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Innovative Research in Applied science, engineering and Technology (IRASET),Conference on

作者： Abdelkarim Ou-khouya Hassane Mes-adi Meryem Taoufiki Imane Ait Ibrahim Amin El Harfouf Rachid Herbazi Mohamed Tahiri Process Engineering Computer Science and Mathematics Laboratory Khouribga National School of Applied Sciences Sultan Moulay Slimane University Khouribga Morocco Faculty of Sciences and Techniques Sustainable Development Laboratory Sultan Moulay Slimane University Beni-Mellal Morocco Departement of Physics Laboratory of Condensed Matter and interdisciplinary Sciences CNRST Labeled Research Unit Faculty of Sciences Mohamed V University Rabat Morocco Department of Physics Polydisciplinary Faculty of Khouribga University of Sultan Moulay Slimane Khouribga Morocco Intelligent Systems and Applications Laboratory (LSIA) EMSI Tangier Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

In this study, we investigate the mechanical properties of gold (Au) materials using molecular dynamics simulations. In this simulation, the nanoindentation process was performed to analyze the deformation defects and hardness properties of Au under different indentation velocities. The results suggest that the deformation behavior of the Au specimen was affected by different indentation velocities. These findings were confirmed by examining the load-displacement curve, which indicated elastic deformation through the appearance of a linear load-displacement relationship at low indentation velocities. In contrast, plastic deformation was identified by the presence of intensified serrations in the load-displacement curve at higher indentation velocities. Under such conditions, the hardness exhibited minimal variation when the indenter velocity increased from 5 mls to 10 mls. However, a significant increase in hardness was noted when the indentation velocity was further raised to 15 mls.

关键词： Deformable models Gold Analytical models Deformation Dynamics Plastics Load modeling

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High-speed multiwavelength InGaAs/InP quantum well nanowire array micro-LEDs for next generation optical communications

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Opto-Electronic science 2023年第5期2卷 1-11页

作者： Fanlu Zhang Zhicheng Su Zhe Li Yi Zhu Nikita Gagrani Ziyuan Li Mark Lockrey Li Li Igor Aharonovich Yuerui Lu Hark Hoe Tan Chennupati Jagadish Lan Fu Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems Department of Electronic Materials EngineeringResearch School of PhysicsThe Australian National UniversityCanberra ACT 2601Australia School of Electronic Science and Engineering Southeast UniversityNanjing 210096China Microstructural Analysis Unit University of Technology SydneySydney NSW 2007Australia Australian National Fabrication Facility ACT Node Research School of PhysicsThe Australian National UniversityCanberra ACT 2601Australia ARC Centre of Excellence for Transformative Meta-Optical Systems Faculty of ScienceUniversity of Technology SydneySydney NSW 2007Australia School of Engineering College of Engineering and Computer ScienceThe Australia National UniversityCanberraCanberra ACT 2601Australia

Miniaturized light sources at telecommunication wavelengths are essential components for on-chip optical communication ***,we report the growth and fabrication of highly uniform p-i-n core-shell InGaAs/InP single quantum well(QW)nanowire array light emitting diodes(LEDs)with multi-wavelength and high-speed ***-dimensional cathodoluminescence mapping reveals that axial and radial QWs in the nanowire structure contribute to strong emission at the wavelength of~1.35 and~1.55μm,respectively,ideal for low-loss optical *** a result of simultaneous contributions from both axial and radial QWs,broadband electroluminescence emission with a linewidth of 286 nm is achieved with a peak power of~17μW.A large spectral blueshift is observed with the increase of applied bias,which is ascribed to the band-filling effect based on device simulation,and enables voltage tunable multi-wavelength operation at the telecommunication wavelength ***-wavelength operation is also achieved by fabricating nanowire array LEDs with different pitch sizes on the same substrate,leading to QW formation with different emission ***,high-speed GHz-level modulation and small pixel size LED are demonstrated,showing the promise for ultrafast operation and ultracompact *** voltage and pitch size controlled multi-wavelength highspeed nanowire array LED presents a compact and efficient scheme for developing high-performance nanoscale light sources for future optical communication applications.

关键词： InGaAs/InP quantum well nanowires LEDs

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Ameliorated Golden Jackal Optimization (Agjo) with Enhanced Movement and Multi-Angle Position Updating Strategy

SSRN

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SSRN 2023年

作者： Wahab, Magd Abdel Bai, Jianfu Khatir, Samir Abualigah, Laith Soete Laboratory Department of Electrical Energy Metals Mechanical Constructions and Systems Faculty of Engineering and Architecture Ghent University Belgium Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taiwan

