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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kashyap, Vijaita Ahuja, Rakesh Gautam, Shivani Kumar, Ashok Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India School of Computer Application Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350350357

The use of Blockchain technology (BCT) has become widespread across several scientific fields and is projected to see substantial growth in the near future. Blockchain (BC) functions as a digital record-keeping system, enabling transactions to occur in a decentralized manner. BC applications are being developed in a variety of areas, including agriculture, healthcare, and the Internet of Things. BCT has recently sparked significant interest in the healthcare industry. It provides trustworthy and convenient services, and the utmost importance to recognise and address the security issues and obstacles that are linked to this innovative approach. This study introduces the Proof-of-Health (PoH) consensus method, which improves the security and efficiency of the integrated system. It also provides a contemporary analysis and overview of the numerous challenges and unresolved issues that conventional security methods have introduced to smart healthcare. Here, the analysis of loT and BC implementation with some different factors is shown. This study also explores the many components used in BC methodologies and incorporates BCT as a feasible security measure. It evaluates possible obstacles in sectors such as healthcare, agriculture, and numerous other domains. © 2024 IEEE.

关键词： Blockchain

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Sentiment Analysis Scheme for Image-Text Comments based on Multimodal Deep Learning and Spatiotemporal Attention 6

Sentiment Analysis Scheme for Image-Text Comments based on M...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Fanchang TangYaLing Yadav, Amit Sah, Abhishek Pratap Khan, Asif Department of Software Engineering Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Application Integral University Lucknow India

ISBN: (纸本)9798331522667

A sentiment analysis scheme for image and text comments based on multimodal deep learning and spatiotemporal attention is proposed to address the issues of incomplete spatiotemporal considerations, incomplete implementation details, and cutting-edge theoretical algorithms in graphic and textual sentiment analysis schemes. The proposed model has clear layering including data preprocessing layer, modal encoding layer, modal fusion layer, sentiment classification layer, loss function and optimizer, evaluation and feedback. The implementation details of each layer are introduced. The entire scheme model incorporates Multimodal Fusion Neural Network (MFNN) deep learning and spatiotemporal attention mechanism, which makes the scheme perform well in terms of security, robustness and performance, making up for the shortcomings of existing research schemes. © 2025 IEEE.

关键词： Contrastive Learning

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Review of Totem Pole PFC Soft-switching Methods with Market Survey

Review of Totem Pole PFC Soft-switching Methods with Market ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Ghosh, Sumana Hu, Yuequan Batarseh, Issa Wolfspeed Inc Power Application DurhamNC United States University of Central Florida Department of Electrical and Computer Engineering OrlandoFL United States

ISBN: (纸本)9798350316445

This paper presents a comprehensive review of the soft switching methods for the totem pole (TTP) Power Factor Correction (PFC) circuit. Bridgeless PFC circuits are widely used in several applications like EV chargers, data centers, consumer service, and telecommunication equipment. The paper begins by conducting a market survey to gain an overall understanding of the present market products and the importance of soft switching methods. The article then proceeds to present a comprehensive review of TTP soft switching topologies for both GaN and SiC-based methodologies, categorized based on their soft switching approach on the AC or DC side. Finally, the paper concludes with a summary of these ZVS, ZCS, and ZVT techniques, including their advantages, disadvantages, and directions for future research. © 2023 IEEE.

关键词： ac/dc converter Power Factor Correction soft-switching Totempole PFC

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lassi onk: a system framework to annotate and classify vehicular honk from road traffic

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Environmental Monitoring and Assessment 2024年第10期196卷 1-29页

作者： Maity, Biswajit Alim, Abdul Rama Charan, Popuri Sree Nandi, Subrata Bhattacharjee, Sanghita Computer Application and Science Institute of Engineering and Management Kolkata India CSE National Institute and Technology Durgapur India

