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Intelligent traffic prediction system using hybrid convolutional neural networks for smart cities

Multimedia Tools and Applications 2024年 1-19页

作者： J, Jeba Sonia G, Arun Kumar kumar, ERajesh Raju, K. Narasimha Sudha, V. Kshirsagar, Pravin R. Tirth, Vineet Rajaram, A. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Uttar Pradesh Noida201301 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Andhra Pradesh Visakhapatnam530048 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Mechanical Engineering Department Centre for Engineering and Technology Innovations College of Engineering King Khalid University Asir Abha61421 Saudi Arabia Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India

Intelligent Traffic Management Systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffic prediction is essential to avoid congestion, yet existing systems often struggle with high delays and insufficient accuracy. This paper introduces an innovative ITMS based on a Hybrid Convolutional Neural Network (HCNN) to overcome these challenges. The proposed system integrates numerous smart devices, including smart vehicles, with an edge computing framework where the HCNN model is deployed. The HCNN combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and reduce latency. Experimental results demonstrate that the HCNN model achieves up to 95% prediction accuracy while reducing delays by up to 50% compared to conventional methods. These improvements are significant for smart city applications, ensuring timely and precise traffic management. The study validates the effectiveness of the HCNN model in improving traffic prediction and proposes its potential for broader smart city implementation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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A critical review on techno-economic analysis of hybrid renewable energy resources-based microgrids

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Journal of engineering and Applied Science 2023年第1期70卷 148页

作者： Manas, Munish Sharma, Shivi Reddy, K. Shashidhar Srivastava, Abhinav Department of Electrical Engineering School of Engineering and Technology Central University of Haryana Mahendragarh India Department of Computer Science and Engineering Jain University Bangalore India Department of Electrical and Electronics Engineering CVR College of Engineering Hyderabad India

Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings. © 2023, The Author(s).

关键词： Renewable energy resources

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Semantics-Aware Dual Graph Convolutional Networks for Argument Pair Extraction 30

Semantics-Aware Dual Graph Convolutional Networks for Argume...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Guan, Minzhao Qiu, Zhixun Li, Fenghuan Xue, Yun School of Electronics and Information Engineering South China Normal University China School of Computer Science and Technology Guangdong University of Technology China

ISBN: (纸本)9782493814104

Argument pair extraction (APE) is a task that aims to extract interactive argument pairs from two argument passages. Generally, existing works focus on either simple argument interaction or task form conversion, instead of thorough deep-level feature exploitation of argument pairs. To address this issue, a Semantics-Aware Dual Graph Convolutional Networks (SADGCN) is proposed for APE. Specifically, the co-occurring word graph is designed to tackle the lexical and semantic relevance of arguments with a pre-trained Rouge-guided Transformer (ROT). Considering the topic relevance in argument pairs, a topic graph is constructed by the neural topic model to leverage the topic information of argument passages. The two graphs are fused via a gating mechanism, which contributes to the extraction of argument pairs. Experimental results indicate that our approach achieves the state-of-the-art performance. The performance on F1 score is significantly improved by 6.56% against the existing best alternative. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Extraction

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Path Planning for Self-driving Vehicles using Metaverse in 6G era with AI-enabled Networks 18

Path Planning for Self-driving Vehicles using Metaverse in 6...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Choudhary, Rachit Prakash, Rahul Chaudhary, Rajat Budhiraja, Ishan Choudhary, Amit Sir Chhotu Ram Institute of Engineering and Technology Electronics and Communication Uttar Pradesh Meerut India Bennett University School of Computer Science and Engineering Technology Greater Noida India Kth Royal Institute of Technology Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350391725

Self-driving vehicles are the future of technology which is having the potential to reshape mobility by improving the safety, efficiency, and accessibility of transportation system in 6G networks. Critical task like safety is a crucial factor which is included in the planning of motions of the vehicle through an environment where other vehicles and other objects are part of analysis based on feedback control. This paper mainly focuses on decision-making for autonomous driving, specifically on lane change decisions by using deep reinforcement learning (DRL) in metaverse environment. The challenges of the lane change and path planning addresses the importance of considering both longitudinal speed and lateral lane changes in self-driving vehicle scenarios. The study defines the actions for the autonomous agent like either to stay in the current lane otherwise, changing lanes to the left and right. Thus, safety and efficiency are key concerns in the decision-making process based on rewards and penalties defined to encourage safe driving behavior. The paper limits the self-driving vehicle to drive in the three right lanes of the road to ensure safety and adherence to lane regulations. The goal is to drive as fast as possible within the speed limits, avoid collisions, and minimize unnecessary lane changes in self-driving robots environment. This paper implements longitudinal speed control through rule-based methods while focusing on the lane change decision-making process using reinforcement learning algorithms. Finally, the performance evaluation is performed on Matlab and Simulink to compute the path followed by the proposed model. © 2024 IEEE.

