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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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A New Profiling-Based Side-Channel Attack on Graphics Processing Units

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Journal of Computing science and engineering 2023年第1期17卷 30-40页

作者： Wang, Xin Zhang, Wei Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA United States Department of Computer Science and Engineering University of Louisville LouisvilleKY United States

Graphics processing units (GPUs) have been increasingly used to solve a range of compute-intensive and data-parallel scientific computing problems that can be perfectly parallelized for performance speedups. Particularly, GPUs have recently become popular to host the encryption/decryption algorithms due to its high-throughput computing capability. However, the security issues of moving the cryptographic algorithms onto GPUs have not been studied adequately. Consequently, with absence of any protection strategy, the potential vulnerabilities of GPUs to side-channel attacks (SCAs) may expose the confidential information with high risk. In this paper, we proposed a new profiling-assisted correlationbased side-channel attack (pacSCA) to demonstrate that ignoring security issues and naively moving security services onto GPUs can offer adversaries fatal vulnerabilities to thieve critical information. The results showed that the proposed SCA can rebuild the secure key of the AES-128 algorithm in less than 6 seconds, revealing the urgency of protecting GPUs against side-channel threats. © Copyright 2023. The Korean Institute of Information Scientists and Engineers

关键词： Graphics processing unit

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Efficacy of Ensemble Learning Method over Individual Classifier for Rainfall Forecasting in Eastern India

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Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 929-939页

作者： Karmakar, Rahul Kundu, Saranagta Biswas, Saroj Kumar Tripathi, Deeksha Department of Computer Science The University of Burdwan West Bengal Bardhaman India Department of Computer Science and Engineering National Institute of Technology Assam Silchar India

Severe rainfall has seriously threatened human health and survival. Natural catastrophes such as floods, droughts, and many other natural disasters are caused by heavy rains, which people worldwide have to deal with throughout the year. Accurate rainfall forecasting is essential in nations like India where cultivation is the main occupation. Machine learning (ML) techniques are more effective than many other strategies because of the unpredictable nature of rainfall. Ensemble learning (EL) methods yield higher accuracy than ML specific classifiers. Therefore, this paper proposes an EL-based prediction model for not only a specific state of India but for the entire eastern India region, which includes ten states: West Bengal, Jharkhand, Bihar, Odisha, Assam, Tripura, Mizoram, Manipur, Nagaland and Meghalaya. We acquired the rainfall data for eastern India using NASA’s power data access view for 22 years (2000–2021). In this model, apart from classification feature selection is done using Pearson correlation and ANOVA. This study looks at the accuracy of several different classifiers, including DT, LR, and SVM. The acceptable accuracy of 96.53% for rainfall prediction in eastern India was determined using an ensemble model that includes the XGBoost ensemble technique and the classifiers DT, LR, and SVM. The findings of our experiment reveal that EL-based prediction model outperforms the individual classifiers utilized in the experiment. © The Institution of Engineers (India) 2024.

关键词： Decision trees

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An enhanced AMBTC for color image compression using color palette

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Multimedia Tools and Applications 2024年第11期83卷 31783-31803页

作者： Xiong, Lizhi Zhang, Mengtao Yang, Ching-Nung Kim, Cheonshik School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Computer Engineering Sejong University Seoul Korea Republic of

Digital image has been used in various fields as an essential carrier. Many color images have been constantly produced since their more realistic description, which takes up much storage space and network bandwidth. Thus, color image compression has become an essential key technology. Absolute Moment Block Truncation Coding (AMBTC) has been widely studied as one of the classical image compression methods. However, in the existing methods, the visual quality of the reconstructed images and the compression rate are all relatively low. Therefore, this paper proposes an enhanced AMBTC for color image compression using a color palette. In the proposed method, the K-means clustering algorithm is utilized for training the image's palette pattern. The color palette obtained by K-mean will be more suitable for reconstructing this image than the standard color palette, and the visual quality will be higher. The six clustered central pixels are matched with the palette through a color difference formula, and the obtained index values are used as the quantization levels. Huffman coding is used to build a bitmap to achieve a higher compression rate, that is, a lower bit rate. At last, a block of a color image can be represented by six index values and a bitmap. Experimental results and theoretical analysis demonstrate that the proposed method has better visual quality and bit rate than similar schemes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

