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6th International Conference on Soft Computing and its Engineering Applications, icSoftComp 2024

作者： Kavaiya, Sagar Chauhan, Dharmendra Chang, Yoong Choon Alias, Mohamad Yusoff Bin Hai, Nguyen Tri Phung, Bui Minh Chauhan, Narendrakumar Dalal, Purvang Smt. Chandaben Mohanbhai Patel Institute for Computer Applications Charotar University Anand India Chadubhai S Patel Institute of Technology Charotar University of Science and Technology Anand India Department of Electrical and Electronic Engineering Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Bandar Sungai Long Kajang Malaysia Faculty of Engineering Multimedia University Persiaran Multimedia Cyberjaya Selangor Malaysia Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Electronics and Communication Engineering Dharmsinh Desai University Nadiad India

ISBN: (纸本)9783031880414

The rapid development of connected car technology imposes vital requirements to develop adaptive and robust intelligent computational models to help enhance communication, safety, and autonomous decision-making for vehicles. This paper deals with how to incorporate neuro-evolutionary computing-the hybrid paradigm of neural networks and evolutionary algorithms-into the connected car ecosystem. We thus propose a holistic framework that harnesses the adaptive learning capabilities of neural networks and the optimization strengths of evolutionary algorithms to ensure optimal real-time decision-making processes for efficient traffic management and safe passage by passengers. The framework houses a multi-agent system, wherein every connected vehicle is an intelligent agent in itself-learning and evolving toward dynamic traffic surroundings. We present simulations and experimental results showing the effectiveness of our neuro-evolutionary approach to reduce communications latencies and lead to better optimization of routes through proactive collision avoidance. From these results, it is suggested that there exists significant promise for neuro-evolutionary computing to address computational challenges in connected car networks, paving the way to safe, resilient, and efficient intelligent transportation systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Traffic control

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Web-Based Automation Speech-to-Text Application using Audio Recording for Meeting Speech 2

Web-Based Automation Speech-to-Text Application using Audio ...

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2nd International Conference on Mechanical, Electronics, computer, and Industrial Technology, MECnIT 2018

作者： Yasir, Muhammad Nababan, Marlince N.K. Laia, Yonata Purba, Windania Robin Gea, Asaziduhu Faculty of Technology and Computer Science Universitas Prima Indonesia Indonesia STMIK Mikroskil Medan Indonesia Department of Information System Faculty of Technology and Computer Sciences Universitas Methodist Indonesia Indonesia

The use of technology is very important nowadays. Most of everyday activities are documented and stored in digital form. One of those activity is recording. Activity of an organization is very important, therefore recording of meeting material is one of those important activities. Usually the meeting material is documented by writing them into papers or typing them and stored them into computer. Sometimes, the meeting information is written or typed incorrectly so a speech-to-text application is required to solve this problem. In this study, the solution offered is to implement a web-based automation speed-to-text application which can record the voice of meeting participants then converted them into text automatically, so the results of the recording process of meeting materials are more effective and efficient By using voice recognition feature its called web kit Speech Recognition, this system can be implemented. After successfully implementing this application which is so called Speech Meeting Web kit system, the average value for duration system for Indonesia and English language is 96,63 % and 82,78 %. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Speech recognition

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A hybrid approach with balanced score card and fuzzy topsis method for identifying critical factors affecting smart regency development 5

A hybrid approach with balanced score card and fuzzy topsis ...

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5th International Conference on Informatics and Computing, ICIC 2020

作者： Kisnu Darmawan, Aang Siahaan, Daniel Oranova Susanto, Tony Dwi Hoiriyah Umam, Busro Akramul Yulianto, Tony Walid, Miftahul Puspasari, Betty Dewi Hidayanto, Achmad Nizar Universitas Islam Madura Department of Information System Pamekasan Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Information System Surabaya Indonesia Universitas Islam Madura Department of Information Technology Pamekasan Indonesia Universitas Islam Madura Department of Mathematics Pamekasan Indonesia Universitas Islam Madura Department of Informatics Pamekasan Indonesia Sekolah Tinggi Teknik Atlas Nusantara Department of Informatics Malang Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9781728180472

