No abstract

No abstract

Modern organizations are keen to work towards their customer needs. To achieve this, analyzing their activities and identifying their interest in any entity becomes important. Every user has been identified as the most important factor in point of organization, and they never give up even a single user. Several approaches have been discussed earlier, which use artificial intelligence to mine the users and their interest in the problem. However, the deep learning algorithms are identified as most efficient in identifying the user interest but suffer to achieve higher performance. Towards this issue, an efficient multi-feature semantic similarity-based online social recommendation system has been proposed. The method uses Convolution Neural Network (CNN) to train and predict user interest in any topic. Each layer has been identified as a single interest, and neurons of the layers are initialized with huge data set. The neuron estimates the Multi-Feature Semantic Similarity (MFSS) towards each interest of the user. Finally, the method identifies the single interest for the user by ranking each interest to produce recommendations to the user. The proposed algorithm improves the performance of recommendation generation with less false ratio.

In 2018, an invariant numbers ranging from 10 million people suffered from Tuberculosis (TB) approximately that has remained quite stable in recent years, based on the WHO 2019 survey report. This infection rate differs invariable among countries, from less than 5 to more than 500 new infections per 1,00,000 people each year, with a global average of around 130. Around 1.2 million HIV negative deaths existed in 2018. If this prevailing disease were diagnosed earlier, the death rate would have been under control, however sophisticated testing techniques tend to be cost prohibitive of wider acceptance. Some of the most important methods for TB diagnosis include thoracic X-ray image interpretation through image processing by the identification of various structures on thoracic X-rays and anomaly assessment is an important stage in computer-aided diagnosis systems. Chest form and size may contain indications for serious disorders such as pneumothorax, pneumoconiosis, tuberculosis and emphysema. Substantial work might have contributed to simplify diagnosis through implementing various statistical strategies to medical images, minimizing overtime and dramatically lowering overhead costs. In addition, recent advances in deep learning have provided magnificent results in the detection of images in different fields, but their use in diagnose TB remains limited. Thus, this work focuses on the development of a novel approach in disease detection. The concepts presented in this work are placed into practice and linked to current literature. We also proposed an automatic approach in conventional poster anterior chest X-rays for TB identification and diagnosis. We use the chest X-ray image with modified discrete grey wolf optimizer for segmentation techniques to eradicate abnormal areas and shape abnormality. We extract various features from the X-ray image with a shear let extraction that allows the image to be classified as normal or abnormal, based on a deep learning classifier, via the improved residual VGG net CNN with big data. Using Shenzhen Hospital Chest X-ray data set we test the efficiency of our system. The suggested technique has competitive results with comparatively shorter training period and greater precision depending on Masientropy based discrete gray wolf optimizer segmentation with an improved residual VGG net CNN. All the simulations are carried out in a mat lab environment.