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Event-driven bi-fidelity control for efficient and reliable robotic manipulation

Results in engineering 2025年 26卷

作者： alsaade, Fawaz W. al-zahrani, Mohammed S. alsaadi, Fuad E. Department of Computer Science College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Department of Computer Networks and Communications College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Communication Systems and Networks Research Group Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

This paper proposes a novel bi-fidelity control framework for robotic manipulators that integrates a high-fidelity model predictive control (MPC) scheme with a low-fidelity Long Short-Term Memory (LSTM) neural network surrogate. Unlike conventional fixed-schedule approaches, our method employs an event-triggered mechanism that dynamically selects the appropriate controller based on a real-time error metric. This mechanism ensures that the computationally intensive MPC is invoked only when the LSTM approximation deviates beyond acceptable bounds. We rigorously established the near-optimal performance and closed-loop stability of our approach via Lyapunov analysis under mild assumptions, instilling confidence in its reliability. The simulation results on a three-arm manipulator, subject to low-frequency sinusoidal and high-frequency chirp trajectories, demonstrate that the proposed approach achieves up to 80–90% reduction in MPC calls, significantly improving the accuracy of the follow-up and computational efficiency. These findings highlight the potential of integrating learning-based approximations with conventional optimization-based control to achieve reliable and time-efficient performance in complex robotic manipulation tasks. © 2025 The Authors

关键词： Predictive control systems

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Enhanced Metaheuristics-Based Clustering Scheme for Wireless Multimedia Sensor Networks

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computers, Materials & Continua 2022年第11期73卷 4179-4192页

作者： R.Uma Mageswari Sara A.althubiti Fayadh alenezi E.Laxmi Lydia Gyanendra Prasad Joshi Woong Cho Department of Computer Science and Engineering Vardhaman College of Engineering(Autonomous)HyderabadTelangana501218India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

Traditional Wireless Sensor Networks(WSNs)comprise of costeffective sensors that can send physical parameters of the target environment to an intended *** the evolution of technology,multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target *** existence of multimedia sensors,integrated with effective signal processing and multimedia source coding approaches,has led to the increased application of Wireless Multimedia Sensor Network(WMSN).This sort of network has the potential to capture,transmit,and receive multimedia *** energy is a major source in WMSN,novel clustering approaches are essential to deal with adaptive topologies of WMSN and prolonged network *** this motivation,the current study develops an Enhanced Spider Monkey Optimization-based Energy-Aware Clustering Scheme(ESMO-EACS)for *** proposed ESMO-EACS model derives ESMO algorithm by incorporating the concepts of SMO algorithm and quantum *** proposed ESMO-EACS model involves the design of fitness functions using distinct input parameters for effective construction of clusters.A comprehensive experimental analysis was conducted to validate the effectiveness of the proposed ESMO-EACS technique in terms of different performance *** simulation outcome established the superiority of the proposed ESMO-EACS technique to other methods under various measures.

关键词： Wireless multimedia sensor networks clustering spider monkey optimization algorithm energy efficiency metaheuristics quantum computing

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Rehabilitation robot with a new diagnostic and control protocol 20

Rehabilitation robot with a new diagnostic and control proto...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Abdallah, Ismail Ben Bouteraa, Yassine Sfax Tunisia Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Department of Computer Engineering Al-Kharj11942 Saudi Arabia

ISBN: (纸本)9798350332568

Using a connected robot for wrist and forearm rehabilitation, a new rehabilitation system is developed. Forearm and wrist biomechanics are studied as part of a rehabilitation protocol. An integrated wrist-forearm joint mathematical model is developed and integrated into the main controller. The proposed new rehabilitation protocol is composed of three main sessions: the first is dedicated to the extraction of the passive components of the dynamic model of the wrist-forearm biomechanics while the active components are extracted in the second session. The third session consists of performing the continuous exercises using the determined dynamic model of the forearm-wrist joints taking into account the torque due to muscle fatigue. In addition to determining the wrist condition and capturing the torque produced by the robot and that provided by the patient, this protocol aims to identify the level of fatigue faced by the wrist. An EMG signal is used to estimate muscle fatigue using a fuzzy classifier designed and implemented for this purpose. The results show that the rehabilitation system developed allows a good progression of the articular amplitudes as well as the resistive-active couple. © 2023 IEEE.

