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Deep Neural Network Empowered Movie Recommender System Using Hesitant Fuzzy Bi Objective Clustering

Journal of The Institution of Engineers (India): Series B 2024年 1-11页

作者： Shrivastava, Vineet Kumar, Suresh Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Haryana Faridabad India

The movie recommender system is a highly influential and practical tool that assists individuals in efficiently choosing films to watch. Although recommender systems have been extensively used in academic research for various objectives, such as suggesting movies and recommending books, there has been a lack of focus on providing personalized movie recommendations for individual users. This research presents a new method for recommending movies that combines the Hesitant Fuzzy Clustering technique with a Convolutional Spiking Neural Network Movie Recommender System. The first phase entails obtaining input data from benchmark datasets such as MovieLens 100 K and MovieLens 1 M. These datasets are analysed using Ternary Pattern and Discrete Wavelet Transforms along with Hesitant Fuzzy Bi-objective Clustering technique to choose clusters depending on the retrieved attributes. After that, recommendation of movies uses a Deep Convolutional Spiking Neural Network to forecast user preferences. The efficiency of the proposed model is specifically compared to recent existing methods, like the Multi-Model Trust Based Movie Recommender Scheme (MT-ML-MRS) and the Graph-Dependent Hybrid Movie Recommendation Scheme (GHRS-MRS), particularly for movie recommendations. The results demonstrate a substantial enhancement, as the suggested model achieves 2.30% and 4.71% greater accuracy for MovieLens 100 k and MovieLens 1 M datasets respectively. The proposed system shows significant improvement over some traditional recommendation models indicating avenues for future research in scalable and intricate systems. © The Institution of Engineers (India) 2024.

关键词： Convolutional neural networks

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Out-of-Distribution Data: An Acquaintance of Adversarial Examples - A Survey

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ACM computing Surveys 2025年第8期57卷 1-40页

作者： Karunanayake, Naveen Gunawardena, Ravin Seneviratne, Suranga Chawla, Sanjay School of Computer Science The University of Sydney Sydney Australia Computer Science and Engineering University of New South Wales Sydney Australia Qatar Computing Research Institute Doha Qatar and Hamad Bin Khalifa University Doha Qatar

Deep neural networks (DNNs) deployed in real-world applications can encounter out-of-distribution (OOD) data and adversarial examples. These represent distinct forms of distributional shifts that can significantly impact DNNs’ reliability and robustness. Traditionally, research has addressed OOD detection and adversarial robustness as separate challenges. This survey focuses on the intersection of these two areas, examining how the research community has investigated them together. Consequently, we identify two key research directions: robust OOD detection and unified robustness. Robust OOD detection aims to differentiate between in-distribution (ID) data and OOD data, even when they are adversarially manipulated to deceive the OOD detector. Unified robustness seeks a single approach to make DNNs robust against both adversarial attacks and OOD inputs. Accordingly, first, we establish a taxonomy based on the concept of distributional shifts. This framework clarifies how robust OOD detection and unified robustness relate to other research areas addressing distributional shifts, such as OOD detection, open set recognition, and anomaly detection. Subsequently, we review existing work on robust OOD detection and unified robustness. Finally, we highlight the limitations of the existing work and propose promising research directions that explore adversarial and OOD inputs within a unified framework. © 2025 Copyright held by the owner/author(s)

关键词： Generative adversarial networks

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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Committed-programming reductions: formalizations,implications and relations

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science China(Information sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

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An Assessment Towards 2D and 3D Human Pose Estimation and its Applications to Activity Recognition: A Review

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SN computer science 2025年第2期6卷 1-24页

作者： Verma, Pratishtha Srivastava, Rajeev Tripathy, Santosh Kumar Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India Computer Science and Engineering IIT BHU Varanasi India Department of Computing Science and Engineering National Institute of Technology Patna Bihar Patna India

