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检索条件"任意字段=4th International Conference on Art, Science, and Engineering of Programming, Programming 2020"

共 1467 条记录，以下是1371-1380 订阅

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A Reinforcement Learning Driven Iterated Greedy Algorithm for Energy-Efficiency Flexible Job-Shop Scheduling Problem

A Reinforcement Learning Driven Iterated Greedy Algorithm fo...

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Computer, Control and Robotics (ICCCR), international conference on

作者： Haizhu Bao Chuang Wang Quanke Pan Bingtao Wang Miao Rong Aolei Yang Xiaohua Wang School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

the escalating environmental challenges have sparked significant interest in energy-efficient scheduling as a potent strategy for realizing sustainable development and fostering green manufacturing. this approach is characterized by its dual focus on economic efficiency and energy conservation. this paper tackles the energy-efficient scheduling of the flexible job-shop scheduling problem (EFJSP) with the dual objective of minimizing both makespan and total energy consumption. the mixed-integer linear programming (MILP) model of EFJSP is devised. A reinforcement learning driven iterated greedy algorithm (RLIGA) is proposed to solve the EFJSP. Furthermore, upon a thorough analysis of the problem's characteristics, two optimization strategies-a energy-saving strategy and an acceleration strategy-are developed to enhance the solution further. Extensive benchmark tests have substantiated the superior efficiency and significance of the RLIGA over state-of-the-art algorithms in addressing the EFJSP.

关键词： Greedy algorithms Energy consumption Job shop scheduling Reinforcement learning Search problems Energy efficiency Vectors

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Applying Web Mining and Sentiment Analysis to Assess Tourists Review on Batu City Tourist Destination

Applying Web Mining and Sentiment Analysis to Assess Tourist...

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Vocational Education and Training (ICOVET), international conference on

作者： Aisyah Larasati Joko Sayono Agus Purnomo Effendi Mohamad Muhammad Farhan Puji Rahmawati Universitas Negeri Malang Malang Indonesia Faculty of Manufacturing Engineering University Teknikal Malaysia Melaka Malaysia

ISBN: (数字)9781728181318

ISBN: (纸本)9781728181325

Tourist attractions are one of the destinations for the community to eliminate fatigue and to function as a self-entertainment. Lots of favorite cities chosen by the community as a tourist destination, one of which is Batu City, East Java. this city has a variety of tourist sites, which can be categorized into two types, namely natural and artificial. thousands of people come every year to spend their holidays in the city of Batu. For this reason, this study aims to apply web mining and sentiment analysis to determine people's sentiment or perspective on tourist attractions in Batu through the reviews found on the website. the web mining method is used to retrieve information from the travel website. then, the retrieve information is analyzed by using python programming language. there are eight tourist sites whose reviews were taken, with the total number of reviews are 4887 reviews. the results show that the tourists sites have a polarity value with a range between 0 to 1, which indicates that all the tourists' site gets a positive sentiment. In addition, the resulting subjectivity value is in the range of 0.4 to 0.6, which indicates that all reviews given by visitors are opinions or personal opinions. It implies that each tourist site has sufficiently met the needs of visitors, regarding the facilities and infrastructure that are provided.

关键词： Urban areas Sentiment analysis Python Web mining Web pages Training programming

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Agile Ontology: A Dynamic Framework for E-Business Evolution

Agile Ontology: A Dynamic Framework for E-Business Evolution

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Emerging Smart Technologies and Applications (eSmartA), international conference on

作者： Mohammad Mustafa Taye Rawan Abulail Belal Al-Ifan Software Engineering Department Philadelphia University Amman Jordan Computer Science Department Philadelphia University Amman Jordan Data Science and Artificial Intelligence Department Philadelphia University Amman Jordan

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

In this work, a progressed version of Extreme programming (XP) for e-business is provided. In an effort to efficiently address the precise troubles of this dynamic industry, the strategy promotes iterative procedures and more functions. By including new elements that guide growth, independence, and fine manipulation, Enhanced XP for E-business (EXPe) builds upon the fundamental XP concepts of continuous feedback and collaboration. the e-business enterprise faces challenges such as rapidly evolving marketplace needs and the want for scalable, adaptive solutions. EXPe targets to cope with these by using selling iterative development and incorporating more suitable functionalities to help non-stop adaptation and scalability. the layout prioritizes user standards, adds more scalability mechanisms, and employs iterative improvement techniques to help development teams adapt quickly and efficaciously to changing E-enterprise desires. this study indicates that EXPe is a great choice for quickly and efficaciously growing E-business solutions due to its many benefits and capabilities. this technique aims to provide a strong foundation for engaging in non-intrusive studies on agile methods. Enhanced XP for E-Business (EXPe) uniquely integrates ontology-unique requirements and semantic research into the agile framework, extensively enhancing the adaptability and scalability of e-commercial enterprise solutions. Furthermore, listed for this purpose are applicable files and XP artifacts, including user stories.

