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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Kumar, V Sunil Sowmyashree, S. Pareek, Piyush Kumar Khanna, Ashish Costa De Albuquerque, Victor Hugo Gupta, Deepak Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Computer Science & Engineering East West Institute of Technology Bengaluru India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Cearά Fortaleza Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Federal University of Cearά Department of Teleinformatics Engineering Brazil

ISBN: (纸本)9781665493963

It is essential to develop an efficient Client Relationship Model in order to make use of the estimations about customer turnover (CRM). Finding and breaking down the facts surrounding the business information understanding is necessary for client produce prediction, which should be made possible effectively by altering the strategies used for business insight. The tools that are provided by company insight allow for the historical, current, and prospective perspectives of business activity to be anticipated and dissected. It is possible to deal with it in an efficient way by providing the information mining approaches that choose the data from the created informative index that is the most helpful. Fundamental leadership should be possible to achieve in a fruitful way if one has this information. The actions that consumers have taken in the recent past are going to be analyzed, and an accurate forecast is going to be formed about those customers who are likely going to get unhappy in the near future based on the results of that prediction. The problem is that deciding on a certain period to investigate the client's activities and also establishing on a specific day and age for each of the clients won't be appropriate6. This is due to the fact that the problem is caused by the fact that deciding on a certain period to investigate the client's activities will take too long. The challenge lies just in this aspect. How to decide on a certain amount of time to spend researching the activities of the customer. For instance, when planning a model, 100 customers are used, 70 of those customers are considered 'active,' which indicates that they are still conducting business with the organisation, and the remaining 30 customers are considered 'agitate,' which indicates that they have severed their ties with the organisation. The primary purpose of the work that has been suggested is to carry out exploratory research and management as a multicriteria basic leadership issue, a

关键词： Forecasting

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Implementation and Performance Evaluation of Collective Communications Using CIRCUS on Multiple FPGAs

Implementation and Performance Evaluation of Collective Comm...

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2023 International Conference on High Performance Computing in Asia-Pacific Region Workshops, HPC Asia 2023

作者： Kikuchi, Kohei Fujita, Norihisa Kobayashi, Ryohei Boku, Taisuke Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781450399890

In the high-performance computing domain, Field programmable Gate Array (FPGA) is a novel accelerator that exhibits high flexibility and performance characteristics distinct from other accelerators such as the Graphics Processing Unit (GPU). Recent advanced high-end FPGA is equipped with multiple channels of high speed optical link up to 100Gbps performance for each. This is a crucial feature when we construct PC clusters with FPGAs as accelerators, however it is not easy to utilize from user kernels because this feature is implemented in low level and simple direct communication between neighboring FPGAs. In order to provide the communication feature between FPGAs for accelerated PC clusters, we developed a communication system named CIRCUS which implies a user-friendly API from OpenCL and is equipped with routing function over multi-hop communication on multi-dimensional torus network of FPGAs. However, current CIRCUS only provides a point-to-point communication between source and destination FPGAs. In ordinary parallel processing environment such as MPI, the user program the message passing with various collective communication functions for parallel algorithm, for instance Allreduce, Allgather, etc. In this paper, we implement the collective communication function over CIRCUS for user-friendly programming of ordinary parallel algorithms on FPGAs. As the first target, we implement Allreduce function which is the most essential and important function. The paper describes the CIRCUS system briefly followed by the design, implementation and preliminary performance evaluation on Intel Stratix10 FPGAs. © 2023 ACM.

关键词： Field programmable gate arrays (FPGA)

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New microbial electrosynthesis system for methane production from carbon dioxide coupled with oxidation of sulfide to sulfate

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Journal of Environmental Sciences 2023年第3期125卷 786-797页

作者： Hiromi Kambara Ha T.T.Dinh Shuji Matsushita Yoshiteru Aoi Tomonori Kindaichi Noriatsu Ozaki Akiyoshi Ohashi Department of Civil and Environmental Engineering Graduate School of EngineeringHiroshima University1–4–1KagamiyamaHigashi-HiroshimaHiroshima 739–8527Japan Faculty of Environment Ho Chi Minh City University of Natural Resources and Environment236 Le Van Sy1 WardTan Binh DistrictHo Chi Minh CityVietnam Agricultural Technology Research Center Hiroshima Prefectural Technology Research Institute6869HaraHachihonmatsuHigashihiroshimaHiroshima 739–0151Japan Program of Biotechnology Graduate School of Integrated Sciences for LifeHiroshima University1–3–1KagamiyamaHigashi-HiroshimaHiroshima 739–8530Japan Department of Civil and Environmental Engineering Graduate School of Advanced Science and EngineeringHiroshima University1–4–1KagamiyamaHigashi-HiroshimaHiroshima 739–8527Japan

