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computer Modeling in engineering & Sciences 2023年第5期135卷 1345-1369页

作者： Adedoyin A.Hussain Fadi Al-Turjman Computer Engineering Department and Research Centre for AI and IoT Near East UniversityNicosiaTurkey Computer Engineering Department CyprusWest UniversityGazimagusaTurkey Artificial Intelligence Engineering Department and AI and Robotics Institutes Near East UniversityNicosiaTurkey Research Centre for AI and IoT Faculty of EngineeringUniversity of KyreniaKyreniaTurkey

The internet of medical things(IoMT)empowers patients to get adaptable,and virtualized gear over the *** scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on ***,a proposition is being made for a distinct scheduling technique to suitably meet these *** manage the scheduling issue,an artificial intelligence(ai)method known as a hybrid genetic algorithm(HGA)is *** proposed ai method will be justified by contrasting it with other traditional optimization and ai scheduling *** CloudSim is utilized to quantify its effect on various parameters like time,resource utilization,cost,and *** proposed ai technique enhanced the viability of task scheduling with a better execution rate of 32.47ms and a reduced time of ***,the experimented outcomes show that the HGA reduces cost as well as time profoundly.

关键词： Artificial intelligence IoMT hybrid genetic algorithm cloud

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Power synchronization: taming massive diversified serverless functions under power constraints

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Science China(Information Sciences) 2025年第3期68卷 47-65页

作者： Du LIU Lu ZHANG Yechen XU Xinkai WANG Lingyu SUN Yifei PU Xiaofeng HOU Chao LI Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong University AI Chip Center for Emerging Smart Systems The Hong Kong University of Science and Technology

Carbon neutrality has become an important design objective worldwide. However, the on-going shift to cloud-naive era does not necessarily mean energy efficiency. From the perspective of power management, co-hosted serverless functions are difficult to tame. They are lightweight, short-lived applications sensitive to power capping activities. In addition, they exhibit great individual and temporal variability, presenting idiosyncratic performance/power scaling goals that are often at odds with one another. To date, very few proposals exist in terms of tailored power management for serverless platforms. In this work, we introduce power synchronization, a novel yet generic mechanism for managing serverless functions in a power-efficient way. Our insight with power synchronization is that uniform application power behavior enables consistent and uncompromised function operation on shared host machines. We also propose PowerSync, a synchronization-based power management framework that ensures optimal efficiency based on a clear understanding of functions. Our evaluation shows that PowerSync can improve the energy efficiency of functions by up to 16% without performance loss compared to conventional power management strategies.

关键词： serverless computing variability energy efficiency power management power synchronization

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Reward-Based Energy-Aware Routing Protocols in Wireless Sensor Networks

Reward-Based Energy-Aware Routing Protocols in Wireless Sens...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-aioT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software and Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Smart cities, industrial automation, healthcare, and environmental monitoring are just a few of the industries that have seen revolutionary changes as a result of Wireless Sensor Networks’ (WSNs) explosive growth. WSNs are significantly hampered by the limited energy resources of their sensor nodes, which are usually battery-powered and placed in inaccessible locations, despite their wide-ranging applications. As a result, energy efficiency is now a crucial component of these networks’ operation and design. Though these protocols frequently fail to adjust to the dynamic and unpredictable nature of WSNs, traditional energy-aware routing methods have been created to decrease energy consumption by choosing the best paths for data transfer. By allowing sensor nodes to learn from their surroundings and make wise routing decisions, the machine learning field of reinforcement learning (RL) provides a viable answer to these problems. In contrast to conventional techniques, RL-based protocols provide continuous energy optimization by dynamically modifying their routing strategies in response to real-time feedback. This paper investigates how to include RL methods—specifically, Q-learning and SARSA—into WSN energy-aware routing protocols. The suggested method creates a more distributed and scalable solution by enabling each node to independently learn and modify its routing choices. The effectiveness of RL-based routing protocols is compared to traditional methods through comprehensive simulations. The findings show that by slowing down the rate at which energy is being used up by each node, RL not only increases energy efficiency but also lengthens the network's operational life. In particular, Q-learning demonstrates a notable improvement in sustaining data delivery rates and network connectivity under various circumstances. Although a little more computationally demanding, the SARSA algorithm offers greater flexibility in situations where network topology changes often. Th

关键词： Multi agent systems

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Using Gamification to Increase Healthcare Professionals’ Cybersecurity Awareness