Golden jackal optimization (GJO), a lately published meta-heuristic optimization algorithm, is inspired by the foraging behavior of pairs of golden jackals and shows an acceptable optimization performance. However, GJO exists a shortage in balancing exploration and exploitation, as it completely focused on exploitation in the later iterations. This study proposes a new variant of GJO, named Ameliorated Golden jackal optimization (AGJO). AGJO uses three strategies to alleviate the imbalance between the exploration and exploitation of GJO: the enhanced movement strategy, the global search strategy, and the multi-angle position update strategy for prey. An environmental disturbance factor is added to the third strategy to strengthen GJO's ability to evade the local optimal solution. The performance of AGJO is tested and compared with GJO and seven well-known meta-heuristic algorithms for 23 classical benchmark functions, CEC 2017 and CEC 2019. These results determined that AGJO performs better over 90% of these functions compared to GJO. AGJO is also highly competitive in terms of optimization capability and convergence speed compared with other algorithms. Finally, AGJO is applied to optimize five engineering design problems with constraints. Results show that AGJO is a more potential algorithm for solving real problems with constraints than GJO and other algorithms. © 2023, The Authors. All rights reserved.

关键词： Optimization

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Level of Trust and Privacy Management in 6G Intent-based Networks for Vertical Scenarios

Level of Trust and Privacy Management in 6G Intent-based Net...

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6G Networking (6GNet), International Conference on

作者： Jesú s A. Alonso-Lupez Luis Alberto Martí nez Herná ndez Sandra Pé rez Arteaga Ana Lucila Sandoval Orozco Luis Javier Garcí a Villalba Antonio Pastor Diego Ló pez Garcí a Department of Software Engineering and Artificial Intelligence (DISIA) Faculty of Computer Science and Engineering Office 431 Group of Analysis Security and Systems (GASS) Universidad Complutense de Madrid (UCM) Madrid Spain CTIO Unit Telef&#x00F3 nica I+D Ronda de la Comunicacion S/N Madrid Spain

Security is one of the main concerns when designing future 6G networks. Most of the components of service chains are software-based and their security must be ensured. In this work we propose a system that will check the level of trust of 6G services following 3 complementary approaches: attestation, generation of proofs of transit and usage of smart contracts whose execution will be published via Distributed Ledger Technology in order to enable their traceability. The service orchestration function will use reinforcement learning to formulate optimised service orchestration decisions. Services will be specified in a declarative model, following a privacy-aware flavour of intent-based networking. The proposed architecture will take into account privacy of every stakeholder: final users, service providers and infrastructure providers.

关键词： 6G mobile communication Privacy Distributed ledger Network topology Smart contracts Reinforcement learning Service-oriented architecture

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Arabic stems are being built in a database utilising an automated process.

Arabic stems are being built in a database utilising an auto...

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International Conference on Intelligent systems: Theories and Applications (SITA)

作者： Abderrahman Laabidi Abdellah Yousfi Said Iazzi Lrit Si Lhoussain Aouragh Laboratory of Engineering Sciences ICES Team ENSIAS Mohammed V University in Rabat Rabat Morocco FSJES-Souissi Mohammed V University in Rabat Rabat Morocco Associated Unit to the CNRST-URAC29 Faculty of Sciences Mohammed V University in Rabat Rabat Morocco High National School for Computer Science and Systems Analysis Mohammed V University in Robat Morocco

This study explores the use of machine learning methods to improve morphological analysis of the Arabic language. In order to create a database of Arabic stems that academics can utilize, it examines the most well-known techniques and introduces a novel tool for finding Arabic stems from a corpus made up entirely of Arabic *** used a corpus of 30 million Arabic words to create this database. From this corpus of words, we were able to attain a precision of 94% using our stem detection method. The benchmarking strategy and these in-depth experimental findings offer a strong basis for assessing how well our novel strategy performs.

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Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction

arXiv

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arXiv 2023年

作者： Wang, Xinshun Zhang, Wanying Wang, Can Gao, Yuan Liu, Mengyuan the National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School China the School of Intelligent Systems Engineering Sun Yat-sen University China the Multimedia Information Processing Laboratory the Department of Computer Science Kiel University Germany Peking University China the Unit of Computing Sciences CS Faculty of Information Technology and Communication Sciences ITC Tampere University Finland

Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task. Nevertheless, how to construct a graph from a skeleton sequence and how to perform message passing on the graph are still open problems, which severely affect the performance of GCN. To solve both problems, this paper presents a Dynamic Dense Graph Convolutional Network (DD-GCN), which constructs a dense graph and implements an integrated dynamic message passing. More specifically, we construct a dense graph with 4D adjacency modeling as a comprehensive representation of motion sequence at different levels of abstraction. Based on the dense graph, we propose a dynamic message passing framework that learns dynamically from data to generate distinctive messages reflecting sample-specific relevance among nodes in the graph. Extensive experiments on benchmark Human 3.6M and CMU Mocap datasets verify the effectiveness of our DD-GCN which obviously outperforms state-of-the-art GCN-based methods, especially when using long-term and our proposed extremely long-term protocol. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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A low-cost device-based data approach to Eight Hop Test

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Procedia computer science 2025年 256卷 1135-1142页

