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IEEE Networking Letters 2022年第3期4卷 137-141页

作者： Hieu, Nguyen Quang Tran, The Anh Nguyen, Cong Luong Niyato, Dusit Kim, Dong In Elmroth, Erik University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore Phenikaa University Faculty of Computer Science Hanoi10000 Viet Nam Sungkyunkwan University Department of Electrical and Computer Engineering Seoul16419 Korea Republic of Umeå University Department of Computing Science Umeå901 87 Sweden

Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in blockchain in a reliable manner. However, one problem is the increase of the training latency due to the mining process. Moreover, mobile devices have energy and CPU constraints. Therefore, the machine learning model owner (MLMO) needs to decide the data and energy that the mobile devices use for the training and determine the block generation rate to minimize the system latency and mining cost while achieving the target accuracy. Under the uncertainty of BFL, we propose to use deep reinforcement learning to find the optimal decisions for the MLMO. © 2019 IEEE.

关键词： Neural networks

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Integrating Cloud computing and Naive Bayes for Precision Detection and Classification of Sheet and Rill Erosion in Agriculture 2

Integrating Cloud Computing and Naive Bayes for Precision De...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Deshpande, Ashish Govindrao Raman, Ramakrishnan Vekariya, Vipul Mishra, Nilamadhab Arumugam, Sundaram Srinivasan, C. Pune Maharashtra Pune India Symbiosis Institute of Business Management Pune Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Navajo Technical University Department of Electrical Engineering Crown pointNM United States Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Sustainable agriculture has significant challenges from sheet and rill erosion for effective techniques. A new way to accurately identify and classifier sheet and rill erosion in agricultural landscapes with the combination of cloud computing and Naïve Bayes. To differentiate between these erosion patterns work proposes a technique that uses ground truth data and high-resolution satellite images. The technique gets results by using the Naïve Bayes algorithm, which is famous for being simple and successful in classification problems. More efficient and accurate and advanced methods by conducting comprehensive experiments and validations. This technology takes use of the scalability and accessibility of cloud computing to conduct erosion risk assessments in real-time, providing agricultural stakeholders with valuable information to enhance soil conservation initiatives. It highlights the revolutionary power of new technology to improve agricultural land management methods and address environmental issues. Sustainable agricultural practices mitigate the adverse implications of soil erosion by offering exact detection and classification of erosion events, which lead to better informed decision-making. © 2024 IEEE.

关键词： Environmental Monitoring Erosion Risk Assessment Ground Truth Data Land Management Satellite Imagery Soil Conservation Sustainable Agriculture

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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

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computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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On the Accuracy of Ble Indoor Localization Systems: An Assessment Survey

SSRN

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SSRN 2024年

作者： Morgan, Ahmed A. Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah21955 Saudi Arabia Department of Computer Engineering Faculty of Engineering Cairo University Giza12613 Egypt

Bluetooth Low Energy (BLE) is one of the most widely used technologies to carry out the essential task of indoor localization. Hundreds of studies are presented to use, analyze, and improve the performance of BLE-based localization systems. These studies include a multitude of localization techniques, application domains, and evaluation metrics. This paper presents a state-of-the-art survey of these studies, taking the accuracy metric into main consideration. Using a majority analysis of reported values in these studies, the presented survey has four key contributions. First, it quantifies the level of accuracy reached by previous localization techniques. Throughout the presented survey, it becomes evident that sub meter accuracy is currently achievable using advanced localization techniques, whereas sub decimeter counterpart is still an open area that requires further research efforts. Second, different domains of BLE-based localization applications are classified into three groups of high, moderate, and low accuracy. Localization techniques are similarly summarized and categorized into the same three groups, such that future researchers could easily find the suitable technique for each domain of applications. Third, categories of techniques that are capable of realizing sub meter accuracy are enumerated. These are the ones that primarily use Machine Learning (ML), filtration, fusion, or the Angle of Arrival (AoA) feature of BLE v5.1 and above. Individual techniques within these categories are further investigated and assessed. Fourth, evaluation metrics, which are targeted by these highly accurate categories of techniques, are reviewed. This review reflects that some important metrics, such as security, still require much attention from the BLE localization community. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Chest Radiographs Based Pneumothorax Detection Using Federated Learning

