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International IEEE Conference on Intelligent systems, IS

作者： Man Tianxing Nataly Zhukova Georgi Tsochev ITMO University St. Petersburg Russia Laboratory of Computer-Information Systems and Software Engineering St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences St. Petersburg Russia Laboratory of Telematics Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (数字)9781728154565

ISBN: (纸本)9781728154572

In recent years, social network data analysis is an emerging field. Multilevel social network analysis could help researchers to figure out comprehensive influencing factors. But the raw data extracted from life is complicated and inoperable so that the entire analysis process is multi-step and changeable. The characteristics of social network datasets at different levels are diverse. There is no general data analysis algorithms for each level. The characteristics of the data and the requirements of the task are important basis for choosing suitable analysis methods. But it is hard for non-expert researchers. This paper proposes an intelligent multilevel assistant for multilevel social network analysis and implements it based on ontology technology. Algorithms at different levels process the corresponding data forms until the appropriate output model is generated. The use of ontology technology makes this framework extensible and understandable. Such a framework is significant for non-experts.

关键词： Social network services Ontologies Machine learning algorithms Data mining Data analysis Task analysis

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COVID-19 pneumonia level detection using deep learning algorithm

TechRxiv

引用

TechRxiv 2020年

作者： Ghafoor, Kayhan Zrar Ali, Abbas M. Mulahuwaish, Aos Maghdid, Halgurd S. Hussein, Safwan Mawlood Mohammed, Marwan A. Department of Software Engineering Salahaddin University-Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wulfruna Street WolverhamptonWV1 1LY United Kingdom Department of Computer Science and Information Systems Saginaw Valley State University University Center 7400 Bay Rd Science East 174 MI48710 United States Department of Software Engineering Faculty of Engineering Koya University Kurdistan Iraq Computer Engineering Department Tishk International University Iraq

The first COVID-19 confirmed case is reported in Wuhan, China and spread across the globe with unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, it is significant to develop smart, fast and efficient detection technique. To this end, we developed an Artificial Intelligence (AI) engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases;in the first phase, we calculate the volume and density of lesions and opacities of the CT images of the confirmed COVID-19 patient using Morphological approaches. In the second phase, the second phase classifies the pneumonia level of the confirmed COVID-19 patient. To achieve precise classification of lung inflammation, we use modified Convolution Neural Network (CNN) and k-Nearest Neighbor (kNN). The result of the experiments show that the utilized models can provide the accuracy up to 95.65% and 91.304 % of CNN and kNN respectively. © 2020, CC BY.

关键词： COVID-19

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Review of Machine Learning Applications for Predicting the Quality Biomass Briquettes for Sustainable and Low-Carbon Energy Solutions

Green Energy and Resources

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Green Energy and Resources 2025年

作者： Constance Nakato Nakimuli Fred Kaggwa Johan De Greef David Kilama Okot Julien Blondeau Simon Kawuma Department of Information Technology Mbarara University of Science and Technology Uganda Brussels Institute for Thermal-Fluid Systems and Clean Energy(BRITE) Vrije Universiteit Brussel Belgium Thermodynamics and Fluid Mechanics Group Faculty of Engineering Vrije Universiteit Brussel (VUB) Belgium Department of Computer Science Mbarara University of Science and Technology Uganda Department of Materials Engineering – Leuven Group T Campus KU Leuven Leuven Belgium Department of Physics Mbarara University of Science and Technology Uganda Department of Software Engineering Mbarara University of Science and Technology Uganda

This review discusses how Machine Learning is applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper has reviewed literature covering various Machine Learning models applied for predicting and optimizing briquette quality parameters including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example Random Forest with R2 of 0.9936 in deformation energy prediction and Artificial Neural Networks with R2 of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstock and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policy makers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.

关键词： Machine learning briquettes sustainable energy low-carbon solutions agricultural waste municipal waste

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Business model canvas should pay more attention to the software startup team

arXiv

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arXiv 2021年

作者： Kemell, Kai-Kristian Elonen, Atte Suoranta, Mari Nguyen-Duc, Anh Garbajosa, Juan Chanin, Rafael Melegati, Jorge Rafiq, Usman Aldaeej, Abdullah Assyne, Nana Sales, Afonso Hyrynsalmi, Sami Risku, Juhani Edison, Henry Abrahamsson, Pekka Faculty of Information Technology University of Jyvaskyla Jyvaskyla Finland School of Business and Economics University of Jyväskylä Jyväskylä Finland Department of Business and IT University of Southeast Norway Bø Norway Universidad Politécnica de Madrid Madrid Spain School of Technology PUCRS Porto Alegre Brazil Faculty of Computer Science Free University of Bozen-Bolzano Bolzano Italy Department of Information Systems University of Maryland Baltimore Country Baltimore United States Department of Software Engineering LUT University Lahti Finland Lero NUI Galway Galway Ireland

