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arXiv 2021年

作者： Zaky, Ahmed B. Khamis, Mohamed A. Gomaa, Walid Benha University Cairo11240 Egypt Big Data Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China New Borg El-Arab City Alexandria21934 Egypt Faculty of Engineering Alexandria University Alexandria21544 Egypt

Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situations, we present a driver behavior switching model validated by a low-cost data collection solution using smartphones. The proposed model is validated using a real dataset to predict the driver behavior in short duration periods. A literature survey on motion detection (specifically driving behavior detection using smartphones) is presented. Multiple Markov Switching Variable Auto-Regression (MSVAR) models are implemented to achieve a sophisticated fitting with the collected driver behavior data. This yields more accurate predictions not only for driver behavior but also for the entire driving situation. The performance of the presented models together with a suitable model selection criteria is also presented. The proposed driver behavior prediction framework can potentially be used in accident prediction and driver safety systems. © 2021, CC BY.

关键词： Smartphones

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Correction to: Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN

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Journal of Cloud Computing 2024年第1期13卷 1-1页

作者： Zhang, Chengping Aamir, Muhammad Guan, Yurong Al-Razgan, Muna Awwad, Emad Mahrous Ullah, Rizwan Bhatti, Uzair Aslam Ghadi, Yazeed Yasin Mechanical and Electrical Engineering College Hainan Vocational University of Science and Technology Haikou China College of Computer Science Huanggang Normal University Huanggang China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Electrical Engineering College of Engineering King Saud University Riyadh Saudi Arabia Faculty of Engineering Chulalongkorn University Bangkok Thailand Bangkok Thailand School of Information and Communication Engineering Hainan University Haikou China Department of Computer Science Al Ain University Al Ain UAE

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Scenario-Based Learning Exemplification with a Dynamic Video Retrieving Tool for the Second Language Teaching 2nd

Scenario-Based Learning Exemplification with a Dynamic Video...

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2nd International Conference on Innovative Technologies and Learning, ICITL 2019

作者： Wang, Kun-Te Lu, Lele Sun, Qiao Huang, Tien-Chi Faculty of Computer and Software Engineering Huaiyin Institute of Technology Hangzhou China National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9783030353421

In most second language teaching environments, the traditional teaching instruction tends to focus on language elements, grammatical rules, and vocabulary memorizing rather than learning on the language in meaningful discourse for successful communication. A simple memory of abstract language elements in a context-free learning environment will make language learning a very difficult task and often frustrate teachers in providing examples of relevant contexts with phrases and sentences. This study describes an interactive video retrieval tool that helps English teachers provide the authentic material of a particular language element which can be viewed in different environments. The tool allows students to access 110 video clips containing examples of contextualized language elements to help them build their perception of the target language. Besides, the tool analyzes the emotional polarity of each subtitle. The result shows that 5814/6000 words could be found by using the tool for Chinese CET-6 training, and the repetition rate the examples was 85% (CET-4) and 87% (CET-6). In a short time, the tool is designed to give teachers access to many dynamic examples from a multimedia corpus, and the choice of teachers can be easily used as a teaching resource for students. The design of this study not only reveals the importance of using dynamic instances in the classroom, but also provides an effective strategy for English teachers to present and explain new vocabulary to students. © Springer Nature Switzerland AG 2019.

关键词： Students

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Research on Restructuring Path of Children’s Digital Publishing Supply and Demand Chain

Research on Restructuring Path of Children’s Digital Publis...

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Information science and Education (ICISE-IE), International Conference on

作者： Huan Chen Ying Liu Chan Shan Hequn Zhang School of Journalism and Communication Minjiang University Fuzhou China Xiamen Institute of Software Technology Xiamen China Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia College of Information Engineering Jimei University Xiamen China

ISBN: (纸本)9781665438308

In the era of rapid development of intelligent technology, the children’s digital publishing industry is facing a disruptive change, and this change may be realized through the reconstruction of the supply and demand chain. The value power of source content creation and the ability to gather users have been deconstructed. The increase in the amount of content requires platform institutions to have a more professional content operation system. The convenience of payment technology has greatly increased the digital content payment rate of users of children’s digital products. A development model of the supply and demand chain of children’s digital publishing with more innovative flexibility and enabling effects.

关键词： Electronic publishing Information science Supply and demand Statistical analysis Transfer learning Refining Tail

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Transformers in Medical Image Analysis: A Review

arXiv

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arXiv 2022年

作者： He, Kelei Gan, Chen Li, Zhuoyuan Rekik, Islem Yin, Zihao Ji, Wen Gao, Yang Wang, Qian Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis. © 2022, CC BY-SA.

关键词： Diagnosis

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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, scienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation 2

A Time Effect based Collaborative Filtering Approach for Use...

