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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Borah, Asha Rani Gupta, Manik Arashingodi, Sudeep M New Horizon College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

This research introduces a novel approach to safeguarding aquatic ecosystems through the integration of Internet of Things (IoT) and machine learning technologies. Our system monitors critical parameters such as pH level, hardness, dissolved oxygen, and temperature in real-time. Leveraging machine learning algorithms, it can detect potential health issues in fish by analyzing images, water quality, or fish residue. Alerts are then seamlessly communicated to users via a dedicated website or mobile app. This comprehensive solution ensures promoting the well-being of aquatic life and contributing to sustainable water management practices. © 2024 IEEE.

关键词： Alerts Aquatic Health Disease Detection MobileApp Real Time Monitoring Water Quality Website

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Smart Problem-Solving Using AI Driven Resource Kit 2

Smart Problem-Solving Using AI Driven Resource Kit

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Mohanraj, A. Nithyasarathi, M. Mohammed Safil, M. Ravena, S. Narthika, M. Sri Eshwar College of Engineering Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

SPARK (Smart Problem-Solving and AI-Driven Resource Kit) aims to revolutionize programming education by offering personalized, AI-driven problem sets that adapt to each student's skill level. Unlike existing platforms like LeetCode and CodeChef, which rely on user-driven problem selection, SPARK integrates advanced clustering algorithms, real-time adaptive feedback, and dynamic difficulty adjustments to ensure challenges are optimally tailored to individual learners. Leveraging Intelligent Tutoring Systems (ITS) and data-driven performance analysis, SPARK continuously tracks user progress, analyzes strengths and weaknesses, and adjusts recommendations to enhance learning efficiency. This approach addresses gaps in existing systems, providing actionable feedback and helping students avoid frustration from misaligned challenges. While the prototype is still under development, initial plans suggest that SPARK will improve coding fluency, engagement, and problem-solving capabilities. Future testing will evaluate its effectiveness in preparing students for technical interviews and coding competitions, positioning SPARK as an innovative and scalable solution for personalized programming education. © 2024 IEEE.

关键词： Adversarial machine learning

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Chronic Obstructive Pulmonary Disease Severity Classification using lung sound 10

Chronic Obstructive Pulmonary Disease Severity Classificatio...

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

作者： Khanaghavalle, G.R. Rahul, G. Senajith, S.R. Vishnuvasan, T.S. Keerthana, S. Computer Science and Engineering Sri Venkateswara College of Engineering Chennai India

ISBN: (纸本)9798350353068

A global health concern, chronic obstructive pulmonary disease (COPD) demands early detection and intervention for effective treatment. Leveraging the extensive 12-channel lung sound dataset, RespiratoryDatabase@TR, our study establishes a robust multi-class COPD severity diagnostic system. We employ a rigorous feature extraction procedure, including spectrogram, Mel spectrogram, and chromogram analysis, alongside specific data preprocessing and augmentation methods. The RESNET50 model is chosen for training, ensuring precise classification across COPD severity levels. Our findings underscore the significance of sound-based prognoses, particularly in early COPD diagnosis. With an estimated 251 million people globally affected by COPD, innovative solutions like ours are crucial. The amalgamation of extensive datasets and advanced machine learning techniques holds the promise of transforming COPD diagnosis and treatment on a global scale, improving the lives of millions affected by this illness. © 2024 IEEE.

关键词： Deep learning

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A Survey on Fish Detection and Species Recognition 2

A Survey on Fish Detection and Species Recognition

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Sheela, A Jeba Madhan Raj, R. Manoj, E. Roshan, G.K. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

Automated fish identification is essential to overcome the challenges and time constraints associated with manual identification processes. Various techniques are explored, evaluating their performance based on factors like pre-processing methods, significant characteristics, and accuracy. Fish classification is a well-studied problem with applications in diverse fields, including target marketing and government initiatives to manage fish supply and maintain ecological balance. These automated methods play a pivotal role in sectors like commercial fisheries, agriculture, marine science, and the broader industrial fish market, supporting industries such as nutrition and canning factories. One approach involves enhancing fish recognition algorithms based on models like AlexNet, IDNet, SAFNet. Methods such as item-based soft attention mechanisms, reduced structural complexity, and transfer learning have been employed to improve accuracy and reduce training time. Similarly, researchers have applied deep learning algorithms like Mask R-CNN, MobileNet, and Fast RCNN to tackle species identification, length estimation, and object detection in marine environments. These techniques have shown promising results, especially when dealing with overlapping fish or elongated ***, lightweight models have been developed for underwater object detection, considering issues like low visibility, colour distortion, and small target size. Techniques like attentional feature fusion, modified feature pyramid generation, and adaptive anchor generators have been proposed to optimize performance, achieving a balance between accuracy and processing speed. © 2024 IEEE.

