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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Ragam, Prashanth Sai, Botcha Taraka Rutesha Mekatotti, Anshita Priyadarshini Ojasvi, Nallamala Jahnavi, Adigoppula Danaveni, Pavithra School of Computer Science & Engineering VIT-AP UNIVERSITY Inavolu AP Vijayawada522237 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science & Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798331533205

The project proposes an Examination Feedback System utilizing machine learning to automate the evaluation of subjective answers in educational assessments. The system will analyze student responses and classify them under a number of predefined topics while making sure that the feedback it generates is tailored and specific to the needs of the students. The primary goal is to ensure improved outcomes through enhanced information to students, where improvement is needed, while helping educators identify performance trends. The system is powered by DistilBERT, which is a transformer-based natural language processing model which is known for its efficiency and contextual knowledge. The proposed system differs from previous manual grading systems in that it offers data-driven information while addressing confidentiality issues with a computationally efficient design. Thus, the examination process becomes scalable, accurate, and data-driven in satisfying the demands of upstream education systems with very limited computational infrastructure. © 2025 IEEE.

关键词： Natural language processing systems

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Recognition of Fingerprint Images using CNN for Cybercrime Detection System 2

Recognition of Fingerprint Images using CNN for Cybercrime D...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Kavitha, T. Patil, Bhimaraya Saraswathi, S. Rajarajeswari, S. Patil, Anita New Horizon College of engineering Dept. of Computer Engineering Bangalore India Sri Sivasubramaniya Nadar College of Engineering Dept. of Computer Science Engineering Chennai India Vellore Institute of Technology School of computer science and engineering Vellore India City Engineering College Dept. of Information Science Bengaluru India

ISBN: (纸本)9798350372892

Fingerprint identification plays a crucial role in crime detection, but manual analysis is time-consuming and error-prone. This research aims to develop an automated system for accurate and efficient fingerprint matching using machine learning. The proposed approach involves image acquisition, preprocessing, feature extraction, and classification. Fingerprint images are enhanced, binarized, and segmented. Minutiae (ridge endings and bifurcations) are extracted as features. A convolutional neural network (CNN) is trained on a dataset of fingerprint images for classification. By comparing algorithms, Random Forest achieved over 8 1 % accuracy, outperforming Decision Trees (nearly 75% accuracy). The trained CNN model can accurately match fingerprints from crime scenes with potential suspects, providing reliable evidence. The developed system offers law enforcement agencies an automated and accurate fingerprint identification tool, improving crime-solving efficiency and reducing manual effort. The proposed approach showcases the potential of machine learning in forensic applications. © 2024 IEEE.

关键词： Random forests

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Convolutional Neural Networks for Automated Diagnosis of Diabetic Retinopathy in Fundus Images

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Journal of Artificial Intelligence and technology 2023年第4期3卷 205-214页

作者： S.Rama Krishna Naresh Cherukuri Y.Dileep Kumar R.Jayakarthik B.Nagarajan Allam Balaram G.Divya Jyothi Department of Computer Science&Engineering GSoTGITAM(Deemed-to-be-University)HyderabadIndia Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of Technology(Autonomous)VijayawadaIndia Department of Electronics&Communication Engineering School of EngineeringMohan Babu UniversityTirupatiAndhra PradeshIndia Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATSIndia Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatoreIndia Department of Computer Science and Engineering MLR Institute of TechnologyHyderabadIndia Department of Computer Science and Engineering MLR Institute of TechnologyDundigalMedchalTelanganaIndia

Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of *** diagnostic procedures for DR now include optical coherence tomography and digital fundus *** digital fundus images alone could provide a reliable diagnosis,then eliminating the costly optical coherence tomography would be beneficial for all parties *** and their patients will find this *** deep convolutional neural networks(DCNNs),we provide a novel approach to this *** approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling *** order to collect more subtle information for categorization,two such DCNNs,each with a different number of layers,are *** establish these limits,we use DCNNs and features extracted from picture metadata to train a support vector machine *** our experiments,we used information from Kaggle’s open DR detection *** fed our model 34,124 training images,1,000 validation examples,and 53,572 test images to train and test *** of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and *** results show a higher identification rate(86.17%)than those found in the existing literature,indicating the suggested strategy may be *** have jointly developed an algorithm for machine learning and accompanying software,and we’ve named it deep *** of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our *** technology might be used for self-diagnosis,at-home care,and telemedicine.

