Alzheimer's disease is a neurodegenerative condition that gradually impairs cognitive abilities. Recently, various neuroimaging modalities and machine learning methods have surfaced to diagnose Alzheimer's disease. Resting-state fMRI is a neuroimaging modality that has been widely utilized to study brain activity related to neurodegenerative diseases. In literature, the previous studies are limited to the binary classification of Alzheimer's disease and Mild Cognitive Impairment. The application of computer-aided diagnosis for the numerous advancing phases of Alzheimer's disease, on the other hand, remains understudied. This research analyzes and presents methods for multi-label classification of six Alzheimer's stages using rs-fMRI and deep learning. The proposed model solves the multi-class classification problem by extracting the brain's functional connectivity networks from rs-fMRI data and employing two deep learning approaches, Stacked Sparse Autoencoder and Brain Connectivity Graph Convolutional Network. The suggested models' results were assessed using the k-fold cross-validation approach, and an average accuracy of 77.13% and 84.03% was reached for multi-label classification using Stacked Sparse Autoencoders and Brain Connectivity Based Convolutional Network, respectively. An analysis of brain regions was also performed by using the network's learned weights, leading to the conclusion that the precentral gyrus, frontal gyrus, lingual gyrus, and supplementary motor area are the significant brain regions of interest.

Global climate change has intensified the search for renewable energy sources. Solar power is a cost‐effective option for electricity generation. Accurate energy forecasting is crucial for efficient planning. While various techniques have been introduced for energy forecasting, transformer‐based models are effective for capturing long‐range dependencies in data. This study proposes N hours‐ahead solar irradiance forecasting framework based on variational mode decomposition (VMD) for handling meteorological data and a modified temporal fusion transformer (TFT) for forecasting solar irradiance. The proposed model decomposes raw solar irradiance sequences into intrinsic mode functions (IMFs) using VMD and optimizes the TFT using a variable screening network and a gated recurrent unit (GRU)‐based encoder–decoder. Our study specifically targets the 1‐h as well as different forecasting horizons for solar irradiance. The resulting deep learning model offers insights, including the prioritization of solar irradiance subsequences and an analysis of various forecasting window sizes. An empirical study shows that our proposed method has achieved high performance compared to other time series models, such as artificial neural network (ANN), long short‐term memory (LSTM), CNN–LSTM, CNN–LSTM with temporal attention (CNN–LSTM‐t), transformer, and the original TFT model.

Alzheimer’s disease (AD) is a form of dementia that progressively impairs a person’s mental abilities. Current classification methods for the six AD stages perform poorly in multiclass classification and are computationally expensive, which hinders their clinical use. An efficient, low-computational model for accurate multiclass classification across all AD stages is needed that can integrate both local and global feature extraction. This study uses rs-fMRI, clinical data, and transformer-based models to classify six AD stages. The proposed network is a hybrid of two architectures, namely a transformer and a convolutional neural network (CNN). The model addresses multiclass classification by examining the brain’s functional connectivity networks based on rs-fMRI data and clinical data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed architecture leverages CNNs for local feature extraction and transformers for global context; this method employs the contextual attention power of transformers to improve the multiclass classification accuracy of AD. The k-fold cross-validation method was employed to evaluate the performance of the proposed model. For the multiclass classification of six stages, the average accuracy of the model was 96%. For binary classification, accuracies were 98.96% (AD vs. MCI), 99.65% (AD vs. CN), 98.44% (AD vs. LMCI), 96.88% (AD vs. EMCI), and 98.36% (AD vs. SMC). These results highlight the potential of the proposed network in achieving high accuracy for both binary and multistage AD classification with limited computational resources. The proposed method was also compared to benchmark algorithms and outperformed them; it was substantially less computationally expensive while maintaining its accuracy.

Since January 2020, the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected the whole world, producing a respiratory disease that can become severe and even cause death in certain groups of people. The main method for diagnosing coronavirus disease 2019 (COVID-19) is performing viral tests. However, the kits for carrying out these tests are scarce in certain regions of the world. Lung conditions as perceived in computed tomography and radiography images exhibit a high correlation with the presence of COVID-19 infections. This work attempted to assess the feasibility of using convolutional neural networks for the analysis of pulmonary radiography images to distinguish COVID-19 infections from non-infected cases and other types of viral or bacterial pulmonary conditions. The results obtained indicate that these networks can successfully distinguish the pulmonary radiographies of COVID-19-infected patients from radiographies that exhibit other or no pathology, with a sensitivity of 100% and specificity of 97.6%. This could help future efforts to automate the process of identifying lung radiography images of suspicious cases, thereby supporting medical personnel when many patients need to be rapidly checked. The automated analysis of pulmonary radiography is not intended to be a substitute for formal viral tests or formal diagnosis by a properly trained physician but rather to assist with identification when the need arises.

Nowadays, smart electricity grids are managed through advanced tools and techniques. The advent of Artificial Intelligence (AI) and network technology helps to control the energy demand. These advanced technologies can resolve common issues such as blackouts, optimal energy generation costs, and peak-hours congestion. In this paper, the residential energy demand has been investigated and optimized to enhance the Quality of Service (QoS) to consumers. The energy consumption is distributed throughout the day to fulfill the demand in peak hours. Therefore, an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption. This model gives priority to consumer preferences while planning the operation of appliances. A distributed system using non-cooperative game theory has been designed to minimize the communication overhead between the edge nodes. Furthermore, the allotment mechanism has been designed to manage the grid appliances through the edge node. The proposed model helps to improve the latency in the grid appliances scheduling process.