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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mihir, D.V.S. Pavan, Baramkula Vishnu Datta Aralaguppi, Yogen Avinash Nair, Pranav Biju Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Monitoring of environmental parameters in areas with a sparsely populated and less infrastructure is quite challenging due to issues such as high power consumption, limited range for transmission, and lack of scalability. The innovation of the system proposed here deals with these problems using an IoT-based monitoring system, using LoRa technology and NodeMCU microcontrollers. It can collect and transmit real-time data on temperature, humidity, air quality, and sound levels efficiently. Key outcomes of this research work are reliable range communication up to 2 km under line-of-sight conditions, minimal power consumption, and robust data integrity through sensor calibration and error-checking mechanisms. The solution, which is scalable and low-cost, is proven feasible in real-world applications, particularly in remote and underserved areas, and promises a great future enhancement, including range extension and energy harvesting. © 2025 IEEE.

关键词： Environmental monitoring

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Simulation and study of maximum power point tracking for rim-driven tidal current energy power generation systems

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ENERGY REPORTS 2023年 9卷 792-801页

作者： Ouyang, Yani Zhao, Wei Wang, Haifeng Chinese Acad Sci Inst Elect Engn Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

This paper presents a maximum power point tracking (MPPT) control algorithm based on an intelligent reinforcement learning. The proposed model-free Q-learning algorithm realizes the online learning of the control algorithm of the tidal power generation system by updating the action values stored in the Q-table. By learning the optimal rotor speed-output power curve, the algorithm fits the optimal generator curve and applies the optimal P-e -omega(r) curve to the optimal control method of the tidal power generation systems. (c) 2023 Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Tidal current energy power generation systems Rim-driven MPPT Q-learning

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A Meta-learning Based Resource Allocation Algorithm in Vehicular Edge Computing Networks 3

A Meta-Learning Based Resource Allocation Algorithm in Vehic...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Mo, Wenshuai Zhuang, Lei Xu, Zexi Song, Yanrui Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350380989

With the rapid development of intelligent transportation systems, in-vehicle edge computing networks serve as key infrastructures to provide strong support for vehicle communication and coordination. However, the traditional resource allocation methods face many challenges due to the changing network topology, the uncertainty of data load, and the spatio-temporal relationship between traveling vehicles. Therefore, in this paper, a meta-learning based resource allocation algorithm is designed to overcome these problems. By introducing meta-learning, the aim is to enable the system to quickly adapt to different scenarios and requirements so as to improve the resource utilization. The algorithm not only fully considers the network performance index, but also comprehensively solves the real-time and reliability requirements to achieve comprehensive optimization. The resource allocation algorithm based on meta-learning designed in this paper simulates different car networking tasks during the training process, so that the network learns a more robust resource allocation strategy, which not only fully takes into account the network performance index, but also comprehensively solves the real-time and reliability requirements and achieves comprehensive optimization. Through a large number of simulation experiments and comparisons in Telematics, the algorithm proposed in this paper outperforms the current state-of-the-art algorithms by 20% in terms of request acceptance rate, and leads by 15% in terms of overall gain. The experimental results show that the algorithm in this paper outperforms the current state-of-the-art algorithms in solving the resource allocation problem for car networking applications in edge computing networks. © 2024 IEEE.

关键词： edge computing network Internet of Vehicles meta-learning NFV Resource allocation

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Performance Analysis and Improvement of Lithium Battery Charging System Based on Deep learning Algorithms 3

Performance Analysis and Improvement of Lithium Battery Char...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Lv, Hongtu Zhang, Hongyao Li, Gongju College Of Intelligent Systems Science And Engineering Hubei Minzu University Hubei Enshi China Jiangsu Fuyuan Technology Jiangsu Changzhou China Anhui Chery Ruifu Special Vehicle Technology Anhui Wuhu China

ISBN: (纸本)9798350318609

The research on traditional lithium battery charging systems has problems such as model simplification, insufficient data, insufficient accuracy, and poor real-time performance. Simplified electrochemical models cannot accurately reflect the complexity of actual systems, and limited experimental data limits the understanding of system behavior. Neglecting influencing factors leads to low prediction accuracy and a lack of real-time monitoring mechanisms to cope with dynamic changes in the system. These limitations affect the comprehensive analysis and optimization of system performance. This paper designed a Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU) based on convolutional neural networks to extract local and global features from time series data, and combined it with time series prediction to predict charging temperature. In experimental verification, the quadratic mean error of the CNN-GRU algorithm model is 0.011, and the mean absolute deviation is 0.018. The error between the predicted electricity price and the actual value is within 2%. © 2024 IEEE.

