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Comparative research on block design-based protocol and blowfish for secured key generation and data sharing on cloud

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Soft Computing 2024年第23期28卷 13701-13713页

作者： Hombal, Uma Rangapura Basavaraju, Dayananda Shinde, Ashwini S. Department of Computer Science and Engineering KSIT Bengaluru India Department of Computer Science and Engineering MSRIT Bengaluru India Department of CSE-AI Nutan College of Engineering and Research Pune India

Confidentiality of maintaining the Electronic Health Records of patients is a major concern to both the patient and Doctor. Sharing the data on cloud is one of the most efficient technology infrastructures with extensive potential application, but at the same time there is a threat of hacking the data. Here we implement the concept for sharing of electronic health record on cloud that permits multiple members of the group to easily share the group data. With the increase in size of healthcare data the time and space complexity increases. The main challenge here is data security. This research article emphasis on a secured method for sharing the data on cloud and generation of the key that is safe from unauthorized user. In this research article, we implement the soft computing approach for key agreement protocol using Block based design to support several users that can adapt it-self to upsurge the number of users in the cloud which depends on the assembly of the block design. This protocol generates a common conference key for all members. On comparing our results with some prevailing techniques, we accomplish that the results of our research have a better computational complexity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Personal computing

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Bidirectional Sign Language Translation System 8

Bidirectional Sign Language Translation System

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Mahjan, Aditya Hendre, Pradnya Mankhair, Gauri Satpute, Shubhangi Chaudhari, Deepti A. Nutan College of Engineering and Research Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350391770

Sign language is an essential form of communication for the deaf and hard-of-hearing community. The creation of Bidirectional Sign Language Translation Systems (BSLTS) has drawn a lot of attention as technology has advanced. The first section of the article gives a general review of the difficulties in translating and recognising sign language, highlighting the urgent need for precise and timely solutions. After that, it explores the fundamental ideas of CNN and Sign language, emphasising their advantages. The survey discusses different architectures and techniques used for feature extraction and classification, and it thoroughly evaluates the body of research on CNN's use in sign language recognition. Additionally, the study investigates how the cutting-edge object detection technology YOLO3 improves the spatial comprehension of sign language gestures. This investigation investigates how real-time object identification capabilities of YOLO3 can enhance the accuracy and efficiency of BSLTS, enabling smooth communication between sign language users and non-signers. Sign language is a communication method used by people with speech and hearing impairments to interact with others who are not handicapped. Normal people don't use sign language very often, and it takes a lot of time and effort to understand signs. People with hearing impairments are cut off from the outside world and seldom engage in social interactions due to this communication gap. A system for converting text to sign and sign to text is required to tackle this issue and facilitate communication. For system training, a variety of methods have been employed, including Sign language and Convolutional Neural Networks (CNN). © 2024 IEEE.

关键词： Convolutional neural networks

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Sentimental Analysis on Zomato Restaurant Reviews using Bi-LSTM

Sentimental Analysis on Zomato Restaurant Reviews using Bi-L...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Chaudhari, Deepti Angadi, Sanjeevkumar Sable, Saili Patil, Uma Chaudhari, Dipamala Jadhav, Kavita Nutan College of Engineering and Research Computer Science and Engineering Pune India

ISBN: (纸本)9798350315455

Sentiment analysis is a vital aspect of understanding public opinion and sentiment towards products and services. This paper presents a sentiment analysis work focused on Zomato restaurant reviews in Bangalore, aiming to classify restaurants into positive, negative, or neutral sentiment categories based on customer reviews. Bi-LSTM and Bi-GRU models are employed to capture contextual information in the sequential data of reviews. Additionally, sentiment analysis techniques, including Word2Vec, VADER, and Sentiment Intensity Analyzer, are integrated to enhance the sentiment classification process. Through rigorous experimentation, the performance of these models and techniques is evaluated. The proposed models demonstrate promising accuracy rates in sentiment classification. By enhancing and expanding the sentiment analysis framework, this paper contributes to a deeper understanding of public sentiment towards Zomato restaurants in Bangalore. The insights derived from this study can facilitate informed decision-making for both restaurant owners and customers, ultimately improving the dining experience and customer satisfaction with accuracy of 98.6%. © 2023 IEEE.

关键词： Bi-GRU Bi-LSTM Restaurant Reviews Sentiment Analysis Text Classification Word2Vec

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Bridging the gap: combining expert guidance and self-learned knowledge for efficient and robust autonomous navigation in complex settings

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Journal of Ambient Intelligence and Humanized Computing 2025年第2期16卷 425-441页

作者： Kumar, Dharmendra Computer Science and Engineering United College of Engineering and Research Naini Uttar Pradesh Prayagraj211010 India

