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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Singh, Shubhanshi Tyagi, Neha Amity School of Engineering and Technology Dept. Computer Science and Engineering Noida India

ISBN: (纸本)9798350353648

Weather forecasting predicts atmospheric conditions at a particular time and place. It is an important task with a wide range of applications, including agriculture, transportation, and disaster prevention. Traditional weather forecasting methods rely on physical models of the atmosphere, which can be complex and computationally expensive. In recent years, deep learning techniques are effective for weather forecasting. Deep learning models can learn complex patterns from data, which can be used to predict future weather conditions accurately. This project aims to create a deep-learning model for forecasting weather. A huge dataset of historical meteorological data, including radar, satellite, and surface measurements, will be used to train the model. Numerous meteorological factors, such as temperature, precipitation, and wind speed, will be predicted by the model. We will compare the deep learning model's performance with conventional weather forecasting techniques. Creating a deep learning model that is more accurate and effective than current approaches is the project's main objective. The initiative may result in improved weather forecasts that are prompt and accurate, better agricultural, transportation, and disaster prevention decision-making, and less money lost because of weather-related incidents. Possible obstacles for the project are gathering and purifying a sizable dataset of meteorological data, using this data to train a deep learning model, and then comparing the model's output with more conventional techniques. © 2024 IEEE.

关键词： Wind forecasting

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FORECASTING ELECTRICITY PRICES FOR INTRADAY MARKETS USING MACHINE LEARNING 14

FORECASTING ELECTRICITY PRICES FOR INTRADAY MARKETS USING MA...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Kouveliotis-Lysikatos, Iasonas Kotsias, Panagiotis-Christos Amelin, Mikael School of Electrical and Computer Engineering University of Peloponnese Patras Greece School of Electrical and Computer Engineering KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781837242689

This paper studies the problem of forecasting electricity prices in continuous short-term electricity markets, specifically focusing on the intraday volume-weighted average price of hourly products in the last three hours of trading. Two state-of-the-art recurrent neural network architectures, namely the Temporal Fusion Transformer and the DeepAR network, are compared against well-established statistical models, such as the Linear Regression-LR, ARX, and SARIMAX models, concerning their forecast accuracy. Historical electricity market and grid data from European Energy Exchanges were used to create a forecasting dataset and train and compare five different model structures stemming from traditional statistical methods or contemporary deep learning-based counterparts. © The Institution of engineering & technology 2024.

关键词： ELECTRICITY TRADING INTRADAY MARKET MACHINE LEARNING PRICE FORECASTING

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Study of Electrical Characteristics for Dual-Gate TFTs With Asymmetric Defect Distributions and Gate Work Functions

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IEEE Transactions on Electron Devices 2023年第6期70卷 3390-3393页

作者： Hsu, Chih-Chieh Li, Jin-Xian Huang, Po-Cheng Jhang, Wun-Ciang Joodaki, Mojtaba National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Graduate School of Electronic Engineering Yunlin640301 Taiwan Constructor University School of Computer Science and Engineering Bremen28759 Germany

Combined effects of asymmetric defect distributions and asymmetric gate work functions (WFs) on the performances of self-aligned dual-gate poly-Si TFTs are investigated. Normally, small grains with plentiful grain boundaries (GBs) or other structural defects appear at different positions of the poly-Si film, which is dependent on the growth process of the film. Subgap states of acceptor-like tail, acceptor-like deep-level, donor-like tail, and donor-like deep-level states are used to emulate the defects. Two sets of density of states (DOS) are employed. We find that defects at different positions of the source-side and drain-side channels exhibit different influences on TFT performance and the influences are dependent on the WFs of the gates. TFTs with a higher gate WF can have a higher tolerance to the depth of the defect region. Besides the electrical characteristics, the combined effects of defects and gate WFs on current density distributions and electric field distributions in the channel regions are explored. The performance variations caused by the asymmetric defects along with asymmetric gate WFs can be explained. © 1963-2012 IEEE.