Some recent studies highlight that vehicular traffic and honking contribute to more than 50% of noise pollution in urban or sub-urban areas in developing countries, including Indian cities. Frequent honking has an adverse effect on health and hampers road safety, the environment, etc. Therefore, recognizing the various vehicle honks and classifying the honk of different vehicles can provide good insights into environmental noise pollution. Moreover, classifying honks based on vehicle types allows for the inference of contextual information of a location, area, or traffic. So far, the researchers have done outdoor sound classification and honk detection, where vehicular honks are collected in a controlled environment or in the absence of ambient noise. Such classification models fail to classify honk based on vehicle types. Therefore, it becomes imperative to design a system that can detect and classify honks of different types of vehicles to infer some contextual information. This paper presents a novel framework $${\varvec{\mathcal {A}}}{\varvec{\mathcal {C}}}$$ lassi $${\varvec{\mathcal {H}}}$$ onk that performs raw vehicular honk sensing, data labeling, and classifies the honk into three major groups, i.e., light-weight vehicles, medium-weight vehicles, and heavy-weight vehicles. Raw audio samples of different vehicular honking are collected based on spatio-temporal characteristics and converted them into spectrogram images. A deep learning-based multi-label autoencoder model (MAE) is proposed for automated labeling of the unlabeled data samples, which provides 97.64% accuracy in contrast to existing deep learning-based data labeling methods. Further, various pre-trained models, namely Inception V3, ResNet50, MobileNet, and ShuffleNet are used and proposed an Ensembled Transfer Learning model (EnTL) for vehicle honks classification and performed comparative analysis. Results reveal that EnTL exhibits the best performance compared to pre-trained models and ac

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Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity

Advancing Phishing Detection with Random Forest: A Machine L...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Sucheta Chandra Priyanka Das Ankita Mandal Sriparno Chakroborty Ishika Chowdhury Kathamrita Ghosh Dept. of Computer Application and Science Institute of Engineering & Management University of Engineering & Management Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection.

关键词： Support vector machines Uniform resource locators Accuracy Machine learning algorithms Phishing Feature extraction Ensemble learning Noise measurement Random forests Overfitting

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FRANN: Fuzzy Rough Set Based Artificial Neural Network for Prediction of Rainfall Status 5

FRANN: Fuzzy Rough Set Based Artificial Neural Network for P...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Mallick, Bishnupriya Mantri, Jibendu Kumar Ray, Niranjan Kumar Mscb University Department of Computer Application India Deemed to be University School of Computer Engineering Bhubaneswar751024 India

ISBN: (纸本)9798331522100

Rainfall status prediction is a process of forecasting the future status or condition of rainfall for a particular region. Prediction of rainfall status is essential because heavy or low rainfall may have many adverse effects on agriculture, transportation, environment, living conditions and economic strength. So, accurate prediction of rainfall status is helpful in designing better action plans for water resource management, disaster risk reduction, sustainable development, emergency management and improving living conditions. Computational intelligence and approximate reasoning of conventional machine learning models are limited by incomplete or imperfect knowledge. However, the soft computing techniques have the capabilities to handle uncertainty, imprecision, partial truth and approximate reasoning to extract information in a number of ambiguous situations. In this aspect, a fuzzy rough set (FRST) based artificial neural network (ANN) model is proposed to predict the future rainfall status. Incorporating FRST method in ANN model can improve the prediction accuracy and reduce the overheads associated with both memory as well as computation time by selecting the dominant attributes and handling inconsistent data present in the information system. Rainfall status prediction accuracy of the proposed hybrid model is compared with two other hybrid models namely FRST based decision tree (DCST), FRST based support vector machine (SVM) and three other existing classification based prediction models namely DCST, SVM and ANN. The findings of the result suggest that the proposed model outperform its counterparts. © 2025 IEEE.

关键词： Risk management

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A Design and Implementation of M Tyoe Systems wnhcih uses ICT Technology in Grids

A Design and Implementation of M Tyoe Systems wnhcih uses IC...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Jangid, Satish Kumar Singh, Alka Pushparajesh, V. Vivekananda Global University Department of Electrical Engineering Jaipur India Noida Institute of Engineering & Technology Computer Application Uttar Pradesh Greater Noida India Faculty of Engineering and Technology Department of Electrical and Elceltronics Engineering Bangalore India

ISBN: (纸本)9798350357769

In the context of smart grids, multi-agent-based applications have garnered a lot of interest recently. Concurrently, integration of IT (information and communication technology) has emerged as a crucial element of the infrastructure of smart grids. This research attempts to give a comprehensive assessment of the technological framework and literature pertaining to the utilization of Multi-Agent Societies (MAS) and the addition of information and communication technologies in electrical power system deployments. In the environment of artificially intelligent grids, agents are thought of as sentient beings that can make decisions and behave in a dynamic and autonomous way by drawing on their innate intellect and past experiences. ICT also makes data and information transmission easier, which is a game-changer in the transition from conventional to smart grids. This article summarizes the multi-agent concept in smart grids and offers a thorough literature study on related topics to elucidate their applications. Additionally, it pays particular attention to Cti standards, particularly IEC 61850, which integrates MAS with ICT. The last section of the paper introduces laboratory framework concepts, which provide insight on the actual use of IEC 61850. © 2023 IEEE.

