Rutgers University Management Science and Information Systems Department NewarkNJ07102-3122 United States

Interest in supporting Federated Learning (FL) using blockchains has grown significantly in recent years. However, restricting access to the trained models only to actively participating nodes remains a challenge even today. To address this concern, we propose a methodology that incentivizes model parameter sharing in an FL setup under Local Differential Privacy (LDP). The nodes that share less obfuscated data under LDP are awarded higher quantum of tokens, which they can later use to obtain session keys for accessing encrypted model parameters updated by the server. If one or more of the nodes do not contribute to the learning process by sharing their data, or share only highly perturbed data, they earn less number of tokens. As a result, such nodes may not be able to read the new global model parameters if required. Local parameter sharing and updating of global parameters are done using the distributed ledger of a permissioned blockchain, namely HyperLedger Fabric (HLF). Being a blockchain-based approach, the risk of a single point of failure is also mitigated. Appropriate chaincodes, which are smart contracts in the HLF framework, have been developed for implementing the proposed methodology. Results of an extensive set of experiments firmly establish the feasibility of our approach. © 2024 IEEE.

关键词： Federated learning

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TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

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computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi Li Longfei Ma Zheng Jiang Mingyong Li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low ***-modal retrieval technology can be applied to search engines,crossmodalmedical processing,*** existing main method is to use amulti-label matching paradigm to finish the retrieval ***,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal *** avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal ***,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for ***,this method uses the inference capabilities of the transformer encoder to generate global fine-grained ***,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching *** article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous *** experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

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Unveiling Veracity: An Integrated Semi-Supervised Algorithm for Fake News Detection 16

Unveiling Veracity: An Integrated Semi-Supervised Algorithm ...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Malik, Aman Behera, Dayal Kumar Swain, Amulya Ratna Hota, Jhalak Birla Institute of Technology and Science Department of Computer Science & Information Systems Hyderabad India Kiit University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350371154

In recent years, the pervasive dissemination of misinformation and deliberately falsified content, commonly referred to as 'fake news,' has become a critical challenge in the realm of information dissemination. With the exponential growth of digital media and online platforms, the ease and speed at which misleading information can be generated and spread have increased substantially. Addressing the formidable task of detecting fake news has thus become an imperative within the field of information technology. The supervised model gives promising results. However, the availability of labelled data is critical in the realm of fake news detection. Leveraging unlabeled data alongside labelled data is an effective approach, but it requires careful handling. This paper explores the application of Semi-Supervised Learning for fake news detection by integrating Label Propagation and various ML models as base classifiers. By leveraging both labelled and unlabeled data, Label Propagation with CatBoost Classifier seeks to overcome traditional limitations, offering a promising avenue for bolstering the reliability of automated systems in distinguishing between authentic and deceptive information in the vast landscape of digital content. © 2024 IEEE.

关键词： Information dissemination

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Empirical techniques for effort estimation in designing effective ML models

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International Journal of computers and Applications 2025年第2期47卷 226-234页

作者： Padmanabha Reddy, Y.C.A. Kosuru, Sai Sathwik Sirisalla, Nageswara Rao Vivekananda, G.N. Perala, Venkata Akanksha Department of Artificial Intelligence and Machine Learning Chaitanya Bharati Institute of Technology Telangana Hyderabad India Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering K.S.R.M. College of Engineering AP Kadapa India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine Learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cumulative processes is rarely discussed in the existing works and The time to reach the desired level of the model's functionality is essential to gauge the environment training for the model training and pre-deployment testing. In this study, we empirically defined the manual-cum-computational effort required for the model development in terms of 2-time factors: time-to-live (time until 1st code modification) and time-for-modification (time between 2 codebase changes) taken together in a mathematical model to compute Effort Factor(quantitative measure of determining effort needed to design ML algorithms). The study is novel in terms of how the effort required to create ML models is calculated with a particular focus on the manual effort direction. The results and the findings obtained can be used for determining the total time needed to synthesize ML models and frameworks in terms of code change cycles and implementation strategies marking a 25% performance increase in the current method above the Standard Pipeline. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pipeline codes

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