Accurate and early prediction of Cardiovascular Disease (CVD) plays an important role in improving patient survival and enabling timely preventive or therapeutic interventions. In this study, we propose an enhanced Crow Search Algorithm (eCSA) that integrates feature selection and hyperparameter optimization into a unified framework to improve the performance of a Bidirectional Long Short-Term Memory (Bi-LSTM) based heart disease prediction network. By employing a hybrid binary-continuous search strategy, the proposed eCSA jointly identifies discriminative features and optimizes Bi-LSTM hyperparameters, resulting in improved predictive performance and enhanced feature-level interpretability. Experimental evaluations were conducted on two benchmark datasets, namely the Cleveland Heart Disease dataset and the Framingham Heart Study dataset, under multiple train-test split conditions (80:20 and 70:30). Comparative analysis against state-of-the-art approaches, including Stochastic Configuration Network–Deep Bidirectional Long Short-Term Memory (SCN-Deep Bi-LSTM), Fitness-based Horse Optimization–Bidirectional Long Short-Term Memory (FHO-Bi-LSTM), Quantum Hybrid Deep Network (QHDN), and Convolutional Transformer Network (CTN-Trans), demonstrated that the proposed eCSA-BiLSTM model consistently outperformed competing methods across multiple evaluation metrics. Performance was assessed using accuracy, macro-averaged F1-Score (Macro-F1), precision, recall, Matthews Correlation Coefficient (MCC), and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The proposed model achieved an accuracy of up to 0.95 on the Cleveland dataset and 0.91 on the Framingham dataset, while maintaining stable and consistent performance across stratified 5-fold cross-validation and repeated train–test split experiments.
In this paper, we present a comprehensive approach for depth map completion using a variable pipeline model. The pipeline is designed to be flexible, and its structure is dynamically determined during the training phase by minimizing a fitness function. To achieve this objective, we used the Particle Swarm Optimization (PSO) technique, which efficiently ex-plores the solution space to optimize key parameters. The pipeline itself consists of three main stages: a convolutional stage (Lab Convolution), an Interpolation Model (IM), and a post-Convolutional Stage (SC2). During training, our model automatically adjusts several aspects, including the selection of filter parameters, interpolator settings, and the number of filters used at each stage. Moreover, the pipeline is capable of interchanging the execution order between the stages, either processing in the sequence of Lab Convolution-IM-SC2 or SC2-IM-Lab Convolution. This flexible structure allows for the discovery of better configurations that significantly im-prove the performance of depth map completion tasks. Our approach demonstrates superior results by adapting to the specific characteristics of the input data.
This paper explores and evaluates advanced signal processing techniques for blind channel identification and equalization in modern 5G networks. The focus is on traditional statistical methods and emerging artificial intelligence based on the autoencoder method. The study concentrates on two principal frameworks: high-order cumulants, specifically the fourth-order and sixth-order cumulants, which are capable of exploiting non-Gaussian and higher-order statistical dependencies in digitally modulated signals, and Artificial Intelligence (AI)-based models, particularly autoencoders, which use deep learning architectures to reconstruct or denoise received signals without prior knowledge of the transmission channel. To systematically assess the capabilities of each method, three distinct algorithms were developed and implemented: the fourth-order cumulant, the sixth-order cumulant, and the autoencoder algorithm. These algorithms were applied to 5G signals transmitted over Rayleigh multipath fading channels, modeled using standard propagation profiles such as BRAN-A, BRAN-B, and Proakis B. Simulations were conducted over a wide range of Signal-to-Noise Ratios (SNRs) and varying levels of noise and channel dynamics. Key findings from the simulation results are as follows: The sixth-order cumulant demonstrates superior noise resilience compared to the fourth-order cumulant, making it more suitable for environments with stronger interference or non-Gaussian noise. The autoencoder algorithm achieves the highest estimation and identification accuracy after sufficient training, due to its ability to model complex, nonlinear transformations. However, this comes with increased data requirements and computational cost. The fourth-order cumulant algorithm is computationally efficient, theoretically tractable, and performs well under stationary and low-noise conditions. However, it exhibits a notable performance decline in time-varying or high-noise scenarios. In contrast, the autoencoder algorithm shows enhanced robustness and adaptability to nonlinear, time-varying, and imperfectly modeled environments. Its strength lies in learning the underlying channel effects implicitly from raw data, without requiring a structured analytical model. Nevertheless, this benefit is offset by higher computational complexity and a lack of interpretability compared to the cumulant-based methods. In conclusion, while cumulant-based techniques remain valuable in structured or lightly impaired channels, AI based on autoencoder models, especially autoencoders, offers a more powerful and flexible solution for blind equalization under realistic and challenging 5G conditions.
Grading tobacco leaves is crucial for ensuring fair pricing and quality control, however, the process is still largely carried out manually, resulting in a slow, subjective, and often inconsistent outcome. In this work, we present a multi-task deep learning approach designed to automate the grading of air-cured Burley tobacco leaves in controlled settings. The model is constructed with shared convolutional layers and separate task-specific branches, allowing it to predict stalk group, quality, and color at the same time, in line with the hierarchical grading system. To improve consistency, images were preprocessed using coin-based size normalization, rotation alignment, and segmentation. In our experiments, the multi-task model with EfficientNetB0 achieved an accuracy of 94.82% and significantly outperformed the multi-class and single-task baselines, while reducing both training time and inference delay. These findings suggest that multi-task learning can be a valuable and robust method for automated tobacco grading, showing gains in accuracy, speed, and scalability compared to other algorithms.
Accurate ambient temperature prediction is essential for climate monitoring, urban planning, and environmental management, particularly in regions experiencing rapid climatic variability such as Sri Lanka. This study investigates the application of explainable machine learning models for short-term ambient temperature prediction in Battaramulla, Sri Lanka. Five regression algorithms-K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), and Histogram-based Gradient Boosting Regressor (HGBR) were evaluated using 14 meteorological and environmental predictors, including temporal variables, relative humidity, solar radiation, rainfall, wind speed, and air pollutant concentrations (CO2, NOₓ, CH4, O3, CO, PM2.5, and PM10). Among the models tested, HGBR demonstrated superior predictive performance, achieving R² values of 1.00 (training) and 0.96 (testing), with corresponding mean squared error values of 0.01 and 0.11. Model interpretability was examined using SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) analyses, and Accumulated Local Effects (ALE), which identified several features as the most influential predictors. Model validation using 192 real-time observations showed close agreement between predicted and measured temperatures, although the evaluation was limited to a single location and time period. A web-based application, ‘Therma’, was developed to facilitate practical deployment of the model for localized temperature estimation. Overall, this study demonstrates the utility of explainable machine learning for localized climate prediction while highlighting the need for broader spatiotemporal validation in future work.
