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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Ahmed Hassan Khaled M Fouad Alaa Eldin Abdalla Yassin Somia Mohamed ElSewedy University of Technology Cairo Egypt Faculty of computer science and engineering New Mansoura University Mansoura Egypt Information System department Benha University Benha Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

The medical community has been concerned about how to increase the accuracy of different classification methods with large data that are being generated every day. The traditional KNN method has many limitations, such as dealing with large-scale data, handling uncertain data, and also determining the k parameter for KNN that gives the best result. In this research, three limitations are solved. By using optimized feature selection methods, optimal features are chosen from many irrelevant features. Then, uncertain data that has a conflict with the class label is handled. Finally, the optimal number of k in the KNN method that gives better accuracy is chosen using the ROC curve. The prime objective of this paper is to develop a hybrid optimal model for medical data classification that handles these challenges. The results are evaluated using the accuracy metric. Experimental results show that the enhanced KNN method outperforms the previously used KNN method in used medical datasets

关键词： Training Measurement Accuracy Computational modeling Data handling Nearest neighbor methods Ubiquitous computing Feature extraction Data models Testing

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Navigating the Future: Artificial Intelligence and Sustainable Transportation in Morocco 9th

Navigating the Future: Artificial Intelligence and Sustainab...

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9th International Conference on Smart City Applications, SCA 2024

作者： Chegri, B.E. Rhajbal, Z. El Mettiti, A. Berhich, A. Saadane, R. Charif, M. Chehri, A. Mohammed V University in Rabat Rabat Morocco LRIT-GSCM Faculty of Sciences University Mohamed V Rabat Rabat Morocco AMIPS Research Team Computer Science Department EMI Mohammed V University in Rabat Rabat Morocco Laboratory Engineering System SIRC/LAGeS EHTP Casablanca Morocco Department of Mathematics and Computer Science Royal Military College Kingston Canada

ISBN: (纸本)9783031886522

The integration of Artificial Intelligence (AI) into sustainable mobility represents a transformative paradigm for transportation systems, particularly within the unique socioeconomic landscape of Morocco. This study delves into the dynamic intersection of AI and sustainable mobility, examining its implications for enhancing efficiency, reducing environmental impact, and improving overall accessibility in Moroccan transportation. Against the backdrop of Morocco’s commitment to sustainable development, the research navigates through the intricate web of challenges and opportunities posed by the fusion of AI technologies and mobility solutions. Through a comprehensive literature review, case studies, and recommendations, this article provides a roadmap for policymakers, researchers, and stakeholders to harness the potential of AI in shaping a more resilient and technologically advanced transportation infrastructure. With a focus on both global best practices and Morocco’s specific needs, the study not only underscores the benefits of AI integration but also addresses ethical considerations and potential socio-cultural implications. As Morocco advances towards a sustainable future, the synergies between AI and mobility emerge as a pivotal force in steering transportation towards efficiency, accessibility, and environmental responsibility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Autonomous vehicles

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Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed system

Preliminary Exploration on Node-To-Node Fault Tolerance Coor...

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Computing (ICOCO), IEEE International Conference on

作者： Muntadher Saadoon Siti Hafizah Ab. Hamid Hazrina Sofian Hamza Altarturi Nur Nasuha Zati Hakim Azizul Haqi Khalid Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer and Communication Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism.

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Black Hole Attacks Detection in Mobile Ad Hoc Networks (MANETs) using Bayesian Inference Method

Black Hole Attacks Detection in Mobile Ad Hoc Networks (MANE...

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International Conference on Electrical engineering, computer science and Informatics (EECSI)

作者： Nurul Afifah Muhammad Fadli Tri Wanda Septian Winda Kurnia Sari Adi Hermansyah Mastura Diana Marieska Computer Engineering Dept. Faculty of Comp. Science Sriwijaya University Palembang Indonesia COMNETS Research Group Faculty of Comp. Science Sriwijaya University Palembang Indonesia Information System Dept. Faculty of Comp. Science Sriwijaya University Palembang Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

MANET has vulnerability in terms of security systems against black hole attacks. The main target of black hole as a malicious node is to absorbs all data. The aim of this research is to identify black hole attacks against MANE Tusing Bayesian inference. The methodology of this research involves several testing phases. In the first phase, all data packets sent by the source node to destination within the range of the black hole node are lost. The second and third testing phases showed that the Bayesian method was successful in detecting black holes by preventing data transmission. The results indicated that traffic in the MANET runs smoothly when the nodes are operating under normal conditions. However, when a black hole node is introduced, a significant number of data packets are lost. Implementing the Bayesian method offers a solution to this issue.

