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13th International Conference on Knowledge Management and Information Systems, KMIS 2021 as part of 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2021

作者： Nurhas, Irawan Pirkkalainen, Henri Geisler, Stefan Pawlowski, Jan Institute of Positive Computing Hochschule Ruhr West University of Applied Sciences Bottrop Germany Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Faculty of Management and Business Tampere University Tampere Finland

ISBN: (纸本)9789897585333

This study aims to determine the competing concerns of people interested in startup development and entrepreneurship by using topic modeling and sentiment analysis on a social question-and-answer (SQA) website. Understanding the underlying concerns of startup entrepreneurs is critical to society and economic growth. Therefore, greater scientific support for entrepreneurship remains necessary, including data mining from virtual social communities. In this study, an SQA platform was used to identify the sentiment of thirty concerns of people interested in startup entrepreneurship. Based on topic modeling and sentiment analysis of 18819 inquiries in various forums on an SQA, we identified additional questions about founder figures, keys to success, and the location of a startup. In addition, we found that general questions were rated more positively, especially when it came to pitching, finding good sources, disruptive innovation, idea generation, and marketing advice. On average, the identified concerns were considered 48.9 percent positive, 41 percent neutral, and 10.1 percent negative. This research establishes a critical foundation for future research and development of digital startups by outlining a variety of different concerns associated with startup development in the digital age. Copyright © 2021 by SCITEPRESS – Science and technology Publications, *** rights reserved.

关键词： Sentiment analysis

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Automatic classification of histology images into normal/cancer class with pre-trained CNN 8

Automatic classification of histology images into normal/can...

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8th International Virtual Conference on Biosignals, Images, and Instrumentation, ICBSII 2022

作者： Achuthan, Geetha Kadry, Seifedine Suresh Manic, K. Meqdad, Maytham N. Department of Electrical and Communication Engineering National University of Science and Technology Oman Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Al-Mustaqbal University College Babil Hillah Iraq

Deep-Learning-Scheme (DLS) based medical data assessment has been widely employed in recent years due to its improved accuracy. Our goal is to study the performance of the pre-trained DLS on RGB-scale breast-histology images. The implemented idea holds these phases;(i) Data collection, pre-processing and resizing, (ii) Training the DLS with chosen test-pictures, (iii) Testing and validating the performance of the DLS with 5-fold cross-validation. This investigation considered the breast-histology pictures for the study and binary classification is employed to achieve Normal/Cancer class grouping of images. The proposed work compared the classification performance of AlexNet, VGG16 and *** experimental outcome of this study authenticates that the AlexNet with the Random-Forest (RF) classifier helps to get a higher classification accuracy (>87%) compared to VGG16 and VGG19. © Published under licence by IOP Publishing Ltd.

关键词： Histology

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The benefit and impact of E-commerce in tourism enterprises 2

The benefit and impact of E-commerce in tourism enterprises

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Pathmanathan, P. Ravindran Baker El-Ebiary, Yousef A. Yusoff, M. Hafiz Aseh, Khairi Al-Qudah, Omar Mohammed Ali Abdullah Pande, Bishwajeet Abu-Ulbeh, Waheeb Al Moaiad, Yazeed Bamansoor, Samer International Institute of Applied Science Swiss School of Management Netherlands UniSZA Faculty of Informatics and Computing Malaysia Albukhary International University School of Computing and Informatic Kedah Malaysia Faculty of Informatics and Computing UniSZA Malaysia Irbid National University Faculty of Financial and Business Science Head of E-Marketing Department Jordan Gyancity Research Consultancy Pvt Ltd Motihari India Faculty of Computer Science and Informatics Cybersecurity Department Jordan MEDIU Faculty of Computer and Information Technology Malaysia Faculty of Computer and Information Technology MEDIU Malaysia

ISBN: (纸本)9781665432221

It is generally accepted that the advancement of information and communication technology (ICT) has contributed to the exponential growth of the electronic marketplace. ICT, especially the internet, is believed to be the most cost-effective tool to help brick and mortar companies gain greater markets and the opportunity to compete with other competitive organizations in attracting consumers to their goods, services and knowledge with the strong waves of globalization and liberalization around the world. Nowadays, technology is developing and making our world borderless and infinite. E-commerce is one of the innovations that is transforming the demand for businesses. Electronic commerce (e-commerce) is considered to be an effective method for promoting, selling and incorporating online services that can play an important role in customer recognition, acquisition and maintenance. Through the e-commerce site, people can also promote services, replacing the conventional process, which is cheaper and more efficient. There is a set of effective organizational and external organizational variables in e-commerce that should be taken into consideration. The aim of this study is to suggest an e-commerce solution that can solve the problem for tourism companies. The paper focuses on Malaysia's tourism industry and focuses only on the angle of e-commerce adoption management. © 2021 IEEE.

