Daily Archives: June 16, 2025

Authors: Mr. Uppala Haresh, Assistant Professor Mrs. Perla Ratna Kumari

Abstract: Recent years have witnessed a notable rise in phishing attacks targeting websites. Many researchers have developed tools aimed at identifying such fraudulent sites. Nevertheless, these tools are not fully capable of recognizing all threats. There are several minor challenges in detecting fake websites. Therefore, incorporating machine learning techniques into the detection process is the most effective approach. This enhances the overall accuracy of the project. Moreover, it allows for more efficient computation. Utilizing machine learning methods can also help tackle the challenges posed by existing phishing detection models. The main objective of this project is to use the dataset designed to train the ENASSEMBLE Machine Learning (ML) model for identifying phishing websites.

 

 

Published by: vikaspatanker

Authors: Sneha Sankeshwari, Santosh Gaikwad, Arshiya Khan, R.S. Deshpande

Abstract: Lung cancer is one of the leading causes of cancer-related mortality worldwide, primarily due to delayed diagnosis and limited access to timely screening. Early detection is essential for improving survival outcomes, yet conventional diagnostic techniques such as CT scans, X-rays, and biopsies are often expensive, time-consuming, and not readily available in all healthcare settings. This study explores the potential of machine learning (ML) techniques in facilitating early and accurate lung cancer prediction by leveraging structured patient data, including age, smoking history, environmental exposures, and family medical background. Various ML models—including Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines—are evaluated for their effectiveness in identifying high-risk individuals. Publicly available datasets, such as the UCI Lung Cancer Dataset, SEER database, and PLCO trial data, are utilized for training and validation. The study also addresses key challenges in ML-based diagnosis, including data imbalance, feature selection, and model interpretability. Additionally, future research directions are highlighted, particularly the integration of multi-modal data and the deployment of interpretable AI solutions in clinical practice. The findings underscore the promise of ML in making lung cancer detection more accessible, efficient, and cost-effective, ultimately contributing to reduced mortality rates.

 

 

Published by: vikaspatanker

Authors: Raghu V Kaspa

 

Abstract: High Performing Organizations (HPOs) consistently outperform their peers in metrics such as innovation, agility, financial results, and employee engagement. This paper explores the critical attributes that characterize HPOs and applies these attributes to Tesla, Inc., as a case study. Through an analytical lens grounded in organizational theory, performance frameworks, and empirical evidence, Tesla’s rise as a global automotive and energy leader is examined to identify the drivers of its high performance.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.154

 

Published by: vikaspatanker

Authors: Rushali Manwatkar, Saloni Jaiswal, Professor Yogesh Patidar

Abstract: Image retrieval is a poor stepchild to other forms of information retrieval (IR). Image retrieval has been one of the most interesting and research areas in the field of computer vision over the last decades. Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. Colour, shape and texture features are important properties in content-based image retrieval systems. In this paper, we have mentioned detailed classification of CBIR system. We have defined different techniques as well as the combinations of them to improve the performance. We have also defined the effect of different matching techniques on the retrieval process. Most content-based image retrievals (CBIR) use color as image features. However, image retrieval using color features often gives disappointing results because in many cases, images with similar colors do not have similar content. Color methods incorporating spatial information have been proposed to solve this problem, however, these methods often result in very high dimensions of features which drastically slow down the retrieval speed. In this paper, a method combining color, shape and texture features of image is proposed to improve the retrieval performance. Given a query, images in the database are firstly ranked using color features. Then the top ranked images are re-ranked according to their texture features. Results show the second process improves retrieval performance significantly.

 

 

Published by: vikaspatanker