关键词： automated diagnosis of diabetic retinopathy DCNNs diabetic retinopathy self-diagnosis

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Explore the Impact of Quantum Computing to Enhance Cryptographic Protocols and Network Security Measures 5

Explore the Impact of Quantum Computing to Enhance Cryptogra...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Ambika, S. Balaji, V. Rajasekaran, R.Thalapathi Periyasamy, P.N. Kamal, N. E. G. S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Hindusthan Institute of Technology Department of Computer Science and Engineering India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350383522

The revolutionary effects of quantum computing on security procedures for networks and cryptographic protocols are the focus of this study. Our research shows that conventional cryptographic algorithms are susceptible to quantum assaults;specifically, RSA and ECC encryption are very vulnerable. On the other hand, cryptographic algorithms that are immune to quantum attacks, such as multivariate polynomial cryptography and lattice-based algorithms, show promise in meeting these challenges. Key exchange rates between 200 kbps and 450 kbps and error rates as low as 0.5% are some of the significant achievements shown by Quantum Key Distribution (QKD) implementations. These applications show that QKD is a viable solution for protecting communication channels up to 200 km away. On top of that, there are remarkable outcomes when network security systems incorporate quantum advancements. The detection accuracy of Quantum-Enhanced Intrusion Detection Systems (QIDS) is 98.5% and the false positive rate is minimally 0.2%. The detection accuracy of quantum-secured communication systems is 99.2% and the false positive rate is an extremely low 0.1%. While quantum firewalls keep a detection accuracy of 97.8% and a false positive rate of 0.3%, quantum-safe cryptographic modules show a low latency of 3 ms. © 2024 IEEE.

关键词： cryptographic protocol data security quantum key distribution quantum-enhanced intrusion detection

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Weave defect detection in jute manufacturing value chain using artificial intelligence and computer vision

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International Journal of System Assurance engineering and Management 2025年 1-9页

作者： Vadivel, S.M. Madhumitha, R. Nair, Yash Aniruddha, P. Manish Mithun, S.P. Sarathy, Yashwanth VIT Business School Operations and Quantitative Techniques Division Vellore Institute of Technology Chennai Vandalur-Kelambakkam Road Tamil Nadu Chennai600127 India School of Computer Science and Engineering Vellore Institute of Technology Vandalur-Kelambakkam Road Tamil Nadu Chennai600127 India

This study aims to introduce a cutting-edge method for weave defect detection using Artificial intelligence (AI) and computer vision (CV) technologies. By employing a Convolutional neural network (CNN) trained on a comprehensive dataset (Python programming codes mentioned in appendix) of textile images with annotated defects, the system automates the identification and classification of fabric irregularities, such as stains and tears. Testing in an industrial setting has shown that this AI-driven approach significantly outperforms traditional manual inspections, enhancing defect detection accuracy and production efficiency. The research highlights the potential of AI and CV to revolutionize Quality control (QC) processes in jute manufacturing industries and beyond. © The Author(s) under exclusive licence to The Society for Reliability engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of technology, Sweden 2025.

关键词： computer control

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LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association

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computers, Materials & Continua 2024年第10期81卷 1449-1470页

作者： Suya Li Ying Cao Hengyi Ren Dongsheng Zhu Xin Xie Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng475001China College of Information Science and Technology&Artificial Intelligence Nanjing Forestry UniversityNanjing210037China School of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China

Multi-object tracking(MOT)has seen rapid improvements in recent ***,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking *** from some existing methods,which discarded the low-quality targets or ignored low-quality target ***,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality *** the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data ***,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target ***,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking *** experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).

关键词： Low-quality targets association strategy feature fusion multi-object tracking tracking-by-detection

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Differential Privacy Preserving Quantum Computing via Projection Operator Measurements 1

Differential Privacy Preserving Quantum Computing via Projec...

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1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024

作者： Li, Yuqing Zhao, Yusheng Zhang, Xinyue Zhong, Hui Pan, Miao Zhang, Chi School of Cyberspace Science and Technology University of Science and Technology of China Hefei China Kennesaw State University Department of Computer Science Marietta United States University of Houston Department of Electrical and Computer Engineering Houston United States

ISBN: (纸本)9798350366778

Quantum computing has been widely applied in various fields, such as quantum physics simulations, quantum machine learning, and big data analysis. However, in the domains of data-driven paradigms, ensuring databases privacy. For classical computing, we can incorporate the concept of differential privacy (DP) to meet the standard of privacy preservation by manually adding noise. In quantum computing scenario, researchers have extended classic DP to quantum differential privacy (QDP) by considering quantum noise. In this paper, we propose a novel approach to satisfy the QDP definition by considering the errors generated by the projection operator measurement, which is denoted as shot noise. Then, we discuss the amount of privacy budget that can be achieved with shot noise, which serves as a metric for the level of privacy protection. Furthermore, we provide the QDP of shot noise in quantum circuits with depolarizing noise. Through numerical simulations, we show that shot noise can effectively provide privacy protection in quantum computing. © 2024 IEEE.

