An enhanced drought forecasting in coastal arid regions using deep learning approach with evaporation index.

Coastal arid regions are similar to deserts, where it receives significantly less rainfall, less than 10 cm. Perhaps the world's worst natural disaster, coastal area droughts, can only be detected using reliable monitoring systems. Creating a reliable drought forecast model and figuring out how well various models can analyze drought factors in coastal arid regions are two of the biggest obstacles in this field. Different time-series methods and machine-learning models have traditionally been utilized in forecasting strategies. Deep learning is promising when describing the complex interplay between coastal drought and its contributing variables. Considering the possibility of enhancing our understanding of drought features, applying deep learning approaches has yet to be tried widely. The current investigation employs a deep learning strategy. Coastal Drought indices are commonly used to comprehend the situation better; hence the Standard Precipitation Evaporation Index (SPEI) was used since it incorporates temperatures and precipitation into its computation. An integrated coastal drought monitoring model was presented and validated using convolutional long short-term memory with self-attention (SA-CLSTM). The Climatic Research Unit (CRU) dataset, which spans 1901-2018, was mined for the drought index and predictor data. To learn how LSTM forecasting could enhance drought forecasting, we analyzed the findings regarding numerous drought parameters (drought severity, drought category, or geographic variation). The model's ability to predict drought intensity was assessed using the Coefficient of Determination (R2), the Root Mean Square Error (RMSE), and the Mean Absolute Error (MAE). Both the SPEI 1 and SPEI 3 examples had R2 values more than 0.99 for the model. The range of predicted outcomes for each drought group was analyzed using a multi-class Receiver Operating Characteristic based Area under Curves (ROC-AUC) method. The research showed that the AUC for SPEI 1 was 0.99 and for SPEI 3, 0.99. The study's results indicate progress over machine learning models for one month in advance, accounting for various drought conditions. This work's findings may be used to mitigate drought, and additional improvement can be achieved by testing other models.

Henry Gas Solubility Optimization Algorithm based Feature Extraction in Dermoscopic Images Analysis of Skin Cancer.

Artificial Intelligence (AI) techniques have changed the general perceptions about medical diagnostics, especially after the introduction and development of Convolutional Neural Networks (CNN) and advanced Deep Learning (DL) and Machine Learning (ML) approaches. In general, dermatologists visually inspect the images and assess the morphological variables such as borders, colors, and shapes to diagnose the disease. In this background, AI techniques make use of algorithms and computer systems to mimic the cognitive functions of the human brain and assist clinicians and researchers. In recent years, AI has been applied extensively in the domain of dermatology, especially for the detection and classification of skin cancer and other general skin diseases. In this research article, the authors propose an Optimal Multi-Attention Fusion Convolutional Neural Network-based Skin Cancer Diagnosis (MAFCNN-SCD) technique for the detection of skin cancer in dermoscopic images. The primary aim of the proposed MAFCNN-SCD technique is to classify skin cancer on dermoscopic images. In the presented MAFCNN-SCD technique, the data pre-processing is performed at the initial stage. Next, the MAFNet method is applied as a feature extractor with Henry Gas Solubility Optimization (HGSO) algorithm as a hyperparameter optimizer. Finally, the Deep Belief Network (DBN) method is exploited for the detection and classification of skin cancer. A sequence of simulations was conducted to establish the superior performance of the proposed MAFCNN-SCD approach. The comprehensive comparative analysis outcomes confirmed the supreme performance of the proposed MAFCNN-SCD technique over other methodologies.

Machine learning based skin lesion segmentation method with novel borders and hair removal techniques.

The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided Diagnosis (CAD) systems in improving the diagnosing rate of skin cancer. The results of the existing skin lesion segmentation techniques are not up to the mark for dermoscopic images with artifacts like varying size corner borders with color similar to lesion(s) and/or hairs having low contrast with surrounding background. To improve the results of the existing skin lesion segmentation techniques for such kinds of dermoscopic images, an effective skin lesion segmentation method is proposed in this research work. The proposed method searches for the presence of corner borders in the given dermoscopc image and removes them if found otherwise it starts searching for the presence of hairs on it and eliminate them if present. Next, it enhances the resultant image using state-of-the-art image enhancement method and segments lesion from it using machine learning technique namely, GrabCut method. The proposed method was tested on PH2 and ISIC 2018 datasets containing 200 images each and its accuracy was measured with two evaluation metrics, i.e., Jaccard index, and Dice index. The evaluation results show that our proposed skin lesion segmentation method obtained Jaccard Index of 0.77, 0.80 and Dice index of 0.87, 0.82 values on PH2, and ISIC2018 datasets, respectively, which are better than state-of-the-art skin lesion segmentation techniques.