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Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

computers, Materials & Continua 2023年第5期75卷 3239-3255页

作者： Majdy M.Eltahir Adil Yousif Fadwa Alrowais Mohamed K.Nour Radwa Marzouk Hatim Dafaalla Asma Abbas Hassan Elnour Amira Sayed A.Aziz Manar Ahmed Hamza Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Arts and Science Najran UniversitySharourahSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Applied SciencesKing Khalid UniversityMuhayil63772Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event *** is especially applicable in the case of elderly or disabled people who live self-reliantly in their *** sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during *** is one of the most important problems confronted by older people and people with movement *** previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled ***,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor ***,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s *** this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)*** presented IWODL-FDDP model aims to identify the fall events to assist disabled *** presented IWODLFDDP model applies an image filtering approach to pre-process the ***,the EfficientNet-B0 model is utilized to generate valuable feature vector ***,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall ***,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the *** experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

关键词： Fall detection disabled people deep learning improved whale optimization assisted living

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MODEL OF THE DECISION-MAKING SYSTEM FOR ASSESSMENT OF THE STATE OF TECHNICAL OBJECTS 28

MODEL OF THE DECISION-MAKING SYSTEM FOR ASSESSMENT OF THE ST...

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28th International Congress on Sound and Vibration, ICSV 2022

作者： Peter, Brancevich Yibin, Li Belarusian State University of Informatics and Radioelectronics Faculty of Computer Systems and Networks Minsk Belarus Shandong University School of Information Science and Engineering Jinan China

ISBN: (纸本)9789811850707

The most wear-out is production equipment with rotary motion (turbines, generators, motors, gearboxes, pumps, compressors, fans, etc.). It is possible to reduce the cost of its operation by introducing modern maintenance systems, which are based on the use of technologies for monitoring, assessing the condition, diagnosing, predicting the development of defects, which, in terms of their organization and functioning, are intelligent systems. The state of the observed technical objects is determined by the parameters and characteristics. The aim of the work is to develop a model of the basic element of the decision-making system and consideration of options for its practical application. The proposed model of the basic element of the decision-making system allows you to create various structures designed to assess the state of complex technical and natural objects. An example of the implementation of a decision-making system for assessing the state of a multi-support power unit is presented, as a combination of basic solver modules. © International Institute of Acoustics and Vibration (IIAV), 2022.

关键词： Intelligent systems

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Robust Active and Reactive Power Control by using Single-Phase Grid connected H-Bridge Inverter

Robust Active and Reactive Power Control by using Single-Pha...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Resul Coteli Korhan Kayisli Ruhi Zafer Caglayan Dept. of Energy Systems Engineering Technology Faculty Firat University Elazig Turkiye Dept. of Electrical- Electronic Engineering Engineering Faculty Gazi University Ankara Turkiye Dept. of Computer Engineering Engineering Faculty TED University Ankara Turkiye

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

In a single-phase power network, inverters can be used to control the active and reactive power between sources. This task can be achieved by proper control of the current of the single-phase inverter. The current controllers using dq-frame components have a wide application area because of their superior features such as zero steady-state error and using the DC quantities. In this study, the active and reactive power between sources in a single-phase power system consisting of an AC voltage source and a DC voltage source are controlled via dq-frame based super twisting sliding mode (ST-SMC) current controller. For this aim, a Matlab/Simulink model is constructed. The model includes single phase H-bridge inverter, DC voltage source, AC voltage source, AC load, LCL filter and control unit. Two super twisting sliding mode controllers are used to control d and q-axis currents. Decoupling terms are added to output of each controller for independent control of active and reactive power. The simulation studies are performed to demonstrate the controller's ability to provide/absorb active and reactive power. To demonstrate the ability to provide/absorb active power, the d-axis current command is set to 10 A up to 2 s from the start of the simulation, and then, it is set to – 10 A between 2 s to 4 s. To demonstrate the ability to provide/absorb reactive power, the q-axis current command is set to 10 A up to 2 s from the start of the simulation, and then, it is set to – 10 A between 2 s to 4 s. The simulation results show that the controller provides/absorbs the desired active and reactive power quickly and without steady-state error.

