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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Prajapati, Abhijit Nigam, Muskan Priyanka, R. School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur603203 India

ISBN: (纸本)9798350375237

In today's fast-paced lifestyle, the importance of preparing meals at home has significantly increased due to the high costs and time constraints associated with dining out. However, while home-cooked meals offer numerous benefits, such as cost-effectiveness and convenience, maintaining variety in one's diet is crucial to preventing culinary monotony. Contrary to common belief, creating diverse recipes does not necessarily require an extensive array of ingredients;rather, innovative combinations and techniques can transform familiar ingredients into exciting culinary experiences. This research paper introduces RecipeLens, a novel approach that combines food ingredient detection with recipe suggestions to streamline the cooking process and enhance culinary creativity. Building upon existing studies in food ingredient detection and recipe recommendation, RecipeLens integrates both functionalities into a unified platform, offering users a seamless and efficient way to discover and prepare diverse dishes using available ingredients. To expedite ingredient classification, RecipeLens employs the MobileNet_v2 model, renowned for its accuracy and efficiency in image recognition tasks. Additionally, recipe suggestions are sourced from the Food Recipe API, ensuring the validity and relevance of each recommended dish. Furthermore, RecipeLens accommodates users with limited knowledge of fruit and vegetable names, making it accessible to individuals of varying culinary expertise. Through the integration of ingredient detection and recipe recommendation, RecipeLens empowers users to unleash their culinary creativity, discover new dishes, and enjoy diverse homemade meals with ease and efficiency. © 2024 IEEE.

关键词： Image classification

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A Novel Deep Learning Based Framework for Cardiac Arrest Prediction

A Novel Deep Learning Based Framework for Cardiac Arrest Pre...

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2023 International Conference on Robotics and Automation in Industry, ICRAI 2023

作者： Fatima, Nargis Irtaza, Aun Ali, Rehan University of Engineering and Technology Department of Computer Sciences Taxila Pakistan Iqra University Department of Computing and Technology Pakistan

ISBN: (纸本)9781665464727

Cardiovascular diseases are a major health issue that calls for prompt medical attention. In order to determine the most advantageous methods in this field, numerous techniques and studies have been carried out over the past few years. It should be mentioned that the majority of these cardiovascular illnesses are treatable with earlier detection and prediction. This paper suggests using an automated methodology to predict and classify the likelihood of cardiac arrests in a patient. Due to their collection from various sources, the Electrocardiogram (ECG) signals are initially preprocessed by normalizing them to the [0 1] range. The significant features from these preprocessed signals are then extracted using Mel-Frequency Cepstrum (MFCC), Melspectrogram, MFCC Delta 1 & 2, MFCC Merge File, etc. We also propose a novel feature vector using ensemble of MFCC features. The features are then classified using Artificial Neural Network (ANN), Support Vector Machine (SVM), TPOT and K-Nearest Neighbor (KNN). The proposed vector outperformed all other feature vectors and obtained highest accuracy of 95.8% via ANN. The study is conducted using publicly accessible dataset composed of approximately 52 thousand ECG signals. The suggested strategy is compared with existing techniques and the results indicate the robustness and effectiveness of our approach. Therefore, the proposed methodology can be effectively deployed in a clinical setting to classify ECG data and identify the risks likelihood of cardiac arrests in a patient. © 2023 IEEE.

关键词： Forecasting

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Comprehensive analysis of BLDC motor faults in electric vehicle transportation

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International Journal of Power Electronics 2023年第1期18卷 40-64页

作者： Usman, Adil Rajpurohit, Bharat Singh School of Computing and Electrical Engineering Indian Institute of Technology HP Mandi India Electrical and Computer Engineering Department University of California Santa CruzCA United States

