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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Thingom, Chintureena Kumar, N.M.G. Gogula, Sreenivasulu Mukherjee, Aparajita Bhosale, Supriya Sarojwal, Atul Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Karnataka Bengaluru562106 India Mohan Babu University Sree Vidyanikethan Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh 517102 India Telangana Hyderabad501218 India Iem Newtown Campus Department of Computer Science and Engineering West Bengal New Town700160 India Sandip University Department of Computer Science and Engineering Maharashtra Nashik422213 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India

ISBN: (纸本)9798350364699

The operation of deep learning ways for prophetic conservation in power systems, aiming to enhance trustability, effectiveness, and cost-effectiveness in conservation operations. Power systems, pivotal for icing nonstop electricity force, face challenges similar to growing structure, outfit declination, and unanticipated failures. Traditional conservation strategies frequently calculate on periodic examinations and preventative measures, which may be expensive and hamstrung. Deep learning offers a data-driven approach to prophetic conservation by using literal data from detectors and functional parameters. Convolutional Neural Networks( CNNs) and intermittent Neural Networks( RNNs) are employed to dissect time-series data and describe patterns reflective of implicit outfit failures or declination. By prognosticating conservation requirements in advance, serviceability can record interventions more effectively, reduce time-out, and optimize resource allocation. Through case studies and simulations, this exploration demonstrates the efficacity of deep learning models in perfecting prophetic conservation delicacy and trustability in power systems. The integration of AI-driven prophetic conservation holds a pledge for transubstantiating conservation practices, enhancing grid adaptability, and icing sustainable energy forces for unborn demands. © 2024 IEEE.

关键词： Predictive maintenance

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Revolutionizing Potato Farming: A CNN-Powered Approach for Early Blight and Late Blight Detection to Ensure Global Food Security

Revolutionizing Potato Farming: A CNN-Powered Approach for E...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Mahesh Datta Sai Ponnuru Likhitha Amasala School of Computing SRM Institute of Science and Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Amaravathi India

Potato, a crucial global food crop, faces persistent threats from diseases like early blight and late blight, jeopardizing both yields and economic stability. In response, we present an innovative approach using CNN for early disease detection in potato crops. Our objective is to empower farmers with rapid and accurate disease identification, facilitating timely treatments to prevent economic losses and reduce agricultural waste. Our CNN model achieved a remarkable validation accuracy of 99.59%, outperforming conventional methods. With 183,747 trainable parameters, our efficient and accurate model is set to revolutionize agricultural practices. Furthermore, we are deploying our model to Google Cloud and developing a React Native mobile app for potato prediction. This integration aims to provide farmers with real-time disease insights, preventing economic losses and ensuring global food security, thereby enhancing economic stability in the potato farming sector.

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Advancements in Cricket Shot Detection: Integrating Human Pose Estimation and Deep Learning for Automated Analysis

Advancements in Cricket Shot Detection: Integrating Human Po...

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International Conference on computing and Networking Technology (ICCNT)

作者： Ks Sreekar Datta G Narasimha Naidu Rahul Dasari T V Jayakumar Dept. Of Computer Science and Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Amritapuri India Dept. Of Computer Science and Applications Amrita School Of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cricket, a sport loved worldwide, often presents challenges for aspiring players due to limited access to infrastructure and coaching resources. Cricket’s automated learning solutions are still largely unexplored, despite its popularity. By concentrating on the application of cricket shot detection, a crucial component of the game, this research fills this gap. One essential component of computer vision that becomes apparent in deciphering the subtleties of cricket shots is human stance estimate. Pose estimate provides a detailed examination of batting skills and footwork by tracking important areas on the batsman’s body, such as joints and limbs. MoveNet is a significant framework in this domain that has garnered accolades for its real-time performance and lightweight architecture, which enable accurate identification of human poses, which is a crucial requirement in the dynamic world of cricket. This study explores the combination of deep learning techniques and human pose estimate for cricket shot recognition. More precise and reliable cricket shot identification and classification are made possible by the model’s ability to extract complex patterns and features from the input data by utilizing deep learning techniques. Our goal is to automatically identify and classify cricket shots based on players’ minor motions through theoretical investigation, empirical validation, and real-world applications. Through the integration of cutting-edge technologies, such as deep learning, with traditional cricket analysis, our goal is to gain a deeper understanding of the game and discover new avenues for sports analytics, coaching, and broadcasting.

