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Hybrid Recommender System Using Systolic Tree for Pattern Mining

computer Systems science & engineering 2023年第2期44卷 1251-1262页

作者： S.Rajalakshmi K.R.Santha Department of Computer Science Engineering Sri Venkateswara College of EngineeringSriperumbudurIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudurIndia

A recommender system is an approach performed by e-commerce for increasing smooth users’*** pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of *** work will present the implementation of sequence pattern mining for recommender systems within the domain of *** work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender *** feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target *** will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy *** work pre-sents a new hybrid recommender system based on optimized feature selection and systolic *** features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)*** systolic tree is used for pattern mining,and based on this,the recommendations are *** proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes conﬁrmed the efﬁciency of the *** was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativeﬁltering,the precision of 0.89 was achieved.

关键词： Recommender systems hybrid recommender systems frequent pattern mining collaborativeﬁltering systolic tree river formation dynamics particle swarm optimization

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Adaptive coupled-sliding-variable-based finite-time control of composite formation for multi-robot systems

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science China(Information sciences) 2024年第10期67卷 244-260页

作者： Xinru MA Hengyu LI Jun LIU Yueying WANG Shaorong XIE Jun LUO School of Mechatronic Engineering and Automation Shanghai University School of Mathematics and Computer Application Technology Jining University School of Computer Science Shanghai University College of Mechanical and Vehicle Engineering Chongqing University

This paper focuses on the finite-time control(FTC) of the composite formation consensus(CFC)problems for multi-robot systems(MRSs). The CFC problems are firstly proposed for MRSs under the complex network topology of cooperative or cooperative-competitive networks. Regarding the problems of FTC and CFC on multiple Lagrange systems(MLSs), coupled sliding variables are introduced to deal with the robustness and consistent convergence. Then, the adaptive finite-time protocols are given based on the displacement approaches. With the premised FTC, tender-tracking methods are further developed for the problems of tracking information disparity. Stability analyses of those MLSs mentioned above are clarified with Lyapunov candidates considering the coupled sliding vectors, which provide new verification for tender-tracking systems. Under the given coupled-sliding-variable-based finite-time protocols, MLSs distributively adjust the local formation error to achieve global CFC and perform uniform convergence in time-varying tracking. Finally, simulation experiments are conducted while providing practical solutions for the theoretical results.

关键词： composite formation time-vary tracking information disparity tender-tracking finite-time control nonlinear robots complex networks symmetry consensus

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Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

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Intelligent Automation & Soft Computing 2023年第6期36卷 2491-2504页

作者： B.Sophia D.Chitra Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatoreTamilnaduIndia Department of Computer Science and Engineering P.A College of Engineering and TechnologyPollachiTamilnaduIndia

Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so ***,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian *** recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,*** this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian *** proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,*** proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition *** object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized *** ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)*** simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.

关键词： Panoptic segmentation object detection deep learning tracking model anomaly detection pedestrian walkway

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Enhanced Machine Learning Based Techniques for Security in Vehicular Ad-Hoc Networks

Enhanced Machine Learning Based Techniques for Security in V...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Jyothi, N. Patil, Rekha Dept of Computer Science and Engineering H. K.E. Society's S.L.N College of Engineering Raichur India Dept of Computer Science and Engineering PDA College of Engineering Kalaburagi India

ISBN: (纸本)9798350396485

Vehicular Ad-Hoc Networks, also referred as VANETs, have emerged as an interesting area of research as a result of ever significant increase in the number of automobiles on roads. Built-in safety smart vehicular traffic infrastructure protects both passengers and drivers, but due to its dynamic nature, real-Time implementation is difficult. They lay the groundwork for the creation of intelligent transportation systems (IPS) and frameworks, which allow road entities to communicate with one another and build new applications and services with the goal of improving both the driving experience and overall road safety. The demanding features of VANETs make it difficult to establish security measures, resulting in gaps that attackers could exploit. This research provides smart structured protective systems for VANETs that use machine learning (ML) based algorithms. The enhanced ML algorithms improves attack detection, protecting data inter-communications between various sources and destinations, and ensuring strong anonymity, authentication, and privacy. The proposed system trains and tests its machine learning algorithms on publicly available datasets of vehicle communications. As a result, the outcomes are repeatable and verifiable. The machine learning-based security system can detect attacks while maintaining low False Positive Rate values (FPR). The findings also suggest that the framework may benefit from employing a variety of algorithms present at various hierarchical levels, selecting algorithms with high performance and focus at the cost of preciseness in lower levels and additionally sophisticated, detailed, and accurate algorithms present in top levels. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Strain modified nano-scale Ge/Ge0.98Sn0.02 exotic pin photo-sensor array for IR sensing: theoretical reliability and experimental feasibility studies

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Optical and Quantum Electronics 2024年第12期56卷 1-20页

