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

作者： Ahmed, Amjed Abbas Fadhil, Saif Aamer Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad10011 Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Mobile Ad Hoc Networks (MANETs) are characterized by some important attributes, including infrastructure, mobile, and dynamic nature, which makes them have vast applications in different areas of computer networks. The black hole attack falls among the strongly criticized security threats within the framework of the MANETs. This deceptive behavior captures data packets from a source destined for the malicious node and then declines them when they are received. This paper comprehensively reviews prior studies done to analyze and mitigate black hole attacks in MANETs. Besides this, we will conduct a comparative analysis of the research above projects. The present paper extends prior work by actually performing the black hole assault simulations in a MANETs environment. This is done with the aid of the NS2 simulator and the Ad Hoc On-Demands Distance Vector (AODV) routing protocol. In this study, the black hole attack is analyzed and the consequences of the attack are assessed in terms of the system performance and security. We analyze crucial parameters like end-to-end latency, PDR, packet drop, and throughput to evaluate the consequences of this assault. From our results derived from the analysis of our simulations, we aim to present substantial points to do with the impacts of blackhole attacks on MANETs. In this way, our goal is to participate in the perpetually evolving efforts aimed at enhancing the security and reliability of such dynamic networks. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Machine Learning-Based Channel State Estimators for 5G Wireless Communication systems

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computer Modeling in engineering & Sciences 2023年第4期135卷 755-778页

作者： Mohamed Hassan Essai Ali Fahad Alraddady Mo’ath Y.Al-Thunaibat Shaima Elnazer Department of Electrical Engineering Faculty of EngineeringAl-Azhar UniversityQena83513Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Management Information Systems Taif UniversityTaif21944Saudi Arabia

For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state ***,it utilizes pilots to offer more helpful information about the communication *** proposedCNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory(BiLSTM/LSTM)NNs-based *** CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based *** three different loss function-based classification layers and the Adam optimization algorithm,a comparative study was conducted to assess the performance of the presented DNNs-based *** BiLSTM-CSE outperforms LSTM,CNN,conventional least squares(LS),and minimum mean square error(MMSE)*** addition,the computational and learning time complexities for DNN-CSEs are *** estimators are promising for 5G and future communication systems because they can analyze large amounts of data,discover statistical dependencies,learn correlations between features,and generalize the gotten knowledge.

关键词： DLNNs channel state estimator 5G and beyond communication systems robust loss functions

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A Quantum Q-Learning Model for the Capacitated Vehicle Routing Problem 17

A Quantum Q-Learning Model for the Capacitated Vehicle Routi...

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17th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Xu, Jiahua Xie, Ningyi Lee, Xinwei Saito, Yoshiyuki Asai, Nobuyoshi Cai, Dongsheng University of Tsukuba Graduate School of Science and Technology Japan Singapore Management University School of Computing and Information Systems Singapore University of Aizu Graduate School of Computer Science and Engineering Japan University of Aizu School of Computer Science and Engineering Japan University of Tsukuba Faculty of Engineering Information and Systems Japan

ISBN: (纸本)9798331530471

The research on Variational Quantum Algorithms (VQAs) has gained significant momentum because of their promising practicality in the noisy intermediate-scale quantum (NISQ) era. Recent studies highlight the potential of VQAs in reinforcement learning (RL) by replacing classical components with parameterized quantum circuits (PQCs). Building on this, we propose a quantum Q-learning model for the Capacitated Vehicle Routing Problem (CVRP), combining a PQC with an RL training process. We present a PQC for embedding both static and dynamic states in RL environments. The proposed PQC approximates the Q-value function by using the expectation values of different observables. Experimental results demonstrate that the proposed PQC outperforms the existing PQC for the CVRP with fewer quantum resources. Furthermore, our method shows competitive performance compared to the classical counterpart. © 2024 IEEE.

关键词： Vehicle routing

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"Hand Model" - A Static Sinhala Sign Language Translation Using Media-Pipe and SVM Compared with Hybrid Model of KNN, SVM and Random Forest Algorithms 5

"Hand Model" - A Static Sinhala Sign Language Translation Us...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Gedaragoda, G.H.M.S.C. Wijesinghe, W.R.S. Abeywickrama, T.R. Lokuliyana, Shashika Mahaadikara, Hansika Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study of sign language recognition has been a thriving area of research for nearly twenty years. SSL translation using computer vision relies on extensive training using large number of images or video sequences. Yet, it poses a challenge for languages with limited resources, like Sinhala Sign Language. Due to the absence of accessible datasets for such investigations and lack of SSL recognition systems for real-time scenarios, the authors of this research have developed a completed system that consist of three components which are, static sign recognition, dynamic sign recognition and natural language processing to output a meaningful sentence. This paper describes the models and the results which are developed for static sign recognition. The final model for the Sinhala Static recognition has developed using media-pipe and SVM algorithm. And to compare the results and performance authors have used CNN, SVM, KNN and Random-forest algorithms to compare the results and performance. In this research, authors have made a valuable contribution by introducing a new dataset and developing a system for translating static SSL that gives 99.67% of accuracy and best performance when compared to the other two models and algorithms described in this paper. © 2023 IEEE.

