Excellence in transmission can be assessed in optical transport networks before providing any additional connections or upgrading the connections. Generally, the Physical Layer Model (PLM) is used to assess the transmission quality which has high probability in uncertainty and inaccuracy due to the circumstances of physical layer. The network efficiency is directly proportional to the margins. If the margins getting increases in the PLM, the efficiency of the network decreases. Maintaining the excellence in transmission is the biggest challenge when the margins getting increased. Other significant factors for excellence in transmission is scalable, minimum latency with maximum speed and energy efficient. Photonic switching is a hopeful solution for handling these challenges. Machine learning technique is proposed to assess the excellence of transmission and flow detection. ML-E and Precedence based scheduling algorithms are proposed for excellence of transmission and flow detection respectively. The proposed techniques justify variations, uncertainties in kits like fiber dilution, dispersion and optimizes PSON (packet switched optical network). Simulation results are demonstrated and the proposed work results indicates that it can outperform a benchmark in all aspects.

A brain tumour is the growth of brain cells that are abnormal, some of which may progress into cancer. Magnetic Resonance Imaging (MRI) scans are the method used most frequently to detect brain tumours. The brain's abnormal tissue growth can be seen on the MRI images, which reveal. Deep learning and machine learning techniques are employed to identify brain tumours in a number of research publications. It only takes a very short amount of time to predict a brain tumour when these algorithms are applied to MRI images, and the increased accuracy makes patient treatment simpler. Thanks to these forecasts, the radiologist can make quick decisions. The suggested approach employs deep learning, a convolution neural network (CNN), an artificial neural network (ANN), a self-defined neural network, andthe existence of brain tumor.

A new method for the annotation of multispectral satellite images based on image segmentation is proposed in this paper. This method performs the multispectral image annotation by incorporating a modified locally excitatory globally inhibitory oscillatory network (LEGION) algorithm and cascaded support vector machine (SVM) classifier. Initially, images in the training set are represented with semantic concepts. The testing image is segmented into various image regions based on the color information. Segmented image regions are classified using cascaded SVM classifier based on the probabilities of semantic classes. Experiments are conducted on multispectral images of Coimbatore, Tamil Nadu, India and the result validates the effectiveness of the proposed image annotation algorithm.

The problem of high quality training samples and high-dimensional data is encountered in high-resolution image change detection. To address these problems, a novel automatic change detection algorithm in bitemporal multispectral images of the same scene using multiindex information is presented. The conspicuous advantages of the proposed algorithm are: (i) the complicated urban scenes are represented by a set of low-level semantic information index (e.g., textural and structural features), the information indices can directly indicate the primitive urban classes and (ii) change detection is carried out automatically using unsupervised approach. The multiindex information map contains vegetation, water and building extracted using enhanced vegetation index, normalized difference water index and developed efficient morphological building index respectively. The proposed algorithm is validated on the multitemporal Landsat ETM+ images over Coimbatore, Tamilnadu, India where auspicious results were achieved by the proposed method. Moreover, the traditional method based on the pixel based change detection has also been implemented for the purpose of comparison to further validate the advantages of the proposed model.