Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of $0.735$ and an area under the ROC curve (AUC) of $0.832$. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.

Planning effective routes and monitoring vehicle traffic are essential for creating sustainable smart cities. Accurate speed prediction is a key component of these efforts, as it aids in alleviating traffic congestion. While their physical proximity is important, the interconnection of these road segments is what significantly contributes to the increase of traffic congestion. This interconnectedness poses a significant challenge to increasing prediction accuracy. To address this, we propose a novel approach based on Deep Graph Neural Networks (DGNNs), which represent the connectedness of road sections as a graph using Graph Neural Networks (GNNs). In this study, we implement the proposed approach, called STGGAN, for real-time traffic-speed estimation using two different actual traffic datasets: PeMSD4 and PeMSD8. The experimental results validate the prediction accuracy values of 96.67% and 98.75% for the PeMSD4 and PeMSD8 datasets, respectively. The computation of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) also shows a progressive decline in these error values with increasing iteration count, demonstrating the success of the suggested technique. To confirm the feasibility, reliability, and applicability of the suggested STGGAN technique, we also perform a comparison analysis, including several statistical, analytical, and machine-learning- and deep-learning-based approaches. Our work contributes significantly to the field of traffic-speed estimation by considering the structure and characteristics of road networks through the implementation of DGNNs. The proposed technique trains a neural network to accurately predict traffic flow using data from the entire road network. Additionally, we extend DGNNs by incorporating Gated Graph Attention Network (GGAN) blocks, enabling the modification of the input and output to sequential graphs. The prediction accuracy of the proposed model based on DGNNs is thoroughly evaluated through extensive tests on real-world datasets, providing a comprehensive comparison with existing state-of-the-art models for traffic-flow forecasting.

Sunflower is a crop that has many economic values and ornamental usages. However, its production can be hampered due to various diseases such as downy mildew, gray mold, and leaf scars, and it is challenging for farmers to identify disease-prone conditions with traditional approaches. Thus, a computerized model composed of vision, artificial intelligence, and machine learning is the demand of the age to detect diseases in plants efficiently. In this paper, we develop a hybrid model with transfer learning (TL) and a simple CNN using a small dataset for detecting sunflower diseases. Out of the eight models tested on the dataset of four different classes (downy mildew, gray mold, leaf scars, and fresh leaf), the VGG19 + CNN hybrid model achieves the best results in terms of precision, recall, F1-score, accuracy, Hamming loss, Matthews coefficient, Jaccard score, and Cohen’s kappa metrics. The experimental outcomes show that the proposed model provides better precision, recall, and accuracy than other approaches on the benchmark dataset.

AIBTBF (Artificial Intelligence and Big data Techniques Based Framework) is a framework designed to enhance the DSS (Decision Support System) of MDAS (Metering Data Acquisition System) in electricity consumption for improved energy efficiency. The core processes of the Revenue Management System (RMS) i.e., Metering, Billing, and collection (Mare) are addressed in the context of the Electricity Consumption. This paper proposed an efficient framework after reviewing & analyzing various methods in practice along with their impacts on electricity consumption. The framework integrates Information Technology (IT) with Operation Technology (OT) of the electrical system to handle the challenges of high volume of data that needs velocity in processing and accumulated variety of data in structured, semi-structured, and unstructured variables. The proposed framework implemented big data analytics and artificial intelligence to enhance the decision support system with real-time accurate and effective decisions. The direct variables for energy efficiency i.e. billing efficiency and collection efficiency are minutely studied in context with IoT techniques. Key indicators for energy efficiency and demand management are also studied to achieve the primary goals of the distribution companies to provide quality supply 24X7 to their valued consumers. It introduces novel methodologies by integrating external factors like weather conditions, seasonal and geographical diversity and socio-economic indicators into predictive models, thereby addressing gaps in traditional forecasting approaches. The framework is structured into four key parts: Extraction & Preparation, Transformation, Analysis, and Suggestions & Recommendations Generation. The paper analyzes the current emerging techniques with limitations and suggested recommendations for best use case scenarios in the prediction and optimization of the existing business process. The contributions of the framework include Efficient data management and processing from diverse sources. Advanced analysis using deep learning algorithms for forecasting and optimization. Real-time decision support for energy management and operational efficiency. Reduction of commercial and technical losses in power distribution. Similarly, the proposed framework uses MapReduce and Parallelization packages. The data is stored at the data lake on HDFS. The framework is evaluated through a series of practical case studies and performance metrics, demonstrating its effectiveness in improving energy management and operational processes. AIBTBF framework not only meets the primary goals of reducing technical and commercial losses but also provides a scalable solution for modernizing legacy systems in power distribution.