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Learning and technology Conference (L&T)

作者： Jawahir Che Mustapha Muhammad Azmin Mohamed Ghazali Munaisyah Abdullah Nor Intan Saniah Sulaiman Hezlin Aryani Abd Rahman Informatics & Analytics Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Computer Engineering Tech. Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Software Engineering Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia College of Arts and Science School of Quantitative Sciences Universiti Utara Malaysia Kedah Malaysia Centre of Statistical and Decision Science Studies Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Selangor Malaysia

This research investigates the complex relationships between social media, body image, and bullying among adolescent boarding school students, with an emphasis on the Malaysian context. Utilizing science fiction as a conceptual lens, the study presents hypothetical future scenarios to investigate how technological advancements could impact these interconnected issues. The paper seeks to provide nuanced insights into the ethical and policy implications of social media use in boarding schools by drawing on key theories such as Social Comparison Theory, Social-Ecological Model and Uses and Gratifications Theory. Through comparative analysis of hypothetical scenarios, this study aims to provide educators, policymakers, and technologists with a comprehensive understanding of the challenges and opportunities at this intersection. The ultimate objective is to contribute to the ongoing discourse concerning adolescent mental health and to propose actionable strategies for mitigating negative outcomes in the unique context of Malaysian boarding schools.

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Line-of-Sight Visual Target Tracking via Particle-Based Belief Propagation

Line-of-Sight Visual Target Tracking via Particle-Based Beli...

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American Control Conference (ACC)

作者： Tony X. Lin Manav Gagvani Sean Lindstrom Donald A. Sofge Fumin Zhang School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Distributed Autonomous Systems Section U.S. Naval Research Laboratory Washington DC USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper considers the problem of controlling a team of camera-equipped robots to ensure a mobile human target is observed by at least one robot at all times. We focus on determining team trajectories over a fixed time horizon such that the human target is in at least one robot's field of view even while the line of sight to the target can be obstructed by obstacles. Our approach to solving this problem lies in using a particle-based Belief Propagation method to estimate the most likely poses for each robot over time such that the visual tracking requirement is satisfied. Our approach is validated in simulation studies and an experimental trial where a human target must be tracked by a pair of miniature autonomous robot blimps.

关键词： Legged locomotion Visualization Target tracking Smoothing methods Soft sensors Robot vision systems Search problems

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Nest: A Novel Ensemble Method for Spatio-Temporal Gait Parameters Measurement

SSRN

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SSRN 2024年

作者： Hsu, Chih-Chao Lai, Hsu-Chao Jhang, Guan-Yi Huang, Jiun-Long Wang, Jun-Zhe Department of Computer Science National Yang Ming Chiao Tung University 1001 University Road Hsinchu City30010 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology No. 123 Section 3 Daxue Road Yunlin County640301 Taiwan Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology No. 123 Section 3 Daxue Road Yunlin County640301 Taiwan

Accurately estimating spatio-temporal gait parameters such as stride height, stride length, stance time, swing time, and stride speed, is crucial for sports medicine and preventive healthcare. Therefore, this paper proposes a novel ensemble method, NEST (standing for Novel Ensemble method for Spatio-Temporal gait parameters measurement), for the multi-task measurement of the aforementioned five spatio-temporal gait parameters. NEST consists of a K-Nearest Neighbor (KNN) regressor branch and a deep learning branch. The KNN regressor branch provides initial estimates, allowing other neural networks to learn to reduce the residual between these estimates and the ground truths. This helps NEST rapidly identify a good optimization direction during the early stage of fine-tuning and expedite convergence speed. The deep learning branch facilitates information sharing among multiple task-specific representations through fully-connected layers, effectively preserving the interdependencies among gait parameters. Several experiments are conducted to evaluate the performance of NEST and other prior methods. Compared to prior handcrafted-statistics-based methods, NEST demonstrates over 65.1% improvement in RMSE (Root-Mean-Square Error) when predicting spatial parameters. NEST also outperforms prior deep-learning-based methods not only in measurement accuracy but also in reducing training time by more than 14.6%. © 2024, The Authors. All rights reserved.

关键词： Sports medicine

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Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model

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computers, Materials & Continua 2021年第8期68卷 2413-2429页

作者： Tapan Kumar Das Pradeep Kumar Roy Mohy Uddin Kathiravan Srinivasan Chuan-Yu Chang Shabbir Syed-Abdul School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering Indian Institute of Information TechnologySurat395007India Research Quality Management Section King Abdullah International Medical Research CenterKing Saud bin Abdulaziz University for Health SciencesMinistry of National Guard-Health AffairsRiyadh11426Kingdom of Saudi Arabia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Graduate Institute of Biomedical Informatics Taipei Medical UniversityTaipeiTaiwan

Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s ***,the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory,clinical mapping,and delusion *** this proposed study,a deep learning based framework that employs deep convolution neural network(Deep-CNN),by utilizing both clinical presentations and conventional magnetic resonance imaging(MRI)investigations,for diagnosing tumors is *** research aims to develop a model that can be used for abnormality detection over MRI data quite efficiently with high *** research is based on deep learning and Deep-CNN was deployed to examine the MR brain image for tracing the *** system runs on Tensor flow and uses a feature extraction module in DeepCNN to elicit the factors of that part of the image from where underlying issues are identified and subsequently succeeded in prediction of the disease in the MR *** results of this study showed that our model did not have any adverse effect on classification,achieved higher accuracy than the peers in recent years,and attained good detection outcomes including case of *** the future work,further improvement can be made by designing models that can drastically reduce the parameter space without affecting classification accuracy.

