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8th IEEE International Conference on Computing, engineering and Design, ICCED 2022

作者： Attarbashi, Zainab S. Altaleb, Abdulazeez E. Mohd Aman, Azana Hafizah Hassan Abdalla Hashim, Aisha Eker, Sebahattin International Islamic University Malaysia Information Communication Technology Kuala Lumpur Malaysia Informatics Institute Istanbul Technical University Communication Systems Department Istanbul Turkey University Kebangsaan Malaysia Faculty of Information Science Technology Bangi Malaysia International Islamic University Malaysia Computer Information Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665453899

5G Networks are considered as a new evolution since it aims to increase the wireless capacity 1000 times the previous generations. By 2025, it is expected that 5G will be connecting around 3 billion people and 41.6 billion devices enabling the Internet of Things (IoT) and the Internet of Everything (IoE) while increasing energy efficiency 90% more than 4G and reducing 20% of carbon emissions. In this paper, the deployment requirements and challenges of 5G Networks are reviewed. Starting with the overall network architecture and network slicing framework including the service-type slicing and the infrastructure slicing are investigated. Later, the spectrum and multiple access techniques are explained. Finally, the challenges and the available solutions have been discussed. © 2022 IEEE.

关键词： 5G mobile communication systems

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DERs Orchestration and Gen3 Blockchain Integration in P2P Trading Based Distribution Network

DERs Orchestration and Gen3 Blockchain Integration in P2P Tr...

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Artificial Intelligence and Machine Learning for Energy Transformation (AIE), International Workshop on

作者： Liaqat Ali M. Imran Azim Jan Peters Nabin B. Ojha M.G. Simoes S.M. Muyeen Department of Electrical Engineering Curtin University Perth Australia Electrical and Computer Systems Engineering Monash University Clayton Victoria Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Berlin Germany Department of Energy Environment and Climate Change Asian Institute of Technology Pathum Thani Thailand Department of Electrical Engineering University of Vaasa Vaasa Finland Department of Electrical Engineering Qatar University Doha Qatar

ISBN: (数字)9798350364965

ISBN: (纸本)9798350364972

This paper discusses the advantages of orchestrating distribution energy resources (DERs) within the distribution network and integrating third generation (Gen3) blockchain technology among a diverse range of customers. The implementation of peer-to-peer (P2P) energy trading aims to optimize local energy utilization, reduce energy costs for participants, and provide insights into profit margins for aggregators and the distribution system operator (DSO). The study focuses on three types of DERs, namely electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESSs), to facilitate transparent and secure P2P energy trading. The results underscore the importance of Gen3 blockchain technology in expediting and enhancing energy trading processes, thereby benefiting a wide range of participants.

关键词： Transactive energy Photovoltaic systems Costs Distribution networks Pricing Throughput Blockchains

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Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization

arXiv

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arXiv 2022年

作者： Peng, Chong Liu, Yang Chen, Yongyong Wu, Xinxin Cheng, Andrew Kang, Zhao Chen, Chenglizhao Cheng, Qiang College of Computer Science and Technology Qingdao University China The College of Computer Science Harbin Institute of Technology The School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science University of Kentucky Department of Statistics and Applied Probability University of California Santa Barbara United States The Institute of Biomedical Informatics University of Kentucky

In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively. In particular, the new method adopts the log-determinant rank approximation and a novel 2,log norm, to restrict the local low-rank or column-wisely sparse properties for the component matrices, respectively. For the 2,log-regularized shrinkage problem, we develop an efficient, closed-form solution, which is named 2,log-shrinkage operator. The new regularization and the corresponding operator can be generally used in other problems that require column-wise sparsity. Moreover, we impose the spatial-spectral total variation regularization in the log-based nonconvex RPCA model, which enhances the global piece-wise smoothness and spectral consistency from the spatial and spectral views in the recovered HSI. Extensive experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method in denoising HSIs. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectroscopy

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Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides

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ACS Chemical Biology 2023年第3期18卷 476-483页

作者： Jian, Bo-Siyuan Chiou, Shao-Lun Hsu, Chun-Chia Ho, Josh Wu, Yu-Wei Chu, John Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Chemistry National Taiwan University Taipei 10617 Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 10675 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei 10675 Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei 10675 Taiwan

The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms. © 2023 The Authors. Published by American Chemical Society.

