As the issue of agricultural sustainability has continued to increase, there has been a need to use data based solutions to improve agricultural productivity. This paper proposes a machine learning system combining Random Forest and XGBoost to combine prediction-forecasting crop yield and classification of crop type rice and wheat in Indian state of Uttarakhand. The model is tested using a library of 6, 000 samples containing 12 soil and climatic characteristics and measured on regression and classification quality. The hybrid ensemble with hyperparameter optimization and verified on the basis of 10-fold cross-validation performed better than single base learners in all measures. It achieved a classification accuracy of 96.3 and R2 = 0.927. Statistically significant developments that were formed using paired t-tests were set at p = 0.05. The SHAP and ablation analysis found out nitrogen, rainfall, and pH as the most influential features. The forecasted framework provides a better generalizability, interpretability, and computational effectiveness, which is appropriate to be applied in the designs of real life in precision agronomy. The new result is novel, interpretable, and high-performative to crop yield intelligence in data-scarce areas and provides a contribution to this study.
The study compares the capabilities of various time series and machine learning models including ARIMA, LSTM, CatBoost, XGBoost, and LightGBM, by predicting the equity movement for major Indian infrastructure and energy companies with hydrogen related exposure, namely Larsen & Toubro, NTPC Limited, JSW Energy Limited, and Adani Green Energy Limited. Hydrogen fuel is considered the most promising energy provider of the future, and an understanding of its position in the market is vital for its growth. The study uses historical data involving stock prices from April 2019 through April 2024 obtained from the National Stock Exchange of India. Using open price as the primary variable, the performance of the models is measured. Additional variables such as close price, highest price, lowest price, and volume are used for gradient boosting. Output graphs comparing actual prices and predicted prices are obtained. The results indicate that deep learning and gradient boosting outperform the statistical model. LSTM demonstrated the strongest short-term predictive accuracy through sequential learning among all models. Among the gradient boosting models, LightGBM provides consistent and robust performance by effectively capturing nonlinear feature interactions. Overall, the study highlights the growing importance of machine learning in interpreting India’s renewable energy equity markets.
Reliable and efficient functioning of thermal power plants is necessary for a steady power supply and economic viability. In the present research, the reliability and availability of a subsystem of a thermal power plant are modelled through a Continuous-Time Markov Process (CTMP), which reflects the stochastic change from working to failed states. Human error is incorporated as an additional failure factor to reflect practical operating conditions. To further improve performance, a Particle Swarm Optimization (PSO) algorithm, a nature-inspired metaheuristic approach, is used to maximize system availability. In addition to enhancing availability, the research uses PSO to maximize the expected profit of the system using a unified economic objective function. The Markov-based model yields an initial system availability of 0.9117. After applying Particle Swarm Optimization, the availability improves to 0.9199 with variation in population size and further increases to 0.9240 with variation in the number of iterations, representing an overall enhancement of approximately 1.23%. The optimized results also increase the expected profit. The outcomes demonstrate that the integration of Markov modelling with PSO ensures accurate reliability and availability analysis and provides a robust framework for economic optimization.
Medical imaging is essential for obtaining precise information to assess patient health and provide effective treatment. The initial analysis of a wide range of medical images is vital for identifying abnormalities. However, limitations during the image acquisition process can lead to poor image quality. To address the issues of reduced information and contrast in medical images, the proposed method is designed. The method integrates the spatial frequencies (SF) of both the original image and the image enhanced through Triple Clipped Dynamic Histogram Equalization (TCDHE). Additionally, the discrete wavelet transform and singular value decomposition are applied simultaneously to both images to obtain an improvement factor (gamma), which controls the contrast enhancement rate. The use of spatial frequencies helps preserve the detailed components of an image, such as edges and sharp features. For experimental purposes, three types of datasets are utilized, including MRI, X-ray, and ultrasound. Objective evaluation is performed using seven performance metrics: AMBE, PSNR, SSIM, GMSD, REC, entropy, and subjective evaluation with a mean opinion score. The experimental results demonstrate that AMBE (4.08), SSIM (0.99), PSNR (37.67 dB), and GMSD (0.13) are the best among state-of-the-art methods. However, the results for REC and entropy are comparable to those of state-of-the-art methods. Furthermore, the average values of all performance parameters have been computed across three categories of medical datasets to demonstrate the efficacy of the proposed method.
Supply Chains (SC) are undergoing rapid transformation with the advent of Industry 4.0 (I4.0) technologies. In highly dynamic and volatile market conditions, Supply Chain Flexibility (SCF) that is the capability to adapt and respond to dynamic changes in the market environment and Supply Chain Sustainability (SCS) that is the ability of managing SCs to meet sustainability requirements and improve performance have become strategically critical. Despite growing interest, limited research has quantitatively examined which factors create the greatest synergy between SCF and SCS. This study addresses this gap by quantitatively prioritizing Organizational Practices (OP) and Underlying Drivers (UD) in the SC of pharmaceutical context using the Analytic Hierarchy Process (AHP). Findings reveal that OP such as ‘Integration of sustainability principles into SCF’ and ‘Instrumental, interconnected and intelligent SC’ holds the highest relative importance, demonstrating its dominant influence on the decision context and UD such as ‘Transparency, Traceability and Visibility’ holds the highest relative significance, exhibiting its dominant effect in the decision environment, followed by ‘Technological changes/ adoption’ and ‘Resilience’ have the highest impact on achieving simultaneous improvements in flexibility and sustainability. The results provide a structured decision hierarchy, highlighting actionable areas for managerial investment and resource allocation. By identifying and ranking these key enablers, the study demonstrates that SCF and SCS can co-evolve rather than compete, offering both practical guidance for pharmaceutical managers and a foundation for future research on optimizing SC performance in the digital era.