关键词： Deep learning convolutional neural network brain tumor magnetic resonance imaging

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Novel intelligent exogenous neuro-architecture–driven machine learning approach for nonlinear fractional breast cancer risk system

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Communications in Nonlinear Science and Numerical Simulation 2025年 149卷

作者： Fida, Afshan Raja, Muhammad Asif Zahoor Chang, Chuan-Yu Raja, Muhammad Junaid Ali Asif Khan, Zeshan Aslam Shoaib, Muhammad of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan AI center Yuan Ze University Taoyuan 320 Taiwan

Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, necessitating mathematical modelling frameworks to capture the complexity of its progression and risk factors. This research endeavor uncovers the novel machine learning expedition using an Adaptive Nonlinear AutoRegressive eXogenous (ANARX) neural network on a Fractional Order Breast Cancer Risk (FO-BCR) model. A novel Caputo fractional operator-based breast cancer risk model is presented using a five compartmental system reflected by healthy, tumor, immune, estrogen, and fatty cells. A modified fractional Adams PECE method is opted to generate solutions of the five fractional order variants on the four diverse BCR scenarios. These temporal sequences are parsed as ground truth for the adept ANARX network, which is iteratively refined using the Levenberg-Marquardt (LM) algorithm. The performance evaluation of the temporal feature learning of the ANARX-LM algorithm is comprehensively evaluated against reference numerical outcomes using mean square error (MSE) performance graphics, input-error cross correlation, error autocorrelation, error histogram analysis, sequential response and comparative error analysis charts. Low disparity between reference solutions is observed for all FO-BCR systems, with MSE errors in the range of 10−8 to 10−11. Finally, the ANARX-LM's predictive prowess is evaluated using the single and multistep configurations. Minute errors in the range of 10−9 to 10−11, 10−8 to 10−10 suggest accurate anticipation of the FO-BCR system enabling preventive and prognostic measures for breast cancer models. These empirical findings underscore the potential of advanced machine-learning-driven neuro-architecture for next-generation predictive-oncology solutions that may facilitate treatment strategies. © 2025 Elsevier B.V.

关键词： Risk assessment

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A computational supervised neural network procedure for the fractional SIQ mathematical model

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European Physical Journal: Special Topics 2023年第5期232卷 535-546页

作者： Mukdasai, Kanit Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Singkibud, Peerapongpat Sadat, R. Ali, Mohamed R. Department of Mathematics Faculty of Science Khon Kaen University Khon Kaen 40002 Thailand Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Applied Mathematics and Statistics Faculty of Science and Liberal Arts Rajamangala University of Technology Isan Nakhon Ratchasima 30000 Thailand Department of Mathematics Faculty of Engineering Zagazig University Zagazig Egypt Faculty of Engineering and Technology Future University in Egypt New Cairo 11835 Egypt Basic Engineering Science Department Benha Faculty of Engineering Benha University Benha Egypt Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error. © 2022, The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature.

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Analysis of Convective Transport of Temperature-Dependent Viscosity forNon-Newtonian Erying Powell Fluid: A Numerical Approach

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computers, Materials & Continua 2021年第1期66卷 675-689页

作者： Ahlam Aljabali Abdul Rahman Mohd Kasim Nur Syamilah Arifin Sharena Mohamad Isa Noor Amalina Nisa Ariffin Centre for Mathematical Sciences UMPGambang26300Malaysia Faculty of Computer and Math Sciences UiTM JohorPasir Gudang CampusMasai81750Malaysia Manufacturing Engineering Technology Section UniKLItaly Design InstituteKuala Lumpur56100Malaysia

Non-Newtonian is a type of fluid that does not comply with the viscosity under the Law of Newton and is being widely used in industrial *** include those related to chemical industries,cosmetics manufacturing,pharmaceutical field,food processing,as well as oil and gas *** inability of the conventional equations of Navier–Stokes to accurately depict rheological behavior for certain fluids led to an emergence study for non-Newtonian fluids’*** line with this,a mathematical model of forced convective flow on non-Newtonian Eyring Powell fluid under temperature-dependent viscosity(TDV)circumstance is *** fluid model is embedded with the Newtonian heating(NH)boundary condition as a heating circumstance and is assumed to move over a stretching sheet acting *** appropriate similarity variables,the respective model was converted into ordinary differential equations(ODE),which was later solved utilizing the Keller box *** present model is validated by comparing the existing output in literature at certain special limiting cases,where the validation results display a firm *** current outputs for the proposed model are shown in tabular and graphical form for variation of skin friction plus Nusselt number,velocity and temperature distribution,respectively.