关键词： Electrical engineering computer science Data transfer Bayes methods Security Informatics Mobile ad hoc networks Testing

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A Current Sensor Fault-Tolerant Control Solution for Induction Motor Drive

A Current Sensor Fault-Tolerant Control Solution for Inducti...

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International Conference on Advanced engineering Theory and Applications, AETA 2022

作者： Nguyen, Minh Chau Huu Tran, Cuong Dinh Dinh, Bach Hoang Phan, Tai Thanh Nguyen, Thong Tien Lai, Giang Thi Tuyet Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Czech Republic17. Listopadu 15/2172 Ostrava-Poruba708 33 Czech Republic Faculty of Electrical and Electronics Engineering Aviation Academy 104 Nguyen Van Troi Dist. Phu Nhuan Ho Chi Minh City Viet Nam Power System Optimization Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University 19 Nguyen Huu Tho Dist. 7 Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819987023

This paper proposes a fault-tolerant control solution for the induction motor drive against total current sensor failures. An improved proposal based on the combination of the measured current signal and its delay signal is applied to diagnose the fault of the current sensors in various conditions. The state of the current sensors is determined by the diagnosis method during the operation of the induction motor. In normal conditions, measured current signals will be supplied to the field-oriented control loop for motor speed control. In contrast, the estimated current signals will be used to replace measured currents when the sensor fault state is determined. The performance of the fault-tolerant control solution will be verified through simulations under various changing load conditions in the Matlab/Simulink environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Induction motors

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Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

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computers, Materials & Continua 2022年第12期73卷 6019-6035页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohammad Alamgeer Mohamed K.Nour Anas Abdelrahman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Mechanical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication *** SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity *** the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s *** advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in *** this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)*** aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient *** attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform ***,WWO algorithm is applied to choose an optimal subset of features from the pre-processed ***,Deep Belief Network(DBN)model is followed to predict the stability level of ***,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN *** order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was *** simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.

关键词： Smart grid stability prediction deep learning energy systems machine learning metaheursitics

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Design and Implementation of a Smart Human Posture Monitoring Device Using a Wearable Vest 7

Design and Implementation of a Smart Human Posture Monitorin...

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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Daou, Roy Abi Zeid Geha, Elio Aoun, Youssef Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Biomedical Engineering Department Furn El Chebbak Beirut Lebanon Mart Learning Education and Research Center Chananir Lebanon Lebanese German University Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Germany Universidad Politecnica de Madrid Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Spain

ISBN: (纸本)9798350325850

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant. © 2023 IEEE.

关键词： Wearable technology

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Synergistic effects of heating and traction during fibrous tissue elongation

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Journal of Biomechanical science and engineering 2023年第2期18卷

作者： Yagi, Ippei Koike, Kazuki Uchida, Satoshi Department of Electrical Engineering and Computer Science Faculty of System Design Tokyo Metropolitan University 1-1 Minami-Osawa Hachioji-shi Tokyo192-0397 Japan

Fibrosis involves the abnormal accumulation of components of the extracellular matrix, such as collagen, and can lead to organ failure, which accounts for approximately one-third of all deaths worldwide. Accordingly, there is an urgent need to develop low-invasive treatment strategies for fibrosis as there is currently a lack of anti-fibrotic pharmacological agents available. In the present study, we tested the hypothesis that applying traction to fibrous tissues while using heat to denature proteins can be used to adjust the shape and flexibility of tissues. Bovine Achilles tendon samples as a model of fibrous tissue contracted by approximately 25% compared to the original length in response to heating, in line with previous findings. In contrast, the application of traction during heating and cooling increased tissue length by 25% and decreased Young’s modulus by 16%. Tissue staining and detection of unfolded collagen using collagen-hybridizing peptide demonstrated thermal degradation of collagen fibers and conversion to gelatinous material throughout samples, confirming that traction, and heating damages collagen. These results indicate that this technique can be used to alter the shape and reduce the rigidity of organs affected by fibrosis. © 2023 The Japan Society of Mechanical Engineers. This is an open access article under the terms of the Creative Commons Attribution 4.0 International license (https://***/licenses/by/4.0/).

