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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Selva Jeba, G. Chitra, P. Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India Thiagarajar College of Engineering Dept. of Computer Applications Madurai India

ISBN: (纸本)9798350304268

Floods occur when water overflows onto normally dry land and are a destructive natural disaster. In recent times, deep learning models have demonstrated their remarkable capabilities in identifying objects and classifying images. As a result, the utilization of deep learning in remote sensing, especially for the crucial task of flood detection, has become an increasingly prominent and promising area of research and application. This study proposes a lightweight yet powerful feature extraction framework by leveraging a self-attention(SA) based EfficientNetV2. The EfficientNetV2 architecture serves as the backbone of the model and has exhibited effectiveness in image classification tasks. By incorporating self-attention mechanisms, the model can capture spatial relationships within remote sensing images, enhancing its accuracy and efficiency in flood detection. The model was trained and assessed using the SEN12-FLOOD dataset, and the outcomes demonstrate that the suggested framework exhibits enhanced flood detection performance, achieving an accuracy of 95.9%. © 2023 IEEE.

关键词： attention mechanism deep learning EfficientNetV2 flood detection remote sensing

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A Few-Shot Learning for HAR Using 3D Siamese Network 15

A Few-Shot Learning for HAR Using 3D Siamese Network

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Guanghui, Hua Kumar, G. Hemantha Aradhya, V.N. Manjunath Punithavalli University of Mysore Dept. of studies in Computer Science Mysuru India Jss Science and Technology University Dept. of Computer Applications Mysuru India Bharathiar University Department of Computer Applications Coimbatore India

ISBN: (纸本)9798350370249

In artificial intelligence, deep learning techniques were applied in a wide field due to their ability to automatically learn complex patterns from raw data, such as image classification, object detection, image generation, and video analysis in computer vision. These applications are just the tip of a substantial iceberg. With the development of deep techniques to achieve state-of-art accuracy, learn precisely relevant features from raw data, and improve the robustness of models to make them versatile solutions. The traditional deep learning techniques require vast computational resources, including high-performance computer GPUs or TPUs, and can be expensive in terms of hardware. In Human Activity Recognition Systems (HARs). The video needs more computational resources than image processing. In this paper, we proposed a few-shot learning approach based on a 3D Siamese Network with dept. video sequences to address the action video similarity or dissimilar problem. Convolutional Neural Networks (CNNs) are well-suited for images and spatial data processing. The 3DCNN is an extension of CNNs to incorporate an additional temporal dimension to capture the temporal pattern. From the 3D Siamese network, we extract more effective features with spatial-temporal action information from raw data. We employed the 3DCNNs Siamese Network (3DCNNs+SN) with shared weight to recognize or classify a new class of data without new sample data only based on learning training existing sample constructed model. In addition, we divided the benchmark dataset MSR Action 3D dataset into sub-dataset1 (SD-1), sub-dataset2 (SD-2) and sub-dataset3(SD-3) to train and validate our proposed method. For dissimilar sub-dataset 2 we got a 98.1% accuracy, 33% on high-similar sub-dataset 3 and 63.5% on merged sub-dataset SD-4 from sub-dataset2 (SD-2) and sub-dataset3(SD-3). © 2024 IEEE.

关键词： Spatio-temporal data

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A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students 3

A Machine Learning Model for Predicting Internet Addiction i...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Johar, Geetika Patel, Ravindra University Institute of Technology Rgpv Dept. of Computer Science & Engineering Bhopal India University Institute of Technology Rgpv Dept. of Computer Applications Bhopal India

ISBN: (纸本)9798350384598

Internet addiction is becoming one of the critical issues among teenagers and young university students. This habit not only negatively impacts the student's learning performance, but also affects the student's physical and mental health. This paper is focusing on designing a Machine Learning (ML) model for accurately identifying internet addiction. The model incorporates data preprocessing and feature selection techniques for reducing the computational complexity. Next, five different ML algorithms have been implemented and compared to identify appropriate ML algorithms. The identified ML model has been trained using the behavioral features of the students for predicting internet addiction. The experiments have been carried out and the performance of the models has been measured. The results demonstrate that a random forest classifier provides 83% accurate detection for university students and 96% accurate results are provided by an SVM-based classifier for teenager internet addiction detection. Therefore, by using the entire observation of experiments it is found that the proposed model can successfully identify internet addiction in students by analyzing the student's behavioral features. © 2024 IEEE.