Microbial electrosynthesis system (MES) is a promising method that can use carbon dioxide,which is a greenhouse gas,to produce methane which acts as an energy source,without using organic ***,this bioelectrical reduction reaction can proceed at a certain high applied voltage when coupled with water oxidation in the anode coated with metallic *** coupled with the oxidation of HS–to SO_(4)^(2-),methane production is thermodynamically more feasible,thus implying its production at a considerably lower applied *** this study,we demonstrated the possibility of electrotrophic methane production coupled with HS–oxidation in a cost-effective bioanode chamber in the MES without organic substrates at a low applied voltage of 0.2 *** addition,microbial community analyses of biomass enriched in the bioanode and biocathode were used to reveal the most probable pathway for methane production from HS–*** the bioanode,electroautotrophic SO_(4)^(2-)production accompanied with electron donation to the electrode is performed mainly by the following two steps:first,incomplete sulfide oxidation to sulfur cycle intermediates (SCI) is performed;then the produced SCI are disproportionated to HS^(–)and SO_(4)^(2-).In the biocathode,methane is produced mainly via H_(2)and acetate by electronaccepting syntrophic bacteria,homoacetogens,and acetoclastic ***,a new ecofriendly MES with biological H_(2)S removal is established.

关键词： Microbial electrosynthesis system Low applied voltage Organic substrates Methane production Sulfur oxidation

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Multi-modal Multitask Learning Model for Simultaneous Classification of Two Epilepsy Biomarkers 27th

Multi-modal Multitask Learning Model for Simultaneous Classi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Broti, Nawara Mahmood Sawada, Masaki Takayama, Yutaro Iijima, Keiya Iwasaki, Masaki Ono, Yumie Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Kawasaki Japan Department of Neurosurgery Yokohama City University Hospital Kanagawa Yokohama Japan Department of Neurosurgery National Center Hospital National Center of Neurology and Psychiatry Tokyo Kodaira Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Kawasaki Japan

ISBN: (纸本)9783031781971

Accurate estimation of the epileptogenic zone is critical for surgical treatment of drug-resistant epilepsy. While epilepsy biomarkers detection for epileptogenic zone localization has traditionally relied on expert clinician knowledge, the use of deep learning in this context has recently gained appeal for objective diagnosis and reducing clinician burden. However, previously proposed classifiers for electrocorticogram data focused on only single biomarkers among many types, requiring separate models and large datasets for each. To minimize clinicians’ workload and patients’ sufferings, innovative methods to classify multiple biomarkers using small training datasets and minimal computation time is required. This research marks the first implementation of multi-modal multitask learning in epilepsy biomarker classification, proposing a model to automatically classify two biomarkers—high-frequency oscillations (HFO) and interictal epileptiform discharges (IED)—from separate datasets simultaneously. We validated the proposed model on 1500 annotated HFO and IED candidate signals each, obtained from 5 patients. Our proposed model can perform both HFO and IED classification simultaneously, with cross-validation accuracies of 90.99% and 93.99%;F1 scores were reported as 0.80 and 0.82, respectively. Our leave-one-patient-out experiments achieved 78.03% and 81.19% accuracy for HFO and IED classification, respectively. Compared to existing state-of-the-art classification architectures, our model is shown to be efficient and robust, yet simple and lightweight. The usefulness of sharing parameters between the two classifiers was confirmed in this practical clinical setting of epileptic biomarker detection, potentially contributing to minimizing the number of annotated datasets and realizing robust, accurate, and simultaneous identification of two biomarkers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Analysis and structural design of a building with multiple floors in composite steel and concrete structure

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Discover Civil engineering 2024年第1期1卷 1-19页

作者： de Lima, Douglas Mateus Silva, Waltner Wagner Nunes López-Yánez, Pablo Aníbal da Silva Medeiros, Iálysson Technology Center Graduate Program in Civil and Environmental Engineering Federal University of Pernambuco AcademicCenterof Agreste Caruaru Brazil Department of Civil and Environmental Engineering Federal University of Pernambuco Recife Brazil