关键词： Algorithms Bacteria Bioinformatics and computational biology Peptides and proteins Pharmaceuticals

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Comparing the Effectiveness of Generative Adversarial Networks for Clustering Tasks

Comparing the Effectiveness of Generative Adversarial Networ...

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International computer Sciences and Information Technologies (CSIT)

作者： Volodymyr Lytvynenko Serge Olszewski Volodymyr Osypenko Iryna Lurie Violeta Demchenko Dariia Dontsova Department of Informatics and Computer Science Kherson National Technical University Kherson Ukraine Department of Radio Engineering and Electronic Systems Taras Shevchenko National University Kyiv Ukraine Department of Computer Science Kyiv National University of Technology and Design Kyiv Ukraine State Institution «Kundiiev Institute of Occupational Health of the National Academy of Medical Sciences of Ukraine» Kyiv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

This study compares the effectiveness of various Generative Adversarial Network architectures, including WGAN and WGAN-GP, in data clustering using the Iris dataset. Performance was evaluated with metrics such as Silhouette Score, Davies-Bouldin Index, Adjusted Rand Index, and Mutual Information. WGANs demonstrated the best clustering performance, while GANs showed promising results but were outperformed by WGAN. Contrary to expectations, WGAN-GP performed the worst. The research underscores the potential of GANs in clustering and highlights the need for further optimization based on specific datasets.

关键词： Training Measurement Iris Stability criteria computer architecture Generative adversarial networks Indexes Information technology Mutual information Optimization

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Optimal One- and Two-Sided Multi-Level ASK for Noncoherent SIMO Systems Over Correlated Rician Fading

arXiv

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arXiv 2025年

作者： Reddy, Badri Ramanjaneya Dash, Soumya P. Alexandropoulos, George C. Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens16122 Greece Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60601 United States School of Electrical Sciences Indian Institute of Technology Bhubaneswar Argul Odisha Khordha752050 India

This paper analyzes the performance of a single-input multiple-output (SIMO) wireless communication system employing one- and two-sided amplitude shift keying (ASK) modulation schemes for data transmission and operating under correlated Rician fading channels. The receiver deploys an optimal noncoherent maximum likelihood detector, which exploits statistical knowledge of the channel state information for signal decoding. An optimal receiver structure is derived, from which series-form and closed-form expressions for the union bound on the symbol error probability (SEP) are obtained for general and massive SIMO systems, respectively. Furthermore, an optimization framework to derive the optimal one- and two-sided ASK modulation schemes is proposed, which focuses on minimizing SEP performance under an average transmit energy constraint. The conducted numerical investigations for various system parameters demonstrate that the proposed noncoherent SIMO system with the designed optimal ASK modulation schemes achieves superior error performance compared to traditional equispaced ASK modulation. It is also shown that, when the proposed system employs traditional two-sided ASK modulation, superior error performance from the case of using one-sided ASK is obtained. © 2025, CC BY.

关键词： Fading channels

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Evaluating ExcellenceWebometric's CriteriaWeight using Consistent Fuzzy Preference Relation Method

Evaluating ExcellenceWebometric's CriteriaWeight using Consi...

