Stock market prediction plays an important role in financial decision-making for investors. Many of them rely on news disclosures to make their decisions buying or selling stocks. However, accurate modelling stock trends via is a challenging task, considering the complexity and ambiguity natural languages used. Unlike previous work along this line research, which typically applies bag-of-words extract tens thousands features build model, we propose sentiment analysis-based approach using...

Financial news disclosures provide valuable information for traders and investors while making stock market investment decisions. Essential but challenging, the prediction problem has attracted significant attention from both researchers practitioners. Conventional machine learning models often fail to interpret content of financial due complexity ambiguity natural language used in news. Inspired by success recurrent neural networks (RNNs) sequential data processing, we propose an ensemble...

Pneumoconiosis is a group of occupational lung diseases induced by mineral dust inhalation and subsequent tissue reactions. It can eventually cause irreparable damage, as well gradual permanent physical impairments. has affected millions workers in hazardous industries throughout the world, it leading death. difficult to diagnose early pneumoconiosis because low sensitivity chest radiographs, wide variation interpretation between among readers, scarcity B-readers, which all add difficulty...

Although machine learning classifiers have been successfully used in the medical and engineering fields, there is still room for improving predictive accuracy of model classification. The higher classifier, better suggestions can be provided decision makers. Therefore, this study, we propose an ensemble approach, called Feature generation-based Ensemble Support Vector Machine (FESVM), classification tasks. We first apply feature selection technique to select most related features. Next,...

Cybersecurity is one of the important considerations when adopting IoT devices in smart applications. Even though a huge volume data available, related to attacks are generally significantly smaller proportion. Although machine learning models have been successfully applied for detecting security on applications, their performance affected by problem such imbalance. In this case, prediction model preferable majority class, while predicting minority class poor. To address problems, we apply...

In recent years cybersecurity has become a major concern in the adaptation of smart applications. A secure and trusted mechanism can provide peace mind for users, especially homes where large number IoT devices are used. Artificial Neural Networks (ANN) promising results detecting any security attacks on However, due to complex nature model used this technique, it is not easy common users trust ANN-based solutions. Also, selection right hyperparameters ANN architecture plays crucial role...

Happiness refers to an emotional state of well-being and contentment. Accurate prediction happiness is important for people in promoting a healthy lifestyle, helping reduce stress, enhancing humans' immune system. In this paper, we propose novel fuzzy feature generation approach prediction. We design weighted operation based on the IF-THEN rules generate feature. This generated (new information) added model training achieve more accurate model. addition, considering high interpretability...

Student performance prediction is vital for identifying at-risk students and providing support to help them succeed academically. In this paper, we propose a feature importance-based multi-layer CatBoost approach predict the students' grade in period exam. The idea construct structure with increasingly important features layer by layer. Specifically, importance are first calculated sorted ascending order. each layer, least accumulated until reaching given threshold. Then, these selected used...

Education greatly aids in the process of students' growth; therefore, education institutions try to provide high-quality their students. A possible remedy is by discovering knowledge from educational data. However, accurately evaluating performance very challenging due different sources and structures In addition, teaching strategies are required because learning ability different. One way discover hidden data use clustering algorithms, which capable mining interesting patterns Thus, this...

Obesity, associated with having excess body fat, is a critical public health problem that can cause serious diseases. Although range of techniques for fat estimation have been developed to assess obesity, these typically involve high-cost tests requiring special equipment. Thus, the accurate prediction percentage based on easily accessed measurements important assessing obesity and its related By considering characteristics different features (e.g. measurements), this study investigates...

The airfoil noise problem is highly nonlinear, but its prediction very important to broadband helicopter rotors, wind turbines, and airframe noise. Thus, this paper presents a novel strategy whereby the minimum-of-maximum relative error support vector machine (RE-SVM) used improve appr oximation ability of fuzzy system. In preliminary design stage, antecedents rule base are cluster rules. Then, those rules with same antecedent clustered. Next, in each cluster, that has highest degree...