RECENT TRENDS IN PROGRAMMING LANGUAGES

From Text to Revolution: Unfolding ChatGPT's Impact on Library Services

Suman Jain — 2024-02-14

This study explores ChatGPT, an advanced language model by OpenAI, and its potential to revolutionize library services. In order to arouse interest in the opportunities it presents, we examine the advantages, difficulties, and ethical issues associated with incorporating ChatGPT into libraries.As we illustrate ChatGPT's technology, we are able to see how well it performs on various language-based tasks. As a result, the user experience for library reference services will be improved. It also promises lightning-fast responses, improved quality, and less work for librarians.However, we face moral challenges with unfairness and privacy, highlighting the demand for a fair and responsible AI tool. We anticipate a successful relationship by balancing ChatGPT's expertise with the irreplaceable touch of human librarians.It is exciting to see how ChatGPT is used in cataloguing, contributing to academic papers, and reference services. Real-world case studies show its effect and provide us with optimum implementation practices.As we look to the future, AI developments provide more applications in user support and research by scholars. Libraries can embark on an exciting journey into an enhanced future with ChatGPT by merging innovation with ethical responsibility.

AI Impact: Unpacking Brain Duplication in Everyday Life

Om K. Siddhapura — 2024-02-09

Brain duplication technique is impactful in several industries to reduce the cost and human efforts. Ai-based systems are being implemented in education, health, technology, manufacturing, production and other sectors. Hence, the following work has developed a clear understanding of the brain duplication technique and its impact on human activities.The essential elements have encapsulated the utilization of computer algorithms like machine learning, natural language processing, deep learning, and similar technologies.Hence, the impact on human activities has been produced which developed the statement of how AI has been replacing human jobs as well as finding opportunities for new job sectors.Furthermore, a brief visualization has also displayed the data of recent pictures while comparing with past years as how AI has been implemented in job sectors. On the other hand, implications and discussion have been made to understand the key findings of the research. Moreover, a recommendation has shown the suggestion to improve the abilities in future use.

A Review: Identification of Credit Card Fraud Using Machine Learning and Anomaly Detection Approach’s on Imbalanced Data

Ayush Bilgaiyan — 2024-02-26

Credit cards are among the most widely used payment methods in today's culture, and online purchases have become quite popular. Credit card fraud has emerged as a problem in this industry as a direct result of its popularity. The issue of credit card fraud is becoming a global problem. Credit card fraud has increased due to the widespread use of these payment methods. Thanks to credit card use, e-commerce has flourished, and the infrastructure for electronic payments has become more user-friendly. To combat fraud, machine learning techniques are being used on a larger scale.When it comes to inspecting customer data, ML algorithms are crucial. Security of credit card transactions and the efficiency of online banking are, therefore, primary concerns for financial organizations. They want to do this by creating more effective methods of detecting fraudulent transactions, which will lead to a reduction in overall fraud. This study aims to define fraud detection, provide fraud detection methods, discuss banking industry fraud difficulties and challenges, and outline contemporary solutions based on ML techniques. This work presents an analysis of the existing literature on machine learning and anomaly detection methods utilized in credit card fraud detection (CCFD).As far as ML is concerned, it is the best way to guarantee privacy while increasing CCFD accuracy.Different class imbalances, machine learning, data mining, as well as anomaly detection techniques have been reviewed and compared in the domain of fraud detection systems.This review provides the overview of CCFD, key features, trends, anomaly detection techniques, machine learning techniques, class imbalance problems and existing work on CCFD.

The Evolution of Programming Languages: A Survey from Early Achievements to Modern Advancements

Ushaa Eswaran — 2024-02-09

Programming languages are fundamental to software development, providing the means for developers to communicate instructions to computers. This study provides a broad overview of programming languages and their history, classifications, key elements, the compiler and interpreter systems that implement them, and notable trends driving innovation. It examines imperative, object-oriented, functional, logical, and scripting languages while exploring syntax, semantics, typing systems, abstraction, variables and bindings, control flow, and runtime environment considerations. The compilation and interpretation processes that translate high-level languages into machine code are delineated. Significant programming paradigms like procedural, structured, modular, declarative, concurrent, and event-driven models are surveyed. Modern developments such as increased support for parallelism and domains like artificial intelligence and data science have become robust areas of focus. The study synthesizes concepts central to understanding the landscape of computer languages spanning from foundational machinations to state-of-the-art advancements.

A Comprehensive Review on Python based NLP Approaches for Sentiment Analysis

Rajesh Yadav — 2023-12-20

Sentiment analysis, also known as opinion mining, is a burgeoning field in natural language processing that involves the computational analysis of textual data to determine and quantify the sentiment expressed within. This study presents a comprehensive exploration of sentiment analysis using Python, focusing on techniques, methodologies, and tools that enable the extraction of sentiment polarity from diverse textual sources. The review commences with an overview of the importance of sentiment analysis in understanding public opinion, customer feedback, and social media dynamics. A significant portion of the study is dedicated to practical implementation using Python, showcasing popular libraries such as NLTK, TextBlob. It also covers portions such as summarization, challenges in sentiment analysis, and applications.