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The Impact of AI-Powered Personalization on Academic Performance in Students 5

The Impact of AI-Powered Personalization on Academic Perform...

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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Shete, Shweta G. Koshti, Priyanka Pujari, Vinayak I. Sanjay Ghodawat University Department of Artificial Intelligence and Machine Learning Kolhapur India Sanjay Ghodawat University Department of Computer Science Kolhapur India D.Y. Patil School of Engineering and Management Department of Computer Application Kolhapur India

ISBN: (纸本)9798350351378

This study investigates how learners' performance is affected by AI-driven personalization. This goal compares students' academic performance in AI-driven personalized learning environments to that of students in conventional learning environments. This study shows the rapid development and application of artificial intelligence (AI) in education has led to the emergence of personalized learning platforms that utilize AI to provide tailored learning experiences for individual students. The study shows a beneficial relationship between enhanced academic accomplishment, engagement, and satisfaction with individualized AI-based adaptive learning through quantitative and qualitative analysis. The results demonstrate how AI-driven customization can improve student achievement and change the way that education is delivered. © 2024 IEEE.

关键词： Students

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Implementing RNN and LSTM Models to Electrical Load Predictions 2

Implementing RNN and LSTM Models to Electrical Load Predicti...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ramesh, T.R. Kumar, Kirana Asha, V. Kumar, S. Naveen Kumar, Manish Kareem, Ali School of Sciences and Computer Studies Bengaluru India Institute of Aeronautical Engineering Department of Electronics and Communication Engineering Hyderabad India New Horizon College of Engineering Department of Master of Computer Application Bangalore India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (纸本)9798350364699

Electrical utilities attach significant emphasis to power load forecasting to design the generating, transmission, and distribution systems. Additional uses of load forecasting in the power network include demand and supply management, determination of power plant operational resource needs, planning of spinning reserve, and scheduling power unit activities. Additionally, there is the option to use a plethora of other applications. The researcher used two distinct machine learning approaches, including Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), also known to forecast the expected future demand for electrical power in this study. Historical load information's are used to assess the efficiency of implemented measures. The Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are analytical metrics used to assess the accuracy of the methods being used. © 2024 IEEE.

关键词： Electric load forecasting

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Comparative Analysis of CNN Architectures for Maize Crop Disease 10

Comparative Analysis of CNN Architectures for Maize Crop Dis...

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10th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Agrawal, Rakshit Singh, Vinayak Gourisaria, Mahendra Kumar Sharma, Ashish Das, Himansu School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India GLA University Department of Computer Engineering & Application UP Mathura281406 India

ISBN: (纸本)9781665467414

In the current scenario, the Agriculture industry is regarded as the leading industry for society, serving all the needs for the betterment of humanity. Plants are considered to be one of the primary sources of humanity's energy production concealed with nutrients, medicinal cures, etc. Any harm or disease due to exposure of pathogens, viruses, bacteria, etc. to the plants during agriculture leads to the downfall of productivity making it a crucial concern to prevent such diseases and take necessary steps to avoid them. Making accurate identification of such fatal diseases is an important step for the industry as well as for the farmers. In our study, we have implemented fifteen different Convolutional Neural Networks (CNN) which takes plant leaf image as an input source. These architectures have different layers, neurons per layer, optimizers, etc. Our goal is to provide a detailed comparative analysis between the various frameworks based on accuracy, precision, Least Validation Cross-Entropy Loss (LVCEL), etc. parameters in the most effective way. From the experimental results, we found the sixth architecture to be the most accurate model. With the modification of convolutional layers and the use of the correct optimizer, results can be improved to a great extends. © 2022 IEEE.

关键词： Productivity Humanities Pathogens Plants (biology) Neurons Crops Market research

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