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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4470-4478页

作者： Dhanaraj, Rachel Selva Sridevi, M. Goa College of Engineering Computer Engineering Department Goa 403401 India National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli620015 India

Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence (AI) researchers are constantly trying to find a better balance to develop new techniques and approaches to minimize loss of accuracy and increase robustness. To address these gaps, this article proposes a hybrid adversarial attack strategy by utilizing the Fast Gradient Sign Method and Projected Gradient Descent effectively to compute the perturbations that deceive deep neural networks, thus quantifying robustness without compromising its accuracy. Three distinct datasets - CelebA, CIFAR-10, and MNIST - were used in the extensive experiment, and six analyses were carried out to assess how well the suggested technique performed against attacks and defense mechanisms. The proposed model yielded confidence values of 99.99% for the MNIST dataset, 99.93% for the CelebA dataset, and 99.99% for the CIFAR-10 dataset. Defense study revealed that the proposed model outperformed previous models with a robust accuracy of 75.33% for the CelebA dataset, 55.4% for the CIFAR-10 dataset, and 98.65% for the MNIST dataset. The results of the experiment demonstrate that the proposed model is better than the other existing methods in computing the adversarial test and improvising the robustness of the system, thereby minimizing the accuracy loss. © 2020 IEEE.

关键词： Deep neural networks

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A Survey of Federated Learning for IoT: Addressing Resource Constraints and Heterogeneous Challenges

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第17期49卷 1-10页

作者： Vashisth, Sristi Goyal, Anjali Sharda University Department of Computer Science and Engineering Greater Noida India

Federated Learning (FL) has emerged as a promising approach to address the challenges of data privacy, security, and scalability in Internet of Things (IoT) environments. This paper provides a comprehensive survey of recent advances in FL for resource-constrained IoT systems, focusing on addressing the challenges of heterogeneous data, limited computational resources, and dynamic network environments. The survey highlights key achievements, including accuracy improvements of over 90% in domains such as smart homes, industrial IoT, and healthcare. Furthermore, FL solutions leveraging edge and fog computing have demonstrated significant energy efficiency improvements, reducing power consumption by up to 30%. A comparative analysis of state-of-the-art FL frameworks is presented, identifying critical research gaps in scalability, adaptive frameworks, and the integration of blockchain for enhanced security. Finally, the paper proposes future research directions to develop robust, efficient, and scalable FL solutions tailored for diverse IoT applications. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Federated learning

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Video surveillance in smart cities: current status, challenges & future directions

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Multimedia Tools and Applications 2025年第16期84卷 15787-15832页

作者： Sharma, Himani Kanwal, Navdeep Department of Computer Science & Engineering Punjabi University Punjab Patiala India

People across the world aspire to settle in urban areas for better opportunities in career, education, and healthcare facilities. The increased proportion of people living in urban areas requires an improvement of smart habitat(s) robust enough to deal with the daily needs of citizens such as personal information management, security and surveillance to deal with anomalous activities in real-time. The present paper aims to provide an extensive review based on 213 research articles published from 2001 to 2023 highlighting various technologies for smart cities and intelligent video surveillance techniques, in order to: (i) Highlight the significance of smart city surveillance as well as the current research tendencies in this field. (ii) To present and explicate a standardized model of a smart city based on video surveillance. (iii) To analyze the current status and highlight challenges, and limitations of surveillance systems in different smart city applications. The paper outlines the critical role of video surveillance as a necessary feature of every subdomain of the smart city model. The fundamental element that defines the soon-to-come victorious period is the most recent technological developments for the detection of anomalous activity, fire, digital tampering, and objects, which are thoroughly examined in existing research papers and elucidated. The article further presents a well outlined bibliographic classification of state-of-the-art techniques. A comparison of the existing video surveillance datasets has also been thoroughly analyzed. Finally, the current work identifies major research challenges and future opportunities in this domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object detection

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Hybrid DF and SIR Forwarding Strategy in Conventional and Distributed Alamouti Space-Time Coded Cooperative Networks

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computer Modeling in engineering.& sciences 2025年第2期142卷 1933-1954页

作者： Slim Chaoui Omar Alruwaili Faeiz Alserhani Haifa Harrouch Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Computer Science Department Applied CollegeUniversity of HailHail55424Saudi Arabia

