M. A. Ansari

Lung-related ailments are prevalent all over the world which majorly includes asthma, chronic obstructive pulmonary disease (COPD), tuberculosis, pneumonia, fibrosis, etc. and now COVID-19 is added to this list. Infection of COVID-19 poses respirational complications with other indications like cough, high fever, and pneumonia. WHO had identified cancer in the lungs as a fatal cancer type amongst others and thus, the timely detection of such cancer is pivotal for an individual’s health. Since the elementary convolutional neural networks have not performed fairly well in identifying atypical image types hence, we recommend a novel and completely automated framework with a deep learning approach for the recognition and classification of chronic pulmonary disorders (CPD) and COVID-pneumonia using Thoracic or Chest X-Ray (CXR) images. A novel three-step, completely automated, approach is presented that first extracts the region of interest from CXR images for preprocessing, and they are then used to detects infected lungs X-rays from the Normal ones. Thereafter, the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD), which might be utilized in the current scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases. And finally, highlight the regions in the CXR which are indicative of severe chronic pulmonary disorders like COVID-19 and pneumonia. A detailed investigation of various pivotal parameters based on several experimental outcomes are made here. This paper presents an approach that detects the Normal lung X-rays from infected ones and the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders with an utmost accuracy of 96.8%. Several other collective performance measurements validate the superiority of the presented model. The proposed framework shows effective results in classifying lung images into Normal, COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD). This framework can be effectively utilized in this current pandemic scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases.

Controlling schemes for microturbine plays an important role on its performance. To control the various parameters of microturbine, the mathematical modeling of the system has been done. Permanent magnet synchronous generator is used for microturbine and modelled is done in d-q reference frame. An efficient power electronics interface has been used to change the high frequency into useful frequency range. The speed of the turbine is increased and decreased according to the load variation. The turbine is running on 100%, 90% and 80% of its rated speed. The turbine is connected to the different loads for analyzing its overall performance and controller. MATLAB/Simulation based microturbine system with PMSG has been developed to the analysis for the controlling of parameters. The system is tested on different loading conditions of 3 kW, 6.5 kW and 13 kW to obtained the optimize working of microturbine.

The medical image compression plays an important role in digital image compression. Compression reduces the redundancy and encodes the data. To use different techniques and algorithms we can get the desired result. But it will depend on the used algorithm, which algorithm we have used in which circumstances. In this paper we have used SPIHT method with wavelet bior4.4 on brain surface medical image with different type of image parameters. And observe the result of this method with different types of image parameters at different level of iterations.

The MRI brain image detection and classification is very crucial for medical science. From last few years, various methods are being presented. Here in this paper, we proposed a novel approach to classify a given MRI brain image as normal or abnormal i.e. affected through tumor. The performed method firstly taken the wavelet transform for feature extraction from the images then applied principle component analysis to decrease the dimensions of features. Then the decreased features are being employed to kernel-support vector machine (k-SVM). Here the data has been taken for seven general brain diseases viz. glioma, Alzheimer's disease, Sarcoma, coma, LINN, IPOL, HPOL. We collected the data of 20 normal and 140 abnormal patients from Harvard Medical Laboratory website. Here four methods of SVM are performed such as L-Kernel, RBF, Poly, and NN. The comparative analysis is also being performed by the existing literatures which illustrates the result by DWT+PCA+KSVM by applying with GRB Kernel. Our classification gives fast and accurate results and assist doctors to distinguish between normal or abnormal to certain precision values.

Face alignment has been neatly done using deep convolutional neural networks. For achieving desired performance, the selecting strategy of local patches for training and their input range is crucial along with configuration of each network. There is double benefit of this. First, the texture background data over the full face is exploited to detect every key point. Second, the training has been given to networks to forecast every key point together; the geometric constraints amid key points are essentially encoded. So, the method can avert local minimum created by uncertainty and data corruption in challenging image samples because of occlusions, broad pose deviations and extreme lightings. Experimental results prove that the approach has high accuracy and is more robust and less complex in nature.