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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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Open-source solutions for real-time data retrieval in industrial automation and IoT environments

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International Journal of Internet Manufacturing and Services 2025年第1期11卷 73-92页

作者： Hegde, Seema B. Reddy, Tadigotla Narendra Prasannan, Prasad Manjunath, K.V. Herbert, Mervin A. Rao, Shrikantha S. Department of Computer Science and Engineering Global Academy of Technology Bengaluru560098 India Smart Manufacturing Precision Machine Tools Central Manufacturing Technology Institute Bengaluru22 India Department of Industrial Engineering and Management Siddaganga Institute of Technology Tumkur572103 India Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal India

Digitalisation of the manufacturing industries due to the implementation of the ‘industrial internet of things (IIOT)’ is a key enabler for improved productivity and reliability at a reduced labour cost. The industrial IOT connects all the industrial machines such as PLCs, CNCs, and robots through a robust network. The generated data by these end devices plays a vital role in industrial automation, however acquiring the data from machines specifically legacy machines using various communication protocols is the biggest challenge and costly process, especially for MSMEs. Hence this paper discusses the usage of the open-source framework for real-time data acquisition from industrial machines and its implication in Industry 4.0. The paper implements and validates the possibility of the usage of an open-source framework for data acquisition instead of vendor-specific licensed software using several test cases. The paper also validates and proves ‘Wireshark’ can be a universal open-source solution for data acquisition using any standard communication protocols from various vendor-specific machines. Hence this work provides a novel solution for the digitalisation of the MSME manufacturing industries efficiently at the reduced maintenance cost and improve their productivity. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Cost reduction

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Carbon Dioxide Emission Forecasting using Machine Learning and Time Series Statistical Models 25

Carbon Dioxide Emission Forecasting using Machine Learning a...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Hassan, Tahir Mansour, Khalid Waseemullah, Waseemullah Memon, Muhammad Qasim Ned University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan Arab Open University Faculty of Computer Studies Bahrain Sindh Bhitshah Pakistan

ISBN: (纸本)9798331540012

Climate change is considered a global disaster that has wreaked havoc worldwide. Climate change conditions are primarily driven due to emission of carbon dioxide and other greenhouse gases. Around the globe, several countries are facing serious challenges related to global warming and climate change. The adverse effects of global warming and climate change are recently observed in the form heat waves, floods, extreme rainfall events and drought conditions. The escalating CO2 emission level causes threats of food security and water scarcity. To overcome this, timely and accurate CO2 emissions forecasting is essential for developing effective mitigation strategies in such a manner that CO2 emission impacts can be reduced. The aim of this study is to forecast the total CO2 emissions of upcoming years using various statistical, machine learning models such as ARIMA (Auto-Regressive Integrated Moving Average), SARIMA (Seasonal ARIMA), Linear Regression, and Decision tree regressor. We had forecasted CO2 emission on the basis of influencing factors, which highly contributes in CO2 emission. The performance of the models is validated using multiple evaluation metrics such as MAE, MSE RMSE,MAPE and R2 square. The results show that CO2 emission is highly affected by fossil fuel consumption. In particular, Linear regression performs better among all applied models, with R2 value 0.93 and MAE, RMSE and MAPE values 5.52, 6.47 and 0.03, respectively. Our research delineates the results and insights that will assist concerned authorities, policymakers and environmental agencies. The present study also aim to inform authorities, helping them to better understand the future of CO2 emission trajectories and make timely efforts and interventions to tackle the climate change and global warming challenges. © 2024 IEEE.

关键词： Low emission

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Prediction of segregation characterization based on granular velocity and concentration in rotating drum

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Particuology 2023年第2期73卷 17-25页

作者： Wenzheng Xiu Ra Li Quan Chen Qicheng Sun Vladimir Zivkovic Hui Yang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai200093China State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing100084China School of Engineering Newcastle UniversityNE17RUUnited Kingdom

In a binary granular system composed of two types of particles with different granule sizes and the same density,particle sorting occurs easily during the flow *** segregation pattern structure is mainly affected by the granular velocity and granular concentration in the flow *** paper reports on the experimental velocity and concentration measurement results for spherical particles in a quasi-two-dimensional rotating *** relationship between the granular velocity along the depth direction of the flow layer and granular concentration was established to characterize structures with different degrees of *** corresponding relationships between the granular velocity and concentration and the segregation pattern were further analyzed to improve the theoretical models of segregation(convection-diffusion model and continuous flow model)and provide a reference for granular segregation control in the production process.

