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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Bouthaina, Rechachi Rezeg, Khaled Okba, Kazar Merizig, Abdelhak Mohamed Khider University LINFI Laboratory Biskra Algeria Sharjah University Computing and Informatics College Arts Sciences & Information Technology College Kalba United Arab Emirates

ISBN: (纸本)9798350309249

Agriculture is a sector that represents a real indicator of the country's development, and it is a sector that affects national security. The palm tree is a significant, affordable, and nutrient-rich food source that gives millions of people access to wholesome food globally. It can get a lot of illnesses, much like other plants. DL has been effectively implemented in agriculture and has been employed in many other industries. This paper examines the latest advancements in the use of deep learning (DL) for the diagnosis of palm tree diseases. Particularly Convolutional Neural Networks (CNN) are often utilised in agricultural research due to its strong image processing ability. © 2024 IEEE.

关键词： Precision agriculture

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UD-assisted Multi-path Transport in RDMA 13

UD-assisted Multi-path Transport in RDMA

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Choi, Mingyu Lee, Sugi Kim, Younghoon College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Although Remote Direct Memory Access (RDMA) has become one of the most promising networking technologies in data centers, it is prone to link failures and several schemes have been proposed to utilize redundant network paths in RDMA for mitigating the problem. However, they still do not scale well since their schemes rely on a connection-based transport mode which uses more cache memory than a datagram-based mode in RNICs. In this paper, we propose a novel UD-based multi-path transport in RDMA (MPRUD). MPRUD not only addresses the scalability problem by employing UD QPs which occupy only a small amount of cache memory but also obtains robustness with failure detection and recovery algorithms. Our evaluation shows that MPRUD can achieve the line rate bandwidth with utilizing multi-paths and successfully recover from link failures about 72x faster compared to a default single-path flow, significantly decreasing data loss to 0.12% (from 4.5GB to 5.6MB). © 2022 IEEE.

关键词： Cache memory

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The convergence of Education 4.0 and Industry 4.0: a Twin Peaks model

Journal of Innovative Digital Transformation

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Journal of Innovative Digital Transformation 2024年第1期1卷 68-83页

作者： Ehsan Ahmad College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

Purpose This paper explores the convergence of Education 4.0 and Industry 4.0 and presents a Twin Peaks model for their seamless integration. Design/methodology/approach A high-level literature review is conducted to identify and discuss the important challenges and opportunities offered by both Education 4.0 and Industry 4.0. A novel Twin Peaks model is devised for the convergence of these domains and to cope with the challenges effectively. Findings The proposed Twin Peak model for the convergence of Education 4.0 and Industry 4.0 suggests that the development of these two domains is interdependent. It emphasizes ethical considerations, inclusivity and understanding the concerns of stakeholders from both education and industry. We have also explained how continuous incremental adaptation within the proposed Twin Peaks model might assist in addressing concerns of one sector with the opportunities of the other. Originality/value First, Education 4.0 and Industry 4.0 are reviewed in terms of opportunities and challenges they present. Second, a novel Twin Peaks model for the convergence of Education 4.0 and Industry 4.0 is presented. The proposed discovers that the convergence is adaptive, iterative and must be ethically sound while considering the broader societal implications of the digital transformation. Third, this study also acts as a torch-bearer for the necessity for more research of this kind to guarantee that our educational ecosystem is adaptable and capable of producing the skills required for success in the era of IR4.0.

关键词： Digital transformation Education 4.0 Industry 4.0 Twin Peaks model E-manufacturing

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The Role of Organizational Culture in Business Process Management Adoption: A Case Study

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Procedia Computer Science 2024年 234卷 663-673页

作者： Naif Aljlayel Computing and Informatics College Saudi Electronic University Riyadh Saudi Arabia

Business Process Management adoption requires extensive effort, time, resources and discipline. Therefore, several studies have attempted to identify the factors that influence BPM adoption. Most of studies identify organizational culture among the key factors, but few attempted to explore its influence on BPM adoption. Thus, this study sets out to explore the role of organizational culture in BPM adoption. This study employed a qualitative approach and held in-depth interviews. Twenty BPM professionals who are working in different Saudi Arabian organizations started adopting BPM have been selected. Commitment, Continuous improvement, Cross-functional teamwork, Customer centricity, Innovation and Process ownership are important organizational culture values influencing BPM adoption positively. This study contributes to theory by proposing a model including six important cultural values in BPM adoption and also increases awareness among practitioners of the importance of organizational culture in BPM adoption.

