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An Experimental Comparison Between Tree-Based and Machine Learning Approaches for Predicting an Oil Temperature in an Electricity Transformer Dataset 2nd

An Experimental Comparison Between Tree-Based and Machine Le...

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2nd International Conference on From Smart City to Smart Factory for a Sustainable Future, SCFF 2024

作者： Do, Anh Kim Thi Nguyen, Vinh Dinh School of Computing and Information Technology Eastern International University Thủ Dầu Một Viet Nam Department of Software Engineering FPT University Can Tho City Viet Nam

ISBN: (纸本)9783031656552

Time series data forecasting remains a challenging problem within research communities. A plethora of machine learning and deep learning architectures have been devised to tackle the wide array of time series datasets across different domains. Recent advancements in machine learning algorithms have demonstrated impressive performance. However, there remains a limitation regarding the time-consuming nature of both training and testing phases. Hence, this study endeavors to address whether any methods can achieve comparable performance to recent machine learning algorithms without requiring extensive training time. Through our research, we have identified XGBoost as the optimal solution for addressing this issue. Through comprehensive experiments across three study cases encompassing multivariate to multivariate prediction, multivariate to univariate prediction, and univariate to univariate prediction scenarios, we discovered that XGBoost outperformed other state-of-the-art machine learning techniques in the multivariate-to-multivariate prediction case under ETD dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： machine learning time series forecasting tree-based method

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LigCDnet:Remote Sensing Image Cloud Detection Based on Lightweight Framework 30th

LigCDnet:Remote Sensing Image Cloud Detection Based on Ligh...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Su, Baotong Zheng, Wenguang School of Computer Science and Engineering Tianjin University of Technology TianJin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology TianJin300384 China

ISBN: (纸本)9783031533075

Cloud contamination is inevitable in remote sensing images, resulting in a large number of images that cannot be applied in various fields. Therefore, cloud detection is one of the important tasks in remote sensing image preprocessing, aimed at removing images obstructed by clouds. Most existing methods are mostly based on CNN and feature a complex network structure, requiring a significant amount of computational resources, making it challenging to deploy them in practical applications. To tackle this problem, we propose a lightweight cloud detection framework (LigCDnet) with a lightweight feature extraction module (LFEM), a channel attention module (CAM), and a lightweight feature pyramid module (LFPM). The LFEM serves as the backbone of the network to capture rich spatial and contextual information;the CAM adaptively adjusts the channel weights of the feature maps;and the LFPM extracts cloud features at multiple scales. The effectiveness of our approach is evaluated on two public datasets, GF-1 and LandSat8. Extensive experiments have demonstrated that the proposed LigCDnet achieves state-of-the-art detection accuracy while significantly reducing computational burden and having a smaller model size. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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A Study on the Number of Composite Layers and Effectiveness of Metamorphic Relation

A Study on the Number of Composite Layers and Effectiveness ...

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2023 IEEE International Conference on Electrical, Automation and Computer engineering, ICEACE 2023

作者： Zhang, Xueyu Yang, Xiaohua Li, Meng Liu, Jie Wu, Qujin Yan, Shiyu School of Computing University of South China Hengyang China CNNC Key Laboratory on High Trusted Computing Hengyang China Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment Hengyang China

ISBN: (纸本)9798350309614

Metamorphic Testing is one of the main methods to alleviate the test Oracle problem, centered on metamorphic relation. Currently, the construction of the metamorphic relation mainly relies on manual analysis. Therefore, the systematic generation method has been the research concern. One of the construction methods is the composition of existing metamorphic relations. Research shows that the composited metamorphic relations have better fault detection capability than component ones. However, the more composite layers there are, the more time-consuming the computation becomes;even a combination explosion will be triggered. So, studying the mechanism of the number of composite layers on cost-effectiveness is very important. Firstly, combined operation is defined for the numerical expression type metamorphic relation. Then, experiments carry out the trend of the number of composite layers and cost-effectiveness and conclude that three is the most cost-effective, which is good guidance for the systematic generation of metamorphic relations. © 2023 IEEE.

关键词： composite metamorphic relation metamorphic relation metamorphic testing

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Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications

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Computers, Materials & Continua 2022年第6期71卷 5659-5676页

作者： Punit Gupta Sanjit Bhagat Dinesh Kumar Saini Ashish Kumar Mohammad Alahmadi Prakash Chandra Sharma School of Computing&Information Technology Manipal University JaipurJaipur303007India School of Basic Sciences Manipal University JaipurJaipur303007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia

In the next generation of computing environment e-health care services depend on cloud *** Cloud computing environment provides a real-time computing environment for e-health care *** these services generate a huge number of computational tasks,real-time computing and comes with a deadline,so conventional cloud optimizationmodels cannot fulfil the task in the least time and within the *** overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation *** order to overcome existing issues,an artificial neural-inspired whale optimization is proposed to provide a reliable solution for healthcare *** this work,two models are proposed one for reliability estimation and the other is based on whale optimization technique and neural network-based binary *** predictive model enhances the quality of service using performance metrics,makespan,least average task completion time,resource usages cost and utilization of the *** as compared to existing algorithms the proposedANN-WHOalgorithms prove to improve the average start time by 29.3%,average finish time by 29.5%and utilization by 11%.

关键词： Cloud computing whale optimization health care resource optimization

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Reinforcement learning

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Research on NetPad Teaching Mode Based on ARCS Motivational Model 17th

Research on NetPad Teaching Mode Based on ARCS Motivational ...

