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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Aggarwal, Aarush Pannu, Husanbir Singh Kaur, Varleen Sidhu, Sanmeet Thapar Institute of Engineering and Technology Computer Science and Engineering Patiala India Thapar Institute of Engineering and Technology Centre for Training & Development Patiala India

ISBN: (纸本)9798331508685

The transition from traditional pedagogy to self-learning represents a significant shift in educational practice, propelled by technological advancements and the evolving needs of learners. This study explores the evolution from ancient Gurukul systems to modern digital learning platforms, incorporating data from student survey responses. The analysis reveals a marked preference for hybrid learning models (62.4%) and the widespread use of online resources among participants (91%). Supported by established learning theories and quantitative analysis, the findings indicate that self-learning enhances critical thinking, adaptability, and lifelong learning competencies. The results suggest a future where technology-integrated, personalised learning complements conventional classroom instruction, fostering more independent and proficient learners. © 2025 IEEE.

关键词： Federated learning

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Investigation of sustainable strategies with metaheuristic algorithm on surface roughness, cutting temperature, and chip morphology during machining of Ti6Al4V alloy

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International Journal of Machining and Machinability of Materials 2024年第3期26卷 223-243页

作者： Misra, Sutanu Kumar, Yogesh Paul, Goutam Pradhan, Buddhadeb Department of Mechanical Engineering University of Engineering and Management Kolkata India Department of Mechanical Engineering National Institute of Technology Patna Bihar Patna India Department of Computer Science and Engineering University of Engineering and Management Kolkata India

This research work explores the effects of dry, liquid N2-based cryogenic cooling and cryogenic plus MQL hybrid strategy on surface roughness, rake surface temperature, principal cutting-edge temperature, auxiliary cutting-edge temperature, and chip morphology to understand the machinability of Ti-6Al-4V alloy using sustainable manufacturing techniques. Here, a metaheuristic algorithm approach has been introduced to determine the experimental data’s accuracy, which is approximately 75% for further investigation. Logistic regression and support vector machine provided higher accuracy and best fitted with the dataset and problem formulation. During this work, it has been observed that chip morphology is influenced by cutting parameters and lubrication strategy. The cryogenic plus MQL hybrid approach is the best method over cryogenic cooling by decreasing the heat by 12% and giving 8% better surface quality. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Titanium alloys

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Exploration of Semantic Priming Studies in Native Language of Children:Study on Bengali L1 and English L2 Bilingual Children

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SN computer science 2025年第2期6卷 1-18页

作者： Banerjee, Jaya Nithin, Kota Sai Sai, Uppalapati Hari Krishna Chakraborty, Baisakhi Basu, Anupam Department of Computer Science & Engineering National Institute of Technology West Bengal Durgapur 713209 India National Institute of Technology West Bengal Durgapur India Department of Computer Science & Engineering Indian Institute of Technology (IIT) Kharagpur 721302 India

Word Priming is an event in which prior subjection to a particular word impacts the reply of the individual to the upcoming correlated word. The response time is inferred to be less in identifying the target word preceded by an associated word compared to the word in seclusion or isolation as per the experiments undertaken on an Indian language where research incorporates Bengali-speaking children. Bengali is an important language of consideration on national and international platforms. A set of twenty-one school-going children with ages in the range of 7 to 10 years are subjected to a word priming assessment. Analysis established a positive impact of word prime in the reaction of a set of children aged seven to ten to certain listed words Due to the presence of word prime, the average error measure of the lower progressing answerers reduced by a significant 19.8%. With reference to the Bengali script, the average error measure is 10 percent. With the involvement of the word prime, the mean error measure is 1.6 percent. This paper proposes first of its kind of research studies and analysis with regard to the positive effect of word priming on the response time of target word identification on Bengali speaking bi-lingual dyslexic children to yield positive results. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bengali Children English Learning impairment Priming Psycholinguistics Second language Word identification Word priming

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Blockchain-based layered architecture for traceability in dairy supply chain management

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International Journal of Information technology (Singapore) 2025年 1-8页

作者： Sharma, Deepti Singh, Gurpreet Kaur, Amanpreet Department of Computer Science & Engineering Giani Zail Singh Campus College of Engineering and Technology Maharaja Ranjit Singh Punjab Technical University (MRSPTU) Punjab Bathinda India Department of Computer Science & Engineering Punjab Institute of Technology Rajpura (MRSPTU Bathinda) Punjab Rajpura India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