作者： Bhadra, Bias Chinara, Suchismita Kundu, Abhijit Department of Computer Science and Engineering Chaibasa Engineering College Jharkhand Chaibasa India Department of Computer Science and Engineering NIT Rourkela Odisha Rourkela India Department of Electronics and Communication Engineering Chaibasa Engineering College Jharkhand Chaibasa India

In this paper, the superiority of Ge/Ge0.98Sn0.02asymmetrical supper lattice structure based vertically doped nano-scale pin photo-sensor under operating wavelength of 1200 nm to 2200 nm is reported. The authors have developed non-linear Strain Modified Quantum-Corrected Drift–Diffusion (SMQCDD) model for analyzing the electrical and optical characteristics of the photo-sensor. The inclusion of a small amount of Sn (2%) into the pure Ge material creates in-plane bi-axial strain in the intrinsic region (i-region) of the device. This results in increases in the value of the out-plane mobility of the charge particles. As a result, the overall performance of the photo-sensor enhances significantly. The authors have used in-plane induced bi-axial strain to accelerate the out-plane mobility of the charge particles by incorporation of the exotic asymmetrical supper lattice structure in the i-region of the photo-sensor. The validation of the non-linear SMQCDD model is performed through comparison of the simulated data obtained from SMQCDD model with the experimental results under a-like thermal/structural/electrical conditions. Additionally, the authors have designed 3X2 array of photo-sensors and studied the photo-electrical characteristics at the said operating wavelength. The proposed device offers better performance in terms of quantum efficiency (0.619: single-type photo-sensor;0.708: array-type photo) and photo-responsivity (0.056 A/W: single-type photo-sensor;0.808 A/W:for array-type photo-sensor)at 1600 nm wavelength compared to its conventional flat Si counterpart. The developed exotic pin photo-sensor can be used as a sensing device for applications in optical communication and bio-medical systems. As far as the authors are aware, this is the first report on nano-scale Ge/Ge0.98Sn0.02exotic pin photo-sensor. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nanocrystals

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A Color Image Encryption Scheme Based on Singular Values and Chaos

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computer Modeling in engineering & sciences 2023年第10期137卷 965-999页

作者： Adnan Malik Muhammad Ali Faisal S.Alsubaei Nisar Ahmed Harish Kumar Electrical Engineering Department University of Engineering and TechnologyLahore54000Pakistan Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Computer Engineering Department University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The security of digital images transmitted via the Internet or other public media is of the utmost *** encryption is a method of keeping an image secure while it travels across a non-secure communication medium where it could be intercepted by unauthorized *** study provides an approach to color image encryption that could find practical use in various *** proposed method,which combines four chaotic systems,employs singular value decomposition and a chaotic sequence,making it both secure and *** unified average change intensity,the number of pixels’change rate,information entropy analysis,correlation coefficient analysis,compression friendliness,and security against brute force,statistical analysis and differential attacks are all used to evaluate the algorithm’s *** a thorough investigation of the experimental data,it is concluded that the proposed image encryption approach is secure against a wide range of attacks and provides superior compression friendliness when compared to chaos-based alternatives.

关键词： Encryption image encryption chaos theory color image encryption singular value decomposition compression friendliness

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PeriodicMFD: A Periodic-based Framework for Multi-source Fault Diagnosis

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7252-7260页

作者： Zheng, Jianbo Yang, Chao Zhang, Tairui Jiang, Bin Fan, Xuhui Wu, Xiao-Ming Shao, Haidong Hunan University College of Computer Science and Electronic Engineering Changsha China Macquarie University School of Computing Australia Sun Yat-sen University School of Computer Science and Engineering China Hunan University College of Mechanical and Vehicle Engineering Changsha China

Cross-speed bearing fault diagnosis based on multiple source domains and their data enables high-performance condition monitoring for variable-speed equipment, such as engines and turbines. Current multi-source methods typically employ a fixed-length sampling strategy to construct samples and then align the distributions of these samples from different domains. However, these methods neglect the inherent periodic characteristics of bearing data, resulting in incomplete or redundant periodic features in the samples. To address this challenge, we propose a periodic-based framework, PeriodicMFD, for multi-source cross-speed fault diagnosis, which ensures complete periodic information. Our PeriodicMFD framework begins with a periodic sampling strategy designed to construct periodic samples that effectively capture periodic features while maintaining their periodic integrity. Nevertheless, periodic samples from different domains exhibit inconsistencies at both the sample and domain levels. To reconcile these inconsistencies, we introduce sample-level matching to address inconsistencies in feature dimensions and fault patterns among samples from various domains. Additionally, we propose domain-level alignment to handle inconsistencies in space and distribution across different domains. Extensive experiments across three datasets highlight the effectiveness of the PeriodicMFD framework, with a stable average accuracy of 99.55%. © 2015 IEEE.