关键词： Thin film transistors

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Broad Siamese Network for Facial Beauty Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5786-5800页

作者： Li, Yikai Zhang, Tong Chen, C. L. Philip South China University of Technology School of Computer Sciences and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China

Facial beauty prediction (FBP) aims to automatically predict beauty scores of facial images according to human perception. Usually, facial images contain lots of information irrelevant to facial beauty, such as information about pose, emotion, and illumination, which interferes with the prediction of facial beauty. To overcome interferences, we develop a broad Siamese network (BSN) to focus more on the task of beauty prediction. Specifically, BSN consists mainly of three components: a multitask Siamese network (MTSN), a multilayer attention (MLA) module, and a broad representation learning (BRL) module. First, MTSN is proposed with different tasks about facial beauty to fully mine knowledge about attractiveness and guide the network to neglect interference information. In the subnetwork of MTSN, the MLA module is proposed to focus more on salient features about facial beauty and reduce the impact of interference information. Then, the BRL module based on broad learning system (BLS) is developed to learn discriminative features with the guidance of beauty scores. It further releases facial features from the impact of interference information. Comparisons with state-of-the-art methods demonstrate the effectiveness of BSN. © 2024 IEEE.

关键词： Forecasting

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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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ACbot:an IIoT platform for industrial robots

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Frontiers of computer Science 2025年第4期19卷 13-27页

作者： Rui WANG Xudong MOU Tianyu WO Mingyang ZHANG Yuxin LIU Tiejun WANG Pin LIU Jihong YAN Xudong LIU School of Computer Science and Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100194China School of Software Beihang UniversityBeijing 100191China School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Engineering China University of GeosciencesBeijing 100083China

As the application of Industrial Robots(IRs)scales and related participants increase,the demands for intelligent Operation and Maintenance(O&M)and multi-tenant collaboration *** methods could no longer cover the requirements,while the Industrial Internet of Things(IIoT)has been considered a promising ***,there’s a lack of IIoT platforms dedicated to IR O&M,including IR maintenance,process optimization,and knowledge *** this context,this paper puts forward the multi-tenant-oriented ACbot platform,which attempts to provide the first holistic IIoT-based solution for O&M of *** on an information model designed for the IR field,ACbot has implemented an application architecture with resource and microservice management across the cloud and multiple *** this basis,we develop four vital applications including real-time monitoring,health management,process optimization,and knowledge *** have deployed the ACbot platform in real-world scenarios that contain various participants,types of IRs,and *** date,ACbot has been accessed by 10 organizations and managed 60 industrial robots,demonstrating that the platform fulfills our ***,the application results also showcase its robustness,versatility,and adaptability for developing and hosting intelligent robot applications.

关键词： IIoT platform industrial robots cloud-edge collaboration intelligent applications

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Enhancing Traffic Control Strategies through Dynamic Simulation and Reinforcement Learning 3

Enhancing Traffic Control Strategies through Dynamic Simulat...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sebastian, Alona Mary Athulram, K.R. Michael, Colin Sunil, Diya Anna Preetha, K.G. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798350375190

In urban areas, traffic congestion is a major problem causing increased travel times, higher consumption of fuel, and environmental pollution. The traditional traffic control systems cannot adapt to dynamic traffic conditions, causing inefficient traffic flow and inefficient use of road infrastructure. In this paper, we propose a novel method to enhance traffic management strategies through dynamic simulation and reinforcement learning (RL). A simulation framework is presented that models the complex interactions among vehicles, traffic signals, and infrastructure in a realistic urban environment. Within this framework, RL agents are deployed to learn adaptive traffic control policies that minimize congestion and optimize traffic flow. This approach uses the ability of RL to continuously learn and adjust to shifting traffic circumstances, which results in improved efficiency and responsiveness compared to static or rule-based control systems. Finally, evaluate the effectiveness of the approach through extensive simulations and demonstrate its capability to significantly reduce congestion, decrease travel times, decrease average waiting time and enhance overall traffic management. By integrating dynamic simulation and RL-based control strategies, the proposed approach offers a guaranteed solution for addressing the challenges of urban traffic congestion and advancing the field of intelligent transportation systems. © 2024 IEEE.

