Weather prediction and meteorological analysis contribute significantly towards sustainable development to reduce the damage from extreme events which could otherwise set-back the progress in development by years. The change in surface temperature is one of the important indicators in detecting climate change. In this research, we propose a novel deep learning model named Spatial Feature Attention Long Short-Term Memory (SFA-LSTM) model to capture accurate spatial and temporal relations of multiple meteorological features to forecast temperature. Significant spatial feature and temporal interpretations of historical data aligned directly to output feature helps the model to forecast data accurately. The spatial feature attention captures mutual influence of input features on the target feature. The model is built using encoder-decoder architecture, where the temporal dependencies in data are learnt using LSTM layers in the encoder phase and spatial feature relations in the decoder phase. SFA-LSTM forecasts temperature by simultaneously learning most important time steps and weather variables. When compared with baseline models, SFA-LSTM maintains the state-of the-art prediction accuracy while offering the benefit of appropriate spatial feature interpretability. The learned spatial feature attention weights are validated from magnitude of correlation with target feature obtained from the dataset.

Medical practitioners examine medical images, such as X-rays, write reports based on the findings, and provide conclusive statements. Manual interpretation of the results and report generation by examiners are time-consuming processes that lead to potential delays in diagnosis. We propose an automated report generation model for medical images leveraging an encoder–decoder architecture. Our model utilizes transformer architectures, including Vision Transformer (ViT) and its variants like Data Efficient Image Transformer (DEiT) and BERT pre-training image transformer (BEiT), as an encoder. These transformers are adapted for processing to extract and gain visual information from medical images. Reports are transformed into text embeddings, and the Generative Pre-trained Transformer (GPT2) model is used as a decoder to generate medical reports. Our model utilizes a cross-attention mechanism between the vision transformer and GPT2, which enables it to create detailed and coherent medical reports based on the visual information extracted by the encoder. In our model, we have extended the report generation with general knowledge, which is independent of the inputs and provides a comprehensive report in a broad sense. We conduct our experiments on the Indiana University X-ray dataset to demonstrate the effectiveness of our models. Generated medical reports from the model are evaluated using word overlap metrics such as Bleu scores, Rouge-L, retrieval augmentation answer correctness, and similarity metrics such as skip thought cs, greedy matching, vector extrema, and RAG answer similarity. Results show that our model is performing better than the recurrent models in terms of report generation, answer similarity, and word overlap metrics. By automating the report generation process and incorporating advanced transformer architectures and general knowledge, our approach has the potential to significantly improve the efficiency and accuracy of medical image analysis and report generation.

Medicine recommendation systems are designed to aid healthcare professionals by analysing a patient’s admission data to recommend safe and effective medications. These systems are categorised into two types: instance-based and longitudinal-based. Instance-based models only consider the current admission, while longitudinal models consider the patient’s medical history. Electronic Health Records are used to incorporate medical history into longitudinal models. This project proposes a novel K nowledge G raph- D riven Medicine Recommendation System using Graph Neural Net works, KGDNet , that utilises longitudinal EHR data along with ontologies and Drug-Drug Interaction knowledge to construct admission-wise clinical and medicine Knowledge Graphs for every patient. Recurrent Neural Networks are employed to model a patient’s historical data, and Graph Neural Networks are used to learn embeddings from the Knowledge Graphs. A Transformer-based Attention mechanism is then used to generate medication recommendations for the patient, considering their current clinical state, medication history, and joint medical records. The model is evaluated on the MIMIC-IV EHR data and outperforms existing methods in terms of precision, recall, F1 score, Jaccard score, and Drug-Drug Interaction control. An ablation study on our models various inputs and components to provide evidence for the importance of each component in providing the best performance. Case study is also performed to demonstrate the real-world effectiveness of KGDNet.

The proposed research deals with constructing a sign gesture recognition system to enable improved interaction between sign and non-sign users. With respect to this goal, five types of features are utilized—hand coordinates, convolutional features, convolutional features with finger angles, convolutional features on hand edges and convolutional features on binary robust invariant scalable keypoints—and trained on ensemble classifiers to accurately predict the label of the sign image provided as input. In addition, a hybrid artificial neural network is also fabricated that takes two of the aforementioned features, namely convolutional features and convolutional features on hand edges to precisely locate the hand region of the sign gesture under consideration in an attempt for classification. Experiments are also performed with convolutional neural networks on those benchmark datasets which are not accurately classified by the previous two methods. Overall, the proposed methodologies are able to handle a diverse variety of images that include labyrinthine backgrounds, user-specific distinctions, minuscule discrepancies between classes and image alterations. As a result, they are able to produce accuracies comparable with state-of-the-art literature.

Microarray data examination is a relatively new technology that intends to determine the proper treatment for various diseases and a precise medical diagnosis by analyzing a massive number of genes in various experimental conditions. The conventional data classification techniques suffer from overfitting and the high dimensionality of gene expression data. Therefore, the feature (gene) selection approach plays a vital role in handling a high dimensionality of data. Data science concepts can be widely employed in several data classification problems, and they identify different class labels. In this aspect, we developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features. The RFODL-MGEC model uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features. Moreover, the RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN technique were tuned using the chaos game optimization (CGO) algorithm. Comprehensive experiments on benchmark datasets indicated that the RFODL-MGEC model accomplished superior results for subtype classifications. Therefore, the RFODL-MGEC model was found to be effective for the identification of various classes for high-dimensional and small-scale microarray data.