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Improved Secure Identification-Based Multilevel Structure of Data Sharing in Cloud Environments

computer Systems science & engineering 2022年第11期43卷 785-801页

作者： Saraswathi Shunmuganathan Sridharan Kannan T.V.Madhusudhana Rao K.Ambika T.Jayasankar Department of Computer Science and Engineering SSN College of EngineeringChennaiTamil Nadu603110India Department of Computer Science and Engineering J.K.K.Munirajah College of TechnologyErodeTamil Nadu638506India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalliTamil Nadu620024India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India

The Cloud Computing Environment(CCE)developed for using the dynamic cloud is the ability of software and services likely to grow with any *** has transformed the methodology for storing the enterprise data,accessing the data,and Data Sharing(DS).Big data frame a constant way of uploading and sharing the cloud data in a hierarchical architecture with different kinds of separate privileges to access the *** the requirement of vast volumes of storage area in the CCEs,capturing a secured data access framework is an important *** paper proposes an Improved Secure Identification-based Multilevel Structure of Data Sharing(ISIMSDS)to hold the DS of big data in *** complex file partitioning technique is proposed to verify the access privilege context for sharing data in complex *** access control Encryption Method(EM)is used to improve the *** Complexity is measured to increase the authentication *** active attack is protected using this ISIMSDS *** proposed ISIMSDS method assists in diminishing the Complexity whenever the user’s population is increasing *** security analysis proves that the proposed ISIMSDS methodology is more secure against the chosen-PlainText(PT)attack and provides more efficient computation and storage space than the related *** performance of the proposed ISIMSDS methodology provides more efficiency in communication costs such as encryption,decryption,and retrieval of the data.

关键词： Data sharing cloud environments big data chosen-plaintext attack security

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Hybrid Attention Vision Transformer-based Deep Learning Model for Video Caption Generation 3

Hybrid Attention Vision Transformer-based Deep Learning Mode...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ansari, Khustar Srivastava, Priyanka Sarala Birla University Jharkhand Ranchi835103 India Bit Sindri Dhanbad Department of Computer Science and Engineering Jharkhand Sindri828123 India Sarala Birla University Computer Science and Engineering Jharkhand Ranchi835103 India

ISBN: (纸本)9798331509675

Video caption generation is an extremely challenging computer vision task that automatically generates the caption for video clips with a clear understanding of the embedded semantics. Due to the extensive use of images for conveying large volumes of information, there exists a huge demand for image analytics in information processing systems. Various techniques are designed for automatic image caption generation to solve the computer vision challenges, but highlighting the contextual information more accurately still results in a challenging task. To overcome such issues, a Hybrid Attention Vision Transformer-based Convolutional Neural Network-Gated Recurrent Unit-Bidirectional Long Short-Term Memory (HAVisT-CNN-GRU-BiLSTM) is developed for video caption generation. Specifically, the Vision transformer (VisT) effectively captures the global dependencies and learns the intricate relationships present in the image. Moreover, the proposed approach combines the salient information extracted from the CNN, GRU, and BiLSTM models resulting in less memory usage, high dimensional data processing, and improved convergence speed. Moreover, the hybrid attention-based Vision transformer facilitates capturing the global context information and enhances computational efficiency. The experimental results demonstrate that the HAVisT-CNN-GRU-BiLSTM model achieves the efficient metric values of BLEU, METEOR, and ROUGE as 0.48, 0.28, and 0.71 respectively outperforming the other existing techniques. © 2025 IEEE.

关键词： Convolutional neural networks

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Blockchain and Data Integrity Authentication Technique for Secure Cloud Environment

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Intelligent Automation & Soft Computing 2023年第5期36卷 2055-2070页

作者： A.Ramachandran P.Ramadevi Ahmed Alkhayyat Yousif Kerrar Yousif Department of Computer Science and Engineering University College of EngineeringPanruti607106India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirapalli620025India College of Technical Engineering The Islamic UniversityNajafIraq Department of Computer Technical Engineering Al-Hadba University CollegeMosulIraq

Nowadays,numerous applications are associated with cloud and user data gets collected globally and stored in cloud *** addition to shared data storage,cloud computing technique offers multiple advantages for the user through different distribution designs like hybrid cloud,public cloud,community cloud and private *** cloud-based computing solutions are highly con-venient to the users,it also brings a challenge i.e.,security of the data ***,in current research paper,blockchain with data integrity authentication technique is developed for an efﬁcient and secure operation with user authentica-tion *** technology is utilized in this study to enable efﬁcient and secure operation which not only empowers cloud security but also avoids threats and ***,the data integrity authentication technique is also uti-lized to limit the unwanted access of data in cloud storage *** major objec-tive of the projected technique is to empower data security and user authentication in cloud computing *** improve the proposed authentication pro-cess,cuckooﬁlter and Merkle Hash Tree(MHT)are *** proposed meth-odology was validated using few performance metrics such as processing time,uploading time,downloading time,authentication time,consensus time,waiting time,initialization time,in addition to storage *** proposed method was compared with conventional cloud security techniques and the outcomes establish the supremacy of the proposed method.

