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In this paper, Convolutional Neural Network Segmentation (CNNS) by Dual-Interface Reign Architecture is proposed to figure out the condition of breast tumor. The existing novel approaches from machine learning may not be voluntarily useful as they require a huge training data bit with segmented pixels. This architecture is built upon a CNN and a dual mode network which is used to segment and can non-linearly maps the input data into a deep latent space respectively. the two paths of dual-interface networks are locality preserving learner and a conditional graph learner, both are used for the extraction of images and exploit the input features. The complemented dual-interface architecture is further used to improve the rate of classification. This proposed method can overwhelm the restriction of some benchmarks for the classification in breast imageries. In addition, to perform extensive error analysis, an analytical model is developed for mammograms and tested for varying pixel widths and input probabilities. The analytical model is validated through simulation. The results of our proposed method demonstrate that it can effectively detect the different kinds of mammograms in database by segmenting them more precisely. When comparing with previous works the precision is improved up to 5% and 8% than the GSA-ANN and ANN respectively. [ABSTRACT FROM AUTHOR]

Nowadays social media plays a significant role in all sorts of our activities ranging from analysing the attitude of a person for the job, getting opinions towards buying a product, acting as a forum for exchanging thoughts about the current events of various domains, creating awareness to the public about the natural calamities, educating the public about the fraudulent news spread by the fakers, initiating the young aspirant to protest against any societal issues, etc. Grasping the opinions shared by the experienced people towards a product, film, event, news, or politics like any subject of matter is one among the worth noting applications for a common man. It extends its application to making decisions about our day-to-day activities. The text reviews consist of enormous, sparse, non-uniform distribution of words represented as features. Text mining is the backend process for those applications. It includes techniques such as feature representation, sentiment classification, feature optimization, etc. Analysing the opinions suggested by the experienced people as positive and negative reviews is a challenging process and it is the baseline of our work. This paper contributes to the related processes involved in analysing the sentiments from the text reviews and accurately classifying them based on their polarity. In the proposed work, we particularly focus on feature representation techniques that have a major effect on enhancing the performance of sentiment classification. We explore different feature representation models such as TF-IDF vectorizer, word2vec vectorizer, and glove vectorizer as these word embedding models are interpreting the words and their syntactic and semantic relationships differently from the corpus. Also, we employ machine learning algorithms and a deep convolution neural network to perform comparative studies in classifying the sentiments. The word2vec in combination with Deep Convolution Neural Network provides the accuracy of 85.7%, precision of 84.4%, recall of 87%, and F-measure of 85.7% compared to other models. [ABSTRACT FROM AUTHOR]