The faults prospective are higher for any BLDC motor, driven under a constant and continuous operating period. The possibility of getting susceptible to winding short-circuit faults and rotor demagnetisation effects, is more frequently discerned. The winding insulation breakdown introduces stator inter-turn fault (SITF) conditions in the machine which has consequent detrimental impact on the rotor permanent magnets (PMs). The cumulative effect of fault stimulates rotor demagnetisation, intensifying the effect of faults. This paper aims in classifying the motor faults employed in electric vehicle (EV) transportation. Depending upon the machine’s characteristic performance in terms of electrical signatures like, motor current signature (MCS) and back-EMF (BEMF), and mechanical signatures like frequency and vibration spectrum;a unique classification to fault is provided. The proposed research models investigate the performance of a machine drive under both individual and combined fault conditions, to give a significant outcome to the research findings. The closed loop motor drive operation is performed using an advanced field remodelling (AFR)-based technique which employs fuzzy logic controller (FLC). The developed drive-controller-system minimises the steady state error under motor operating conditions and has a clear discrepancy in terms of machine quantities during faults. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Numerical methods

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Blockchain-Enabled Energy Trading in Microgrids for Sustainable computing Infrastructure 5

Blockchain-Enabled Energy Trading in Microgrids for Sustaina...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kokila, M.L. Sworna Sunitha, R. Kalyanakumar, P. Raj, J. Relin Francis Krishnan, R. Santhana Gopikumar, S. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur India Rajarajeswari College of Engineering Department of Electronics and Communication Engineering Bangalore India Velammal College of Engineering and Technology Department of Information Technology Madurai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Civil Engineering Chennai India

ISBN: (纸本)9798350379945

This research investigates Blockchain-enabled energy trading within microgrids to enhance sustainable computing infrastructure. A novel algorithm, GridCoin, is proposed and evaluated against existing methodologies including Optimal Power Flow (OPF), Microgrid Energy Management System (MEMS), and Hierarchical Control Algorithms (HCA). The study employs simulation metrics such as Transaction Throughput, Energy Trading Efficiency, and Transaction Latency to compare the performance of these algorithms. GridCoin demonstrates superior capabilities in optimizing energy trading operations, achieving higher transaction throughput, improved efficiency in energy utilization, and reduced transaction latency. These results highlight the potential of Blockchain technology to revolutionize energy markets by providing secure, transparent, and decentralized transaction mechanisms. Future research directions include enhancing optimization techniques, integrating renewable energy sources, addressing scalability and security challenges, conducting real-world deployments, and analyzing economic and policy implications. This study contributes to advancing sustainable computing infrastructure through innovative Blockchain-based solutions. © 2024 IEEE.

关键词： Blockchain technology energy trading GridCoin algorithm microgrids optimization renewable energy integration sustainable computing

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NLP Techniques Cyberbullying Text Analysis on Twitter 10

NLP Techniques Cyberbullying Text Analysis on Twitter

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rishi, R. Mohammed Irfan, M.A. Balamurugan, G. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Tamil Nadu Chengalpattu India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Campus Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350353068

With the pervasive nature of social media, the rise of cyberbullying has become a critical concern, necessitating advanced methodologies for timely identification and analysis. This research delves into the application of Natural Language Processing (NLP) mechanisms to mention the challenges of cyberbullying within the dynamic and fast-paced environment of Twitter. The study explores the utilization of state-of-the-art NLP algorithms and methodologies for the classification and analysis of cyberbullying text. Techniques such as sentiment analysis, text categorization, and semantic understanding are employed to discern the nuanced and context-dependent nature of cyberbullying content on the Twitter platform. We present a comprehensive examination of various machine learning models, including but not limited to recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer methods like BERT, for their effectiveness in accurately classifying cyberbullying text. Additionally, attention is given to feature engineering and pre-processing strategies tailored to the unique characteristics of Twitter data. © 2024 IEEE.