关键词： Fault detection

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A Novel Enhanced Approach for Security and Privacy Preserving in IoT Devices with Federal Learning Technique

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SN computer science 2024年第6期5卷 750页

作者： Moeed, Syed Abdul Karnati, Ramesh Ashmitha, G. Mohammad, Gouse Baig Mohanty, Sachi Nandan Department of Computer Science and Engineering Kakatiya Institute of Technology and Science Department of Computer Science and Engineering Vardhaman College of Engineering School of Computer Science & amp Engineering (SCOPE) VIT-AP University

The Internet of Things (IoT) has revolutionized our lives, but it has also introduced significant security and privacy challenges. The vast amount of data collected by these devices, often containing sensitive information, makes them prime targets for cyberattacks. Traditional security methods struggle to keep pace with the evolving threat landscape of the interconnected IoT world. In response, this paper explores the transformative potential of federated learning (FL) in safeguarding both the privacy and security of IoT devices. FL keeps data on individual devices, only sharing updated models, not raw data, thus protecting user privacy and eliminating the need for a central data storage server, which reduces the risk of data breaches and security vulnerabilities. The effectiveness of the proposed model is evaluated using established open-source datasets like NSL-KDD and UNSW NB15, ensuring real-world applicability. Analysis of the dataset's features enabled the development of a model utilizing federated learning. Notably, the proposed model achieved superior performance with FL in detecting attacks on IoT networks. Furthermore, this research investigates the transformative potential of FL to address the inherent security and privacy challenges plaguing traditional, centralized data collection methods in the ever-expanding realm of IoT devices. FL empowers collaborative learning on distributed datasets, allowing devices to collectively improve security without compromising user privacy. The findings of this study demonstrate the promise of FL as a secure and privacy-preserving solution for the future of IoT. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cyberattacks Federal learning Internet of Things Machine learning Security and privacy

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Multi-objective Cluster-head-based Energy-aware Routing using a Separable Convolution Neural Network in a Wireless Sensor Network

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IEIE Transactions on Smart Processing and Computing 2024年第2期13卷 105-112页

作者： Ather, Danish Mary, J. Prisca Singh, Pooja Garg, Kanika Priyadharshini, T.R. Princy, B. Anni Tiwari, Mohit Department of IT and Engineering Amity University in Tashkent Uzbekistan Department of Computer Science and Engineering NPR College of Engineering and Technology Tamil Nadu Dindigul India Department of Physics Faculty of Science SGT University Haryana Gurugram India Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Ghaziabad Modinagar India Department of Information Technology Hindusthan College of Engineering and Technology Pollachi main road Coimbatore India Department of Computer and Communication Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

Wireless sensor network (WSN) applications are added day by day owing to numerous global uses (by the military, for monitoring the atmosphere, in disaster relief, and so on). Here, trust management is a main challenge. Sensor nodes are important in wireless sensor networks, but they are easily depleted because of their short lifespan from continuous sensing activity and low battery capacity. So efficient energy utilization is a challenging task in a WSN. To minimize energy loss, clustering with an optimum path selection process is needed to retain energy in sensor nodes. This manuscript proposes multi-objective Pelican Optimization Algorithm (POA) routing to maintain energy efficiency and minimize transmission distances in wireless sensor networks. A cluster head (CH) is selected by using a Separable Convolution Neural Network (SCNN). Simulation outcomes prove the proposed technique attains 22.3% and 25.04% improvements in energy consumption when compared to the Multi-Objective CH Energy-aware Optimized Routing Approach at WSN (MOCH-EORA-WSN) and Multiple Optimum Cluster Head Multi-Objective Grasshopper Optimization with Harmony-search at WSN (MOCH-MOGOH-WSN), respectively. © 2024 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Energy utilization

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Seizure Prediction on EEG Signals using Feature Augmentation based Multi Model Ensemble

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Recent Patents on engineering 2025年第1期19卷 41-49页

作者： Anandaraj, A. Alphonse, P.J.A. Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

Background: Epilepsy is a neurological disorder that leads to seizures. This occurs due to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid in improving the lifestyle of epilepsy patients. After analyzing various patents related to seizure prediction, it is observed that monitoring electroencephalography (EEG) signals of epileptic patients is an important task for the early diagnosis of seizures. Objective: The main objective of this paper is to assist epileptic patients to enhance their way of living by predicting the seizure in advance. Methods: This paper builds a feature augmentation-based multi-model ensemble-based architecture for seizure prediction. The proposed technique is divided into 2 broad categories;feature augmentation and ensemble modeling. The feature augmentation process builds temporal features while the multi-model ensemble has been designed to handle the high complexity levels of the EEG data. The first phase of the multi-model ensemble has been designed with heterogeneous classifier models. The second phase is based on the prediction results obtained from the first phase. Experiments were performed using the seizure prediction dataset from the University Hospital of Bonn. Results: Comparison indicates 98.7% accuracy, with improvement of 5% from the existing model. High prediction levels indicate that the model is highly capable of providing accurate seizure predictions, hence ensuring its applicability in real time. Conclusion: The result of this paper has been compared with existing methods of predicting seizures and it indicated that the proposed model has better enhancement in the accuracy levels. © 2025 Bentham science Publishers.

关键词： Electroencephalography

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