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12th Annual IEEE Global Humanitarian technology Conference, GHTC 2022

作者： Palawat, Nattacha Kiattisin, Supaporn Mayakul, Theeraya Mahidol University Information Technology Management Faculty of Engineering Nakhon Pathom Thailand

ISBN: (数字)9781665450973

ISBN: (纸本)9781665450973

Gestational Diabetes Mellitus (GDM) is a high blood glucose level during pregnancy. Patients have frequent follow-ups throughout pregnancy. The concept of health technology enables the accessibility and efficiency of therapy in terms of time-saving and promotes adherence, especially during Covid-19. The study conducts the predictive of GDM risk using a data classification model, which has high accuracy (more than 90%). The model is used for improving the patient's self-awareness through the color notification feature. In addition, we design the GDM's system, including the electronic health information exchange, to ensure interoperability, improve service accessibility and increase patient participation. Finally, this prototype is evaluated by medical staff using the technology Acceptance Model. The results are satisfactory and accepted because the data technology and standard are incorporated to deliver high performance. Besides, this system is expected to reduce workload and provide convenience. © 2022 IEEE.

关键词： Interoperability

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Modeling indices using partial least squares: How to determine the optimum weights?

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Quality and Quantity 2023年第4-Sup期57卷 521-535页

作者： Dirsehan, Taşkın Henseler, Jörg Department of Business Administration Faculty of Business Administration Marmara University Istanbul Turkey Visiting Researcher at Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Nova Information Management School Universidade Nova de Lisboa Lisbon Portugal

Indices are often used to model theoretical concepts in economics and finance. Beyond the econometric models used to test the relationships between these variables, partial least squares path modeling (PLS-PM) allows the study of complex models, but it is an estimator that is still in its infancy in economics and finance research. Thus, the use of PLS-PM for composite analysis needs to be explored further. As one such attempt, this paper is focused on the determination of the indices’ optimum weights. For this purpose, the effects of the market potential index (MPI) on foreign direct investment (FDI) and gross domestic product (GDP) were analysed by implementing different weighting schemes. The assessment of the model shows that PLS Mode B leads to better model fit. © 2022, The Author(s).

关键词： Composite measurement Indices Partial least squares path modeling PLS Mode A PLS Mode B Weighting schemes

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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Computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

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Computers, Materials & Continua 2022年第8期72卷 3985-3997页

作者： Rabab Hamed M.Aly Aziza I.Hussein Kamel H.Rahouma The Higher Institute for Management and Information Technology Minya61768Egypt Department of Electrical and Computer Engineering Effat UniversityJeddahKSA Electrical Engineering Department Faculty of EngineeringMinia UniversityMinia6111Egypt

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’*** algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best *** work of this paper consists of two *** first part is based on collecting data of employees to calculate and illustrate the performance of each *** second part is based on the classification and prediction techniques of the employee *** model is designed to help companies in their decisions about the employees’*** classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the *** of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming *** also show that the Grasshopper Optimization,followed by“KF”with the Gradient Boosting Tree as classifier and predictor,is characterized by a high *** proposed algorithm is compared with other known techniques where our results are fund to be superior.

关键词： Metaheuristic algorithm KUWAHARA filter Grasshopper optimization algorithm and Gradient boosting tree

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Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation

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Energy and AI 2024年第4期18卷 258-280页

作者： Junhao Song Yingfang Yuan Kaiwen Chang Bing Xu Jin Xuan Wei Pang School of Mathematical and Computer Sciences Heriot-Watt UniversityEdinburghUnited Kingdom Faculty of Engineering Imperial College LondonLondonUnited Kingdom Edinburgh Business School Heriot-Watt UniversityEdinburghUnited Kingdom Faculty of Engineering and Physical Sciences University of SurreySurreyUnited Kingdom School of Management Science and Engineering Nanjing University of Information Science and TechnologyNanjingChina

To advance the circular economy(CE),it is crucial to gain insights into the evolution of public attention,cognitive pathways related to circular products,and key public *** achieve these objectives,we collected data from diverse platforms,including Twitter,Reddit,and The Guardian,and utilised three topic models to analyse the *** the performance of topic modelling may vary depending on hyperparameter settings,we proposed a novel framework that integrates twin(single-and multi-objective)hyperparameter timisation op-for CE *** experiments were conducted to determine appropriate hyperparameters under different constraints,providing valuable insights into the correlations between CE and public *** findings reveal that economic implications of sustainability and circular practices,particularly around recyclable materials and environmentally sustainable technologies,remain a significant public *** related to sustainable development and environmental protection technologies are particularly prominent on The Guardian,while Twitter discussions are comparatively *** insights highlight the importance of targeted education programmes,business incentives adopt CE practices,and stringent waste management policies alongside improved recycling processes.

