Understanding the Potential Risks and Uses of SLS in Daily Life
A popular chemical in personal care goods like shampoos and cleaning solutions is sodium lauryl sulfate, or SLS. Its emulsifying and foaming qualities make it valuable. Many Concerns have been voiced about its possible negative effects on health, especially skin irritation, as well as its environmental impact. This paper discusses the history of SLS, its uses, and the safety issues that surround it. Further covered is the topic of substitute ingredients for SLS, especially in the cosmetics sector. Despite SLS's effectiveness, these results imply that safer and more environmentally friendly options exist.
Published by: Tunisha Chaudhary
Author: Tunisha Chaudhary
Paper ID: V10I5-1271
Paper Status: published
Published: October 4, 2024
Categorization and Forecasting of Hepatitis C Diagnosis via an Unconventional Consensus Classifier
Liver diseases are increasingly becoming one of the most fatal health conditions in several countries, especially after Covid-19 (i.e., after 2019) and the prevalence of liver disease has been rising since then due to factors such as excessive alcohol consumption, inhalation of harmful gases, and the intake of contaminated food, pickles, drugs and medications and not to miss, also due to the Covid-19 virus. To address this issue, several multimodal data are collected and given as input to build categorization and forecasting models aimed at predicting liver diseases, especially Hepatitis C and, by utilizing machine learning approaches, we comprehensively assess the patients' liver conditions and the stage of Hepatitis C. We first categorize the results into positive and negative outcomes using rudimentary machine learning algorithms. As we process the liver parameters and their percentages, we present the results as votes derived using the Unconventional Consensus Classifier Algorithm to classify the stages of Hepatitis C. This project aims to develop a robust machine-learning model for the categorization and forecasting of liver disease diagnosis. Leveraging various machine learning algorithms, including decision trees, support vector machines, and so on, the project focuses on accurately predicting liver disease based on a set of medical and demographic features. By analyzing the available existing data and utilizing advanced data preprocessing and feature engineering methods, the proposed system seeks to assist healthcare professionals in early diagnosis and treatment planning, ultimately improving patient outcomes.
Published by: S. Sri Krishna, N T Sunil Kumar, K S Saran, Dr. B. Aarthi
Author: S. Sri Krishna
Paper ID: V10I5-1273
Paper Status: published
Published: October 4, 2024
Efficient Calorie Counter
This paper presents a novel formula for calculating total calories burned, incorporating both physical activity and basal metabolic rate (BMR). The model integrates mass, time, distance covered, and heart rate intensity into a comprehensive equation. By adjusting for heart rate zones and converting energy units from joules to calories, the formula provides a more personalized estimate of energy expenditure. This approach improves accuracy by accounting for individual metabolic differences, aiming to enhance current methods for fitness tracking and calorie estimation.
Published by: Makwana Krishna
Author: Makwana Krishna
Paper ID: V10I5-1264
Paper Status: published
Published: October 3, 2024
Data Mining
Data mining is the process of discovering patterns, correlations, and anomalies within large datasets to predict outcomes. By applying a variety of techniques from statistics, machine learning, and database systems, data mining transforms raw data into valuable insights. This paper explores the methodologies and applications of data mining, highlighting its significance in fields such as finance, healthcare, and marketing. Key techniques discussed include classification, clustering, regression, and association rule learning. The study also addresses the challenges and future directions in data mining, emphasizing the need for scalable and efficient algorithms to handle the ever-increasing volume of data.
Published by: V.Jyothika, A.MEENA
Author: V.Jyothika
Paper ID: V10I5-1252
Paper Status: published
Published: October 2, 2024
Adoption and Impact of Cloud Computing in Enterprise and Business Management: A Literature Survey
Cloud computing has emerged as a transformative force in enterprise and business management, offering scalable, flexible, and cost-effective solutions. This literature review examines the adoption patterns and impact of cloud computing across various business environments, including large enterprises, small and medium-sized enterprises (SMEs), human resource (HR) management, and Enterprise Resource Planning (ERP) systems. The review reveals that cloud computing enables organizations to streamline processes, reduce overhead costs, and better manage resources. Cloud ERP systems improve operational workflows and boost productivity, while cloud-based HRMS enhance the flexibility and scalability of HR functions. Successful cloud adoption requires strong top management support and robust security frameworks. As businesses increasingly turn to cloud technologies for competitive advantages, developing advanced frameworks and solutions that address the unique challenges of SMEs and dynamic HR environments will be crucial. Cloud computing is poised to continue playing a transformative role in shaping the future of business management, offering unprecedented opportunities for efficiency and growth in a rapidly evolving technological landscape.
Published by: Spoorthy Reddy Maguluri
Author: Spoorthy Reddy Maguluri
Paper ID: V10I5-1256
Paper Status: published
Published: October 2, 2024
The Rise and Impact of Deepfakes: A Comprehensive Analysis of Detection Criteria and Societal Implications
Some believe that the new era of deepfake technology has improved digital media, but others believe it has brought up major risks as well as creative opportunities. This study offers an investigation of deepfakes, concentrating on the detection criteria found by analyzing more than a thousand movies that were selected from Kaggle datasets. The study is based on formulae for inconsistent lighting and shadows, visual transitions, and auditory synchronization.
Published by: Samayra Chawla
Author: Samayra Chawla
Paper ID: V10I5-1236
Paper Status: published
Published: September 28, 2024
