Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice.

To effectively solve the problems faced by modern electrical engineering, the design of an electrical automation control system based on artificial intelligence technology is realized. A model of electrical automation control system based on an artificial intelligence algorithm is presented. The control parameters are optimized by using the artificial intelligence algorithm control strategy. The research results show that when there is 20% load interference and 2.1 Hz frequency interference, the maximum failure rate of the turbine under system control is 0.02, indicating that the system has high anti-interference. Therefore, in the automatic control of electrification, the application of the artificial intelligence algorithm can greatly improve the control response time of the automatic control of electrification, save cost, and achieve efficient production.

The classification process of MRI (magnetic resonance imaging) is frequently used for making medical diagnoses for conditions including pituitary, glioma, meningioma, and no tumor. For this reason, determining the type of MRI and its quantity are significant and valuable measurements that reveal the brain’s state of health. To segment and classify brain analysis, laboratory personnel employ manual examination via screen; this requires a lot of labour and time. On the other hand, the devices used by specialists are not practical or inexpensive for every doctor or institution. In recent years, a variety of computational algorithms for segmentation and classification have been developed with improved results to get around the issue. Artificial neural networks (ANNs) have the capability and promise to classify in this regard. The purpose of this paper is to create and put into practice a system for classifying different types of MRI images of brain tumor samples. As a result, this paper concentrated on the tasks of segmentation, feature extraction, classifier building, and classification into four categories using various machine learning algorithms. The authors used VGG‐16, ResNet‐50, and AlexNet models based on the transfer learning algorithm for three models to classify images as an ensemble model. As a result, MRI brain tumor segmentation is more precise because each spatial feature point can now refer to all other contextual data. In the specifics, our models outperformed every other published modern ensemble model in the official deep learning challenge without any postprocessing. The ensemble model achieved an accuracy of 99.16%, a sensitivity of 98.47%, a specificity of 98.57%, a precision of 98.74%, a recall of 98.49%, and an F1‐score of 98.18%. These results significantly surpass the accuracy of other methods such as Naive Bayes, decision tree classifier, random forest, and DNN models.

The different types of white blood cells equips us an important data for diagnosing and identifying of many diseases. The automation of this task can save time and avoid errors in the identification process. In this paper, we explore whether using shape features of nucleus is sufficient to classify white blood cells or not. According to this, an automatic system is implemented that is able to identify and analyze White Blood Cells (WBCs) into five categories (Basophil, Eosinophil, Lymphocyte, Monocyte, and Neutrophil). Four steps are required for such a system; the first step represents the segmentation of the cell images and the second step involves the scanning of each segmented image to prepare its dataset. Extracting the shapes and textures from scanned image are performed in the third step. Finally, different machine learning algorithms such as (K* classifier, Additive Regression, Bagging, Input Mapped Classifier, or Decision Table) is separately applied to the extracted (shapes and textures) to obtain the results. Each algorithm results are compared to select the best one according to different criteria’s.

Automated flaw identification has become more important in medical imaging. For patient preparation, unaided prediction of tumor (brain) detection in the magnetic resonance imaging process (MRI) is critical. Traditional ways of recognizing z are intended to make radiologists' jobs easier. The size and variety of molecular structures in brain tumors is one of the issues with MRI brain tumor diagnosis. Deep learning (DL) techniques (artificial neural network (ANN), naive Bayes (NB), multi-layer perceptron (MLP)) are used in this article to detect brain cancers in MRI data. The preprocessing techniques are used to eliminate textural features from the brain MRI images. These characteristics are then utilized to train a machine-learning system.

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning. Bayesian networks can represent probabilistic dependency relationships among the variables. One strategy of Bayesian Networks structure learning is the score and search technique. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) as a novel approach to Bayesian network structure learning. In the algorithm; Deleting, Reversing, Inserting, and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly, water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with simulated annealing and greedy search using BDe score function. The authors have also investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of the evaluations, the proposed algorithm has better performance than the other algorithms and produces better scores and accuracy values.