Business Model Canvas (BMC) is a tool widely used to describe startup business models. Despite the various business aspects described, BMC pays a little emphasis on team-related factors. The importance of team-related factors in software development has been acknowledged widely in literature. While not as extensively studied, the importance of teams in software startups is also known in both literature and among practitioners. In this paper, we propose potential changes to BMC to have the tool better reflect the importance of the team, especially in a software startup environment. Based on a literature review, we identify various components related to the team, which we then further support with empirical data. We do so by means of a qualitative case study of five startups. Copyright © 2021, The Authors. All rights reserved.

关键词： software design

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The use of public-private partnership in the machine-building industry

引用

AIP Conference Proceedings 2022年第1期2467卷

作者： Oksana Bondarskaya Tatyana Bondarskaya Sergey Pichugin Anna Drozdova Elena Avksentieva Sergey Yekimov 1Tambov State Technical University Tambov Russian 2S. P. Korolev Rocket and Space Corporation Energia Moscow Russia 3Faculty of Business Informatics and Integrated Systems Management Department of Industrial Economics and Management National Research Nuclear University MEPhI Moscow Russia 4Faculty of Software Engineering and Computer Systems ITMO University St. Peterburg Russia 5Publishing House “Education and Science” Prague The Czech Republic

The process of reengineering business processes, in our opinion, is an iterative procedure in the process of enterprise management. Each subsequent business process in the business process chain is a logical continuation of the previous one. The reengineering procedure is characterized by the presence of some feedback about the impact of each individual business process on the overall set of business processes. During the implementation of reengineering, traditional business approaches may undergo revision. Often, many business rules need to be improved and are outdated. The use of public-private partnerships, in our opinion, can be considered as elements of reengineering, since partnerships with the state can open up new business development prospects for an enterprise, and many business processes used at this enterprise can be rethought and updated. The production of high-quality and high-performance agricultural machinery can be of great importance for solving the problem of ensuring food security of the state. The use of various forms of public-private partnership allows us to speed up the solution of this problem. The implementation of economic modernization requires the full use of the economic potential of the state. This can be achieved only if public-private partnerships are effectively implemented, since they allow combining the resources of the state and private business to achieve common goals.

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Correction to: Feature selection and risk prediction for patients with coronary artery disease using data mining

引用

Medical & biological engineering & computing 2022年第3期60卷 887页

作者： Nashreen Md Idris Yin Kia Chiam Kasturi Dewi Varathan Wan Azman Wan Ahmad Kok Han Chee Yih Miin Liew Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia. Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia. yinkia@um.edu.my. Department of Information Systems Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia. Department of Medicine University Malaya Medical Centre 50603 Kuala Lumpur Malaysia. Department of Biomedical Engineering Faculty of Engineering Universiti Malaya 50603 Kuala Lumpur Malaysia.

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Retraction notice to "Modeling of adsorptive removal of azithromycin from aquatic media by CoFe2O4/NiO anchored microalgae-derived nitrogen-doped porous activated carbon adsorbent and colorimetric quantifying of azithromycin in pharmaceutical products" [Chemosphere 329 (2023) 138635]

引用

Chemosphere 2025年 375卷 144147页

作者： Fuad Ameen Reza Mostafazadeh Yasamin Hamidian Nevin Erk Afsaneh L Sanati Ceren Karaman Ali Ayati Department of Botany and Microbiology College of Science King Saud University Riyadh 11451 Saudi Arabia. Ankara University Faculty of Pharmacy Department of Analytical Chemistry 06560 Ankara Turkey. Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Polo II 3030-290 Coimbra Portugal. Akdeniz University Vocational School of Technical Sciences Department of Electricity and Energy Antalya 07070 Turkey. ChemBio Cluster ITMO University Lomonosova Street 9 Saint Petersburg 191002 Russia.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://***/about/policies/article-withdrawal). This article has been retracted at the request of the Editors-in-Chief. A journal-wide investigation by Elsevier's Research Integrity & Publishing Ethics team identified violations of the journal's policies on conflict of interest related to the submission and review of this paper. Review of this submission was handled by Guest Editor Elena-Niculina Dragoi despite an extensive recent record of collaboration, including co-publication, with two of the paper co-authors (Ceren Karaman, Nevin Erk). In addition, there was a significant increase of citations of papers authored by the Guest Editor and by author Karaman between the original submission and the revised version; 6 papers by Dragoi and 8 by Karaman were added in the revised version of the paper. This compromised the editorial process and breached the journal's policies. The authors disagree with this retraction and dispute the grounds for it.

