Journal of Management and Service Science (JMSS)

Automation with Reinforcement Learning in Driving

Anushka Tyagi, Prof. (Dr) S.W.A. Rizvi — Mon, 25 Nov 2024 00:00:00 +0530

In recent years, the field of automatic driving technology has grown significantly, with the goal of driving a car without the need for human interaction. Reinforcement learning approaches have been important in this area. The application of reinforce-ment learning to automated driving techniques is examined and discussed in this work. The reinforcement learning process is where the study starts. A specific focus of the architectural framework is creating novel reward functions that promote safe and socially acceptable driving behavior while taking uncertainty factors into account with the use of sophisticated Bayesian neural networks. Understanding the scene, localization and mapping, planning and driving techniques, and control are the main topics of this work. The study also explores the particular complications connected to each of the main components of automated driving. It draws attention to how rein-forcement learning is applied in the field of autonomous driving. Autonomous vehi-cles use reinforcement learning to help them comprehend their surroundings, recog-nize roads with accuracy, drive wisely, and maintain safe control of the vehicle. The implementation and ongoing enhancement of automated driving heavily relies on reinforcement learning, particularly when combined with deep learning. Lastly, a summary and forecast covering the full study round up the publication.

Chatbot: Chatbot Assistant

Rakshita Rajan Singh, Dr. P. Singh — Mon, 25 Nov 2024 00:00:00 +0530

Chatbots are a promising solution for order tracking in the digitally connected world. They offer real-time updates and personalized assistance, reducing the need for customers to constantly check their emails or websites. They also provide 24/7 availability, allowing customers to check their order status or seek assistance at any time. Chatbots also collect valuable data on customer inquiries and behaviors, enabling businesses to identify trends and refine their order-tracking systems. They can seamlessly integrate with existing order management systems, making it easier for businesses to adopt this technology without significant disruptions. However, challenges such as data security, user experience, and conversational skills remain.

Attrition Unveiled: Analyzing Trends and Strategies in Employee Turnover

Aman Shrivastava, Dr Pooja Khanna — Mon, 25 Nov 2024 00:00:00 +0530

This study performs an Exploratory Data Analysis (EDA) on employee attrition data using Python to identify key factors influencing turnover. Employee attrition affects organizational performance, making it crucial to understand its root causes for effective retention strategies. The analysis considers various employee attributes, including demographics (age, gender) and job-related factors (salary, satisfaction, tenure). The process involves data cleaning, followed by univariate, bivariate, and multivariate analyses to explore variable relationships. Visualizations such as bar charts, scatter plots, and heatmaps aid in identifying patterns and high-risk employees. The findings offer actionable recommendations to enhance retention and serve as a foundation for an organization to make data-driven decisions.

Streamlining Information: Creating YouTube Video Summarizer Using Machine Learning

Abhash Srivastava, Dr. Bramah Hazela, Dr. Shikha Singh, Vineet Singh — Mon, 25 Nov 2024 00:00:00 +0530

The aim of this study is to develop a user interface facilitating the retrieval of YouTube video summaries through the integration of Natural Language Processing (NLP) and Machine Learning techniques. With the continuous influx of videos uploaded to YouTube on a daily basis, locating relevant content has become increasingly challenging. Often, significant time and effort are expended in searching for desired content, with outcomes often proving futile due to the inability to extract meaningful information. Our project addresses this issue by providing a solution that efficiently summarizes videos, presenting users with concise yet comprehensive insights. Utilizing an abstractive summarization model, the system extracts transcripts from YouTube videos and generates condensed summaries, effectively reducing the time required for content consumption while preserving crucial information. While the implementation phase is still in progress, this paper presents the conceptual framework and initial findings of our research endeavor.