The advancement of automated number plate recognition (ANPR) systems has garnered noteworthy attention in recent times owing to their diverse applications across multiple domains, including traffic management, parking...
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Speech Emotion Recognition (SER) has seen much research done recently, but little is being done to minimize the effect of environmental noise on the predictions. Existing SER models primarily aim to learn the best fea...
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The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain *** brain tumor is characterized by an anomalous proliferation of brain c...
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The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain *** brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or *** tumors are misdiagnosed due to the variabil-ity and complexity of lesions,which reduces the survival rate in ***-sis of brain tumors via computer vision algorithms is a challenging *** and classification of brain tumors are currently one of the most essential surgical and pharmaceutical *** brain tumor identi-fication techniques require manual segmentation or handcrafted feature extraction that is error-prone and *** the proposed research work is mainly focused on medical image processing,which takes Magnetic Resonance Imaging(MRI)images as input and performs preprocessing,segmentation,fea-ture extraction,feature selection,similarity measurement,and classification steps for identifying brain ***,the medianfilter is practically applied to the input image to reduce the *** graph-cut segmentation technique is used to segment the tumor *** texture feature is extracted from the output of the segmented *** extracted feature is selected by using the Ant Colony Opti-mization(ACO)algorithm to improve the performance of the classifi*** prob-abilistic approach is used to solve computing *** Euclidean distance is used to calculate the degree of similarity for each extracted *** selected feature value is given to the Relevance Vector Machine(RVM)which is a multi-class classification ***,the tumor is classified as abnormal or *** experimental result reveals that the proposed RVM technique gives a better accuracy range of 98.87%when compared to the traditional Support Vector Machine(SVM)technique.
Most of the traditional cloud-based applications are insecure and difficult to compute the data integrity with variable hash size on heterogeneous supply chain datasets. Also, cloud storage systems are independent of ...
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Research into Medicare fraud detection that utilizes machine learning methodologies is of great national interest due to the significant fiscal ramifications of this type of fraud. Our big data analysis pertains to th...
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A dynamic video summarization system detects key parts of the input video to generate its compact representation. The summaries can be used for efficient management of video data. This paper proposes an approach, Vide...
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Investigating trip purposes represents an important phase of travel demand modeling which allows to correctly infer mobility patterns and to better understand travel behavior. Until now, researchers collected informat...
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Wireless Sensor Networks (WSNs) play an important role in the modern era and security has become an important research area. Intrusion Detection System (IDS) improve network security by monitoring the network state so...
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Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, ...
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The landscape of the Internet of Things is evolving rapidly. The security and efficiency of data handling and device management are multifaceted and complex problems, which are only exacerbated by the huge volumes of ...
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ISBN:
(纸本)9798350387490
The landscape of the Internet of Things is evolving rapidly. The security and efficiency of data handling and device management are multifaceted and complex problems, which are only exacerbated by the huge volumes of data and the sprawling growth of IoT deployments. The goal of this paper is to outline innovative solutions to the most pressing issues in the field through the use of advanced cryptographic protocols and scalable management systems. The integrity and maintainability of IoT ecosystems face threats not only from quantum-capable attackers but also from vendor lock-in, which necessitates special attention. First, the Secure Data Retrieval Encryption system will be presented, which is based on the Learning with Errors assumptions, Self-Managed Public Key Infrastructure, Next-generation Hashed Elliptic Curve Protocol, and Dynamic Unclonable Physical Properties. In a single round of queries, SDRE enables forward security and efficient searches defined by authorized clients. This work presents a distinctive innovation. Initially, the search process was accelerated greatly, and the data being transmitted and decrypted was implemented based on secure computation. In addition, the Self-Managed Public Key Infrastructure protocol is a simple and standard-acquiescent answer for changing control between IoT service providers. SMS-PKI seeks to diminish this necessity of manual effort, especially in light of the massive upsurge in IoT deployments. SM-PKI also mechanizes the IoT PKI credentials updating and trusted domains process, allowing multiple domains to be run on one client apparatus. Tamarin is used to prove security and efficiency, which demonstrates the necessity of using SM-PKI to maintain desired security properties with the minimal cost and device overhead. Finally, a universal authentication scheme grounded on edge computing and involving Next-generation Hashed Elliptic Curve Protocol and Dynamic Unclonable Physical Properties will be shown. This approach
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