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3rd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Thorat, Nilesh N. Kulal, Nilesh M. Baburao, Khedekar Vilas Hirve, Sumit Arun Kolekar, Sachin Sangore, Rohidas Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350354348

In order to facilitate intuitive control and communication across a variety of areas, hand gesture recognition is an essential component of human-computer interaction systems. In this paper, a real-time hand gesture recognition system developed with the MediaPipe library, and convolutional neural networks (CNNs) is presented. The system utilizes a CNN model provided by MediaPipe for accurate detection of hand landmarks in camera feed images. Through extensive preprocessing and feature extraction, hand gestures, including finger gestures and palm orientations, are classified in real-time. The system offers dynamic selection between two modes: logging key points and logging point history, facilitating data collection for training or analysis *** features of the system include bounding rectangle calculation, landmark visualization, and gesture classification, providing users with immediate feedback on detected hand gestures. The modular design and parameter configurability make the system versatile for a wide range of applications, from interactive interfaces to sign language interpretation. The outcomes of the experiments showcase the practicality and efficacy of the suggested method, indicating that it has the potential to improve the experiences of people interacting with computers. Incorporating AI security measures, the system can significantly enhance security protocols in various applications. For instance, it can be used for gesture-based authentication, where specific hand gestures act as passwords to grant access to secure systems. This method can be further secured by combining it with traditional authentication techniques, creating a robust multi-factor authentication framework. Additionally, the real-time detection capability allows for immediate response to unauthorized access attempts, triggering alerts or lockdown procedures when unfamiliar gestures are detected. © 2024 IEEE.

关键词： Convolutional neural networks

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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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An Efficient Method For Skin Cancer Detection Using Convolutional Neural Network 2

An Efficient Method For Skin Cancer Detection Using Convolut...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Rajagopal, R. Karthik Kumar, G.V. Senbagavalli, M. Sai Priya, P. Divya Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India Department of Computer Science & Information Technology Alliance College of Engineering and Design Alliance University Bangalore India

ISBN: (纸本)9798350383867

Skin cancer poses a significant health risk, often overlooked despite its prevalence. Manual detection by clinicians may sometimes be inadequate, leading to the necessity of automated systems utilizing deep learning. In this study, we propose a computerized solution employing Convolutional Neural Networks (CNNs) to detect skin diseases from imaging data. By leveraging the Kera's Sequential API, we design and train various neural network architectures, analyzing their performances in skin cancer detection. Our custom CNN model achieves an accuracy of approximately 80%. Subsequently, we explore the efficiency of transfer learning models such as VGG11 to improve accuracy and for comparative analysis. Notably, the vgg16 architecture achieves the highest accuracy of 86% making it a competitive solution for skin cancer detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Exact controllability of rational expectations model with multiplicative noise and input delay

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Journal of Automation and Intelligence 2024年第1期3卷 19-25页

作者： Wenjing Wang Juanjuan Xu Huanshui Zhang Minyue Fu School of Control Science and Engineering Shandong UniversityJinan 250061China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China

This paper considers the rational expectations model with multiplicative noise and input delay,where the system dynamics rely on the conditional expectations of future *** main contribution is to obtain a sufficient condition for the exact controllability of the rational expectations *** particular,we derive a sufficient Gramian matrix condition and a rank condition for the delay-free *** key is the solvability of the backward stochastic difference equations with input delay which is derived from the forward and backward stochastic system.

关键词： Rational expectations model Exact controllability Exactly null controllability Multiplicative noise Input delay

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Study on the Influence of Reactor system LOCA Modeling Mode on Dynamic Analysis 23rd

Study on the Influence of Reactor System LOCA Modeling Mode ...

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23rd Pacific Basin Nuclear Conference, PBNC 2022

作者： Shuai, Liu Xuan, Huang Zhipeng, Feng Zhongxiu, Zeng Huanhuan, Qi Xiaozhou, Jiang Harbin Engineering University Harbin China Science and Technology on Reactor System Design Technology Laboratory Sichuan Chengdu China

ISBN: (纸本)9789811987793

The reactor system has a large scale of components. And the system dynamic analysis model for loss of coolant accidents (LOCA) contains many non-linear factors. The transient calculation analysis takes a long time and the convergence is difficult to guarantee. In this paper, two different analysis models (complete three-loop model and single-loop model) of the first loop system of reactor are established for comparative analysis. The analysis shows that the calculation error of the main position load in the two different model is within 10%. The decoupling influence of the wave tube and the reactor inlet and outlet on the system dynamic characteristics is within an acceptable range. When the single loop model is used, it is necessary to decouple the reactor pressure vessel and pressurizer. And the simplified boundary is adapted to simulate its effect on the whole loop. The single-loop dynamic analysis model based on the decoupling criterion can greatly reduce the calculation scale and meet the calculation accuracy, which can be used to calculate the LOCA dynamic characteristics of the reactor system quickly. The model can also be used for seismic analysis of reactor system. And using this model for reactor system dynamic analysis can greatly improve the calculation efficiency. Please copy you summary here. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Loss of coolant accidents