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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Zhang, Jing Gao, Jinghui Lei, Siran Chen, Ruxian Li, Ya Rao, Yongsheng Institute of Computing Science and Technology Guangzhou University Guangzhou China Software Engineering Institute of Guangzhou Guangzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (纸本)9789819924455

Stimulating and maintaining students’ learning motivation is the key to fully mobilizing the enthusiasm of students’ active participation and realizing the unity of students’ learning and teachers’ teaching. The ARCS motivational model is an instructional design model proposed by Professor Keller that focuses on motivating and sustaining students’ learning motivation. Additionally, NetPad is a web-based dynamic mathematics software with powerful functions and user-friendly interface, it can be organically integrated with teaching, enrich the teaching style, and strengthen students’ learning motivation. In this paper, we propose the NetPad motivational teaching mode based on the ARCS model and NetPad. This mode consists of four procedures: interesting cases introduction, detailed cases explanation, group inquiry, and mutual evaluation and sharing. Each procedure is progressive and complementary, reflecting the hierarchy of motivational teaching from shallow to deep and the synergy of motivation stimulating and maintaining. The teaching practice under the guidance of this mode has verified its effectiveness. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motivation

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A Review of Metamorphic Relation Representation Forms 10

A Review of Metamorphic Relation Representation Forms

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Zhao, Yin Li, Meng Liu, Jie University of South China School of Computing Hengyang China Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment Hengyang China Cnnc Key Laboratory on High Trusted Computing Hengyang China

ISBN: (纸本)9798350304770

Metamorphic testing can effectively alleviate the problem of testing oracle. The lack of specification descriptions of metamorphic relations affects its generalized application. This text categorizes metamorphic relations into three types based on their representation forms: arithmetic expressions, predicate expressions, and other categories. Among the one hundred and sixty-two relations classified, arithmetic expressions account for sixty-four percent. Within geometry, numerical programs, and algorithms, the main to employ relations on arithmetic expressions, while machine learning and optimization algorithms are mainly inclined toward hiring predicate expression relations. Meanwhile, the research findings about the construction methods of metamorphic relations, the generation of original test cases, and the utilization of testing tools have been systematically organized to guide the application of the above techniques based on representation forms. Moreover, it lays the foundation for studying the fundamental nature of relationships, such as complexity, similarity, and relation composite. © 2023 IEEE.

关键词： Machine learning

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A Short Review of Truck and Drone Collaborative Delivery Problem 10

A Short Review of Truck and Drone Collaborative Delivery Pro...

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhu, Yena Liu, Min Jin, Dongyao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

ISBN: (纸本)9798350312461

With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. The advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. Therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented. © 2023 IEEE.

关键词： Reinforcement learning

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Analysis of Sentiments on Russia-Ukraine Conflict using Machine Learning Techniques 1

Analysis of Sentiments on Russia-Ukraine Conflict using Mach...

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1st IEEE International Conference on Contemporary computing and Communications, InC4 2023

作者： Negi, Sakshi Dawar, Ishaan Prakash, Rishabh Tiwari, Prashant Bhushan, Megha Kumar, Ajay DIT University School of Computing Uttarakhand Dehradun India DTU Department of Software Engineering Delhi India KIET Group of Institutions CSIT Department Gaziabad India

ISBN: (纸本)9798350335774

Nowadays, the internet has become an essential part of the current scenario. Due to the rapid expansion of the social network industry, people are using its platforms to share their opinions on current events. It is important to collect and evaluate consumer feedback on products, services, and other topics. It is of utmost importance to determine if an endorsement or criticism of war operations leads to the universal message as the crisis between Russia and Ukraine has worsened and more foreign powers are involved. The abundant writings on the critical connection between public opinion to international relations support the above-mentioned objective. This study focuses mainly on Twitter text data extracted by Twitter API during the Ukraine-Russia war timeline and is available on Kaggle. Sentimental analysis using various supervised machine learning techniques, including Random Forest, Xtreme Gradient Boosting, Support Vector Machine, and Logistic Regression. Also, the comparison of their performance based on various metrics, including the accuracy and precision of the data is presented. As a result, it investigates the opinions and reactions of the global world to Russia's invasion of Ukraine. © 2023 IEEE.

关键词： Sentiment analysis

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Adaptive Occlusion Face Recognition Feature Fusion Network Based on Precise Guidance Mask Extraction

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Journal of Shanghai Jiaotong University (Science) 2025年 1-12页

作者： Ye, Jihua Zhang, Lei Wang, Chao Tu, Qixuan Zheng, Rehua Huang, Huiyuan Jiang, Aiwen School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Jiangxi Provincial Key Laboratory for High Performance Computing Jiangxi Normal University Nanchang330022 China State International Science & Technology Cooperation Base of Networked Supporting Software Jiangxi Normal University Nanchang330022 China

The uncertainty in the position and size of occluding objects greatly affects the extraction of identity features in facial recognition, which is a challenge that existing methods fail to effectively address. To tackle this issue, this paper introduces a novel feature fusion network for occluded facial recognition that adaptively handles arbitrary occlusions and accurately extracts occlusion information masks. The network effectively mitigates the impact of occlusions on facial identity features through a feature fusion module. The proposed method employs an occlusion classification module and an occlusion classification loss to precisely guide the extraction of accurate occlusion masks. Subsequently, an occlusion-aware hybrid attention module is introduced, which focuses on the relationship between facial features and occlusion masks across channel and spatial dimensions. This fusion strategy enables the extraction of facial identity features that are resilient to occlusions. Experimental results indicate that the proposed method holds a significant advantage in handling facial recognition tasks with arbitrary occlusions. On the LFW and MF1 synthetic occlusion datasets, the average accuracy rates reached 93.19% and 72.57%, respectively, showing improvements of 1.7 and 2.39 percent points over mainstream methods. © Shanghai Jiao Tong University 2025.

关键词： A feature fusion occluded facial recognition occlusion mask occlusion perception TP391.41

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