关键词： Collagen

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Towards an Accurate Liver Disease Prediction Based on Two-level Ensemble Stacking Model

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IEEE Access 2024年

作者： Mohamed, Marghny H. Ali, Botheina H. Taloba, Ahmed I. Aseeri, Ahmad O. Elaziz, Mohamed Abd El-Sappgah, Shaker El-Rashidy, Nora Assiut University Faculty of Computers and Information Department of Computer Science Assiut Egypt Japan University of Science and Technology - P.O Box 179 Computer science and information technology New Borg El-Arab City Alexandria21934 Egypt Arish University Faculty of Computers and Information Department of Information System Egypt Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Department of Computer Science Al Kharj11942 Saudi Arabia Galala University Faculty of Computer Science and Engineering Suez435611 Egypt Benha University Faculty of Computers and Artificial Intelligence Information Systems Department Banha13518 Egypt Kafrelsheikh University Faculty of Artificial Intelligence Department of Machine Learning and Information Retrieval El-Geish st Kafrelsheikh33516 Egypt

The difficulty of detecting liver disease at an early stage goes back to its limited number of symptoms. In this study, single and ensemble machine learning (ML) algorithms are applied to the Indian Liver Patient Dataset (ILPD) dataset, and their results, without and with feature selection techniques, are compared between each other and to the existing studies. Also, a two-level ensemble stacking model is applied based on several meta-ensemble classifiers and the feature selection technique to optimize the accuracy of the ensemble classifiers. Several data preprocessing techniques are employed to optimize the accuracy of the proposed work, including data encoding, data cleaning, data scaling, data skewing transformation, data balancing, and feature selection. The choices of single model ML are logistic regression (LR), K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), and multilayer perceptron (MLP). In contrast, the choices of ensemble ML models are extra tree classifier, random forest (RF), gradient boosting, AdaBoost, extreme gradient boosting (XGBoost), and ensemble stacking classifier. Among the ensemble models, the ensemble stacking model achieved the highest accuracies (93.88% and 94.12%) when trained without and with the feature selection technique using the 10-fold cross-validation. The two-level ensemble stacking model achieved the highest performance with the metrics values: accuracy (94.01%), Precision (94.44%), Recall (94.25%), F1-score (94.01%), and area under the ROC curve (94.25%) when trained with feature selection technique. These results indicate that our proposed technique achieved a high prediction model for liver disease. © 2013 IEEE.

关键词： Prediction models

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Model Predictive Control for B-Spline Trajectory Tracking in Omnidirectional Robots

Model Predictive Control for B-Spline Trajectory Tracking in...

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Iberian Robotics Conference (ROBOT)

作者： José Pedro Carvalho António Paulo Moreira A. Pedro Aguiar Faculty of Engineering University of Porto (FEUP) Porto Portugal SYSTEC-ARISE Research Center for System and Technologies Porto Portugal INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

In the field of intelligent autonomous robots, integrating optimization techniques with classical control theory methods for mobile robot control is an increasingly prominent area of research. The combination enhances robots' ability to perform their tasks more efficiently, reliably, and safely. This paper addresses the development of a path and motion planning framework for omnidirectional robots, leveraging B-Splines and Trajectory Tracking with Model Predictive Control. The proposed framework is evaluated through software-in-the-loop tests using two distinct dynamical models and sets of hyperparameters. Final validation is conducted by implementing the framework within a ROS environment and performing field tests on a robotic platform. The results demonstrate that the robot can reliably track trajectories at its actuation limits, and the proposed framework enables the robot to increase its velocity up to 50% when compared to a PID path-following controller.

关键词： Uncertainty Trajectory tracking Wheels Predictive models Trajectory Planning Mobile robots Splines (mathematics) Robots Predictive control

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