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Discrepancies of remote techno-tolerance due to COVID-19 pandemic within Arab middle-east countries

Journal of Umm Al-Qura University for engineering and Architecture 2023年第3期14卷 151-165页

作者： Abu-Hashem, Muhannad A. Gutub, Adnan Salem, Osama Shambour, Mohd Khaled Shambour, Qusai Shehab, Mohammad Izzat, Ahmad Alrawashdeh, Mufda J. Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia Computer Engineering Department College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Educational Technology and E-Learning Department College of Education Umm Al-Qura University Makkah Saudi Arabia The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Software Engineering Department Faculty of IT Al-Ahliyya Amman University Amman Jordan Artificial Intelligence & Software Engineering Departments Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Faculty of Engineering and Information Technology Israa University Gaza Palestine Department of Mathematics College of Sciences and Arts Qassim University Al-Ras Saudi Arabia

The coronavirus disease (COVID-19) changed the world’s lifestyle switching many techno-services to be provided remotely instead of direct usual physical interactions between people. This study focused on university students’ perceptions of this virtual technology-engineering change as discrepancies to be analyzed. The research surveyed 777 different students from four Arab middle-east neighboring countries, with related demographics and specifications, expressing full remarkable experiences of e-technology in virtual as well as traditional performances helping reveal overall tolerance possibilities. The study adopted examination technology via ANOVA to test discrepancies within students' perceptions for overall multi-factors deep analysis classification. The exploration highlighted an interesting range of pros and cons remarks including feminism and economic effect as well as other cultural and engineering interaction issues, raising signals to essentially consider and mutually benefit from adequate future generation e-techno adaptation within the region. © The Author(s) 2023.

关键词： ANOVA COVID-19 E-techno Students’ perceptions Traditional and virtual classes

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Analysis of Legged Wheels with Spring Suspension for Stair-Climbing Robotics

Analysis of Legged Wheels with Spring Suspension for Stair-C...

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Magomedov, Islam Aygumov, Timur Ordonez-Avila, Jose Luis Kadyrov Chechen state university Grozny Russia Dagestan State Technical University Department at of Software Engineering Computer Technology and Automated Systems Dagestan Russia San Pedro Sula Honduras

ISBN: (纸本)9798331511241

The following article looked into legged wheels with spring suspension. The idea behind the work was to see how adding springs in various places and their stiffness would impact the robot's ability to navigate through stairs. Due to the unconventional appearance of legged wheels, they are not suitable for even terrain passage. Hence, the idea was to add springs to minimize shock occurrence each time a leg touches the even environment. It was also important to equip springs throughout the whole path. As the robot progresses through steps, a greater disturbance of the structure occurs. Two different ideas were tested. In the first scenario, springs were attached between the holder of the wheels and the main body. In the second scenario, springs were directly added to the legs. The extruded part of the leg was divided into two, and a spring was embedded between these two parts. Different values of spring stiffness were also examined. The results illustrated that the structure would go further by implementing springs in all four wheels or in the rear. However, the structure was tested only on an upward incline. With other setups, it might be different with the rearrangement of the springs. It was also noted that with higher stiffness of the springs, the structure became unstable. In the second scenario, results illustrated the slightest progress. For instance, when the front wheels' legs were modified, they showed better results. To understand the influence in more depth, further investigation of the problem must be conducted. © 2025 IEEE.

关键词： Industrial robots

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Smart Advertising Based on Customer Preferences and Manage the Supermarket 4

Smart Advertising Based on Customer Preferences and Manage t...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Eishan Dinuka, W.H.A. Wickramasinghe, A.Y.S.W. Weerasinghe, W.S.H. Karunaratne, K.P.G. Liyanapathirana, Chethana Rupasinghe, Lakmal Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

As a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users' feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users' preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application Programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly. © 2022 IEEE.

关键词： Sales

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Beyond N-Grams: Enhancing String Kernels With Transformer-Guided Semantic Insights

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IEEE Access 2025年 13卷 97779-97793页

作者： Nazar Zaki Reem Alderei Mahra Alketbi Alia Alkaabi Fatima Alneyadi Nadeen Zaki Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates Department of Information Systems and Security College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

The rapid advancements in large language models (LLMs) have led to the generation of sophisticated AI-produced texts, posing significant challenges in distinguishing machine-generated content from authentic human writing. This study presents a novel hybrid framework that effectively integrates string kernel approaches with deep contextual embeddings from state-of-the-art transformers for robust AI-generated text detection. We propose and evaluate four innovative kernel-based methods namely Attention-Augmented Kernel, Error Pattern Analysis, Transformer-Guided N-gram Selection, and a Custom Kernel Function each designed to uniquely capture semantic and structural distinctions of text. Extensive experiments conducted on eight diverse datasets comprising 2,501 total samples, featuring texts generated and enhanced by leading LLMs including GPT-3.5, GPT-4, DeepSeek, and KIMI, demonstrate superior performance of the proposed methods. Particularly, the Transformer-Guided N-gram Selection and the Custom Kernel Function consistently outperform baseline models, achieving near-perfect detection accuracy with significantly reduced computational complexity. Comprehensive hyperparameter optimization further solidifies our methods’ effectiveness and practical applicability. The publicly available datasets and robust empirical evaluations contribute valuable benchmarks for future research. This work sets a new standard in AI-text detection methodologies, enhancing reliability, efficiency, and scalability for real-world applications.

