Flight delays continue to pose a substantial concern in the aviation sector, impacting both operational efficiency and passenger satisfaction. Existing systems, while attempting to predict delays, often lack accurate predictive capabilities due to poor modeling setups, insufficient feature engineering, and inadequate feature selection processes, leading to suboptimal predictions and ineffective decision-making. Precisely forecasting flight arrival delays is essential for improving airline scheduling and resource allocation. The aim of our research is to create a superior prediction model that surpasses current modeling approaches. This study aims to forecast airline arrival delays by examining data from five prominent U.S. states in 2023—California (CA), Texas (TX), Florida (FL), New York (NY), and Georgia (GA). Our proposed modeling approach involves feature engineering to identify significant variables, followed by a novel feature selection algorithm (CFS) designed to retain only the most relevant features. Delay forecasts were generated using our proposed Deep Feed Forward Regression Network (DFFRN), a five-layer deep learning approach designed to enhance predictive accuracy by incorporating extensively selected features. The findings indicate that the DFFRN model substantially outperformed conventional models documented in the literature. The DFFRN had the highest R² score (99.916%), indicating exceptional predictive efficacy, highlighting the efficacy of the DFFRN model for predicting flight delays and establishing it as a significant asset for improving decision-making and minimizing operational delays in the aviation sector.

Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability across geographically distributed agrarian systems. To transcend these limitations, we propose DERIENet, a robust and scalable classification approach within a deep ensemble learning framework. It is meticulously engineered by integrating three high-performing convolutional neural networks—ResNet50, InceptionV3, and EfficientNetB0—along with regularization, batch normalization, and dropout strategies, to accurately classify jute leaf diseases such as Cercospora Leaf Spot, Golden Mosaic Virus, and healthy leaves. A key methodological contribution is the design of a novel augmentation pipeline, termed Geometric Localized Occlusion and Adaptive Rescaling (GLOAR), which dynamically modulates photometric and geometric distortions based on image entropy and luminance to synthetically upscale a limited dataset (920 images) into a significantly enriched and diverse dataset of 7800 samples, thereby mitigating overfitting and enhancing domain generalizability. Empirical evaluation, utilizing a comprehensive set of performance metrics—accuracy, precision, recall, F1-score, confusion matrices, and ROC curves—demonstrates that DERIENet achieves a state-of-the-art classification accuracy of 99.89%, with macro-averaged and weighted average precision, recall, and F1-score uniformly at 99.89%, and an AUC of 1.0 across all disease categories. The reliability of the model is validated by the confusion matrix, which shows that 899 out of 900 test images were correctly identified and that there was only one misclassification. Comparative evaluations of the various ensemble baselines, such as DenseNet201, MobileNetV2, and VGG16, and individual base learners demonstrate that DERIENet performs noticeably superior to all baseline models. It provides a highly interpretable, deployment-ready, and computationally efficient architecture that is ideal for integrating into edge or mobile platforms to facilitate in situ, real-time disease diagnostics in precision agriculture.

Data-driven approaches are widely used to improve the productivity of organizations. The availability of data related to business requirements and employee benefits even allowed us to predict productivity at the employee level. In our study, we investigated several machine learning approaches to predict productivity and estimation approaches to estimate team operating factors such as the amount of incentive and overtime that ensure a target productivity by the employees of a garment industry. This study used 3 months of data from a garment industry that recorded department ID, standard minute value, incentive, overtime, number of workers, and actual productivity. Our main contribution was to find the best estimation approach integrated with the best machine learning technique that recommends the required amount of incentive and overtime for a business to obtain a target productivity. For estimating business requirements, we explored the performance of Simulated Annealing, Particle Filter (PF), Fixed Point Particle Filter, and Particle Swarm Optimization algorithms. The PF displayed notable accuracy for incentive and overtime estimation, achieving the highest R-squared value of 0.60 for incentive estimation and 0.08 for overtime estimation. Additionally, the proposed machine learning model outperformed the existing Random Forest and Deep Learning model with a minimalist MAPE (Mean Absolute Percentage Error) of 6.5906. Our study concluded that the PF estimation algorithm has a strong potential to find the garment industry's business requirements.

Accurate and reliable plant disease detection systems have to be implemented for optimizing agricultural profitability and preserving global food security. Deep learning-based image classification has shown significant potential in addressing these challenges. However, its application in resource-limited demands of ecological responsibility solutions that are fast, accurate, and computationally efficient. In order to satisfy these requirements, we have employed a compact transfer learning architecture for rice leaf disease identification that uses nine disease classes. The framework features preprocessing approaches such as illumination correction to enhance image quality. The method we propose integrates a tailored classifier network with a pretrained EfficientNetB0 model for optimal feature extraction and precise classification. With the objective to ensure its efficacy, we evaluate the proposed framework against multiple transfer learning models, including DenseNet121, VGG16, InceptionV3, MobileNetV2, and ResNet50. In accordance with the experimental results, the proposed customized EfficientNetB0 framework exceeds the other frameworks, attaining 98.16% training accuracy and 94.47% testing accuracy. In comparison, DenseNet121, VGG16, InceptionV3, MobileNetV2, and ResNet50 achieved training accuracies of 77.67%, 75.59%, 91.74%, 78.94%, and 93.16%, and testing accuracies of 71.75%, 71.58%, 89.02%, 77.28%, and 89.52%, respectively.

In the field of electrical power systems, effective fault detection, and minimization are crucial for maintaining system reliability and operational efficiency. This paper introduces machine learning based Adaptive Fault Reduction Algorithm (AFRA), a novel performance optimization approach designed to reduce fault occurrences significantly of electrical power systems. We used a binary classification dataset with a total of 12,001 data samples for this research. Random Forest (RF) model was used for fault detection and compared its performance against the Artificial Neural Network (ANN) model. RF model demonstrated accuracy, precision, and recall, with scores of 99.79%, 99.64%, and 99.91 % respectively, compared to the ANN's 99.54%, 99.54%, and 99.45%. By integrating AFRA, we reduced the number of fault events from 5,496 to just 6, achieving a 99.89% reduction. Our approach highlights the importance of optimization in fault detection and minimization methodologies, offering a robust framework for improving system reliability. The promising results achieved by AFRA suggest significant potential for its application in real-time monitoring and dynamic fault detection and minimization in power systems.