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Hierarchical blockchain-based secure data storage in cloud using Merkle tree approach

International Journal of Cloud Computing 2024年第6期13卷 605-626页

作者： Bruno, Judy Flavia Chelliah, Balika J. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India

An authenticated manager must reinforce huge applications and operating systems, keeping information in the cloud while resisting potentially unreliable service providers. This article explores the presence of multiple service providers based on the cloud to store and manage customer information. It has investigated the creation of an optimal system for shared cloud storage to facilitate flexible operations and information migration. This paper uses the Merkle tree approach to describe the experimental outcomes and designs of security systems provided by blockchain in cloud computing. We proposed that good authenticity and security be implemented with minimal significant degradation. The method was based on the position of the conscious Merkle tree that employs a three-tuple technique that has proven reliable in terms of authentication and encryption of security functions. The proposed system performance was measured by parameters such as retrieval time of information in blockchain technology, cost of storage, and average time with conventional information storage methods. The results indicate that the proposed blockchain technology solution has a response time almost 50% faster than conventional methods. Also, it claims that the storage cost would be around 20% lower. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Cloud storage

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Hybrid heuristic driving training-rider optimization algorithm for QoS-aware multicast communication system in MANET

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Australian Journal of Electrical and Electronics engineering 2024年第1期21卷 59-78页

作者： Sarangi, Sanjaya Kumar Nanda, Arabinda Lenka, Rasmita Behera, Prafulla Kumar Department of Computer Science and Applications Utkal University Bhubaneswar India Department of Computer Science & Engineering Krupajal Engineering College Bhubaneswar India School of electronics engineering KIIT University Bhubaneswar India

Mobile Ad hoc Network (MANET) is broadly applicable in various sectors within a short amount of time, which is connected to mobile developments. However, the communication in the MANET faces several issues like synchronization, managing power, resources, and routing management. Often, finding the optimal path in the MANET becomes complicated in the existing approaches. In this work, the latest QoS-assisted multiple-cast communication system in MANET is presented using hybridized approaches for better communication over the MANET domain. Especially, an Exploitation-assisted Driving Training-Rider Optimization (EDT-RO) model is suggested for the multicast routing in MANET. The developed EDT-RO algorithm optimizes the paths for communication in MANET, where the network considers more than two receivers. The proposed EDT-RO-based multicast routing algorithm aims to attain the objective function considering the data packet delivery rate, the throughput, the end-to-end latency, and the Quality of Service (QoS). Throughout the result analysis, the offered model shows better performance in terms of 0.2%, 0.3%, 0.5%, and 1.5% than DHOA, Jaya, DTBO, and ROA regarding mean. Simulations are conducted by comparing with other optimization algorithms used for QoS-assisted multiple casts routing to showcase and attain better routing performance of the developed models. ©, Engineers Australia.

关键词： Optimization

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LEViT- Leaf Disease identification and classification using an enhanced Vision transformers(ViT) model

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Multimedia Tools and Applications 2024年 1-32页

作者： Prashanthi, Bh. Krishna, A. V. Praveen Rao, Ch. Mallikarjuna Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram522502 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad500090 India

The variety of crops, differences in climate, and the multiplicity of disease symptoms make early identification and evaluation of leaf diseases a challenging task. Although deep-learning methods have been created for this very reason, they often need adjustments to suit various regions and types of crops. This study presents a vision transformer-based deep learning model incorporating Explainable Artificial Intelligence (XAI) features to effectively detect and classify leaf diseases. The model is evaluated using the New Plant Disease dataset, encompassing data from 14 plants with 38 leaf disease classifications. The primary objective is to enhance accuracy in leaf disease classification through the utilization of vision transformer learning. The proposed model, LEViT (Leaf Disease Identification and Classification using an Enhanced Vision Transformers Model), builds upon the ViT_B_32 model. It incorporates additional layers such as dense layers, dropout, and L2 regularization to improve performance and mitigate overfitting. Specialized preprocessing techniques tailored for transformers are employed to refine data quality. Incorporating Grad-CAM (Gradient-weighted Class Activation Mapping) enhances interpretability, enabling the identification of crucial regions influencing the model’s decisions within images. The model exhibits remarkable efficacy, boasting a training accuracy of 95.22%, validation accuracy of 96.19%, and test accuracy of 92.33%. These results underscore its robust ability to accurately identify leaf diseases across diverse crop types. Comparative analysis with state-of-the-art models in the field demonstrates LEViT’s superior performance, establishing it as a leading solution for leaf disease detection and classification. LEViT’s advanced architecture, coupled with fine-tuning, positions it as a highly promising tool for advancing crop management and yield improvement, thereby significantly contributing to the field of precision agriculture. © The