关键词： Reinforcement learning

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A novel dominating set and centrality based graph convolutional network for node classification

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Multimedia Tools and Applications 2025年第14期84卷 12979-13004页

作者： Garg, Neeraj Garg, Sneha Jadeja, Mahipal Meena, Yogesh Kumar Gopalani, Dinesh Chauhan, Ganpat Singh Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Information Technology School of IT Manipal University Jaipur Rajasthan Jaipur302034 India

To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are particularly designed to record dependencies and relations in large and complicated networks such as social networks, molecular structures, citation networks and recommendation systems. The main idea behind GNN is the message-passing mechanism that allows sharing of information between nodes and edges in a graph network, enabling the model to learn about graph structures and perform well even with limited training data. GNNs have three different architectures: Graph Convolutional Network (GCN), GraphSAGE, Graph Attention Model (GAT) and other hybrid models. In this paper, we have proposed novel GCN models namely dominating Set with Degree Centrality based GCN (DS_DC_GCN) and dominating Set with Betweenness Centrality based GCN (DS_BWC_GCN) model, for node classification task. In the traditional GNN models, training nodes are selected randomly. In our proposed models, we first identify a subset of the nodes with the help of a dominating set (D) of the underlying network. Then we consider final training nodes (say D′) from set D based on the centrality scores (degree or betweenness). We give preference to nodes having high centrality scores for training. To evaluate the performance of the proposed models, we have used three standard citation network datasets - Cora, Citeseer and Pubmed. Our models obtain improved node classification performance as compared to other state-of-the-art-methods. The consideration of high centrality nodes of dominating set in training helps to improve the feature availability in the underlying computational graphs, which in turn improves learning and hence, model performance. These models also have the potential to offer better results for other network tasks, including link prediction and community detection. © The Author(s), under

关键词： Graph neural networks

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SkinSage - Lesion Diagnosis Using Deep Learning Techniques 14

SkinSage - Lesion Diagnosis Using Deep Learning Techniques

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Geetika Bhardwaj, Paarth Nimish Bhargava, Vaibhav Singh, Shivendra Amity School of Engineering and Technology Department of Computer Science and Engineering Noida201301 India

ISBN: (纸本)9798350344837

The alarming rise in instances of skin cancer in recent years, one of the most prevalent malignancies worldwide, emphasises how important early and accurate identification is. The SkinSage project, which combines the precision of deep learning models and technologies. Earlier, dermatologists relied on eye exams to spot skin lesions. But with datasets like HAM10000 and digital photography, it became easy to record a variety of skin conditions. SkinSage uses cutting-edge neural architectures like CNN, ResNet, DenseNet, and InceptionV3 to recognise patterns and nuances that are much above the capabilities of the human eye. In addition to being a computational marvel, the SkinSage project also exemplifies accessibility by making its analytical capabilities available via a smartphone application in future. High-quality skin lesion diagnostics will be accessible to everyone thanks to this platform, regardless of location or degree of training. With the confluence of these developments, SkinSage becomes more than just a tool for diagnosing skin cancer;it signals a paradigm shift that has the potential to democratise early detection and save countless lives in the future. © 2024 IEEE.

关键词： Deep learning

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DUE-MVSNet: Learning Multi-view Stereo Based on Dual Uncertainty Estimation 41st

DUE-MVSNet: Learning Multi-view Stereo Based on Dual Uncert...

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41st computer Graphics International Conference, CGI 2024

作者： Cao, Mingwei Nian, Siqi Yi, Jun School of Computer Science and Technology Anhui University Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

ISBN: (纸本)9783031820205

Many multi-view stereo networks employing a cascade structure can efficiently estimate depth while conserving memory. However, the accuracy of the depth map in the fine stage heavily relies on the depth map estimated in the coarse stage. Additionally, the multi-stage depth maps generated by the cascaded structure are simply used to calculate losses without further utilization, which results in the loss of inter-stage differentiation information. To address these issues, we propose a method called DUE-MVSNet. We use dual uncertainty estimation to mitigate the adverse effects introduced by the cascade structure. Specifically, we design an adjacent stage uncertainty (ASU) module and a pair-wise stage uncertainty (PSU) module. The ASU module dynamically adjusts the depth hypothesis range for the current stage by learning the uncertainty of the previous stage. The PSU module estimates the uncertainty between each pair of stages, mitigating the adverse effects of high uncertainty regions. We evaluated our method on the DTU and Tanks & Temples datasets. Experimental results show that our method achieves superior reconstruction results compared to the state-of-the-art methods. Code is available at https://***/caomw/due-mvsnet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stereo image processing

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