关键词： Biomechanics

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain ali Haider Khan Malek alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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A new design of a bionic hand controlled by EMG signals : A preliminary study 20

A new design of a bionic hand controlled by EMG signals : A ...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

ISBN: (纸本)9798350332568

A bionic hand might provide a good replacement for the human hand when carrying out various tasks. In recent years, hand movements have been widely recognized via myoelectric control. although a few research has looked at the hand-grip position, the majority of earlier investigations have concentrated on whole-hand movements. This study's objective was to build a prototype system for determining hand-grip position in order to control a bionic hand by examining EMG signals obtained from the associated muscles. We employed the fuzzy logic method as the classifier to perform hand-grip estimation and several characteristics that are frequently used in literature, such as mean absolute value and root mean square, in the approach for extracting hand-grip features. We developed a two-channel EMG measurement system that can recognize the human handgrip position and control the bionic hand to make the appropriate hand movement. The results of the experiments demonstrate that the bionic hand could accurately imitate the grasping position of the human hand. © 2023 IEEE.

关键词： Bionics

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Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

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computers, Materials & Continua 2022年第12期73卷 5543-5557页

作者： Thavavel Vaiyapuri S.Srinivasan Mohamed Yacin Sikkandar T.S.Balaji Seifedine Kadry Maytham N.Meqdad Yunyoung Nam College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering College of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Applied Data Science Noroff University CollegeKristiansand4608Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillahBabil51001Iraq Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

In past decades,retinal diseases have become more common and affect people of all age grounds over the *** examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated *** analyze the retinal malady,the system proposes a multiclass and multi-label arrangement ***,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive ***,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)*** this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus *** proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological ***,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly *** addition,SqueezeNet based feature extractor is employed to generate a collection of feature ***,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal *** efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different *** experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

关键词： Multi-retinal disease computer aided diagnosis fundus images deep learning segmentation intelligent models

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Business Process Remaining Time Prediction Based on Incremental Event Logs

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IEEE Transactions on Services Computing 2025年

作者： Guo, Na Liu, Cong Mo, Qi Cao, Jian Ouyang, Chun Lu, Xixi Zeng, Qingtian Shandong University of Technology School of Electrical and Electronic Engineering Zibo255000 China Nova University of Lisbon NOVA Information Management School Lisbon1070-312 Portugal Shandong University of Technology School of Computer Science and Technology Zibo255000 China Yunnan University School of Software Kunming650091 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200030 China Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Utrecht University Department of Information and Computing Sciences CS Utrecht3584 Netherlands Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China

Predictive Process Monitoring (PPM) aims to predict the future state of running process instances to enable timely interventions to mitigate potential risks. As one of the most fundamental tasks in PPM, process remaining time prediction focuses on preventing timeout occurrences. While various deep learning-based approaches have been developed for this purpose, they often rely on pre-established static prediction models and struggle to maintain accurate predictions when the process undergoes dynamic changes, such as an expanding sales channels. To tackle this challenge, this paper proposes an incremental process remaining time prediction framework by continuously updating the prediction model based on an incremental event log. Specifically, a feature selection strategy is first introduced to extract effective features from event logs. Leveraging effective features can significantly improve the prediction quality by capturing the changes in process information. Then, three incremental log-based updating mechanisms, including period-based updating, quantity-based updating, and concept-drift-based updating, along with a reconstruction strategy, are proposed to dynamically adjust the prediction model in response to business changes. Finally, LSTM, Transformer, and Auto-encoder models are adapted and integrated into the proposed framework. The approach has been implemented and publicly released. Experimental evaluation using nine real-life event logs demonstrate that the proposed framework and its three instantiations (i.e., LSTM-based, Transformer-based, and Auto-encoder-based ones) outperform state-of-the-art techniques in terms of prediction accuracy. © 2008-2012 IEEE.