关键词： Granular segregation Rotating drum Granular velocity Granular concentration

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Retinal Disease Detection for Low Resource Devices Through Pruned and Quantized CNNs

Retinal Disease Detection for Low Resource Devices Through P...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Gavel, Harish Azad, Chandrashekhar National Institute of Technology Jamshedpur Department of Computer Science and Engineering Jamshedpur India

ISBN: (纸本)9798331529833

Retinal disease diagnosis is one of the crucial step for preventing vision loss, especially in conditions such as Diabetic Retinopathy(DR), Glaucoma, and Age-related Macular Degeneration(AMD). In this paper, we propose an efficient approach for retinal disease classification using lightweight convolutional neural networks (CNNs) deployed on a low-resource device, the Raspberry Pi 4. To address the limitations of resource-constrained hardware, we employ model optimization techniques, including pruning and quantization, to reduce computational requirements while maintaining classification accuracy. The proposed system is evaluated on the Messidor datasets. Our optimized MobileNet and EfficientNetLite models decreased the accuracy scores by 1.5% to 2%, while reducing inference time by over 50% and memory usage by 45%. These results show the feasibility of deploying real-time retinal disease models on edge devices for telemedicine and mobile health in resource-limited settings. © 2025 IEEE.

关键词： Constrained optimization

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Automated Traffic Signaling System for Ambulance Crossing using IOT 15

Automated Traffic Signaling System for Ambulance Crossing us...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pattabiraman, V. Parvathi, R. Jai Ganesh, C. Kaushal, C. Mohnish Kumaar, D. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331300579

The research aims to improve traffic management and response time of emergency vehicles, specifically ambulances, at four-lane traffic junctions. The primary objectives are to detect the presence of emergency vehicles, reduce traffic congestion, and design an efficient traffic management system. The proposed solution combines radio frequency (RF) technology and sound sensing for emergency vehicle detection, along with density-based traffic regulation using ultrasonic sensors. The methods employed include installing RF transmitters in ambulances and RF receivers with sound sensors at traffic junctions, interfaced with an Arduino microcontroller. The project's outcomes are expected to include more efficient traffic flow, faster passage for emergency vehicles, and overall reduced traffic congestion. As a result, the response time of emergency services will be improved, contributing to saving lives and promoting sustainable urban transportation infrastructure. The scope of this project will include traffic management in urban environments, specifically at four-lane traffic junctions. The proposed system has the potential to be scaled up and adapted for larger or more complex traffic networks, as well as integrated with other smart city technologies, enhancing traffic management and emergency response efficiency in various urban settings. © Grenze Scientific Society, 2024.

关键词： Traffic congestion

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Research On Fatigue Driving Detection Based On Deep Learning

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engineering Letters 2025年第2期33卷 348-356页

作者： Zhou, Huinan Dai, Hong University of Science and Technology Liaoning Liaoning AnshanCO 114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning AnshanCO 114051 China

Fatigue driving is one of the main causes of traffic accidents. Effective fatigue driving detection technology can reduce traffic accidents caused by fatigue driving. Traditional fatigue driving detection methods usually use a two-stage detection method, which has the problems of low efficiency and poor accuracy. To solve these problems, this paper designs a single-stage fatigue driving detection model based on separable convolution Road Safety Net (RSNet), which extracts the features in the driver's image or video and classifies these features to determine whether the driver is in an abnormal driving state. RSNet borrows the structure of Swin Transformer and adopts a hierarchical design, which makes the model better able to extract features and classify. Each level is composed of a GDS Layer and multiple RS Blocks. To solve the problem of low efficiency of traditional methods, we use separable convolution in RS Block to replace the traditional convolutional neural network, which reduces the complexity of the model and effectively improves the computational efficiency of the model. Experimental results show that the Top-1 and Top-5 Accuracy of RSNet on the HMDB51 dataset reaches 85 % and 97.35 %, respectively, and the delay is 5.97 ms. Compared with ResNet, EfficientNet, and Swin Transformer, RSNet has higher accuracy and efficiency. In addition, this paper takes the YOLACT algorithm as an example, selects different backbone networks for object detection, and compares the indicators of mean Average Precision (mAP) and Frames Per Second (FPS). Experiments show that RSNet performs better when used as the backbone network for object detection, with mAP reaching 32.4 % and FPS of 39.6 ms. It indicates that RSNet performs well in action recognition tasks. © 2025, International Association of Engineers. All rights reserved.