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2019 2nd International Conference on computer Information science and Artificial Intelligence, CISAI 2019

作者： Chen, Yuxi Zhang, Xiaotong Zhao, Qing Akhtar, Faheem School of Statistics and Mathematics Country Central University of Finance and Economics Beijing100081 China Harpur College of Arts and Sciences Binghamton University-SUNY New York13902 United States Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Vrije Universiteit Amsterdam1081 HV Netherlands School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for IoT Software and Systems Beijing100124 China Department of Computer Science Sukkur IBA University Sukkur65200 Pakistan

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user's behaviour, which means a user's interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn't consider the time effect;next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative filtering

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Wind Speed Time Series Prediction Using a Single Dendritic Neuron Model

Wind Speed Time Series Prediction Using a Single Dendritic N...

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International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Zhenyu Song Tianle Zhou Xuemei Yan Cheng Tang Junkai Ji College of Computer Science and Technology Taizhou University Taizhou China School of Computer Engineering Jiangsu University of Technology Changzhou China Faculty of Engineering University of Toyama Toyama Japan College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

The chaotic and intrinsic complexity of wind speed time series calls for an appropriate model to accurately predict the moving tendency. In this study, we proposed a single dendritic neuron model (S-DNM) using a back-propagation algorithm to accomplish wind speed forecasting. First, based on mutual information method and false nearest neighbor method, the time delay and embedding dimension are calculated. Second, the phase space is reconstructed by time delay and embedding dimension, and the characteristics of wind speed time series are analyzed. Then, the maximum Lyapunov exponent is applied to confirm the chaotic properties of the wind speed time series. Finally, using wind speed data from Sotavento located in Galicia, Spain, the performance of the forecasting method is evaluated for short-term horizons (1 hour ahead). Experimental results show that the proposed S-DNM performed better than the traditional ELMAN model and classic multi-layered perceptron network. Thus, it is concluded that the proposed model is suitable for wind speed forecasting.

关键词： Wind speed Delay effects Time series analysis Neurons Predictive models Prediction algorithms Forecasting

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A Quantum System Control Method Based on Enhanced Reinforcement Learning

Research Square

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Research Square 2021年

作者： Liu, Wenjie Wang, Bosi Fan, Jihao Ge, Yebo Zidan, Mohammed School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Hurghada Faculty of Computers and Artificial Intelligence South Valley University Egypt

The design of quantum system control is a key task to a powerful quantum information technology. In practical, traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to complete the quantum system control task. So a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. A satisfactory control strategy is obtained through enhanced reinforcement learning so that the quantum system can be evolved accurately from the initial state to the target state. According to the number of candidate unitary operations, the three-switch control is used for simulation experiments. Compared with other methods, the QSC-ERL can achieve high fidelity learning control of quantum systems and improve the efficiency of quantum system control. © 2021, CC BY.

关键词： Reinforcement learning

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Performance Evaluation of PID Controller Parameters Gain Optimization for Wheel Mobile Robot Based on Bat Algorithm and Particle Swarm Optimization 10th

Performance Evaluation of PID Controller Parameters Gain Opt...

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10th National Technical Seminar on Underwater System Technology, NUSYS 2018

作者： Suarin, Nur Aisyah Syafinaz Pebrianti, Dwi Ann, Nurnajmin Qasrina Bayuaji, Luhur Syafrullah, Muhammad Riyanto, Indra Faculty of Electrical and Electronics Engineering Universiti Malaysia Pahang Pekan Malaysia Faculty of Computer Science and Software Engineering Universiti Malaysia Pahang Pekan Malaysia Magister of Computer Science Universitas Budi Luhur Jakarta12260 Indonesia Faculty of Engineering Universitas Budi Luhur Jakarta12260 Indonesia

ISBN: (纸本)9789811337079

Tuning Proportional Integral Differential (PID) controller to the best value of gains is essential to develop a reliable controller for wheel mobile robot (WMR). WMR is a nonlinear system that falls into category of underactuated system where the inputs number is less than output number. The selection of PID gains for such system is highly difficult. Optimization of PID controller using Bat Algorithm (BA) is presented in this paper. BA as a nature inspired algorithm is used to search the optimum PID gains for wheel mobile robot i.e. an off-the-shelf mobile robot called mBot so that the system will have good performance in term of steady state error and time response. Kinematic model of mBot robot is used to develop a simulation model to simulate the system. The result of tuning and optimizing PID gains using BA is compared with Particle Swarm Optimization (PSO). The tuning result by using BA outperformed PSO methods with faster processing time and best values of gain Kp and Kd to be applied in the WMR. The PID gain values obtained from the BA and PSO are then applied on the WMR model. The performance of BA shows better result compared to PSO. Settling time for BA is 10.62 s compared with PSO 11.1 s, rise time for BA is 3.24 s while PSO 2.68 s, percentage overshoot of BA 28.2% compared with PSO 28.4%. Thus, the result proven that BA is able to optimize gain of PID controller better than PSO. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Controllers

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