关键词： learning algorithms

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intelligent data Pipelines for Predictive Maintenance in Saas products 3

Intelligent Data Pipelines for Predictive Maintenance in Saa...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Dubey, Shubham Priyadarshni, Sweta Covetrus Inc NJ United States Deloitte Uttar Pradesh India

ISBN: (纸本)9798331532420

In the rapid pace of today’s digital world, one thing that is most important is to make sure SaaS works without any setbacks. Downtime or performance may result in less customer satisfaction and huge revenue losses. Predictive maintenance allows one to undertake an immediate, proactive strategy to anticipate events and resolve problems well before the users are affected. However, its implementation in SaaS environments has complexities of its own because these are built up with a very distributed nature, and the volume of data is huge in these *** paper will review how intelligent data pipelines can help in predictive maintenance for SaaS products. By using smart data pipelines to intelligently gather, process, and analyze real-time data from all constituent parts of the SaaS infrastructure, patterns and anomalies may be spotted that predict an impending issue. In this paper, we present a design and implementation of scalable data pipelines with machine learning algorithms on logs, performance metrics, and user behavior data. We demonstrate, through case studies and experimental results, how such intelligent data pipelines reduce downtime, improve system reliability, and enhance customer satisfaction. Application of intelligent data pipes in SaaS operations marks one of the most important steps toward more resilient and effective software services. © 2024 IEEE.

关键词： Predictive maintenance

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Plant Diseases Detection Using Deep learning and Machine Vision 3

Plant Diseases Detection Using Deep Learning and Machine Vis...

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3rd IEEE international conference on intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Singh, Nidhi School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9798350361186

This research delves into deep learning and machine vision applications for plant leaf disease detection in agricultural settings, focusing on farm village datasets. Utilizing a blend of authentic farm village data and synthetic data from Generative Adversarial Networks (GANs), three advanced convolutional neural network (CNN) models VGG16, ResNet50, and InceptionNet V3 are employed with transfer learning. Leveraging transfer learning enhances model performance through fine-tuning pre-trained networks. The study systematically evaluates models based on key metrics like accuracy, precision, recall, and F1 score. Results showcase the methodology's robustness, with ResNet50 emerging as the leading performer at 83.23%, contributing to precision agriculture's advancements with promising implications for sustainable farming and crop yield optimization. © 2024 IEEE.

关键词： Generative adversarial networks

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Kannada Continuous Speech Recognition Using Deep learning 3rd

Kannada Continuous Speech Recognition Using Deep Learning

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3rd international conference on Advanced Network Technologies and intelligent Computing (ANTIC)

作者： Paul, Shubhojeet Bhattacharjee, Vandana Saha, Sujan Kumar Birla Inst Technol Ranchi Mesra India Natl Inst Technol Durgapur India

ISBN: (纸本)9783031640667;9783031640674

Automatic Speech Recognition (ASR) is a prevalent approach for attaining human-machine interaction by enabling machines to transcribe speech data. We propose a Continuous Speech Recognition model in the Kannada language using deep learning techniques such as Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (Bi-GRU). The model was trained and validated using 100 and 20 h of data, respectively. The experiment has generated encouraging results with a Character Error Rate (CER) of 15.62% and a Word Error Rate of 34.47% (WER).

关键词： Kannada Automatic Speech Recognition Continuous Speech Recognition Deep learning

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Deep Reinforcement learning for Dynamic VNF Profiling and Resource Optimization 3

Deep Reinforcement Learning for Dynamic VNF Profiling and Re...