关键词： Convolutional neural networks

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Design of Neural Network Based Wind Speed Prediction Model Using GWO

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computer Systems science & engineering 2022年第2期40卷 593-606页

作者： R.Kingsy Grace R.Manimegalai Department of Computer Science and Engineering Sri Ramakrishna Engineering CollegeCoimbatore641022India Department of Computer Science and Engineering PSG Institute of Technology and Applied ResearchCoimbatore641062India

The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind *** power is the clean,free and conservative renewable *** is necessary to predict the wind speed,to implement wind power *** paper proposes a new model,named WT-GWO-BPNN,by integrating Wavelet Transform(WT),Back Propagation Neural Network(BPNN)and GreyWolf Optimization(GWO).The wavelet transform is adopted to decompose the original time series data(wind speed)into approximation and detailed ***-BPNN is applied to predict the wind *** is used to optimize the parameters of back propagation neural network and to improve the convergence *** work uses wind power data of six months with 25,086 data points to test and verify the performance of the proposed *** proposed work,WT-GWO-BPNN,predicts the wind speed using a three-step procedure and provides better *** Absolute Error(MAE),Mean Squared Error(MSE),Mean absolute percentage error(MAPE)and Root mean squared error(RMSE)are calculated to validate the performance of the proposed *** results demonstrate that the proposed model has better performance when compared to other methods in the literature.

关键词： Wind speed wavelet transform back propagation neural network grey wolf optimization time series

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Improving the Extraction of Aspect Terms from Customer Reviews using MSRL-NET and BERT 2

Improving the Extraction of Aspect Terms from Customer Revie...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Gupta, Shivani Rakhesh, K. Velmurugan, S. Brindha, T.V. Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India Shri Jagdishprasad Jhabarmal Tibrewala University Department of Computer Science and Engineering Rajasthan India Rmd Engineering Colleg Department of Information and Technology Kavarapettai Chennai India Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350384277

Aspect Term Extraction (ATE) is a significant task in natural language processing that determinations to normalize and extract particular aspects or properties of things referenced in text. This technique is essential in a dissimilarity of applications, with sentiment analysis, opinion mining, and review summarization. The determination of aspect term extraction is to automatically label and classify words or phrases that express aspects of a specific entity, which could be products, services, or any topic under discussion. In ATE, static word embeddings are required to capture dynamic word meanings properly. We designate a original method for dealing with the intricacies of this difficult and enhancing sentiment analysis accuracy, which uses a Multi-level Semantic Relation-enhanced Learning Network (MSRL-Net) and Bidirectional Encoder Representations from Transformers (BERT). In this paper, we suggest a synergistic framework that uses MSRL-Nets to develop the resolution and clarity of text representations, subsequent by BERT's input format, which is limited to a collection of words and cannot include more context knowledge. MSRL-Net and BERT addresses aspect extraction, sentiment polarity identification, and context-dependent sentiment comprehension by leveraging MSRL-Net's capabilities to create high-resolution text representation and BERT's contextualized language considerate. The combination of MSRL-Nets with BERT designates a promising future path for aspect-based sentiment analysis, with applications such as customer feedback analysis, market research, and social media sentiment monitoring. The experimental evaluation revealed that the proposed technique outperforms the SemEval dataset regarding precision, F1 score, and accuracy. © 2024 IEEE.

关键词： Semantics

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A CNN and HBA Based Approach for Grape Disease Identification 4

A CNN and HBA Based Approach for Grape Disease Identificatio...

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

作者： Vishnuvarthan, K. Renugadevi, R. Santhi, S. Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Grapes are among the most extensively cultivated fruit crops globally, with over 10,000 varieties. They serve as a significant economic resource for many nations due to their diverse applications, including wine production, fresh juice consumption, raisin production, and juice manufacturing. However, grape diseases pose a severe threat to the viticulture industry, leading to substantial financial losses and a decline in wine quality. Manual evaluation is the foundation of traditional disease detection techniques, which are error-prone in addition to saving time. This highlights the need for an automated system capable of analyzing plant health, identifying diseases, and addressing undernourishment efficiently and reliably. Accurate disease diagnosis and timely control measures are crucial for mitigating these challenges. Many scholars have studied various algorithms such as CNN, SVM, Neural Networks, Random Forests, etc., yet each has disadvantages. So we have proposed an automated deep learning-based approach that integrates the Honey Badger Algorithm (HBA) with Convolutional Neural Networks (CNN) for the detection of grape diseases. A phenomenal 95% accuracy rate was attained by the prototype after it was trained on a dataset of 10,000 photos. Its performance was evaluated across various grape varieties and environmental conditions, demonstrating its robustness and adaptability. © 2025 IEEE.

关键词： Wine

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