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Improved Monarchy Butterfly Optimization Algorithm (IMBO): Intrusion Detection Using Mapreduce Framework Based Optimized ANU-Net

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computers, Materials & Continua 2023年第6期75卷 5887-5909页

作者： Kunda Suresh Babu Yamarthi Narasimha Rao School of Computer Science and Engineering VIT-AP UniversityAmaravathi522237Andhra PradeshIndia

The demand for cybersecurity is rising recently due to the rapid improvement of network *** a primary defense mechanism,an intrusion detection system(IDS)was anticipated to adapt and secure com-puting infrastructures from the constantly evolving,sophisticated threat ***,various deep learning methods have been put forth;however,these methods struggle to recognize all forms of assaults,especially infrequent attacks,because of network traffic imbalances and a shortage of aberrant traffic samples for model *** work introduces deep learning(DL)based Attention based Nested U-Net(ANU-Net)for intrusion detection to address these issues and enhance detection *** this IDS model,the first data preprocessing is carried out in three stages:duplication elimi-nation,label transformation,and data *** the features are extracted and selected based on the Improved Flower Pollination Algorithm(IFPA).The Improved Monarchy Butterfly Optimization Algorithm(IMBO),a new metaheuristic,is used to modify the hyper-parameters in ANU-Net,effectively increasing the learning rate for spatial-temporal information and resolving the imbalance *** the use of parallel programming,the MapReduce architecture reduces computation complexity while signifi-cantly accelerating *** publicly available data sets were used to evaluate and test the *** investigational outcomes suggest that the proposed technique can more efficiently boost the performances of IDS under the scenario of unbalanced *** proposed method achieves above 98%accuracy and classifies various attacks significantly well compared to other classifiers.

关键词： Intrusion detection system(IDS) attention based nested U-Net

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Analysis of Software Trustworthiness Based on FAHP-CRITIC Method

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Journal of Shanghai Jiaotong university(science) 2024年第3期29卷 588-600页

作者：高晓彤马艳芳周伟 School of Computer Science and Technology Huaibei Normal UniversityHuaibei 235000AnhuiChina School of Computer Science and Information Engineering Changzhou Institute of TechnologyChangzhou 213000JiangsuChina

Software trustworthiness includes many *** weight allocation of trustworthy at-tributes plays a key role in the software trustworthiness *** practical application,attribute weight usually comes from experts'evaluation to attributes and hidden information derived from ***,when the weight of attributes is researched,it is necessary to consider weight from subjective and objective ***,a novel weight allocation method is proposed by combining the fuzzy analytical hierarchy process(FAHP)method and the criteria importance though intercrieria correlation(CRITIC)***,based on the weight allocation method,the trustworthiness measurement models of component-based software are estab-lished according to the seven combination structures of ***,the model reasonability is verified via proving some metric ***,a case is carried *** to the comparison with other models,the result shows that the model has the advantage of utilizing hidden information fully and analyzing the com-bination of components *** is an important guide for measuring the trustworthiness measurement of component-based software.

关键词： trustworthiness weight component-based software measurement

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Speed Harmonic based Saturation Free Inductance Modeling and Estimation of Interior PMSM Using Measurements Under One Load Condition

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CES Transactions on Electrical Machines and Systems 2025年第1期9卷 91-99页

作者： Guodong Feng Yuting Lu Zhe Tong Beichen Ding Guishan Yan Chunyan Lai the School of Intelligent Systems Engineering Sun Yat-sen UniversityShenzhenChina the School of Advanced Manufacturing Sun Yat-sen University the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) China the Department of Electrical and Computer Engineering Concordia UniversityQCCanada

For permanent magnet synchronous machines(PMSMs),accurate inductance is critical for control design and condition *** to magnetic saturation,existing methods require nonlinear saturation model and measurements from multiple load/current conditions,and the estimation is relying on the accuracy of saturation model and other machine parameters in the *** harmonic produced by harmonic currents is inductance-dependent,and thus this paper explores the use of magnitude and phase angle of the speed harmonic for accurate inductance *** estimation models are built based on either the magnitude or phase angle,and the inductances can be from d-axis voltage and the magnitude or phase angle,in which the filter influence in harmonic extraction is considered to ensure the estimation *** inductances can be estimated from the measurements under one load condition,which is free of saturation ***,the inductance estimation is robust to the change of other machine *** proposed approach can effectively improve estimation accuracy especially under the condition with low current *** and comparisons are conducted on a test PMSM to validate the proposed approach.