关键词： Crops

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LesionAid: vision transformers-based skin lesion generation and classification – A practical review

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Multimedia Tools and Applications 2025年 1-22页

作者： K, Mallikharjuna Rao Krishna, Ghanta Sai Supriya, Kundrapu Sorgile, Meetiksha Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur India Computer Science and Engineering International Institute of Information Technology Naya Raipur India

Skin cancer is one of the most prevalent forms of human cancer. It is recognized mainly visually, beginning with clinical screening and continuing with the dermoscopic examination, histological assessment, and specimen collection. Deep convolutional neural networks (CNNs) perform highly segregated and potentially universal tasks against a classified fine-grained object. This study suggests a novel multi-class prediction framework that uses ViT and ViTGAN to categories skin lesions. To address the class disparity, GANs (Generative Adversarial Networks) based on vision transformers are used. The framework comprises four main phases: ViTGANs, Image processing, and explainable AI. Phase 1 consists of generating synthetic images using ViTGAN to balance all the classes in the dataset. To enhance the amount of the data, the second phase involves using various morphological processes and data augmentation techniques. In phases three and four, a ViT model for edge computing systems that can recognize patterns and classify skin lesions from the user's skin that is visible in the picture is developed. In phase 3, after classifying the lesions into the desired class with ViT, we will use explainable AI (XAI) that leads to more explainable results (using activation maps, etc.) while ensuring high predictive accuracy. The results demonstrate that the model used for generating synthetic images has achieved an FID score of 13.32 and the ViT model has achieved 99.2% as its training accuracy and 97.4% as its validation accuracy. The whole framework is compared with the existing frameworks for skin lesion detection. And explainable AI (XAI) is used in the proposed framework in order to increase model transparency and boost trust among users by illustrating the main factors affecting predictions. This interpretability helps physicians in making more informed decisions by providing clear insights into the model's reasoning. © The Author(s), under exclusive licence to Springer science+Bu

关键词： Generative adversarial networks

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FTBAC: fuzzy trust based access control for healthcare cross-domain environment

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Soft Computing 2025年第7期29卷 3349-3366页

作者： Roy, Sujoy Kumar, Alok Rao, Udai Pratap Department of Computer Science and Engineering SVNIT Keval Chowk Gujarat Surat395007 India Department of Computer Science and Engineering NIT-Patna Patna University Campus Bihar Patna800005 India

Internet of Thing based healthcare ecosystems are extremely popular as they are built on a network of devices that are connected directly to one another in order to collect, share, and use important contextual information. However, in a cross-domain environment, the absence of a delicate, fine-grained access control mechanism may lead to the disruption or leakage of sensitive data. In addition, the plug-and-play nature of the users with wireless healthcare devices may result in disturbances in the accessibility of data. In this article, we propose a Fuzzy Trust-Based Access Control scheme for healthcare environment consisting of dynamically sensitive data. Trust being dynamic in nature has been considered an as attribute along with other static and dynamic attributes for the proposed scheme. The dynamic nature of trust as an attribute prompts us to use fuzzy logic for its calculation. Parameter-based analysis and security analysis exhibits that the proposed scheme is highly scalable in nature and is resilient to reply attack, Distributed-Denial-of-Service attack, and collusion attack. Further, the proposed access control scheme ensures proper delegation, high context awareness, high granularity, and ensures interoperability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Authorization