关键词： Deep learning Training Analytical models Accuracy Refining Pose estimation Games Transforms Feature extraction Sports

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Enhanced Layer Extraction for Efficient Plant Disease Classification using Efficientnet B1

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SN computer science 2025年第4期6卷 1-15页

作者： Thakur, Arastu Thakur, Abhilasha Vivek, V. Mahesh, T.R. Krishna, K. Murali Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India Department of Agriculture Marwadi University Gujarat Rajkot India Alliance School of Advanced Computing Alliance University Chandapura - Anekal Main Road Bengaluru Anekal 562106 India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India

Agriculture is crucial for the global economy, providing sustenance and resources for various industries. However, plant diseases threaten crop quality and yield, risking severe economic impacts. Traditional plant disease diagnosis methods, like manual inspection or basic machine learning, are slow, require expertise, and struggle with scalability and adapting to new diseases. This study introduces a deep learning model using convolutional neural networks (cnns) to autonomously detect and classify plant diseases from images, enhancing speed, scalability, and accuracy. We utilize the efficientnetb1 architecture, pretrained on imagenet and fine-tuned on a dataset of bell pepper, potato, and tomato diseases, recognizing 14 diseases plus healthy plants. The model achieves 98.80% accuracy on the test dataset, with high precision, recall, and F1-scores, demonstrating its effectiveness in plant disease diagnosis. This advancement marks a significant step in automating plant disease diagnosis, offering a robust tool for effective disease management in agriculture. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agriculture Automated diagnosis Deep learning Disease management Efficientnetb1 Image classification Plant disease detection Transfer learning

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Minimize the Energy Consumption to Increase the Network Lifetime for Green IoT Environment

Minimize the Energy Consumption to Increase the Network Life...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Dekka, Satish Raju, K. Narasimha Sai, D. Manendra Pallavi, M. Sambana, Bosubabu JNTU-GV Andhra Pradesh Vizianagaram India Andhra University Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Vignan’s Institute of Engineering for Women Visakhapatnam JNTU-GV Andhra Pradesh Vizianagaram India Department of Data Science School of Computing Mohan Babu University Andhra Pradesh Tirupathi India

ISBN: (纸本)9783031513374

One of the most important and complex issues in the IoT context is energy usage. Because the many nodes utilized in IoT contexts are energy-constrained and must operate for long periods of time without human interaction, their batteries quickly run out of power. As a result, nodes must manage their energy effectively. Sensing, communicating, and processing are regarded as the primary functions of IoT sensors. IoT connectivity uses more energy at the node level than at other times. The main motto of this study is to reduce energy consumption and lengthen network life. Although routing also has a major impact on energy usage, the first one deals with employing an energy model, and the second one deals with selecting an effective routing protocol. When picking a protocol, various other performance indicators such as throughput, delay, packet delivery ratio, and packet loss are also considered. The Network Simulator 3 is used as a tool for testing these strategies (NS3). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy Model Green IoT Green networks MANET NS3 Routing

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Chaotic Grey Wolf Optimization for Energy-Efficient Clustering and Routing in Wireless Sensor Networks 2

Chaotic Grey Wolf Optimization for Energy-Efficient Clusteri...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Almusawi, Muntather Ravindran, Gobinath Parameshachari, B.D. Bhasker, B. Lavanya, N.L. The Islamic University Najaf Iraq SR University Department of Civil Engineering Telangana Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India REVA University School of Computing and Information Technology Bengaluru India Sapthagiri College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350317558

Wireless Sensor Networks (WSNs), the imperative challenge of energy efficiency takes center stage. This paper delves into inventive methodologies to combat energy depletion in sensor nodes, introducing the Chaotic Grey Wolf Optimization Algorithm (CGWOA) for optimal Cluster Head (CH) selection in WSNs. An evolution strategy and a mutation operation operator, CGWOA aims to amplify energy efficiency, scalability, and minimize network overhead through streamlined clustering. The outcomes underscore CGWOA's potential as a solution for advancing energy-efficient clustering and routing in WSNs, particularly in optimizing CH selection. The ensuing numerical validation underscores CGWOA's revealing an energy consumption of 1100 J, a throughput ratio of 69,785 kbps, 4900 active nodes, and a network lifetime extension to 8021. This comparative analysis shows the emphasis on problems to overcome existing methods like Particle Swarm Optimization for Energy-Efficient Clustering (PSO-EEC), Adaptive Hybrid Clustering Scheme with Grey Wolf Optimization (AHCS-GWO), Hybrid Optimization for Cluster Head Selection (HOCK). © 2024 IEEE.

