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Implementation of Secure and Verifiable Access Control Procedures using the NTRU Cryptosystem to Store Big Data in the Cloud Environment

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Recent Patents on engineering 2025年第2期19卷 57-70页

作者： Babu B, Ravindra Saxena, Swati Widjaja, Gunawan Vinodha D, Vedha Omarov, Batyrkhan Ramaiah, Gurumurthy B. Vats, Prashant Adama Ethiopia Department of Computer Science KCG College of Technology Chennai India Universitas Krisnadwipayana Jawa Barat 13077 Indonesia Department of Electronics and Communication Engineering JCT College Of Engineering and Technology Pichanur Coimbatore India International University of Tourism and Hospitality Turkistan Kazakhstan Technical and Vocational Training Institute Addis Ababa Ethiopia Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India

Background: Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores. Methods: However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility. Results: In this patent paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour. Conclusion: It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users. © 2025 Bentham science Publishers.

关键词： Cloud security

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An Optimized Hybrid Soil Texture Classification Model using Stacked Sparse Autoencoder 1

An Optimized Hybrid Soil Texture Classification Model using ...

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1st IEEE International Conference on Emerging research in Computational science, ICERCS 2023

作者： Prabavathi, R. Chelliah, Balika J Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

With effective soil management, which lowers the costs associated with soil erosion, improves water utilisation, and other factors, soil data analysis can offer vital information to raise agricultural yield and nutrient utilisation efficiency. An unsupervised method of data analysis called clustering finds similar groups of objects by analysing attribute values. The dynamic subject of soil classification starts with the model and moves on to explain the classes and, at the end, apply the knowledge gained from the field. In light of this, the study presents a novel clustering and classification-based hybrid soil texture classification model (HSTC). Soil data will be clustered and categorised into multiple classes by the proposed (HSTC) model. Hydrological soil groupings (HSG) of various kinds are used as classes to accomplish this. Based on characteristics like the proportion of clay, silt, and sand, the HGS can be categorised. The density-based clustering (DBSCAN) algorithm is used to group the soil texture data. The stacked sparse auto encoder (SSAE) model is used to categorise the clustered data. In addition, the parameter values of the SSAE technique are determined using a new barnacle mating optimizer (BMO). The HSTC model has achieved the best results, with accuracy of 95.66%. The experimental validation of the proposed HSTC model on soil texture dataset revealed that the suggested model outperformed previous techniques. © 2023 IEEE.

关键词： Deep learning

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Compositional design and phase formation capability of high-entropy rare-earth disilicates from machine learning and decision fusion

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npj Computational Materials 2024年第1期10卷 2257-2271页

作者： Yun Fan Yuelei Bai Qian Li Zhiyao Lu Dong Chen Yuchen Liu Wenxian Li Bin Liu National Key Laboratory of Science and Technology on Advanced Composites in Special Environments and Center for Composite Materials and Structure Harbin Institute of TechnologyHarbin 150080China School of Materials Science and Engineering Shanghai UniversityShanghai 200444China National Engineering Research Center for Magnesium Alloy Chongqing UniversityChongqing 400044China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150080China UNSW Materials&Manufacturing Futures Institute School of Materials Science and EngineeringUniversity of New South WalesSydney NSW 2052Australia Institute of Coating Technology for Hydrogen Gas Turbines Liaoning Academy of MaterialsShenyang 110004China

A key strategy for designing environmental barrier coatings is to incorporate multiple rare-earth(RE)components intoβ-andγ-RE_(2)Si_(2)O_(7)to achieve multifunctional performance ***,the polymorphic phase presents s... 详细信息

关键词： rare earth silicate

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Mechanical Properties of Railway High-strength Manufactured Sand Concrete: Typical Lithology, Stone Powder Content and Strength Grade

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Journal of Wuhan University of technology(Materials science) 2025年第1期40卷 194-203页

作者： WANG Zhen LI Huajian HUANG Fali YANG Zhiqiang WEN Jiaxin SHI Henan Railway Engineering Research Institute China Academy of Railway Science Corporation LimitedBeijing 100081China State Key Laboratory for Track Technology of High-Speed Railway Beijing 100081China

In order to achieve the large-scale application of manufactured sand in railway high-strength concrete structure,a series of high-strength manufactured sand concrete(HMC)are prepared by taking the manufactured sand lithology(tuff,limestone,basalt,granite),stone powder content(0,5%,10%,15%)and concrete strength grade(C60,C80,C100)as *** evolution of mechanical properties of HMC and the correlation between cubic compressive strength and other mechanical properties are *** to river sand,manufactured sand enhances the cubic compressive strength,axial compressive strength and elastic modulus of concrete,while its potential microcracks weaken the flexural strength and splitting tensile strength of *** powder content displays both positive and negative effects on mechanical properties of HMC,and the stone powder content is suggested to be less than 10%.The empirical formulas between cubic compressive strength and other mechanical properties are proposed.

