Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classification. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the final phase, the best features are subjected to various classifiers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifier, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept.

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Peptides, short-chained amino acids, have shown great potentials toward the investigation and evolution of novel medications for treatment or therapy. The wet-lab based discovery of potential therapeutic peptides and eventually drug development is a hard and time-consuming process. The computational prediction using machine learning (ML) methods can expedite and facilitate the discovery process of potential prospects with therapeutic effects. ML approaches have been practiced favorably and extensively within the area of proteins, DNA, and RNA to discover the hidden features and functional activities, moreover, recently been utilized for functional discovery of peptides for various therapeutics. In this paper, a systematic literature review (SLR) has been presented to recognize the data-sources, ML classifiers, and encoding schemes being utilized in the state-of-the-art computational models to predict therapeutic peptides. To conduct the SLR, fourty-one research articles have been selected carefully based on well-defined selection criteria. To the best of our knowledge, there is no such SLR available that provides a comprehensive review in this domain. In this article, we have proposed a taxonomy based on identified feature encodings, which may offer relational understandings to researchers. Similarly, the framework model for the computational prediction of the therapeutic peptides has been introduced to characterize the best practices and levels involved in the development of peptide prediction models. Lastly, common issues and challenges have been discussed to facilitate the researchers with encouraging future directions in the field of computational prediction of therapeutic peptides.

Gait is a vital biometric process for human identification in the domain of machine learning. In this article, a new method is implemented for human gait recognition based on accurate segmentation and multi-level features extraction. Four major steps are performed including: a) enhancement of motion region in frame by the implementation of linear transformation with HSI color space; b) Region of Interest (ROI) detection based on parallel implementation of optical flow and background subtraction; c) shape and geometric features extraction and parallel fusion; d) Multi-class support vector machine (MSVM) utilization for recognition. The presented approach reduces error rate and increases the CCR. Extensive experiments are done on three data sets namely CASIA-A, CASIA-B and CASIA-C which present different variations in clothing and carrying conditions. The proposed method achieved maximum recognition results of 98.6% on CASIA-A, 93.5% on CASIA-B and 97.3% on CASIA-C, respectively.

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.

In this paper, a hybrid scheme of Dynamic wireless charging (DWC) for electric vehicles EV(s) is proposed to resolve this issue in a network topological infrastructure. The proposed hybrid scheme uses different parameters to allow DWC in EVs. The network infrastructure was established through an enhanced destination sequential distance vector (Enhanced-DSDV) protocol for participating EVs. The DWC charge between paired EV(s) was enabled by magnetic coupling, where the Charge State Estimator (CSE) was used as an unsupervised machine learning technique to learn the current charging status of each EV. Similarly, the captured data of CSE is shared via embedded wireless nodes in the network following enhanced-DSDV routing protocol. Moreover, the proposed model enables each participating EV to transfer charge to another EV participating in the network in DWC environment. To allow, the drivers to monitor the participating EVs in close proximity with their current charge status, location, and distance information, we have have used a dashboard screen in each EV. In addition, each EV uses a generator to produce a magnetic field for magnetic coupling between paired EV(s) to exchange power in wireless environment. The feasibility of the proposed model was thoroughly examined in the real environment of DWC. The results show that the proposed scheme is reliable in terms of DWC in both static and dynamic. Moreover, the enhanced-DSDV routing protocol performed significantly well than existing schemes particularly in terms of throughput, packet lost ratio and latency.