关键词： PMSM Inductance estimation Speed harmonic High frequency signal injection

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Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 3952-3967页

作者： Yemini, Michal Nedic, Angelia Goldsmith, Andrea J. Gil, Stephanie Bar-Ilan University Faculty of Engineering Israel Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Harvard University Computer Science Department School of Engineering and Applied Sciences CambridgeMA02139 United States

This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points. © 1963-2012 IEEE.

关键词： Multi agent systems

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Event Management System Using Spatial and Event Attribute Information

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SN computer science 2025年第3期6卷 1-9页

作者： Setia, Sonia Anjli, Km Bisht, Upasana Jyoti Raj, Dharm Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science Institute of Innovation in Technology & Management Delhi Janakpuri India Department of Computer Science and Engineering ITS Engineering College Greater Noida India

The event management mechanism matches messages that have been subscribed to and events that have been published. To identify the subscriptions that correspond to the occurrence inside the category, it must first run a clustering operation on the subscription message data and determine the category to which the event belongs. In this work, we offer an on-the-fly publish-subscribe technique that can handle on-the-fly subscription changes and category modifications in a distributed environment, as well as the synchronization of spatial information and event attribute information. Additionally, without any prior information, clustering and matching processes can be carried out rapidly. The publish-subscribe method is implemented on a distributed system, and a load-balancing solution is proposed for the algorithm on the distributed system. We then constructed our cluster and used real data to empirically evaluate the suggested publish-subscribe technique. Experiments show that incorporating both similarity and relevance with frequency, compared to focusing on similarity alone, significantly decreases the number of categories which need to be matched for events. This leads to a 15.5% improvement in the speed of matching events and subscriptions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clustering algorithms Distributed systems Publish-and-subscribe systems

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Blockchain-Empowered Asynchronous Federated Reinforcement Learning for IoT-Based Traffic Trajectory Prediction

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IEEE Internet of Things Journal 2025年第11期12卷 17095-17109页

作者： Wang, Bin Tian, Zhao Tang, Fengxiao Pan, Heng She, Wei Liu, Wei Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450002 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Central South University School of Computer Science and Engineering Changsha410083 China Zhongyuan University of Technology School of Cyber Space Security Zhengzhou450007 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou450002 China

Vehicle trajectory prediction plays a crucial role in IoT-based intelligent transportation systems, which can effectively address key issues, such as driving safety and multivehicle collaboration. However, the sensitivity of trajectory data and the reluctance of data holders to share it constrain the prediction model’s ability to capture vehicle behavior patterns in different scenarios. To address the above problems, we propose a blockchain-enabled asynchronous federated proximal policy optimization framework (BE-AFPPO) for the trajectory prediction of self-driving vehicles. First, we propose a curiosity proximal policy optimization (C-PPO) algorithm. The method utilizes a driven exploration strategy to actively motivate the intelligent agent to explore the unknown state space. The avoidance policy model reaches a local optimum when processing trajectory data. In addition, we design historical gated recurrent unit (GRU) and future GRU as input layers. The target’s historical motion features and future trajectory features are extracted, respectively. Then, various data is received through asynchronous federated learning. This model can fully learn the vehicle’s behavior patterns in different scenarios, which improves prediction accuracy. Based on this, we develop a blockchain-based dynamic group practical Byzantine fault tolerance (DG-PBFT) consensus algorithm. This enhances the credibility and integrity of the data while enriching the sources of trajectory data. Finally, we perform the experiments on the publicly available dataset nuScenes. The results demonstrate that the proposed method improves the robustness and accuracy of trajectory prediction. © 2014 IEEE.

