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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Kozakai, Makiko Hayashi, Emiri Nishi, Hiroaki Graduate School of Science and Technology Keio University Yokohama Japan Department of System Design Faculty of Science and Technology Keio University Yokohama Japan

ISBN: (纸本)9781665464543

Recently, smart agriculture utilizing Information and Communication technology (ICT) and robotics has gained attention. The Institute of Electrical and Electronics Engineers (IEEE) Standards Association is developing standard P2992 for smart agricultural data design. Research on crop feature representation exists but lacks universal methods and real-world application. This study proposes a method to represent crop growth and environments in JavaScript Object Notation (JSON) format based on P2992, applied to cooperative farmers’ greenhouse management. The method includes feature extraction, environment data acquisition, and JSON representation. As a result of the designed application, flower detection achieved 84.5% conformance and 86.1% recall, with a data size reduction of over 99% compared to traditional methods. Applications using temperature and crop data show potential for effective cultivation and sales management by using simple JSONata scripts, suggesting P2992's practicality and usefulness in real-world environments. © 2024 IEEE.

关键词： Crops

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Unsupervised Anomaly Sound Analysis Method Using StyleGAN2 to Estimate the Degree and Type of Abnormalities 50

Unsupervised Anomaly Sound Analysis Method Using StyleGAN2 t...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Yasuda, Riko Sugawara, Ayaki Nishi, Hiroaki Graduate School of Science and Technology Keio University Yokohama Japan Department of System Design Faculty of Science and Technology Keio University Yokohama Japan

ISBN: (纸本)9781665464543

This study proposes a pioneering approach for anomaly detection in smart factories through the analysis of equipment acoustic data, leveraging the StyleGAN2-ADA deep generative model, originally developed for facial image generation, to analyze acoustic data. This method shows the potential to accurately estimate the severity and type of unusual sounds, allowing for early identification of equipment faults or wear and tear, and subsequently estimating their operational lifespan. Specifically, acoustic data from small motors are converted into Mel spectrograms, which are treated as images. By training the StyleGAN2-ADA model with these spectrograms and analyzing the resulting latent vectors, the technique classifies the nature and extent of anomalies in previously unseen acoustic data. Using unsupervised learning to analyze anomalous sounds holds significant potential for improving industrial monitoring and maintenance planning. © 2024 IEEE.

关键词： Spectrographs

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A Deep Learning Based system to Predict the Plant Disease Using Streamlit 4

A Deep Learning Based System to Predict the Plant Disease Us...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Warbhe, Mohan K. Bore, Joy Jordan Chaudari, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering MaharashtraSawangi Wardha442001 India

ISBN: (纸本)9798331523923

The proposed web application for tomato leaf disease detection exemplifies the transformative power of Artificial Intelligence and Computer Vision in modern agriculture. Addressing the critical issue of early and accurate disease detection, this solution bridges the gap between advanced technological innovations and the practical needs of farming. The application is designed using Streamlit and Python to combine the usability of a friendly interface with the power of robust analytical capabilities in CNN, offering real-time reliable diagnosis and actionable insights to farmers. This application provides the highest possible accuracy and adaptability through the thorough training and evaluation of models and is a good asset for disease management. Deployment on scalable cloud platforms ensures access, reliability, and global reach. Future improvements, including extending disease detection to other crops, multilingual support, real-time recommendations, and integration with IoT devices, promise to expand its reach and utility. This initiative is a shining example of how AI-based solutions can transform agricultural practices by enhancing productivity, reducing losses, and ensuring sustainability. The application empowers farmers and contributes to a resilient agricultural ecosystem, addressing the pressing need for innovative and sustainable solutions in a rapidly evolving world. © 2025 IEEE.