作者： Luís Pimenta Paulo Jorge Coelho Norberto Jorge Gonçalves José Paulo Lousado Carlos Albuquerque Nuno M. Garcia Eftim Zdravevski Petre Lameski Ivan Miguel Pires Escola de Ciências e Tecnologia University of Trás-os-Montes e Alto Douro Vila Real Portugal Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Research Centre in Digital Services (CISeD) Polytechnic Institute of Viseu Portugal Health Sciences Research Unit: Nursing (UICISA: E) Nursing School of Coimbra (ESEnfC) Coimbra Portugal Higher School of Health Polytechnic Institute of Viseu Viseu Portugal Child Studies Research Center (CIEC) University of Minho Braga Portugal Institute of Biophysics and Biomedical Engineering (IBEB) Faculty of Sciences University of Lisbon Lisbon Portugal Faculty of Computer Science and Engineering University Ss Cyril and Methodius Skopje Macedonia Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

The Eight Hop Test is a physical rehabilitation exercise involving jumping with one leg in a circuit similar to the number eight. This study aims to analyze data related to the performance of the test, relating it to gender and pathologies in the population. A mobile application was implemented to acquire accelerometer data, which was then analyzed using various artificial intelligence methods. The study found reliable accuracy in identifying participants’ gender (60%) and specific pathologies (50%), suggesting that mobile sensors can effectively support medical evaluations. Neural Network showed the highest accuracy for multivariate data, while the k-Nearest Neighbor was most effective for binary classifications. The mobile device-based method helps healthcare professionals measure physical strength evolution in lower limb injuries and predicts pathologies with near-100% accuracy using machine learning methods. Future research will explore more individuals and data to improve healthcare treatments and explore new techniques and analyses.

关键词： Eight Hop Test machine learning accelerometer diseases gender

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A novel ensemble method for enhancing Internet of Things device security against botnet attacks

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Decision Analytics Journal 2023年 8卷

作者： Arshad, Amina Jabeen, Maira Ubaid, Saqib Raza, Ali Abualigah, Laith Aldiabat, Khaled Jia, Heming Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Department of Management Information Systems Ajloun National University Jordan School of Information Engineering Sanming University Sanming 365004 China

The growing number of connected Internet of Things (IoT) devices has led to the daily growth of network botnet attacks. The networks of compromised devices controlled by a single entity can be used for malicious purposes such as denial of service distributed IoT botnet attacks and theft of personal information. The weak security measures of many IoT devices make them easy targets for compromise and inclusion in botnets. In this research, we propose a system for detecting botnet attacks. We develop an ensemble learning system to detect botnets in network traffic with high-performance scores. The system will analyze the traffic and identify any suspicious behavior that may indicate the presence of a botnet. For this purpose, we use the benchmark CTU-13 dataset to build the applied machine learning and deep learning techniques for comparison. We propose a novel ensemble technique, K-neighbors, Decision tree, and Random forest (KDR), to achieve high performance for botnet attack detection. Study results show that the proposed KDR gives 99.7% accuracy in 12.99 s. Hyperparameter optimization and k-fold cross-validation are employed to substantiate the performance. Our research study contributes to the body of knowledge on the detection of botnet attacks and provides a practical solution for securing IoT devices against botnet attacks. © 2023 The Author(s)

关键词： Botnet attacks Cyber security Deep learning Ensemble learning IoT devices Network security

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CPS, Sustainability and Efficiency in an Aquaponic Greenhouse

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2022年 20卷 210-215页

作者： Galvão, J. Santos, P. Aires, L. Neves, F. Department of Electrical Engineering/ESTG Polytechnic of Leiria Leiria2411-901 Portugal LSRE-LCM-Laboratory of Separation and Reaction Engineering Laboratory of Catalysis and Materials Escola Superior de Tecnologia e Gestão Instituto Politécnico de Leiria Leiria2411-901 Portugal ALiCE-Associate Laboratory in Chemical Engineering Faculty of Engineering University of Porto Rua Dr. Roberto Frias Porto4200-465 Portugal R&D Unit Institute for Systems Engineering and Computers/INESC Coimbra Portugal Department of Computer Science Polytechnic of Leiria Leiria2411-901 Portugal

Nowadays the humanity needs to produce food in greater quantity and quality, in order to satisfy citizens and markets, supported by advanced technologies such as cyber-physical system (CPS) or intelligent systems where software and electronic devices and receivers of physical variables are mechanisms that control the management of multiple actions inherent to plant growth. Electronic devices and receivers of physical variables are mechanisms that involve a network of IoT components and control multiple actions inherent to plant growth in agricultural greenhouses, which in short, are intensive food production systems. In this context, this paper presents a case study that uses a monitoring and management system that considers environmental parameters measured inside a greenhouse, allowing the manager to assess the working status of an aquaponic systems, and detect critical conditions that require quick decisions in real time. It is intended to develop technology that can be used in greenhouses, close to consumer centres or cities, to mitigate the impact of transport, lowering CO2 emissions resulting from the distribution channels between the producers and consumers. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Energy efficiency

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