关键词： Blockchain security data integrity authentication cloud computing signature hash tree

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An Enhanced Item-based Collaborative Filtering Approach for Book Recommender System Design 1

An Enhanced Item-based Collaborative Filtering Approach for ...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Verma, Monika Rawal, Arpana Department of Computer Science & Engineering BIT Durg CG India

ISBN: (纸本)9781607685395

The massive amounts of web-sourced data have made accessibility of precise(tailored) information for end-users, a challenging task. If this personalized information-filtering technology, is possibly thought to get automated at the machine level, this necessitates the design of an appropriate machine-assisted recommender system that suffices both system and end-user requirements. This paper attempts to make use of an innovative hybrid approach to build a prototype of a machine-assisted recommender that can be used as a tool in the physical library of Universities, organizations, and institutions. In this manuscript, an Enhanced Item-based Collaborative Filtering Approach for Book Recommender System Design is proposed to predict the popular Books based on the transactions found in the issue/return transaction database. Demographic attributes of books are used for the precise calculation of item-item similarity with cosine/correlation coefficient by discovering all significant associations rules in the formulated item set and it provides the recommendations. The proposed algorithm's performances are calculated based on accuracy, Precision, Recall. © The Electrochemical Society

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Soil Contamination Level in Cauvery Delta Region Through Machine Learning and GIS Techniques

Soil Contamination Level in Cauvery Delta Region Through Mac...

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Indian Geotechnical Conference, IGC 2021

作者： Valarmathi, A. Nalini, S. Kalidhas, M. Department of Computer Applications University College of Engineering BIT Campus Anna University Trichy620024 India Department of Computer Science and Engineering University College of Engineering BIT Campus Anna University Trichy620024 India University College of Engineering BIT Campus Anna University Trichy620024 India

ISBN: (纸本)9789811965128

The Cauvery River plays an important role and provides significant agricultural productivity in Tamil Nadu. Agricultural activities are carried out in the delta area due to the availability of water resources. In recent years, river water has been polluted in various ways through human activities and the process of industrialization. Consequently, contamination has an impact on the soil health of agricultural land. As a result, our focus has been on soil health analysis by measuring the chemical properties and heavy metals that exist in the soil. A classification model has been developed to determine the contamination level that exists in the soil through interpolation techniques. Soil samples are analysed in terms of soil chemical properties such as macronutrient, micronutrient, pH, and electric conductivity. Samples are collected at different locations at various depths where the sample size is 33. Samples are taken from the different regions that cover the category of farmland, urban areas, industrial areas, and river beds in the Cauvery delta region. A continuous surface had created for the concentration of the chemical constituents that are present in the soil through the space interpolation techniques using GIS technology which was determined from the analytical classification of the amount of the chemicals or the heavy metals that present in the soil has been determined through the fuzzy logic membership function. The soil contamination analysis based on the GIS approach provides an ongoing basis for monitoring contaminated sites. The contamination level along with to which extent it was contaminated is determined. The classification of the overall soil contamination levels of the Cauvery delta region is provided in detail, and its dispersed outcome is represented as a statistical analysis report in the form of the spatial variation maps. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Soils

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Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algor

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computers, Materials & Continua 2024年第4期79卷 1727-1755页

作者： Parth Khandelwal Harshit Indranil Manna Metallurgical&Materials Engineering Department Indian Institute of TechnologyKharagpurWest Bengal721302India Computer Science&Engineering Department Indian Institute of TechnologyKharagpurWest Bengal721302India Vice Chancellor Office Birla Institute of Technology(BIT)MesraRanchiJharkhand835215India

Metallic alloys for a given application are usually designed to achieve the desired properties by devising experimentsbased on experience, thermodynamic and kinetic principles, and various modeling and simulation ***, the influence of process parameters and material properties is often non-linear and non-colligative. Inrecent years, machine learning (ML) has emerged as a promising tool to dealwith the complex interrelation betweencomposition, properties, and process parameters to facilitate accelerated discovery and development of new alloysand functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles,to design novel copper alloys for achieving seemingly contradictory targets of high strength and high electricalconductivity. Initially, we establish a correlation between the alloy composition (binary to multi-component) andthe target properties, namely, electrical conductivity and mechanical strength. Catboost, an ML model coupledwith GA, was used for this task. The accuracy of the model was above 93.5%. Next, for obtaining the optimizedcompositions the outputs fromthe initial model were refined by combining the concepts of data augmentation andPareto front. Finally, the ultimate objective of predicting the target composition that would deliver the desired rangeof properties was achieved by developing an advancedMLmodel through data segregation and data *** examine the reliability of this model, results were rigorously compared and verified using several independentdata reported in the literature. This comparison substantiates that the results predicted by our model regarding thevariation of conductivity and evolution ofmicrostructure and mechanical properties with composition are in goodagreement with the reports published in the literature.

关键词： Machine learning genetic algorithm solid-solution precipitation strengthening pareto front data augmentation

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Student Performance Monitoring System Using Artificial Intelligence Models 1st

Student Performance Monitoring System Using Artificial Intel...