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ImageNet Classification Using WordNet Hierarchy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1718-1727页

作者： Chatterjee, Ankita Mukherjee, Jayanta Das, Partha Pratim Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India Ashoka University Department of Computer Science Sonipat131029 India

Convolutional neural networks (ConvNets) have become increasingly popular for image classification tasks. All contemporary computer vision problems are being dominated by ConvNets. Conventional training methods using cross-entropy loss for training have constantly outperformed the state-of-the-art technique to set a new standard in the ImageNet classification challenge. However, growing accuracy come at the cost of enormous number of parameters and computations. Further, classical learning algorithms do not utilize the semantic relationship between the classes present in the dataset. Thus, interpreting the behavior of the model become difficult even though the results may be desirable. Hence, we demonstrate a classification method by leveraging the WordNet hierarchy on the ImageNet dataset to establish class relationships and label embedding. The model is trained using cross entropy with soft labels based on the semantic similarity between the generated output and the ground truth. Unlike categorical cross entropy, it does not treat every predicted label as equally erroneous. The method generates meaningful neighboring classes in the feature space of the true label. © 2020 IEEE.

关键词： Neural networks

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Emergency Vehicle Direction Detection Using Mel-Frequency Cepstral Coefficients and Deep Learning 18

Emergency Vehicle Direction Detection Using Mel-Frequency Ce...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Bhushan, Shourya Chaturvedi, Manish Iitram Department of Electrical and Computer Science Engineering Ahmedabad India

ISBN: (纸本)9798350387278

Emergency vehicles face difficulty in navigating through traffic and locating the shortest route to the incident site. In the developing countries or under disaster conditions when limited communication and Intelligent Transportation System (ITS) infrastructure is available, the situation becomes more challenging. This paper addresses these challenges by leveraging the unique sound generated by emergency vehicles to detect the direction of the emergency vehicles. We use the Mel-frequency cepstral coefficients (MFCC) and a Convolutional Neural Network (CNN) stacked with Long Short-Term Memory (LSTM) layers to accurately detect the movement direction of outgoing emergency vehicles at a junction. MFCC facilitates effective feature extraction from audio signals, while the CNN-LSTM architecture enables robust temporal and spatial pattern recognition. The information can be used to adapt the traffic lights at the downstream junction to enable the uninterrupted movement of the emergency vehicle through the junction. The experiment results demonstrate 98.56% accuracy in detecting movement direction of an emergency vehicle. The solution can improve emergency response under resource constrained environment and in turn improve public safety and well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Alkali Metal Doping on Sol-Gel-Derived Copper Oxide for Performance Enhancement of Thin-Film Transistors

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第6期3卷 228-233页

作者： Baek, Seokhyeon Choi, Jun-Gyu Park, Sungjun Ajou University Department of Intelligence Semiconductor and Engineering Suwon16449 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16449 Korea Republic of Ajou University The Department of Electrical and Computer Engineering Suwon16449 Korea Republic of

In recent years, copper oxide (CuxO) has emerged as a promising p-type oxide semiconductor owing to its high Hall mobility. However, its inherent drawbacks, such as the substantial native defects and uncontrolled stoichiometry, limit its application in thin-film transistors (TFTs) for energy-efficient complementary devices. In this study, we employ the sol-gel synthetic approach for facile doping to investigate the doping effects of alkali metals (Li, Na, and K) on the electrical performance of CuxO TFTs. The results demonstrate that doping, particularly with lithium (Li), significantly improves the electrical performance of CuxO TFTs. The similarity in ionic radius facilitates efficient hole transport, which considerably enhances the field-effect mobility (4.7×10-3 cm2/Vs), subthreshold swing (SS) (8.3 V/dec), and on-off current ratio (∼104) with minimized hysteresis. The structural analysis of the Li-doped CuxO films using X-ray diffraction (XRD) does not exhibit any significant lattice distortion and an increase in the grain size implies the reduction of trap sites. Consequently, the successful fabrication of Li-doped CuxO TFTs on polyimide substrates, using ZrOxO as a gate dielectric layer, demonstrates its compatibility with flexible electronics. This approach enhances the electrical performance of p-type CuxO TFTs and presents a scalable and efficient pathway for the development of advanced TFT technology in flexible electronics. © 2022 IEEE.

