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Application of Artificial Intelligence in Healthcare systems: A Scientometric Analysis

Application of Artificial Intelligence in Healthcare Systems...

2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ajibade, Samuel-Soma M. Lee, Angela Siew Hoong Jasser, Muhammed Basheer Akinola, Tomiwa Faith Sunway University Dept of Computing and Information Systems Selangor Malaysia Istanbul Ticaret Univ Dept of Comp. Engineering Turkey Sunway University School of Engineering and Technology Department of Computing and Information Systems Selangor Malaysia University of Ibadan Faculty of Public Health Department of Health Promotion Nigeria

ISBN: (纸本)9798350358155

This bibliometric study examines the worldwide progression of machine learning applications in medical and healthcare research between 2010 and 2023. A large dataset of published articles pertaining to machine learning applications in the medical and healthcare sectors is mined for useful information in this study. The data extraction procedure involves retrieving pertinent information from primary sources such as journals, books, and conference proceedings. Subsequently, the retrieved data is subjected to analysis to discern the patterns and tendencies in the use of machine learning in medical and healthcare research. A total of 1,220 publications were found in the Scopus database over the past 14 years. In addition, the study demonstrated that most AIHS research has concentrated on artificial intelligence applications to address a wide range of problems, including patient data security and chronic medical difficulties (such as cardiovascular disorders). Policymakers, healthcare practitioners, and researchers around the world may find this study's conclusions helpful. Emerging ethical concerns, integration, and real-world uses in smart healthcare systems, and the Internet of Things (IoT), could be the focus of future research. © 2024 IEEE.

关键词： artificial intelligence bibliometric analysis healthcare systems machine learning medical research

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Monochromatic Image Dehazing Using Enhanced Feature Extraction Techniques in Deep Learning

Monochromatic Image Dehazing Using Enhanced Feature Extracti...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Doshi, Nisarg Bhavsar, Sagar Rajeswari, D. Srinivasan, R. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9798350334357

Images photographed in foggy weather usually have poor visibility. To mitigate this problem researchers have come up with various image dehazing techniques. Now, more than ever, high-quality images that can be used to glean maximum information from autonomous systems are in high demand. This research work uses different Deep Learning (DL) architectures to draw out essential details from the picture and localize the information recovered to reduce the haze from the picture. The paper investigates to remove the hazes from the dehazed images using DL techniques. The first task of this proposed work attempts three pre-processing techniques namely, air light estimation, contextual regularization and boundary constraint. The second task of this work is to identify the suitable DL model to extract clear images from dehazed images. Evaluation metrics are PSNR value and SSIM value are used to estimate the values of dehazed images compared with clear images. Experimental results proves that AOD-Net outperforms good result with respect to PSNR value. © 2023 IEEE.

关键词： Image enhancement

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Distributed intelligence for IoT-based smart cities: a survey

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Neural computing and Applications 2024年第27期36卷 16621-16656页

作者： Hashem, Ibrahim Abaker Siddiqa, Aisha Alaba, Fadele Ayotunde Bilal, Muhammad Alhashmi, Saadat Mehmood Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science Federal College of Education Zaria Kaduna Tudun Wada810282 Nigeria Department of Pharmaceutical Outcomes and Policy University of Florida GainesvilleFL32610 United States Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are conjoined. On the one hand, IoT provides a digital connection to everyday physical devices, and on the other hand, distributed intelligence allows these connected devices to work together in a distributed environment to solve complex problems through learning and reasoning. In this article, we investigate and report the recent premier advances in distributed intelligence for IoT-based smart cities. We use bibliometric techniques which provide essential insights into many studies to evaluate the literature including publication year, type, and citation status on distributed intelligence and its variations published between 2013 and 2023. We categorize and classify literature by devising a taxonomy based on essential parameters, technologies, approaches, applications, and implications with respect to distributed intelligence. Moreover, we provide recent ongoing advancements in distributed intelligence and IoT such as sustainability, blockchain, 5G technologies, AI, edge computing expansion and IoT expansion. We also highlight some of the use cases which include traffic management, energy management, waste management, public safety, and healthcare. We provide core layers and services for implementing distributed intelligence in IoT-based smart cities and present several research challenges and issues. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smart city

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Development of Kansei-Based Visualization Pattern for E-Learning Website 4th

Development of Kansei-Based Visualization Pattern for E-Lear...