关键词： incremental event log Predictive process monitoring process mining remaining time prediction

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Retraction notice to "Factors affecting the adoption of metaverse in healthcare: The moderating role of digital division, and meta-culture" [Heliyon 10 (2024) e28778]

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Heliyon 2025年第3期11卷 e42486页

作者： Jibo He Sayed Fayaz Ahmad Muna al-Razgan Yasser A ali Muhammad Irshad School of Psychology Nanjing Normal University Nanjing 210023 China. Department of Engineering Management Institute of Business Management Karachi Pakistan. Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia. Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia. Department of Management Sciences University of Gwadar Pakistan.

[This retracts the article DOI: 10.1016/***.2024.e28778.].

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SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments

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Heliyon 2025年第1期11卷 e40874页

作者： Awan, Kamran Ahmad Ud Din, Ikram almogren, Ahmad Nawaz, ali Khan, Muhammad Yasar altameem, Ayman Department of Information Technology The University of Haripur Khyber Pakhtunkhwa Haripur 22620 Pakistan Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh 11633 Saudi Arabia College of Information Technology United Arab Emirates University Al Ain 15551 United Arab Emirates SFI-Funded E-Governance Unit Insight Centre for Data Analytics University of Galway Galway H91 CF50 Ireland Department of Natural and Engineering Sciences College of Applied Studies and Community Services King Saud University Riyadh 11633 Saudi Arabia

The rapid growth of Internet of Things (IoT) devices presents significant cybersecurity challenges due to their diverse and resource-constrained nature. Existing security solutions often fall short in addressing the dynamic and distributed environments of IoT systems. This study aims to propose a novel deep learning framework, SecEdge, designed to enhance real-time cybersecurity in mobile IoT environments. The SecEdge framework integrates transformer-based models for efficient handling of long-range dependencies and Graph Neural Networks (GNNs) for modeling relational data, coupled with federated learning to ensure data privacy and reduce latency. The adaptive learning mechanism continuously updates model parameters to counter evolving cyber threats. The framework's performance was evaluated in a simulation environment using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. Results demonstrated that SecEdge outperformed state-of-the-art methods with a detection rate of 98.8% for DoS attacks on NSL-KDD, 98.5% for MitM attacks on UNSW-NB15, and 98.7% for data injection attacks on CICIDS2017. © 2024

关键词： Anamoly detection Cyber security Deep learning Federated learning Graph neural network Internet of things Mobile computing

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Neighborhood Search Based Improved Bat algorithm for Web Service Composition

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computer Systems Science & engineering 2023年第5期45卷 1343-1356页

作者： Fadl Dahan Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology AlturbahTaiz UniversityTaiz9674Yemen

Web services are provided as reusable software components in the services-oriented *** complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service(QoS)*** workflow consists of tasks where many services can be considered for each *** for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial(NP)-hard *** work focuses on the Web Service Composition(WSC)problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the *** proposed algorithm determines the optimal combination of the web services to satisfy the complex user *** also addresses the Bat algorithm(BA)shortcomings,such as the tradeoff among exploration and exploitation searching mechanisms,local optima,and convergence *** proposed enhancement includes a developed cooperative and adaptive population initialization *** elitist mechanism is utilized to address the BA convergence *** tradeoff between exploration and exploitation is handled through a neighborhood search *** benchmark datasets are selected to evaluate the proposed bat algorithm’s *** simulation results are estimated using the average fitness value,the standard deviation of the fitness value,and an average of the execution time and compared with four bat-inspired *** is observed from the simulation results that introduced enhancement obtains significant results.

关键词： Cloud computing web service composition bat algorithm serviceoriented architecture

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