关键词： Contrastive Learning

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Decision Support System for Crop Management using Convolutional Neural Network 3

Decision Support System for Crop Management using Convolutio...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Immaculate Mercy, A. Ruby, D. Logesh, T. Shahidha Shireen, S. Periyar Maniammai Institute of Science & Technology Dept. of Computer Applications Tamilnadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523893

Effective crop management is critical for enhancing agricultural productivity and sustainability. Traditional farming methods often face challenges in accurately forecasting yields, optimizing resources, and determining the right harvest timing. To address these challenges, this study develops a decision support system for rice farming in South India by utilizing deep learning techniques, specifically convolutional neural networks (CNN s), combined with dimensionality reduction methods. The system uses a comprehensive dataset that includes factors such as rainfall, temperature, soil type, and rice variety to predict rice yield. Additionally, it optimizes the use of resources such as water, fertilizer, and labor on the basis of expected yields. The proposed CNN model, coupled with squared exponential kernel PCA (SEKPCA), achieved a prediction accuracy of 83.12% and an F1 score of 0.63, enabling farmers to make informed decisions and improve productivity. The system also evaluates resource requirements, offering insights into water usage, fertilizer, and labor needs. This approach enhances agricultural efficiency, reduces waste, and promotes sustainable farming practices. By integrating advanced deep learning technologies into traditional agriculture, the system contributes to more precise yield predictions and better resource management, paving the way for improved crop management and sustainability in rice farming in South India. © 2025 IEEE.

关键词： Resource allocation

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Blockchain Technology: Reshaping Telemedicine and Telehealth Services for Emergency Management

Blockchain Technology: Reshaping Telemedicine and Telehealth...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Neelam Mittal, Varsha Rawat, Vandana Kumar, Priyansh Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Uttrakhand Dehradun India Graphic Era Deemed to Be University Dept. of Computer Applications Uttrakhand Dehradun India

ISBN: (纸本)9798331508685

Both consumers and healthcare providers have benefited greatly from the quick development of software-based technology. The dominance of blockchains is impending as the medical care sector moves quickly towards digitization and customization. The development of blockchain provided answers to several problems that had long plagued the healthcare sector. However, not only in healthcare due to its capabilities, blockchain technology has found great success in a variety of industrial fields. The important factors propelling the growth of the healthcare blockchain market are the rise in data breaches, privacy concerns, fake pharmaceuticals, and provided answers to numerous problems that had long plagued the healthcare sector. Although telemedicine had been developing for a while, the covid-19 epidemic gave it the necessary kick. In order to facilitate social isolation, the pandemic made it necessary to expand virtual healthcare and distant therapy. Over the past year, the telemedicine market has experienced rapid expansion. In the current study, the adaptation issues and prospective prospects for blockchain technology in the telehealth and telemedicine sectors are *** on a systematic review, we have determined the existing state of the use of blockchain technology in a number of healthcare-related areas, the ideal state that would be achieved, the research gap regarding the usage of blockchain technology in a number of healthcare-related application areas, as well as a number of potential research agendas for future studies. © 2025 IEEE.

关键词： Telemedicine

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A MMAMF Mitigation Strategy for Overcoming Cyber Vulnerabilties in a Cyber-Physical System 3

A MMAMF Mitigation Strategy for Overcoming Cyber Vulnerabilt...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ahuja, Sidak Rana, Lakshya Wason, Ritika Arora, Parul Hoda, M.N. Arora, Devansh Dept. of Computer Science and Applications Delhi India Sprinklr Gurugram India

ISBN: (纸本)9798331518592

The rise in the frequency and complexity of cyber-attacks against all enterprises alike, has fueled the research on mitigation techniques for cyber-physical vulnerabilities. The 2014 Sony Pictures cyber-attack was a significant event that was unique in its nature, orchestration as well as effects. This manuscript provides a detailed assessment of the cyber physical vulnerabilities exposed by the attackers while assessing the implications for future mitigation strategies. The manuscript also lays an effective mitigation framework to avoid such attacks in the future on any significant, cyberphysical systems. © 2024 IEEE.

关键词： Cyber attacks

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Artificial Intelligence and Machine Learning Algorithms to Analyse Integrated Nutrient Management Strategies for Optimal Yield and Quality of Oilseeds Crops for Sustainable Development 4

Artificial Intelligence and Machine Learning Algorithms to A...

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4th International Conference on Soft Computing for Security applications, ICSCSA 2024

作者： Mithra, C. Dar, Showkat A. Preethi, A. Dept. of Computer Science and Engineering Chennai India Githam University Dept. of Computer Science and Engineeing India Dept. of Computer Science and Engineering Chidambaram India