Using Gamification to Increase Healthcare Professionals’ Cy...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-aioT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Cybersecurity knowledge is essential in today’s increasingly digital healthcare environment to protect sensitive patient data and guarantee the integrity of healthcare systems. But conventional cybersecurity training approaches frequently fail to hold healthcare professionals’ attention, which results in inadequate application and retention of the material. In order to increase cybersecurity awareness among healthcare professionals, this study investigates the use of gamification—a technological technique that incorporates game design aspects into non-gaming contexts—as an innovative method. A mixed-methods research methodology is used in the study to assess the effectiveness of a cybersecurity training program that is gamified. To encourage user involvement and motivation, the program incorporates aspects like leaderboards, interactive scenarios, badges, and points. The study included a total of 100 healthcare professionals from various departments, offering a thorough understanding of the program’s effects across a range of positions and experience levels. Pre- and post-training surveys were used to gather quantitative data on participant changes in knowledge, attitudes, and behaviors pertaining to cybersecurity procedures. Furthermore, semi-structured interviews yielded qualitative insights that further elucidated the participants’ perspectives and experiences with the gamified training method. Comparing gamification to conventional training approaches, the results show that gamification dramatically improves engagement and knowledge retention. The willingness of the participants to acquire and use cybersecurity techniques in their daily lives has reportedly increased. Additionally, the gamified components strengthened the training objectives by fostering a competitive yet cooperative learning environment. The potential of gamification as a game-changing instrument for cybersecurity education in healthcare settings is highlighted by this study. Healthcare busines

关键词： Cybersecurity

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Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

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computers, Materials & Continua 2023年第8期76卷 1721-1744页

作者： Seshu Bhavani Mallampati Seetha Hari School of Computer Science and Engineering VIT-AP UniversityAndhra PradeshIndia Center of Excellence AIand RoboticsVIT-AP UniversityAndhra PradeshIndia

Expanding internet-connected services has increased cyberattacks,many of which have grave and disastrous *** Intrusion Detection System(IDS)plays an essential role in network security since it helps to protect the network from vulnerabilities and *** extensive research was reported in IDS,detecting novel intrusions with optimal features and reducing false alarm rates are still ***,we developed a novel fusion-based feature importance method to reduce the high dimensional feature space,which helps to identify attacks accurately with less false alarm ***,to improve training data quality,various preprocessing techniques are *** Adaptive Synthetic oversampling technique generates synthetic samples for minority *** the proposed fusion-based feature importance,we use different approaches from the filter,wrapper,and embedded methods like mutual information,random forest importance,permutation importance,Shapley Additive exPlanations(SHAP)-based feature importance,and statistical feature importance methods like the difference of mean and median and standard deviation to rank each feature according to its *** by simple plurality voting,the most optimal features are *** the optimal features are fed to various models like Extra Tree(ET),Logistic Regression(LR),Support vector Machine(SVM),Decision Tree(DT),and Extreme Gradient Boosting Machine(XGBM).Then the hyperparameters of classification models are tuned with Halving Random Search cross-validation to enhance the *** experiments were carried out on the original imbalanced data and balanced *** outcomes demonstrate that the balanced data scenario knocked out the imbalanced ***,the experimental analysis proved that our proposed fusionbased feature importance performed well with XGBM giving an accuracy of 99.86%,99.68%,and 92.4%,with 9,7 and 8 features by training time of 1.5,4.5 and 5.5 s on Network Security Laborat

关键词： Cyber security feature ranking imbalance preprocessing IDS SHAP

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SEMG-Based Gesture Recognition in Multiple Scenarios Using a Phase Locked Value-Based Deep Learning Method

SEMG-Based Gesture Recognition in Multiple Scenarios Using a...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Xueze Wang, Minjie Chen, Zihua Wang, Lu Wang, Hongbo Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai & Robotics Ministry of Education Shanghai Engineering Research Center of Ai & Robotics Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai & Robotics Institude of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (纸本)9798350386226

Surface electromyography (sEMG) signals-based gesture recognition method is widely employed in human-computer interaction task. In this paper, we proposed a phase locked value (PLV)-based feature extraction method for sEMG-based gesture recognition. We conducted validation experiments on public datasets by integrating the previous proposed gesture recognition algorithms, STCN-GR and ConSSL. PLV-STCN-GR and PLV-ConSSL were utilized to test the performance of proposed feature extraction method in intra-session, inter-session and inter-subject scenarios. In general, the proposed PLV-based sEMG decoding framework has achieved good recognition results on public datasets and demonstrated the possibility of its application in practical scenarios. © 2024 IEEE.

