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22nd International Conference on Intelligent data Engineering and Automated Learning, IDEAL 2021

作者： Salamon, Vilmos Tibor Tashu, Tsegaye Misikir Horváth, Tomáš ELTE - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Telekom Innovation Laboratories Pázmány Péter sétány 1/C Budapest1117 Hungary Kombolcha Institute of Technology College of Informatics Wollo University Kombolcha208 Ethiopia Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia

ISBN: (纸本)9783030916077

In this paper, we proposed Linear Concept Approximation, a novel multilingual document representation approach for the task of multilingual document representation and recommendation. The main idea is in creating representations by using mappings to align monolingual representation spaces using linear concept approximation, that in turn will enhance the quality of content-based Multilingual Document Recommendation Systems. The experimental results on JRC-Acquis have shown that our proposed approach outperformed traditional methods on the task of multilingual document recommendation. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Internet of Thing (IoT) and data Analytics with Challenges and Future Applications 1

Internet of Thing (IoT) and Data Analytics with Challenges a...

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1st International Conference on Computer science and Emerging Technologies, CSET 2023

作者： Aljarrah, Mohammmadnour Mosbah Zawaideh, Farah H. Magableh, Murad Al Wahshat, Hassan Mohamed, Rajina R. Archana, V.K. Irbid National University Faculty of Business and Financial Science MIS Bi and Data Analysis Department Irbid Jordan Irbid National University Faculty of Financial Sciences and Business Department of Bi and Data Analysi Irbid Jordan Ajloun National University Faculty of Business Department of Management Information System Ajloun Jordan College of Computing Dan Informatics Universiti Tenaga Nasional Malaysia Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

The Internet of Things (IoT) has emerged as a transformative technology, enabling seamless connectivity and communication between smart devices and the digital world. IoT's integration with data analytics has opened up a myriad of possibilities for extracting valuable insights from the vast volumes of data generated by connected devices. This research paper explores the convergence of IoT and data analytics, shedding light on the challenges encountered during their integration and envisioning future applications that can revolutionize various domains. The paper begins with a comprehensive review of the IoT ecosystem and its significance in shaping the modern era of interconnected devices. It highlights the diverse range of IoT applications across industries, including smart cities, healthcare, agriculture, manufacturing, transportation, and more. Moreover, it delves into the underlying technologies and communication protocols that enable IoT devices to gather, transmit, and process data efficiently. As data generated by IoT devices continues to escalate exponentially, effective data analytics becomes paramount to extract meaningful insights and support informed decision-making. The research delves into various data analytics techniques, such as machine learning, artificial intelligence, and big data analytics, illustrating their relevance in harnessing the potential of IoT-generated data. However, amidst the promises, several challenges impede the seamless amalgamation of IoT and data analytics. Issues concerning data privacy, security, interoperability, scalability, and data quality are thoroughly examined. The research emphasizes the need for robust frameworks and standards to address these challenges and ensure the secure and efficient functioning of IoT ecosystems. In the final section, this paper envisions the future applications of IoT and data analytics. It envisions intelligent and autonomous systems that adapt to dynamic environments, leading to advancement

关键词： Internet of things

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High data Rate Mobile Video Communication

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Computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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Improving internet of vehicles research: A systematic preprocessing framework for the VeReMi dataset

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data in Brief 2025年 60卷

作者： Aparup Roy Debotosh Bhattacharjee Ondrej Krejcar Bachelor of Science (B.S.) in Data Science and Applications (Pursuing) Indian Institute of Technology Madras BS Degree Office 3rd Floor ICSR Building IIT Madras Chennai 600036 India Department of Computer Science & Engineering Jadavpur University 188 Raja S.C. Mallick Rd Kolkata 700032 West Bengal India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove 50003 Czech Republic Research Center Skoda Auto University Na Karmeli 1457 293 01 Mlada Boleslav Czech Republic

The Vehicular Reference Misbehavior dataset (VeReMi) is a vital resource for advancing Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV). However, its large size (∼7 GB) and inherent class imbalance pose significant challenges for machine learning model development. This paper presents a preprocessing framework to enhance VeReMi’s usability and relevance. Through 10 % down-sampling, the dataset was reduced to ∼724MB, making it computationally manageable. Biases were addressed by balancing benign and malicious samples through synthesis and identifying benign instances using predefined criteria. A refined feature set, including key attributes like rcvTime, pos_0, pos_1, and attack_type (renamed attacker_type ), was selected to improve machine learning compatibility. This preprocessing pipeline effectively maintains data integrity and preserves the representativeness of malicious patterns. The optimized dataset is well-suited for ITS and IoV applications, such as anomaly detection and network security, underscoring the crucial role of preprocessing in overcoming real-world constraints and enhancing model performance.