关键词： manufactured sand concrete railway mechanical property lithology stone powder content

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Analyzing the Prediction Accuracy of Corn Leaf Diseases Using A Pre-Trained Network Model

Analyzing the Prediction Accuracy of Corn Leaf Diseases Usin...

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

作者： Ashwini, C. Sellam, V. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396485

During corn's exploration and manufacturing phases, farmers have a complicated issue in accurately diagnosing corn crop infections. To solve this issue, this work provides a method for specific position three prevalent diseases of corn leaves: grey spot, leaf blight, and rusty depending on grouping and an upgraded deep learning method. Deep learning advancements paved the path for enhancing prediction accuracy as its significance is adopted over various research fields. Disease prediction and classification is time-consuming due to the lesser foreground and background intensity information. To identify three illnesses, first cluster reference images using the clustering technique, then input them into the enhanced deep network. The influences of various 'k' values on corn diagnostic techniques are investigated in this research. The trial findings show that the approach has the most significant identification impact on observations with the analytical prediction of corn disease, rust, and grey spot disease. Here, VGG-16 and ResNet18 likewise produce the best diagnostic findings, with an average diagnostic accuracy, respectively. The technique presented in this study has a diagnostic performance of 95% for the three corn diseases. It has a more substantial diagnostic impact than another four techniques and can be used to safeguard crops in the agricultural area. The simulation is done in the MATLAB 2020a environment, and various performance metrics like accuracy, precision, F1-score, recall and some other statistical measures are evaluated and compared with existing approaches. © 2023 IEEE.

关键词： Forecasting

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Contagion Classification using Deep Learning Approach 2

Contagion Classification using Deep Learning Approach

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Kacham, Sai Prajwal Angayarkanni, S.A. Rohithshan, S. Mishra, Arpan Vignesh, K. SRM Institute of Science and Technology College of Engineering and Technology Department of Networking and Communication Tamilnadu Kattankulathur603203 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil626126 India

ISBN: (纸本)9798350365269

Potatoes are a major crop globally, but diseases can seriously harm their growth and quality. Recently, there's been a lot of interest in using machine learning to identify potato diseases from pictures. In this study, we've developed a way to automatically classify potato diseases using deep learning methods. By training a model with CNN and SVM algorithms on a dataset of potato disease images, we achieved an accuracy rate of 93.95%. This shows that deep learning is effective in identifying potato diseases. Our method could be a big help to farmers and researchers, making it faster and easier to spot and deal with potato diseases, which could lead to better crop yield and quality. © 2024 IEEE.

关键词： Support vector machines

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An empirical intelligent water irrigation system using soft computing and IoT

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International Journal of Information technology (Singapore) 2024年第6期16卷 3983-3989页

作者： Patel, Warish Sangeetha, Y. Gopagoni, Deepa Rani Mallik, D. M. Arvind Agrawal, Rakshal Mishra, Saket Sunil, M.P. Kolandaisamy, Raenu Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Information Technology VR Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India PES Institute of Technology and Management Karnataka Shivamogga India Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India Centre of Interdisciplinary Research in Business and Technology Chitkara Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka Bengaluru India Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia

Water loss from fruit and vegetable production is mostly caused by leakage in the transportation system and handling of land, as well as by using traditional processing techniques. In order to increase the wellness of plants, save power, and optimize consumption of water, information should be widely applied in designing intelligent irrigation systems, which is possible with the help of the Internet of Things (IoT). In the given paper, an IoT-based soft computing recommendation framework is proposed for performing irrigation in a smarter way. The system works in such a way that IoT devices are positioned in the field to gather information about crops and groundwater levels. The acquired data is then fed to a cloud repository, which makes use of soft computing techniques such as neural networks for analysing and providing effective recommendations to farmers on their smartphones. In this way, the framework is tuned and the farmers are also updated from time to time regarding judicious irrigation processes. Lastly, the performance of the proposed system is validated against traditional techniques by taking crop datasets into consideration. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Efficient water management Intelligent devices IoT Smart irrigation Soft computing

