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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Ghodeswar, Ujwala Puri, Chetan Shingade, Sharvari Waware, Teena Ladhe, Ajinkya Durge, Tejas Department of Electronics Engineering Yeshwantrao chavan College of Engineering Nagpur India Department of Computer Science & Design Faculty of Engineeering & Technology Wardha India

ISBN: (纸本)9798350361155

The research investigates how machine learning algorithms and Internet of Things (IoT) technologies might be combined to solve the crucial problem of separating fresh fruits from damaged apple fruits. Using Internet of Things (IoT) devices with sensors, the system collects extensive data on important characteristics, such as real-time apple picture analysis and available apple database. Convolutional neural networks (CNNs) and YOLO are two examples of sophisticated machine learning models that are used with this data to accurately classify the apples into different groups according to their quality. This initiative fosters enhanced efficiency, production, and profitability in the agricultural supply chain while providing a sustainable solution to the problems connected with fruit sorting. It represents a synergistic convergence of IoT, machine learning, and agriculture. © 2024 IEEE.

关键词： Internet of things

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STaaS: Cost Effective and Scalable Union Mounting in ICP for Aerospace Applications

STaaS: Cost Effective and Scalable Union Mounting in ICP for...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Sakthivel, V. Li, Jueying Rama Abirami, K. Min, Dugki Lee, Jae Woo Choi, Eunmi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Konkuk University Department of Computer Science and Engineering Seoul Korea Republic of Curtin University Department of Electrical and Computer Engineering Malaysia Konkuk Aerospace Design - Airworthiness Institute Konkuk University Seoul Korea Republic of Kookmin University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350357905

Cloud computing widely used in various specific areas leads to the emergence of 'Short-Time as a Service' (STaaS) as a cost-effective and scalable way for enterprises and organizations to access and utilize computing resources. Union mounting, a method that enables multiple file systems to be combined into one virtual file system, has been proposed as a strategy to optimize STaaS by allowing resources to be allocated dynamically based on user requirements. This research paper explores the feasibility and benefits of STaaS with the union mounting method, using a project providing a simulation environment on the Industry Cloud Platform (ICP) for the aerospace organization that requires an environment that can automatically and quickly create cluster groups of Cloud In the Loop Simulation (CILS) for Unmanned Aerial Vehicle (UAV) operations. The results demonstrate that STaaS with a union mounting method can provide significant benefits in terms of CILS cluster group creation, performance optimization, and flexibility. Specifically, our proposed research could reduce instance creation time costs by up to 90% compared to the traditional creation method. Overall, STaaS with union mounting method improves the creation time of the CILS cluster group for the aerospace organization requiring flexibility and cost-effectiveness. © 2024 IEEE.

关键词： Antennas

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The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities:A comprehensive systematic review

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Environmental science and Ecotechnology 2024年第4期20卷 79-104页

作者： Simon Elias Bibri Jeffrey Huang Senthil Kumar Jagatheesaperumal John Krogstie Swiss Federal Institute of Technology Lausanne(EPFL) Institute of Computer and Communication Sciences(IINFCOM)School of ArchitectureCivil and Environmental Engineering(ENAC)Media and Design Laboratory(LDM)1015LausanneSwitzerland Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative *** advancements are reshaping data-driven planning strategies,practices,and approaches,thereby facilitating the achievement of environmental sustainability *** transformative wave signals a fundamental shift d marked by the synergistic operation of artificial intelligence(AI),artificial intelligence of things(AIoT),and urban digital twin(UDT)*** previous research has largely explored urban AI,urban AIoT,and UDT in isolation,a significant knowledge gap exists regarding their synergistic interplay,collaborative integration,and collective impact on data-driven environmental planning in the dynamic context of sustainable smart *** address this gap,this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies,models,and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart *** to this study are four guiding research questions:*** theoretical and practical foundations underpin the convergence of AI,AIoT,UDT,data-driven planning,and environmental sustainability in sustainable smart cities,and how can these components be synthesized into a novel comprehensive framework?*** does integrating AI and AIoT reshape the landscape of datadriven planning to improve the environmental performance of sustainable smart cities?*** can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities?*** challenges and barriers arise in integrating and implementing AI,AIoT,and UDT in data-driven environmental urban planning,and what strategies can be devised to surmount or mitigate them?Methodologically,this study involves a rigorous analysis and synthesis of studies publis

关键词： Sustainable smart cities Artificial intelligence Artificial intelligence of things Urban digital twin Data-driven urban planning Environmental planning Environmental sustainability

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Performance Analysis of the Machine Learning Algorithms for the Early Detection of Breast Carcinoma 2

Performance Analysis of the Machine Learning Algorithms for ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Vanitha, K. Karpagavalli, S.M. Mahesh, T.R. Ali, A. Althaf Sridhar, T. Anitha, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Al Ameen Engineering College Department of Computer Science and Engg Tamilnadu Erode India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh India Alliance University Alliance College of Engineering and Design Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350360523

Breast Carcinoma, generally known as breast cancer, primarily affects women, though men can develop it as well. Because of the existence of breast tissue and exposure to female hormones, notably oestrogen, women are at a higher risk It's critical to diagnose breast tumors early. Several techniques based on machine learning (ML were used in this study to classify breast cancer using a dataset that was made available to the public. F-score, recall, precision, preciseness, and other performance metrics were used to evaluate these ML algorithms. Previous research and experimental findings indicate that Random Forest achieved the highest accuracy, with a remarkable accuracy rate of 99.12%. © 2024 IEEE.