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2021 International Conference on computer, Control, informatics and Its Applications - Learning Experience: Raising and Leveraging the Digital Technologies During the COVID-19 Pandemic, IC3INA

作者： Wahyuningrum, Tenia Kartiko, Condro Soares, Teotino Gomez Department of Informatics Institut Teknologi Telkom Purwokerto Indonesia Department of Software Engineering Institut Teknologi Telkom Purwokerto Indonesia Department of Computer Science Dili Institute of Technology Timor Leste Dili Timor-leste

ISBN: (纸本)9781450385244

Some methods in the world university ranking institutions still have weaknesses. Among the university rankings, Webometrics focused on quantitative studies related to website and content phenomena and is considered easy to measure because it bases ranking criteria on university activities on the internet. On the webometrics, the percentage of weight on the criteria of excellence which is quite high (40 %), makes the university pay attention to achieving that score. The existing method in weighting the criteria using the analytical hierarchy process (AHP) needs to include =n(n-1)/2 questions for each group of n-criteria paired comparisons. In this study, the Consistent Fuzzy Preference Relation (CFPR) method is used to study the factors/criteria to determine what strategy universities will take to improve the webometrics ranking in terms of excellence. The CFPR techniques are used to reduce expert assessment steps to only as much as n-1 to ensure consistency at the level with n criteria. Based on the calculation results, it can be concluded that the strategy for improving the excellence score is prioritized on three main criteria in sequence, namely improving scholarly rank (A), measuring the number of scientific papers (B), recognizing scholarly ranks (C). The weight of each criterion in the sequence is 0.51, 0.32, and 0.17. Some strategies to increase the excellence score based on the main priority of sub-criteria and sub-sub criteria are explained in this study. © 2021 ACM.

关键词： Consistent Fuzzy Preference Relations Excellence University rank Webometrics

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Introducing QuantConn: Overcoming Challenging Diffusion Acquisitions with Harmonization 15th

Introducing QuantConn: Overcoming Challenging Diffusion Acqu...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Newlin, Nancy Schilling, Kurt Koudoro, Serge Chandio, Bramsh Qamar Kanakaraj, Praitayini Moyer, Daniel Kelly, Claire E. Genc, Sila Yang, Joseph Yuan-Mou Wu, Ye Adluru, Nagesh Nath, Vishwesh Pathak, Sudhir Schneider, Walter Gade, Anurag Consagra, William Rathi, Yogesh Hendriks, Tom Vilanova, Anna Chamberland, Maxime Pieciak, Tomasz Ciupek, Dominika Vega, Antonio Tristán Aja-Fernández, Santiago Malawski, Maciej Ouedraogo, Gani Machnio, Julia Thompson, Paul M. Jahanshad, Neda Garyfallidis, Eleftherios Landman, Bennett Department of Computer Science Vanderbilt University NashvilleTN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville United States Indiana University Bloomington BloomingtonIN United States Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC Los AngelesCA United States Developmental Imaging Murdoch Children’ Research Institute Melbourne Australia Murdoch Children’s Research Institute Melbourne Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Melbourne Australia Department of Neurosurgery Royal Children’s Hospital Melbourne Australia Neuroscience Research Murdoch Children’s Research Institute Melbourne Australia Department of Paediatrics University of Melbourne Melbourne Australia School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Waisman Center Department of Radiology University of Wisconsin Madison United States NVIDIA Santa Clara United States Learning Research and Development Center University of Pittsburgh Pittsburgh United States Brigham and Women’s Hospital Boston United States Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computer Science and Mathmatics Eindhoven University of Technology Eindhoven Netherlands LPI ETSI Telecomunicación Universidad de Valladolid Castilla y León Spain Sano Centre for Computational Medicine Kraków30-054 Poland Aix-Marseille Université Marseille France Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States