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3rd international conference on intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Jaisudthi, Pratchaya Phungkoed, Natthakran Srisuk, Kanyaphak Phueaknumpol, Vasupon Sridee, Pachara Threerapat Computer Engineering Rambhai Barni Rajabhat University Chanthaburi Thailand Computer Network Technology Rambhai Barni Rajabhat University Chanthaburi Thailand

ISBN: (纸本)9798331529222

This paper presents an AI-driven framework for multi-objective Virtual Network Function (VNF) profiling, utilizing deep reinforcement learning (DRL) to optimize key resources such as CPU, memory (MEM), and link capacity (LC) in dynamic network environments for next-generation networks. The proposed DRL model was tested in two scenarios: vFW VNF and Snort Inline Mode VNF, to evaluate its effectiveness in resource optimization. Compared to traditional reinforcement learning (RL), DRL demonstrated approximately a 10% reduction in CPU usage across both scenarios. Additionally, DRL achieved an 8% reduction in memory usage for the vFW VNF scenario and optimized LC allocation by approximately 23% for Snort Inline Mode VNF and 8% for vFW VNF. These results highlight DRL’s superior adaptability and efficiency in complex, fluctuating environments, making it a promising approach for real-time VNF resource management in next-generation networks. ©2024 IEEE.

关键词： Deep reinforcement learning

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Joint Extraction of Entity Relationships in Walnut Disease and Pest Based on Chinese NLP Models 3

Joint Extraction of Entity Relationships in Walnut Disease a...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Zhu, Ruiliang Song, Xiangshuai Zhang, Hao Cai, Xuli School of Computer and Electronic Information Guangxi University Nanning Guangxi China

ISBN: (纸本)9798350380989

This study addresses the limited application of deep learning techniques in the field of walnut disease and pest and the challenges posed by complex relationships and diverse entity types in this domain. We propose a deep learning-based method for constructing a knowledge graph in the walnut disease and pest domain, incorporating ontologies to establish a conceptual model for the disease and pest knowledge graph. To overcome issues such as relationship overlap (e.g., one-to-many, many-to-many) and loss of relationship chains, we introduce a novel labeling scheme called 'based on ontology binding BIESO (Begin-Inside-End-Single-Other)' that directly models triplets. By employing a label matching algorithm, we obtain triplet data. We train and predict on the dataset using an end-to-end model consisting of Bidirectional Encoder Representations from Transformers (BERT), Bi-directional Gate Recurrent Unit (BiGRU), and Conditional Random Field (CRF). Experimental results show an F1 score of 75.79%, outperforming models such as BERT-BiLSTM-CRF and word2vec-BiGRU-CRF. We semiautomatically extract unstructured knowledge and store the extracted triplets in a Neo4j graph database, enabling visualization of the knowledge. The research methodology of this knowledge graph can serve as a reference for constructing knowledge graphs in walnut agriculture and developing intelligent question-answering systems for walnut disease and pest. © 2024 IEEE.

关键词： BERTBiGRU-CRF deep learning knowledge graph ontology Walnut disease and pest

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Real-Time Pedestrian Detection and Tracking in Dynamic Urban Environments Using YOLO for intelligent Video Systems 3

Real-Time Pedestrian Detection and Tracking in Dynamic Urban...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Aatrey Anand, V. Gautham, B. Sangeetha, K. Rajalakshmi Engineering College Department of AIML India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523893

This paper describes a comprehensive system for real-time pedestrian detection and tracking, which is intended to suit the needs of intelligent video surveillance in dynamic metropolitan areas. The main detection component makes use of the most recent version of the YOLO (You Only Look Once) algorithm, which is noted for its speed and accuracy in identifying pedestrians in complicated settings. The CrowdHuman Pedestrain dataset, a difficult dataset that replicates actual urban situations where a variety of environments, fluctuating lighting, and occlusions provide major obstacles, is used to benchmark the system. Our findings show that the system can be used for intelligent traffic management, public safety, and urban surveillance applications by improving both detection accuracy and tracking resilience while preserving real-time performance. This work contributes to the growing field of intelligent video analytics by combining cutting-edge deep learning techniques with practical tracking and interpretability solutions. © 2025 IEEE.

关键词： Deep learning

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