In developing countries, the concept of Smart Cities and Smart Regencies has become the trend and the primary choice in solving increasingly complex and multidimensional city governance problems. The idea of smart cities is an integrated ICT -based city governance concept that offers solutions to manage city resources more efficiently. However, in the smart city development process, various problems make the smart city development process fails and is not as expected before. Previous researchers have conducted research on the key and critical factors that have an essential influence in building smart cities, but of the many critical factors, the list has not been made to highlight development. This study aims to identify the priority key factors that need to be considered in the event of smart regencies. The method used is a mixed-method, a combination of qualitative and quantitative research. At the qualitative research stage, critical factor investigation modeling is carried out using the Balanced Scorecard technique. While the qualitative research stage is carried out using the Fuzzy TOPSIS analysis method, a technique in Multi-Criteria Decision Making to rank and prioritize. The results of this study indicate that the factors in the category of Procedure Bureaucracy Factors (PBF) are very influential factors to consider compared to other factors. This research contributes to two things, methodologically provides a variety of combining the balance scored card and Fuzzy- Topsis methods, then practically, it gives the government and policymakers suggestions that the essential predictors of the growth of smart regencies be given more attention. © 2020 IEEE.

关键词： Smart city

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Towards Data-driven Education with Learning Analytics for Educator 4.0 1

Towards Data-driven Education with Learning Analytics for Ed...

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1st International Conference computer science and Engineering, IC2SE 2019

作者： Mokhtar, Salimah Alshboul, Jawad A. Q. Shahin, Ghassan O. A. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Department of Information Systems and Multimedia Palestine Polytechnic University Hebron Palestine

Learning analytics has not been extensively used yet as necessary tools in the management and operation of public universities in Malaysia. Massive amount of data been created and collected on students at the faculty but mostly remain dark and unexplored. Generating many reports, having lots of alerts or dashboards does not make a faculty data-driven. To be smart, a faculty must utilize technology to enable and support better planning and decision-making, and to be data-driven, a faculty must have analytics to drive actions for value. This paper intends to explore the impact of IR 4.0 in the field of education and research into the possibility on how a university or a faculty can adapt to IR 4.0 and function in the big data environment. It will present the concept of Education 4.0, data-driven education and learning analytics. To transform, universities must rethink the current teaching practices and then redesign learning to suit future demand. This is discuss next. Finally, it summarizes the roles that educators 4.0 should play in Education 4.0. © Published under licence by IOP Publishing Ltd.

关键词： Decision making

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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A study of ontology-based knowledge management system in academic domain 7

A study of ontology-based knowledge management system in aca...

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7th International Conference on information and Communication Technology, ICoICT 2019

作者： Rokhman, Mokhammad Fathoni Sensuse, Dana Indra Hakim, Shidiq Al Satria, Deki Information System Department Universitas Maarif Hasyim Latif Sidoarjo Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (纸本)9781538680520

The purpose of this paper is to review the use of ontology in the field of knowledge management in the academic domain. To conducting this systematic review, we used the guidelines from Kitchenham and used collected data sources from IEEE Xplore, Scopus, Emerald Insight, and ACM Digital Library. We collect paper from data sources by using boolean search through available tools in each publisher. The papers collected were only limited to the last five years and those using ontology as taxonomies. Our data search results found 346 articles, and there were 26 suitable articles selected based on the selection criteria we defined. Our findings concluded six types of ontology models contained in ontology-based knowledge management and three areas that mostly used ontology-based knowledge management systems. Finally, we also found important classes that form the basis for making an ontology used in knowledge management. © 2019 IEEE.

关键词： Knowledge management

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Deep Learning Methods for Calibrated Photometric Stereo and Beyond

arXiv

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arXiv 2022年

作者： Ju, Yakun Lam, Kin-Man Xie, Wuyuan Zhou, Huiyu Dong, Junyu Shi, Boxin School of Electrical and Electronic Engineering Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong The Research Institute for Future Media Computing Shenzhen University Shenzhen China The Department of Informatics University of Leicester Leicester United Kingdom The Faculty of Information Science and Engineering The Institute for Advanced Ocean Study Ocean University of China Qingdao China The National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models. Copyright © 2022, The Authors. All rights reserved.

关键词： Light sources

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Digital Health and Deep Learning: Opportunities, Challenges, and the Imperative of Data Protection in Wearable Devices

SSRN

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SSRN 2024年

作者： Norman, Azah Anir Eke, Christopher Ifeanyi Phiri, Musa Al-Garadi, M.A. Ahmed, Abdulaziz Yeh, Wan-Ju Department of Information System FCSIT University of Malaya Malaysia Department of Computer Science Faculty of Computing Federal University of Lafia Nasarawa state Nigeria Department of Engineering and Technology Mulungushi University PO Box 80415 Kabwe Zambia Department of Biomedical Informatics School of Medicine Vanderbilt University 1161 21st Ave S # D3300 NashvilleTN37232 United States Department of Health Services Administration School of Health Professions University of Alabama at Birmingham 1716 9th Ave S BirminghamAL35233 United States National Cheng-Kung University Department of Law No. 1 University Road Tainan City70 Taiwan