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Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing

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Discover Artificial Intelligence 2025年第1期5卷 1-29页

作者： Islam, Md. Rezaul Chowdhury, Aniruddha Islam Shama, Sharmin Lamyea, Md. Masudul Hasan Department of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet3114 Bangladesh Department of Educational Technology and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur1750 Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka1212 Bangladesh

Thyroid disorders are increasingly prevalent, making early detection crucial for reducing mortality and complications. Accurate prediction of disease progression and understanding the interplay of clinical features are essential for effective diagnosis and treatment. Our study addresses these challenges by employing a standard machine learning model, enhanced with comprehensive clinical feature analysis and an ensemble learning technique. By leveraging machine learning, we can identify key risk factors and improve diagnostic accuracy. To achieve optimal prediction outcomes, we evaluated seventeen machine learning models and implemented an Ensemble ML classifier using a hard voting strategy. Class balancing techniques, particularly random oversampling, significantly improved classification performance. Our experimental results demonstrate that the proposed model outperforms existing methods, achieving 100% sensitivity and 99.72% accuracy using the XGBoost algorithm and SelectKBest feature selection. By addressing feature reduction and high class-imbalance, the ensemble ML classifier with hard voting proves more effective in handling classification challenges. © The Author(s) 2025.

关键词： Adversarial machine learning

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Evaluation of the Practicability of Internet of Things in Fighting Against COVID-19

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IEEE Internet of Things Journal 2024年第5期11卷 8064-8077页

作者： Lin, Yan-Ming Chu, Edward T.-H. National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliou640 Taiwan

The COVID-19 pandemic has already ravaged the world for two years and infected more than 600 million people, having an irreparable impact on the health, economic, and political dimensions of human society. There have been many papers proposing various Internet of Things (IoT) solutions to stop the spread of COVID-19, such as virus tracking, infection control, medical treatment, and health management. Due to the high popularity of IoT devices, many studies have systematically investigated related technologies, evaluating the development, and application of IoT solutions from different aspects, such as electronic security, personal privacy, disaster prediction, and artificial intelligence (AI). However, the usefulness of IoT in combating COVID-19 has not been thoroughly evaluated. Therefore, this article systematically analyzes high-quality articles published in well-known international journals and conferences from 2019 to 2022 and divides them into the technical development of prevention and treatment according to the application field of the solution, cross-comparing the research results. It provides a comprehensive assessment of the maturity of the current IoT solutions through the Standard Technology Readiness Level (TRL). Finally, through the technical efficacy matrix, the application status and development direction of the international IoT technology are analyzed by patent. Our results reveal the maturity and limitations of existing technologies to fight COVID-19. Possible future research directions are also discussed. © 2014 IEEE.

关键词： COVID-19

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CDS: Coupled data Storage to Enhance Read Performance of 3D TLC NAND Flash Memory

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IEEE Transactions on computers 2024年第3期73卷 694-707页

作者： Wu, Wan-Ling Hsieh, Jen-Wei Ku, Hao-Yu National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan

Due to the strong demand of massive storage capacity, the density of flash memory has been improved in terms of technology node scaling, multi-bit per cell technique, and 3D stacking. However, these techniques also degrade read performance and reliability. The long read latency comes from increased data sensing time and time-consuming ECC decoding time. Storing multiple bits per cell results in more read reference voltages and increased latency of identifying appropriate threshold voltages. To deal with error correction, LDPC is widely used in flash memory to provide stronger ECC capability. However, LDPC incurs a long decoding latency when bit errors are numerous. In this work, we propose coupled data storage (CDS) to improve the read performance of 3D NAND flash-memory storage devices. CDS supports two modes to improve read latency: The high read-speed mode is designed to improve data sensing time with reduced voltage states, while the data correction mode is designed to mitigate bit errors and LDPC overhead. Experiment results showed that CDS could reduce 50∼66.6% read latency and 25.7∼27.5% write latency under the high read-speed mode. For the data correction mode, RBER could be decreased by 37∼52% and the lifetime could be prolonged to 1.6 to 3 times. © 1968-2012 IEEE.

