Understanding the impact of input variables on black-box machine learning and deep learning models is necessary. Therefore, this study proposed SHapley Additive exPlanations (SHAP) values to address the problem of the interpretability of the output of the support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and long short-term memory (LSTM) models to forecast climatic water balance (CWB) within 1-3 months lead-time.The current study uses two Koppen-Geiger climate zones over Bangladesh: the humid subtropical climate with dry winter and hot summer (Cwa) and the tropical climate (Af-Am). Monthly antecedent CWB, potential evapotranspira-

The study aims to present an architecture for a recommendation system based on user items that are transformed into narrow categories. In particular, to identify the movies a user will likely watch based on their favorite items. The recommendation system focuses on the shortest connections between item correlations. The degree of attention paid to user-group relationships provides another valuable piece of information obtained by joining the sub-groups. Various relationships have been used to reduce the data sparsity problem. We reformulate the existing data into several groups of items and users. As part of the calculations and containment of activities, we consider Pearson similarity, cosine similarity, Euclidean distance, the Gaussian distribution rule, matrix factorization, EM algorithm, and k-nearest neighbors (KNN). It is also demonstrated that the proposed methods could moderate possible recommendations from diverse perspectives.

Due to greater accessibility, healthcare databases have grown over the years. In this paper, we practice locating and associating data points or observations that pertain to similar entities across several datasets in public healthcare. Based on the methods proposed in this study, all sources are allocated using AI-based approaches to consider non-unique features and calculate similarity indices. Critical components discussed include accuracy assessment, blocking criteria, and linkage processes. Accurate measurements develop methods for manually evaluating and validating matched pairs to purify connecting parameters and boost the process efficacy. This study aims to assess and raise the standard of healthcare datasets that aid doctors’ comprehension of patients’ physical characteristics by using NARX to detect errors and machine learning models for the decision-making process. Consequently, our findings on the mortality rate of patients with COVID-19 revealed a gender bias: female 15.91% and male 22.73%. We also found a gender bias with mild symptoms such as shortness of breath: female 31.82% and male 32.87%. With congestive heart disease symptoms, the bias was as follows: female 5.07% and male 7.58%. Finally, with typical symptoms, the overall mortality rate for both males and females was 13.2%.

Recently, various purposes such as computer vision, self-driving vehicles, self-directed shipping, and so on. Therefore, machine learning and deep learning algorithms have demonstrated strong routine. Using machine learning applications, we can implement sophisticated functionality without relying on complex code constructions. An alternative is to train a learning system on a collected training dataset and ensure that it performs as expected. Two main benefits of a deep-learning-based system over a hard-coded one exist. A Reinforcement Learning, and deep learning-based system does not require such complex hard-coded algorithms, which makes it less prone to error and easier to implement. In this study we proposed a deep learning-driven systems are able to adapt to different situations by retraining their algorithms on collected data. Proposed method influenced varying obtain leaning to another example of changing conditions. Systems can adapt even when input distributions change over time.

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