A BCI is a system of hardware and software integrated as an interface between the brain and the computer. A BCI translates the EEG signals, originating from the brain, into computer commands, commands that help the user interact with the external real world in a useful manner. The possibility that EEG signals can translated into any open-ended computer command, opened endless possibility. What a person can do with EEG-based BCI is now just limited by imagination. This paper will discuss advances in the practical aspects of different classifiers for EEG-based BCI, as well as the theoretical advances in signal processing and user relevance of these advance in EEG-based BCI in real-time applications.

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Localization is one of the challenges in achieving reliable communication in Wireless Sensor Networks (WSN). Estimating a sensor's node's position is known as Localization. Nonlinear version of Kalman filtering is known as the ExtendedKalman Filter which deals with the case governed by the nonlinear stochastic differential equations, Extended kalman filter is nonlinear filter having their own problem of consistency. In this paper proposed efficient localization algorithm that enables sensor nodes to estimate their location with high accuracy. The purpose of this paper is to develop the particle swarm optimization assisted Extended Kalman Filter (PSO-EKF) for Localization in WSN. Performance evaluation for the PSO-EKF as compared to the conventional KF could be better for time critical applications.

In Wireless Sensor Networks, nodes are positioned arbitrarily and finding location of nodes is difficult. In this network, the nodes need to know their location is important for indoor applications. In this applications signals are affected by various factors such as noise, multipath, NLOS etc. This impact on inaccurate location information of node, which leads finding path to the destination node is difficult. Cooperative location based routing is alternative solution for finding better path. In this paper a solution is proposed for effective route in indoor application of WSN. The proposed solution uses Particle Swarm Optimization assisted Adaptive Extended Kalman Filter (PSO-AKF) for finding location of nodes. In this mechanism, finding accurate position of node impact on network performance such as minimization of delay, location error and also minimizes complexity.

Deep learning is frequently used to classify medical images. Surgeons may know the type of tumor before doing surgery on a patient. Transfer learning was used to alleviate the overfitting issue of deep networks in classification since the training samples, such as a brain MRI dataset, were insufficient. To overcome this issue, We introduce a new deep‐learning methodology for the categorization of MRI brain tumor images. This method combines a unique data augmentation model with modified AlexNet and network‐based deep transfer learning. We used Lipschitz‐based data augmentation on a dataset, and the output of the augmentation model was fed into a modified AlexNet that uses network‐based deep transfer learning to extract features from a dataset. The proposed model is trained and tested using the BraTS 2020 and Figshare datasets. The proposed model's performance is assessed using sensitivity, specificity, accuracy, precision, F1‐score, and MCC. The proposed model yields superior results.