关键词： Hormones

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Spectral Data Classification for Supply Chain Management 5

Spectral Data Classification for Supply Chain Management

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Ananthi, P. Sangavi, N. Ashish, G. Jabeen Begum, S. Mahalakhsmi, V. Kongu Engineering College Computer Science and Design Erode India Bannari Amman Institute of Technology Computer Science and Engineering Erode India Sengunthar College of Engineering Information Technology Thriuchangode India Velalar College of Engineering and Technology Computer Science and Engineering Erode India

ISBN: (纸本)9798350321425

Supply chain management plays a pivotal role in business operations, specifically with regard to the distribution network and ensuring the pristine condition of delivered products. Damages that may occur during shipping pose significant concerns, encompassing accidents, improper cargo handling, and related factors. To address this challenge, the implementation of machine learning algorithms, particularly through motion detection, emerges as a promising solution. By meticulously analyzing spectral features and accelerometric properties, the algorithm proficiently classifies motions across three-dimensional axes. Leveraging a K-Nearest Neighbor classifier, the system accurately categorizes these motions based on a carefully curated dataset of labeled non-damaging motions. Through the identification of outlier motions, the system effectively detects potential risks, thus facilitating the implementation of streamlined and optimized transportation mechanisms. This refined approach harnesses the power of machine learning to mitigate damages and elevate overall transportation practices within the supply chain. By enhancing the accuracy of motion classification and identifying deviations, proactive measures can be taken to prevent damage during transportation. This ultimately leads to improved efficiency and customer satisfaction throughout the supply chain. © 2023 IEEE.

关键词： Classification (of information)

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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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Efficient Radon Levels Detection in Residential Areas using Cloud-Enabled Decision Trees for Real-Time Analysis 8

Efficient Radon Levels Detection in Residential Areas using ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lenin, J. Rohit, V.Robin Nisha, F. Vasudevan, B. Sivakumar, S. Murugan, S. Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Bangalore India Presidency University Computer Science & Engineering School of Computer Science & Engineering and Information Science Bengaluru India Cmr University School of Engineering and Technology Computer Science & Engineering Bangalore India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur SRM Nagar Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

This research introduces a new approach to radon detection in homes utilizing Decision Trees (DTs) enabled by the cloud in real-time. High radon levels, a natural radioactive gas, are dangerous to human health. Quick evaluation and mitigation efforts are hindered by the long processes required by traditional detection systems. Using DTs, a machine learning methodology, the proposed method sorts of radon levels according to variables, including building materials, ventilation systems, and geographic location. These trees may handle both numerical and categorical data and provide interpretability. Fast processing of massive data sets and real-time analysis across varied residential areas are made possible by integrating cloud computing, increasing efficiency and scalability. Stakeholders may remotely access crucial information due to the solution's accessibility and flexibility. Results from controlled experiments demonstrate that this method can accurately identify radon levels in the air in real time, allowing for preventative actions to lessen potential health hazards. People living in areas prone to radon may benefit from this practice in terms of their health. © 2024 IEEE.

关键词： Indoor air pollution

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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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CodGRU: Covariate gated attention GRU for short term runoff forecasting 24

CodGRU: Covariate gated attention GRU for short term runoff ...

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4th International Conference on Signal Processing and Communication Technology, SPCT 2024

作者： Cao, Jiahao Xiao, Tong Cao, Zhe School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China Shenzhen Branch of China Academy of Urban Planning and Design Guangdong Shenzhen China

ISBN: (纸本)9798400710636

Runoff simulation and forecasting are critical for effective watershed management, yet their complexity and nonlinear characteristics pose significant challenges. The generation of runoff is the result of the interaction between multiple factors such as precipitation, soil moisture, topography, and human activities, making this dynamic process extremely complex and difficult to model accurately. Traditional models are often based on an understanding of these natural processes but may fall short when dealing with complex real-world scenarios. To address these issues, this study has developed a dynamic gated feature selection model with attention mechanisms (CodGRU), specifically designed for multi-watershed hourly runoff forecasting. By dynamically adjusting the weights of input variables and selecting the most important features, CodGRU can better capture the key factors influencing runoff, thereby improving the accuracy of forecasting. The test results of five watersheds on the real dataset show that the CodGRU model performs excellently in multi-watershed hourly-scale forecasting, outperforming the existing mainstream models and providing reliable support for water resource management and disaster forecasting and mitigation. © 2024 Copyright held by the owner/author(s).

关键词： Soil moisture

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Enhanced Attitude Stabilization of Quadrotors Using a Single-Axis Constraint Moment Disturbance Obserner

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IEEJ Transactions on Industry Applications 2024年第4期144卷 257-264页

作者： Ichiryu, Atsuhiro Shimada, Akira Electrical Engineering and Computer Science Graduate School of Engineering and Science Shibaura Institute of Technology 3-7-5 Toyosu Koto-ku Tokyo135-8548 Japan Dept. of Engineering and Design Shibaura Institute of Technology 3-7-5 Toyosu Koto-ku Tokyo135-8548 Japan

This study proposes a maintaining a quadrotor’s attitude system using a ‘moment disturbance observer’ aimed at enhancing stability. In recent years, quadrotors have become increasingly prevalent in sectors such as agriculture, surveying, logistics, aerial photography, infrastructure inspection, and security. However, with their growing, there have been concerns about potential physical and human harm stemming from accidents. The Ministry of Land, Infrastructure, Transport, and Tourism has reported accidents attributed to challenging maneuvering conditions that have resulted in crashes. This study aims to develop a quadrotor that can maintain a stable attitude during sudden and significant alterations in its orientation. We suggest employing a moment disturbance observer to estimate the necessary moments for stable attitude maintenance. Numerical simulation results show that the proposed method surpasses traditional PID control in terms of attitude stabilization. Furthermore, this method resulted in superior attitude stability in single-axis experiments with a mechanically constrained quadrotor compared to conventional PID control methods. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Aerial photography

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