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3rd International Conference on Electronics and renewable Systems, ICEArS 2025

作者： Pradeep Kumar, S. Basha, C.H. Hussaian Prasath, C. Arun Amirthayogam, G. Lathif, Syed Ismail Abdul Leelavathi, r. Rajalakshmi Engineering College Computer Science Design Department Thandalam Chennai602105 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Mahendra Engineering College Department of Ece Namakkal637503 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331509675

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods. © 2025 IEEE.

关键词： Smart contract

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GrAFrAUd: Fraud detection using Graph databases and Neural Networks

GRAFRAUD: Fraud Detection using Graph Databases and Neural N...

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2023 4th International Informatics and Software Engineering Conference (IISEC)

作者： Alperen Sayar Şuayip Arslan Ajeet Singh raina Seyit Ertuğrul Tuna Çakar Data Science Tam Finans R&D Istanbul Türkiye Big Data and Data Governance Massachusetts Institute of Technology Massachusetts USA Docker Containerized Software Docker Inc. California USA Computer Engineering MEF University Istanbul Türkiye

The issue of fraud has become a significant concern for many companies, particularly in the finance sector, but the traditional methods of detecting fraud are no longer adequate. Innovative technologies are necessary to identify complex fraudulent activities, and redisGraph, a high-performance graph database, may offer a solution. With the assistance of neural networks, redisGraph can accurately and efficiently detect fraudulent transactions in vast and intricate environments. Companies typically use a combination of Python and Oracle databases to design fraud detection systems. which provide robust data management and real time AI processing capabilities. These technologies allow to create fraud detection systems that can determine fraudulent activities in real-time. But according to advancements of fraud methods only using of these systems not efficient nowadays. This article presents a proof of concept based on an essential use case of redisGraph-powered neural networks in detecting financial fraud. It demonstrates the value of carefully employing Python and Oracle database to construct and deploy real-time systems that can efficiently detect fraudulent activities.

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Implementing Machine Learning for Early detection and Prognostic Modeling of Chronic diseases

Implementing Machine Learning for Early Detection and Progno...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Iqbal, J. L. Mazher Gurrapu, Omprakash Saritha, P.S. rajesh Kanna, r. Jayanthi, r. Tejaskumar, M.B. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India Volvo Trucks NC United States Dhanalakshmi Srinivasan College of Engineering Department of Artificial intelligence and data science Coimbatore India Christ University Department of Computer Science Bangalore India Dayananda Sagar College of Engineering Department of Master of Computer Applications Bengaluru India Don Bosco Institute of Technology Bangalore India

ISBN: (纸本)9798331538538

The employ of deep learning methods for the diagnosis and prognosis model of chronic diseases is an important discovery to change the healthcare service. Some of the chronic diseases which prevalence and incidence rates remain high globally include diabetes, cardiovascular diseases, chronic kidney diseases, and cancers. There is nothing more critical than early diagnosis and accurate prediction of the patients' condition and the best course of action that has to be taken. This paper aims at examining the possibility of utilizing ANN, random Forest, XGBoost, and CNN to forecast the occurrence of the. due to integration of big and varied data which involve clinical characteristics, biochemical parameters and medical images among others, ML models have the ability recognize complex relations not easily recognizable by conventional diagnostic procedures. These illustrations prove that deep learning models or more specifically the convolutional neural networks for image diagnosis outperform other traditional methods in performance and prognosis. Nevertheless, some issues, such as data quality, model's interpretability, and its implementation into clinical practice, are still present. The challenges appeared in this paper are key to understanding the future of ML in healthcare as they can pave the way to the integration of such models into practice, therefore leading to early detection, better prognosis, and effective management of chronic diseases. This paper aims at exploring on how ML can be of significance in transformation of the health care sector and orderly improve patients care. © 2025 IEEE.

关键词： diseases

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Federated Learning-Based Intelligent Indoor Smoke and Fire detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Aymene Berkani, Mohamed rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1d for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable dL-CNN1d model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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Image Analysis of MrI-based Brain Tumor Classification and Segmentation using BSA and rELM Networks 2

Image Analysis of MRI-based Brain Tumor Classification and S...