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Enhancing structural health monitoring with AI-ML algorithms: a focus on crack detection and prediction

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Asian Journal of Civil engineering 2025年第5期26卷 1907-1918页

作者： Bader, Ahmad Shtayat, Amir Al-Mistarehi, Bara’ Communication and Computer Department Faculty of Engineering Jadara University Irbid Jordan Department of City Planning and Design Jordan University of Science and Technology Irbid 22110 Jordan Department of Civil Engineering Jordan University of Science and Technology Irbid Jordan

SHM is a very important process in terms of the safety and durability of infrastructure. Traditional SHM often faces problems detecting minor structural defects and handling large datasets. Therefore, certain more advanced approaches are called for. The paper discussed the applications of AI and ML algorithms, such as CatBoost and the African Vultures Optimization Algorithm, for such challenges. The research is based on a unique dataset of 8,541 rows and diverse features, developing a predictive framework that enhances crack detection and forecast capabilities. The approach mainly deals with heterogeneous data using the CatBoost algorithm, given its capability for high-accuracy predictions, while AVOA optimizes feature selection, reduces the computational cost, and guarantees no loss in model performance. This methodology has resulted in a significant enhancement of the prediction accuracy, which states the importance of AI-ML integration in SHM. The key results demonstrate the effectiveness of the model in detecting structural anomalies and crack propagation to enable proactive maintenance strategies. This study’s contributions have gone toward advancing SHM with scalable and efficient AI-ML frameworks, enabling real-time monitoring for better infrastructure management. Such development might have a transforming potential to cut down on maintenance costs and enhance operational safety, thus further encouraging sustainable infrastructure systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： African vultures optimization algorithm CatBoost Crack Detection Feature selection Machine learning Structural health monitoring

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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 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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The impact of densification and orientation manipulation on outdoor thermal comfort at social housing in arid regions: a sensitivity analysis

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HBRC Journal 2023年第1期19卷 523-541页

作者： Adly, Mariam A. Mahmoud, Hatem Galal, Omar M. Arab Academy for Science Technology and Maritime Transport Department of Architectural Engineering and Environmental Design Giza Egypt Department of Environmental Engineering Egypt-Japan University of Science and Technology E-JUST Alexandria Egypt Department of Architecture Engineering Engineering Faculty Aswan University Aswan Egypt

Many developing countries are experiencing rapid urban growth, resulting in a high demand for social housing schemes. In Egypt, newly built communities and potential extensions are incompatible with its hot climate due to low aspect ratios, high sky view factors values (SVF) and less shaded areas, which negatively affect outdoor thermal comfort at the pedestrian level. This study aims to find a relationship between density (represented in SVF), Physiological Equivalent Temperature (PET) and orientation through a sensitivity analysis to improve the thermal comfort of outdoor spaces in new cities. The research was held on the peakiest summer day using a validated ENVI-met simulation model for a residential area in New October city. The simulation results of 36 scenarios with a total of 37,138 spatial grids were examined through sensitivity analysis to quantify the impact of densification parameters like building coverage ratio, floor area ratio, building height variation, and orientation on PET. PET was found to be more sensitive to densification in cardinal directions. With Higher mid-range densities PET at cardinal orientation reached up to 10℃ higher than in diagonal. Also, the results revealed that optimizing density and building heights could improve average PET (08:00 to 18:00) in diagonal orientation with up to 8℃ in the mid-range densities. However, orientation is insignificant in low densities. Out of the tested parameters, densification was found to be the most impactful strategy than height variation. These results can aid in developing public and private housing schemes that are more climate responsive. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Sensitivity analysis

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Real-Time Kannada Sign Language Recognition to Speech and Text for Rural Primary Healthcare Centers 1

Real-Time Kannada Sign Language Recognition to Speech and Te...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Swetha, P. Sreenivasa, B.R. Pes University Department of Computer Science and Engineering Bangalore India Bapuji Institute of Engineering & Technology Department of Computer Science and Design Davangere India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Understanding and interpreting sign languages are crucial for promoting communication among the Deaf and Hard of Hearing (DHH) populations [1]. Kannada Sign Language (KSL), primarily utilized in Karnataka, lacks sufficient digital tools and technical support compared to several other well-studied sign languages. To reduce this gap, this study uses machine language learning (ML) techniques to convert KSL gestures into text and speech. The hand movements and facial expressions used in the KSL signs were gathered and examined for this purpose. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two examples of advanced machine-learning methods, have been used to create a reliable recognition system that can perform KSL in real time. The model has shown promising accuracy rates in the preliminary findings, instilling confidence in its potential to enhance KSL users' communication significantly. This study aims to improve education and daily communication among KSL speakers. The proposed model can be used at the regional Primary Healthcare Centers (PHC) for the effective interaction of a patient and doctors. In the future, we plan to expand the dataset, improve the algorithmic performance, and create user-friendly programs to make such programs useful at urban hospitals, too. © 2024 IEEE.

关键词： Convolutional neural networks

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