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Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud computing Environment

computer Systems science & Engineering 2023年第3期44卷 1881-1895页

作者： S.Stewart Kirubakaran V.P.Arunachalam S.Karthik S.K annan Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia School of Computing Science and Engineering Vellore Institute of TechnologyBhopalMadhya PradheshIndia

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud ***,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data *** addressing and handling the security-related issues on Cloud,several models were *** that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud *** preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data ***,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the ***,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

关键词： Third-party auditor(TPA) efficient auditing technique(EAT) cloud service provider(CSP) data user(DU) data security privacy-preserving cloud computing cloud security

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A Design and Challenges in Energy Optimizing CR-Wireless Sensor Networks

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Recent Advances in computer science and Communications 2023年第5期16卷 82-92页

作者： Reddy, Pundru Chandra Shaker Sucharitha, Yadala School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Computer Science and Engineering CMR Institute of Technology TS Hyderabad India

Background: The progress of the Cognitive Radio-Wireless Sensor Network is being influenced by advancements in wireless sensor networks (WSNs), which significantly have unique features of cognitive radio technology (CR-WSN). Enhancing the network lifespan of any network requires better utilization of the available spectrum as well as the selection of a good routing mechanism for transmitting informational data to the base station from the sensor node without data conflict. Aims: Cognitive radio methods play a significant part in achieving this, and when paired with WSNs, the above-mentioned objectives can be met to a large extent. Methods: A unique energy-saving Distance-Based Multi-hop Clustering and Routing (DBMCR) methodology in association with spectrum allocation is proposed as a heterogeneous CR-WSN model. The supplied heterogeneous CR-wireless sensor networks are separated into areas and assigned a different spectrum depending on the distance. Information is sent over a multi-hop connection after dynamic clustering using distance computation. Results: The findings show that the suggested method achieves higher stability and ensures the energy-optimizing CR-WSN. The enhanced scalability can be seen in the First Node Death (FND). Additionally, the improved throughput helps to preserve the residual energy of the network which helps to address the issue of load balancing across nodes. Conclusion: Thus, the result acquired from the above findings shows that the proposed heterogeneous model achieves the enhanced network lifetime and ensures the energy optimizing CR-WSN. © 2023 Bentham science Publishers.

关键词： Cognitive radio

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Algorithms for online fault tolerance server consolidation

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Digital Communications and Networks 2025年第2期11卷 514-523页

作者： Boyu Li Bin Wu Meng Shen Hao Peng Weisheng Li Hong Zhang Jie Gan Zhihong Tian Guangquan Xu College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Computer Science and Technology Zhejiang Normal UniversityJinhua 321004China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400054China School of Astronautics Beihang UniversityBeijing100191China School of Computer Science University of SydneySydneyNSW 20528Australia Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou 511370China

We study a novel replication mechanism to ensure service continuity against multiple simultaneous server *** this mechanism,each item represents a computing task and is replicated intoξ+1 servers for some integerξ≥1,with workloads specified by the amount of required *** one or more servers fail,the affected workloads can be redirected to other servers that host replicas associated with the same item,such that the service is not interrupted by the failure of up toξ*** requires that any feasible assignment algorithm must reserve some capacity in each server to accommodate the workload redirected from potential failed servers without overloading,and determining the optimal method for reserving capacity becomes a key *** existing algorithms that assume that no two servers share replicas of more than one item,we first formulate capacity reservation for a general arbitrary *** to the combinatorial nature of this problem,finding the optimal solution is *** this end,we propose a Generalized and Simple Calculating Reserved Capacity(GSCRC)algorithm,with a time complexity only related to the number of items packed in the *** conjunction with GSCRC,we propose a robust replica packing algorithm with capacity optimization(RobustPack),which aims to minimize the number of servers hosting replicas and tolerate multiple server *** theoretical analysis and experimental evaluations,we show that the RobustPack algorithm can achieve better performance.

关键词： Cloud computing Server consolidation Replica Fault tolerance

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, V. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 IEEE.

关键词： computerized tomography

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Automated AI-Powered Fruit Identification Using Convolutional Neural Network

Automated AI-Powered Fruit Identification Using Convolutiona...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sai Tejasree, K. Surendran, R. Rajakumar, B. Gomathi, R.M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India J.N.N Institute of Engineering Department of Artificial Intelligence and Data Science Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recognition. This method teaches the ML Model we clearly explained the Model using application's agent using PEAS and the application's task environment using the 6 dimensions by showing it tons of fruit images, so over time, it gets really good at spotting the differences and similarities between, say, a banana and a grape. We also used another technique called pattern recognition, which helps the computer pay attention to specific details like the fruit's color, shape, size and texture. Overcoming multiple obstacles in order to automatically identify the type of fruit from the picture. The variety of images is influencing the color, texture, and shape of many different types of fruits. When it came to fruit picture detection, Convolutional Neural Network (CNN) Algorithm outperformed standard support-vector-machine-based approaches using handcrafted features in terms of accuracy also, it is a lot faster to implement for new fruits. By integrating deep learning and pattern recognition techniques such as Convolutional Neural Network Algorithm we have got the accuracy of 84%, our system efficiently identified different fruit types from images, demonstrating the power and effectiveness of our methods. The goal of our project is to create a tool that can quickly and correctly identify many types of fruits in photos, which could be useful for things like sorting fruits in a grocery store or helping people learn about different fruits by using Convolutional Neural Network Algorithm. This is not just about teaching a computer to recognize fruits;it is about making technology that can understand and interact with the world in a way that is helpful to us. © 2025 IEEE.