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computer Systems Science & engineering 2023年第11期47卷 1775-1791页

作者： Ahmad Almadhor Arfat Ahmad Khan Chitapong Wechtaisong Iqra Yousaf Natalia Kryvinska Usman Tariq Haithem Ben Chikha Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Prince Sattam bin Abdulaziz University AlKharjSaudi Arabia

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest *** most persistent disease,as well as one that necessitates particular patient care and the privacy of their health *** radiologists find it challenging to diagnose pneumothorax due to the variations in *** learning-based techniques are commonly employed to solve image categorization and segmentation ***,it is challenging to employ it in the medical field due to privacy issues and a lack of *** address this issue,a federated learning framework based on an Xception neural network model is proposed in this *** pneumothorax medical image dataset is obtained from the Kaggle *** preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s ***-max normalization technique is used to normalize the data,and the features are extracted from chest Xray *** dataset converts into two windows to make two clients for local model *** neural network model is trained on the dataset individually and aggregates model updates from two clients on the server *** decrease the over-fitting effect,every client analyses the results three *** 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%*** experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

关键词： Privacy preserving pneumothorax disease federated learning chest x-ray images healthcare machine learning deep learning

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A Bibliometric Analysis and Research Landscape of Artificial Intelligence in Education 14

A Bibliometric Analysis and Research Landscape of Artificial...

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14th IEEE International Conference on control System, computing and engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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An Optimized Design of Antenna Arrays for the Smart Antenna Systems

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computers, Materials & Continua 2021年第11期69卷 1979-1994页

作者： Fahd N.Al-Wesabi Murad A.A.Almekhlafi Huda G.Iskandar Adnan Zain Saleh Alzahrani Mohammed Alamgeer Nadhem Nemri Sami Dhabi Mohammad Medani Ali.M.Al-Sharafi Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia Department of Electronics and Communication Engineering Faculty of EngineeringUniversity of AdenYemen Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA

In recent years,there has been an increasing demand to improve cellular communication services in several *** aspect that received the most attention is improving the quality of coverage through using smart antennas which consist of array *** paper investigates the main characteristics and design of the three types of array antennas of the base station for better coverage through simulation(MATLAB)which provides field and strength patterns measured in polar and rectangular coordinates for a variety of conditions including broadsides,ordinary End-fire,and increasing directivity End-fire which is typically used in smart *** method of analysis was applied to twenty experiments of process design to each antenna type separately,so sixty results were obtained from the radiation pattern indicating the parameters for each radiation ***,nineteen design experiments were described in this *** is hoped that the results obtained from this study will help engineers solve coverage problems as well as improve the quality of cellular communication networks.

关键词： Smart antenna antenna arrays patterns construction coverage

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Semantic Segmentation Based Real-Time Traffic Monitoring via Res-UNet Classifier and Kalman Filter

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SN computer Science 2025年第1期6卷 1-17页

作者： Qureshi, Asifa Mehmood Algarni, Asaad Aljuaid, Hanan Alatiyyah, Mohammed Hamad Alnowaiser, Khaled Jalal, Ahmad Department of Creative Technologies Air University E-9 Islamabad 44000 Pakistan Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha 91911 Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Science College of Sciences and Humanities in Aflaj Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Computer Science Air University E-9 Islamabad 44000 Pakistan

