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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Jagadeesan, S. Ramalingam, Venkadeshan Abinash, P. Ilayaraja, V. Masood, Jafar Ali Ibrahim Syed Tamil Priya, D. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India UTAS-Shinas branch Department of Computing and Information Sciences Oman School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331542573

Stroke and related neurological disorders are difficult to classify accurately, hence new computational approaches are needed to enhance diagnosis. In order to transform the categorization of brain strokes, this study presents Neuro-Xception, a novel framework that combines the Xception architecture with Extreme Learning Machines (ELM). The rapid learning speed and enhanced generalisation capability of ELMs are combined with the feature extraction expertise of Xception to create Neuro-Xception. Through training on large neuroimaging datasets, Neuro-Xception learns complex patterns that are essential for differentiating between different types and intensities of stroke. By means of extensive testing on various datasets, Neuro-Xception exhibits notable gains in performance compared to traditional techniques, indicating its potential to revolutionise stroke detection in medical environments. Furthermore, Neuro-Xception's scalability and flexibility are enhanced by the combination of Xception and ELMs, which makes it a viable tool for real-time clinical decision support systems and personalised medicine. This study highlights how merging Xception and ELMs can revolutionise neurological healthcare and this development could lead to more accurate and efficient diagnostic techniques for treating stroke patients. Neuro-Xception offers a potential path for increasing diagnosis accuracy and expediting timely medical intervention. It is a major step forward in the use of artificial intelligence to enhance patient outcomes and advance the profession of stroke care. © 2024 IEEE.

关键词： Personalized medicine

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Benaddi, Lamya Ouaddi, Charaf Naimi, Lahbib Souha, Adnane Jakimi, Abdeslam Ouchao, Brahim Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction. © 2024 IEEE.

关键词： Customer satisfaction

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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Computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Intensive Tasks Offloading Decision Algorithms in Satellite Edge Computing systems 22

Intensive Tasks Offloading Decision Algorithms in Satellite ...

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22nd International Conference on Optical Communications and Networks, ICOCN 2024

作者： Wang, Yanxiang Chai, Furong Zhang, Qi Li, Yuanfeng Sun, Meng Sun, Dandan Tian, Feng Wang, Fu Wang, Yongjun Tian, Qinghua Yang, Leijing Beijing100876 China BUPT Beijing Key Laboratory of Space-ground Interconnection and Convergence Beijing100876 China National Univ. of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha410073 China

To address the offloading allocation problem for intensive tasks in Satellite Edge Computing (SEC) systems, a Sequence-to-Sequence (Seq2Seq) and Deep Reinforcement Learning (DRL) based decision generation algorithm, e... 详细信息

ISBN: (纸本)9798350367652

关键词： Computation offloading

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DaC-GANSAEBF:Divide and Conquer-Generative Adversarial Network-Squeeze and Excitation-Based Framework for Spam Email Identification

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Computer Modeling in engineering & sciences 2025年第3期142卷 3181-3212页

作者： Tawfeeq Shawly Ahmed A.Alsheikhy Yahia Said Shaaban M.Shaaban Husam Lahza Aws I.Abu Eid Abdulrahman Alzahrani Department of Electrical Engineering Faculty of Engineering at RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Information Technology College of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information and Communication Technology College of Computing StudiesArab Open UniversityKuwait BranchKuwait City13033Al-SafatState of Kuwait Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia

Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security *** spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data *** address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam *** framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual *** approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated *** model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the *** findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection *** framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.

关键词： Email spam fraud light dual attention squeeze and excitation divide and conquer-generative adversarial network-squeeze and excitation-based framework security

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A Metamodel for a DSL to Design Decision Support systems in Smart Tourism

A Metamodel for a DSL to Design Decision Support Systems in ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ouaddi, Charaf Benaddi, Lamya Souha, Adnane Naimi, Lahbib Bouziane, El Mahi Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

The development of Decision Support systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism. © 2024 IEEE.

关键词： Digital subscriber lines

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UAV path planning based on hybrid particle swarm and gray wolf optimization algorithm 4

UAV path planning based on hybrid particle swarm and gray wo...

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4th International Conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Wang, Kaijiang Zhao, Zipeng College of Information Science and Engineering Lanzhou University Lanzhou China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Addressing the problem of sensitivity in parameter selection within traditional particle swarm optimization (PSO), where varying parameter values can significantly impact path optimization performance, this study replaces PSO with gray wolf optimization (GWO). An improved algorithm, grounded in GWO, is proposed for static three-dimensional (3D) path planning of unmanned aerial vehicles (UAVs). The improved algorithm combines GWO and PSO. By improving the position update formula, the individual gray wolf can adjust its position more flexibly during the search process, and improve the issue that GWO is prone to stuck in local optimum prematurely when searching for optimization. Test and simulation outcomes demonstrate the improved algorithm's superior performance in path planning. © 2024 SPIE.

