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

作者： Sharvesh, S. Kaileshwar, M. Arthi, B. Jothikumar, C. Srm Institute of Science and Technology Computing Technologies School of computing Kattankulathur Chennai India

ISBN: (纸本)9798350372892

This study explores the efficacy of CNNs in classifying images into predefined categories, highlighting advancements in automated image recognition technology. The project aims to develop a robust CNN model that can accurately identify and classify images based on their content, which has significant implications for fields requiring automated image analysis. A sequential CNN architecture featuring layers of convolution, max pooling, and dropout, complemented by fully connected layers for feature learning and classification, developed and validated using TensorFlow and Keras. The model was trained and tested on a curated dataset comprising diverse images categorized into seven distinct classes, preprocessed to normalize image sizes and pixel values to enhance model training efficiency. 8L-CThe model used in the research includes 2L-CNN, 3L-CNN, and 4L-CNN architecture are used. In that, better accuracy of 62.2 % for 3-layer architecture received. The CNN demonstrated high accuracy and generalizability in image classification, proving capable of handling complex pattern recognition tasks, underscoring the potential of CNNs in transforming image processing applications. © 2024 IEEE.

关键词： Emotion Recognition

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Deoxyribonucleic Acid Based Oryza Sativa Phenotype Prediction Using Molecular Neural Network

Deoxyribonucleic Acid Based Oryza Sativa Phenotype Predictio...

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2024 International Conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Soren, Nikita Rajendran, P. Selvi School Of Computing Science Hindustan Institute Of Technology And Science Chennai India

ISBN: (纸本)9798350364828

Precise phenotypic trait prediction in Oryza sativa (rice) is essential for breeding initiatives, agricultural innovations, and maintaining food security. In this work, we present a unique method for phenotype prediction based on the examination of Oryza sativa chromosomes 1, 2, 4, 7, 11, and 12 deoxyribonucleic acid (DNA) sequences. In order to capture the complex genetic properties linked to phenotypic variances, we created a Molecular Neural Network (MNN) architecture specifically designed for processing molecular data. This architecture integrates both recurrent and convolutional layers. We preprocess the DNA sequences using one-hot encoding and data partitioning for testing, validation, and training as part of our technique. A dataset of DNA sequences and associated phenotypic labels is used to train the MNN. The effectiveness of the model is assessed using common measures like accuracy. The model's performance on a test set is being measured in the proposed study by computing Pearson correlation coefficients between expected and actual trait values. It also calculates the (PCC) for each feature in the model, and in 152 seconds, we obtained an accuracy of 98%. © 2024 IEEE.

关键词： DNA

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Comparison Between Yoga And Non Yoga Eeg Signal Analysis 2

Comparison Between Yoga And Non Yoga Eeg Signal Analysis

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Rajalakshmi, A. Sridhar, S.S. SRM Institute of Science and Technology Department of Computing Technology Chennai India

ISBN: (纸本)9798350365269

Electroencephalography is a non-invasive technique used to monitor brain activity and make a variety of neurological problems diagnoses. The electrical activity of the brain is measured using an EEG instrument, which converts chemical variations in the brain into voltage. With either the intracranial EEG method or the Scalp EEG approach, several electrodes are implanted at the right location on the brain to measure EEG signals. Analysis of electroencephalograms, or EEGs, has grown to be crucial for identifying many human disorders. The most crucial and straightforward step in processing EEG readings is using temporal frequency analysis to make a potential diagnosis. EEG signals are the important data for any type of analysis of brain activity. In this study, we first analysis the EEG signal characteristics that have been used in the literature for a variety of activities, then we concentrate on looking at EEG feature applications, and finally we talk about the potential and unresolved issues with EEG feature extraction. Everyone should assess the wide variety of EEG signal properties and their effects on BCI interfaces in order to improve the accuracy of different brain activity detection in the future. © 2024 IEEE.

关键词： Electroencephalography

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Enhancing Farming Practices through a Comprehensive Recommendation System 7

Enhancing Farming Practices through a Comprehensive Recommen...