关键词： Convolutional neural networks

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Intelligent Control Mechanisms for Renewable Energy Integration Using Machine Learning Models

Intelligent Control Mechanisms for Renewable Energy Integrat...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Chahar, Suman Kaur, Kawaljit Kaswan, Kuldeep Singh Malik, Kiran University Institute of Computing Chandigarh University Mohali India Galgotias University Department of Computer Science and Engineering Greater Noida India Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering-AIML Greater Noida India

ISBN: (纸本)9798331508685

The deployment of renewable energy sources (RES) in power systems used to be a major challenge owing to their intermittent and uncertain nature. Therefore, in this paper, the use of ML models as an intelligent control system to improve the operation of RES is studied. We survey different types of ML approaches, covering supervised learning for energy predic- tion, unsupervised learning for clustering demand, reinforcement learning for energy storage management, and deep learning for real time data processing. The intent of these advanced control strategies are to balance and stabilize the grid, harmonize demand response, and augment energy storage. We highlight the use-cases and impacts of ML from real-world cases across various geographies, showing operational gains and peak-demand savings. While these technologies hold great promise, they also face challenges like data privacy, interpretability of models, and availability of high-quality datasets. Moving forward, there is a need to improve transparency of algorithms and to strengthen the collaboration of different stakeholders. Our work paves the way for a systematic and automated approach to renewable energy integration, which can serve as a critical factor to build a reliable and efficient future modern power systems with machine learning. © 2025 IEEE.

关键词： Adversarial machine learning

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Text and Voice Conversion for Machine Recognition Using NLP 8th

Text and Voice Conversion for Machine Recognition Using NLP

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Singh, Sujit Kumar Kaur, Deepinder Dhingra, Isha Computer Science and Engineering BCET Durgapur India Computer Science and Engineering SRM University Delhi NCR India University Institute of Computing Chandigarh University Mohali India

ISBN: (纸本)9789819748945

Recently, Natural Language Processing has drawn a lot of interest for its ability to computationally represent and analyze human language. The current paradigm of Information Technology makes use of natural language—the language we use every day for communication—for human–computer interaction. In recent times, NLP has drawn a lot of interest for its ability to compute a- tionally represent and analyze human language. Text present and speech deliver on various medias are unstructured in nature so we must perform data processing, text normalization, sentence segmentation, tokenization, stemming, lemmatization, bag of words, and finally, using TF-IDF, to see how words will be converted to numbers. In this work, we demonstrate the thorough processing of how, after receiving the input in the form of natural language, it transformed it into numbers using a variety of NLP techniques so that the computer can comprehend what to do next. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data handling

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Harvesting Knowledge: An Innovative Ontology Framework for Agricultural Advancement using NPL methodologies 3

Harvesting Knowledge: An Innovative Ontology Framework for A...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Deepa, R. Das, Manisha Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Andhrapradesh Rajam India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Kone Elevator India Technology & Engineering Centre Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

This paper introduces Agricultural Advancement using NLP (AA-NLP) techniques as a strategy to overcome the limitations of traditional agricultural management information systems. There is a lot of unstructured data available but this situation makes agricultural research inefficient because of its knowledge extraction and usage failure. This problem is addressed by combining an ontology framework developed for agricultural information management with Natural Language Processing (NLP) methods. The solution, AA-NLP, utilizes modern NLP algorithms which extract, classify and evaluate agricultural information from many textual sources that include research papers, reports and online repositories. AA-NLP achieves effective information retrieval and exploitation through named entity identification, sentiment analysis and semantic similarity as well as other techniques to convert unstructured input into structured ontological representations. Applying AA-NLP has several benefits such as increased discoverability of relevant agricultural information, automatic classification of scientific findings, and generation of actionable insights for decision making in farming practices, crop management and formulation of agricultural policies. The effectiveness of AA-NLP was analyzed in this paper by measuring the precision associated with knowledge extraction and ontology creation through various performance metrics including accuracy, sustainability farming practices, agricultural productivity and precision. © 2024 IEEE.

关键词： Semantics

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scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics

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BRIEFINGS IN BIOINFORMATICS 2024年第3期25卷 bbae112-bbae112页

作者： Zheng, Chuanyang Wang, Yixuan Cheng, Yuqi Wang, Xuesong Wei, Hongxin King, Irwin Li, Yu Department of Computer Science and Engineering CUHK Hong Kong SAR China College of Computing Georgia Institute of Technology Atlanta GA USA MLR Lab Southern University of Science and Technology The CUHK Shenzhen Research Institute Hi-Tech Park Nanshan Shenzhen 518057 China Institute for Medical Enginering and Science Massachusetts Institute of Technology Cambridge MA USA Wyss Institute for Biologically Inspired Engineering Harvard University Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA

Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.

关键词： novel rare cell discovery single-cell analysis neural network

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Latent Concept-based Explanation of NLP Models

Latent Concept-based Explanation of NLP Models

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yu, Xuemin Dalvi, Fahim Durrani, Nadir Nouri, Marzia Sajjad, Hassan Faculty of Computer Science Dalhousie University Canada Qatar Computing Research Institute Hamad Bin Khalifa University Qatar

ISBN: (纸本)9798891761643

Interpreting and understanding the predictions made by deep learning models poses a formidable challenge due to their inherently opaque nature. Many previous efforts to explain these predictions rely on input features, specifically, the words within NLP models. However, such explanations are often less informative due to the discrete nature of the words and their lack of contextual verbosity. To address this limitation, we introduce Latent Concept Attribution (LACOAT), which generates explanations for predictions based on latent concepts. Our intuition is that a word can exhibit multiple facets depending on the context in which it is used. Therefore, given a word in context, the latent space derived from our training process reflects a specific facet of that word. LACOAT functions by mapping the representations of salient input words into the training latent space, enabling it to provide latent context-based explanations of the prediction. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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