关键词： Cluster head Clustering Energy consumption Network Lifetime Wireless senor network

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Short-term speed prediction of urban roads based on multi-source feature fusion 23

Short-term speed prediction of urban roads based on multi-so...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Silin Liao, Zhuhua Liu, Yizhi Yi, Aiping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China

ISBN: (纸本)9781665494571

Short-term traffic forecasting is an important means to prevent urban congestion. In this paper, the urban traffic operation time series data sets are used for modeling, combining with various characteristic variables of weather conditions, air quality index (AQI) and travel time index (TTI). A bidirectional long and short-term memory network framework (MF-BiLSTM) based on multi-source feature fusion was established to predict the average road speed. The comparison experiment selects three benchmark models: ARIMA, LSTM and CNN, and a bidirectional long short-term memory network (BiLSTM) model using only road speed time series data sets, and compares the prediction accuracy of these models under three prediction durations. The results show that MF-BiLSTM has the best prediction performance in terms of prediction error measurement, the prediction accuracy is 2.93%, 1.95%, 2.87%, 0.89% higher than that of ARIMA, LSTM, CNN, and BiLSTM, respectively. In addition, comparing the prediction accuracy of each model under different weather conditions and air pollution levels, MF-BiLSTM has the smallest deviation. © 2021 IEEE.

关键词： Correlation Roads Atmospheric modeling Time series analysis Weather forecasting Predictive models Air pollution

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IoT-Enabled Horticultural Lighting for Optimizing Plant Growth and Agriculture Operations 2

IoT-Enabled Horticultural Lighting for Optimizing Plant Grow...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Srinivasan, S. Lk, Shoba Alavanthar, T. Srinivasan, C. Murugan, S. Sujatha, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India Department of Biomedical Engineering Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350365269

The Internet of Things (IoT) and horticulture lighting systems may improve plant growth and agricultural operations. The variation in the light spectrum, intensities, and durations affect plant physiological systems. The proposed system can dynamically adjust and control lighting settings using real-time sensor data by seamlessly integrating IoT capabilities. These sensors carefully track plant health, ambient conditions, and energy use. This dynamic feedback system allows operators to make informed choices and adjust lighting techniques for optimum growth, yields, and resource use. Plant growth and resource efficiency benefit from light parameter improvement. The connection between IoT and horticulture lighting leads to sustainable agriculture that maximizes agricultural yields and energy efficiency. Moving from static to adaptive lighting systems represents a paradigm change in agriculture, as data-driven decisions enable precision farming. Combining IoT's real-time abilities with horticulture lighting systems promises unique yields, energy savings, and sustainable agriculture practices. © 2024 IEEE.

关键词： Horticulture

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Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities

Feasibility of Federated Learning from Client Databases with...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Wagner, Felix Xu, Wentian Saha, Pramit Liang, Ziyun Whitehouse, Daniel Menon, David Newcombe, Virginia Voets, Natalie Noble, J. Alison Kamnitsas, Konstantinos University of Cambridge Department of Medicine United Kingdom University of Oxford Nuffield Department of Clinical Neurosciences United Kingdom University of Oxford Department of Engineering Science United Kingdom Imperial College London Department of Computing United Kingdom School of Computer Science University of Birmingham United Kingdom

ISBN: (纸本)9798331510831

Segmentation models for brain lesions in MRI are commonly developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment the disease using data with a different set of MRI modalities, nor can they segment other types of diseases. Moreover, this training paradigm prevents a model from using the advantages of learning from heterogeneous databases that may contain scans and segmentation labels for different brain pathologies and diverse sets of MRI modalities. Additionally, the confidentiality of patient data often prevents central data aggregation, necessitating a decentralized approach. Is it feasible to use Federated Learning (FL) to train a single model on client databases that contain scans and labels of different brain pathologies and diverse sets of MRI modalities? We demonstrate promising results by combining appropriate, simple, and practical modifications to the model and training strategy: Designing a model with input channels that cover the whole set of modalities available across clients, training with random modality drop, and exploring the effects of feature normalization methods. Evaluation on 7 brain MRI databases with 5 different diseases shows that this FL framework can train a single model that achieves very promising results in segmenting all disease types seen during training. Importantly, it can segment these diseases in new databases that contain sets of modalities different from those in training clients. These results demonstrate, for the first time, the feasibility and effectiveness of using FL to train a single 3D segmentation model on decentralised data with diverse brain diseases and MRI modalities, a necessary step towards leveraging heterogeneous real-world databases. Code: https://***/FelixWag/FedUniBrain © 2025 IEEE.

关键词： Multi-task learning

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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