ISBN: (纸本)9798331515720

The main objective of this research is to deduce the efficacy of integrated nutrient management (INM) technologies in production of oilseed crops for sustainable development. A great amount of experience is needed in selecting the most effective INM strategy. A new recommendation system to circumnavigate this issue is proposed. It lets farmers decide on the best INM strategy to maximize oilseed crop yield and quality. This system is built on the techniques of advanced machine Learning (ML) and aritifical Intelligence (AI). Oilseed crop date in Tamil Nadu from 1961 to 2019 was used to develop the proposed algorithm. The proposed algorithm for crop yield prediction (CYP) which includes a Soft Voting Ensemble Classifier with weights (SVECWW), a Soft Voting Ensemble Classifier without weights (SVECWOW) along with the SVM technique are compared and contrasted with existing algorithms and also proves that SVECWW outperforms other ML algorithms with an accuracy rate of 97.2%. Furthermore, the Stacked Generalization Ensemble model is employed and compared with another Deep Neural Network (DNN) for the INM crop recommendation system which offers a simple graphical user interface (GUI) for farmers to use and received an accuracy of 97.5%. This GUI enables farmers to access valuable information such as the optimal timing for cultivating oilseed crops, the appropriate types and quantities of organic manures, inorganic fertilizers, and bio-fertilizers required for successful oilseed crop production. The study shows, on its whole, how to create tailored recommendation systems for farmers using GUI models with artificial intelligence and machine learning algorithms. Implementing these systems is expected to significantly improve oilseed crop production and quality significantly, benefiting the whole agricultural sector for sustainable development. Artificial intelligence (AI) makes a recommendation system more accurate and adaptable by looking through complex datasets and patterns

关键词： Adversarial machine learning

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Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第14期48卷 27-42页

作者： Alabbasi, Awab Aljabery, Maalim A. Bölük, Pınar Sarısaray Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Bahcesehir University Istanbul34353 Turkey Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah61004 Iraq Faculty of Engineering and Natural Sciences Software Engineering Dept. Bahçeşehir University Istanbul34353 Turkey

VGIS (Virtual Geographic Information System) Platform is a unified oilfield operations management platform based on MaaS (Management as a Service) that integrates advanced technologies such as AIoT (Artificial Intelligence & Internet of Things), GIS (Geographic Information System), Digital Twin, and AR (Augmented Reality)/ VR (Virtual Reality). The VGIS platform connects, collects, integrates, analyses, and acts on disparate data sources to provide the oilfield with a centralized view to help make informed decisions fast, bring operational visibility across all assets to improve safety and operational efficiency, reduce costs and improve margins, provide comprehensive analytics and decision support, increase business agility, and reduce management complexity. This is a case study of the application of the VGIS platform Majnoon oilfield, Basrah, Iraq. VGIS digital transformation, production process, and KPIs (Key performance indicators) are highly managed taking oilfield management to a higher level globally, in this study, the objective is to address and resolve the issue of high costs associated with using cloud services by utilising a local Data Center (a large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data). This approach involves directly uploading 3D maps to ensure faster service on the intranet, eliminating the need to pay for cloud *** aims providing a platform for researching and implementing monitoring and controlling processes in a more convenient and remote manner. The vGIS platform has emerged as a crucial instrument in the realm of disaster response and crisis management. This technology enables responders to rapidly evaluate, analyse, and respond to crucial information in real-time. This research examined the application of vGIS technology in disaster response and analyse the potential advantages and obstacles associated with its utilisation. © 2

关键词： Information management

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Security of Examination Question Paper Through Blockchain – SecureQ

Security of Examination Question Paper Through Blockchain –...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Solomon Raju, G. Sowmya, K.N. Chennamma, H.R. Dept. of Computer Applications JSS Science and Technology University Mysuru India Dept. of Information Science and Engineering JSS Academy of Technical Education Bengaluru India

ISBN: (纸本)9798331505134

Ensuring the security of confidential information is a daunting task for any concerned authority. Proving the authenticity and integrity of exam question papers is a prime responsibility of examination authorities. Storing and sharing question papers on open networks makes them vulnerable to unauthorized access and alterations. The proposed approach redefines question paper management using the Interplanetary File System (IPFS) and blockchain technology, providing a secure, decentralized, and tamper-resistant platform. This solution addresses long-standing challenges and ensures security, accessibility, and data integrity, establishing new standards for examination management. A user-friendly text editor in the framework allows paper setters to create, modify, and format questions effortlessly while preserving content integrity. The integration of this editor streamlines question paper preparation and enhances both efficiency and security. By leveraging a decentralized framework, the proposed solution ensures that the critical tasks of any concerned authority are handled securely and efficiently, overcoming limitations of conventional methods. © 2024 IEEE.

关键词： Data integrity

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Integrating Cypress Testing with Web Interfaces in Cyber-Physical Systems 8

Integrating Cypress Testing with Web Interfaces in Cyber-Phy...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Jasmine, K.S. Savitha, R. Bhat, Prajna R. Rv College of Engineering Dept. of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798331505462

As cyber threats increasingly target critical infrastructure, safeguarding power systems has become a top priority. This paper investigates the integration of advanced simulation tools to enhance the resilience of power systems against evolving cyber threats. By leveraging the CYPRESS tool, which offers a comprehensive approach to simulating both power systems and cyber networks, the study demonstrates how these simulations can reveal vulnerabilities and inform mitigation strategies. The paper explores how real-time co-simulation of physical and cyber systems, combined with hardware-in-the-loop testing, provides valuable insights into the impact of cyber attacks on power system stability and security. © 2024 IEEE.

关键词： co-simulation cyber threats cybersecurity CYPRESS tool industrial hardware power system security real-time simulation

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