ISBN: (纸本)9783031869198

White matter alterations are increasingly implicated in neurological diseases and their progression. Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many international-scale studies to identify alterations in white matter microstructure and connectivity. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variations in acquisition protocols, sites, and scanners. Specifically, there is a need to harmonize the preprocessing of DW-MRI datasets to ensure that compatible and reproducible quantitative metrics are derived from each site, including (1) bundle-wise microstructure measures, (2) features of white matter fiber bundles, and (3) connectomics measures. In the MICCAI CDMRI 2023 QuantConn challenge, participants are provided raw data from the same individuals taken with two different acquisition protocols on a single 4 tesla scanner in the same scanning session and asked to preprocess the data in order to minimize acquisition differences while retaining biological variation. Here, we outline the testing framework, provide baseline pre-harmonized results, and discuss the learning implications of this challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photointerpretation

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Exploring Relationships Between Cryptocurrency News Outlets and Influencers’ Twitter Activity and Market Prices

arXiv

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arXiv 2024年

作者： Alizadeh, Meysam Asgari, Yasaman Samei, Zeynab Yari, Sara Dehghani, Shirin Kubli, Mael Zare, Darya Bermeo, Juan Diego Batzdorfer, Veronika Gilardi, Fabrizio Digital Democracy Lab University of Zurich Zurich Switzerland Digital Society Initiative Department of Mathematical Modeling and Machine learning University of Zurich Zurich Switzerland Department of Computer Science IPM Tehran Iran Department of Computer Engineering Allameh Tabataba’i University Tehran Iran Department of Informatics University of Zurich Zurich Switzerland Institute for Sociology and Computational Social Science Karlsruhe Institute of Technology Karlsruhe Germany

Academics increasingly acknowledge the predictive power of social media for a wide variety of events and, more specifically, for financial markets. Anecdotal and empirical findings show that cryptocurrencies are among the financial assets that have been affected by news and influencers’ activities on Twitter. However, the extent to which Twitter crypto influencer’s posts about trading signals and their effect on market prices is mostly unexplored. In this paper, we use LLMs to uncover buy and not-buy signals from influencers and news outlets’ Twitter posts and use a VAR analysis with Granger Causality tests and cross-correlation analysis to understand how these trading signals are temporally correlated with the top nine major cryptocurrencies’ prices. Overall, the results show a mixed pattern across cryptocurrencies and temporal periods. However, we found that for the top three cryptocurrencies with the highest presence within news and influencer posts, their aggregated LLM-detected trading signal over the preceding 24 hours granger-causes fluctuations in their market prices, exhibiting a lag of at least 6 hours. In addition, the results reveal fundamental differences in how influencers and news outlets cover cryptocurrencies. © 2024, CC BY.

关键词： Tweets

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Implementation of Knowledge based Collaborative Filtering and Machine Learning for E-Commerce Recommendation System 3

Implementation of Knowledge based Collaborative Filtering an...

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3rd International Conference on Computational and Experimental Methods in Mechanical engineering, ICCEMME 2021

作者： Singh, Mahesh Kumar Rishi, Om Prakash Singh, Akhilesh Kumar Singh, Pushpendra Choudhary, Pushpa Department of Information Technology G L Bajaj Institute of Technology and Management UP Greater Noida India Department of Computer Science and Informatics University of Kota Rajasthan Kota India Department of Information Technology Indraprastha Engineering College UP Ghaziabad India

This is the era of I-way. The development of high-speed computing and huge storage devices change the working culture of human. It affects the traditional business processes and shifted towards online business. It creates huge problems like overload and irrelevant information which are the causes of confusion both customers as well as enterprise. Recommendation system solves these problems. Design and development of efficient system is one of the key areas of the recent researchers. Collaborative filtering (CF) and content-based filtering algorithms are widely used in the implementation of such system. Collaborative used user's features while content-based used item's features. Most of the CFS are rating or review based processed homogeneous information. In this paper we proposed knowledge-based collaborative filtering algorithm for large data set that uses various activities done by users during interaction of item through E-commerce web site like clicks, select and purchase. The performance of the system is compared with the base models using real time Amazon E-commerce dataset using precession, recall and NDCG evaluation parameters in various combinations of activities performed by users on items. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Collaborative filtering

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