Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A Smart Healthcare Monitoring System for Heart Disease Prediction Based On Ensemble Deep Learning and Feature Fusion

This study provides both a spherical understanding about autonomous ship navigation for collision avoidance (CA) and a theoretical background of the reviewed work. Additionally, the human cognitive abilities and the collision avoidance regulations (COLREGs) for ship navigation are examined together with water based collision avoidance algorithms. The requirements for autonomous ship navigation are addressed in conjunction with the factors influencing ship collision avoidance. Humans are able to appreciate these factors and also perform ship navigation at a satisfactory level, but their critical decisions are highly subjective and can lead to error and potentially, to ship collision. The research for autonomous ship navigation may be grouped into the classical and soft computing based categories. Classical techniques are based on mathematical models and algorithms while soft-computing techniques are based on Artificial Intelligence (AI). The areas of AI for autonomous ship collision avoidance are examined in this paper are evolutionary algorithms, fuzzy logic, expert systems, and neural networks (NN), as well as a combination of them (hybrid system).

Autonomous ship collision avoidance navigation concepts, technologies and techniques

Fuzzy logic control, due to its simple control structure, easy and cost-effective design, has been successfully employed to the application of guidance and control in robotic fields. This paper aims to review fuzzy-logic-based guidance and control in an important branch of robots—marine robotic vehicles. First, guidance and motion forms including the maneuvering, path following, trajectory tracking, and position stabilization are described. Subsequently, the application of three major classes of fuzzy logic control, including the conventional fuzzy control (Mamdani fuzzy control and Takagi–Sugeno–Kang fuzzy control), adaptive fuzzy control (self-tuning fuzzy control and direct/indirect adaptive fuzzy control), and hybrid fuzzy control (fuzzy PID control, fuzzy sliding mode control, and neuro-fuzzy control) are presented. In particular, we summarize the design and analysis process of direct/indirect adaptive fuzzy control and fuzzy PID control in marine robotic fields. In addition, two comparative results between hybrid fuzzy control and the corresponding single control are provided to illustrate the superiority of hybrid fuzzy control. Finally, trends of the fuzzy future in marine robotic vehicles are concluded based on its state of the art.

Survey on Fuzzy-Logic-Based Guidance and Control of Marine Surface Vehicles and Underwater Vehicles

Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a new emotion recognition system based on facial expression images. We enrolled 20 subjects and let each subject pose seven different emotions: happy, sadness, surprise, anger, disgust, fear, and neutral. Afterward, we employed biorthogonal wavelet entropy to extract multiscale features, and used fuzzy multiclass support vector machine to be the classifier. The stratified cross validation was employed as a strict validation model. The statistical analysis showed our method achieved an overall accuracy of 96.77±0.10%. Besides, our method is superior to three state-of-the-art methods. In all, this proposed method is efficient.

Facial Emotion Recognition Based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation

Due to brisk industrial growth, the marine traffic has become an imperative subject in the open sea nowadays. The crew inside the vehicle traffic service (VTS) centre is facing challenging issues on account of continuous growth in vessel number. Currently, most of VTS centers' are using the ARPA RADAR based conventional vehicle traffic management system and VTS staff has to carry out most of the things manually to guide the ship's captain properly. Therefore, there is a strong impetus in the field of ocean engineering to develop a smart system which can take the data from RADAR and autonomously manipulate it, to calculate the degree of collision risk among all vessels from the VTS centre. Later on, the traffic management officer utilizes this information for intelligent decision making. In the past, several researchers have addressed this issue to facilities the VTS crew and captain of the ship but mostly, their research work was for academic purposes and could not get popularity because of extra manual workload. Our proposed vessel collision risk assessment system is an intelligent solution which is based on fuzzy inference system and has the ability to solve the said issues. We calculated the DCPA, TCPA, bearing and VCD among all vessels ships from the VTS centre by using conventional marine equipments and exploited the extracted information to calculate and display the degree of collision risk among all vessels. Furthermore, we developed the RADAR filtration algorithm which helps the VTS officer to gauge out the degree of collision risk around a particular ship. To authenticate the validity and to monitor the performance efficiency, we developed RADAR operated intelligent software which directly gets the required data from RADAR and displays the vessels list based on their degree of collision severity. The laboratory experiments confirm the validity of the proposed system.

An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system

The available classical ontology-based systems are inadequate and limit the information extraction from the internet.An ontology with fuzzy logic is effective technology for precise information extraction from blurred data environment.We proposed fuzzy domain ontology with SVM to extract feature's opinion from reviews and to compute polarity.The result of opinion mining by using SVM with FDO for online large data set is better than SVM-based existing systems.The proposed system thoroughly explains the feature extraction and polarity computation. With the explosion of Social media, Opinion mining has been used rapidly in recent years. However, a few studies focused on the precision rate of feature review's and opinion word's extraction. These studies do not come with any optimum mechanism of supplying required precision rate for effective opinion mining. Most of these studies are based on Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and classical ontology. These systems are still imperfect for classifying the feature reviews into more degrees of polarity terms (strong negative, negative, neutral, positive and strong positive). Further, the existing classical ontology-based systems cannot extract blurred information from reviews; thus, it provides poor results. In this regard, this paper proposes a robust classification technique for feature review's identification and semantic knowledge for opinion mining based on SVM and Fuzzy Domain Ontology (FDO). The proposed system retrieves a collection of reviews about hotel and hotel features. The SVM identifies hotel feature reviews and filter out irrelevant reviews (noises) and the FDO is then used to compute the polarity term of each feature. The amalgamation of FDO and SVM significantly increases the precision rate of review's and opinion word's extraction and accuracy of opinion mining. The FDO and intelligent prototype are developed using Protege OWL-2 (Ontology Web Language) tool and JAVA, respectively. The experimental result shows considerable performance improvement in feature review's classification and opinion mining.

Opinion mining based on fuzzy domain ontology and Support Vector Machine

Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles

The volume of traveling websites is rapidly increasing. This makes relevant information extraction more challenging. Several fuzzy ontology-based systems have been proposed to decrease the manual work of a full-text query search engine and opinion mining. However, most search engines are keyword-based, and available full-text search engine systems are still imperfect at extracting precise information using different types of user queries. In opinion mining, travelers do not declare their hotel opinions entirely but express individual feature opinions in reviews. Hotel reviews have numerous uncertainties, and most featured opinions are based on complex linguistic wording (small, big, very good and very bad). Available ontology-based systems cannot extract blurred information from reviews to provide better solutions. To solve these problems, this paper proposes a new extraction and opinion mining system based on a type-2 fuzzy ontology called T2FOBOMIE. The system reformulates the user's full-text query to extract the user requirement and convert it into the format of a proper classical full-text search engine query. The proposed system retrieves targeted hotel reviews and extracts feature opinions from reviews using a fuzzy domain ontology. The fuzzy domain ontology, user information and hotel information are integrated to form a type-2 fuzzy merged ontology for the retrieving of feature polarity and individual hotel polarity. The Protege OWL-2 (Ontology Web Language) tool is used to develop the type-2 fuzzy ontology. A series of experiments were designed and demonstrated that T2FOBOMIE performance is highly productive for analyzing reviews and accurate opinion mining.

Yong-Gi Kim

Papers

An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system

Opinion mining based on fuzzy domain ontology and Support Vector Machine

Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles

Type-2 fuzzy ontology-based opinion mining and information extraction: A proposal to automate the hotel reservation system

An intelligent collision avoidance system for AUVs using fuzzy relational products