关键词： Motion planning

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Reconstructing the Image Stitching Pipeline: Integrating Fusion and Rectangling into a Unified Inpainting Model 38

Reconstructing the Image Stitching Pipeline: Integrating Fus...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xie, Ziqi Zhao, Weidong Liu, Xianhui Zhao, Jian Jia, Ning College of Computer Science and Technology Tongji University China College of Electronics and Information Engineering Tongji University China

Deep learning-based image stitching pipelines are typically divided into three cascading stages: registration, fusion, and rectangling. Each stage requires its own network training and is tightly coupled to the others, leading to error propagation and posing significant challenges to parameter tuning and system stability. This paper proposes the Simple and Robust Stitcher (SRStitcher), which revolutionizes the image stitching pipeline by simplifying the fusion and rectangling stages into a unified inpainting model, requiring no model training or fine-tuning. We reformulate the problem definitions of the fusion and rectangling stages and demonstrate that they can be effectively integrated into an inpainting task. Furthermore, we design the weighted masks to guide the reverse process in a pre-trained large-scale diffusion model, implementing this integrated inpainting task in a single inference. Through extensive experimentation, we verify the interpretability and generalization capabilities of this unified model, demonstrating that SRStitcher outperforms state-of-the-art methods in both performance and stability. Code: https://***/yayoyo66/SRStitcher. © 2024 Neural information processing systems foundation. All rights reserved.

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Topology, Size, and Shape Optimization in Civil engineering Structures: A Review

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computer Modeling in engineering & Sciences 2025年第2期142卷 933-971页

作者： Ahmed Manguri Hogr Hassan Najmadeen Saeed Robert Jankowski Faculty of Civil and Environmental Engineering Gdansk University of TechnologyGdansk80-223Poland Department of Computer Science ModelingElectronics and Systems EngineeringUniversity of CalabriaCosenza87036Italy Civil Engineering Department University of RaparinRania46012Iraq Civil Engineering Department Tishk International UniversityErbil44001Iraq

The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering *** optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural *** design approaches aim to reduce the built environment’s energy use and carbon *** comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these *** trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were *** review also discusses emerging technologies,such as machine learning applications with different optimization *** of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and *** techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure *** and non-linear structures,including geometric and material nonlinearity,are *** role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.

关键词： Structural optimization topology optimization size optimization shape optimization multi-objective optimization

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Improving Tomato Disease Diagnosis Through ResNet50: An Innovative Approach 4

Improving Tomato Disease Diagnosis Through ResNet50: An Inno...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Dhamotharan, R. Raj, J. Relin Francis Krishnan, R. Santhana Kavitha, S. Soundiraraj, N. Kumar, V. Vinoth Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331540364

Tomato crops are vulnerable to numerous diseases that can severely affect both yield and quality. Early and precise identification of these diseases is indispensable for implementing active management and intervention measures. This research introduces an advanced approach to tomato disease detection by employing the ResNet50 architecture, pretrained on the ImageNet dataset. Through transfer learning, the ResNet50 model leverages its pre-learned features to classify tomato plant images affected by diseases such as Tomato Mosaic Virus (ToMV), Bacterial Spot, Blossom End Rot, Early Blight, and Late Blight. The ResNet50 model is fine-tuned with custom classification layers tailored to the specific diseases, allowing it to achieve high accuracy in disease detection. Preliminary results indicate that the ResNet50 model significantly outperforms traditional machine learning approaches, demonstrating its efficacy in accurately diagnosing tomato diseases. This study provides an effective solution for improving disease detection in tomatoes and highlights the transformative role of deep learning in advancing agricultural practices, fostering sustainability, and enhancing productivity. © 2024 IEEE.

