Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Emerging Advancements in Sustainable Entrepreneurship: A Systematic Literature Review and Bibliometric Analysis(Springer Nature, 2023) Mishra, R.; Kiran, K.B.Entrepreneurship has been recognized as a crucial driver for a nation’s economic growth, wherein the entrepreneurs exploit social and environmental assets to generate financial gains, making them accountable to the community and the environment. The current state of the environment, the socioeconomic divide, and insufficient access to resources and opportunities are the primary concerns. Recent academic works have highlighted the concept of sustainable entrepreneurship as a solution to minimizing the negative effects of entrepreneurial industries on environmental deterioration and social inequality. Scholarly interest in this field has grown in recent years as a result of its ability to incorporate the triple bottom line of people, planet, and profit. However, there is still a lack of awareness and understanding of nature and the future of sustainable entrepreneurship in both theory and practice. Therefore, this systematic review paper along with the bibliometric analysis aims to show how sustainable entrepreneurship has changed after the introduction of Sustainable Development Goals (SDGs), focusing on recent research trends. This literature review will reveal the trend and ascertain the future directions for novel and senior researchers in the field of sustainable entrepreneurship. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.Item Exploring Hidden Behaviors in OpenMP Multi-threaded Applications for Anomaly Detection in HPC Environments(Springer Science and Business Media Deutschland GmbH, 2025) Bhowmik, B.; Girish, K.K.; Mishra, P.; Mishra, R.In high-performance computing (HPC), multi-threaded applications using OpenMP face complex challenges in identifying hidden performance issues, often due to resource conflicts, software inefficiencies, and hardware anomalies. These subtle issues can significantly degrade performance and reduce system reliability. This paper introduces an innovative approach designed to address these concealed issues in OpenMP multi-threaded applications. The proposed method integrates a Random Forest classifier with anthropomorphic diagnosis to effectively identify and diagnose performance-affecting problems. The approach has demonstrated a remarkable ability to detect 90% of performance-affecting issues that are often obscured within complex HPC environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.