Securing the availability of high-quality and safe food for people is the primary goal of food supply chain systems. Traditional food supply systems have inherent difficulties such as the possibility of data loss, consistency problems with the product, quality compromises, and single points of failure. Blockchain technology exhibits significant potential because of its auditability, immutability, and decentralization. Thus, blockchain-based layered architecture is proposed in the given paper to guarantee traceability of dairy products and information sharing across supply chain participants. The proposed architecture is based on the concept of sharding, which is based on the idea of parallel processing. Other components of the dairy supply chain, including quality assurance, automated payment settlements, sales analysis, and consumption monitoring, are also supported by the proposed architecture. For overall implementation, Hyperledger Fabric is used, and the algorithms for all the chain codes used for traceability of dairy items are described. A comparative analysis of Hyperledger-based systems and other existing approaches is presented, which shows the suitability of the platform for supply chain networks. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Blockchain Chain codes Dairy supply chain management Smart Contracts Traceability

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Kookaburra Optimization Algorithm and Feature Aggregated Region based Differential network for Colon Cancer Detection from Histopathological Images 8

Kookaburra Optimization Algorithm and Feature Aggregated Reg...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Gowthamy, J. Ramesh, S.S. Subashka Rm Institute of Science and Technology Amapuram Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Ramapuram Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Colon cancer is a significant health concern, ranking as the third leading cause of cancer-related deaths in men and the second in women. Early detection is crucial in reducing the incidence and mortality rates associated with this malignancy. This paper introduces a novel Feature Aggregated Region-based Differential Network (FA-RDNet) designed for the accurate classification of colon cancer from histopathological images. Our approach leverages Multiple Instance Learning (MIL) to effectively handle large datasets structured as bags of instances, addressing the limitations of conventional MIL techniques. A key innovation of our work is the integration of an adaptive feature enhancement module that bilaterally updates multi-level deep features, allowing for efficient extraction of both low-level details and high-level semantic information. Furthermore, we introduce the Kookaburra Optimization Algorithm (KOA) to fine-tune the parameters of FA-RDNet, utilizing a unique two-phase mathematical modeling process inspired by the hunting behaviors of kookaburras. This enhances the model's optimization and classification accuracy. The effectiveness of our proposed system is validated using the publicly available LC25000 dataset, where it achieved a remarkable accuracy of 94.42%, outperforming existing state-of-the-art methods in colon cancer detection. The high computational efficiency and accuracy of FA-RDNet highlight its potential for practical applications in clinical settings, marking a significant advancement in the field of medical image analysis. © 2024 IEEE.

关键词： Lung cancer

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Enhancing IoT ECO System - Smart Parking System

Enhancing IoT ECO System - Smart Parking System

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Avinash, S. Muthukrishnan, S. Aishwarya, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529833

The Smart Parking System is an innovative solution designed to tackle the challenges of parking in densely populated urban areas. By integrating advanced technologies such as RFID (Radio Frequency Identification) sensors, automated gate systems, and proximity sensors, the system delivers a streamlined and efficient parking experience. RFID technology automates entry and exit processes by recognizing vehicles equipped with RFID tags, reducing wait times and enhancing security through controlled access. Proximity sensors at each parking spot monitor occupancy in real-time, feeding data to a central management system that directs drivers to available spaces via digital signage or a mobile app. This real-time guidance decreases search time, reduces congestion, and improves overall parking organization. Designed with scalability and future-readiness, the system's central management unit can integrate additional features like payment processing, reservation systems, and predictive analytics powered by machine learning. This adaptability ensures the system remains relevant as technology advances and user needs evolve. The Smart Parking System also promotes sustainability by minimizing vehicle idling and carbon emissions, contributing to a reduced environmental impact. For users, the system offers a more convenient, secure, and efficient parking experience. For operators, it enhances operational efficiency, reduces manual labor, and provides valuable insights into parking patterns, aiding in pricing, expansion, and maintenance decisions. The system's modular design allows for cost-effective implementation, making it suitable for various facilities from small lots to large complexes. In summary, the Smart Parking System represents a forward-thinking approach to urban parking, combining technology, efficiency, and sustainability to improve urban living. © 2025 IEEE.

关键词： Proximity sensors

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Enhancing Diabetic Retinopathy Diagnosis with ResNet-50-Based Transfer Learning: A Promising Approach

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Annals of Data science 2024年第1期11卷 1-24页

作者： Karthika, S. Durgadevi, M. Rani, T. Yamuna Department of Computer Science and Engineering SRM Institute of Science and Technology College of Engineering and Technology Vadapalani Campus Tamil Nadu Chennai India ACMS Railway Hospital ICF Chennai Tamil Nadu India

