In India, where over 60% of the youth reside in rural areas, agriculture remains the primary source of livelihood. However, the sector faces a critical challenge: its young workforce is increasingly migrating towards urban areas in search of better opportunities. This research, c...
Open access
Research Article10.9734/ajaees/2024/v42i42399
Supersaturated designs (SSDs) are crucial in factor screening experiments, especially when factor sparsity is assumed, meaning only a few factors are expected to be significant. Building on the foundational work of Jones and Majumdar [1], who introduced the UE(\(S^2 )\) criterio...
Open access
Research Article10.9734/jsrr/2024/v30i82304
This paper explores three pivotal government initiatives in India—Beti Bachao-Beti Padhao (BBBP), Stand-Up India, and Skill India Mission—aimed at enhancing the education and skill development of women. By examining the genesis, objectives, key components, and impacts of each pro...
Open access
Research Article10.9734/ajaees/2024/v42i72521
This review paper explores the impact and contributions of community radio stations in Karnataka, India. Since the inception of community radio in India, these stations have evolved to address various local needs and issues. Karnataka, with its diverse array of community radios,...
Open access
Research Article10.9734/ajaar/2024/v24i8534
B. Manjunatha, Appaji Pundalik Naik, K. R. Mahendra, Manju Prem S., Gunashekhar H., N. R. Kiran, Damodhara G. N. & Karthik R.·Archives of Current Research International·2024
Multi-Dimensional Scaling (MDS) is a data visualization method that identifies clusters of points by representing the distances or dissimilarities between sets of objects in a lower-dimensional space. This paper explores the theoretical concepts of MDS, various methods of impleme...
Open access
Research Article10.9734/acri/2024/v24i6814
B. Manjunatha, Karthik R., N. R. Kiran, Appaji Pundalik Naik, Damodhara G. N., Gunashekhar H., Manju Prem S. & K. R. Mahendra·Journal of Scientific Research and Reports·2024
Hidden Markov Models (HMMs) are effective statistical techniques used to uncover underlying patterns in observable sequential data This paper provides a comprehensive overview of the theoretical foundations of HMMs, including key algorithms essential for their implementation, suc...
Open access
Research Article10.9734/jsrr/2024/v30i82303
This study explores the efficacy of combining forecasts using the median operator to enhance forecasting performance. The traditional approach of assigning equal weights to individual models often struggles with extreme forecasts. A new method Simple Combination of Univariate Mod...
Open access
Research Article10.9734/jeai/2024/v46i82782