关键词： Scalability Layout User centered design Semantics Collaboration Ontologies programming

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th international conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. the results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Minimizing the Makespan on Parallel Machines with Flexible Maintenances and Dynamic Release Times of Jobs

Minimizing the Makespan on Parallel Machines with Flexible M...

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2017 4th international conference on Advanced Education Technology and Management science(AETMS 2017)

作者： Jihong Pang Fuh-Der Chou Ya-Chih Tsai College of Mechanical and Electronic Engineering Wenzhou University Department of Hotel Management Vanung University

this paper considers the problem of scheduling n jobs and a number of preventive maintenances(PMs) on parallel machines to minimize the makespan. Each job has a release time, a processing time, and jobs could be continuously processed on the machine as long as the maximum allowed working time is not violated. the maximum allowed working time is given and the PMs are performed periodically once the maximum allowed working time is going to be violated. the problem is NP-hard due to the case of single machine has proven to be NP-hard. For the problem, a mixed integer programming(MIP) model is introduced and optimally solved small-size problems.

关键词： Parallel machines Flexible Maintenances Makespan Release time

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Performance Evaluation of K-Means Clustering Using MapReduce programming Model

Performance Evaluation of K-Means Clustering Using MapReduce...

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Technological Advancements in Computational sciences (ICTACS), international conference on

作者： Akshita Semwal Anirudh Purohit Pallava Joshi Manisha Basera Vihan Singh Bhakuni Manika Manwal Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India R/S Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

In the current era of unparalleled data expansion, effective handling. large datasets have emerged as a crucial obstacle. When using enormous datasets that are terabytes or petabytes in size, the conventional k-means clustering approach has computational time limits. In the MapReduce framework, we assess the k-means algorithm.'s performance using multiple methods: K-means simple, K-means with Initial Equidistant Centres (IEC), and K-means Java implementation. on MapReduce. We investigate the newsgroup dataset and evaluate their performance in various infrastructures. settings. We also perform a comparative study at different iteration levels between the above-mentioned K-means methods. We use this study to demonstrate improvement in calculated time performance with various infrastructures. Additionally, we also analyze k means algorithms and their behavior with respect to centroids and various iteration levels, and hence provide deeper insights into their dynamics. Our paper offers useful benchmarks for further research and practices working with large-scale data clustering, illuminating the best methods to make use of parallel computation.

关键词： Performance evaluation Java Analytical models Heuristic algorithms Computational modeling Clustering algorithms IEC programming Benchmark testing

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Classification and Evaluation Framework of Automated testing tools for agile software: Technical Review

Classification and Evaluation Framework of Automated testing...

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Emerging Smart Technologies and Applications (eSmartA), international conference on

作者： Mogeeb A.A. Moseh Nashwan Ameen Al-Khulaidi Abdu H. Gumaei Ayman Alsabry Ali A. A. Musleh Software Engineering Dept Faculty of Engineering & Information Technology Taiz University Taiz Yemen Industrial Dept. Engineering Dep Faculty of Engineering & Information Technology Taiz Univerity Taiz Yemen Computer Science Dept. College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Science International University of Technology Twintech Sana’a Yemen Faculty of Computer Science & Information Technology International University of Technology Twintech Sana’a Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Test automation is crucial for agile software projects to enable frequent delivery of working software with cost and time and minimal bugs. However, selecting the right automated testing tool is considered challenging due to the wide range of such existing tools. Additionally, the challenges occur clearly due to several issues such as the programming code language, the categorization of the developed system, and the tester’s knowledge and skills. this paper aims to address this gap by proposing an evaluation framework for comparing and classifying the existing automated testing tools used in agile projects. the framework is developed based on an extensive literature review of existing agile testing methodologies and common commercial automation testing techniques. the key criteria for tool evaluation are identified to cover the main testing objective aspects such as test design support, testing interfaces, reporting capabilities, etc. these criteria are considered the core methodology for this study used to analyze and compare the popular open-source and commercial tools. the proposed evaluation framework provides agile practitioners with guidelines to assist in selecting the appropriate tools based on their specific project needs such as budget, timelines, and technical expertise. this study is considered a comparative evaluation of existing agile testing tools to highlight their key strengths and limitations. the findings of this study categorized the testing tools based on the interface, code, design, and report features. this research contributes to assisting the project developer and tester in selecting suitable tools for the adoption of automated testing tools in their agile software projects. It also identifies the direction for future work, such as integrations with modern development methodologies and technologies.

