Random Forest and Support Vector Machine based Hybrid Approach to Sentiment Analysis

In this paper we present a cascaded framework of feature selection and classifier ensemble using particle swarm optimization (PSO) for aspect based sentiment analysis. Aspect based sentiment analysis is performed in two steps, viz. aspect term extraction and sentiment classification. The pruned, compact set of features performs better compared to the baseline model that makes use of the complete set of features for aspect term extraction and sentiment classification. We further construct an ensemble based on PSO, and put it in cascade after the feature selection module. We use the features that are identified based on the properties of different classifiers and domains. As base learning algorithms we use three classifiers, namely Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM). Experiments for aspect term extraction and sentiment analysis on two different kinds of domains show the effectiveness of our proposed approach.

Feature selection and ensemble construction

Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework

An expert system for selecting wart treatment method

Melanoma is a deadly skin cancer that breaks out in the skin’s pigment cells on the skin surface. Melanoma causes 75% of the skin cancer-related deaths. This disease can be diagnosed by a dermatology specialist through the interpretation of the dermoscopy images in accordance with ABCD rule. Even if dermatology experts use dermatological images for diagnosis, the rate of the correct diagnosis of experts is estimated to be 75-84%. The purpose of this study is to pre-classify the skin lesions in three groups as normal, abnormal and melanoma by machine learning methods and to develop a decision support system that should make the decision easier for a doctor. The objective of this study is skin lesions based on dermoscopic images PH 2 datasets using 4 different machine learning methods namely; ANN, SVM, KNN and Decision Tree. Correctly classified instances were found as 92.50%, 89.50%, 82.00% and 90.00% for ANN, SVM, KNN and DT respectively. The findings show that the system developed in this study has the feature of a medical decision support system which can help dermatologists in diagnosing of the skin lesions.

https://www.ijisae.org/IJISAE/article/download/714/pdf

Skin Lesion Classification using Machine Learning Algorithms

https://cyberleninka.org/article/n/1174470.pdf

Computer Aided Melanoma Skin Cancer Detection Using Image Processing

Sentiment classification is a way to analyze the subjective information in the text and then mine the opinion. Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease, making choices with regards to online shopping, choosing events, products, entities. The approaches of text sentiment analysis typically work at a particular level like phrase, sentence or document level. This paper aims at analyzing a solution for the sentiment classification at a fine-grained level, namely the sentence level in which polarity of the sentence can be given by three categories as positive, negative and neutral.

Analysis of different approaches to Sentence-Level Sentiment Classification

Emotions are fundamental to human lives and their decision-making. Understanding an expression of emotional feeling between people forms an intricate web. There are systems, been developed that attempt to recognize aspects of emotion related behaviors and to respond to these, for example systems designed to improve the user experience or to change user behavior. The emotion recognition through facial images in recent times have proved to be an interesting topic for the researchers. Since two decades, a big research has been addressed to enhance Human Computer Interaction (HCI). There are a lot of applications and API-accessible software online that parallels the human ability to discern emotional behavior. The visual detection market is expanding enormously that can be seen noted from various system developed and described in this paper. This paper discuss various attributes, methods and emotional labels that are considered by various emotion API system. This paper also gives an overview of the databases available for inferring emotion through human facial features. The paper.

A survey: Software API and database for emotion recognition

— Artificial Intelligence (AI) is the younger field in computer science ready to accept challenges. Software engineering (SE) is the dominating industrial field. So, automating SE is the most relevant challenge today. AI has the capacity to empower SE in that way. Here in this paper we present a state of the art literature review which reveals the past and present work done for automating Software Development Life Cycle (SDLC) using AI. Keywords — Artificial Intelligence, Code Generation, Requirements Engineering, SDLC, Software Design, Software Estimation, Software Testing. I. I NTRODUCTION The disciplines of artificial intelligence and software engineering have developed separately. There is not much exchange of research results between them. AI research techniques make it possible to perceive, reason and act. Research in software engineering is concerned with supporting engineers to developed better software in less period. Rech and Altoff(2008) say "The disciplines of artificial intelligences and software engineering have many commonalities. Both deals with modeling real world objects from the real world like business process, expert knowledge, or process models." Now a day's several research directions of both disciplines come closer together and are beginning to build new research areas. Software agents play an important role as research objects in distributed AI(DAI) as well as in Agent Oriented Software Engineering(AOSE). Knowledge-based System(KBS) are being examine for Learning Software Organizations (LSO) as well as Knowledge Engineering(KE). Ambient intelligence(AmI) a new research area for distributed, non-intrusive, and intelligent software system both from the direction of how to build these system as well as how to designed the collaboration between system. Lastly computational intelligence(CI) plays an important role in research about software analysis or project management as well as knowledge discovery in machine learning or databases.[1] Artificial Intelligence techniques, which aim to create software systems that exhibit some form of human intelligence, have been employed to assist or automate the activities in software engineering. Software inspections are been applied with great success to detect defects in different kinds of software documents such as specifications, design, test plans, or source code by many researchers.[2] Automated software engineering is a research area which is constantly developing new methodologies and technologies. It includes toolsets and frameworks based on mathematical models (theorem provers and model checkers), requirements-driven developments and reverse engineering (design, coding, verification validation), software management (configurations and projects), and code drivers (generators, analyzers, and visualizers). In the following sections we have tried to review some research techniques to automate each phase of software development life cycle using artificial intelligence.

http://www.irdindia.in/journal_ijacect/pdf/vol4_iss1/1.pdf

Use of Artificial Intelligence in Software Development Life Cycle: A state of the Art Review

Data mining also sometimes called data or knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information. Image processing is related to Computer vision, which is a high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images. One of the ways to do this is by comparing selected facial features from the image and a facial database. Recognizing emotion from images has become one of the active research themes in image processing and in applications based on human-computer interaction. This research conducts an experimental study on recognizing facial emotions. The flow of our emotion recognition system include the basic process in FER system. These include image acquisition, preprocessing of an image, face detection, feature extraction, classification and then when the emotions are classified the system assigns the user particular music according to his emotion. Our system focuses on live images taken from the webcam. The aim of this research is to develop automatic facial emotion recognition system for stressed individuals thus assigning them music therapy so as to relief stress. The emotions considered for the experiments include happiness, Sadness, Surprise, Fear, Disgust, and Anger that are universally accepted.

Vandana Jagtap

Papers

Computer Aided Melanoma Skin Cancer Detection Using Image Processing

Analysis of different approaches to Sentence-Level Sentiment Classification

A survey: Software API and database for emotion recognition

Use of Artificial Intelligence in Software Development Life Cycle: A state of the Art Review

Facial emotion recognition system through machine learning approach