A Robust Learning Algorithm Based on Particle Swarm Optimization for Pi-Sigma Artificial Neural Networks.

Artificial neural networks (ANNs) have been frequently used in forecasting problems in recent years. One of the most popular types of ANNs in these days is Pi-Sigma artificial neural networks (PS-ANNs). PS-ANNs have a high order ANN structure and they use both multiplicative and additive neuron models in their architecture. PS-ANNs produce superior forecasting performance because of their high order structure. PS-ANNs are affected negatively by an outlier or outliers in a data set because of having a multiplicative neuron model in their architecture. In this study, a new robust learning algorithm based on particle swarm optimization and Huber's loss function for PS-ANNs is proposed. To evaluate the performance of the proposed method, Dow Jones stock exchange and Australian beer consumption data sets are analyzed and the obtained results are compared with many ANNs types proposed in the literature. Besides, the performance of the proposed method in outlier cases is also investigated by injecting outliers into these data sets. It is seen that the proposed learning algorithm has a satisfying performance both the data have an outlier or outliers' case and original case.

Cognitive Depression Detection Cyber-Medical System Based on EEG Analysis and Deep Learning Approaches.

Long-term depression and negative emotional cycles affect life quality and work productivity. However, depression is not easy to detect, with current methods mostly relying on scales that make it impossible to quickly and directly measure the severity of depression. This study seeks to empirically identify brainwave stimulation feedback electrode points and brain regions related to potential depression. Using brainwave data collected by mood-induction procedures, the front and occipital lobes have the greatest role in the operation of depressive emotions, especially the Fp1 and Fp2 positions and the O1 and O2 positions. The Fourier brainwave bands are mainly affected in the α and θ band, while the wavelet brainwave bands have a significant impact on the minimum value of approximated signals. This study uses two signal processing methods, combined with deep neural network techniques (Multilayer perceptron, Deep neural network, Deep belief network, and Long Short-Term Memory) to develop 8 potential depression assessment models, with models constructed using deep neural networks providing the best and most stable performance. Therefore, this model can be developed as an auxiliary system for rapid and objective assessment of underlying depression, thereby assisting in the autonomous management of emotions and early detection and treatment of depression. In addition, the individual abnormality is found in the low mood stage and appropriate relief methods are provided, potentially reducing the occurrence of depression.

[Information Strategies in Indigenous Communities During the COVID-19 Pandemic].

The COVID-19 pandemic has had an unprecedented impact on society, especially in densely populated areas. Schools have implemented distance learning, which has spawned many related problems. This paper focuses on the difficulties arising from the epidemic in indigenous communities and how appropriate information strategies may be used to solve these. Four main suggestions are provided to assist indigenous students and their teachers to protect themselves and learn during the pandemic and to ensure that educational goals are achieved.

The key successful factors of video and mobile game crowdfunding projects using a lexicon-based feature selection approach.

The emergence of crowdfunding has given many capital demanders a new fund-raising channel, but the overall project success rate is very low. Many scholars have begun to discover key suscessful factors of crowdfunding projects. Previous studies have used questionnaires survey to identify important project features. In addition to requiring a lot of manpower and time, there may also be sampling bias. Moreover, related studies also reported that the project description will affect the success of the crowdfunding project, but there is no research to tell fundraisers which success factors should be included in the content of the project description. Besides, in recent years, game crowdfunding projects have been attracted lots of attention in terms of total fundraising amount and number of projects. Moreover, in traditional feature selection and text mining approaches, the selected terms are un-organized and hard to be explained. Therefore, this study will focus on real video and mobile game project descriptions to replace conventional questionnaires. To solve these issues, we present a lexicon-based feature selection method. We attempt to define "content features" and build lexicons to determine the attributes' values. Three feature selection methods including decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE) will be employed to find organized candidate key successful factors. Then, support vector machines (SVM) will be used to evaluate the performances of candidate feature subsets. Finally, this study has identified the key successful factors for video and mobile games, respectively. Based on the experimental results, we can give fundraisers some useful suggestions to improve the success rate of crowdfunding projects.

