In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from or...
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ISBN:
(数字)9798331534967
ISBN:
(纸本)9798331534974
In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from original look into Strawberry corrected one. Key part of ourresearch is to preprocess the images by using Gaussian preprocessing at initial front levels followed with Gabor filtering which improves clarity and focus in x-ray image. Findings also contribute to the field of biomedical science in studies related to lung cancerdiagnosis, by proving that advanced image processing techniques are useful. These techniques are essential tools in many medical fields ensuring high quality imaging, which allows early detection and treatment planning. Lung cancer, characterized by rapid uncontrolled proliferation of cells in the lungs with a potential fordistant spread either via blood or lymphatic system requires precise diagnostic techniques. After preprocessing, we apply segmentation and enhancement techniques on images to detect the salient features separating normal from affected lung tissues. Extensive experiments are conducted to validate our method compared with other methods using pixel level percentage and mask labels for anomaly detection. This work highlights the importance of a robust imaging approach for health care, especially in biomedical research where various diseases could be detected and treated.
Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing ...
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ISBN:
(数字)9798331518097
ISBN:
(纸本)9798331518103
Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts andreducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture.
Cloud computing is transforming the healthcare industry by providing scalable, and cost-effective solutions for storage, analysis, and access to health information and services. This comprehensive review examines the ...
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As the retail industry continues to evolve in the digital era, businesses are exploring innovative solutions to enhance customer engagement and loyalty. This research proposes a novel approach to loyalty programs by l...
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ISBN:
(数字)9798350305463
ISBN:
(纸本)9798350305470
As the retail industry continues to evolve in the digital era, businesses are exploring innovative solutions to enhance customer engagement and loyalty. This research proposes a novel approach to loyalty programs by leveraging blockchain technology to create a secure and transparent model for customerrewards. Traditional loyalty programs often face challenges related to security, transparency, and trust, leading to issues such as fraud and customer skepticism. The proposed model outlines the design and implementation of a blockchainbased loyalty program, ensuring a secure and immutable ledger for tracking customer transactions andrewards. Smart contracts, self-executing contracts with predefinedrules, are utilized to automate and enforce the terms of the loyalty program, reducing the potential fordisputes and enhancing transparency. The decentralized nature of the blockchain ensures that customerdata is securely stored and accessible only to authorized parties, addressing privacy concerns prevalent in centralized loyalty systems. This research employs a mix of theoretical analysis and practical implementation to validate the feasibility and effectiveness of the blockchain-based loyalty program model. The proposed Ethereum Blockchain model stands out with a significantly higher throughput of 5300 transactions perday, accompanied by an exceptional transaction transparency score of 99.5 .
OLAP-based middleware for parallel OLAP query processing is presented in this study, which builds on previous research results to provide two new areas of authority application built on top of OLAP. This multi-faceted...
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Purpose: To improve the reliability and performance of Large Language Models (LLMs) in extracting structureddata from radiology reports, particularly in domains with complex and non-English texts (e.g., Hebrew), by i...
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Credit card fraud poses a serious threat to financial institutions and their customers; hence, stringent detection protocols are necessary. This study introduces an approach known as Enhanced Learning for Credit Card ...
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ISBN:
(数字)9798350367171
ISBN:
(纸本)9798350367188
Credit card fraud poses a serious threat to financial institutions and their customers; hence, stringent detection protocols are necessary. This study introduces an approach known as Enhanced Learning for Credit Card Frauddetection (ELCCFd) to enhance the accuracy of credit card frauddetection. To improve the frauddetection process, the proposed method combines the strengths of Convolutional Neural Networks (CNNs), AlexNet architecture, and Gradient Boosting Machines (GBM). The proposed approach begins with cleaning up the credit carddata to get useful features, then trains a Convolutional Neural Network (CNN) using AlexNet to figure out complex patterns andrepresentations on its own. This study generates a complete set of features by merging the CNN’s output with features generated using GBM. The final model is trained by using a combination of deep learning and other conventional machine learning techniques to achieve the best results. Experimental findings on benchmark datasets demonstrate the effectiveness of the ELCCFd methodology, achieving an accuracy rate of 98%. This study combines AlexNet with GBM to get a model to capture the complex patterns and is easier to understand with the feature importance analysis. With its strong accuracy andreliability, the proposed methodology offers a strong option to fight credit card fraud, and it shows the potential for actual use in financial systems.
In the field of cloud computing, ensuring secure and efficient key agreement among multiple parties has emerged as a paramount challenge. Traditional key agreement protocols often rely on central authorities or truste...
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ISBN:
(数字)9798350379990
ISBN:
(纸本)9798350391558
In the field of cloud computing, ensuring secure and efficient key agreement among multiple parties has emerged as a paramount challenge. Traditional key agreement protocols often rely on central authorities or trusted third parties, posing significant security and privacy concerns. To address these challenges, this paper introduces a novel key agreement protocol designed specifically for cloud computing platforms, emphasizing security, efficiency, andresilience without depending on a trusted third party. The proposed protocol innovatively combines distributed Key Generation (dKG) with a dynamic Consensus Mechanism, Zero-Knowledge Proof (ZKP) based authentication, and a Multi-Cloudredundancy approach, offering a comprehensive solution to secure multi-party communication in distributed cloud environments. The dKG protocol facilitates the collaborative generation of a shared secret among participants, significantly enhancing security by eliminating single points of failure. The proposeddynamic Consensus Mechanism ensures the integrity and finality of key agreement transactions on a blockchain-based ledger, adapting to network conditions and participant trust levels to optimize performance without compromising security. ZKP-based authentication allows participants to verify their identities without revealing sensitive information, preserving privacy and thwarting impersonation attacks. Lastly, the Multi-Cloudredundancy strategy enhances the protocol's resilience to cloud-specific vulnerabilities and service outages, ensuring high availability androbustness.
The transmission of information via a variety of channels, such as spoken, written, and visual interaction, has been a defining characteristic of the trajectory of human development. The manner in which information is...
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ISBN:
(数字)9798331533663
ISBN:
(纸本)9798331533670
The transmission of information via a variety of channels, such as spoken, written, and visual interaction, has been a defining characteristic of the trajectory of human development. The manner in which information is sent has a substantial influence on the retention and comprehension of information, especially in situations where context-specific, based on classes, and semantic connections are at play. The interpolation method algorithms have advanced to the point where they allow for the preservation of data usefulness and integrity while simultaneously guaranteeing semantic congruence and confidentiality. In a similar manner, semantic analysis is performed on text that has been extracted from photographs in order to analyse the parallels in composition and interpretation. A further factor that contributes to the complexity of auditory and textual features is the presence of distinctive dialectal tendencies and climatic fluctuations, both of which provide difficulties for algorithmic learners. The incorporation of prosody into learnt variables and taxonomies makes it easier to find interesting things inside data, despite the difficulties that are involved. This ontology-driven method provides a solid foundation for cutting-edge technology and interoperability due to the fact that it incorporates insights from text, audio, and graphical data. The framework that has been suggested enables smooth information distribution, better privacy, and semantic comprehension by synthesized multiple modalities of data. When compared to other approaches, the techniques that were recommended perform very well in all four criteria and have an accuracy rate of 92.8%.
A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project impl...
A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements deep ConvNet2d (Convolutional Network 2d) and Computer Vision emergency vehicle recognition. We propose a CNN-basedreal-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the roundrobin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our methoddetects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses deep ConvNet2d Algorithm. The proposedreal-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases.
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