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Reflecting on a community of practice for engineering education research capacity in Africa: who are we and where are we going?

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Australasian Journal of engineering Education 2023年第1期28卷 74-84页

作者： Matemba, Esther Smith, Lelanie Wolff, Karin Inglis, Helen Mogashana, Disaapele Jansen, Lauren Gwynne-Evans, Alison Campbell, Anita L. Kwuimy, Cedrick Nassar, Shamim Magara, Irene Kloot, Bruce Hattingh, Teresa Raji, Atanda Musa, Tagwa Nyamapfene, Abel School of Civil and Mechanical Engineering Curtin University Perth Australia Faculty of Engineering Built Environment and Information Technology University of Pretoria Pretoria South Africa Faculty of Engineering Stellenbosch University Stellenbosch South Africa Faculty of Engineering and the Built Environment University of Cape Town Cape Town South Africa Department of Engineering and Computing Education University of Cincinnati Cincinnati United States Department of Electrical Electronic and Information Engineering South Eastern Kenya University Kitui Kenya Faculty of Applied Sciences and Technology Mbarara University of Science and Technology Mbarara Uganda Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa Faculty of Engineering and the Built Environment Cape Peninsula University of Technology Cape Town South Africa Department of Petroleum Engineering Sudan University of Science and Technology Khartoum Sudan Faculty of Engineering Sciences University College London London United Kingdom

The engineering Education Research Network in Africa (EERN-Africa) was created to enable connections between practitioners and researchers with a shared interest in African engineering education contexts. Recognising the importance of developing an African voice in the engineering education research space, the EERN-Africa community has interacted in a dynamic and dialogic way with our own teaching and research practices across diverse African contexts, with an ethical commitment to democratic and inclusive community-building. The objective of this paper is to reflect on the current status of the Community of Practice (CoP), and the challenges and opportunities in sustaining and growing the CoP. A collaborative analysis of perspectives on this emerging identity is presented, using an Appreciative Inquiry (AI) methodology and drawing on collective written reflections and discussions. Six broad themes on the value that the CoP has for both individuals and the group were identified: networking, capacity development, emotional support, impact on professional identity, social and environmental impact, and breaking borders. This paper contributes an approach for collaborative capacity-building in EER through a virtual CoP, underpinned by the spirit of ubuntu. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Environmental impact

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Enhanced Layer Extraction for Efficient Plant Disease Classification using Efficientnet B1

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SN Computer Science 2025年第4期6卷 1-15页

作者： Thakur, Arastu Thakur, Abhilasha Vivek, V. Mahesh, T.R. Krishna, K. Murali Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India Department of Agriculture Marwadi University Gujarat Rajkot India Alliance School of Advanced Computing Alliance University Chandapura - Anekal Main Road Bengaluru Anekal 562106 India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India

Agriculture is crucial for the global economy, providing sustenance and resources for various industries. However, plant diseases threaten crop quality and yield, risking severe economic impacts. Traditional plant disease diagnosis methods, like manual inspection or basic machine learning, are slow, require expertise, and struggle with scalability and adapting to new diseases. This study introduces a deep learning model using convolutional neural networks (cnns) to autonomously detect and classify plant diseases from images, enhancing speed, scalability, and accuracy. We utilize the efficientnetb1 architecture, pretrained on imagenet and fine-tuned on a dataset of bell pepper, potato, and tomato diseases, recognizing 14 diseases plus healthy plants. The model achieves 98.80% accuracy on the test dataset, with high precision, recall, and F1-scores, demonstrating its effectiveness in plant disease diagnosis. This advancement marks a significant step in automating plant disease diagnosis, offering a robust tool for effective disease management in agriculture. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agriculture Automated diagnosis Deep learning Disease management Efficientnetb1 Image classification Plant disease detection Transfer learning

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Unveiling Ethical Challenges and Malpractice in Academic Research and Integrity 3