Autonomous navigation in dynamic environments poses significant challenges due to complex agent–environment interactions, real-time decision-making, and adaptability requirements. We propose the Reinforcement-Imitation Learning for Autonomous Navigation (RILAN) algorithm, which integrates reinforcement learning, imitation learning, and uncertainty estimation for robust and adaptable navigation. RILAN combines the strengths of expert demonstrations and self-learned knowledge, overcoming individual limitations. The modular RILAN architecture comprises an imitation learning module, reinforcement learning module, and an uncertainty-aware policy selector, enabling efficient information processing and dynamic policy selection. Extensive experiments compare RILAN to state-of-the-art algorithms, including E2E-IL, CIL, DQN, and PPO. Results show RILAN outperforms these algorithms in success rate, average time to goal, collision rate, and off-road rate, demonstrating the proposed approach’s efficacy for robust autonomous navigation in applications like self-driving vehicles and robotics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Contrastive Learning

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CNN-Based Detection of Tampered Digital Images 2

CNN-Based Detection of Tampered Digital Images

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Kumar, S. Prem Thirumalesh, M. Sai Kumar, T. Pramod Reddy, P. Subba Angadi, Sanjeevkumar Venu, Nookala Computer Science and Engineering G.Pullaiah College of Engineering and Technology Kurnool India Nutan College of Engineering and Research Computer Science and Engineering Talegaon Dabhade Pune India Madhya Pradesh Gwalior474 005 India

ISBN: (纸本)9798350384277

This project presents a novel approach for detecting tampered digital images using Convolutional Neural Networks (CNN). Our methodology leverages the power of deep learning to analyse image content and identify alterations introduced through manipulation. The key innovation lies in the integration of CNN architectures, enabling robust and accurate detection of tampering in various image types. Additionally, the project incorporates Error Level Analysis (ELA) as a complementary feature to enhance the detection accuracy. ELA helps identify areas of an image with inconsistent compression levels, aiding in the identification of potential tampering artifacts. The synergy between CNN-based analysis and ELA creates a comprehensive solution for the reliable detection of tampered digital images. This approach demonstrates promising results in differentiating between authentic and manipulated images, making it a valuable tool for forensic image analysis, and ensuring the integrity of digital content. © 2024 IEEE.

关键词： Convolutional neural networks

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Reinforcement imitation learning for reliable and efficient autonomous navigation in complex environments

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Neural Computing and Applications 2024年第20期36卷 11945-11961页

作者： Kumar, Dharmendra Computer Science and Engineering United College of Engineering and Research Uttar Pradesh Naini Prayagraj211010 India

Reinforcement learning (RL) and imitation learning (IL) are quite two useful machine learning techniques that were shown to be potential in enhancing navigation performance. Basically, both of these methods try to find a policy decision function in a reinforcement learning fashion or through imitation. In this paper, we propose a novel algorithm named Reinforcement Imitation Learning (RIL) that naturally combines RL and IL together in accelerating more reliable and efficient navigation in dynamic environments. RIL is a hybrid approach that utilizes RL for policy optimization and IL as some kind of learning from expert demonstrations with the inclusion of guidance. We present the comparison of the convergence of RIL with conventional RL and IL to provide the support for our algorithm’s performance in a dynamic environment with moving obstacles. The results of the testing indicate that the RIL algorithm has better collision avoidance and navigation efficiency than traditional methods. The proposed RIL algorithm has broad application prospects in many specific areas such as an autonomous driving, unmanned aerial vehicles, and robots. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Reinforcement learning

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning

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Tsinghua science and Technology 2024年第4期29卷 948-958页

作者： A.E.M.Eljialy Mohammed Yousuf Uddin Sultan Ahmad Department of Information Systems College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia

Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.

关键词： random forest feature selection decision tree software-defined network logistic regression intrusion detection system XGB classifier AdaBoost

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CLIP-Flow:Decoding images encoded in CLIP space

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Computational Visual Media 2024年第6期10卷 1157-1168页

作者： Hao Ma Ming Li Jingyuan Yang Or Patashnik Dani Lischinski Daniel Cohen-Or Hui Huang Visual Computing Research Center College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China Department of Computer Science Tel Aviv UniversityTel Aviv 6997801Israel School of Computer Science and Engineering the Hebrew University of JerusalemJerusalem 91904Israel

This study introduces CLIP-Flow,a novel network for generating images from a given image or *** effectively utilize the rich semantics contained in both modalities,we designed a semantics-guided methodology for image-and text-to-image *** particular,we adopted Contrastive Language-Image Pretraining(CLIP)as an encoder to extract semantics and StyleGAN as a decoder to generate images from such ***,to bridge the embedding space of CLIP and latent space of StyleGAN,real NVP is employed and modified with activation normalization and invertible *** the images and text in CLIP share the same representation space,text prompts can be fed directly into CLIP-Flow to achieve text-to-image *** conducted extensive experiments on several datasets to validate the effectiveness of the proposed image-to-image synthesis *** addition,we tested on the public dataset Multi-Modal CelebA-HQ,for text-to-image *** validated that our approach can generate high-quality text-matching images,and is comparable with state-of-the-art methods,both qualitatively and quantitatively.

关键词： image-to-image text-to-image contrastive language-image pretraining(CLIP) flow StyleGAN

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Data-driven output regulation control for constrained linear systems

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science China(Information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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