Exploring Time Series Techniques in Production Function Modeling: ARIMA and VECM Applications Mohammed Ali Shaik, Abdul Rahim, V Subhalakshmi, Dr R Ravi Kumar, Raghunadh Pasunuri, Dhanraj Verma Proceedings of the International Conference on Intelligent Computing and Control Systems Iciccs 2025, 2025 This paper uses both used ARIMA and VECM models to analyze the production function to determine the interactions between GDP, capital and labor inputs in this analysis. The paper explores short-term dynamics as well as equilibrium behavior of these variables using annual data for the period 1984–2023. The coefficients estimated by the ARIMA (2,0,0) model indicate that current GDP depends on its past levels, and both capital and labor, although they have positive signs, are statistically insignificant. Moreover, Johsen cointegration estimates unfold one co-integrating vector; VECM result also confirms the positive association between capital and GDP but an unexpected negative relation with labor. The above study’s findings reaffirm the status of capital accumulation as a foundation for developing states’ growth policy priorities; the results of labor analysis are inconclusive and may indicate issues such as difficulty in measuring labor or the possibility of structural changes. The policy implications are for the continuous long term orientations, reform of the labor market, improving human capital, and expanding economic diversification. The paper recognizes certain limitations in the type of data and model used and recommends that future research consider non linearities, technological progress to transfer technological improvement to the production function as well as examining sectoral differences for a better understanding of growth paths in fast growing economies.
Eco Predict: AI-Based Air Quality Insights Bomma Ramakrishna, Rachapothu Venkata Manikanta, Muntha Raju, Raghunadh Pasunuri, K. Pushpa Latha, Lakshmanarao Talapakula, Lakshmi Prasanna Byrapuneni, S Suman 2025 2nd International Conference on Integration of Computational Intelligent System Icicis 2025, 2025 Air pollution poses a significant threat to global human health, ecosystems, and the climate, maintaining its status as one of the most pressing environmental challenges. Rapid urbanization, along with emissions from vehicles and industrial activities, contributes to high concentrations of pollutants such as PM2.5, SO2, and O3—key factors in the rise of respiratory and cardiovascular diseases. Traditional air quality monitoring methods, which rely on fixed physical sensors, are often limited in geographic coverage, scalability, and predictive capability. Eco Predict addresses these limitations using machine learning techniques, including random forests, support vector machines, and deep neural networks. These models are further enhanced by integrating feature selection methods and Gravitational Search Optimization to improve accuracy and efficiency. Internet of Things (IoT) devices are utilized to collect real-time pollution data, which is transmitted for analysis and presented via an interactive dashboard. Among the models tested, the Random Forest algorithm achieved the highest accuracy in predicting Air Quality Index (AQI) categories. This proposed system leverages a scalable and intelligent solution for air quality forecasting, delivering actionable insights to public health institutions, environmental agencies.
An optimal proximity method for nearest neighbor search in high dimensional data Raghunadh Pasunuri, Vadlamudi China Venkaiah Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics Ic3i 2016, 2016 Nearest Neighbor Search is the basic operation that has been used to perform similarity search in vast areas like Content-Based Image Retrieval (CBIR), Web Search Engines, Micro Array Data Analysis, Recommender Systems, and many more. In this work we propose a data partitioning method based on multiple reference points. Our method partitions the data into multiple groups based on an optimization criteria. The method works by partitioning the data into disjoint partitions based on the distance from a set of reference points to the data objects. We are able to retrieve the nearest neighbours (kNN) by searching in only a single partition group where all the nearest neighbours lie for the given query according to the distance from a reference point. We have used ZINC, AT & T (formarly ORL), Yale, GCM, Luekemia and Lung Data sets to conduct experiments. We compare the results with the similar methods like mean reference, minmax reference and set of reference points methods. We have tested these three methods with the proposed method on six data sets. From the experimental results we can say that the proposed method is giving promising and better results than the state of the art NN search methods. Proposed method works by pruning the search space and also reduces the computation cost and achieves fast search. Performance of the proposed method is compared with a group of queries and the results are promising.