Unveiling Ethical Challenges and Malpractice in Academic Res...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Singh Bist, Ankur Rawat, Bhupesh Rahardja, Untung Hardini, Marviola Kamal, Mustofa Graphic Era Hill University Faculty of Computer Science & Engineering Uttarakhand India Graphic Era Hill University Faculty of Computing Uttarakhand India University of Raharja Faculty of Information Technology Tangerang Indonesia University of Raharja Faculty of Computer System Tangerang Indonesia

ISBN: (纸本)9798350367492

In the pursuit of knowledge and innovation, academic research stands as a cornerstone of intellectual progress. However, the integrity of this process is susceptible to malpractice, raising profound ethical concerns. This research paper delves into the nuanced landscape of malpractice and ethical considerations within the realm of academic research, aiming to shed light on the challenges that undermine the credibility and reliability of scholarly endeavours. The study explores various forms of malpractice, including plagiarism, data fabrication, and authorship disputes, which have the potential to erode the foundations of academic integrity. Through a combination of literature reviews, case studies, and expert interviews, the research seeks to identify patterns and motivations behind such malpractices. The findings reveal that systematic solutions are necessary to address these issues, and propose the integration of advanced technological tools to enhance ethical oversight. This study contributes to the ongoing discourse on ethics in academic research by providing comprehensive insights and recommendations to uphold ethical standards. © 2024 IEEE.

关键词： Confidentiality Ethics Informed Consent Plagiarism Professional Misconduct

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Hybrid programming and multiple GPUs implementation for Particle-In-Cell 8

Hybrid programming and multiple GPUs implementation for Part...

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8th International Conference on Computer and Communication Systems, ICCCS 2023

作者： Krishnasamy, Ezhilmathi Vasileska, Ivona Kos, Leon Bouvry, Pascal University of Luxembourg Parallel Computing and Optimization Group Belval Luxembourg University of Ljubljana Faculty of Mechanical Engineering Ljubljana Slovenia

ISBN: (纸本)9781665456128

Numerical modelling in fusion physics is crucial for studying fusion devices, space, and astrophysical systems. The plasma simulations of fusion devices demand a kinetic approach to handle extreme nonlinearities methods. One of the most used plasma kinetic simulation codes is the Particle-In-Cell (PIC). The HPC systems worldwide are getting more powerful with the combination of CPU, GPU, and other accelerators (e.g., FPGAs and Quantum Processors). Moreover, we can already notice that several exascale machines are operational worldwide;one typical example is the Frontier (Oak Ridge National Laboratory) exascale machine. In parallel, the same effort is being made for scientific algorithms to use robust HPC systems efficiently. Many programming frameworks (e.g., OpenACC, OpenMP offloading, and SYCL) mainly offer excellent support portability to the existing scientific codes to use the exascale HPC systems. This work demonstrates hybrid and multiple GPUs capabilities (or portability) for Simple Particle-In-Cell (SIMPIC) based on the PIC algorithm. First, we have implemented the hybrid (MPI+OpenMP) portability and multiple GPUs (multiple node GPU with the help of MPI) offloading portability. The first implementation gains a speed up to 40% compared to the plain MPI version, and the second implementation achieves up to 40% speedups compared to the hybrid (MPI+OpenMP) implementation. © 2023 IEEE.

关键词： Application programming interfaces (API)

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Parkinson's Disease Induced Gain Freezing Detection using Gated Recurrent Units Optimized by Modified Crayfish Optimization Algorithm 5

Parkinson's Disease Induced Gain Freezing Detection using Ga...