关键词： Fish

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Performance Evaluation of Natural Language Processing Algorithms for Sentiment Analysis

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SN computer science 2024年第6期5卷 724页

作者： Silviya, S. H. Annie Faith, S. Julia Seetha, R. Hemalatha, M. Department of Computer Science and Engineering Rajalakshmi Institute of Technology (RIT) Department of Information Technology S.A.Engineering College Department of Information Technology Vellore Institute of Technology Department of Computer Science and Engineering RMD Engineering College

Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, computer science, and artificial intelligence called “natural language processing” studies how computers and human language interact, with a focus on how to teach computers to process and analyze vast volumes of natural language data. Sentiment analysis, sometimes referred to as opinion mining or emotion artificial intelligence, is the methodical identification, extraction, quantification, and study of affective states and subjective data using natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is frequently used in online and social media platforms, voice of the customer materials (such as reviews and survey replies), and healthcare materials for marketing, customer service, and clinical medicine purposes. This paper discusses different sentiment analysis methods and selects the best approach for a certain task. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics Computational linguistics Natural language processing Sentiment analysis Text analysis

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Rapid Plant Disease Detection Using AI Techniques 5

Rapid Plant Disease Detection Using AI Techniques

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Srivastava, Akhilesh Kumar Chauhan, Saransh Mishra, Tribhuvan Mishra, Vaishnavi Abes Engineering College Department of Computer Science Engineering Ghaziabad India

ISBN: (纸本)9798350383522

India is a country of agriculture where 60 percent of the population, according to the census, 2011 is involved in the agriculture industry. A survey depicts that a huge amount of the worth of the crop gets wasted due to plant disease every year in India. The paper deals with a solution for the problem of plant disease by creating a system that can predict the disease in a plant by referring to the image of the infected plant. The paper aims not only for disease detection but also recommends a treatment for dealing with the disease in the infected plant. The author's team had worked on the PlantVillage dataset for creating the model, which contains a large collection of images of various plant species and their associated diseases. The proposed model uses deep learning technique based on Convolutional Neural Network (CNN) and utilizes the *** library of Python. The ResNet34 model has been used for the analysis. ResNet34 use Convolutional Neural Network (CNN), with residual learning. Research for the treatment of disease is also conducted, by the author's team. This paper aims to bring our research to the knowledge of every farmer, researcher, and inquisitive who wants to put their efforts into the field of agriculture. © 2024 IEEE.

关键词： Agriculture Batches Deep Learning Fast.ai Normalization Plant Disease Python

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Tokenization of Energy Assets: A Multichain Blockchain Approach 5

Tokenization of Energy Assets: A Multichain Blockchain Appro...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Akiladevi, R. Sardha, S. Shruthi, R. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350395235

The biggest barrier is the financial strain caused by rising electricity costs from using too much power. The security of businesses in the energy sector is threatened by this financial burden, therefore finding creative ways to get through these rough waters is imperative. Moreover, the intricacies involved in the issuance and exchange of energy certificates have intensified the need for enhanced openness and economy. Industry stakeholders are now seriously concerned about these nuances. Our project offers a methodical strategy based on multichain blockchain technology to address these enormous obstacles. invested in the future of the industry. This innovative approach focuses on tokenizing energy assets, optimizing energy management procedures, and providing industry stakeholders with a safe environment. The work on the project in stages, starting with collecting a lot of data. The carefully collect standardized data on power use, expressed in terawatt-hours (TWh), and then improve data quality to make sure the information is reliable and credible. The principal aim is to bring in an era characterized by security, efficiency, and transparency in the administration of energy resources, hence fostering growth and resilience within the energy industry. The ultimate objective is to clear the path for a future in energy asset management marked by openness, security, and efficient operations, all of which will support the growth and sustainability of the sector. © 2024 IEEE.

