Due to the growing demand, assessing performance has become obligatory for photovoltaic (PV) energy harvesting systems. Performance assessment involves estimating different PV system parameters. Traditional ways, such as calculating solar radiation using satellite data and the IV characteristics approach as assessment methods, are no longer reliable enough to provide a reasonable projection of PV system parameters. Estimating system parameters using machine learning (ML) approaches has become a reliable and popular method because of its speed and accuracy. This paper systematically reviewed ML-based PV parameter estimation studies published in the last three years (2020 - 2022). Studies were analyzed using several criteria, including ML algorithm, outcome, experimental setup, sample data size, and error metric. The analysis revealed several interesting factors. The neural network was the most popular ML method (32.55%), followed by random vector functional link (13.95%) and support vector machine (9.30%). Dataset was sourced from hardware tests and computer-based simulations: 66% of the studies used data from only computer simulation, 18% used data from only hardware setup, and the 16% experiments used data from both hardware and simulations to evaluate different system parameters. The top three most commonly used error metrics were root mean square error (29.1%), mean absolute error (17.5%), and coefficient of determination (15.9%). Our systematic review will help researchers assess ML algorithms' projection in PV system parameters estimation. Consequently, scopes shall be created to establish more robust governmental frameworks, expand private financing in the PV industry, and optimize PV system parameters.

Brain-computer interfaces (BCI) in electroencephalography (EEG)-based motor imagery classification offer promising solutions in neurorehabilitation and assistive technologies by enabling communication between the brain and external devices. However, the non-stationary nature of EEG signals and significant inter-subject variability cause substantial challenges for developing robust cross-subject classification models. This paper introduces a novel Spatial-Spectral-Temporal Attention Fusion (SSTAF) Transformer specifically designed for upper-limb motor imagery classification. Our architecture consists of a spectral transformer and a spatial transformer, followed by a transformer block and a classifier network. Each module is integrated with attention mechanisms that dynamically attend to the most discriminative patterns across multiple domains, such as spectral frequencies, spatial electrode locations, and temporal dynamics. The short-time Fourier transform is incorporated to extract features in the time-frequency domain to make it easier for the model to obtain a better feature distinction. We evaluated our SSTAF Transformer model on two publicly available datasets, the EEGMMIDB dataset and BCI Competition IV-2a. SSTAF Transformer achieves an accuracy of 76. 83% and 68. 30% in the data sets, respectively, outperforms traditional CNN-based architectures and a few existing transformer-based approaches.

Solar energy adoption is rapidly growing as a sustainable option, with solar panels used on residential buildings, commercial properties, and large-scale farms. However, the unpredictable nature of solar power can lead to suboptimal energy generation from photovoltaic (PV) panels. Despite the high effectiveness of deep learning (DL) models in forecasting PV power, they often struggle with the perception of being “closed boxes” that lack clear explanations for their prediction results, which fail to highlight the key features for PV prediction. To address the critical issue of full transparency, this study explores a well-known DL model named lightweight deep neural network (LWDNN) in PV power forecasting, along with the application of explainable artificial intelligence (XAI) tools like Shapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME). Real-time data collected from a grid-connected solar PV system located in Dhaka were utilized to perform the prediction. By enabling XAI model interpretation, we identified feature contributions and explained individual predictions, reducing training computational demands without compromising accuracy. The reliability of the LWDNN model is assessed using both complete and reduced feature sets through performance metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). The test results show that the proposed LWDNN model based on SHAP analysis outperforms conventional schemes by achieving RMSE = 6.180 kW, MAE = 1.939 kW, and R² = 0.988. Finally, the model was implemented on a Raspberry Pi for low-power solar forecasting, demonstrating the feasibility of edge deployment.

Diabetes Mellitus is one of the world's leading causes of mortality, with a worldwide death toll estimated to be in the millions. It is determined by the concentration of a sugar molecule in the blood, which is produced from glucose. Predicting the likelihood of contracting this illness may now be done using a plethora of methods. Data about diabetic patients must be comprehensive and accurate in order to accurately forecast the onset of the disease. In this paper, we discussed early-stage diabetes prediction using six algorithms. The algorithms are Gradient Boosting, ADA Boosting, XG Boosting, Neural Network, SVM, Random Forest, Stacking Neural Network, Stacking SVM, Stacking Random Forest. We also discussed briefly about the best algorithm among them with detailed accuracy by class and confusion matrix. By this study, we can predict early-stage diabetes disease more accurately.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with early and accurate detection being critical for improving patient outcomes. Automated image segmentation using deep learning has emerged as a transformative tool for identifying colorectal abnormalities in medical imaging. This study conducts a comparative analysis of three prominent deep learning architectures—U-Net, SegNet, and ResNet—for colorectal cancer image segmentation, evaluating their performance on a custom dataset comprising 1,800 images (1,000 polyp images from the Kvasir-SEG dataset and 800 polyp-free images from the WCE Curated Colon Dataset). The dataset was preprocessed to a uniform resolution of 256 × 256 pixels and partitioned into training, validation, and test sets. Quantitative and qualitative results demonstrate that U-Net outperforms SegNet and ResNet, achieving superior segmentation accuracy (validation accuracy of 0.95) and robustness, particularly when trained on datasets that include negative samples. SegNet showed the sign of overfitting and delivered unstable results, while ResNet struggled to generalize effectively. The integration of negative images improved specificity by decreasing false positive rates. Overall, the results demonstrate U-net as the most efficient in precise polyp segmentation, providing significant implications for robust diagnostic system development.