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Rafiqi, Simrah Mahato, Manojeet Minu, R.I. SRM Institute of Science and Technology Dept. of Computing Technology India

ISBN: (纸本)9798350372816

In response to the pervasive challenges faced by farmers, including crop failure, insufficient knowledge, and crop damage, a holistic solution has been introduced. This comprehensive approach encompasses a Crop Recommendation System, Fertilizer Suggestions, and Crop Disease Detection Systems. To systematically address soil quality, a sophisticated examination system measures pH, Nitrogen, Phosphorus, and Potassium levels. Leveraging advanced Machine Learning classification algorithms, the system analyzes soil data to provide tailored crop recommendations, factoring in regional nuances and climate variations. This data-driven decision-making process enhances the precision of recommendations, optimizing agricultural outcomes. The holistic approach not only prevents crop failure but also empowers farmers with valuable insights, fostering knowledge enhancement for sustainable farming practices. Additionally, timely disease management is ensured through the early detection capabilities of the system, minimizing the impact of crop damage. Overall, this integrated solution aims to revolutionize agriculture by mitigating risks, improving productivity, and promoting environmentally conscious farming practices. The project endeavors to create a user-friendly platform tailored for farmers, aiming to empower them with precise guidance on crop selection and soil improvement. © 2024 IEEE.

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A Multi-path Droplet Routing Protocol for Digital Microfluidic Biochip

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Informatica (Slovenia) 2024年第7期48卷 95-102页

作者： Swain, Jyotiranjan Pyne, Sumanta School of Computing and Information Technology REVA University Bangalore India Department of Computer Science and Engineering National Institute of Technology Rourkela India

Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It involves challenges like moving droplets without violating the fluid constraints. This article proposed a multi-path droplet routing protocol. Multiple routes are explored and validated using special packets. These routes are then classified based on user-defined heuristics. A single route is sequentially selected for each droplet. The routes are then compacted to generate a parallel moving sequence. The simulation result shows 2.087% and 4.952% improvement in the latest arrival time for free and virtual topology, respectively. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Biochips

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Leveraging Pre-trained ResNet-18 with Transfer Learning for Yoga Posture Classification 2

Leveraging Pre-trained ResNet-18 with Transfer Learning for ...

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

作者： Aruna, M. Kaneriya, Gruhit Jain, Prashuk Srm Institute of Science and Technology Computing TechnologiesSchool of Computing Kattankulathur Chennai India

ISBN: (纸本)9798350372892

This research paper explores the application of pre-trained ResNet-18 with transfer learning for the classification of yoga postures. The study utilizes a dataset comprising images of various yoga poses taken from Kaggle. Through fine-tuning, the pre-trained model achieved impressive training accuracy of 9 8 %. Furthermore, on unseen data, the model maintained accuracy of 9 4. 9 3 %. Advantages and disadvantages of the methodology are discussed, along with experimental setup details and metrics evaluation. The findings underscore the efficacy of leveraging pre-trained models and transfer learning techniques for complex image classification tasks in specialized domains like yoga postures. However, a notable limitation arises in the difficulty of removing noise during model implementation, leading to a decrease in confidence scores. For future work, the project aims to address this limitation by focusing on noise removal techniques while maintaining accuracy and confidence scores, thus enhancing the robustness of the classification model. © 2024 IEEE.

关键词： Image classification

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Deep Learning Approach for Identifying Red Lesions in Retinal Fundus Imagery 6th

Deep Learning Approach for Identifying Red Lesions in Retina...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Divyashrree, R. Dhawan, Samarth Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9789819774258