Human pose estimation (HPE) from images or video is not only a major issue of computer vision, but also it plays a vital role in many real-world applications. The most challenging problems of human pose estimation are the disparity of poses, disturbing background clutter, different camera views, changes in scene lighting conditions, and occlusion. To solve these issues, a significant amount of research effort has been considered in the past. In this paper, we explore various vision-based activity recognition using 2D and 3D human pose estimations. We explore the conventional system, which includes both statistical and deep learning-based approaches for HPE, and then explore the current progress of each one in comparison to existing ones. At last, we also present various current state-of-the-art human activity recognition (HAR) techniques that use HPE for recognition. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Human activity recognition (HAR) Human body model (HBM) Human pose estimation (HPE)

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Enhancing Healthcare Record Privacy Through the Integration of Visual Cryptography and Diverse Image Encryption Techniques 9

Enhancing Healthcare Record Privacy Through the Integration ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Sandeep, Dayina Ganesh, Addala Siva Krishna, Angalakurthi Jaya Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

The methodology of this research focuses on analyzing real-world breaches, such as the Star Health Insurance leak, and demonstrating how multi-layered encryption techniques could have mitigated the impact. By splitting sensitive healthcare information into multiple shares using visual cryptography, unauthorized access is prevented unless all shares are combined, adding an additional layer of security. Furthermore, image-based encryption adds another line of defense, ensuring that even if hackers gain access to data, it remains unusable without the correct decryption methods. Through the implementation of these techniques, we demonstrate a significant enhancement in the security of healthcare records. By combining diverse encryption methods, our approach not only strengthens the protection of personal health information but also prevents unauthorized data dissemination, as seen in the Star Health breach. The results indicate that our model is resilient to various cyber-attacks, reducing the likelihood of large-scale data leaks and improving the integrity of healthcare systems. © 2024 IEEE.

关键词： Electronic health record

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Clinical deep model to analyse medical multivariate time-series data for health diagnosis

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Cyber-Physical Systems 2025年第2期11卷 139-164页

作者： Almahadeen, Layth Vijay, Richa Shabaz, Mohammad Soni, Mukesh Singh, Pavitar Parkash Patel, Pavan Byeon, Haewon Department of Financial and Administrative Sciences Al- Balqa’Applied University Al-Salt Jordan Department of computer science Amity University UP Noida India Department of Computer Science Engineering Model Institute of Engineering and Technology Jammu Jammu and Kashmir India Department of Computer Science Engineering University Centre for Research & Development Chandigarh University Mohali India Department of Management Lovely Professional University Phagwara India Department of Computer Science Engineering Ahmedabad Institute of Technology Ahmedabad India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features;feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decision making

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Secure Student Record Management using Visual Cryptography in Educational Systems 9

Secure Student Record Management using Visual Cryptography i...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Kiranchandu, E. Gireesh, C. Kiran, G. Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

Cloud computing is vital for data transmission and storage in the digital era, highlighting the urgent requirement for strong data security measures. Conventional encryption techniques such as RSA, while dependable, have considerable computational burden and energy requirements owing to their extensive key sizes. This study examines the efficacy of Elliptic Curve Cryptography (ECC) as a superior solution for data security in cloud environments. The suggested method utilizes ECC's reduced key sizes to attain improved computational efficiency and energy saving, all while upholding rigorous encryption standards. A comparison research indicates that ECC surpasses RSA in performance and resource efficiency, rendering it especially appropriate for extensive cloud systems handling significant amounts of sensitive information. This research examines the implementation of the Advanced Encryption Standard (AES) algorithm in Cipher Block Chaining (CBC) mode for image encryption. The AES-CBC technique efficiently safeguards images by transforming them into encrypted formats, so obstructing illegal access. Comparative assessments using alternative encryption methods highlight AES's enhanced security and computational efficacy. Collectively, these findings underscore the capability of ECC and AES to overcome the shortcomings of conventional encryption, providing secure and resource-efficient solutions for contemporary cloud computing issues. © 2024 IEEE.

关键词： Cloud security

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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TRS Scheduling for Improved QoS Performance in Cloud System

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computers, Materials & Continua 2023年第4期75卷 1547-1559页

作者： G.John Samuel Babu M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India Department of Computing Technologies School of ComputingCollege of Engineering andTechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.

关键词： Cloud task scheduling TRS quality of service RSM route security data security SDE RATRSDS

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