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1st International Conference on Artificial Intelligence and Internet of Things, ICAII 2022

作者： Kujur, A.G.P. Tiwari, Rajesh Kumar Panday, Vijay Department of Computer Science and Engineering BIT Sindri Jharkhand Dhanbad India Department of Computer Science and Engineering RVS College of Engineering and Technology Jharkhand Jamshedpur India Jharkhand University of Technology Jharkhand Ranchi India

ISBN: (纸本)9783031373022

With recent technological advancements, modern educational systems adopted efficient ICT (Information and Communication Technologies) to enhance the quality of education. Higher education is currently being transformed with resourceful learning and assessment techniques like Artificial intelligence. Since students are an important element of learning, the system focuses on learning, performance and reports, a big data education system is an efficient tool for decision-making in an educational environment. The aim to collect competent data is to evaluate quality and support in decision-making in educational processes. The data can help develop a tool that analyses data to practice alternatives in decision processes. Human limitations in decision-making have a greater impact on the education ecosystem. However, machine learning can handle this big data for decision-making. The proposed system is purposed for developing a reliable and efficient model for AI-assisted decision-making for education systems. Increasing competition in the educational scenario needs to accomplish flexibility in learning techniques as well as reform the curriculum. In addition, performance monitoring needs to be evaluated precisely and regularly. Thus, machine learning models are proposed for decision-making based on data on the performance of students, feedback and other related information. Thus, the proposed model extracts data from several levels and areas of the education system for relative scrutiny of decision support system (DSS) and decision-making models. The AI-based model can overcome issues in decision-making due to human interference and use the data from ICT platforms. It includes all the players concerned in the processes to assist decisions in various circumstances. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Decision making

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Classification of MRI Images for Detecting Alzheimer’s Disease Using Convolutional Neural Network

Classification of MRI Images for Detecting Alzheimer’s Dise...

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Virtual International Conference on Soft Computing, Optimization Theory and Applications, SCOTA 2021

作者： Kumari, Rashmi Nigam, Akriti Pushkar, Shashank Department of Computer Science Engineering BIT Mesra Ranchi835215 India

ISBN: (纸本)9789811964053

According to the Alzheimer Foundation of America (AFA), 4 million patients are currently affected due to Alzheimer’s Disease (AD) where it forms 6th most significant cause of death of patients in the world. In 2017, the cost of treating Alzheimer’s Disease had gone up to 1.1 trillion dollars which is the nation’s 10% GDP. Because AD has no cure, detecting it early is the only way to slow down its progression. However, detecting AD is a cumbersome task and requires substantial computational time. Magnetic resonance imaging (MRI) is the most popular modality used by medical practitioners for detecting AD in patients. The present work aims to use a novel convolutional neural network (CNN) as a deep learning technique for accurately classifying the brain’s MRI images into two cohorts, i.e., Normal Control (NC) and Alzheimer’s Disease (AD), where 85% accuracy has been achieved for 100 subjects. Consequently, the accuracy has been increased to 90% when the number of subjects increases to 200. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

关键词： Magnetic resonance imaging

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CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2183-2199页

作者： Tang, Jianheng Fan, Kejia Yang, Shihao Liu, Anfeng Xiong, Neal N. Song, Houbing Herbert Leung, Victor C. M. Central South University School of Computer Science and Engineering Changsha410083 China Peking University School of Computer Science Beijing100871 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches. © 1983-2012 IEEE.

关键词： Differential privacy

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DKINet: Medication Recommendation via Domain Knowledge Informed Deep Learning

DKINet: Medication Recommendation via Domain Knowledge Infor...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Sicen Wang, Xiaolong Zhao, Xianbing Chen, Hao The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong The Shenzhen MSU-BIT University Department of Engineering Shenzhen China Department of Computer Science Shenzhen China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong Hkust ShenzhenHong Kong Collaborative Innovation Research Institute Shenzhen China

ISBN: (纸本)9798350386226

Medication recommendation is a fundamental yet crucial branch of healthcare that presents opportunities to assist physicians in making more accurate medication prescriptions for patients with complex health conditions. Previous studies have primarily concentrated on deriving patient representations from electronic health records (EHRs) to recommend medications, often overlooking the effective integration of domain-specific prior knowledge. However, integrating domain knowledge with the patient's clinical manifestations can be challenging, particularly when dealing with complex clinical manifestations. Therefore, in this paper, we first identify comprehensive domain-specific prior knowledge, namely the Unified Medical Language System (UMLS), which is a comprehensive repository of biomedical vocabularies and standards, for knowledge extraction. Subsequently, we propose a knowledge injection module that addresses the effective integration of domain knowledge with complex clinical manifestations, enabling an effective characterization of the health conditions of the patient. Moreover, acknowledging the influence of historical medications on patients' current treatments, we propose a historical medication-aware patient representation module to capture the longitudinal influence of historical medication information on the representation of current patients. Extensive experiments on three publicly benchmark datasets verify the superiority of our proposed method, which outperformed other methods by a significant margin. The code is available at: https://***/sherry6247/DKINet. © 2024 IEEE.

关键词： Electronic health record

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