关键词： Threshold voltage

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Plant Disease Detection and Classification Using Hybrid Model Based on Convolutional Auto Encoder and Convolutional Neural Network

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computers, Materials & Continua 2025年第6期83卷 5219-5234页

作者： Tajinder Kumar Sarbjit Kaur Purushottam Sharma Ankita Chhikara Xiaochun Cheng Sachin Lalar Vikram Verma Computer Science&Engineering Department Jai Parkash Mukand Lal Innovative Engineering&Technology InstituteRadaurYamunanagar135133India Department of Computer Science Government PG CollegeAmbala CanttAmbala134003India School of Computer Science&Engineering Galgotias UniversityGreater Noida203201India Department of Computer Science and Applications Kurukshetra UniversityKurukshetra136118India Computer Science Department Bay Campus Fabian WaySwansea UniversitySwanseaSA18ENUK Department of Engineering and Technology Gurugram UniversityGurugram122003India Department of CSE Panipat Institute of Engineering and TechnologyPanipat132103India

During its growth stage,the plant is exposed to various *** and early detection of crop diseases is amajor challenge in the horticulture *** infections can harmtotal crop yield and reduce farmers’income if not identified ***’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant *** is an excellent use case for Community Assessment and Treatment Services(CATS)due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of *** alternative to conventional Machine Learning(ML)methods,which require manual identification of parameters for exact results,is to develop a prototype that can be classified without *** automatically diagnose tomato leaf disease,this research proposes a hybrid model using the Convolutional Auto-Encoders(CAE)network and the CNN-based deep learning architecture of *** date,none of the modern systems described in this paper have a combined model based on DenseNet,CAE,and ConvolutionalNeuralNetwork(CNN)todiagnose the ailments of tomato leaves *** trained on a dataset obtained from the Plant Village *** dataset consisted of 9920 tomato leaves,and the model-tomodel accuracy ratio was 98.35%.Unlike other approaches discussed in this paper,this hybrid strategy requires fewer training ***,the training time to classify plant diseases with the trained algorithm,as well as the training time to automatically detect the ailments of tomato leaves,is significantly reduced.

关键词： Tomato leaf disease deep learning DenseNet-121 convolutional autoencoder convolutional neural network

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Diabetes Prediction using Hybrid Machine Learning Techniques

Diabetes Prediction using Hybrid Machine Learning Techniques

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Naik, Bhukya Devendar Spoorthy, Anumula Baherji, Sakshi Rout, Saroja Kumar Sethi, Bijaya Kumar Vardhaman College of Engineering Department of Information Technology Hyderabad India Vardhaman College of Engineering Department of Computer Science & Engineering Data Science Hyderabad India

ISBN: (纸本)9798350373783

The rise in blood glucose levels is the primary factor contributing to the development of diabetes. Given the significance of preventing diabetes or delaying its onset, despite numerous efforts utilizing machine learning for medical diagnostics, there remains a notable gap in research concerning long-term disease prediction, especially for type 2 diabetes. However, the traditional method of diagnosing diabetes involves patients undergoing blood glucose tests administered by doctors, which can be limited by clinical resources. Many patients consequently encounter delays in getting a diagnosis. To create a predictive model for diabetes diagnosis, this study used six traditional machine learning models: boosting, neural networks, decision trees, random forests, logistic regression, and support vector machines. The study employed machine learning (ML) algorithms to predict diabetes using an authentic dataset from Safety Pressure Primary Health Care. With a validation accuracy of 84%, the study offers important new information on who is most likely to develop type 2 diabetes. By precisely predicting the type of diabetes and examining the importance of each indication in the prediction process, the goal of this study is to improve the accuracy of diabetes prediction. © 2024 IEEE.

关键词： Prediction models

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