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Blockchain Merkle-Tree Ethereum Approach in Enterprise Multitenant Cloud Environment

computers, Materials & Continua 2023年第2期74卷 3297-3313页

作者： Pooja Dhiman Santosh Kumar Henge Sartaj Singh Avinash Kaur Parminder Singh Mustapha Hadabou School of Computer Applications Lovely Professional UniversityPhagwara144001India School of Computer Science and Engineering Lovely Professional UniversityPhagwara144001India School of Computer Science Mohammed VI Polytechnic UniversityBen Guerir43150Morocco

This research paper puts emphasis on using cloud computing with Blockchain(BC)to improve the security and privacy in a *** security of data is not guaranteed as there is always a risk of leakage of users’*** can be used in a multi-tenant cloud environment(MTCE)to improve the security of data,as it is a decentralized *** is saved in unaltered ***,Blockchain is not owned by a single *** encryption process can be done using a Homomorphic encryption(HE)algorithm along with hashing technique,hereby allowing computations on encrypted data without the need for *** research paper is composed of four objectives:Analysis of cloud security using Blockchain technology;Exceptional scenario of Blockchain architecture in an enterprise-level MTCE;Implementation of cipher-text policy attribute-based encryption(CPABE)algorithm;Implementation of Merkle tree using Ethereum(MTuE)in a Multi-tenant *** of these four objectives,the main focus is on the implementation of CP-ABE ***-ABE parameters are proposed for different levels of *** levels include inner tenant,outer tenant,Inner-Outer-Tenant,Inner-Outer-External-Tenant,Outer-Inner-Tenant,External-Outer-Inner-Tenant and the parameters such as token,private key,public key,access tree,message,attribute set,node-level,cipher-text,salting which will help in providing better security using CP-ABE algorithm in a multitenant environment(MTE)where tenants can be provided with different levels of security and achieved 92 percentage of authenticity and access-control of the data.

关键词： Blockchain(BC) merkle tree using ethereum(MTuE) multi-tenant environment(MTE) homomorphic encryption(HE) ciphertext policy attribute-based encryption(CP-ABE)

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Deep Learning Model-Based Region Measurement Analysis for Breast Cancer Classification 1

Deep Learning Model-Based Region Measurement Analysis for Br...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Meegada, Radhika Bhuyan, Hemanta Kumar Department of Computer Science Engineering Andhra Pradesh Guntur India Department of Information Technology and Computer Applications Andhra Pradesh Guntur India

ISBN: (纸本)9798331518943

The mortality of Breast cancer patients is issued based on lack of identification and treatment, and mammography is a useful tool for early screening. Deep learning-based computer-aided diagnosis (CAD) of mammography can help radiologists make more accurate and objective decisions. However, a lot of the current approaches rely on datasets that have been manually annotated for segmentation. Furthermore, several techniques are less interested in using ROI for image fusion models because of the high image sizes and modest lesion proportions. These flaws raise the application of the model's labour, cost, and computational overhead. Consequently, a network with regional scoring based on the deep location network (DLN) model is suggested. Utilizing pooling modules, DLN is considered a single feature extractor end-to-end mammography image classification approach that locates lesion sites without the need for training. Different deep learning models have been considered to test our proposed model. The experiments are demonstrated on publicly available IN breast and CBIS-DDSM datasets, and the proposed model fared well when compared to earlier cutting-edge techniques for classifying mammogram images. © 2024 IEEE.

关键词： Mammography

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Two-branch Convolutional Networks for Stress Detection from Biomedical Data 27

Two-branch Convolutional Networks for Stress Detection from ...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Dobrokhvalov, Maksim O. Filatov, Anton Yu. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9798350363708

Stress is considered one of the main causes of health problems. Combining physiological signals from multiple modalities is a promising method for more accurately determining a person's condition. This paper proposes single-branch and two-branch convolutional neural networks for stress detection using biomedical data (electrocardiogram, blood pulse pressure, electrodermal activity). The two-branch model independently processes heart data (electrocardiogram or blood volume pulse) and electrodermal activity, and then merges the results of the branches using a predictor. The work examines various predictors. In some predictors, only the final result of the network is transferred to the loss function, and in one, the results of each branch and the whole network are taken into account. On the WESAD dataset, the single-branch model achieves an accuracy 0.983 and an F1 score 0.976, and the two-branch model achieves an accuracy 0.994 and an F1 score 0.992. On the UBFC-Phys dataset, the single-branch model achieves an accuracy of 0.922 and an F1 score of 0.945. © 2024 IEEE.