In this paper,we propose a hybrid decode-and-forward and soft information relaying(HDFSIR)strategy to mitigate error propagation in coded cooperative *** the HDFSIR approach,the relay operates in decode-and-forward(DF)mode when it successfully decodes the received message;otherwise,it switches to soft information relaying(SIR)*** benefits of the DF and SIR forwarding strategies are combined to achieve better performance than deploying the DF or SIR strategy ***-form expressions for the outage probability and symbol error rate(SER)are derived for coded cooperative communication with HDFSIR and energy-harvesting ***,we introduce a novel normalized log-likelihood-ratio based soft estimation symbol(NL-SES)mapping technique,which enhances soft symbol accuracy for higher-order modulation,and propose a model characterizing the relationship between the estimated complex soft symbol and the actual high-order modulated ***-more,the hybrid DF-SIR strategy is extended to a distributed Alamouti space-time-coded cooperative *** evaluate the~performance of the proposed HDFSIR strategy,we implement extensive Monte Carlo simulations under varying channel *** demonstrate significant improvements with the hybrid technique outperforming individual DF and SIR strategies in both conventional and distributed Alamouti space-time coded cooperative ***,at a SER of 10^(-3),the proposed NL-SES mapping demonstrated a 3.5 dB performance gain over the conventional averaging one,highlighting its superior accuracy in estimating soft symbols for quadrature phase-shift keying modulation.

关键词： Cooperative communication soft information relaying soft symbols modeling cooperative diversity gain distributed Alamouti space-time code

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IDBNWP: Improved deep belief network for workload prediction: Hybrid optimization for load balancing in cloud system

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Multimedia Tools and Applications 2025年第16期84卷 15715-15733页

作者： Ajil, A. Kumar, E. Saravana Department of Computer Science and Engineering The Oxford College of Engineering Visvesvaraya Technological University Karnataka Belagavi India School of Computer Science and Engineering REVA University Karnataka Bengaluru India

The achievement of cloud environment is determined by the efficiency of its load balancing with proper allocation of resources. The proactive forecasting of future workload, accompanied by the allocation of resources, has emerged as a primary method for addressing other inbuilt problems, such as the underneath or over utilization of physical machines, resource wastage, VM migration, Quality-of-Services (QoS) violations, load balancing, and so on. In this paper, we have introduced a novel workload prediction and load balancing approach which includes two major phases like workload prediction with deep learning and optimal load balancing. In the initial workload prediction stage, we have proposed an Improved Deep Belief Network (IDBN), which efficiently predict the load as under load, overload or equally balanced. Afterwards, the load gets balanced by the utilization of the hybrid optimization named Bald Eagle Assisted Butterfly Optimization Algorithm (BEABOA), which consider the constraints like makespan (70), communication cost (4000), response time (1.1), turnaround time (2), migration cost (0.1) during the process of optimal load balancing. Also, the outcomes demonstrate that this proposed workload prediction and load balancing approach can offer superior outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Resource allocation

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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

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computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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Secure container Orchestration: A framework for detecting and mitigating Orchestrator - level vulnerabilities

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Multimedia Tools and Applications 2025年第17期84卷 18351-18371页

作者： Mahavaishnavi, V. Saminathan, R. Prithviraj, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Chidambaram India

This paper presents Secure Orchestration, a novel framework meticulously planned to uphold rigorous security measures over the profound security concerns that lie within the container orchestration platforms, especially in Kubernetes. The widespread adoption of containerized environments has raised concerns about the vulnerabilities at the level of the orchestrator. Secure Orchestration delivers a comprehensive approach by leveraging cutting-edge methods to detect and prevent the exposure of vulnerabilities from orchestrator misconfigurations, privilege escalation exploits that can thwart the orchestration security layer, and unauthorized access attempts targeting the orchestration system itself. In addition, it is further strengthened by deploying an Intrusion Detection and Prevention System (IDPS), which is responsible for actively monitoring the orchestration infrastructure. The IDPS utilizes sophisticated algorithms and mechanisms for detecting anomalies to continuously assess the orchestration environment for indications of intrusion, vulnerabilities, and anomalous activities. By using this proactive approach, the framework is able to rapidly identify potential threats and respond through countermeasures in a timely manner, thus greatly reducing the risks associated with orchestrator level vulnerabilities. Conducting a set of real-world experiments that closely simulate a variety of threat scenarios is imperative for empirically validating the efficiency of this framework. The obtained empirical results yield insightful revelations on the efficacy of the framework in accurately identifying and mitigating vulnerabilities, thereby ensuring the privacy and integrity of container orchestration systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Containers

