NEW FIXED POINT RESULTS FOR T-CONTRACTIVE MAPPING WITH c-DISTANCE IN CONE METRIC SPACES

In this article, we generalize and improve the results of Fadail et al.[Z. M. Fadail and S. M. Abusalim, Int. Jour. of Math. Anal., Vol. 11, No. 8(2017), pp. 397-405.] and Dubey et al.[AnilKumar Dubey and Urmila Mishra, Non. Func. Anal. Appl., Vol. 22, No. 2(2017), pp 275-286.] under the concept of a c-distance in cone metric spaces. We prove the existence and uniqueness of the fixed point for T -contractive type mapping under the concept of c-distance in cone metric spaces

Phishing Detection using Base Classifier and Ensemble Technique

Phishing attacks continue to pose a significant threat in today's digital landscape, with both individuals and organizations falling victim to these attacks on a regular basis. One of the primary methods used to carry out phishing attacks is through the use of phishing websites, which are designed to look like legitimate sites in order to trick users into giving away their personal information, including sensitive data such as credit card details and passwords. This research paper proposes a model that utilizes several benchmark classifiers, including LR, Bagging, RF, K-NN, DT, SVM, and Adaboost, to accurately identify and classify phishing websites based on accuracy, precision, recall, f1-score, and confusion matrix. Additionally, a meta-learner and stacking model were combined to identify phishing websites in existing systems. The proposed ensemble learning approach using stack-based meta-learners proved to be highly effective in identifying both legitimate and phishing websites, achieving an accuracy rate of up to 97.19%, with precision, recall, and f1 scores of 97%, 98%, and 98%, respectively. Thus, it is recommended that ensemble learning, particularly with stacking and its meta-learner variations, be implemented to detect and prevent phishing attacks and other digital cyber threats

A study on effect of eWOM information on purchase intention for electric vehicles

Purpose- Today, customers play an active role in creating, generating, and sharing the electronic Word of mouth (eWOM). As a result, attracting customers through recommendations and WOM has become an important goal for businesses. In addition, Social Networking Sites (SNSs) have created valuable opportunities for eWOM. As a result, the development of Electric Vehicles (EVs) is essential. This study shows how eWOM information affects customers' purchase intention for EVs on social networking sites. Design/methodology/approach- This study employed the Information Adoption Model (IAM). Data was collected using a self-administered questionnaire from 266 respondents in Hyderabad and Secunderabad to evaluate the proposed model using SmartPLS software. Findings –The findings show that information quality and Credibility positively affect the usefulness of the information. Furthermore, information adoption determines purchasing intentions, with information usefulness as a predictor for adoption.  Research limitations/implications- This study aims to fill a gap in the research on SNSs, specifically in the context of eWOM information. The study proposes the IAM model and statistically confirms the hypothesized relationship. This study can be used as a platform for further studies. Practical implications-&nbsp

-an experience of two cases

ABSTRACT Tongue abscess is a rare entity, despite exposure to large number of potential pathogen, relatively resistant to infection. Here in this article we were discussed the two cases of tongue abscess in a young females, with their clinical presentation, differential diagnoses, management and a review of literature