关键词： Electrocardiograms

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Detection of Eye Disease Using Deep Learning Algorithms 4

Detection of Eye Disease Using Deep Learning Algorithms

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Patel, Omkumar J. Verma, Kshitij Vyas, Chirayu Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications Kiit Deemed to be University Bhubaneswar India

ISBN: (纸本)9798331540364

The human eye is a complex organ that can perceive a wide variety of visual inputs. However, its functioning can be severely impaired by diseases such as cataract and diabetic retinopathy which have the potential to cause permanent blindness which not only disrupts the productivity and mobility of patients but also causes a socio-economic impact that decreases the quality of life. This paper reviews the recent advancements in deep learning for diagnosing retinal diseases such as diabetic retinopathy (DR), cataract and glaucoma. The paper presents an extensive comparative analysis of 4 definite deep learning models - Vision Transformers (ViT), ResNet, GoogLeNet and Convolutional Neural Network (CNN). The results from the conducted experiments show that ViT has the highest accuracy followed by CNN. The outcomes highlights the potential of deep learning models in revolutionizing eye disease detection technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving Transfer Learning Performance for Abnormality Detection in Brain MRI Images Using Feature Optimization Techniques 27

Improving Transfer Learning Performance for Abnormality Dete...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Nizamli, Yasser A. Filatov, Anton Yu. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9798350363708

Accurate and early diagnosis of brain tumors plays a vital role in determining appropriate treatment and increasing survival rates. Manual diagnosis using MRI images is often prone to errors and can be a tedious and time-consuming procedure. As a result, automated computer-aided systems that utilize artificial intelligence and machine learning techniques are widely used. However, accurately diagnosing the type of brain tumor remains a significant and complex challenge for today's medical imaging research community. In this paper, we present an enhanced transfer learning approach that aims to differentiate brain tumor types using MRI scans while achieving state-of-the-art performance. In the proposed approach, the pre-trained VGG-19 network with fixed weights is used to map MRI scans to a high-level numerical representation. The extracted features are then passed to a support vector machine algorithm to classify brain tumors into one of three major categories: glioma, meningioma, and pituitary. To further improve the performance of the model, the effectiveness of utilizing feature optimization techniques based on the radial basis similarity function and L1-penalty logistic regression was investigated. For evaluation, the benchmark Figshare dataset containing 3064 MRI images was used after a series of processing operations. The proposed system achieved a high overall accuracy of 98.53%, outperforming other analogues. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Enhancing Decision-Making in Small Retail : ML algorithms For Shop Owners 8

Enhancing Decision-Making in Small Retail : ML algorithms Fo...

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

作者： Parmar, Manish Naik, Tanvi Gostu, Hrishikumar Biradar, Maheshwari D Y Patil International University School of Computer Science Engineering and Applications Akurdi Pune India

ISBN: (纸本)9798350391770

This research reflects on the role of Machine Learning (ML) in making small retail shop owners in India profitable, since they come up with inventory management problems, pricing issues and ways of engaging their customers thus getting better than their competitors. It discusses how, unlike all other apps in the market, "Ganak"is sharpened with a data-driven model and its applications by statistical analysis and ML algorithms. Through the utilization of big data technologies, Ganak intends to assist entrepreneurs who cannot afford the right inputs to improve their decision-making skills. This leads to an increased business efficiency and profitability. The paper has delivered on its objective, by producing findings that prove Ganak's role in the decision making process, and adopting digital application that will thus lead to economic growth of small retailers in India. © 2024 IEEE.

关键词： Recommender systems

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Content Based Image Retrieval: A Study of Approaches and Techniques 4

Content Based Image Retrieval: A Study of Approaches and Tec...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Bano, Mehnaz Matta, Priya Chandel, Shivali Tula's Institute Department of Computer Science and Engineering Dehradun India Tula's Institute Department of Computer Applications Dehradun India

ISBN: (纸本)9798350387490

The Content Based Image Retrieval (CBIR) is basically used to extract images from the collection of images using several image matching and extraction methods. As it is now with the advancement and ongoing consistent technology and negligible cost of the electronic, digital and virtual sensors, a huge collection of image databases is processed by different types of applications. This type of database of images extends the requirement that we should provide effective search methods for the retrieval of images that can fulfill the demands of end-user. The texture, color, or shape of a picture is the most basic feature used to symbolize and index the images. These distinct elements of photographs are retrieved and used as a measure to find image similarities. A search procedure is initiated for relevant images that fulfill the user's information need using a sketch drawing or a query image given by the user that has similar features. The relevance feedback can be used as an optional step to retrieve and enhance the CBIR process. In this paper, after extracting semantic characteristics from photos, an analysis of the content of the images is performed with respect to features connected to the low level. In this paper, an overview of traditional CBIR methods (e.g.: Color histogram-based, texture based and shape based) are discussed. This paper's primary goal is to study the comprehensive details on the various computational techniques used for content-based image retrieval. To further enhance the efficiency of the image retrieval process, their limitations are also addressed. © 2024 IEEE.

