Mines in the waters are just explosives that detonate upon contact with an object. The underwater submarine must foresee if it will encounter a mine or a rock. Lacking the development of the Ranging Sound Navigation approach, which utilizes particular variables to identify whether a surface or a barrier is made of a mine or rock, finding mines or rocks would have been extremely difficult. In our study, we demonstrate a technique for predicting underwater rocks and mines using SONAR waves. At 60 different angles, SONAR pings are employed to record the various frequencies of submerged objects. To identify whether the object in the ocean is a mine or just a rock, the submarine uses SONAR signals, which transmit sound and receive switchbacks. The mine and rock categories are predicted using the prediction models. To create these prediction models, Supervised Machine Learning Classification methods were employed.
Birefringence and order parameter are key points to judge the applications of liquid crystals (LC) in various fields of science and technology. Various methods are proposed to compute these parameters for liquids and liquid crystals [1-5]. Most common method of calculating birefringence is from the measurement of refractive indices [6-10]. However, recent investigations revealed that the birefringence can be calculated simply by using mathematical methods through image analysis of liquid crystal samples [11-15]. Present study also involved calculations of various optical parameters like absorption coefficient, birefringence, order parameter, etc. through statistical methods of textural images. Here, a comparative study was taken to pure and nanoparticles dispersed mesogens. Till now various analyses were done to almost all the pure mesogens [16-21], which found little drawbacks in application point of view [22-24]. However, addition of foreign elements to the lattice of pure mesogen perturbs transition temperatures optical parameters and various thermodynamical parameters, because mesogen is a soft and sensitive matter. Research has been going on in this way and found remarkable variations in the impure mesogens [25-30].
Brain tumor is one of the major causes of death among other types of the cancer because Brain is a very sensitive, complex and central part of the body. Proper and timely diagnosis can prevent the life of a person to some extent. Therefore, in this paper we have introduced brain tumor detection system based on combining wavelet statistical texture features and recurrent neural network (RNN). Basically, the system consists of four phases such as (i) feature extraction (ii) feature selection (iii) classification and (iii) segmentation. First, noise removal is performed as the preprocessing step on the brain MR images. After that texture features (both the dominant run length and co-occurrence texture features) are extracted from these noise free MR images. The high number of features is reduced based on sparse principle component analysis (SPCA) approach. The next step is to classify the brain image using Recurrent Neural Network (RNN). After classification, proposed system extracts tumor region from MRI images using modified region growing segmentation algorithm (MRG). This technique has been tested against the datasets of different patients received from muthu neuro center hospital. The experimentation result proves that the proposed system achieves the better result compared to the existing approaches
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