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2nd International Conference on Automation, Computing and renewable Systems, ICACrS 2023

作者： Saranya, r. Pushpakumar, r. Srinivasan, P. Aparna, N. Karthika, K. Chauhan, Amit Department of Computer science with Data Analytics PSG College of Arts and Science Coimbatore India Department of Information Technology Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of ECE Sona College of Technology Tamilnadu Salem India Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Tamil Nadu Veerappanur India Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Karnataka Bengaluru India

ISBN: (纸本)9798350340235

Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. due to the sheer volume of MrI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-rELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-rELM is used to train the models with the input features. The proposed technique outperforms BSA and rELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method. © 2023 IEEE.

关键词： Brain Tumor Segmentation Central Nervous System (CNS) FuzzyClustering Method (FCM)

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Prediction of gender from structural MrI images using Multiscale ShuffleNet Extreme Learning Machine

Prediction of gender from structural MRI images using Multis...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Berakhah.F. Stanley r Jeen retna Kumar V Gnanaprakash P Bini Palas J Gold Beaulah Patturose d J Joel devadass daniel Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Easwari Engineering College Chennai India Department of Artificial Intelligence and Data Science St. Joseph’s Institute of Technology Chennai India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

In recent years, the application of machine learning techniques to medical imaging data has shown promising results in various clinical tasks. One such task is the prediction of gender from structural MrI (magnetic resonance imaging) images, which holds potential implications for personalized medicine and understanding neurobiological differences between genders. In this study, we propose a novel approach utilizing Multiscale ShuffleNet Extreme Learning Machine (MSEL), a fusion of Multiscale feature extraction and ShuffleNet architecture with Extreme Learning Machine classifier. We demonstrate the effectiveness of our method on a large dataset of structural MrI images, employing state-of-the-art preprocessing techniques and feature extraction methods. Our results indicate significant accuracy and robustness in gender prediction, outperforming existing methodologies. Furthermore, we conduct comprehensive analyses to investigate the contribution of different components in our proposed framework, shedding light on the underlying mechanisms of gender-related brain structural differences. Overall, our study presents a promising avenue for utilizing advanced machine learning techniques in neuroimaging research, with potential applications in clinical diagnostics and personalized healthcare.

关键词： Neuroimaging Accuracy Extreme learning machines Magnetic resonance imaging Computational modeling Computer architecture Feature extraction

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IoT-based trusted wireless communication framework by machine learning approach

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Measurement: Sensors 2024年 34卷

作者： Chakaravarthi, S. Saravanan, S. Jagadeesh, M. Nandhini, S. Department of Artificial Intelligence and Data Science Panimalar Engineering College India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computing Technologies SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

The traditional radio-Frequency Systems (rFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed Ids or asymmetric cryptography through infiltrative, side-channel, training, and computer attacks. In contrast, Unaccounted Information (UAI) has the potential to exploit irregularities in production systems to automatically identify microchips, offering a highly robust and cost-effective security solution. This approach introduces rFS-UAI, a deep neural network-based system that efficiently manages wireless node identification by leveraging synthetic rFS characteristics of remote controls (Tx) learned through supervised methods in Wireless Sensor Networks (WSN). Unlike traditional methods that require the development of specialized transistors for UAI or semantic segmentation, this approach utilizes the existing asymmetrical rFS communication networks. Similar to the way the human brain processes information, rx handles the entire device identification process at the gateway. According to test results, which include assessing process capability at a specified 65 nm threshold voltage and characteristics such as Local Oscillator (LO) misalignment and I-Q disparity using a probabilistic model with 52 hidden units, the system can distinguish up to 4800 transmitters with a remarkable 99.9 % accuracy under various channel conditions, all without the need for regular preambles. This recommended method can serve as a standalone security measure or be integrated into a biometric identification system. © 2024 The Authors

关键词： Authentication

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real Time Crop Health Monitoring Through Convolution Neural Networks