关键词： Content based retrieval

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Natural Language Processing (NLP) based Phonetic Insights for Improving Voice Recognition and Synthesis 4

Natural Language Processing (NLP) based Phonetic Insights fo...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Priyadharshini, N. Subbulakshmi, N. Kalasalingam Academy of Research and Education Department of Computer Applications Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Computer Science and Engineering Krishnankoil India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Voice analysis in Natural Language Processing (NLP) involves the processing of spoken language to develop applications such as speech recognition, synthesis, and speaker identification. This survey explores the phonetic intricacies of Tamil and other Dravidian languages, examining their impact on NLP. The extensive phonetic inventory of the Tamil Language is characterized by its complex vowel system, retroflex consonants, and the distinction between aspirated and unaspirated stops, which presents unique challenges and opportunities for computational linguistics. Phonetics helps to process the human language both accurately and naturally and it works as a bridge between the spoken and written communication forms. This paper emphasizes the necessity of developing specialized phonetic modeling techniques and robust algorithms. Additionally, it highlights the importance of creating comprehensive phonetic datasets and leveraging advancements in machine learning and deep learning to improve NLP tasks such as speech recognition and synthesis. A brief comparison on algorithms and applications associated with voice-assisted technologies. By advocating for interdisciplinary collaboration among linguists, computer scientists, and language technologists, this survey aims to inspire further research and innovation in processing Tamil and other Dravidian languages, ultimately contributing to more inclusive and efficient NLP systems for these linguistically rich languages. © 2024 IEEE.

关键词： Deep learning

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Advance Chess Engine: an use of ML Approach 3

Advance Chess Engine: an use of ML Approach

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Srivastava, Kushagra Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Mishra, Manoj Ranjan De, Utpal Chandra Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350381931

Several researchers have sought to create a software that can learn to play cognitive games with little or no prior understanding of the game's rules. A typical chess playing machine investigates all of the possible moves from a chessboard arrangement before deciding on the next best move. The Deep Blue chess engine's brute-force searching strategy has had a significant influence on artificial intelligence, although it has been discovered to be resource intensive. Using the evolutionary and adaptive computing strategy on learning from human grandmasters, this study proposes a relatively easy and fast approach to developing an intelligent chess engine that can aid and hint at probable moves inside the game. Chess-playing machines may now easily surpass human talent thanks to the introduction of Machine Learning, Deep Learning and Artificial Intelligence. The game of chess is examined through the prism of these algorithms in this study. The comparison of the model performance with the existing ones has been performed to show the efficacy of the model. © 2024 IEEE.

关键词： Engines

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Providing a Robust Blockchain-Based Framework for the Secure Transmission of EHRs across the HioT 5

Providing a Robust Blockchain-Based Framework for the Secure...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Joy, Destin N. Yadav, Chandra Shekhar Jhunjhunu India Noida Institute of Engineering and Technology Greater Noida School of Computer Applications Noida India

ISBN: (纸本)9798350389609

"Electronic-Health Records (EHRs)"requires a safe and effective method of exchange, and the present research aims to provide that requirement. "Health Internet-of-Things (HIoTs)"encompasses the integration of various interconnected gadgets related to healthcare, including fitness bands, heart rate tracks, diabetes meters, as well as additional sensors integrated into healthcare devices. Medical practitioners could remotely monitor the condition of their patients' parameters with the use of HIoT. Potential users include seniors, individuals with chronic diseases, and others who need round-the-clock monitoring. There could be significant costs, complicated encryption procedures, and low transaction rates with the present "Elliptic-Curve Cryptography (ECC)"framework. One major issue is that there is now no reliable or affordable means to secure the transfer of EHR data through connected devices. Lower throughput, excessive transaction and signature costs, and ineffective calculation and transmission distinguish the current approaches. The safe sharing of EHR information and its broad adoption are impeded by these obstacles. The present research proposes a new framework called "Block-Chain Lightweight-Signcryption (BC-LS)"which combines LS methods with the latest BC technology to address the existing obstacles. There are two phases involved in the process that make up the suggested BC-LS framework. The initial phase involves the creation of "Wireless Body-Area Networks (WBANs)"for patients through "Neighborhood Sensor-Nodes (NSNs)". A "Cluster-Head (CH)"defined by the nearby NSNs can communicate EHR metadata through the "Gateway SNs (GSNs)"of the WBAN. Following a subsequent phase, the GSN combines encryption of data and signature using an LS approach for approved participants. Regarding secure access with keys and data sharing across public channels, the framework employs the "Inter-Planetary File-System (IPFS)". Using the metrics of "Security Ratio,""Encryption Time,"

关键词： Blockchain

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