关键词： Transformers Benchmark testing Multilingual Adaptation models Robustness Kernel Computational modeling Text detection Artificial intelligence Semantics

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Network Traffic Classifiers for Internet of Things Intrusion Detection using Machine Learning Classifiers

Network Traffic Classifiers for Internet of Things Intrusion...

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Anita Uniyal Amit Kumar Mishra Manushi Yogesh Pant Computer Science & Engineering GRD IMT Dehradun India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India Department of Computer Science & Engineering GRD IMT Dehradun India Software Business Systems Consultanting India Private Limited

In today's era smart devices are integral part of our life and every device becomes smart if it is equipped with sensors and having ability to connect with internet. Without any hindrance we can connect and interact with soundings via smart devices, this is possible because of the technological advancement such as cloud computing and Internet of things. With the latest technologies we are using advance algorithms such as machine learning to make our decision more effective. The importance and need of smart devices in life have more vulnerability attacks so it needs concrete security mechanism and privacy policies to safeguard from cyber security and threats via intrusion detection. The existing IoT security literature is thoroughly analysed in this paper, outlining the many vulnerabilities and possible defences. The paper also looks into the use of machine learning techniques for traffic inflow classification, highlighting comparisons of different classifiers for problems that are special to different domains and different machine learning techniques for early identification of intrusion detection. The paper is concluded with a review of the current problems and potential future directions for IoT security research.

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Utilization of Machine Learning In RTL-GL Signals Correlation 8

Utilization of Machine Learning In RTL-GL Signals Correlatio...

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8th International Conference on Signal Processing and Integrated Networks, SPIN 2021

作者： Alhaddad, Medhat Ashraf Hussein, Seif Eldin Mohamed Helmy, Abanoub Ghadban Nagy, Nagy Raouf Ghazy, Muhammad Ziad Muhammad Yousef, Ahmed H. Computer Engineering and Software Systems Department Ain Shams University Cairo Egypt Ain Shams University Nile University Egypt

ISBN: (纸本)9781665435642

Verification is an important part of the Electronic Design Automation (EDA) design flow which currently takes a considerable amount of time. During the synthesis process, Different optimizations are done to the Register-Transfer-Level (RTL) code to optimize the power, area, and speed of the circuit. These optimizations result in changes in the names of signals at the gate level. Automatic signal mapping can improve the verification process and can be used to guide functional verification activities between the two presentations using (Clock domain crossing (CDC) analysis in RTL, Gate Level CDC) as well as Formal Sequential Equivalence Checking. Machine Learning has applications everywhere in our lives, and EDA is not an exception. Machine Learning can help EDA tools optimize their results by learning from their previous choices which would improve their results. Machine Learning can help in decreasing the amount of time used in the verification process and increase productivity. In this paper, we discuss the methods used in the signal correlation, propose a modification to one of them as well as present a new technique using Machine Learning to solve the problem of signal correlation between the RTL and the gate level. © 2021 IEEE

关键词： Machine learning

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M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans

arXiv

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arXiv 2024年

作者： Juwita, Juwita Hassan, Ghulam Mubashar Akhtar, Naveed Datta, Amitava Department of Computer Science and Software Engineering The University of Western Australia Australia School of Computing and Information Systems The University of Melbourne Australia Department of Informatics Syiah Kuala University Banda Aceh Indonesia

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on the widely known NIH pancreas dataset and MSD pancreas dataset. Our approach demonstrates a considerable performance improvement, achieving an average Dice Similarity Coefficient (DSC) value of up to 89.53%±1.82 and an Intersection Over Union (IOU) score of up to 81.16±0.03% for the NIH pancreas dataset, and 88.60%±1.48 DSC and 79.90%±2.19 IOU for the MSD Pancreas dataset. © 2024, CC BY-NC-SA.

关键词： computerized tomography

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M3bunet:Mobile Mean Max Unet for Pancreas Segmentation on Ct-Scans

SSRN

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SSRN 2024年

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on NIH and MSD pancreas datasets. Our approach demonstrates performance improvement, achieving an average DSC value of up to 89.53±1.82 and IOU score of up to 81.16±0.03 for the NIH, and 88.60 ±1.48 DSC and 79.90±2.19 IOU for the MSD. © 2024, The Authors. All rights reserved.

关键词： computerized tomography

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Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

arXiv

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arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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