关键词： Motion planning

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Meter-Scale Thin-Walled Structure with Lattice Infill for Fuel Tank Supporting Component of Satellite:Multiscale Design and Experimental Verification

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Computer Modeling in engineering & sciences 2024年第1期138卷 201-220页

作者： Xiaoyu Zhang Huizhong Zeng Shaohui Zhang Yan Zhang Mi Xiao Liping Liu Hao Zhou Hongyou Chai Liang Gao Beijing Key Laboratory of Lightweight Multi-Functional Composite Materials and Structures Beijing Institute of TechnologyBeijing100081China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System EngineeringChina Academy of Space TechnologyBeijing100094China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of EducationWuhan University of Science and TechnologyWuhan430081China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan430074China

Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich *** structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other *** this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the *** designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding ***-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled *** experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.

关键词： Thin-walled structure lattice infill supporting component selective laser melting satellite

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Tunable polarimetric sensitive terahertz absorber 6

Tunable polarimetric sensitive terahertz absorber

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6th Conference on Frontiers in Optical Imaging and technology: Novel Technologies in Optical systems

作者： Zhu, Haixia Ni, Bo Feng, Zhenjie Qian, Yitong Zhou, Xiaoyan Ni, Haibin Yang, Lingsheng Chang, Jianhua Jiangsu Key Laboratory of Meteorological Observation and Information Processing School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (数字)9781510679672

ISBN: (纸本)9781510679665

In this paper, an absorber based on split square graphene is designed, which produces double absorption peaks at different polarization incidence. The absorption rates of x-polarized waves at 7.86 THz and 12.63THz are 97.9% and 91.2% respectively. The absorption rates of y-polarized waves at 6.30 THz and 10.52 THz are 94.1% and 93.2%. In this paper, an absorber based on split square graphene is designed, which produces double absorption peaks at different polarization incidence. The absorption rates of x-polarized waves at 7.86 THz and 12.63THz are 97.9% and 91.2% respectively. The absorption rates of y-polarized waves at 6.30 THz and 10.52 THz are 94.1% and 93.2%. The effect of Fermi level on the dual polarization absorptivity of graphene is discussed. Therefore, this work may have great reference value for terahertz sensing, selective spectral detection and other fields. The effect of Fermi level on the dual polarization absorptivity of graphene is discussed. Therefore, this work may have great reference value for terahertz sensing, selective spectral detection and other fields. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Polarization

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Graphene-supported isolated platinum atoms and platinum dimers for CO_(2) hydrogenation:Catalytic activity and selectivity variations

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Chinese Chemical Letters 2025年第4期36卷 241-245页

作者： Sanmei Wang Dengxin Yan Wenhua Zhang Liangbing Wang Research Institute of Interdisciplinary Sciences(RISE)and School of Materials Science&Engineering Dongguan University of TechnologyDongguan 523808China State Key Laboratory for Powder Metallurgy School of Materials Science and EngineeringCentral South UniversityChangsha 410083China Hefei National Laboratory for Physical Sciences at the Microscale and Synergetic Innovation Centre of Quantum Information&Quantum Physics University of Science and Technology of ChinaHefei 230026China Laboratory for Chemical Technology Ghent UniversityGhent 9052Belgium

Manipulating catalyst structures to control product selectivity while maintaining high activity presents a considerable challenge in CO_(2)*** density functional theory calculations and microkinetic analysis,we proposed that graphene-supported isolated Pt atoms(Pt1/graphene)and Pt_(2)dimers(Pt_(2)/graphene)exhibited distinct selectivity in CO_(2)***_(1)/graphene facilitated the conversion of CO_(2)into formic acid,whereas Pt_(2)/graphene favored methanol *** variation in product selectivity arose from the synergistic interaction of Pt_(2)dimers,which facilitated the migration of H atoms between two Pt atoms and promoted the transformation from*COOH intermediates to*C(OH)_(2)intermediates,altering the reaction pathways compared to isolated Pt ***,an analysis of the catalytic activities of three Pt_(1)/graphene and three Pt_(2)/graphene structures revealed that the turnover frequencies for formic acid generation on Pt_(1ii)/graphene and methanol generation on Pt_(2i)/graphene were as high as 744.48 h-1and 789.48 h^(-1),*** values rivaled or even surpassed those previously reported in the literature under identical *** study provides valuable insights into optimizing catalyst structures to achieve desired products in CO_(2)hydrogenation.

关键词： CO_(2)hydrogenation Graphene Pt single-atom Pt_(2)dimers DFT

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