关键词： Smart agriculture

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ResNet50-Based Deep Learning Model for Lung Disease Detection

ResNet50-Based Deep Learning Model for Lung Disease Detectio...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Hajbe, Janhavi R. Patle, Sneha K. Talvekar, Shreya V. Khonde, Tanvi W. Yesankar, Prajyot R. Faculty of Engineering and Technology Department of Computer Science & Design Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331509828

By enabling a highly accurate examination of the chest x-ray, deep learning, for example, is changing the methods of recognizing lung disorders. In order to classify lung diseases, such as bacterial pneumonia, viral pneumonia, TB, and COVID-19, this work describes the use of ResNet50, a kind of CNN and a deep learning technique. We prove the potential of deep learning to discern these diseases from the two conventional approaches and most importantly, the patterns one could implicitly observe. The article also discusses the challenges that deep learning may pose on to the healthcare industry including data privacy, availability of data and the issue of overfitting. The paper links with this part by looking at the future of this area of specialization, which includes;multimodal fusion, operational AI, real-time identification, personalized care, data issues, and concerns, and ethics. But if deep learning is successful in addressing all the above-discussed challenges then it can bring the situation to the real world where the identification of lung diseases happens in a previous phase which in turn results in better treatment procedures and higher patient outcomes. © 2025 IEEE.

关键词： Lung cancer

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Application of AI and ML in Wound Healing and Skin Regeneration 4

Application of AI and ML in Wound Healing and Skin Regenerat...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Petkar, Taniya G. Kumar, Praveen Sarate, Kirtiksha U. Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

By enabling precise, individualized, and effective treatments, the integration of artificial intelligence (AI) and machine learning (ML) into wound and skin healing is revolutionizing healthcare. Artificial intelligence-controlled tools improve wound assessment, anticipate healing, and collaborate with regular examinations using cutting-edge dressings and imaging technologies. AI computations work on the management of express data to enhance treatment systems, while advancements in nanomaterials like marine collagen and therapies employing young microorganisms further accelerate skin recovery. Despite encouraging findings, there are still problems, such as potential overfitting and dataset limitations. In general, machine learning is changing the way that injuries are treated by providing proactive, tailored solutions that aim to understand outcomes and lower the cost associated with clinical outcomes. © 2025 IEEE.

关键词： Personalized medicine

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A Review on the Role of IoT in Smart Agriculture with Reference to Efficiency, Sustainability and Precision Farming 5

A Review on the Role of IoT in Smart Agriculture with Refere...

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

作者： Yesankar, Prajyot R. Bandre, Prajwal Verma, Prateek Gudadhe, Amit G. Gourshettiwar, Palash Puri, Chetan Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India

ISBN: (纸本)9798350379945

Agriculture is changing into a more sustainable and productive sector as a result of the fusion of modern technologies, especially the Internet of Things (Io'T), with traditional farming methods. This shift, dubbed 'Smart Agriculture,' rethinks how farmers conduct cultivation, managing resources, and sustainability by using the power of online access, data analytics, and automation. A network of linked systems and gadgets that gather, transfer, and evaluate real-time data forms the basis of smart agriculture. With previously unheard-of precision and efficiency, farmers may now remotely track and supervise a variety of aspects of their farming activities thanks to this connectivity. The transformative influence of IoT on productivity, sustainability, or precision farming highlights the need for it in agriculture. A crucial element of smart agriculture is precision farming, which entails exact management of resources like pesticides, fertilizers, and water as well as real-time data processing. This study examines the ways in which integrated geospatial technologies, weather forecasting, and IoT -driven automation and actuation support productive and sustainable farming methods. The emphasis on aquaculture monitoring also draws attention to the wide range of uses for IoT in agriculture that go beyond conventional crop growing. The crucial part that IoT plays in meeting the growing need for food production on a worldwide scale while using scarce resources. Precision farming, management of water, and crop monitoring are made possible by Internet of Things technologies including sensors, drones, or automated machinery. These apps support environmental sustainability in agriculture, improve resource efficiency, and enable data-driven decision-making. This research work goes into more detail on the fundamental components of smart agriculture, such as sensor technologies, infrastructure connectivity, automation, data transfer, acquisition, and integration with geospatial tec

关键词： Smart agriculture

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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A Review on Implementation of Wearable Devices in Healthcare 7

A Review on Implementation of Wearable Devices in Healthcare

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Suman, Saurabh Choudhary, Vishal Kumar Pacharaney, Utkarsha Kumar, Abhishek Kumar, Dhiraj Raushan, Rahul Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Artificial Intelligence And Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350359299