关键词： Fruits

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Transformer Internal and Inrush Current Fault Detection Using Machine Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 153-168页

作者： R.Vidhya P.Vanaja Ranjan N.R.Shanker Department of Electronics and Communication Engineering Loyola-ICAM College of Engineering and TechnologyChennai600034India Embedded System Technologies Department of Electrical and Electronics EngineeringCollege of EngineeringGuindyChennai 600025India Department of Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringChennai 600055India

Preventive maintenance in the transformer is performed through a dif-ferential relay protection system,and it protects the transformer from internal and external ***,the Current Transformer(CT)in the differential protec-tion system mal-operates during inrush *** saturates due to magnetizing inrush currents and causes false tripping of the differential ***,iden-tiﬁcation of tripping in protection relay either due to inrush current or internal faults needs to be *** the above problem,continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the trip-ping in relay due to inrush or internal *** transformer’s internal fault leads to high breathing process in the transformer breather,never for inrush *** inrush currents,CT temperature is *** monitoring of breather and CT of the transformer through thermal imaging and radiometric pix-els detect the causes of CT saturation and differentiates *** wavelet threshold image analytics(HWT-IA)based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95%for identiﬁcation of the false tripping of differential protection system of transformer.

关键词： Wavelet threshold inrush transformer breather current transformer thermal image

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Detection of Cardiovascular Activities of Multiple Patients using MIMO mmWave Radar

Detection of Cardiovascular Activities of Multiple Patients ...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Jena, Paramananda Sahu, Kedar Nath Behera, Sagarika Electronics and Radar Development Establishment DRDO Bengaluru India Dept. of Electronics and Communication Engineering Stanley College of Engineering and Technology for Women Hyderabad India Dept. of Computer Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (纸本)9798350379693

This paper presents the detection and analysis of cardiovascular activities for localisation of multiple patients using Doppler signals associated with their chest wall motion, heart beat and sleep disorder. The modeling of the vital signs has been carried out considering the vibrating surface of human body illuminated by a radar. The main challenge of a medical radar is the noncontact diagnosis of multiple people. MIMO radar has enormous potential in two areas: micro Doppler detection and fine angular resolution. In this work, MIMO radar is designed and analysis of micro Doppler due to (i) chest movement, (ii) heartbeat and (iii) tidal volume of patients in their sleeping conditions has been carried out using MATLAB models. © 2024 IEEE.

关键词： Doppler radar

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FreqEmo: A Twitter Dataset Comprising Users' Tweeting Frequency and Emotions

FreqEmo: A Twitter Dataset Comprising Users' Tweeting Freque...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Agrawal, Aryan Arora, Anshika Chakraborty, Pinaki Netaji Subhas University of Technology Department of Electronics and Communication Engineering New Delhi India School of Computer Science Engineering and Technology Bennett University Greater Noida India Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350379716

Social media is now indispensable, used by all social, economic, age, and demographic groups. It provides extensive user-generated data, offering insights into users' mental states. This paper introduces a novel Twitter dataset comprising users' tweeting frequency and emotions shared. The dataset is based on Tweets collected from the USA and India during a 60-day period using adjectives representing emotions. Further, for frequently tweeting users, NRC emotion lexicon has been implemented which extracted sentiment and emotion scores of the users. The final dataset includes users' tweeting patterns along with the distribution of emotions and sentiments in their tweets. This dataset has been made publicly available and this project aims to help researchers develop models to assess users' emotional states based on social media activity without self-reporting, with applications in correlating usage patterns with well-being, clustering users by emotions and tweeting patterns, and analyzing temporal, spatial, and demographic trends. To provide baseline performance, k-means clustering is implemented in this study to cluster the users with similar usage patterns. Clusters are analyzed to determine the types of emotions and sentiments shared by users within each cluster. Based on the analysis, it is concluded that as Twitter usage increases within the clusters, the proportion of users dominated by negative sentiment also rises significantly, peaking at 41.67%. Additionally, the percentage of users whose predominant emotion is joy decreases with increased usage, reaching a low of 8.33% as opposed to the peak value of 69.44% in the group characterized by least usage, while the percentage of users dominated by anger and disgust grows to 17.24% and 4.17% as their usage intensifies. © 2024 IEEE.

关键词： Tweets

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