关键词： Circular economy Pulic attention Topic modelling Machine learning Hyperparameter optimisation

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Enhancing Collaborative Learning in Mobile Environments through Interactive Virtual Reality Simulations

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第11期18卷 15-26页

作者： Abdullah, Rijal Nawi, Mohd Nasrun Mohd Salameh, Anas A. Deraman, Rafikullah Harun, Aizul Nahar Faculty of Engineering Universitas Negeri Padang Padang Indonesia School of Technology Management and Logistics Universiti Utara Malaysia Kedah Malaysia Department of Management Information Systems College of Business Administration Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Project and Facilities Management Research Group Faculty of Civil Engineering and Built Environment Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Universiti Teknologi Malaysia Kuala Lumpur Malaysia

This study investigates the potential of enhancing collaborative learning in mobile environments through the integration of interactive virtual reality (VR) simulations. With the ubiquity of mobile devices and advancements in VR technology, there is a growing interest in using immersive experiences to promote collaborative learning among students. The study explores the design and implementation of interactive VR simulations customized for mobile platforms. The goal is to create engaging and immersive learning experiences that foster collaboration and knowledge sharing. By immersing students in virtual environments where they can interact with digital objects and manipulate scenarios, the study aims to facilitate active participation and teamwork, thereby enhancing learning outcomes. Furthermore, the study examines the impact of interactive VR simulations on student engagement, motivation, and knowledge retention in collaborative learning settings. Through empirical studies and user evaluations, the effectiveness of interactive VR simulations as a tool for collaborative learning in mobile environments is assessed. The findings provide valuable insights into the design and pedagogical integration of VR technologies in mobile learning contexts. They offer guidance for educators and instructional designers who aim to leverage the potential of immersive experiences to improve collaborative learning outcomes. © 2024 by the authors of this article.

关键词： Virtual environments

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Application of Process Failure Modes and Effect Analysis for Acceptance Quality Limit in Lot Sentencing 24

Application of Process Failure Modes and Effect Analysis for...

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2024 7th International Conference on Computers in management and Business, ICCMB 2024

作者： Reihana, Nafadza Salsabila Nurprihatin, Filscha Islam, Sri Susilawati Redi, Anak Agung Ngurah Perwira Young, Michael Nayat Prasetyo, Yogi Tri Department of Industrial Engineering Faculty of Engineering and Technology Sampoerna University 12780 Indonesia School of Industrial Engineering and Engineering Management Mapua University Manila Philippines International Bachelor Program in Engineering Yuan Ze University Chung Li Taiwan

ISBN: (纸本)9798400716652

The quality control process performs an acceptance sampling plan to determine if a batch of products meets quality specifications. Acceptance Quality Limit (AQL) defines the acceptable level of defects, but determining the appropriate AQL rate is challenging due to manufacturing process variations. This study utilized Process Failure Mode and Effect Analysis (PFMEA) to determine the AQL for acceptance sampling plans and evaluate its performance against the current method using Operating Characteristic Curve. The study identified eighteen failure modes from PFMEA, with severity level 7, occurrence level 3, and detection level 3. The results demonstrated that PFMEA can serve as a valuable reference for modeling acceptance sampling plans. Acceptance sampling with AQL effectively assesses defective batches compared to the current method. Incorporating PFMEA in acceptance sampling plans enables companies to enhance their understanding of process performance and potential risks, empowering them to make informed decisions and improve product quality. © 2024 ACM.

关键词： Failure modes

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Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

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Computers, Materials & Continua 2022年第5期71卷 3407-3423页

作者： Shakra Mehak M.Usman Ashraf Rabia Zafar Ahmed M.Alghamdi Ahmed S.Alfakeeh Fawaz Alassery Habib Hamam Muhammad Shafiq Knowledge Unit of System and Technology University of Management&TechnologySialkot Campus51310Pakistan Department of Computer Science University of Management and TechnologySialkot Campus51310Pakistan Faculty of Computing and Technology University of Engineering&TechnologyNarowal51610Pakistan College of Computer Science and Engineering University of Jeddah21493Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.

关键词： Breast cancer nuclear atypia autoencoder

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Application of Deep Wavelet Kernel Extreme Learning Machine in Fault Diagnosis of Tamping Vehicle

Application of Deep Wavelet Kernel Extreme Learning Machine ...

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2022 International Conference on Applied Mathematics and Digital Simulation, AMDS 2022

作者： Wang, Hairui Li, Dongwen Zhu, Guifu Yang, Xiuqi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Information Technology Construction Management Center Kunming University of Science and Technology Kunming China School of Materials Science and Engineering Kunming University of Science and Technology Kunming China

Since it is easy to overfit due to the long training time of the fault diagnosis model for machinery. Introducing the idea of autoencoder (AE) into the wavelet extreme learning machine (WELM) and then stacking to form WELM-AE can convert the underlying fault features to more abstract and advanced ones. And then the adaptive boosting kernel extreme learning machine (Adaboost-KELM) is used as the top-level classifier for fault recognition. The experimental results verify the feasibility of the proposed algorithm in the fault diagnosis of tamping machine with the characteristics of the fast training speed of the extreme learning machine, and a higher accuracy rate than back propagation (BP), support vector machine (SVM), stacked autoencoder (SAE), and convolutional neural networks (CNN). © Published under licence by IOP Publishing Ltd.

关键词： Failure analysis

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