关键词： Temperature-dependent viscosity erying powell fluid vertical stretching sheet numerical solution boundary layer

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Mixed Convection of Non-Newtonian Erying Powell Fluid with Temperature-Dependent Viscosity over a Vertically Stretched Surface

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computers, Materials & Continua 2021年第1期66卷 421-435页

作者： Ahlam Aljabali Abdul Rahman Mohd Kasim Nur Syamilah Arifin Sharena Mohamad Isa Centre for Mathematical Sciences UMPGambang26300Malaysia Faculty Computer and Mathematical Sciences UiTM JohorPasir Gudang CampusMasai81750Malaysia Manufacturing Engineering Technology Section UniKLItaly Design InstituteKuala Lumpur56100Malaysia

The viscosity of a substance or material is intensely influenced by the temperature,especially in the field of lubricant engineering where the changeable temperature is well *** this paper,the problem of temperature-dependent viscosity on mixed convection flow of Eyring Powell fluid was studied together with Newtonian heating thermal boundary *** flow was assumed to move over a vertical stretching *** model of the problem,which is in partial differential equations,was first transformed to ordinary differential equations using appropriate *** approach was considered to reduce the complexity of the ***,the transformed equations were solved using the Keller box method under the finite difference scheme *** validation process of the results was performed,and it was found to be in an excellent *** results on the present computation are shown in tabular form and also graphical *** major finding was observed where the skin friction and Nusselt number were boosted in the strong viscosity.

关键词： Temperature-dependent viscosity Erying Powell fluid numerical solution combined convection

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5G High Mast Inspection Based on a Decentralized Autonomous Organization in the Framework of the OASEES Project

5G High Mast Inspection Based on a Decentralized Autonomous ...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Chochliouros, Ioannis Kourtis, Michail-Alexandros Xilouris, George Tavernier, Wouter Spiliopoulou, Anastasia Zaharis, Zaharias Lazaridis, Pavlos S.A. Fixed Network R&D Programs Section Maroussi-Athens15122 Greece National Center for Scientific Research "Demokritos" Agia Paraskevi15341 Greece IMEC-Department of Information Technology Ghent University Ghent9000 Belgium School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom

ISBN: (纸本)9783031494123

In this work, based on the framework of the ongoing OASEES EU-funded project, we have discussed the concept of a Decentralized Autonomous Organization (DAO) that operates on a blockchain and governed by smart contracts. Then we have focused upon the description of a specific use case related to high mast inspection of telecommunications infrastructures. OASEES intends to integrate the DAO paradigm for the self-organized operation of a drone swarm, which will rely on the tight consolidation of the networking (e.g., 5G/B5G) and edge acceleration technologies (e.g., Spiking Neural Networks). We have introduced this use case and also discussed current state-of-the-art together with existing limitations. In addition, we have identified intended scenarios of use and proposed specific requirements for conformance to the corresponding project architecture. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Drones

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An optimal control strategy for emergency vehicle priority system in smart cities using edge computing and IOT sensors

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Measurement: Sensors 2023年 26卷

作者： Rosayyan, Prakash Paul, Jasmine Subramaniam, Senthilkumar Ganesan, Saravana Ilango Intelligent Transportation and Networking Section of C-DAC Thiruvananthapuram Thiruvananthapuram695033 India Department of Computer Science and Engineering St. Thomas Institute for Science and Technology Thiruvananthapuram695584 India Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli620015 India

Smart city mission is a concept which improves economic growth and quality of life of people by using Information and Communication technology (ICT). Smart cities collect data from various Internet of Things (IoT) sensors and manage the resources and provide service to the public. Emergency Vehicle Priority/Pre-emption (EVP) is one of the major IoT components in smart cities which saves many lives by giving right of way green signal to the Emergency Vehicles (EVs) such as ambulances and fire engines. The existing EVP system gives green signal to the EVs immediate by stopping the conflicting movements. The sudden stoppage of normal traffic as well as the sequence change generates confusion to the road users and drivers. This needs an optimal control strategy which gives green signal to EVs as well as it should not affect the normal traffic flow. This paper proposes an optimal control strategy for EVP using edge computing and IoT sensor for smart cities. The experiment was conducted using a GPS based IoT sensor which keeps on sending the Location Information (LI) to the edge server. The edge server computes the optimum timings based on the proposed control strategy algorithm and clears the emergency vehicles. Waiting time of other road vehicles before and after implementing the system have been studied at a traffic junction at Thiruvananthapuram, India. The comparison result shows that the average waiting time of vehicles on the other road is reduced by 73.23% in the proposed system when compared with the existing system. Since, edge computing has been deployed the latency in communication has been reduced less than 100 ms. So, the proposed solution reduces the waiting time of other road vehicles and reduces the latency in communication simultaneously. © 2023

关键词： Internet of things

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