关键词： Sentiment analysis

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Deep-CNWO: a deep-chaotic nature whale optimization algorithm for early prediction of blood pressure disorder in smart healthcare settings

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Neural computing and Applications 2024年第24期36卷 15117-15136页

作者： Motwani, Anand Shukla, Piyush Kumar Pawar, Mahesh Arya, Monika Jain, Paras School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan MP Sehore466114 India Department of Computer Science & amp Engineering University Institute of Technology RGPV MP Bhopal462033 India Department of Computer Science & amp Engineering CMR Engineering College Kandlakoya Medchal Telangana Hyderabad501401 India Department of Information Technology University Institute of Technology RGPV MP Bhopal462033 India

The integration of cloud and edge computing, along with machine learning, plays a vital role in the development of efficient healthcare systems in smart cities. However, machine and deep learning (DL) models are prone to delayed convergence and Type-I and Type-II errors due to data vastness and high degree imbalance. To overcome the shortcomings of previous frameworks, this work aims to propose an optimization method with DL, ‘Deep-Chaotic Nature Whale Optimization’ (Deep-CNWO) for early prediction of Blood Pressure disorders among patients under at-home supervision. A simplex search algorithm is integrated to improve the update mechanism of whale optimization algorithm (WOA), thereby creating a CNWO algorithm. The purpose of this hybrid optimization is to increase the accuracy and efficiency of DL models. Leveraging the power of DL and CNWO, this method (Deep-CNWO) provides an effective solution for early detection and proactive management of a chronic disease in at-home healthcare settings. We collected relevant data from clinical studies, including vital signs and patient contextual information, to train and evaluate the deep-CNWO model. The CNWO optimization approach has been used to improve the predictive performance and convergence of DL models. Experiments performed on imbalanced datasets using deep-CNWO have given 99.90% accuracy. The average F-score for emergency cases has improved by 22%, while the average accuracy has increased by 5.72% across all three classes, compared to the results reported in previous related work. Deep-CNWO improves the convergence of DL and reduces Type-I and Type-II errors. The experimental results demonstrate the efficacy of our proposed method for remote patient monitoring and highlight its potential for quick intervention during emergencies. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Insights into research on blockchain for smart contracts: a bibliometric analysis

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Multimedia Tools and Applications 2024年 1-25页

作者： Singh, Renu Gupta, Ashlesha Mittal, Poonam Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India Department of Computer Science & amp Engineering Indraprastha Institute of Information Technology IIIT New Delhi India

Over the past few years, blockchain technology has gained significant attention. This surge in popularity can be attributed to the emergence of cryptocurrencies and the development of smart contracts. Cryptocurrency is a digital currency that eliminates the problem of double spending. Cryptocurrencies like Bitcoin, Ethereum, Litecoin, Stellar, Zcash, Maker, Aave, etc. become popular and are preferred for money transfers. Smart contracts are the next popular technology on the blockchain after cryptocurrency. It can be considered a piece of code that can execute automatically when the predefined conditions are fulfilled. Researchers believe that the potential of blockchain with smart contracts is only in its initial stages and that its true potential has yet to be fully discovered. Hence, an extensive bibliometric analysis is conducted to understand blockchain trends for smart contracts and to give future directions in this field. For this analysis, various steps are followed, starting with formulating the research question, defining the scope of our research, extracting and analyzing data, answering the research question, and finally, drawing a conclusion. This research paper will be fruitful for scholars and researchers, providing an extensive statistical and network analysis of extracted smart contracts publications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bibliometric analysis Blockchain Crypto contract Scientometrics Scopus Smart contracts VOSViewer

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Real-Time On-Site Ground-Motion Prediction Using ResNet

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Seismological Research Letters 2025年第3期96卷 1834-1845页

作者： Yang, Tzu-Yi Chen, Da-Yi Chin, Tai-Lin Mobile Computing and Networking Lab Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Seismological Center of Central Weather Administration Taipei Taiwan

On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023. © Seismological Society of America.

关键词： Convolutional neural networks

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Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

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Computers, Materials & Continua 2023年第9期76卷 2763-2777页

作者： Shuo Cai Tingyu Luo Fei Yu Pradip Kumar Sharma Weizheng Wang Lairong Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenUK School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha410114China

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission *** Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy *** the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting ***,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the ***,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is *** approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing ***,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

关键词： BSN Internet of Things reliability analysis random isolation time

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