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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Studying of Machine Learning Models for Forecasting Macroeconomic Indicators

Studying of Machine Learning Models for Forecasting Macroeco...

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International Conference on Advanced Communication technology (ICACT)

作者： Lan Dong Thi Ngoc Ngo-Thi-Thu-Trang Kyoung-Mok Kim Vung Pham Ha-Nam Nguyen Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi CO Vietnam Department of Medical Information Sahmyook Health University Seoul Republic of Korea Computer Science Department Sam Houston State University Huntsville Texas TX USA Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R 2 metrics to evaluate the accuracy of the *** experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful *** study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes.

关键词： Support vector machines Economic indicators Biological system modeling Predictive models Macroeconomics Forecasting Unemployment Random forests Optimization Long short term memory

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Research on Restructuring Path of Children's Digital Publishing Supply and Demand Chain 2

Research on Restructuring Path of Children's Digital Publish...

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2nd International Conference on Information science and Education, ICISE-IE 2021

作者： Chen, Huan Liu, Ying Shan, Chan Zhang, Hequn School of Journalism and Communication of Minjiang University Fujian Fuzhou350108 China Department of Software Technology Xiamen Institute of Software Technology Fujian Xiamen China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia College of Information Engineering Jimei University Xiamen361021 China

ISBN: (纸本)9781665438292

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. © 2021 IEEE

关键词： Economics

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Disentanglement of intrinsic and extrinsic side-jump scattering induced spin Hall effect in N-implanted Pt

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Physical Review B 2023年第6期107卷 064402-064402页

作者： Utkarsh Shashank Yoji Nakamura Yu Kusaba Takafumi Tomoda Razia Nongjai Asokan Kandasami Rohit Medwal Rajdeep Singh Rawat Hironori Asada Surbhi Gupta Yasuhiro Fukuma Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka 820-8502 Japan Materials Science Division Inter University Accelerator Centre Aruna Asaf Ali Marg New Delhi 110067 India Department of Physics and Centre for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand 248007 India Department of Physics Indian Institute of Technology Kanpur Kanpur 208016 India Natural Sciences and Science Education National Institute of Education Nanyang Technological University 637616 Singapore Graduate School of Sciences and Technology for Innovation Yamaguchi University 2-16-1 Tokiwadai Ube 755-8611 Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu 808-0196 Japan

The rapidly evolving utilization of spin Hall effect (SHE) arising from spin-orbit coupling in 5d transition metals and alloys has made giant strides in the development of designing low-power, robust, and nonvolatile magnetic memory. Recent studies, on incorporating nonmetallic lighter elements such as oxygen, nitrogen, and sulfur into 5d transition metals, have shown an enhancement in dampinglike torque efficiency θDL due to the modified SHE, but the mechanism behind this enhancement is not clear. In this paper, we study θDL at different temperatures (100–293 K) to disentangle the intrinsic and extrinsic side-jump scattering induced SHE in N-implanted Pt. We observe a crossover of intrinsic to extrinsic side-jump mechanism as the implantation dose increases from 2×1016 to 1×1017ions/cm2. A sudden decrease in the intrinsic spin Hall conductivity is counterbalanced by the increase in the extrinsic side-jump induced SHE efficiency. These results conclude that studying θDL as a function of implantation dose, and as a function of temperature, is important to understand the physical mechanism contributing to SHE.

关键词： Spin Hall effect Spin-orbit coupling Spin-orbit torque Spintronics Thin films Ferromagnetic resonance Ion implantation

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Attention-effective multiple instance learning on weakly stem cell colony segmentation

arXiv

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arXiv 2022年

作者： Yudistira, Novanto Kavitha, Muthu Subash Rajan, Jeny Kurita, Takio Intelligent System Lab Faculty of Computer Science Brawijaya University Indonesia School of Information and Data Sciences Nagasaki University Nagasaki Japan Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan

The detection of induced pluripotent stem cell (iPSC) colonies often needs the precise extraction of the colony features. However, existing computerized systems relied on segmentation of contours by preprocessing for classifying the colony conditions were task-extensive. To maximize the efficiency in categorizing colony conditions, we propose a multiple instance learning (MIL) in weakly supervised settings. It is designed in a single model to produce weak segmentation and classification of colonies without using finely labeled samples. As a single model, we employ a U-net-like convolution neural network (CNN) to train on binary image-level labels for MIL colonies classification. Furthermore, to specify the object of interest we used a simple post-processing method. The proposed approach is compared over conventional methods using five-fold cross-validation and receiver operating characteristic (ROC) curve. The maximum accuracy of the MIL-net is 95%, which is 15% higher than the conventional methods. Furthermore, the ability to interpret the location of the iPSC colonies based on the image level label without using a pixel-wise ground truth image is more appealing and cost-effective in colony condition recognition. © 2022, CC BY.

关键词： Learning systems

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Investigation the Performance Effect of QOS in MPLS-TE Network 2

Investigation the Performance Effect of QOS in MPLS-TE Netwo...

