Daily Archives: May 29, 2025

Authors: Nishant Verma, Associate Professor Dr. Mehak

Abstract: This paper explores risk management practices in Small and Medium Enterprises (SMEs) through the interdisciplinary lenses of psychology, economics, and linguistics, emphasizing the role of lifelong learning in achieving sustainable futures. By examining how SMEs perceive, communicate, and economically strategize around risk, we uncover the cognitive, communicative, and systemic factors shaping risk resilience. This study draws on empirical data, theoretical frameworks, and case studies to advocate for integrative, adaptive, and continuous learning mechanisms to enhance SMEs’ sustainability and competitiveness in an increasingly volatile global market.Risk management is a critical component of business strategy, particularly for Small and Medium Enterprises (SMES) that often lack the resources of larger corporations. This paper investigates the risk management practices adopted by SMES, exploring their effectiveness, challenges, and the role of organizational culture, awareness, and external support. Using a mixed-methods approach, the study identifies common risks faced by SMES, evaluates current mitigation strategies, and proposes a framework for improved risk management. The findings highlight a need for enhanced awareness, training, and integration of risk management into business planning. This paper investigates the current landscape of risk management practices among SMES, with a focus on how they perceive, assess, and respond to various types of risks. Drawing on a mixed-methods research approach, including quantitative surveys and qualitative interviews with SME owners and managers, the study reveals that while most SMES recognize the existence of critical risks, few possess structured or formal risk management systems. Instead, risk responses are often reactive, ad hoc, and based on the intuition and personal experiences of the business owner rather than systematic analysis.

Published by: vikaspatanker

Authors: Md Shareef, P Sri Sindu, M Surya Teja, B Prasun Reddy

Abstract: Lung cancer remains a leading cause of cancer-related mortality worldwide, underscoring the critical need for effective predictive models to aid in early detection and intervention. This study presents a comprehensive approach to lung cancer prediction, leveraging advanced machine learning techniques and multimodal data integration. By incorporating diverse sources of information, including medical imaging scans, clinical records, and genetic markers, our proposed model aims to capture the complex interplay of factors influencing lung cancer risk. We employ a combination of feature engineering, feature selection, and ensemble learning methods to develop robust predictive models capable of accurately identifying individuals at elevated risk of developing lung cancer. Furthermore, we explore the interpretability of our models to gain insights into the underlying factors driving lung cancer susceptibility. Through extensive experimentation and validation on large-scale datasets, we demonstrate the efficacy of our approach in achieving superior predictive performance compared to existing methods. The proposed model holds significant promise for facilitating early detection, personalized risk assessment, and targeted interventions in lung cancer management, ultimately improving patient outcomes and reducing the burden of this devastating disease.

Published by: vikaspatanker

Authors: Naaz Parween, Ankita Gupta

 

Abstract: Understanding the candidate's personality in the modern world of the business world is like spotting the technical skills, just as critical as the latter. In fact, the personality an individual applies is the key to success in both personal and professional aspects. Hence, this study features a system using machine learning based on personality prediction from CVs in order to cut short the hiring time of the right employee to the required position by evaluation of personality contours of the candidate. More advanced yet with a combination of other techniques as for the classification model with the Big Five Personality Model along with the NLP technique, this method defines the traits such as Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism using keyword analysis only. To discover the machine learning algorithm of the highest quality, we tested various ones such as Logistics Regression, Naive Bayes, k-Nearest Neighbours (KNN), Support Vector Machines (SVM), and Random Forest. Consequently, it was evidenced after the study period that the Random Forest algorithm indeed showed the most precise result of 71%, thus surpassing other methods in the survey. At the time, the proposed system together with the business planning called "the recruitment tool" helps companies find the best candidate; therefore, the use of personality-based hiring becomes a major trend in them. The next step in the evolution process is that we will include a more extensive dataset and make the model more precise for tours.

DOI: http://doi.org/

 

Published by: vikaspatanker