The capacity to get additional patient data, including clinical, behavioral, and self-monitored data, has enhanced by the expanding usage of wearable technology. Large volumes of previously unobtainable data are now available, providing a chance to create new instruments with sophisticated analysis and decision support systems for application in clinical settings. Additionally, it gives patients additional options by arming them with personalized decision-support resources and information, enabling them to take charge of their own health care while maintaining their sense of autonomy. Especially well-suited for miniaturization and integration into microfluidic devices are biosensors with electrochemical sensors. Applications cover activities in the domains of diagnostics, food and environmental control, and the identification of complete cells, the parts of cells, proteins, and tiny compounds. Microfluidic technologies can be as basic as sample transport tubes or as sophisticated as platforms with extra components for sample preparation and sensing electrode integration. The kind of detection, application, and sample material all influence the microfluidics design. The next issue for the healthcare industry will be how to use newly acquired information in a way that benefits patients, healthcare organizations, and professionals all while reducing costs and workloads. Given the current limitations, the challenge for wearable technology designers is to provide solutions that healthcare practitioners can readily integrate and use. © 2024 IEEE.

关键词： Biosensors

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Kolmogorov Arnold Network for Hyperspectral Image Classification: A Detailed Explanation 2

Kolmogorov Arnold Network for Hyperspectral Image Classifica...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Suman, Saurabh Pacharaney, Utkarsha Choudhary, Vishal Kumar Raushan, Rahul Kumar, Abhishek Kumar, Gulshan Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Hyperspectral imaging (HSI) has been proved to be useful in numerous fields because of its ability to acquire the spectral information across the hundreds of contiguous bands. Nevertheless, the vast dimensionality of the hyperspectral data presents a great problem especially during data processing as well as during classification. Recently, one of the techniques that have attracted most interest for the classification of hyperspectral images is Kolmogorov Arnold Network (KAN), which is efficient with regard to those high-dimensional non- linear data sets found in hyperspectral imagery. This study focuses on how KAN can be utilized for classifying hyperspectral images, how B-spline curves can be used for augmenting the performance of the model, and what experimentation was done with the state-of-the-art hyperspectral data set. From these results, it was established that KAN plus B-spline curves was a good strategy for hyperspectral images classification compared to existing systems in both accuracy and speed. © 2024 IEEE.

关键词： Splines

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Variable Length Multi-Objective Whale Optimization for Trust Aware Data Gathering in Wireless Sensor Network

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IEEE Access 2025年 13卷 32100-32121页

作者： Saad, Mohammed Ayad Jaafar, Rosmina Chellappan, Kalaivani Faculty of Engineering and Built Environment Department of Electrical Electronics and System Engineering Selangor Bangi43600 Malaysia Al-Kitab University Department of Medical Instrumentations Technique Engineering Kirkuk36001 Iraq Universitas Airlangga Biomedical Engineering Study Program Faculty of Science and Technology Surabaya60115 Indonesia

Efficient data collection in wireless sensor networks (WSNs) is crucial. While traditional approaches rely on stationary data sinks, the use of mobile sinks, like unmanned aerial vehicles (UAVs), has shown promise in enhancing data-gathering capabilities. However, existing methods often overlook the importance of trust, leaving the network susceptible to malicious or faulty nodes. To address this issue, we propose a novel trust-aware data-gathering algorithm, Variable Length Multi-Objective Whale Optimization Data Gathering (VLMOWO-DG). Our algorithm simultaneously optimizes energy consumption, delay, and trust while employing mobile sinks to collect data from sensor nodes. By considering trust as a key performance metric, we improve data reliability and security. The VLMOWO-DG algorithm introduces flexibility through variable-length solution representation and application-oriented operators, allowing for efficient exploration of the solution space. Simulation results demonstrate that our proposed algorithm significantly outperforms established benchmarks, NSGA-II and NSGA-III, in terms of domination and hypervolume. This leads to a remarkable improvement of 200% in data gathering performance. © 2015 The Authors.

关键词： Data collection

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