Brain tumor segmentation and classification is a crucial challenge in diagnosing, planning, and treating brain tumors. This article proposes an automatic method that categorizes the severity level of the tumors to render an effective diagnosis. The proposed fractional Jaya optimizer‐deep convolutional neural network undergoes the severity classification based on the features obtained from the segments of the magnetic resonance imaging (MRI) images. The segments are obtained using the particle swarm optimization that ensures the optimal selection of the segments from the MRI image and yields the core tumor and the edema tumor regions. The experimentation using the BRATS database reveals that the proposed method acquired a maximal accuracy, specificity, and sensitivity of 0.9414, 0.9429, and 0.9708, respectively.

Automatic anomaly detection in surveillance videos is a trending research domain, which assures the detection of the anomalies effectively, relieves the time-consumed by the manual interpretation methods without the requirement of the domain knowledge about the anomalous object. Accordingly, this research work proposes an effective anomaly detection approach, named, TimeRide Neural network (TimeRideNN), by modifying the standard RideNN using the Taylor series such that an extra group of rider, named as timerider, is included in the standard rider optimization algorithm. Initially, the face in the videos is subjected to face detection using the Viola Jones algorithm. Then, the object tracking is performed using the knocker and holoentropy-based Bhattacharya distance, which is a modification of the Bhattacharya distance using the knocker and holoentropy. After that, the features, such as object-level features and speed-level features of the objects, are extracted and the features are employed to the proposed TimeRideNN classifier, which declares the anomalous objects in the video. The experimentation of the proposed anomaly detection method is done using the UCSD dataset (Ped1), subway dataset and QMUL junction dataset, and the analysis is performed based on accuracy, sensitivity and specificity. The proposed TimeRideNN classifier obtains the accuracy, sensitivity and specificity of 0.9724, 0.9894 and 0.9691, respectively.

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The wind power industry has seen an unprecedented growth in last few years. The surge in orders for wind turbines has resulted in a producers market. This market imbalance, the relative immaturity of the wind industry, and rapid developments in data processing technology have created an opportunity to improve the performance of wind farms and change misconceptions surrounding their operations. This research offers a new paradigm for the wind power industry, data-driven modeling. Each wind Mast generates extensive data for many parameters, registered as frequently as every minute. As the predictive performance approach is novel to wind industry, it is essential to establish a viable research road map. This paper proposes a data-mining-based methodology for long term wind forecasting (ANN), which is suitable to deal with large real databases. The paper includes a case study based on a real database of five years of wind speed data for a site and discusses results of wind power density was determined by using the Weibull and Rayleigh probability density functions. Wind speed predicted using wind speed data with Datamining methodology using intelligent technology as Artificial Neural Networks (ANN) and a PROLOG program designed to calculate the monthly mean wind speed.