关键词： Vehicular reference misbehavior dataset (VeReMi) Intelligent transportation systems (ITS) Internet of vehicles (IoV) Intrusion detection systems (IDS) data preprocessing dataset optimization Anomaly detection Network security Machine learning (ML) Cybersecurity

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Clinician data Scientists—Preparing for the Future of Medicine in the Digital World

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Health data science 2022年第1期2022卷 80-82页

作者： Fulin Wang Lin Ma Georgina Moulton Mai Wang Luxia Zhang National Institute of Health Data Science at Peking University BeijingChina Institute of Medical Technology Peking University Health Science CenterBeijingChina Peking University Health Science Center BeijingChina Division of Informatics Imaging and Data SciencesFaculty of BiologyMedicine and HealthThe University of ManchesterManchesterUK Advanced Institute of Information Technology Peking UniversityChina Institute of Nephrology Peking University First HospitalChina

*** Clinician-scientists have a unique strength in translational research and medical advances that improve the quality of care and patient *** big data analytics and advanced technologies such as artificial intelligence are being continuously applied in the healthcare scenario,it not only transforms patient care but also creates tremendous opportunities for data-driven *** a digital health era,clinicianscientists proficient in data science knowledge——that is clinician data scientists——are central to harnessing the power of big data analytics and advanced technologies in medicine.

关键词： continuously artificial clinician

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Quality Assessment of Brain MRI Defacing Using Machine Learning

Quality Assessment of Brain MRI Defacing Using Machine Learn...

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34th Medical informatics Europe Conference, MIE 2024

作者： Sadeghi, Sina Khodaei, Maryam Hempel, Lars Kirsten, Toralf Department for Medical Data Science Leipzig University Medical Center Leipzig Germany Institute for Medical Informatics Statistics and Epidemiology Leipzig University Leipzig Germany Faculty Applied Computer and Bio Sciences Mittweida University of Applied Sciences Mittweida Germany

ISBN: (纸本)9781643685335

Defacing of brain magnetic resonance imaging (MRI) scans is a crucial process in medical imaging research aimed at preserving patient privacy while maintaining data integrity. However, existing defacing algorithms are prone to errors, potentially compromising patient anonymity. This paper investigates the feasibility and efficacy of automated quality assessment for defaced brain MRIs using machine learning (ML). Our findings demonstrate the promising capability of ML models in accurately distinguishing between properly and inadequately defaced MRI scans. © 2024 The Authors.

关键词： Anonymity

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A Review of the Effectiveness of Management Information System in Decision Making 1

A Review of the Effectiveness of Management Information Syst...

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1st International Conference on Computer science and Emerging Technologies, CSET 2023

作者： Zeidan Al-Kaseasbeh, Hazem Mohammad Wahsheh, Firas Rashed Mohamad, Fatma Susilawati Jusoh, Julaily Aida Farea, Mazen Mohammed Archana, V.K. Irbid National University Faculty of Business and Financial Science MIS Bi and Data Analysis Department Irbid Jordan Ajloun National University Faculty of Business Department of Management Information Systems Ajloun Jordan UniSZA Faculty of Informatics and Computing Malaysia Al-Madinah International University Faculty of Finance & Administrative Sciences Malaysia Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

The rapid advancement of technology has revolutionized the way organizations operate, resulting in the widespread adoption of Management Information Systems (MIS) as a crucial tool in decision-making processes. This research paper presents a comprehensive review of the effectiveness of Management Information Systems in facilitating decision-making within various organizational contexts. The paper begins with an exploration of the fundamental role of MIS in collecting, processing, and presenting relevant data to decision-makers. Emphasis is placed on understanding how MIS enables the integration of data from diverse sources and transforms it into actionable insights, empowering managers to make informed and timely decisions. Furthermore, this review delves into the different types of Management Information Systems, including Executive Information Systems (EIS), Decision Support Systems (DSS), and Transaction Processing Systems (TPS). By analyzing case studies and real-world examples, the paper highlights the specific applications and benefits of each system type in enhancing decision-making efficiency and accuracy. To assess the effectiveness of MIS in decision-making, the review examines key performance indicators, such as the speed and accuracy of decision-making processes, resource optimization, and overall organizational performance. The role of MIS in improving strategic planning, risk management, and competitive advantage is also thoroughly investigated. Additionally, the review explores potential challenges and limitations associated with the implementation and utilization of MIS in decision-making. Factors such as data security, system reliability, user training, and resistance to change are addressed, offering insights into strategies for mitigating these obstacles. The paper concludes with a synthesized analysis of the existing literature and identifies gaps for future research in the field of Management Information Systems and decision-making effectiveness

关键词： Decision support systems

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Implications of the Usage of Three-Mode Principal Component Analysis with a Fixed Polynomial Basis 15th

Implications of the Usage of Three-Mode Principal Compone...