关键词： Deep learning

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Autonomous Residual Threshold Detection for Decentralized Radar Inertial Odometry in Hostile Environments

Autonomous Residual Threshold Detection for Decentralized Ra...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Mukherjee, Moumita Banerjee, Avijit Koval, Anton Nikolakopoulos, George Luleå University of Technology Robotics and Ai Group Department of Computer Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350361025

The article introduces an adaptive threshold detection mechanism aimed at enhancing a decentralized radar inertial odometry (RIO) framework, enabling resilient localization in challenging hostile environments. Considering that frequency-modulated continuous-wave (FMCW) radars possess characteristics enabling measurements in challenging indoor and outdoor environments, the fusion of multiple radars in an ensemble configuration, along with an inertial measurement unit (IMU), holds promise in surpassing individual sensor limitations. This approach thereby enhances robust perception. The proposed adaptive autonomous residual threshold mechanism employs a real-time residual analysis to dynamically adjust the sensor fusion process by comparing the variance between two extended Kalman filters. This adaptive approach addresses irregularities in data samples from multiple sensors, thereby enhancing the decentralized smoothing estimator's precision in providing localization while navigating through hostile environments marked by limited visibility, extreme weather, or high interference. Consequently, it contributes to the resilience and adaptability of autonomous systems in real-world scenarios. The proposed framework effectively showcases precise localization through decentralized radar inertial odometry(RIO). © 2024 IEEE.

关键词： Kalman filters

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Cloud-Based Scheduling Mechanism for Scalable and Resource-Efficient Centralized Controllers 50

Cloud-Based Scheduling Mechanism for Scalable and Resource-E...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Seisa, Achilleas Santi Satpute, Sumeet Gajanan Nikolakopoulos, George The Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (纸本)9781665464543

This paper proposes a novel approach to address the challenges of deploying complex robotic software in large-scale systems, i.e., Centralized Nonlinear Model Predictive Controllers (CNMPCs) for multi-agent systems. The proposed approach is based on a Kubernetes-based scheduling mechanism designed to monitor and optimize the operation of CNMPCs, while addressing the scalability limitation of centralized control schemes. By leveraging a cluster in a real-time cloud environment, the proposed mechanism effectively offloads the computational burden of CNMPCs. Through experiments, we have demonstrated the effectiveness and performance of our system, especially in scenarios where the number of robots is subject to change. Our work contributes to the advancement of cloud-based control strategies and lays the foundation for enhanced performance in cloud-controlled robotic systems. © 2024 IEEE.

关键词： Multi agent systems

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Data augmentation and denoising of computed tomography scan images in training deep learning models for rapid COVID-19 detection

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International Journal of Business Intelligence and Data Mining 2024年第2期24卷 203-216页

作者： Mubarak, Auwalu Saleh Serte, Sertan Al-Turjman, Fadi Ameen, Zubaida Sa'Id Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin10 Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin10 Turkey Department of Electrical and Electronics Engineering Near East University Mersin10 Turkey

The deadly respiratory disease corona virus-2 (COVID-19) which was declared a pandemic by the World Health Organization (WHO) has resulted in over a million deaths around the world within less than a year. With the rapid spread of the virus, the currently adopted COVID-19 test by the WHO is the reverse transcription polymerase chain reaction (RT-PCR) test, which is expensive, time-consuming and not accessed by underdeveloped countries. Computed tomography (CT) scan images that were used in profiling suspected COVID-19 patients can serve as an alternative to the RT PCR test method. In this study, two different pre-Trained deep learning models ResNet-50 and ResNet-101 were trained to classify positive COVID-19 scan images. The best model which was trained on the augmented CT scan images achieved an accuracy of 98.3%, a sensitivity of 0.984, specificity of 0.983. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

关键词： COVID-19

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Preference detection of the humanoid robot face based on EEG and eye movement

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Neural Computing and Applications 2024年第19期36卷 11603-11621页

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI and Robotics Ministry of Education Shanghai Engineering Research Center of AI and Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

The face of a humanoid robot can affect the user experience, and the detection of face preference is particularly important. Preference detection belongs to a branch of emotion recognition that has received much attention from researchers. Most of the previous preference detection studies have been conducted based on a single modality. In this paper, we detect face preferences of humanoid robots based on electroencephalogram (EEG) signals and eye movement signals for single modality, canonical correlation analysis fusion modality, and bimodal deep autoencoder (BDAE) fusion modality, respectively. We validated the theory of frontal asymmetry by analyzing the preference patterns of EEG and found that participants had higher alpha wave energy for preference faces. In addition, hidden preferences extracted by EEG signals were better classified than preferences from participants' subjective feedback, and also, the classification performance of eye movement data was improved. Finally, experimental results showed that BDAE multimodal fusion using frontal alpha and beta power spectral densities and eye movement information as features performed best, with the highest average accuracy of 83.13% for the SVM and 71.09% for the KNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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