One dangerous side effect of diabetes that affects the eyes is called diabetic retinopathy. It happens as a result of alterations in the retina’s blood vessels, which can cause harm and even blindness. The development of red lesions in the retina is one typical sign of diabetic retinopathy. These red lesions may be a sign of microaneurysms or hemorrhages, which are important characteristics to identify and track as diabetic retinopathy progresses. This process becomes even more demanding in the early stages of the disease where symptoms are less pronounced in the images. Leveraging machine-based learning for medical image analysis, especially deep learning algorithms, has demonstrated its capability to enhance the early diagnosis of diabetic retinopathy, providing a more efficient and effective alternative to traditional manual assessments. The proposed work aims to improve the efficiency of early detection of diabetic retinopathy by utilizing Deep Learning (16-layer CNN) to determine a person’s likelihood of having diabetic retinopathy using a 02-Class model that compiles a variety of datasets, specifically IDRiD dataset. The entire dataset contains 18,590 fundus photographs, in which 3662 are allocated for training, 1928 for validation, and 13,000 for testing. All datasets had a similar distribution of output classes, which is a fundamental property of this type of data. Using the IDRiD dataset, the suggested Red Lesion detection system’s specificity and sensitivity were found to be 98% and 89%, respectively, with an accuracy of 95.32%. Using the MESSIDOR dataset, the Diabetic Retinopathy Severity Classification System’s specificity, sensitivity, and accuracy were found to be 93.8, 92.3, and 94%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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An Enhanced Linearly Homomorphic Network Coding Signature Scheme for Secure Data Delivery in IoT Networks

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5534-5548页

作者： Huang, Hao Wang, Xiaofen Au, Man Ho Cao, Sheng Zhao, Qinglin Yu, Jiguo University of Electronic Science and Technology of China Chengdu611731 China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong Macau University of Science and Technology School of Computer Science and Engineering China

Recently, Li et al. proposed an identity-based linearly homomorphic network coding signature (IB-HNCS) scheme for secure data delivery in Internet of Things (IoT) networks, and they claimed that the IB-HNCS scheme can resist pollution attacks. However, this paper shows that the IB-HNCS scheme is vulnerable to pollution attacks, as anyone who only has the public parameter can forge a new file identifier or a valid signature on a corrupted data packet to pollute legitimate sensor data. To enhance security and performance in network coding-based IoT networks, we propose a secure and efficient certificateless linearly homomorphic network coding signature scheme for IoT data delivery, which is free of burdensome certificate management and key escrow issue. In addition, our scheme is proved to be secure against adaptive chosen identity and adaptive chosen subspace attacks under two types of adversaries in the algebraic group model and random oracle model. Therefore, our scheme can verify the validity of data packets and allow data packets to be computed, so as to resist pollution attacks. The performance evaluation demonstrates that our scheme is more efficient and practical than existing secure schemes. Specifically, for a 73-dimensional data vector, the costs of signature generation and verification in our scheme are reduced by 38.588%-86.076% and 38.570%-85.664% respectively under the symmetric bilinear pairing setting, and the costs of signature generation and verification in our scheme are reduced by 17.740%-49.752% and 29.697%-58.645% respectively under the asymmetric bilinear pairing setting. © 2005-2012 IEEE.

关键词： Internet of things

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Efficient distributed algorithms for holistic aggregation functions on random regular graphs

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science China(Information sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph $\mathcal{CONGEST}$ model communication complexity round complexity

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Privacy and Security Concerns in Edge-Cloud Systems: An In-Depth Analysis 3

Privacy and Security Concerns in Edge-Cloud Systems: An In-D...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kaki, Samuel Suhas Kantareddygari, Jagadeswara Reddy Aishwarya, R. School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Edge-Cloud systems provide efficient computation and storage close to data sources, with lower latency, scalability, and application performance for various applications, such as IoT, autonomous vehicles, and real-time analytics. These systems are highly vulnerable to significant security and privacy challenges, which include the following: data confidentiality and integrity in the process of transmission, secure protection of sensitive data stored on the edge against physical and remote attacks, and maintaining availability under dynamic network conditions. More challenges related to ensuring users' private nature through anonymizing data, obtaining consent for all processes involved, and compliance with specific regulations like GDPR and CCPA. This work incorporates more enhanced cryptographic methods in the transfer of data between heterogeneous systems and environment-based anomaly detection, advanced privacy-preserving analytics to avoid extraction of private data, adaptive control mechanisms in place for Edge-Cloud for minimizing vulnerability with enhanced trust along with the maintenance of data integrity. The key benefits would be robust mitigation of threats, better interoperability between edge and cloud platforms, and alignment with evolving security standards, which promotes wider adoption across various sectors. © 2025 IEEE.

关键词： Cloud platforms

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