关键词： Reactive power Reactive power control Simulation Inverters Mathematical models Steady-state Smart grids

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Clustering graph data: the roadmap to spectral techniques

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Discover Artificial Intelligence 2024年第1期4卷 7页

作者： Mondal, Rahul Ignatova, Evelina Walke, Daniel Broneske, David Saake, Gunter Heyer, Robert Faculty of Computer Science Otto-von-Guericke-University Universitätsplatz 2 Saxony-Anhalt Magdeburg39106 Germany Lange Laube 12 Lower Saxony Hannover30159 Germany Faculty of Technology Bielefeld University Universitätsstraße 25 North Rhine-Westphalia Bielefeld33615 Germany Faculty of Process and Systems Engineering Otto-von-Guericke-University Universitätsplatz 2 Saxony-Anhalt Magdeburg39106 Germany Multidimensional Omics Analyses Group Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Bunsen-Kirchhoff-Straße 11 North Rhine-Westphalia Dortmund44139 Germany Faculty of Computer Science Technische Hochschule Ingolstadt Esplanade 10 Bavaria Ingolstadt85049 Germany

Graph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal scalability and high query performance. Clustering techniques, such as K-means, hierarchical clustering, are highly beneficial tools in data mining and machine learning to find meaningful similarities and differences between data points. Recent developments in graph data models, as well as clustering algorithms for graph data, have shown promising results in image segmentation, gene data analysis, etc. This has been primarily achieved through research and development of algorithms in the field of spectral theory, leading to the conception of spectral clustering algorithms. Spectral clustering algorithms have been one of the most effective in grouping similar data points in graph data models. In this paper, we have compiled 16 spectral clustering algorithms and compared their computational complexities, after an overview of graph data models and graph database models. Furthermore, we provided a broad taxonomy to classify most existing clustering algorithms and discussed the taxonomy in detail. © The Author(s) 2024.

关键词： Digital storage

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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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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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Parallel Path Planning for Multi-Robot Coordination

Parallel Path Planning for Multi-Robot Coordination

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Jorge Ribeiro Marina Brilhante Diogo Miguel Matos Carlos A. Silva Héber Sobreira Pedro Costa CRIIS - Centre of Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto Porto Portugal Flowbotic Mobile Systems S.A Viseu Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.

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Comparing Time-Series Analysis Approaches Utilized in Research Papers to Forecast COVID-19 Cases in Africa: A Literature Review

arXiv

引用

arXiv 2023年

作者： Ebadi, Ali Sahafizadeh, Ebrahim Department of Computer Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran

This literature review aimed to compare various time-series analysis approaches utilized in forecasting COVID-19 cases in Africa. The study involved a methodical search for English-language research papers published between January 2020 and July 2023, focusing specifically on papers that utilized time-series analysis approaches on COVID-19 datasets in Africa. A variety of databases including PubMed, Google Scholar, Scopus, and Web of Science were utilized for this process. The research papers underwent an evaluation process to extract relevant information regarding the implementation and performance of the time-series analysis models. The study highlighted the different methodologies employed, evaluating their effectiveness and limitations in forecasting the spread of the virus. The result of this review could contribute deeper insights into the field, and future research should consider these insights to improve time series analysis models and explore the integration of different approaches for enhanced public health decision-making. © 2023, CC BY-NC-ND.

关键词： Coronavirus

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Exploring the Potential of LLM-based Chatbots for Task Scheduling in Robot Operations 25

Exploring the Potential of LLM-based Chatbots for Task Sched...

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25th IEEE International Conference on Autonomous Robot systems and Competitions, ICARSC 2025

作者： Rema, Catarina Sousa, Armando Sobreira, Heber Costa, Pedro Silva, Manuel F. Faculty of Engineering Porto4200-465 Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science CRIIS - Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto4249-015 Portugal

ISBN: (纸本)9798331538606

The rise of Industry 4.0 has revolutionized manufacturing by integrating real-time data analysis, artificial intelligence (AI), automation, and interconnected systems, enabling adaptive and resilient smart factories. Autonomous Mobile Robots (AMRs), with their advanced mobility and navigation capabilities, are a pillar of this transformation. However, their deployment in job shop environments adds complexity to the already challenging Job Shop Scheduling Problem (JSSP), expanding it to include task allocation, robot scheduling, and travel time optimization, creating a multi-faceted, non-deterministic polynomial-time hardness (NP-hard) problem. Traditional approaches such as heuristics, meta-heuristics, and mixed integer linear programming (MILP) are commonly used. Recent AI advancements, particularly large language models (LLM), have shown potential in addressing these scheduling challenges due to significant improvements in reasoning and decision-making from textual data. This paper examines the application of LLM to tackle scheduling complexities in smart job shops with mobile robots. Guided by tailored prompts inserted manually, LLM are employed to generate scheduling solutions, being these compared to an heuristic-based method. The results indicate that LLM currently have limitations in solving complex combinatorial problems, such as task scheduling with mobile robots. Due to issues with consistency and repeatability, they are not yet reliable enough for practical implementation in industrial environments. However, they offer a promising foundation for augmenting traditional approaches in the future. © 2025 IEEE.

关键词： Integer programming

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