关键词： Quantum noise

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Autonomous face mask detection using single shot multibox detector, and ResNet-50 with identity retrieval through face matching using deep siamese neural network

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11195-11205页

作者： Vignesh Baalaji, S. Sandhya, S. Sajidha, S.A. Nisha, V.M. Vimalapriya, M.D. Tyagi, Amit Kumar School of Compter Science and Engineering Vellore Institute of Technology Chennai India Post Graduate Department of Computer Science and Technology Women’s Christian College Chennai India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

The COVID-19 pandemic poses a global health challenge. The World Health Organization states that face masks are proven to be effective, especially in public areas. Real-time monitoring of face masks is challenging and exhaustive for humans. To reduce human effort and to provide an enforcement mechanism, an autonomous system has been proposed to detect non-masked people and retrieve their identity using computer vision. The proposed method introduces a novel and efficient method that involves fine-tuning the pre-trained ResNet-50 model with a new head layer for classification between masked and non-masked people. The classifier is trained using adaptive momentum optimization algorithm with decaying learning rate and binary cross-entropy loss. Data augmentation and dropout regularization are employed to achieve best convergence. During real-time application of our classifier on videos, a Caffe face detector model based on Single Shot MultiBox Detector is used to extract the face regions of interest from each frame, on which the trained classifier is applied for detecting the non-masked people. The faces of these people are then captured, which is passed on to a deep siamese neural network, based on VGG-Face model for face matching. The captured faces are compared with the reference images from the database, by extracting the features and calculating cosine distance. If the faces match, the details of that person are retrieved from the database and displayed on the web application. The proposed method has secured best results where the trained classifier has achieved 99.74% accuracy, and the identity retrieval model achieved 98.24% accuracy. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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HTDcr:a job execution framework for high-throughput computing on supercomputers

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science China(Information sciences) 2024年第1期67卷 69-85页

作者： Jiazhi JIANG Dan HUANG Hu CHEN Yutong LU Xiangke LIAO School of Computer Science and Engineering Sun Yat-sen University School of Software Engineering South China University of Technology

High-throughput computing(HTC) is a computing paradigm that aims to accomplish jobs by easily breaking them into smaller,independent ***,it requires a large amount of computing power for a long *** existing HTC frameworks are job-oriented without support for coscheduling with hardware architecture and task-level ***,most of the frameworks reach a limited scale,and their usability needs further ***,we present HTDcr,a job execution framework for the HTC on *** study aims to improve the throughput,task dispatching,and usability of the *** detail,the throughput optimizations include a sophisticated designed task management system,a hierarchical scheduler,and the co-optimization of the task-scheduling strategy with the application and hardware *** optimizations for usability include a programable execution workflow,mechanisms for more robust and reliable service qualities,and a fine-grained resource allocation system for the colocation of multiple *** to our evaluations,HTDcr can achieve outstanding scalability and high throughput on large-scale clusters for the HTC *** evaluate HTDcr with several microbenchmarks and real-world applications on Tianhe-2 and Sunway TaihuLight to demonstrate its effects on existing design *** instance,the task scheduling for two real-world applications integrated with the application and hardware characteristics achieves 1.7× and 1.9× speedups over the basic task-scheduling strategy.

关键词： high-throughput computing supercomputer task scheduling middleware password guessing

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Apply Deep Learning-based CNN and LSTM for Visual Image Caption Generator 3

Apply Deep Learning-based CNN and LSTM for Visual Image Capt...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Indumathi, N. Divyalakshmi, R.J. Stalin, J. Ramachandran, V. Rajaram, P. Srm Institute of Science and Technology Computer Science and Applications Chennai India Sethu Institute of Technology Computer Science and Business Systems Virudhunagar India Computer Science and Engineering Gitam School of Computing Bengaluru India

ISBN: (纸本)9798350399264

Implemented Image captioning is becoming a need. Deep neural network models enable integrated applications to generate and caption images. Image captioning describes an image. It requires identifying an image's main objects, properties, and relationships. It produces correct sentences. The creation of image caption generators, which examine the content of an image and offer pertinent descriptions, makes use of deep learning and computer vision techniques. One element of this method involves classifying objects in the image using English keywords derived from training datasets. The caption generator was created by combining LSTM and CNN. In this paper, we propose a deep-learning model that uses computer vision and machine translation to characterize images and generate captions. The model successfully recognizes and labels visual items and their relationships. Transfer Learning will be used to demonstrate the suggested experiment, coupled with the Flickr8k dataset and the Python3 programming language. This study will also look into the functions and construction of neural networks. The proposed model achieves a BELU Score of 69.8. © 2023 IEEE.

关键词： Long short-term memory

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