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5th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Bacanin, Nebojsa Petrovic, Aleksandar Jovanovic, Luka Zivkovic, Miodrag Zivkovic, Tamara Sarac, Marko Singidunum University Department of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia School of Electrical Engineering Belgrade Serbia

ISBN: (纸本)9798350395235

Parkinson's disease belongs to the group of health problems that are incurable but can be mitigated if treated properly. While there is no way of curing the damage caused by the disease, patient's life quality can be improved if diagnosed and treated properly and on time. The role of artificial intelligence (AI) in medicine is increasing. Deep learning algorithms may be utilized to automatically detect freezing of gait episodes. This study is focused on Parkinson's disease diagnosis based on the disturbances in the patient's gait which is affected by this disease. A hybrid deep and machine learning AI solution that employs the gated recurrent unit (GRU) neural network optimized by a hybrid swarm intelligence solution between the crayfish optimization algorithm and the firefly algorithm has been proposed. The proposed solution is compared to other high-performing optimization algorithms to establish objective grounds for comparison. The proposed framework results in the overall best performance and confirms the made improvements. The best-constructed model attained an accuracy of 87.08%. © 2024 IEEE.

关键词： Patient treatment

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Robust DOA Estimation Against Outliers Based on Bias-compensated Adaptive Filtering 7

Robust DOA Estimation Against Outliers Based on Bias-compens...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Xiao, Wudang Yu, Canping Li, Yingsong Li, Liping Zou, Dingcheng Zlatanov, Nikola Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Innopolis University Faculty of Computer and Engineering Sciences Innopolis Russia

ISBN: (纸本)9798350384437

The direction-of-arrival (DOA) estimation approach utilizing the adaptive nulling array framework has gained widespread attention due to its low complexity. However, in impulsive noise environments, traditional least mean squares (LMS) algorithm may experience a sharp decline in performance or even fail to work properly. To address this challenge, we propose a DOA estimation approach designed for impulsive noise situations based on a robust nonlinear function. In addition, the algorithm can mitigate bias caused by auxiliary array noise and uses the variable step-size strategy to enhance its performance. Furthermore, the mean stability of the proposed approach is also assessed. Simulation results demonstrate its superior performance in impulsive noise environments. © 2024 IEEE.

关键词： Adaptive filtering

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AI BASED SHORT-TERM PREDICTION OF Q-BAND RAIN ATTENUATION

AI BASED SHORT-TERM PREDICTION OF Q-BAND RAIN ATTENUATION

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IET 6G and Future Networks Conference, IET 6G 2024

作者： Abolarinwa, Kamolideen Roula, Ali Karasuwa, Abdulkareem Otung, Ifiok Faculty of Computing Engineering and Sciences University of South Wales Treforest United Kingdom Bradford-Renduchintala Centre for Space AI University of Bradford United Kingdom

ISBN: (纸本)9781837241521

In broadband satellite networks, adaptive coding and modulation (ACM) serves as a standard practice to mitigate the disruptive effects of rain fades, particularly at extremely high frequency (EHF) bands. ACM has emerged as an indispensable component of Fade Mitigation Techniques (FMTs) to bolster signal resilience. Nonetheless, a notable challenge lies in ACM's operation without real-time channel state information. To tackle this hurdle, this study leverages Deep Learning (DL) algorithms for rain attenuation prediction, facilitating efficient and optimal selection of modulation and coding (MODCODs) schemes within the ACM framework. The DL algorithm uses recent past rain attenuation data to forecast short-term trends. The investigation delves into the influence of recent past data on the accuracy of DL algorithms and evaluates their efficacy in enhancing predictions. The attained results furnish insights that underpin the development of a novel rain attenuation prediction framework. This framework aims to augment the overall MODCOD selection process of the Digital Video Broadcasting - Return Channel via Satellite - Second Generation (DVB-RCS2) Standards, bolstering reliability and resilience, particularly in adverse weather conditions. © 2024 The Institution of engineering & Technology.