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Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

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computers, Materials & Continua 2024年第4期79卷 1205-1222页

作者： Jungpil Shin Md.AlMehedi Hasan Md.Maniruzzaman Taiki Watanabe Issei Jozume School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

关键词： Person identification leap motion hand gesture random forest support vector machine

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A Developer-Focused Genetic Algorithm for IoT Application Placement in the Computing Continuum

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IEEE Transactions on Services Computing 2025年第3期18卷 1185-1198页

作者： Herrera, Juan Luis Moya, Alejandro Berrocal, Javier Murillo, Juan Manuel Navarro, Elena University of Bologna Department of Computer Science Engineering Italy University of Castilla-La Mancha LoUISE Research Group Spain University of Extremadura Department of Computer Science and Communications Engineering Spain

The rise of the Internet of Things (IoT) paradigm has led to an interest in applying it not only in tasks for the general public but also to stringent domains such as healthcare. However, the developers of these next-generation IoT applications must consider additional non-functional requirements related to the criticality of the processes they automate, such as low response times or low deployment costs, as well as technical constraints, which include organizational, legal and policy-related constraints on where data can be processed or stored. While the Computing Continuum paradigm emerges as a valuable alternative for placing such applications, identifying the deployments that satisfy all these requirements becomes a tough challenge. The NP-hard nature of the problem makes it impractical to manually find such a deployment, and traditional approaches fail to consider the technical constraints. In this paper, we present the Genetic Algorithm for Application Placement (GAAP), an evolutionary computing-based meta-heuristic designed to help IoT application developers find deployments that satisfy their Quality of Service, business and technical constraints. Our evaluation of an Internet of Medical Things use case shows that GAAP supports larger scenarios than traditional approaches and gives IoT application developers more options while providing better scalability. © 2008-2012 IEEE.

关键词： Internet Of Things Microservice Architectures Quality Of Service Costs Time Factors Electrocardiography Biomedical Monitoring Blood Pressure Genetic Algorithms Business Internet Of Things Computing Continuum Quality Of Service Service Placement Deployment Genetic Algorithm Internet Of Things Internet Of Things Applications Response Time Service Quality Technical Constraints Deployment Cost Low Response Time Internet Of Medical Things Heuristic Objective Function Replica Decision Variables Solution Space Quality Of Experience Internet Of Things Devices Pareto Front Multi Objective Algorithm Capital Expenditure Crossover Operator Quality Of Service Requirements Operational Expenditure Set Of Hosts Workflow Dijkstras Algorithm Non Dominated Solutions Average Response Time Pareto Optimal Solutions Response Cost Multi Objective Genetic Algorithm

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An Anomaly Detection Enhancement Method Using Interpretable Unsupervised Machine Learning in Industrial Information Systems at Multivariate Time Series Environment

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Journal of Institute of Control, Robotics and Systems 2024年第3期30卷 245-252页

作者： Jeon, Janggun Kim, Namgi Department of Computer Science Kyonggi University Korea Republic of Department of AI Computer Engineering Kyonggi University Korea Republic of

Time series anomaly detection is a technology that finds outliers in observed data over time, and is a significant research field associated with many applications or platforms. In this paper, we propose a method called Encoded-IF, a new and improved version of the isolation forest (IF) machine learning algorithm using Auto-Encoder to detect time series anomalies. Encoded-IF is a non-parametric and unsupervised method for detecting anomalies in time series data. It efficiently detects anomalies in each dimension of high-dimensional data and quickly learns nonlinear correlations between dimensions. Experiments were conducted on four publicly available time series data sets to evaluate the performance of the proposed method. The results improvement in performance of up to 4%. © ICROS 2024.

关键词： Time series

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