Real Time Crop Health Monitoring Through Convolution Neural ...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Bommagani Sridevi Nanda Padma Puram Manjusha Sharanya Tirunagari Shanmugasundaram Hariharan S. Famila Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of Electronics & Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The agriculture sector, also known as the primary sector in India, is currently facing significant challenges due to the increasing number of pests and insects. These pests can cause serious damage to crop fields, negatively affect crop health, reduce yields, and lead to substantial economic losses for farmers. Early detection of crop diseases can help minimize yield losses by 20-40%, depending on the specific crop type and its various growth stages. The paper discusses the important use of deep learning algorithms in agriculture, specifically focusing on developing an effective approach for the automatic detection of crop diseases through advanced image analysis techniques. The proposed Convolutional Neural Network (CNN) model architecture consists of multiple layers that extract meaningful features from images using convolution and pooling operations. After the feature extraction process, additional layers are used for accurately classifying the images into different disease categories. This model is trained using the Adam optimizer, which is known for its efficiency in handling large datasets and improving the overall training speed. It employs sparse categorical cross-entropy loss over 50 epochs, with a batch size of 32, to effectively optimize the learning process. during the training phase, the model's accuracy is continuously monitored to assess its performance and make any necessary adjustments to enhance its effectiveness. One of the key innovations of this approach is the ability to perform real-time crop disease detection using a camera. This advancement eliminates the need for manual picture collection from the fields, making the entire process faster and much more efficient. By enabling farmers to quickly identify diseases, this cutting-edge technology can significantly help improve crop management practices and increase overall agricultural productivity. Ultimately, the goal is to enhance food security and reduce economic losses in the agriculture sect

关键词： Productivity Training Neural networks Crops Feature extraction Agriculture real-time systems Convolutional neural networks Monitoring diseases

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Modeling PrOTAC degradation Activity with Machine Learning

arXiv

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arXiv 2024年

作者： ribes, Stefano Nittinger, Eva Tyrchan, Christian Mercado, rocío Department of Computer Science and Engineering Section for Data Science and AI Chalmers University of Technology Chalmersplatsen 4 Gothenburg412 96 Sweden BioPharmaceuticals R&D AstraZeneca Pepparedsleden 1 Mölndal431 83 Sweden

PrOTACs are a promising therapeutic modality that harnesses the cell’s built-in degradation machinery to degrade specific proteins. despite their potential, developing new PrOTACs is challenging and requires significant domain expertise, time, and cost. Meanwhile, machine learning has transformed drug design and development. In this work, we present a strategy for curating open-source PrOTAC data and an open-source deep learning tool for predicting the degradation activity of novel PrOTAC molecules. The curated dataset incorporates important information such as pdC50 , dmax , E3 ligase type, POI amino acid sequence, and experimental cell type. Our model architecture leverages learned embeddings from pretrained machine learning models, in particular for encoding protein sequences and cell type information. We assessed the quality of the curated data and the generalization ability of our model architecture against new PrOTACs and targets via three tailored studies, which we recommend other researchers to use in evaluating their degradation activity models. In each study, three models predict protein degradation in a majority vote setting, reaching a top test accuracy of 80.8% and 0.865 rOC AUC, and a test accuracy of 62.3% and 0.604 rOC AUC when generalizing to novel protein targets. Our results are not only comparable to state-of-the-art models for protein degradation prediction, but also part of an open-source implementation which is easily reproducible and less computationally complex than existing approaches. © 2024, CC BY.

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Global visibility of publications through digital Object Identifiers

arXiv

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arXiv 2023年

作者： Turki, Houcemeddine Fraumann, Grischa Taieb, Mohamed Ali Hadj Aouicha, Mohamed Ben Data Engineering and Semantics Research Unit Faculty of Sciences of Sfax University of Sfax Sfax Tunisia R&D Department TIB – Leibniz Information Centre for Science and Technology Hannover Germany

This brief research report analyzes the availability of digital Object Identifiers (dOIs) worldwide, highlighting the dominance of large publishing houses and the need for unique persistent identifiers to increase the visibility of publications from developing countries. The study reveals that a considerable amount of publications from developing countries are excluded from the global flow of scientific information due to the absence of dOIs, emphasizing the need for alternative publishing models. The authors suggest that the availability of dOIs should receive more attention in scholarly communication and scientometrics, contributing to a necessary debate on dOIs relevant for librarians, publishers, and scientometricians. © 2023, CC BY.

关键词： developing countries

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