Diabetic retinopathy is considered the leading cause of blindness in the population. High blood sugar levels can damage the tiny blood vessels in the retina at any time, leading to retinal detachment and sometimes glaucoma blindness. Treatment involves maintaining the current visual quality of the patient, as the disease is irreversible. Early diagnosis and timely treatment are crucial to minimizing the risk of vision loss. However, existing DR recognition strategies face numerous challenges, such as limited training datasets, high training loss, high-dimensional features, and high misclassification rates, which can significantly affect classification accuracies. In this paper, we propose a ResNet-50-based transfer learning method for classifying DR, which leverages the knowledge and expertise gained from training on a large dataset such as ImageNet. Our method involves preprocessing and segmenting the input images, which are then fed into ResNet-50 for extracting optimal features. We freeze a few layers of the pre-trained ResNet-50 and add Global Average Pooling to generate feature maps. The reduced feature maps are then classified to categorize the type of diabetic retinopathy. We evaluated the proposed method on 40 Real-time fundus images gathered from ICF Hospital together with the APTOS-2019 dataset and used various metrics to evaluate its performance. The experimentation results revealed that the proposed method achieved an accuracy of 99.82%, a sensitivity of 99%, a specificity of 96%, and an AUC score of 0.99 compared to existing DR recognition techniques. Overall, our ResNet-50-based transfer learning method presents a promising approach for DR classification and addresses the existing challenges of DR recognition strategies. It has the potential to aid in early DR diagnosis, leading to timely treatment and improved visual outcomes for patients. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer

关键词： Blood vessels

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Novel Deep Learning Architecture for Automated Identification of Obstructive Sleep Apnea from Polysomnographic Data 2

Novel Deep Learning Architecture for Automated Identificatio...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Shabu, S.L. Jany Indhuja, V. Jeevitha, R. Refonaa, J. Dhamodaran, S. Poornima, D. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331505745

Sleep apnea disorder manifests as obstructive sleep apnea, which affects immense portions of society because it causes life-threatening health problems that include cardiovascular disease alongside diabetes and decreased productivity. The diagnosis of OSA through manual methods is both time demanding and expensive and contains a significant potential for human errors while requiring heavy resources. This research presents deep learning automation as the novel approach for automatic OSA detection through polysomnographic assessments. Our proposed system implements Genetics Algorithm optimized machine learning algorithms which include Logistic Regression, Support Vector Machines, Random Forests, Artificial Neural Networks and more. The analysis utilizes data from the Sleep Health and Lifestyle Dataset for preprocessing operations and features extraction that leads to better sleep disorder and stage classification accuracy. Through hyperparameter adjustment the Random Forest predictive model reached 95% F1 score which proved most effective for OSA detection. Through automation the classification process benefits from reliability with scalability and efficiency and produces better patient outcomes. © 2025 IEEE.

关键词： Deep learning

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A comprehensive analysis of threat vectors in software-defined networking

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Multimedia Tools and Applications 2024年第39期83卷 86791-86832页

作者： Dahiya, Seema Sehrawat, Harkesh Kharb, Seema Siwach, Vikas University Institute of Engineering and Technology Maharshi Dayanand University Rohtak Rohtak India Department of Computer Science and Engineering SRM University Sonepat Sonipat India

In recent years, cyber-attacks have become more frequent and advanced, targeting critical infrastructure, businesses, homes, and government agencies. Detecting and preventing these attacks at the earliest stage possible is crucial to avoid serious harm, including data breaches. Researchers and experts in cybersecurity are looking to Software-Defined Networking (SDN) technologies as a solution to enhance real-time defense against cyber-attacks. SDN revolutionizes traditional networking by offering unprecedented flexibility and control over network resources, making it possible to adapt quickly to emerging threats. SDN provides logically centralized network control by separating the control plane from the data plane. This enables network programming and can block network activity when malicious movement is spotted. This paper presents a comprehensive analysis of threat vectors in SDN. It examines the various ways in which SDN networks are vulnerable to cyber-attacks, including network infrastructure, application layer, and SDN controller. The paper also evaluates the effectiveness of existing security measures and proposes future research directions to enhance SDN security. Overall, the paper highlights the potential of SDN as a powerful tool in the fight against cybercrime and emphasizes the importance of continued research and development to improve SDN security. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

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Enhancing Lung Cancer Early Detection Using Deep Learning Techniques 7th

Enhancing Lung Cancer Early Detection Using Deep Learning Te...

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7th International Conference on Soft Computing and Signal Processing, ICSCSP 2024

作者： Kranthi Reddy, S. Moinuddin, Khaja Waseem Akram, Md. Sachithananda, Sai Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India

ISBN: (纸本)9789819799251

Exposure to carcinogens, particularly those found in cigarette smoke, destroys lung cells DNA and promotes their unchecked proliferation, which is the main cause of lung cancer. Its growth is also greatly influenced by genetic predisposition, family history, and environmental factors such as radon and asbestos. Lung cancer can affect nonsmokers due to these genetic and environmental factors, even though smoking is the main cause. These malignancies have the potential to impair breathing and the exchange of oxygen and carbon dioxide by interfering with the lungs natural function. When applied to tabular datasets, machine learning and deep learning algorithms can help forecast the prognosis of lung cancer and the response to treatment, enabling tailored care plans and enhancing patient outcomes. This study suggests using a convolutional neural network (CNN) architecture to accurately identify lung cancer from medical photos. Clinicians might possibly improve patient outcomes and survival rates by using CNNs’ sophisticated feature extraction capabilities to produce more accurate diagnoses and customized treatment programs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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