关键词： Codes Automation Reviews Agile software development programming Software Software measurement

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Bridging Traditional and AI Methods: A Novel Approach to Breast Cancer Prediction

Bridging Traditional and AI Methods: A Novel Approach to Bre...

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Advancement in Electronics & Communication engineering (AECE), international conference on

作者： Aditya Kumar Kanwarpartap Singh Gill Mukesh Kumar Ruchira Rawat Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Cancer ranks as the second leading cause of death worldwide, with 8.8 million fatalities attributed to it in 2015. Among women, breast cancer is the foremost cause of mortality. Numerous studies have been conducted to identify breast cancer at an early stage, which is critical for initiating treatment and improving survival rates. Breast cancer is a complex disease characterized by various types and forms. It uniquely affects women globally and can often result in death. the primary objective of this paper is to develop a predictive model for breast cancer using different machine learning techniques. It will also evaluate and compare the performance of various classifiers based on metrics such as recall, accuracy, precision, F1-score, and support. We propose employing a machine learning approach to identify patients with triple-negative breast cancer by analyzing their gene expression data. Researchers in machine learning have been experimenting with different methods to enhance outcomes, particularly focusing on accurately classifying samples from the larger population while giving less emphasis to those from smaller groups. Machine learning refers to the process of teaching computers to learn and perform tasks independently, without explicit programming or instructions. In this context, the data is utilized to estimate the likelihood of developing breast cancer. this article also includes the data used for the diagnosis and detection of breast cancer. the proposed model is capable of utilizing various types of information, including imaging and blood samples. the findings illustrate the potential of machine learning to effectively distinguish between triple-negative breast cancer and other cancer types.

关键词： Measurement Logistic regression Accuracy Mortality Focusing Machine learning Predictive models programming Breast cancer Data models

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Optimal Scheduling Strategy for Electric Vehicle Charging and Discharging Based on Two-layer Optimization Model

Optimal Scheduling Strategy for Electric Vehicle Charging an...

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Electrical engineering and Control science (IC2ECS), international conference on

作者： Bo Wu Hairong Zou School of Electrical Engineering Shanghai Dianji University China

ISBN: (数字)9798331510107

ISBN: (纸本)9798331510114

With the rapid promotion of new energy generation and electric vehicles around the world, the volatility and uncertainty of new energy and the randomness of electric vehicle charging have brought great challenges to the stable operation of the power grid. therefore, this paper investigates the EV charging and discharging scheduling strategy and proposes an optimal EV charging and discharging scheduling strategy based on a two-layer optimization model. First, the reactive power interaction capabilities of distributed power sources and EV charging piles are analyzed. Second, a two-layer optimization model considering the reactive power response of bidirectional charging and discharging piles is established, with the upper layer aiming at minimizing the equivalent load mean square deviation and the lower layer aiming at minimizing the network loss, which are solved by the sparrow search algorithm and the second-order cone planning method, respectively. Finally, simulations are performed on the improved IEEE 33-node distribution network system for verification. the results show that the proposed scheduling strategy can effectively reduce the load peak-to-valley difference, decrease the network losses, and improve the voltage level of the system.

关键词： Reactive power Schedules Uncertainty Optimization models Tariffs Optimal scheduling programming Electric vehicle charging Voltage control Load modeling

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Application of Improved Genetic Algorithm in Parameter Identification of Synchronous Condenser

Application of Improved Genetic Algorithm in Parameter Ident...

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Data science and Computer Application (ICDSCA), IEEE international conference on

作者： Zhipeng Liu Yanling Lv Baihan Guo Wanlin Guan Huaiyu Guo Meilun Zhang Jiapeng Cui State Grid Heilongjiang Electric Power Company Limited State Grid Heilongjiang Electric Power Co. Ltd. Electric Power Research Institute Harbin China College of Electrical and Electronic Engineering Harbin University of Science and Technology Harbin China State Grid Heilongjiang Electric Power Transmission and Transformation Engineering Company Limited State Grid Heilongjiang Electric Power Company Limited Harbin China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

A new generation of 300Mvar synchronous condenser is widely used in UHVDC transmission system., but its transient operation parameters need to be further studied and analyzed. In order to obtain the accurate motor model and parameters., this paper proposes an improved genetic algorithm., which improves the population initialization., crossover and mutation operation on the basis of introducing elite retention strategy. this method effectively avoids the traditional genetic algorithm falling into the local optimal problem. Finally., the advantages., stability and accuracy of the improved genetic algorithm and the necessity of parameter identification are verified by the actual operation data.

关键词： Parameter estimation Accuracy Measurement uncertainty Fitting Linear programming Motors Stability analysis Object recognition Transient analysis Genetic algorithms

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