Course Design for College Entrepreneurship Education - From Personal Trait Analysis to Operation in Practice.

Nowadays, many countries are promoting entrepreneurial education or the "innovation, entrepreneurship, and creativity" education. Entrepreneurial education can enhance a nation's economic competitiveness and give rise to new business. At the moment, entrepreneurial courses are mostly designed by school teachers; however, while school teachers may possess business experience, they lack in entrepreneurial experience. Hence, entrepreneurial education courses call for experts with entrepreneurial experience to contribute to course designs and assist with course teachings. Entrepreneurial education not only improves a student's entrepreneurial skills, but also enables each student to explore their personal characteristics in order to advance the collaboration efficacy of the team as a whole. This study asked six experts with entrepreneurial experience in the information industry to work with school teachers in course design as well as teaching collaboration. The course design starts with three talk sessions given by professionals who share with students their thoughts and experiences in entrepreneurial products, team organization, fund raising, and profit calculation. Following that, each student is asked to share their own thoughts on entrepreneurial products and start searching for team members and planning their project. During the course, each team receives six individual advising sessions from the professionals, with topics ranging from product modeling, feasibility, product market estimation, fundraising methods, and profit calculation. The experts also provide each team member with personal trait analysis. Last but not least, the course invites five management-level industry professionals to play the role of venture capital investor, and evaluate each team's product modeling based on their presentation. This study reviews the grades given by the experts as well as the evaluations given by the three industry managers to assess whether the entrepreneurial education course's student entrepreneur teams satisfy the industry's expectations.

[Artificial Intelligence in Smart Health: Investigation of Theory and Practice].

The World Health Organization defines Smart Healthcare as "Information and Communication Technology applications in the medical and health fields, including medical care, disease management, public health monitoring, education, and research." In addition, many scholars believe that "Smart Healthcare" refers also to the integration of medical informatics, public health, and business applications mainly through the Internet and related artificial intelligence and data mining technologies in order to provide more accurate personal healthcare services and health information. The concept of deep learning has gained ground rapidly in recent years. While deep learning is usually applied to the studies of image/object recognition such as board game notations, paintings, people/things/objects in pictures, and so on, it is also often applied to the extraction of features. However, researchers have rarely used deep learning methods to predict outcomes in the medical and healthcare fields, preferring instead to make these predictions using algorithms based in traditional statistical methods and regression analysis. This paper introduces and investigates deep learning methods in the context of predicting outcomes in the medical and healthcare fields.

A bio-inspired computing model for ovarian carcinoma classification and oncogene detection.

MOTIVATION: Ovarian cancer is the fifth leading cause of cancer deaths in women in the western world for 2013. In ovarian cancer, benign tumors turn malignant, but the point of transition is difficult to predict and diagnose. The 5-year survival rate of all types of ovarian cancer is 44%, but this can be improved to 92% if the cancer is found and treated before it spreads beyond the ovary. However, only 15% of all ovarian cancers are found at this early stage. Therefore, the ability to automatically identify and diagnose ovarian cancer precisely and efficiently as the tissue changes from benign to invasive is important for clinical treatment and for increasing the cure rate. This study proposes a new ovarian carcinoma classification model using two algorithms: a novel discretization of food sources for an artificial bee colony (DfABC), and a support vector machine (SVM). For the first time in the literature, oncogene detection using this method is also investigated. RESULTS: A novel bio-inspired computing model and hybrid algorithms combining DfABC and SVM was applied to ovarian carcinoma and oncogene classification. This study used the human ovarian cDNA expression database to collect 41 patient samples and 9600 genes in each pathological stage. Feature selection methods were used to detect and extract 15 notable oncogenes. We then used the DfABC-SVM model to examine these 15 oncogenes, dividing them into eight different classifications according to their gene expressions of various pathological stages. The average accuracyof the eight classification experiments was 94.76%. This research also found some oncogenes that had not been discovered or indicated in previous scientific studies. The main contribution of this research is the proof that these newly discovered oncogenes are highly related to ovarian or other cancers. AVAILABILITY AND IMPLEMENTATION: http://mht.mis.nchu.edu.tw/moodle/course/view.php?id=7.