Big data is an important factor almost in all nowadays technologies, such as, social media, smart cities, and internet of things. Most of standard classifiers tends to be trapped in local optima problem when dealing with such massive datasets. Hence, investigating new techniques for dealing with such massive data sets is required. This paper presents a novel imbalanced big data mining framework for improving optimization algorithms performance by eliminating the local optima problem consists of three main stages. Firstly, the preprocessing stage, which uses the LSH-SMOTE algorithm for solving the class imbalance problem, then it uses the LSH algorithm for hashing the data set instances into buckets. Secondly, the bucket search stage, which uses the GWO for training bidirectional recurrent neural network BRNN and searching for the global optimum in each bucket. Lastly, the final tournament winner stage, which uses the GWO+BRNN for finding the global optimum of the whole data set among all global optimums from all buckets. Our proposed framework LSHGWOBRNN has been tested over 9 data sets one of them is big data set in terms of AUC, MSE, against seven well-known machine-learning algorithms (Naive Bayes, Random Tree, Decision Table, and AdaBoostM1, WOA+MLP, GWO+MLP, and WOA+BRNN), then, we tested our algorithm over four well-known data sets against GWO+MLP, ACO+MLP, GA+MLP, PSO+MLP, PBIL+MLP, and ES+MLP in terms of classification accuracy and MSE. Our experimental results have proved that our proposed framework LSHGWOBRNN has provided high local optima avoidance, and higher accuracy, less complexity and overhead.

The outbreak of coronavirus diseases (COVID-19) has rabidly spread all over the world. The World Health Organization (WHO) has announced that coronavirus COVID-19 is an international pandemic. The Real-Time Reverse transcription-polymerase Chain Reaction (RT-PCR) has a low positive and sensitivity rate in the early stage of COVID-19. As a result, the Computed Tomography (CT) imaging is used for diagnosing. COVID-19 has different key signs on a CT scan differ from other viral pneumonia. These signs include ground-glass opacities, consolidations, and crazy paving. In this paper, an Artificial Intelli-gence-inspired Model for COVID-19 Diagnosis and Prediction for Patient Response to Treatment (AIMDP) is proposed. AIMDP model has two main functions reflected in two proposed modules, namely, the Diagnosis Module (DM) and Prediction Module (PM). The Diagnosis Module (DM) is proposed for early and accurately detecting the patients with COVID-19 and distinguish it from other viral pneumonias using COVID-19 signs obtained from CT scans. The DM model, uses Convolutional Neural Networks (CNNs) as a Deep learning technique for segmentation, can process hundreds of CT images in seconds to speed up diagnosis of COVID-19 and contribute in its containment. In addition, some countries haven’t the ability to provide all patients with the treatment and intensive care services, so it will be mandatory to give treatment to only responding patients. In this context, the Prediction Module (PM) is proposed for predicting the ability of the patient to respond to treatment based on different factors e.g. age, infection stage, respiratory failure, multi-organ failure and the treatment regimens. PM implement the Whale Optimization Algorithm for selecting the most relevant patient’s features. The experimental results show promising performance for the proposed diagnosing and prediction modules, using a dataset with hundreds of real data and CT images.