Land cover and land use classification have been important areas of research in the remote sensing (RS) field. It can give data assistance for many real-world applications, including land planning and use. Convolutional neural networks (CNNs), in particular, demonstrate the remarkable power of feature learning in remote sensing for land use and cover classification, as demonstrated by recent deep learning techniques driven by vast amounts of data. Although the pre-trained models obtained good classification results, it is difficult for them to classify RS images into a correct class with a higher precision rate. In this work, we proposed a new network-level fusion deep architecture based on 16-tiny Vision Transformer and SIBNet. In the initial phase, data augmentation has been performed to resolve the problem of data imbalances. For this purpose, traditional mathematical operations have been performed. In the next step, we proposed a self-attention bottleneck-based inception CNN network named SIBNet. In this network, two architectures are followed. The blocks are designed using inception architecture, and each inception module is created with bottleneck blocks. The 16-tiny vision transformer architecture has been implemented for RS images and fused using a network-level fusion with SIBNet for the first time. Hyperparameters of the proposed model have been initialized using Bayesian Optimization for better training on the RS images. After the fusion, the model was on RS image datasets and extracted deep features from the self-attention layer. The extracted features are classified using a neural network classifier with multiple hidden layers. The experimental process of the proposed architecture has been performed on two publically available datasets, such as EuroSAT and NWPU, and obtained an accuracy of 97.8 and 98.9%, respectively. A detailed ablation study has been performed to test the proposed models and shows that the fusion model achieved improved accuracy. In addition, a comparison is conducted with recent techniques and proposed methods, showing improved precision, recall, and accuracy.

Advancements in multimodal biometrics, which integrate multiple biometric traits, promise to enhance the accuracy and robustness of identification systems. This study focuses on improving multimodal biometric identification by using fingerprint and finger vein images as the primary traits. We utilized the “NUPT-FPV” dataset, which contains a substantial number of finger vein and fingerprint images, which significantly aided our research. Convolutional neural networks (CNNs), renowned for their efficacy in computer vision tasks, are used in our model to extract distinct discriminative features. Specifically, we incorporate three popular CNN architectures: ResNet, VGGNet, and DenseNet. We explore three fusion strategies used in security applications: early fusion, late fusion, and score-level fusion. Early fusion integrates raw images at the input layer of a single CNN, combining information at the initial stages. Late fusion, in contrast, merges features after individual learning from each CNN model. Score-level fusion employs weighted aggregation to combine scores from each modality, leveraging the complementary information they provide. We also use contrast limited adaptive histogram equalization (CLAHE) to enhance fingerprint contrast and vein pattern features, improving feature visibility and extraction. Our evaluation metrics include accuracy, equal error rate (EER), and ROC curves. The fusion of CNN architectures and enhancement methods shows promising performance in identifying multimodal biometrics, aiming to increase identification accuracy. The proposed model offers a reliable authentication system using multiple biometrics to verify identity.

Brain tumors, along with other diseases that harm the neurological system, are a significant contributor to global mortality. Early diagnosis plays a crucial role in effectively treating brain tumors. To distinguish individuals with tumors from those without, this study employs a combination of images and data-based features. In the initial phase, the image dataset is enhanced, followed by the application of a UNet transfer-learning-based model to accurately classify patients as either having tumors or being normal. In the second phase, this research utilizes 13 features in conjunction with a voting classifier. The voting classifier incorporates features extracted from deep convolutional layers and combines stochastic gradient descent with logistic regression to achieve better classification results. The reported accuracy score of 0.99 achieved by both proposed models shows its superior performance. Also, comparing results with other supervised learning algorithms and state-of-the-art models validates its performance.

Wind power is a cheap renewable energy that plays an important role in the economic development of a country. Identifying potential locations for energy production is challenging due to the diverse relationship between wind power potential and the weather characteristics of a location. Many machine learning models were proposed to predict the wind power production level for different locations. There is also a need for a global machine-learning model to enable wind power prediction of multiple locations with a single global model. A Federated Learning (FL) based model is proposed to train and evaluate the global model of wind power prediction of different locations using wind speed and wind direction. The proposed wind power prediction model is implemented in Pakistan to forecast the wind power of four distinct locations in Pakistan, using Linear Regression (LR), Support Vector Regression (SVR), Random Forest Regression (RFR), Extreme Gradient Boosting Regression (XGBR), and Multilayer Perceptron Regression (MLPR) models. The evaluation of the model from 30% of the test dataset reveals that RFR outperformed with a coefficient of determination (R ² ) of 0.9717, a Mean Squared Error (MSE) of 0.0007 kW, a Root Mean Squared Error (RMSE) of 0.0256 kW, and a Mean Absolute Error (MAE) of 0.018 kW. The XGBR model also performed well with R ² of 0.9681, MSE of 0.0007 kW, RMSE of 0.0270 kW, and MAE of 0.0129 kW. The accuracy of global models demonstrated the ability of the FL approach to deal with the heterogeneity of diverse weather characteristics of multiple locations for wind power prediction.