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Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges

arXiv

引用

arXiv 2023年

作者： Jha, Debesh Sharma, Vanshali Banik, Debapriya Bhattacharya, Debayan Roy, Kaushiki Hicks, Steven A. Tomar, Nikhil Kumar Thambawita, Vajira Krenzer, Adrian Ji, Ge-Peng Poudel, Sahadev Batchkala, George Alam, Saruar Ahmed, Awadelrahman M.A. Trinh, Quoc-Huy Khan, Zeshan Nguyen, Tien-Phat Shrestha, Shruti Nathan, Sabari Gwak, Jeonghwan Jha, Ritika K. Zhang, Zheyuan Schlaefer, Alexander Bhattacharjee, Debotosh Bhuyan, M.K. Das, Pradip K. Fan, Deng-Ping Parasa, Sravanthi Ali, Sharib Riegler, Michael A. Halvorsen, Pål de Lange, Thomas Bagci, Ulas Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago United States SimulaMet Oslo Norway Oslo Metropolitan University Oslo Norway Jadavpur University Kolkata India Indian Institute of Technology Guwahati India Institute of Medical Technology and Intelligent Systems Technische Universität Hamburg Germany University of Oslo Norway Julius-Maximilian University of Würzburg Germany Faculty of Information Technology University of Science VNU-HCM Viet Nam Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of School of Computing University of Leeds LeedsLS2 9JT United Kingdom College of Engineering Australian National University Canberra Australia Department of Engineering Science University of Oxford Oxford United Kingdom National University of Computer and Emerging Sciences Karachi Campus Pakistan Swedish Medical Center Seattle United States Kathmandu Nepal Couger Inc Tokyo Japan Department of Software Korea National University of Transportation Chungju Korea Republic of University of Bergen Bergen Norway Department of Medicine and Emergencies Mölndal Sahlgrenska University Hospital Region Västra Götaland Sweden Department of Molecular and Clinical Medicin Sahlgrenska Academy University of Gothenburg Sweden ETH Zurich Zurich Switzerland

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a

关键词： computer aided diagnosis

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software module clustering: An in-depth literature analysis

arXiv

引用

arXiv 2020年

作者： Sarhan, Qusay I. Ahmed, Bestoun S. Bures, Miroslav Zamli, Kamal Z. Department of Computer Science University of Duhok Duhok Kurdistan Region Iraq Department of Mathematics and Computer Science Karlstad University Karlstad651 88 Sweden Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Karlovo nam. 13 Praha 2121 35 Czech Republic IBM Center of Excellence Faculty of Computer Systems and Software Engineering University Malaysia Pahang 26 300 Gambang KuantanPahang Malaysia

software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities’ structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Review on Cost-Value Approach for Requirements Prioritization Techniques 5

Review on Cost-Value Approach for Requirements Prioritizatio...

引用

5th International Conference on Information Technology, computer and Electrical engineering, ICITACEE 2018

作者： Amelia, Tan Mohamed, Rozlina Binti Faculty of Information System Institut Bisnis Dan Informatika Stikom Surabaya Faculty of Computer Systems and Software Engineering University Malaysia Pahang Surabaya Indonesia Faculty of Computer Systems and Software Engineering University Malaysia Pahang Pahang Malaysia

ISBN: (纸本)9781538655276

The use of appropriate requirements prioritization techniques is crucial to the success of a software development project. There are many techniques offered with all the advantages and disadvantages of each. The question that come up frequently when doing requirements prioritization is 'whether the priority list is generated based on customer's required value?' and 'whether the value is generated comparable with the cost incurred for implementation?'. This paper aims to conduct an empirical systematic review to identify and review the requirements prioritization studies based on the cost-value approach. This literature review generates many insights including: reduction of pairwise comparisons, factors and aspects of cost-value, features that support cost value and cost-value constraints. © 2018 IEEE.

关键词： software design

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