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The consistency of user satisfaction on mobile application has been more competitive because of the rapid growth of multi-featured applications. The analysis of user reviews or opinions can play a major role to understand the user’s emotions or demands. Several approaches in different areas of sentiment analysis have been proposed recently. The main objective of this work is to assist the developers in identifying the user’s opinion on their apps whether positive or negative. A sentiment analysis based approach has been proposed in this paper. NLP-based techniques Bags-of-Words, N-Gram, and TF-IDF along with Machine Learning Classifiers, namely, KNN, Random Forest (RF), SVM, Decision Tree, Naive Byes have been used to determine and generate a well-fitted model. It’s been found that RF provides 87.1% accuracy, 91.4% precision, 81.8% recall, 86.3% F1-Score. 88.9% of accuracy, 90.8% of precision, 86.4% of recall, and 88.5% of F1-Score are obtained from SVM.

Nowadays, most of the people are suffering from the attack of chronic diseases because of their lifestyle, food habits, and reduction in physical activities. Diabetes is one of the most common chronic diseases being suffered by the people of all ages. As a result, the healthcare sector is generating extensive data containing huge volume, enormous velocity, and a vast variety of heterogeneous sources. In such scenario, scientific solutions offer to harness these massive, heterogeneous and complex datasets to obtain more meaningful information. Moreover, machine learning algorithms can play a tremendous part in creating a statistical prediction-based model. The aim of this paper is to identify the prevalence of diabetes related to long-term complications among patients with type-2 diabetes mellitus. The processing and statistical analysis require machine learning environment known as Scikit-Learn, Pandas for Python, and R-Studio for R. In this work, machine learning approaches such as decision tree, random forest for developing classification system-based prediction model to assess type-2 diabetes mellitus chronic diseases have been studied. Additionally, we have proposed an algorithm which is solely based on random forest and tried to detect the complicated areas of type-2 diabetes patients.

In today’s era, most of the people are suffering with chronic diseases because of their lifestyle, food habits and reduction in physical activities. Diabetes; is one of the most common chronic diseases which is happened to the people of all ages. Diabetes complication arises in human body due to increase of blood glucose (sugar) level than the normal level. Type-2 diabetes is considered as one of the most prevalent endocrine disorders. Type-2 diabetes is considered one of the most prevalent endocrine disorders. In this circumstance, we have tried to apply Machine learning algorithm to create the statistical prediction based model that people having diabetes can aware of their prevalence. The aim of this paper is to detect the prevalence of diabetes relevant complications among patients with type-2 diabetes mellitus. The processing and statistical analysis we used Scikit-Learn, Pandas for Python. We also have used unsupervised Machine Learning approaches known as Artificial Neural Network (ANN) and K-means Clustering for developing classification system based prediction model to judge type-2 diabetes mellitus chronic diseases.

Sentiment Detection plays a vital role worldwide to measure the acceptance level of any products, movies or facts in the market. Text vectorization (converting text from human readable to machine readable format) and machine learning algorithms are widely used to detect the sentiment of users. This paper presents and evaluates a multi-level architecture based approach using stacked generalization technique named NStackSenti. The presented approach enables the combination of machine learning algorithms to improve the accuracy of detection. Here, Extremely Randomized Tree (ET), Random Forest (RF), Gradient Boost (GB), ADA Boost (ADA), Decision Tree (DT) are used as base classifiers and XGBoost classifier is used as meta estimator. The NStackSenti is applied on two separate datasets to demonstrate the effectiveness in terms of accuracy. NStackSenti provides better accuracy with trigram than unigram and bigram. It provides 83.7% and 86.24% accuracy on 2000 and 50000 data respectively.

Computer vision has been proven a remarkable entity in modern computer science. Different applications of this field have been used on a regular basis. In this paper, we propose a new model for tracking real time object (i.e., car, human face, interior objects, arms, etc.) from video feed by providing training with HAAR features and also with the implementation of Lucas Kanade Optical Flow including Custom Masking technique. Object tracking has been considered to be very much useful in augmented reality, security, virtual reality, training with simulation, etc. In this research, we have trained our classifier of a specific object for detection purpose. Upon successful training of the classifier, a video footage has been passed into the classifier. Firstly, it detects region of interest (ROI) consisting of the object(s). Secondly, with necessary preprocessing techniques we have detected the contour area enclosing the object. Finding out the contour within the detected object(s) enabled us to create green color within the contour enclosing area. We only kept green contours and subtracted everything from the frames of the scene by Custom Masking technique. Finally, we tracked the object by trailing the green contours of the detected object(s) by Lucas Kanade Optical Flow. Our developed system is able to detect and track different object(s) from a video feed with the settings of less than or equal to 30 frames per second (FPS).

The most broadly perceived relative directions are right, left, up, down, backward and forward. This research paper presents a new computational technique to learn human's relative directions, where one intelligent computer can learn any human's right, left, up, down, backward and forward or different relative directions. The present paper portrays models describing the essential structures of relative direction learning process between human and intelligent machine. We developed two proficient algorithms for solving this approach. In our experiment we propose Human Relative Direction Learning (HRDL) algorithm for learning human's relative directions and Human Direction Identification (HDI) algorithm for tracking any human position and identity human's relative directions from different direction points.