关键词： Digital video broadcasting (DVB)

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Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition

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Computers, Materials & Continua 2023年第2期74卷 4135-4147页

作者： Zaid Nidhal Khudhair Farhan Mohamed Amjad Rehman Tanzila Saba Saeed Ali bahaj Faculty of Engineering School of ComputingUniversity Technology of MalaysiaJohar BahruMalaysia UTM-IRDA MaGICX Institute of Human Centered EngineeringUniversiti Teknologi MalaysiaMalaysia Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan University Riyadh Saudi Arabia Computer Techniques Engineering Department Faculty of Information TechnologyImam Ja’afar Al-Sadiq UniversityBaghdadIraq

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined *** each step,the SVD is ***,the diagonal matrix’s maximum value(norm)is selected(representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled).Then,the similar norms are grouped,and each leading group is separated into many subgroups(elements of each subgroup are neighbors)according to 8-adjacency(the subgroups for each leading group must be far from others by a specific distance).After that,a weight is assigned for each subgroup to classify the image as forgery or ***,the F1 score of the proposed system is measured,reaching 99.1%.This approach is robust against rotation,scaling,noisy images,and illumination *** is compared with other similarmethods and presents very promised results.

关键词： Forgery image forensic image processing region duplication SVD transformation technological development

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Dementia Care Using AI: Real-time Patient Trajectory Monitoring System

Dementia Care Using AI: Real-time Patient Trajectory Monitor...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Vikash, P.R. Dharsan, S. Aravindan, Madhu Shree Winster, Godfrey S. Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350305708

Dementia, characterised by cognitive decline, has emerged as a significant global health concern, with a new case diagnosed every 3 seconds worldwide. This study addresses the challenges faced by dementia carers through an innovative approach that combines Progressive Web App (PWA) technology with machine learning and GPS trajectory analysis, customised to aid in care. The proposed method identifies and addresses gaps in existing research, aiming to improve the lives of individuals in the early stages of dementia and their carers by offering real-time anomaly detection and alerts based on patient trajectory data. The methodology encompasses several pivotal stages: Data acquisition and transmission involve collecting and relaying patient trajectory data to the cloud;the isolation forest technique, a sophisticated anomaly detection method, identifies deviations from normal behaviour within patient trajectories. Real-time anomaly detection and alert generation provide swift notification to carers in the event of abnormal patient movements. The approach also encompasses carer engagement and response dynamics. Carers play a crucial role in determining the veracity of detected anomalies and responding accordingly. Based on carer feedback, the system adapts and updates its algorithm for symptom detection and threshold setting. In conclusion, the integrated strategy presented in this study offers an innovative and effective approach to enhancing the quality of care for individuals with dementia and their carers, paving the way for enhanced patient monitoring and carer engagement. © 2023 IEEE.

关键词： anomaly detection artificial intelligence behaviour analysis Dementia patient trajectory monitoring

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An efficient resource optimization scheme for D2D communication

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Digital Communications and Networks 2022年第6期8卷 1122-1129页

作者： Mohammad Haseeb Zafar Imran Khan Madini O.Alassafi Faculty of Computing and IT King Abdulaziz UniversityJeddah21589Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffUK Department of Electrical Engineering University of Engineering&TechnologyPeshawar814Pakistan

With the rapid development of wireless technologies,wireless access networks have entered their Fifth-Generation(5G)system *** heterogeneous and complex nature of a 5G system,with its numerous technological scenarios,poses significant challenges to wireless resource management,making radio resource optimization an important aspect of Device-to-Device(D2D)communication in such *** D2D communication can improve spectrum efficiency,increase system capacity,and reduce base station communication burdens by sharing authorized cell resources;however,can also cause serious ***,research focusing on reducing this interference by optimizing the configuration of shared cellular resources has also grown in *** paper proposes a novel algorithm to address the problems of co-channel interference and energy efficiency optimization in a long-term evolution *** proposed algorithm uses the fuzzy clustering method,which employs minimum outage probability to divide D2D users into several groups in order to improve system throughput and reduce interference between *** efficient power control algorithm based on game theory is also proposed to optimize user transmission power within each group and thereby improve user energy *** results show that these proposed algorithms can effectively improve system throughput,reduce co-channel interference,and enhance energy efficiency.

关键词： Radio resource management Energy efficiency optimization Inter:face mitigation

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