关键词： Business process management BPM adoption Organisational culture values

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Discovering the Conceptual Analysis of Interfusion Augmented Reality Model for Visually Impaired Interaction Experience Through Rapid Review Approach

Discovering the Conceptual Analysis of Interfusion Augmented...

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2024 International Conference on Green Energy, computing and Sustainable Technology, GECOST 2024

作者： Aziz, Nurulnadwan Ahmad, Siti Zulaiha Hamzah, Siti Salbiah Musa, Azuwan Khalid, Aishah Lokman, Anitawati Mohd Universiti Teknologi Mara Cawangan Terengganu Faculty of Business and Management Terengganu Dungun23000 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Cawangan Perlis Perlis Arau02600 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Cawangan Terengganu Terengganu Dungun23000 Malaysia Islamic International University Malaysia Kuliyyah of Medicine Pahang Kuantan25200 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9798350357905

In the era of IR 4.0, augmented reality (AR) has seen widespread growth in the information and communication technology (ICT) domain, yet existing AR models often neglect the interaction experience for visually impaired (VI) individuals. This study addresses this gap by integrating affective design (AD) principles into AR, immersive environments (IE), and assistive technology (AT). The main aim is to construct an interfusion AR model that caters to the specific needs of VI interaction, crucial for overcoming challenges faced by the VI in independent living. Prior to constructing the intended model, this study conducts a rapid review approach to analyze conceptual requirements with the objective of strengthening the research gap of the study. This outcome of this study aligns with Pillar 5 of the 12th Malaysian Plan, emphasizing the empowerment of disabled individuals and addressing social well-being for the VI, contributing to Sustainable Development Goal 10 on reducing inequalities. The research is poised to significantly impact the virtualization of VI interaction experiences, leveraging augmented reality advancements in the era of IR 4.0. © 2024 IEEE.

关键词： Augmented reality

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Super-Resolution Based on Curvelet Transform and Sparse Representation

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Computer Systems Science & Engineering 2023年第4期45卷 167-181页

作者： Israa Ismail Mohamed Meselhy Eltoukhy Ghada Eltaweel Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology University of JeddahCollege of Computing and Information Technology at KhulaisJeddahSaudi Arabia

Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s).In this paper,we proposed a single image super-resolution *** uses the nonlocal mean filter as a prior step to produce a denoised *** proposed algorithm is based on curvelet *** converts the denoised image into low and high frequencies(sub-bands).Then we applied a multi-dimensional interpolation called Lancozos interpolation over both *** parallel,we applied sparse representation with over complete dictionary for the denoised *** proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher *** experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced *** comparison study shows that the proposed super-resolution algorithm outperforms the *** mean absolute error is 0.021±0.008 and the structural similarity index measure is 0.89±0.08.

关键词： Super-resolution Curvelet transform non-local means filter lancozos interpolation sparse representation

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Decision-Aid Framework for Face Authentication Detection Using ResNext50 and BiLSTM to Enhance Media Integrity

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IEEE Access 2025年 13卷 89858-89873页

作者： Alrawahneh, Ayat Abd-Muti Abdullah, Siti Norul Huda Sheikh Abuain, Tarik Abdullah, Sharifah Nurul Asyikin Syed Khadijah Taylor, Sarah Suhaimi, Nur Hanis Sabrina Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi43600 Malaysia Saudi Electronic University College of Computing and Informatics Riyadh11673 Saudi Arabia CyberSecurity Malaysia Cyberjaya63000 Malaysia

This paper presents a decision-aid framework for face authentication detection that integrates ResNext50 with Bidirectional Long Short-Term Memory (BiLSTM) networks to enhance media integrity and improve deepfake detection. Unlike conventional approaches focusing solely on spatial features, the proposed hybrid model incorporates temporal analysis, enabling the detection of subtle manipulations distributed across sequential video frames. ResNext50 is a robust spatial feature extractor from cropped facial regions, while BiLSTM captures bidirectional temporal dependencies, enriching the model's contextual understanding and temporal sensitivity. The model was evaluated on three benchmark datasets - FaceForensics++, DFDC, and Celeb-DF - and demonstrated superior performance in classifying real versus manipulated facial content. The hybrid ResNext50 + BiLSTM model achieved a final accuracy of 96.11%, precision of 96.42%, recall of 96.68%, F1-score of 96.23%, and an AUC of 98.89%, outperforming the standalone ResNext50 and other state-of-the-art CNN-based approaches. These improvements reflect a notable reduction in false positives and negatives, critical for real-world face authentication systems. The proposed framework offers a scalable and robust solution for digital forensics, media verification, and cybersecurity applications, where maintaining trust in visual content is paramount. This study underscores the pivotal role of temporal modeling in improving the reliability of face manipulation detection. Future work will explore advanced fusion strategies, lightweight architectures for real-time deployment, and cross-dataset generalization to enhance adaptability in diverse, real-world scenarios. © 2013 IEEE.

