A Computational Analysis of ECC Based Novel Authentication Scheme in VANET

A recent development in the adhoc network is a vehicular network called VANET (Vehicular Adhoc Network). Intelligent Transportation System is the Intelligent application of VANET. Due to open nature of VANET attacker can launch various kind of attack. As VANET messages are deal with very crucial information’s which may save the life of passengers by avoiding accidents, save the time of people on a trip, exchange of secret information etc., because of this security is must be in the VANET. To ensure the highest level of security the network should be free from attackers, there by all information pass among nodes in the network must be reliable i.e. should be originated by an authenticated node. Authentication is the first line of security in VANET; it avoids nonregistered vehicle in the network. Previous research come up with some Cryptographic, Trust based, Id based, Group signature based authentication schemes. A speed of authentication and privacy preservation is important parameters in VANET authentication. This paper addresses the computational analysis of authentication schemes based on ECC. We started analysis from comparing plain ECC with our proposed AECC (Adaptive Elliptic Curve Cryptography) and EECC (Enhanced Elliptic Curve Cryptography). The result of analysis shows proposed schemes improve speed and security of authentication. In AECC key size is adaptive i.e. different sizes of keys are generated during key generation phase. Three ranges are specified for key sizes small, large and medium. In EECC we added an extra parameter during transmission of information from the vehicle to RSU for key generation. Schemes of authentications are evaluated by comparative analysis of time required for authentication and key breaking possibilities of keys used in authentication

Lightweight MobileNet Model for Image Tempering Detection

In recent years, there has been a wide range of image manipulation identification challenges and an overview of image tampering detection and the relevance of applying deep learning models such as CNN and MobileNet for this purpose. The discussion then delves into the construction and setup of these models, which includes a block diagram as well as mathematical calculations for each layer. A literature study on Image tampering detection is also included in the discussion, comparing and contrasting various articles and their methodologies. The study then moves on to training and assessment datasets, such as the CASIA v2 dataset, and performance indicators like as accuracy and loss. Lastly, the performance characteristics of the MobileNet and CNN designs are compared. This work focuses on Image tampering detection using convolutional neural networks (CNNs) and the MobileNet architecture. We reviewed the MobileNet architecture's setup and block diagram, as well as its application to Image tampering detection. We also looked at significant literature on Image manipulation detection, such as major studies and their methodologies. Using the CASIA v2 dataset, we evaluated the performance of MobileNet and CNN architectures in terms of accuracy and loss. This paper offered an overview of the usage of deep learning and CNN architectures for image tampering detection and proved their accuracy in detecting manipulated images

Early Breast Cancer Prediction using Machine Learning and Deep Learning Techniques

Breast Cancer (BC) is a considered as one of the utmost lethal diseases across the globe that has a very high morbidity and mortality rate. Accurate and early prediction along with diagnosis is one of the most crucial characteristics for the treatment of Breast Cancer. Doctors can have an edge over Breast cancer if they are able to predict it in its early stages using deep learning and machine learning techniques. This paper proposed consists of comparison between the and accuracy of various machine learning models like Support vector machine (SVM), K-Nearest Neighbours (KNN), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), XGB Classifier and deep learning model of Artificial neural networks (ANN) for the precise detection of breast cancer. The most crucial properties from the database have been chosen using one feature-selection technique. Correlation is also used to choose the most correlated features from the data. Implementing the ANN model consists of one input layer, two hidden layers, and one output layer. All Machine Learning models and ANN model are then applied to selected features. The results demonstrated that the SVM classifier achieved the highest performance with an accuracy of ~98.24%

Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making