Paper 1: Impact of Stress and Anxiety on Cognitive Performance

Subiya Ali Kidwai

DOI: https://doi.org/10.5281/zenodo.15333987

Abstract:

Anxiety and stress are common emotions that anyone may experience throughout their lives. These feelings typically diminish quickly once the underlying trigger is addressed. However, they can also manifest as feeling of fear, worry, and unease without specific or immediate reason. On a cognitive level, stress and anxiety can negatively impact attention, memory, decision-making, and problem-solving skills. Furthermore, anxiety-induced rumination and worry occupy working memory resources. Although mild stress can occasionally be helpful, increased and prolonged stress and anxiety typically negatively affect cognitive functioning by interfering with neural processes and diverting cognitive resources.

Paper 2: Design and Implementation of an Intelligent Loan Eligibility System Using Machine Learning Techniques

Authors : Ayush Kashyap, Lucky Mishra, Ashutosh Mishra and Dr. Peeyush Kumar Pathak

Doi: https://doi.org/10.63920/tjths.42002

Abstract

Machine learning (ML) algorithms can bring revolution in the research field in almost all areas. Processes in numerous industries, including finance, real estate, security, and genomics, are being transformed by machine learning (ML) algorithms. One of the major impediments in the banking sector is the loan approval process. Modern tools like ML models help accelerate, streamline, and increase the precision of loan approval procedures. It will benefit both the client and the bank in terms of time and manpower required for loan eligibility prediction. The entire work is centered on a classification problem and is a form of supervised learning in which it is important to determine whether the loan will be approved or not. Also, it is a predictive modeling problem where a class label is predicted from the input data for a specific sample of input data. In this work, we deployed various ML algorithms to identify the loan approval status and compare the performance of implemented models. The implemented models will attempt to predict our target column on the test dataset using information from the loan eligibility prediction dataset obtained from Kaggle, which includes features like loan amount, number of dependents, and education. The parameters like accuracy, confusion matrix, ROC curve, and precision are measured for specific models whose performance is significant.

Paper 3: An AI-Driven System for Monitoring and Enhancing Remote Work Productivity

Authors : Tushar Singh, Prashant Srivastava, Saif Siddiqui, Nitin Singh, and Bibhuti Bhushan Singh

Doi: https://doi.org/10.63920/tjths.42003

Abstract

The transition to remote work has intensified the need for effective productivity tracking solutions that balance employee performance monitoring with engagement and wellbeing. Traditional productivity monitoring systems suffer from significant limitations, including reliance on subjective assessments, lack of contextual understanding, and limited actionable insights. This research investigates the development and implementation of an AI-enhanced remote work productivity tracker that addresses these challenges through automated data collection, real-time insights, personalized recommendations, and predictive analytics. Our approach leverages machine learning algorithms to process data from multiple work applications, communication platforms, and project management tools, providing a comprehensive overview of employee productivity without compromising privacy or autonomy. Preliminary findings suggest that AI-powered productivity tracking can significantly improve performance management, accountability, and transparency in remote work environments while simultaneously enhancing employee engagement and wellbeing through stress reduction mechanisms, collaboration support, and personalized development opportunities. The research concludes that AI-driven productivity tracking represents a transformative solution for remote workforce management, offering organizations the ability to make data-driven decisions regarding resource allocation, goal alignment, and team dynamics in the evolving landscape of distributed work.

Paper 4: Language Detection: Using Natural Language Processing

Authors : Mukesh prajapati, Alok Mishra, Pintu Verma, Abhishek Yadav, Bibhuti Kumar Bhusan

Doi: https://doi.org/10.63920/tjths.42004

Abstract

Natural language processing (NLP) is a method for correctly identifying text based on the provided content or topic matter. An extensive study will make it simple to interpret any language and comprehend what is being said. Despite the fact that NLP is a challenging technique, notable examples include Siri and Alexa. Natural language detection allows us to determine the language being used in a given document. A Python-written model that has been utilised in this work can be used to analyse the basic linguistics of any language. The "words" that make up sentences are the essential building blocks of knowledge and its expression. Correctly identifying them and comprehending the situation in which they are used are essential. NLP steps in to help us in this circumstance by making it easier for us to identify the linguistics used in a particular piece of information, whether it be written or vocal. NLP gives computers the ability to understand human language and respond correctly, performing language detection for us. The current paper provides a summary of developments in tongue process, including analysis, establishment, various areas of rapid advancement in natural language processing research, development tools, and techniques