关键词： Cost effectiveness

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Inertial self-adaptive algorithms with new step size rule for solving split variational inclusion problems in Banach spaces

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Communications in Nonlinear Science and Numerical Simulation 2025年 150卷

作者： Pongsakorn Sunthrayuth Prasit Cholamjiak Issara Siramaneerat Jen-Chih Yao Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Pathumthani 12110 Thailand AI-Powered Digital Platform Research Unit Rajamangala University of Technology Thanyaburi Pathumthani 12110 Thailand School of Science University of Phayao Phayao 56000 Thailand Department of Social Science Faculty of Liberal Arts 3 Rajamangala University of Technology Thanyaburi Pathumthani 12110 Thailand Research Center for Interneural Computing China Medical University Hospital China Medical University Taichung 40447 Taiwan Department of Applied Mathematics National Sun Yat-sen University Kaohsiung 80424 Taiwan

In this study, we introduce two recent algorithms designed to solve the split variational inclusion problem in Banach spaces by using two inertial extrapolations and self-adaptive step size technique. Theoretical analysis demonstrates that the inertial Mann-type self-adaptive algorithm (IMSA) and the inertial Halpern-type self-adaptive algorithm (IHSA) generate strong convergence under the specified conditions. To validate their performance, numerical experiments are conducted to show the effectiveness of the proposed algorithms in both finite-dimensional and infinite-dimensional spaces. Furthermore, practical applications are highlighted through data classification tasks using the heart disease and diabetes datasets.

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Efficient Diagnoses of Breast Cancer Disease Using Deep Learning Technique 24

Efficient Diagnoses of Breast Cancer Disease Using Deep Lear...

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10th International Conference on computing and Artificial Intelligence, ICCAI 2024

作者： Raza, Muhammad Ali Khattak, Asad Masood Abbas, Wasim Asghar, Muhammad Zubair Gomal Research Institute of Computing Faculty of Computing Gomal University D.I.Khan Pakistan Department of Computing and Applied Technology College of Technological Innovation Zayed University Abu Dhabi Campus 144534 United Arab Emirates CEO TECHSACARE PTE. LTD Singapore

ISBN: (纸本)9798400717055

According to WHO 2023 survey, each year more than 2.3 million breast cancer cases are reported. Breast cancer is the either first or second biggest disease in females that is the cause of death in almost 95% of countries. Diagnosing breast cancer in its early stage can be helpful to overcome this disease and result in an increase in the survival chance of the patient. Machine learning (ML) models and well-established methods for encoding categorical data have produced a wide variety of surprising outcomes when used to diagnose breast cancer using datasets that are imbalanced from testing. Early experiments also used an artificial neural network(ANN) to extract characteristics without understanding the sequencing data. In this study, we present a hybrid deep learning (DL) BiLSTM-CNN model, in order to diagnose breast cancer efficiently from patient data. The BiLSTM-CNN model was applied after dataset balancing. Contrasting to previous investigations, the experimental results of our suggested hybrid DL model were outstanding, with an accuracy of 99.3%, a precision of 99%, a recall of 99%, and an F1-score of 99%. © 2024 ACM.

关键词： Lung cancer

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Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

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Computers, Materials & Continua 2021年第6期67卷 2845-2862页

作者： Muhammad Asif Jan Yasir Mahmood Hidayat Ullah Khan Wali Khan Mashwani Muhammad Irfan Uddin Marwan Mahmoud Rashida Adeeb Khanum Ikramullah Noor Mast Institute of Numerical Sciences Kohat University of Science&TechnologyKohatPakistan Department of Economics Abbottabad University of Science&TechnologyAbbottabadPakistan Institute of Computing Kohat University of Science&TechnologyKohatPakistan Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia Jinnah College for Women University of PeshawarPeshawarPakistan Department of Physics Kohat University of Science&TechnologyKohatPakistan

The particle swarm optimization(PSO)algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and *** is essentially an unconstrained algorithm and requires constraint handling techniques(CHTs)to solve constrained optimization problems(COPs).For this purpose,we integrate two CHTs,the superiority of feasibility(SF)and the violation constraint-handling(VCH),with a *** CHTs distinguish feasible solutions from infeasible ***,in SF,the selection of infeasible solutions is based on their degree of constraint violations,whereas in VCH,the number of constraint violations by an infeasible solution is of more ***,a PSO is adapted for constrained optimization,yielding two constrained variants,denoted SF-PSO and *** SF-PSO and VCH-PSO are evaluated with respect to ve engineering problems:the Himmelblau’s nonlinear optimization,the welded beam design,the spring design,the pressure vessel design,and the three-bar truss *** simulation results show that both algorithms are consistent in terms of their solutions to these problems,including their different available *** of the SF-PSO and the VCHPSO with other existing algorithms on the tested problems shows that the proposed algorithms have lower computational cost in terms of the number of function evaluations *** also report our disagreement with some unjust comparisons made by other researchers regarding the tested problems and their different variants.

关键词： Constrained evolutionary optimization constraint handling techniques superiority of feasibility violation constraint-handling technique swarm based evolutionary algorithms particle swarm optimization engineering optimization proble

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Green Hydrogen Microgrid: Household Energy Management System Dedicated to a Hydrogen Storage-based Renewable-Energy-System

Green Hydrogen Microgrid: Household Energy Management System...