The rapid expansion of digital health technologies has been revolutionized by the combination of deep learning and machine learning models with wearable devices, making it possible to perform sophisticated data analytics, predictive health analytics, and real-time monitoring. The paper examines the fascinating possibilities of deep learning, wearable technology, and digital health while emphasizing the challenges and urgent need for data protection. Deep learning and digital health hold great promise for individualized treatment plans, early disease identification, and ongoing health monitoring. Large volumes of health data can be analyzed by machine learning/deep learning models, which can then produce findings and recommendations that are timely and instructive for patients, doctors, and scholars. However, these opportunities also provide significant challenges such as security and privacy. It becomes critical to protect data against misuse, unauthorized access, and data breaches. Strong data protection protocols, compliance with legal requirements, and ethical considerations to uphold user trust are necessary for ensuring the responsible use of machine learning/algorithms in the healthcare industry. This article also highlights the significance of encouraging transparency, guaranteeing accountability among business players, and developing data protection rules and guidelines for wearable devices. Collaboration between policymakers, healthcare professionals, researchers, and technology developers are essential to address the ethical, legal, and technical complexities surrounding data protection in wearable healthcare devices. By addressing the challenges and imperative of data protection, stakeholders can shape a responsible and secure landscape for digital health and deep learning applications, ensuring the long-term success and adoption of wearable devices in healthcare. © 2024, The Authors. All rights reserved.

关键词： Wearable technology

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Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution

arXiv

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Zhou, Jiantao Yang, Jian Qi, Guo-Jun The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Research Center for Industries of the Future The School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer. Specifically, in the CT block, we first propose a CNN-based Cross-Scale information Aggregation Module (CIAM) to enable the model to better focus on potentially helpful information to improve the efficiency of the Transformer phase. Then, we design a novel Cross-receptive Field Guided Transformer (CFGT) to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention. Extensive experiments have shown that our proposed CFIN can effectively reconstruct images using contextual information, and it can strike a good balance between computational cost and model performance as an efficient model. Source codes will be available at https://***/IVIPLab/CFIN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Evidence-Based Quality-Aware Agile Software Development Process: Design and Evaluation

SSRN

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SSRN 2023年

作者： Karhapää, Pertti Behutiye, Woubshet Seppänen, Pertti Rodríguez, Pilar Oivo, Markku Franch, Xavier Martinez-Fernandez, Silverio López, Lidia Choraś, Michal Bagnato, Alessandra Aaramaa, Sanja Partanen, Jari University of Oulu Oulu90014 Finland Department of Service and Information System Engineering UPC-BarcelonaTech Barcelona08034 Spain Kaiserslautern67663 Germany ITTI Sp. z o.o Poznan61-612 Poland University of Science and Technology UTP Univerisy of Science and Technology Bydgoszcz5-796 Poland Softeam Paris75016 France Nokia Oy Oulu90620 Finland Bittium Wireless Ltd. Oulu90590 Finland University of Puget Sound United States Dep. Of Languages Computer Systems and Software Engineering Faculty of Computer Science Universidad Politécnica de Madrid Spain Dep. ESSI GESSI Research Group UPC-BarcelonaTech Barcelona08034 Spain Centro Nacional de Supercomputacíon Barcelona08034 Spain

Context Agile software development (ASD) aims to deliver high-quality software. However, software quality often gets less attention due to focus on *** an evidence-based quality-aware ASD process that organizations can use to create a more quality-aware development process and improve software *** methodUsing design science research, we collaborated with four organizations applying ASD in different contexts. Involving a total of 35 practitioners, we designed company-specific evidence-based quality-aware ASD processes, which were validated through simulated scenarios, implemented, and evaluated by the practitioners. ResultsA general ASD process model, derived from the company-specific models, was created, which shows the specific activities, frequencies, roles, and development phases where evidence can be used to improve software quality. The new process model contributed towards improved bottleneck identification, release readiness, and quality issues prioritization. We also learned that companies must be mindful of the rules that determine the use of evidence. Conclusion Integrating the use of quality related evidence into the ASD process can make it quality-aware, supporting software quality management, and enabling timely reaction through fast feedback loops. The evidence-based quality-aware ASD process is applicable in any Scrum-like ASD process. Applicability in other forms of ASD, however, requires further research. © 2023, The Authors. All rights reserved.

关键词： Quality control

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