关键词： Federated learning

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Research on Internet of Medical Things: Systematic Review, Research Trends and Challenges

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Recent Advances in computer science and Communications 2024年第6期17卷 72-82页

作者： Anand, Dinesh Kaur, Avinash Singh, Manpreet School of Computer Applications Lovely Professional University Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Applications Guru Nanak Dev Engineering College Punjab Ludhiana India

Introduction: Remote data exchange operations in healthcare are observed, consult-ed, monitored and treated by the Internet of Medical Things (IoMT). It is an extension of the Internet of Things (IoT). Method: At the growing stage of IoT, IoMT is speedily drawing researchers’ interest due to its extensive use in healthcare systems. Smaller and lower-priced wireless devices with various communication protocols have led to the formation of IoMT. Healthcare data is exchanged through wireless communication with IoMT. The margining of IoMT and healthcare can yield multiple benefits in terms of: better quality of life, care services and developing solution/s at low cost. In this article, a systematic literature review has been conducted on IoMT. Results: Authors have thoroughly investigated the different versions of healthcare 1.0, 2.0, 3.0 and 4.0 as proposed by the healthcare industry. Furthermore, the taxonomy of IoMT has been designed and compared with existing surveys. Conclusion: This survey is unique and stands different from the point of view of existing sur-veys. It supports the future of IoMT researchers to bring new insight to their researches. © 2024 Bentham science Publishers.

关键词： Electronic data interchange

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Short Term Traffic Flow Prediction Using Hybrid Deep Learning

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computers, Materials & Continua 2023年第4期75卷 1641-1656页

作者： Mohandu Anjaneyulu Mohan Kubendiran School of Computer Science and Engineering(SCOPE) Vellore Institute of Technology(VIT)Vellore632014India

Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential ***, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error *** extensive experimental analysis was performed on the benchmark *** evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.

关键词： Short term traffic flow prediction principal component analysis stacked auto encoders long short term memory k nearest neighbors:intelligent transportation system

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Energy Efficient Unequal Fault Tolerance Clustering Approach

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computer Systems science & engineering 2023年第5期45卷 1971-1983页

作者： Sowjanya Ramisetty Divya Anand Kavita Sahil Verma NZ Jhanjhi Mehedi Masud Mohammed Baz Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadIndia School of Computer Science and Engineering Lovely Professional UniversityPhagwaraIndia Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia

For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network *** saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor *** of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to *** increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor *** Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster *** data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the ***,the MCH overhead *** the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.

关键词： Energy-efficiency unequal fault tolerant clustering approach particle swarm optimization master data aggregator energy efficient time division multiple access optimal nodes

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Pruning techniques for artificial intelligence networks: a deeper look at their engineering design and bias: the first review of its kind

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Multimedia Tools and Applications 2025年第11期84卷 9591-9665页

作者： Mohanty, Lopamudra Kumar, Ashish Mehta, Vivek Agarwal, Mohit Suri, Jasjit S. School of Computer Science Engineering and Technology Bennett University Gr. Noida India Department of Computer Science ABES Engineering College UP Ghaziabad India Stroke Diagnostic and Monitoring Division AtheroPoint™ RosevilleCA United States

Trained Artificial Intelligence (AI) models are challenging to install on edge devices as they are low in memory and computational power. Pruned AI (PAI) models are therefore needed with minimal degradation in performance. This is the first study of its kind where we have characterized three types of PAI paradigms, namely, weight pruning (WP), channel pruning (CP), and hybrid pruning (HP). Further, the study also analyses the risk of bias (RoB) in PAI models, and explainable PAI (XPAI) networks. PRISMA model was adapted for the selection of the best 145 studies. The review had two hypotheses: (i) PAI had bias and (ii) the order of performance for the three PAI is: HP > CP > WP in terms of performance. Thirty-two AI attributes using each of two experts were categorized into four clusters, namely, architecture, optimization, dataset design, and scientific validation. The RoB was analyzed in these PAI studies using AP(ai)Bias 2.0 (AtheroPoint™, CA, USA), which was based on a scoring-and-ranking strategy. This was then compared against the Butterfly model, composed of radial and regional biases, and analyzed using a Venn diagram in three independent bins based on low-moderate and moderate-high cut-offs. Recommendations were laid out for low-bias studies. PAI studies that lack adequate AI attributes were more inclined to the RoB. It has been observed that 19.64%, 21.42%, and 58.92% studies were in low-bias, moderate-bias, and high-bias bins, respectively. Pruning made inference faster and reduced sparsity. We have observed low participation of the PAI studies in the XPAI framework. The study presented the challenges and five-step recommendations for mitigating bias in PAI models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Risk assessment

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