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15th International KES Conference on Intelligent Decision Technologies, KESIDT 2023

作者： Monden, Rei Nagai, Isamu Yanagihara, Hirokazu Informatics and Data Science Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8521 Japan Faculty of Liberal Arts and Science Chukyo University Aichi Nagoya466-8666 Japan Mathematics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8526 Japan

ISBN: (纸本)9789819929689

When I subjects answer questions regarding J variables K times, the data can be stored in a three-mode data set of size I× J× K. Among the various component analysis approaches to summarize such data, Three-mode Principal Component Analysis (3MPCA) is often used. 3MPCA expresses a three-mode data set by means of A-, B-, and C-mode components and a core array, but this approach is not suitable for predicting data at unobserved time points. To handle this issue, the current study suggests a fixed orthonormal polynomial basis for the 3MPCA model. Briefly, we fix the C-mode component matrix with an orthonormal polynomial basis. More specifically, i) measurement time points, and ii) log transformation of the measurement time points were used as an orthonormal polynomial basis. The use of our proposed methods was demonstrated by applying them to a longitudinal data set collected from patients with depression. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Estimation Algorithms for MLE of Three-Mode GMANOVA Model with Kronecker Product Covariance Matrix 15th

Estimation Algorithms for MLE of Three-Mode GMANOVA Model ...

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15th International KES Conference on Intelligent Decision Technologies, KESIDT 2023

作者： Horikawa, Keito Nagai, Isamu Monden, Rei Yanagihara, Hirokazu Informatics and Data Science Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8521 Japan Faculty of Liberal Arts and Science Chukyo University Aichi Nagoya466-8666 Japan Mathematics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8526 Japan

ISBN: (纸本)9789819929689

Currently, there are a lot of measurement data on different items collected over time. The GMANOVA model is appropriate for analyzing the trends in such data, in order to analyze some longitudinal data collected on different items simultaneously, we need to create separate GMANOVA models, but separate GMANOVA models cannot reveal the relationships between items. Also, if we try to apply one model to all of these data, the problem arises that there are too many parameters and the covariance structure is too large. Therefore, we propose an extended GMANOVA model and two estimation algorithms. The proposed model’s covariance matrix has a Kronecker structure. Therefore, because the estimator cannot be uniquely determined using the log-likelihood, we propose two parameter estimation algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Covariance matrix

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A Comparative Study of Job-Related Stress in Long-Term Care Workers in Japan and China 9

A Comparative Study of Job-Related Stress in Long-Term Care ...

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9th International Congress on Advanced Applied informatics, IIAI-AAI 2020

作者： Yu, Yating Taki, Seiko Takanokura, Masato Kawakami, Masaru Yamada, Tetsuo Liang, Shuyu Kitami Institute of Technology Regional Management Engineering Course Kitami Japan Chiba Institute of Technology Faculty of Social Systems Science Department of Management Information Science Chiba Japan Kanagawa University Faculty of Engineering Department of Industrial Engineering and Management Yokohama Japan Jichi Medical University Faculty of Nursing Tochigi Japan The University of Electro-Communications Department of Informatics Tokyo Japan Shandong University Data Science Institute Jian China

ISBN: (纸本)9781728173979

Japan revised and implemented the Occupational Safety and Health Act in December 2015 to mandate that businesses with 50 or more workers have all of their workers tested for stress annually. In China, one effect of the 'one child policy' and the nuclearization of the family has been that the number of children has decreased, while the number of elderly people living alone continues to grow. This growth is increasing the demand for long-term care workers. However, because the aging of the Chinese population has been more rapid than in Japan, and China has yet to develop a long-term care system to cope with the aging of its population, the burden on long-term care staff appears to be increasing. Although the resulting need for more support for long-term care staff has become an issue, China has yet to develop a mental health policy like that of Japan that mandates the monitoring of mental health, for example, through annual testing of workers for stress. As a result, the factors underlying long-term care worker stress have yet to be *** study attempted to discover, through a comparison of long-term care worker stress in Japan and China, the kinds of job-related stress care workers tended to have and what the principal underlying factors resulting in high levels of stress may be in each country. To that end, data were collected on workers' psychological and physical stress using the Brief Job Stress Questionnaire (BJSQ) and analyzed to find out how stress may differ by country, gender, job responsibilities, having to work night shifts, or having certain qualifications. © 2020 IEEE.

关键词： Health

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