关键词： Computer forensics

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Real-time human detection and behavior recognition using low-cost hardware 17

Real-time human detection and behavior recognition using low...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Wang, Bojun Ali, Sajid Fan, Xinyi Abuhmed, Tamer College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9781665453486

Cameras are becoming more pervasive and ubiquitous. The daily activities of individuals are being captured by millions of cameras in public spaces, while individuals are obtaining massive amounts of egocentric videos by employing wearable cameras intended for life-logging. However, recording devices are inexpensive, highly computational, and inconvenient for privacy. We used a low-resolution infrared sensor to detect human activity, including sitting, standing, and lying down, and to locate humans. We acquired the data from a low-cost infrared device and preprocessed them to train the YOLO-v5 network. We developed and tested an infrared technology-based system consisting of 32 × 24 thermal input. Our proposed model is trained on 3,864 low-resolution images and made publicly available. The trained YOLO-v5 achieved 96.34% mean Average Precision (mAP) using our designed lightweight and low-cost activity recognition device. We proposed Artificial Intelligence of Things (A-IoT) system can be used either as a stand-alone data collection such as an IoT device or as a data processing and analysis sub-center. Our system consists of a low-power edge computing device and a cost-effective low-resolution infrared module. Our proposed dataset is now available at https://***/InfoLab-SKKU/Thermal-Human-Detection © 2023 IEEE.

关键词： Cameras

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Multidimensional Data Clustering and Visualization Using Hybrid Artificial Neural Network

Multidimensional Data Clustering and Visualization Using Hyb...

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2022 International Conference on Computer and Drone Applications, IConDA 2022

作者： Leong, Yii Ming Engkamat, Adeline Ismail, Ismariani Binti Sarawak Branch Computing Sciences Studies College of Computing Informatics and Media Kota Samarahan Malaysia

ISBN: (纸本)9781665492355

All data with higher dimensions than 3D require some form of transformation to reveal its potential hidden patterns to assist human perception in making better data-related decisions. The exponential growth of multidimensional unlabeled data generated each day makes unassisted human perception harder to harness the full potential of the collected data that would provide insights. In order to bridge the gap between human visual perception and the multidimensional nature of modern-day data, this paper proposed a unique way of multidimensional data clusters discovery through a hybrid Artificial Neural Network (ANN) algorithms of Self-Organizing Map and modified Adaptive Coordinate (SOM-mAC). A Series of experiments conducted on synthetic 3D Gaussians dataset and benchmarking datasets Iris, Wine, and Wisconsin Breast Cancer (WBC) demonstrated that the proposed SOM-mAC algorithm could identify, cluster, and label the dataset's underlying topologies and densities. The algorithm can also identify and isolate potential outliers associated with each cluster. As clustering can be done in 2D through single-passing of the dataset through trained network, this algorithm has the potential for faster clustering of high dimensional big data. © 2022 IEEE.

关键词： Self organizing maps

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Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

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Computers, Materials & Continua 2022年第6期71卷 5855-5873页

作者： Reya Sharma Baijnath Kaushik Naveen Kumar Gondhi Muhammad Tahir Mohammad Khalid Imam Rahmani School of Computer Science and Engineering SMVDUJ&KIndia College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Even though several advances have been made in recent years,handwritten script recognition is still a challenging task in the pattern recognition *** field has gained much interest lately due to its diverse application ***,different methods are available for automatic script *** most of the reported script recognition techniques,deep neural networks have achieved impressive results and outperformed the classical machine learning ***,the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error,which renders them *** approach often requires manual intervention with domain expertise which consumes substantial time and computational *** alleviate this shortcoming,this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization(QPSO),which is capable of automatically evolving the meaningful convolutional neural network(CNN)*** computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts,namely Bangla,Devanagari,and Dogri,consisting of handwritten characters and ***,the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.

关键词： Neuro-evolution quantum particle swarm optimization deep learning convolutional neural networks handwriting recognition

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