This paper investigates the feasibility of using neural network (NN) and late gamma band (LGB) electroencephalogram (EEG) features extracted from the brain to identify the individuality of subjects. The EEG signals were recorded using 61 active electrodes located on the scalp while the subjects perceived a single picture.LGB EEG signals occur with jittering latency of above 280 ms and are not time-locked to the triggering stimuli. Therefore, LGB EEG could only be computed from single trials of EEG signals and the common method of averaging across trials to remove undesired background EEG (i.e. noise) is not possible. Here, principal component analysis has been used to extract single trials of EEG signals. Zero phase Butterworth filter and Parseval's time-frequency equivalence theorem were used to compute the LGB EEG features. These features were then classified by backpropagation and simplified fuzzy ARTMAP NNs into different categories that represent the individuality of the subjects. The results using a tenfold cross validation scheme gave a maximum classification of 97.33% when tested on 800 unseen LGB EEG features from 40 subjects. This pilot investigation showed that the method of identifying the individuality of subjects using NN classification of LGB EEG features is worth further study.

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Glaucoma is considered as the main source of irrevocable loss of vision. The earlier diagnosis of glaucoma is essential to provide earlier treatment and to reduce vision loss. The fundus images are transfigured in the ophthalmology and are used to visualize the structures of the optic disc. However, accuracy is considered as a major constraint. To increase accuracy, an effective optimization‐driven classifier is developed for glaucoma detection. The proposed Jaya‐chicken swarm optimization (Jaya‐CSO) is employed for training the recurrent neural network (RNN) for glaucoma detection. The proposed Jaya‐CSO is designed by integrating the Jaya algorithm with the chicken swarm optimization (CSO) technique for tuning the weights of the RNN classifier. The method utilized optic disc features, statistical features, and blood vessel features for the determination of the glaucomatous region. The features obtained from the optic disc, blood vessels, and the fundus image is formulated as a feature vector. Finally, the glaucoma classification is done using RNN using the feature vector such that the RNN is trained using the proposed Jaya‐CSO. The proposed Jaya‐CSO outperformed other existing models with maximal accuracy of 0.97, the specificity of 0.97, and sensitivity of 0.97, respectively.