In computer vision, accurate vehicle recognition and tracking is a challenging research topic. Manual surveillance systems are cumbersome, labor-intensive, and inefficient in today's congested traffic environment. Automated traffic monitoring systems are needed in today's world because they provide potential solutions to these issues. However, there are still some challenges in automating these systems which need to be addressed. One of the challenges is the extraction of foreground from a complex environment that only contains vehicles along with spatial and temporal information. In this paper, a semantic segmentation-based foreground extraction model is proposed to overcome these challenges. After segmentation, a blob detection technique is applied to detect each vehicle. To overcome the issue of vehicle-to-vehicle occlusion we extracted Harris corner points and computed the separation line based on the vehicle geometry coordinates. Furthermore, we introduce an ID assignment mechanism based on VGG-16 extracted features. Based on the detections, the number of cars is also counted for each frame. For tracking, we first applied Farneback optical flow algorithm to detect motion in vehicles. Only those vehicles were passed to the tracking algorithm in which motion was detected. To estimate each vehicle's location in succeeding image frames Kalman filter was implemented. A vehicle reidentification and ID recovery module are also proposed to enhance the tracking outcome. Vehicle trajectories based on vehicle location midpoint were also estimated. Extensive experiments are used to validate the proposed system and achieved a precision score of 0.965 and 0.931 over Roundabout and AU-AIR datasets for vehicle detection. However, for vehicle tracking the precision is 0.948 over the Roundabout dataset and 0.839 for the AU-AIR dataset. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blob detection Detection and tracking Kalman filter Occlusion handling Res-Unet Semantic segmentation

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Improvement of Task Scheduling with Energy Efficiency including Fault Tolerant with Resilient computing System in Parallel and Distributed Communication Network 2

Improvement of Task Scheduling with Energy Efficiency includ...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Kumar, Vijay Madhuri, P. Yamini, R. Sravani, Palla Muthukumar, K. Graphic Era Hill University Department of Physics Dehradun India Hyderabad501401 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India Raghu Engineering College Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam531162 India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350325799

In parallel and distributed communication networks, task scheduling is essential for attaining the best system performance. Innovative ways that may intelligently distribute computing resources while minimizing energy consumption and maintaining system resilience are required due to the rising need for energy efficiency and fault tolerance. This system, offers a unique approach to job scheduling in parallel and distributed communication networks that integrate genetic algorithms (GA) and machine learning (ML).Using ML approaches, proposed method analyses historical task execution data to discover patterns that may be applied to task execution time prediction in various computing systems. As a result, this can assign tasks to resources effectively, maximize resource usage, and cut down on execution time as a whole. In addition, using predefined objectives like energy efficiency and fault tolerance, GA is used to find the best task-resource assignments. This applies resilient computing approaches to the job scheduling procedure to overcome problems with fault tolerance. This prevents the execution of crucial operations from being hampered by system faults or resource shortages by utilizing fault detection and recovery technologies. This system can adapt dynamically to shifting network conditions through the combination of ML, GA, and resilient computing, which raises the entire system's reliability and availability. © 2023 IEEE.

关键词： Machine learning

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Grid-Forming PV System for Frequency Ancillary Services Provision

Grid-Forming PV System for Frequency Ancillary Services Prov...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Bojana Barać Tomislav Capuder Mathaios Panteli Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Croatia Department of Electrical and Computer Engineering University of Cyprus Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

With the increasing penetration of photovoltaic (PV) systems posing challenges to power system stability, this paper investigates the effectiveness of grid-forming controlled PV systems in providing frequency ancillary services. The study implements the Virtual Synchronous Machine strategy, utilizing DC-link capacitor energy for inertial response provision (DCVQVSM), integrated within a grid-forming topology. To ensure power-frequency droop control, an additional control loop is introduced within the DCVQ-VSM topology. The efficacy of this control system is proved through simulations conducted on a simple two-bus system and a fully-electrified active distribution network (ADN). Both case studies demonstrate the successful operation of grid-forming controlled PV systems, highlighting their capability to influence power system frequency dynamics. The analysis underscores the significance of deloading PV operation in facilitating flexibility services provision, thereby enhancing the reliability and resilience of system operation.

关键词： Photovoltaic systems Network topology Power system stability Stability analysis Topology Reliability Synchronous machines Frequency control Grid forming Resilience

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