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CoViD19 is a novel disease which has created panic worldwide by infecting millions of people around the world. The last significant variant of this virus, called as omicron, contributed to majority of cases in the third wave across globe. Though lesser in severity as compared to its predecessor, the delta variant, this mutation has shown higher communicable rate. This novel virus with symptoms of pneumonia is dangerous as it is communicable and hence, has engulfed entire world in a very short span of time. With the help of machine learning techniques, entire process of detection can be automated so that direct contacts can be avoided. Therefore, in this paper, experimentation is performed on CoViD19 chest X-ray images using higher order statistics with iterative and non-iterative models. Higher order statistics provide a way of analyzing the disturbances in the chest X-ray images. The results obtained are quite good with 96.64% accuracy using a non-iterative model. For fast testing of the patients, non-iterative model is preferred because it has advantage over iterative model in terms of speed. Comparison with some of the available state-of-the-art methods and some iterative methods proves efficacy of the work. [ABSTRACT FROM AUTHOR]

Every human being has a different electro-cardio-graphy (ECG) waveform that provides information about the well being of a human heart. Therefore, ECG waveform can be used as an effective identification measure in biometrics and many such applications of human identification. To achieve fast and accurate identification of human beings using ECG signals, a novel robust approach has been introduced here. The databases of ECG utilized during the experimentation are MLII, UCI repository arrhythmia and PTBDB databases. All these databases are imbalanced; hence, resampling techniques are helpful in making the databases balanced. Noise removal is performed with discrete wavelet transform (DWT) and features are obtained with multi-cumulants. This approach is mainly based on features extracted from the ECG data in terms of multi-cumulants. The multi-cumulants feature based ECG data is classified using kernel extreme learning machine (KELM). The parameters of multi-cumulants and KELM are optimized using genetic algorithm (GA). Excellent classification rate is achieved with 100% accuracy on MLII and UCI repository arrhythmia databases, and 99.57% on PTBDB database. Comparison with existing state-of-art approaches has also been performed to prove the efficacy of the proposed approach. Here, the process of classification in the proposed approach is named as evolutionary hybrid classifier. [ABSTRACT FROM AUTHOR]

Light variations from different directions on the face images cause severe performance degradation in face recognition system. These variations should be nullified or suppressed so that the recognition performance can be improved. Here, the objective is to develop a robust and pragmatic method which can compensate the effect of uncontrolled light incident from different directions on the person's face. To normalize the effect of unpredictable illumination variations from face images, a novel illumination–normalization method is proposed by utilizing the fractional discrete cosine transform (Fr-DCT) and a nonlinear modifier. Fr-DCT provides a generalized α-domain in which illumination variation's effect can be better processed. The proposed method adaptively selects and processes the low-frequency α-domain coefficients depending upon the light variations incident on the face image. The illumination-normalized images are classified with the help of a non-iterative classifier. The parameters of the proposed method are optimized with the help of genetic algorithm. The proposed method is tested over Extended YALE B, AR, CMU PIE and YALE face databases. Very promising results have been achieved with percentage error rate zero for CMU PIE and subset 3 of Extended YALE B, 0.38% for subset 4 and 0.55% for subset 5. On AR database, error rate is 0.5% when only illumination-varying images are considered for testing. The comparison of the results of the proposed method with those of the existing state-of-the-art methods clearly establishes the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach is presented for adaptive illumination normalization for face recognition under varying illuminations due to change in angle of light projection. Illumination normalization is performed over some of the low frequency discrete Cosine transform (DCT) coefficients which are computed adaptively based upon the significance of alteration of these coefficients. These are, then, modified using a non-linear modifier. The significance of the proposed approach is that the approach is adaptive in normalizing the illumination from the face images as the number of low frequency DCT coefficients that need to be modified using non-linear modifier are selected on the basis of variations of illumination present in the image. This variation in the illumination is also used in developing the non-linear modifier. The proposed approach is important in such a way that the level of illumination variations decides the computations needed i.e. lesser in comparison to the other existing state-of-art approaches as large number of frequency coefficients are not altered. The proposed approach is tested over various face databases: YALE B, Extended YALE B, CMU PIE, AR and YALE face database. The experimental results clearly reveal that the performance of the proposed approach is significantly better than the existing approaches of illumination normalization for face recognition. With the proposed approach, 100% accuracy is attained on all the subsets of YALE B, CMU PIE and on Subset 3 of Extended YALE B database. Promising results are achieved over remaining face databases as well. [ABSTRACT FROM AUTHOR]