关键词： Support vector machines

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Deep Neural Network Inference Processor

Deep Neural Network Inference Processor

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2023 International Conference on Microelectronics, ICM 2023

作者： Hegazy, Ali Salah, Khaled Ain Shams University Faculty of Engineering Department of Computer and Systems Cairo Egypt Siemens Fremont United States

ISBN: (纸本)9798350380828

In recent years, neural networks' architecture has become complicated and it demands special computing requirements. Thus, new specialized microprocessors are proposed for this purpose. In this paper, a new scalable neural network inference processor architecture is proposed and implemented on FPGA. FPGA enables hardware customization to fit the specific computing requirements. Moreover, FPGA-based accelerators demonstrated high energy efficiency compared to GPUs and CPUs. In the proposed FPGA-based architecture, the size of the systolic matrix multiplication array, the size of data flowing inside the processor and all the primitive blocks of the processor are configurable and scalable. Also, in this paper, an illustrative example is introduced to show how a TensorFlow-Based neural network model can be inferred using the proposed architecture. Moreover, synthesis results in terms of area and power are very promising. The design is validated on Virtex UltraScale+ VCUIIS Evaluation platform. It is tested using face recognition algorithm (160×160 face image at 25FPS) where it achieved an accuracy of 95%. Moreover, it shows performance and power efficiency of 55 GOPS and 10 GOPS/W at 100MHz, respectively. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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An Application for Evaluating Proficiency and Expert Knowledge Criteria of Information Technologies (IT) Workers with Using Fuzzy Analytic Hierarchy Process

An Application for Evaluating Proficiency and Expert Knowled...

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Intelligent and Fuzzy systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Tunç, Ali Taşdemir, Sakir Uyar, Mehmet Taha Bingöl, Veysel Can Architecht Information Systems Istanbul Turkey Computer Engineering Department Technology Faculty Selcuk University Konya Turkey

ISBN: (纸本)9783031397769

The information technologies (IT) sector has been an area of study whose significance has grown exponentially, taking into account the effects of growing technology and the importance of technology in recent years, taking into account the effects of the pandemic. The widespread use of remote working and hybrid working models in these sectors reveals the importance of making more careful performance evaluations of employees. Human resources are essential in the upkeep, renewal, and development of existing software products, as well as the creation of new ones. Infrastructures and system algorithms created with human impact as the most important resource can be finished on time for project success and in a manner that addresses future needs. This study, it is aimed to rank and monitor the performances of software engineers and analysts working in the software development sector by measuring both their technical skills and personal competencies. The most important items accepted as evaluation criteria in the sector were weighted by experts in their fields using the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) method. It was ensured that these weighted evaluation items had been scored for the people to be measured and results were obtained. In order to evaluate the employees in the roles of analyst and software developer in terms of both talent and technical knowledge, the evaluation of the criteria of the experts with Fuzzy AHP and the weighting of the criteria are shown in tables in the study. Then, newly hired people and interns to be recruited were evaluated by their mentors, and a method that could be useful for tracking their training and development experiences was provided. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Analytic hierarchy process

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High accuracy, fully reconfigurable DAQ system for sensing temperature 33

High accuracy, fully reconfigurable DAQ system for sensing t...

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33rd International Scientific Conference Electronics, ET 2024

作者： Gatev, Vasil D. Mollov, Valentin S. Petrov, Panayot L. Manoeva, Mariana D. Technical University of Sofia Faculty of Computer Systems and Technologies Department of Computer Systems 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Technical University of Sofia Faculty of Electronic Engineering and Technologies Department of Electronics 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350376449

The FPGA configurable integrated circuits (ICs) are a valid alternative to the common microcontroller solutions for application-specific (ASIC) embedded applications. The FPGAs are applied in the prototype step, before the ASIC synthesis, for end-user devices targeted to real-time applications. Nowadays IoT solutions with post-processing on cloud-based server technology, makes the dedicated DAQ device an important component in the system. The FPGAs are the appropriate basis for such device, where the data acquisition of analog values and its transfer to digital representation must be achieved with high-speed, high-resolution, low power consumption and effective optimization of the IC resources. © 2024 IEEE.

关键词： Application specific integrated circuits

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Meta-Heuristics for Solving Curve Fitting Problems in Optical-Diffraction Based Image Depth Reconstruction 3

Meta-Heuristics for Solving Curve Fitting Problems in Optica...

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3rd IEEE International Conference on Software engineering and Artificial Intelligence, SEAI 2023

作者： Zhu, Qianyao Gao, Kaizhou Liu, Yongjun Macau University of Science and Technology Institute of Systems Engineering China Changshu Institute of Technology Faculty of Computer Science and Engineering Suzhou China

ISBN: (纸本)9798350337952

This paper investigates use meta-heuristic to solve curve fitting problems in Optical-Diffraction Based Image Depth Reconstruction. We aim to accurately establish a relationship curve between object distance and diffraction blur kernel through discrete data points. A mathematical model is first developed to formulate the concerned problems. Then, four meta-heuristic algorithms, particle swarm optimization, artificial bee colony, genetic algorithm and differential evolution, are employed and improved according to the problem's feature. Finally, experiments are executed on a data set. A comparison of the similarity of the least squares method verifies the feasibility of meta-heuristics for solving curve fitting problems. Among the four meta-heuristics, differential evolution algorithm has the best competitiveness. © 2023 IEEE.

关键词： Image reconstruction

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Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

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computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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