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4th International Conference on Advances in Computational Science and engineering, ICACSE 2023

作者： Aning, Alberto Mohd Naim, Nur Faraha Baharum, Aslina Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu88450 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia

ISBN: (纸本)9789819729760

Through their websites, a lot of businesses and people directly offer their services. The way in which these services are offered may affect users emotionally. A website that elicits pleasant emotions in its users can make them feel happy, engaged, trustworthy, or satisfied. Well-known for its ability to analyze customer emotional demands and define the connection between emotions and product design aspects, Kansei engineering (KE) is a user-centered technology. The development of e-learning software, e-learning authoring tools, learning management systems, and other related technologies is currently a major investment made by universities, training centers, and government bodies. Even though there are many advantages for educators using e-learning technology, getting students to use these services can be difficult, especially for the e-learning websites of Malaysia’s higher education institutions (HEIs). Few standards exist for user interface (UI) design that encourages emotional connection on the e-learning platforms of Malaysian HEIs. Students were disengaging from e-learning platforms in substantial numbers because of this issue. Hence, this paper aims to explore users’ needs and emotions while using an HEI website and produce a new guideline for HEIs e-learning website. Using the card sorting technique, a visualization pattern has been presented as a design guideline for an e-learning graphical user interface (GUI) website for HEIs in Malaysia. Positive outcomes have been obtained by implementing the suggested guideline on the GUI of the prototype website. The purpose of this guideline is to help HEIs as well as website developers and designers create e-learning websites that will keep students interested in e-learning over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

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China Communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

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Machine Learning-Driven Worker Activity Recognition with Wearable IoT and Smartphone Sensors 2nd

Machine Learning-Driven Worker Activity Recognition with Wea...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Hafizh, Hadyan Yunus, Muhammad Razi Bin Majeed, Anwar P. P. Abdul Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Sarawak Metro Sarawak Kuching93100 Malaysia

ISBN: (纸本)9789819639489

Worker activity recognition is a crucial area of research with significant applications in manufacturing, healthcare, and security. Traditional methods relying on computer vision-based algorithms often demand extensive infrastructure, such as video cameras. This study presents a more efficient approach by utilizing data from embedded sensors in smartphones and microcontrollers. These wearable devices capture worker activities, including walking, sitting, standing, laying, and climbing stairs, through accelerometer sensors. The data is transmitted and monitored via an IoT platform, where machine learning techniques are employed for activity recognition and classification. The k-NN and Naïve Bayes classifiers achieved the highest accuracy rates, with 77.8% for smartphones and 76.1% for microcontrollers. This system provides a practical solution for recognizing and monitoring worker activities, with potential applications in factories for enhancing health, safety, and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wearable sensors

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Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-Based Feature Selection and PCA for Improved Accuracy 5

Optimized Deep Learning Mechanism for Intrusion Detection: L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rajkumar, M. Vs, Lakshi Karthik, R. Pavithra, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Smart Computing Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331530013

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. By leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-NB15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-NB15 dataset for intrusion classification. © 2024 IEEE.

关键词： Network intrusion

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Comparative Analysis of Simulation Models for Evolving Robot Collective Motion in Industrial Coverage and Inspection Tasks 51

Comparative Analysis of Simulation Models for Evolving Robot...

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51st International Conference on Computers and Industrial engineering, CIE 2024

作者： Ghanem, Reda Ali, Ismail M. Kasmarik, Kathryn Garratt, Matthew School of Systems and Computing University of New South Wales CanberraACT2600 Australia School of Engineering and Technology University of New South Wales CanberraACT2600 Australia

This study presents a comprehensive comparative analysis of simulation approaches for optimizing the collective motion behavior of robot swarms, with a focus on industrial applications. We investigate the feasibility of bridging the simulation-reality gap by utilizing fast, low-fidelity simulators for parameter evolution, thereby enhancing the efficiency of real-world deployments. Our analysis examines transferability of optimized swarm parameters derived from two low-fidelity simulators to a high-fidelity robot simulation environment. These low-fidelity simulators are employed to fine-tune motion parameters for both ground and aerial vehicle swarms addressing complex coverage problems. The results indicate that while a differential drive simulator requires significantly more time to generate parameters compared to a point-mass simulator, it yields superior swarming behavior and coverage performance across both robot types in high-fidelity simulations. This work highlights the potential for effective parameter transfer to real-world scenarios, paving the way for advanced applications in areas such as autonomous inspection, environmental monitoring, and search-and-rescue operations. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Genetic algorithms

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Use of simulation for pre-training of RPAS pilots

Use of simulation for pre-training of RPAS pilots

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Somerville, Alexander Lynar, Tim Joiner, Keith Wild, Graham UNSW Canberra School of Engineering and Technology CanberraACT2612 Australia UNSW Canberra School of Systems and Computing CanberraACT2612 Australia UNSW Canberra School of Science CanberraACT2612 Australia

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This study examines how effective simulator-based training systems are in improving drone piloting skills and performance. Using a quasi-experimental research design, it assesses the impact on accuracy, efficiency, and perceived workload among UAV pilots. By drawing on historical simulation practices from crewed aviation and applying instructivist educational principles, the research evaluates whether structured simulator training can enhance real world drone operation skills. The findings show a significant improvement in flight performance for those who underwent simulator training, emphasizing the importance of integrating advanced simulation technologies and instructivist methods in drone pilot training programs to address the evolving needs of both industry and academia. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Drones

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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