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In recent years, solar irradiance forecasting has become essential to managing, developing, and effectively integrating photovoltaic (PV) systems properly into the smart grid. The foundation of a conventional variational autoencoder (VAE) is an entirely coupled layer that includes both decoder and encoder components. In this study, a novel deep attention-driven model for forecasting named bidirectional long short-term memory (BiLSTM) which is combined with the VAE model is introduced as an enhanced version of the VAE. BiLSTM is integrated at the encoder side of VAE to effectively extract and learn temporal dependencies that are embedded in the panel irradiance data. Additionally, a self-attention mechanism (SAM) is added to bilateral variational autoencoder (BiVAE) which is known as BiVAE-SAM that highlights the important characteristics. The proposed BiVAE-SAM permits the VAE's capacity to design the temporal dependency. The examined models are assessed using sun irradiance measurements from New York City, Turkey, Canopy, Los Angeles, California, and Florida. The outcomes exhibit that the proposed BiVAESAM model performs better mean absolute percentage error (MAPE) with values of 1.7935, 0.7828, 1.3491 and 2.8346 respectively for California, Los Angeles, New York City, and Florida, over existing stacked denoising autoencoders (SDA) model at projecting solar irradiance. [ABSTRACT FROM AUTHOR]

In the present scenario, Alzheimer's Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer's Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models. [ABSTRACT FROM AUTHOR]

Smart healthcare systems in the cloud-IoT framework are designed for the prediction of heart disease. This smart diagnosis improves the patient's health status and minimizes the death rate. Early prediction of heart disease may reduce the risk of patient illness and monitoring in real-time to avoid the risk. The view of existing algorithms is inaccurate in early prediction which took a lot of time for the prediction and inaccurate early prediction of heart disease. To overcome these issues, this paper proposed a sparse autoencoder with Galactic Swarm Optimization (SAE-GSO) algorithm. A sparse encoder predicts heart disease and enhances the accurate prediction, tuning the parameters of sparsity regularity in the sparse autoencoder, Galactic Swarm optimization algorithm is implemented. The proposed work enhances the prediction rate of heart diseases, minimizing the error rate, and maximizing the accuracy. The accuracy rate of the proposed work of SAE-GSO in the Cleveland Dataset produces got 92.23 %, GBT got 65.12 %, SAE got 87.34%, and NB got 83.16 %. The accuracy rate of the proposed work of SAEGSO in the Framingham Dataset produced 92.59 %, GBT got 69.16 %, SAE got 86.25%, and NB got 82.37%. [ABSTRACT FROM AUTHOR]

In the present scenario, the DC-DC converter becomes the most significant device and it is used in various applications such as Electric vehicles, fuel cells, sustainable systems, etc. A bidirectional converter used in the abovementioned applications should be lesser in magnitude and lighter in weight. In case, if the magnitude of the traditional bidirectional DC-DC converter is more, the substituting consistency has been amplified that results in more switching losses. Because of this switching loss, the magnitude of the structure is minimized. To overcome the losses during the process, a novel topology of dc-dc converters is designed with Falcon Optimization Algorithm (FOA) to increase the efficiency of converters. This research proposes the design of Bidirectional DC-DC converter (BDDCDC) for C and π Filter through Fractional Order Proportional Integral Derivative (FOPID) which is tuned by FOA. This proposed controller synthesis considers both robustness and closed-loop performance to ensure a fast and stable regulation characteristic. A simple tuning method provides the appropriate gains to meet design requirements. The outcomes of proposed FOA-FOPID is simulated in MATLAB Simulink which represent the transient performances such as steady state error (1.1 V), rise time (0.21 sec), settling time (0.33 sec) and peak time (0.24 sec) is much better than existing PID and Adaptive Neuro Fuzzy Inference System (ANFIS) control. [ABSTRACT FROM AUTHOR]