关键词： Blockchain

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Review on Blockchain-Enabled Techniques for Secure Storage and Sharing of Patient Healthcare Records 2

Review on Blockchain-Enabled Techniques for Secure Storage a...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Mahmood, Rasmiya George, Vinodu Lbs College of Engineering Department of Computer Science and Engineering Kasaragod India

ISBN: (纸本)9798350328202

The Patient Medical Health Records (PMHR) have always provided a large amount of research data to medical institutions, and pharmaceutical companies and have contributed to the development in medical research. Lack of confidentiality and integrity produces many security problems with sensitive health information and has a significant negative effect on a patient's life. Therefore, securing medical records is much-needed task for the healthcare system. However, the attack's harmfulness, data vastness and complexity have made data protection a complicated mission. Today's healthcare sector use centralized system to record and manage patient data. These may be less secure and subject to misinformation. Therefore, proper and secure management of patient health records is an important task in entire healthcare field. This include secure storing of health records, sharing among various healthcare professionals. In order to overcome the issues regarding the centralized system, various blockchain enabled approaches have been utilized and most of them could result in enhanced confidentiality, high throughput, low processing time, better access management and finally reduced computational overhead. However, still we need more improvements in assessing and addressing the scalability issues considering the increasing volume of data and the growing number of participants in the healthcare ecosystem. Main contribution of this paper is to review the blockchain enabled techniques in securing patient health records and discuss their underlying mechanisms in order to improve confidentiality, security, throughput, processing time and providing effective access control mechanisms. Finally will conclude with realizing a gap for utilizing methodologies for overcoming scalability issues. © 2024 IEEE.

关键词： Blockchain

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Multimodal Knowledge Graph Completion Using CLIP-Enhanced Hyper-Node Relational Graph Attention Networks 5

Multimodal Knowledge Graph Completion Using CLIP-Enhanced Hy...

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

作者： Shashank, R.B. Swami, Saurabh B. Pai, Akanksha A Mady, Pranamya Thomas, Sharon Moharir, Minal Rv College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350379945

Knowledge graph (KG) completion is a critical task in Artificial Intelligence (AI) focused on deducing absent connections between entities. The diverse nature of data, encompassing text, images, and numerical information, introduces considerable difficulties in precisely forecasting these missing links. Multi-modal KGs are necessary to fully capture the diverse and rich information associated with entities, enhancing applications in search, recommendation, and AI. This study presents a new framework for completing multi-modal knowledge graphs that integrates textual, visual, and numerical data. Pre-trained Contrastive Language-Image Pre-training (CLIP) embeddings are leveraged for textual and visual information and a dense layer is utilized for numerical data, creating a unified entity representation through a low-rank multi-modal fusion approach. The proposed model employs a Hyper-node Relational Graph Attention Network (HRGAT) to effectively aggregate and process multi-modal information. The research shows that this new approach performs significantly better than current models in certain metrics, such as Mean Rank (MR), particularly excelling in lowering the mean rank of head entities, showcasing the enhanced capability and accuracy of the method proposed in completing multi-modal KGs. © 2024 IEEE.

关键词： Knowledge graph

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Deep Learning for Crop Disease Detection using YOLOv8 15

Deep Learning for Crop Disease Detection using YOLOv8

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

作者： Rakesh Kumar, M. Rengalakshmi, N. Saghana Shree, R.P. Akiladevi, R. Kumar, P. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Agriculture, deeply rooted in India's history, remains as the root of the country's economy and a primary source of employment. Despite the ageold practice, smallholder farmers in India sometimes face problems, especially in getting critical information that would allow them to make knowledgeable farming decisions. In this era of technology, knowledge is the foundation of advancement, farmers productivity and profitability could be impacted due to a lack of widely accessible agricultural knowledge. Crop health is an important aspect in productivity in agriculture. Crop diseases can have major implications for quality and quantity, as well as farmer's livelihoods. This technology aims to assist farmers, increase productivity, and contribute to agriculture's sustainability by providing up-to-date knowledge regarding crop health, disease identification, and optimal farming practices. Crop disease identification used to rely on digital image processing, but new advances in deep learning have exceptionally outperformed previous techniques. In this paper, we focus on the effectiveness of employing deep learning for crop disease identification. Our research is based on a database of images of diseased crop leaf and healthy leaf, with a focus on prevalent crop diseases in India. An application has been created with this ideology in mind to aid farming practices by detecting plant disease. There are two types of input format can be given to the application such as image and video of the diseased plant leaves. YOLO v8 is the deep learning model that is used in this application to effectively identify the disease. © 2024 IEEE.

关键词： Agriculture productivity Crop disease Crop health Deep Learning YOLOv8

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