This paper consists of the structural design and analysis of a commercial office building with seventeen floors using a composite structure of steel and reinforcement concrete. The static and modal analysis of the structure was performed using SAP 2000 software. To this end, the initial design criteria were defined, and subsequently, the structural design was carried out, defining the structural system, the selection of the construction method and materials used, and the determination of the actions and combinations used. The steel decking slabs, dimensioned using the tables provided by the manufacturer, and the composite beams were verified in both the construction and final stages in accordance with current regulatory criteria, with consideration of the shear connectors in this final post-cure stage. For the analysis of the columns, geometric nonlinear analysis was incorporated to determine the stresses and displacements of the steel columns. The modal analysis of the structure provided the vibration modes, showing that the first and second modes have frequencies lower than 1 Hz, confirming the overall flexibility of the structure against wind actions. Finally, beam-beam and beam-column connections were designed. Thus, this article provides a detailed description of a practical application of the design and analysis of a composite steel and concrete structure, contributing to the advancement of technical and practical knowledge of design, technological innovation, structural safety and direct application in engineering projects.

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Multimodal Sentiment Analysis using Kernel Based Support Vector Machine 2

Multimodal Sentiment Analysis using Kernel Based Support Vec...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Kumar, V Sunil Pareek, Piyush Kumar De Albuquerque, Victor Hugo Costa Khanna, Ashish Gupta, Deepak Madhumala, R.B. Nitte Meenakshi Institute of Technology Department Artificial Intelligence and Machine Learning Bengaluru India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Federal University of Ceará Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Dayananda Sagar Academy of Technology and Management Department of ISE Bangalore India

ISBN: (纸本)9781665493963

There has been a steady expansion in the number of social websites, electronic media outlets, and blogging services, all of which contribute a large number of user messages such as comments, reviews, and opinions from customers. The quantity of these signals, which are used to evaluate the quality of a wide variety of goods and services, is rather substantial. As a result, it is challenging to monitor and identify the feelings of customers. As a result, a method known as Sentiment Analysis (SA), also known as Opinion Mining (OM), was developed. This method gathers data from the internet by using Natural Language Processing (NLP) strategies and then analyses people's thoughts. It does this by extracting subjective information from the words, assessing opinions, and quantifying positive and negative evaluations. The end result is a conclusion about the specific entity, which may be an organisation, the public, or a product. The NLP first gathers the views, and then those opinions are sorted into positive, neutral, and negative categories. Both the marketing promotion and the customer's ability to choose the most appropriate product for their needs may be improved by doing an analysis. The purpose of this study is to evaluate the use of different machine learning and swarm intelligence optimization methods to perform sentiment analysis on data derived from movie reviews. This study helps many businesses increase their profits while simultaneously lowering the number of problems caused by a certain product. Within the scope of this investigation, three distinct models of sentiment analysis were investigated. An effective method for doing sentiment analysis known as a Hybrid Kernel based Support Vector Machine (HKSVM) has been presented. The preprocessing that is described here is meant to cut down on the data inconsistencies and get rid of the data that isn't needed. After that, the Expectation Maximization (EM) technique is used in order to identify an outlier. After t

关键词： Sentiment analysis

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A Method for Automatic Generation of 3D Background Models Using Fractal Models and Genetic Algorithms 11

A Method for Automatic Generation of 3D Background Models Us...

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Watanabe, Kaito Nakayama, Hirotaka Shiraki, Atsushi Ito, Tomoyoshi Hirata, Katsumi Hoshikawa, Naoto National Institute of Technology Oyama College Advanced Course General Engineering Program Oyama Japan National Astronomical Observatory of Japan Center for Computational Astrophysics Mitaka Japan Chiba University Graduate School of Engineering Chiba Japan National Institute of Technology Oyama College Department of Innovative Electrical and Electronic Engineering Oyama Japan University of Tsukuba Graduate School of Science and Tecnology Tsukuba Japan

ISBN: (纸本)9798350306941

In recent years, 3DCG-related industries have grown rapidly and the demand for 3DCG content has increased. However, 3DCG production requires special skills and a lot of time. In particular, the creation of background data requires a lot of human resources at the production site because the data is huge and complex. Therefore, we propose an automatic creation system that generates 3D background data for 3DCG production. The proposed system consists of three functions: 3D model generation, generation parameter optimization, and fitness evaluation. The user inputs a landscape photograph as idea information into the system, and the system optimizes the generation parameters so that the 3D object shape approximates the input image. To evaluate the accuracy of the system's performance, we observe the optimization process and the 3D object output when a previously generated fractal image or landscape photos are input. Experimental results show that the system accurately optimizes the generation parameters and generates 3D objects that are almost identical to the shape of the fractal image. In addition, the parameters were also optimized with a certain degree of accuracy for landscape photographs for which no specific solution had been determined. © 2023 IEEE.