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2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Ben Slama, Sami Zafar, Bassam Shafie, Emad Applied College King Abdelaziz University Saudi Arabia Faculty of Sciences Tunis-El Manar University Tunis Tunisia King Abdulaziz University Faculty of Computing & Information Technology Jeddah Saudi Arabia Applied College Umm Al-Qura Univrsity Makkah Saudi Arabia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Green Hydrogen Microgrid (GHM) is a Hybrid renewable energy that could help the industries reach zero emissions in the future. This paper suggests a hybrid renewable energy system based on microgrids to achieve these goals. The recommended renewable hybrid power system has a PVG, FC, SC, and V2H to meet a set demand. During peak hours, the proposed design is grid-connected and uses solar energy. It makes use of it at night or in the shadow. SC assists FC when the load power is high. SC operates in this manner during load transients. Based on the settings, the RT-Home Energy Management System automatically switches between two modes using a multi-agent system (MAS). This reduces the amount of hydrogen used while keeping SOC at the same level. The scheduling strategy minimizes energy consumption by managing automation devices without network power. It can fulfill residential energy demands in sunny, cloudy, and rainy weather. RT-HEMS is used to meet GHM performance and load requirements. © 2022 IEEE.

关键词： Energy management

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Computer-aided system for extending the performance of diabetes analysis and prediction 7

Computer-aided system for extending the performance of diabe...

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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Murad, Saydul Akbar Azmi, Zafril Rizal M Hakami, Zaid Hafiz Prottasha, Nusrat Jahan Kowsher, Md Universiti Malaysia Pahang Faculty of Computing Malaysia Jazan University Faculty of Computer Science and Information Technology Jazan Saudi Arabia Daffodils International University Department of Computer Science and Engineering Bangladesh Noakhali Science and Technology University Department of Applied Mathematics Bangladesh

ISBN: (纸本)9781665414074

Every year, diabetes causes health difficulties for hundreds of millions of individuals throughout the world. Patients' medical records may be utilized to quantify symptoms, physical characteristics, and clinical laboratory test data, which may then be utilized to undertake biostatistics analysis to uncover patterns or characteristics that are now undetected. In this work, we have used six machine learning algorithms to give the prediction of diabetes patients and the reason for diabetes are illustrated in percentage using pie charts. The machine learning algorithms used to predict the risks of Type 2 diabetes. User can self-assess their diabetes risk once the model has been trained. Based on the experimental results in AdaBoost Classifier's, the accuracy achieved is almost 98 percent. © 2021 IEEE.

关键词： Forecasting

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PVA-based KOH polymer gel electrolyte as a membrane separator for zinc-air flow battery

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Materials Today: Proceedings 2022年 64卷 1649-1654页

作者： Sankaralingam, Ram Kishore Seshadri, Satyanarayanan Sunarso, Jaka Bhatt, Anand I. Kapoor, Ajay Department of Applied Mechanics Indian Institute of Technology Madras Tamil Nadu Chennai 600036 India Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Kuching 93350 Malaysia Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn VIC 3122 Australia CSIRO Energy Research Way Clayton VIC 3168 Australia Chinmaya Vishwavidyapeeth Veliyanad PO Ernakulam Kerala 682313 India

The anticipated future growth in the electric vehicle market is driving research and development of alternative battery technologies to lithium batteries. Among the explored batteries, metal-air batteries are an exciting option given their high potential energy density. Currently, metal-air flow batteries have received more attention over conventional metal-air batteries due to their ability to reduce metal passivation. The separator for a metal-air flow battery should be both ion-conductive and partially hydrophobic to favour the ionic movement and suppress the electrolyte flooding on the cathode. In this work, we investigate the use of polyvinyl alcohol (PVA)-based potassium hydroxide (KOH) polymer gel electrolyte (KPGE) as a membrane separator for planar zinc-air flow battery (ZAFB). The KPGE samples, i.e., S20 and S33 with respective KOH wt.% of 20 and 33 were synthesised. Their surface morphology, porosity, tensile strength, and ionic conductivity were studied. Among the samples, S33 sample with higher KOH content showed higher ionic conductivity than S20 sample however the tensile strength of S33 sample was lower than the S20 sample. When tested in the battery, ZAFB with S33 sample demonstrated higher nominal voltage and improved polarisation characteristics when compared to the ZAFB with S20 sample. © 2022

关键词： Surface morphology

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Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences

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Computers, Materials & Continua 2021年第8期68卷 2693-2709页

作者： Javaria Amin Muhammad Almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam ShuiHua Wang University of Wah Wah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan COMSATS University Islamabad Wah CampusWah CanttPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics University of LeicesterLeicesterUK

Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large ***,a method is proposed for the classification and recognition of different types of human *** proposed approach is consisting of two *** phase I,the new model is proposed named convolutional bidirectional long short-term memory(Conv-BiLSTM)to classify the video frames of human *** this model,features are derived through convolutional neural network(CNN)named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal *** phase II,the YOLOv2-squeezeNet model is designed,where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted *** proposed method achieved up to 90%correct prediction scores on CASIA-A,CASIA-B,and the CASIA-C benchmark *** proposed method achieved better/improved prediction scores as compared to the recent existing works.

关键词： Bi-LSTM YOLOv2 open neural network resNet-18 gait squeezeNet

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