ck+ to simulate and evaluate the performance under the influence of different factors (e.g. network structure, learning … similar expression recognition. Finally, they introduce the k-nearest neighbor (KNN) algorithm compared with CNN to make the …

healthcare data. Design/methodology/approach As the most advanced machine learning (ML) method, static and dynamic DLSTM models … advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method. …

Purpose Artificial intelligence (AI), machine learning (ML) and deep learning (DL) are having a major impact on banking … learning, how financial institutions are currently using AI, and how AI could provide further technological solutions to …

Purpose The purpose of this paper is to catalogue the various ways in which algorithm-driven decision-making now … algorithms. Findings This research determined that perverse and unintended consequences of the spread of algorithm … inequities produced by algorithm-driven decision-making represents a very significant reputation risk and a potential flashpoint …

Deep learning has become popular in all aspect related to human judgments. Most machine learning techniques work well … which includes text classification, text sequence learning, sentiment analysis, question-answer engine, etc. This paper has … paper presents the concept and steps of using deep learning for extraction sentiments from customer reviews. The extraction …

, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an …

purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews …. Design/methodology/approach This paper used machine learning models such as Naïve Bayes, support vector machines (SVM …-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for …

classification of emails using deep learning classifiers such as the long short-term memory (LSTM) model and convolutional neural … classify emails into their relevant categories using machine learning and deep learning models. Two benchmark datasets …, SpamAssassin and Enron, are used in the experimentation. Findings In the first set of experiments, machine learning classifiers …

law course for accounting students and to evaluate its influence on engagement and effective learning. Design …/methodology/approach – The learning activity, known as “corporate villains”, is based on theories of storytelling and engagement. Selected … objectives and identify learning outcomes. Findings – The corporate villains learning activity engaged students at the beginning …

data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain … towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the …