关键词： Genetic algorithms

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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Application of Acinetobacter indicus to promote cigarette smoke particulate matter phytoremediation: removal efficiency and plant–microbe interactions

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Environmental Science and Pollution Research 2024年第39期31卷 52352-52370页

作者： Nookongbut, Phitthaya Thiravetyan, Paitip Salsabila, Salma Widiana, Ana Krobthong, Sucheewin Yingchutrakul, Yodying Treesubsuntorn, Chairat School of Bioresources and Technology King Mongkut’s University of Technology Thonburi Bangkok10150 Thailand Department of Biology Faculty of Science and Technology State Islamic University Sunan Gunung Djati Bandung West Java Bandung City40614 Indonesia Interdisciplinary Graduate Program in Genetic Engineering Kasetsart University Bangkok10900 Thailand Proteomics Research Team National Omics Center NSTDA Khlong Luang Pathum Thani12120 Thailand Pilot Plant Development and Training Institute King Mongkut’s University of Technology Thonburi Bangkok10150 Thailand

Particulate matter (PM) is one of the most hazardous atmospheric pollutants. Several plant species show high potential to reduce air pollutants and are widely used as green belts to provide clean outdoor spaces for human well-being. However, high PM concentrations cause physiological changes and stress in plants. In this study, 11 species of Thai native perennial plants were exposed to PM generated from tobacco smoke. Wrightia religiosa (Teijsm. & Binn.) Benth. ex Kurz, Bauhinia purpurea DC. ex Walp. and Tectona grandis L.f. reduced PM effectively (which is in the typical range of 43.95 to 52.97%) compared to other plant species. In addition, the responses of perennial plants under PM stress at the proteomic level were also evaluated. Proteomic analysis of these three plant species showed that plants respond negatively to high PM concentrations, such as reducing several photosynthetic-related proteins and increasing plant stress response proteins. To improve PM phytoremediation efficiency and reduce plant stress from PM, perennial plant–microbe interactions were investigated. W. religiosa was inoculated with Acinetobacter indicus PS1, and high biosurfactant-producing strains clearly showed a higher PM removal efficiency than non-inoculated plants (9.48, 9.5 and 12.6% for PM1.0, PM2.5 and PM10, respectively). Inoculating W. religiosa with A. indicus PS1 maintained chlorophyll a and b concentrations. Moreover, the malondialdehyde (MDA) concentration of W. religiosa inoculated with A. indicus PS1 was lower than that of non-inoculated W. religiosa. The leaf wax content (µg/cm2) and biosurfactant (µg/cm2) of W. religiosa inoculated with A. indicus PS1 were also higher than those of non-inoculated W. religiosa. This study clearly showed that inoculating plants with A. indicus PS1 can help plants remediate PM and improve their PM stress response. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Air pollution

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Effect of Cu addition on electrochemical activity of ribbons of Mg-Al-Ca anode materials for magnesium rechargeable batteries manufactured by rapid solidification method

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Keikinzoku/Journal of Japan Institute of Light Metals 2024年第1期74卷 14-17页

作者： Fukui, Shunsaku Tsukeda, Tadayoshi Aida, Tetsuo Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama 3190 Gofuku Toyama-shi Toyama930-8555 Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama-shi Toyama Japan CHUETSU METAL WORKS CO. LTD. Nakaniikawa-gun Toyama Japan Saitama Industrial Technology Center Kawaguchi Kawaguchi-shi Saitama Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Imizu-shi Toyama Japan

The potential for magnesium storage batteries to become the next generation of rechargeable batteries is high. This is largely due to the abundance of magnesium metal resources and their high safety and electric capacity per volume. Our research team has been studying magnesium alloys as anode materials for these batteries. Specifically, we have been investigating the electrochemical activity and mechanism of adding copper to Mg-9mass%Al and Mg-9mass%Al-3mass%Ca. Our findings indicate that the electrochemical activity increased with the addition of 1mass%Cu, but decreased with the addition of 3mass%Cu. In addition, changes in lattice constant and basal orientation were also observed. These changes decreased with 1mass%Cu and increased with 3mass%Cu. SEM observations showed that the cell delimitation was indistinct for the 1%Cu addition. © 2024 The Japan Institute of Light Metals.

关键词： Secondary batteries

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