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AI Based Criminal Detection and Recognition System for Public Safety and Security using novel CriminalNet-228 4th

AI Based Criminal Detection and Recognition System for Publi...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

The recent surge in public space criminal activities underscores the need for an efficient system to promptly detect, recognize, and track criminals. Existing AI-based criminal detection literature, while insightful, has limitations such as the complexity to analyze video data, time, and speed of accuracy when it comes to training the algorithms that call for further advancements. Hence proposed AI-driven system addresses this demand, aiming to automate criminal identification, equipping law enforcement with a potent tool for proactive crime prevention and resolution. The system utilizes the innovative "CriminalNet-228" Convolutional Neural Network (CNN) architecture, meticulously trained on a vast criminal image dataset for enhanced detection accuracy. To bolster face detection, additional computational resources like parallel processing and distributed computing are employed, enabling real-time analysis of extensive CCTV footage. Notably, the system tracks identified criminals, providing law enforcement with real-time situational awareness. CriminalNet-228 achieves an impressive overall Mean Average Precision (mAP) of 0.65 and excels in detecting facial features as small as 4 × 4 pixels, demonstrating its detail-oriented recognition capabilities. When evaluating Proposed CriminalNet-228 in comparison to established state-of-the-art techniques like Fast-RCNN, Yolov7, and AlexNet, it surpassed them in terms of precision, recall, f-measure, and accuracy, achieving an impressive accuracy rate of 99.2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Customer Segmentation Analysis Using Clustering Algorithms 3rd

Customer Segmentation Analysis Using Clustering Algorithms

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Reddy, Biyyapu Sri Vardhan Rishikeshan, C.A. Dagumati, VishnuVardhan Prasad, Ashwani Singh, Bhavya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819939312

Customer segmentation has been deployed as a prudent marketing strategy by companies to ensure that their investments are less risky and more judicious. Segmenting customers helps the companies to divide the customers into groups that reflect similarity and maximize the value of each customer to the business. The main goal of this research is to use a machine learning clustering approach called K-means clustering to accomplish consumer segmentation. Besides, the research work is also focused on performing exploratory data analysis on the given dataset. To group the customers into clusters, a K-means clustering algorithm is performed on the customer dataset. To achieve optimization and validation of the clusters, popular heuristic, interpretation, and approximation methods have been included in this paper. Further, for analyzing and visualizing the important facets of the customer dataset and the operation of the K-means algorithm, the paper presents some colorful and informative representations. The implementation of this research work has been done in the R programming language. The outcome of this work includes visualizing the segments of the mall customers in the form of clusters based on their spending scores and annual incomes. Furthermore, a better customer segmentation could be achieved by taking product reviews and customer feedback into consideration. Nevertheless, customer segmentation remains a prospective topic for many researchers and companies due to dynamic customer behavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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Geologs: A Complete end-to-end Classroom Management System 3

Geologs: A Complete end-to-end Classroom Management System

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Mohari, Bishal Dey, Bidipto Ram, Bhera Albert Mayan, J. Mary Posonia, A. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350322842

This classroom management system is designed to simplify attendance tracking and data access. It replaces the traditional pen-and-paper method with a location-based tracking and QR code system, providing administrators with full control over student attendance, classroom routines, and location-based statistics. The proposed system is designed to save time for professors by eliminating manual processes and reducing their workload. Students can access their attendance reports through a detailed dashboard that displays visual statistics and notifications. The system utilizes GPS technology to determine the location of students on campus. If the location of a student and the assigned classroom or block are the same, the system confirms attendance. QR codes are used as a secondary measure to confirm attendance, with randomly generated codes requiring location verification when scanned. This ensures the system is not easily susceptible to malpractices. Administrators have access to a dashboard that displays detailed attendance records of all students, enabling them to override attendance records in exceptional cases. By simplifying attendance tracking, reducing manual processes, and providing easy access to attendance data, this classroom management system streamlines administrative tasks and helps professors focus on what's most essential. © 2023 IEEE.

关键词： Students

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