关键词： Sol-gel process

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REGAC: Multi-Class Offensive Content Identification Using Graphical Approach

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 931-952页

作者： Chinivar, Sneha Roopa, M.S. Arunalatha, J.S. Venugopal, K.R. Department of Computer Science and Engineering Bangalore University K R Circle Karnataka Bengaluru India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering Bangalore University K R Circle Karnataka Bengaluru India Karnataka Bengaluru India

Online offensive behaviour continues to rise with the increasing popularity and use of social media. Various techniques have been used to address this issue. However, most existing studies consider offensive content identification as a binary or ternary problem, disregarding the potential for multi-class classification of derogatory content. In this work, we propose RoBERTa Embedding-based Graphical Approach Classifier (REGAC), which aims to designate the identified offensive content into varied classes,like age, gender, ethnicity, religion, and others to understand what exact qualities the bully usually targets in their victim so that pertinent measures can be taken to battle them. Additionally, the models’ efficiency is evaluated using an unbalanced dataset to identify types of offensive content. The word embeddings are generated for a balanced dataset using RoBERTa embedding model to bring out the best-fitting vectors. These vectors are then input into traditional Machine Learning algorithms (SVM, KNN, Logistic Regression, Random Forest, and XGBoost) and graph-based algorithms (Graph Convolution Network (GCN), GraphSAGE, and Graph Attention Network (GAT)) for fine-grained categorization of offensive textual content. The experimental results demonstrate the efficiency of combating social media’s offensive content of the proposed work with higher Precision, Recall, F1-Score, and Accuracy compared with most state-of-art approaches. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Graph embeddings

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Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

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computer Modeling in engineering & sciences 2023年第5期135卷 1275-1294页

作者： Byoungwook Kim Hong-Jun Jang Department of Computer Engineering Dongshin UniversityNaju58245Korea Department of Computer Science and Engineering Jeonju UniversityJeonju55069Korea

As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is *** of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical *** this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN *** existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the *** solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each *** order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in *** conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force *** experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 ***-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).

关键词： Spatial clustering DBSCAN algorithm genetic algorithm textual information

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A comprehensive systematic review of machine learning in the retail industry: classifications, limitations, opportunities, and challenges

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Neural Computing and Applications 2024年第4期37卷 2035-2070页

作者： Hassan, Dler O. Hassan, Bryar A. Department of Computer Science College of Science Charmo University Kurdistan Region Chamchamal Sulaimani46023 Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Erbil Iraq

Machine learning has profoundly transformed various industries, notably revolutionizing the retail sector through diverse applications that significantly enhance operational efficiency and performance. This comprehensive review examines the state-of-the-art machine learning applications in the retail sector from 2019 to 2024, focusing on supervised learning, unsupervised learning, and ensemble methods. It aims to identify and categorize recent machine learning applications in retail, evaluate the performance of machine learning algorithms, and determine the most suitable algorithms for specific retail use cases. This review article examines 56 studies and identifies 20 unique machine learning applications within the retail sector. This review also discusses the challenges and opportunities of implementing machine learning in retail, offering valuable insights to guide future research and enhance retail performance and customer satisfaction. The findings highlight the strengths and limitations of different machine learning methods, providing insights into their practical applications and future potential. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Ensemble methods Machine learning Retail Supervised learning Unsupervised learning

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