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2nd IEEE International Conference on Signal, Control and Communication, SCC 2021

作者： Bozed, Kenz A. Elbeskri, Ahlam Mohammed Zreg, Jazia R. Ali Zerek, Amer R. Benghazi University Faculty of Information Technology Department of Computer System Design Benghazi Libya Higher Institute of Technical Water Affairs Sabratha Libya Sabratha University College of Science Sabratha Libya Zawia University Faculty of Engineering Electrical and Electronic Eng. Department Zawia Libya

ISBN: (纸本)9781665402705

Due to the need for different communication types (Voice, video, and data) over the network, vendors improving enterprise networks for this purpose to minimize delays in order to support the better result, some applications like voice considered real-time applications which suffer from delay, jitter and packet losses, This paper discusses comparison of QoS performance between three QoS algorithms (PQ, FIFO, and WFQ), when using MPLS-TE technology considering the VoIP QoS parameters (end to end delay, jitter, and packet loss, as simulation done by OPNET modeler 14.5. © 2021 IEEE.

关键词： Quality of service

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Importance of Isoleucine Residue in Ion Channel Formation Ability of 11-Residue Peptaibols

SSRN

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SSRN 2024年

作者： Nakatani, Taiki Goto, Shogo Inoue, Marina Seki, Kiyohiko Shigedomi, Keita Araki, Kaoru Taira, Junichi Kodama, Hiroaki Osada, Satoshi Department of Chemistry and Applied Chemistry Faculty of Science and Engineering Saga University 1 Honjo-machi Saga840–8502 Japan Department of Applied Biochemistry and Food Science Faculty of Agriculture Saga University 1 Honjo-machi Saga840-8502 Japan Health Care Center Saga University 1 Honjo-machi Saga840-8502 Japan Department of Bioscience and Bioinformatics Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka820-8502 Japan

Peptaibols are a class of short peptides, typically 7 to 20 amino acids long, characterized by noncanonical amino acid residues such as aminoisobutyric acid (Aib). Although the helix length is shorter than the membrane thickness, the 11-residue peptaibol trichorovin-XII (TV-XII) can form ion channels in membranes. Assuming that a higher proportion of isoleucine (Ile) relative to leucine (Leu) residues is crucial for maintaining the biological activity of TV-XII, peptide analogues of TV-XII with varying Ile content were designed, synthesized, and evaluated. The secondary structure of all derivatives under hydrophobic conditions was confirmed by CD measurement as an α-helix-like β-bend ribbon spiral structure. The most stable ion channel activity was found in compound 4a with maximum Ile. Furthermore, the C-terminal Ile analog showed greater ion channel activity compared to the Leu analog. This suggests that the choice between Leu and Ile can influence the expression of biological activity, which will be crucial for the de novo designed functional peptides. © 2024, The Authors. All rights reserved.

关键词： Bioactivity

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IoT for Water Quality Categorization

IoT for Water Quality Categorization

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International Conference on Cybernetics and Intelligent system (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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Dense plasma irradiated platinum with improved spin Hall effect

arXiv

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arXiv 2023年

作者： Kumar, Sachin Manna, Sourabh Mohan, John Rex Shashank, Utkarsh Vimal, Jospeh Mishra, Mayank Gupta, Surbhi Asada, Hironori Fukuma, Yasuhiro Rawat, Rajdeep Singh Medwal, Rohit Department of Physics Indian Institute of Technology Kanpur Kanpur208016 India Natural Sciences and Science Education National Institute of Education Nanyang Technological University 637616 Singapore Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka820-8502 Japan The Ernst Ruska-Centre for Microscopy and Spectroscopy Forschungszentrum Jülich52428 Germany Department of Physics Motilal Nehru National Institute of Technology Barrister Mullah Colony Teliarganj Uttar Pradesh Prayagraj211004 India Department of Electronic Devices and Engineering Graduate School of Science and Engineering Yamaguchi University Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu808-0196 Japan

The impurity incorporation in host high spin orbit coupling materials like platinum has shown improved charge to spin conversion by modifying the up-spin and down-spin electron’s trajectories by bending or skewing them in opposite directions. This enables efficient generation, manipulation, and transport of spin currents. In this study, we irradiate the platinum with non-focus dense plasma to incorporate the oxygen ion species. We systematically analyze the spin Hall angle of the oxygen plasma irradiated Pt films using spin torque ferromagnetic resonance. Our results demonstrate a 2.4 times enhancement in the spin Hall effect after plasma treatment of Pt as compared to pristine Pt. This improvement is attributed to the introduction of disorder and defects in the Pt lattice, which enhances the spin-orbit coupling and leads to more efficient charge-to-spin conversion without breaking the spin orbit torque symmetries. Our findings offer a new method of dense plasma-based modification of material for the development of advanced spintronic devices based on Pt and other heavy metals. Copyright © 2023, The Authors. All rights reserved.

关键词： Spin Hall effect

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