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Neuro-fuzzy system is now one of the most widely used tools in the field of artificial intelligence systems. This study proposes a novel approach for time series stock market price prediction using a recurrent error-based neuro-fuzzy system with momentum (RENFSM). The basic idea of this approach is to use time series price momentum and time series prediction error adjusted to the well-known adaptive neuro-fuzzy inference system, ANFIS. Extended from ANFIS, the aim of this study is to propose a reliable prediction system with minimal error. Moreover, to evaluate the proposed model strength, four top-listed stocks from Dhaka stock exchange were applied. In the experiments, several choices of momentum from 3 to 20 days are selected for data preprocessing. It was found that the proposed RENFSM performed superiorly and was more reliable compared to the existing methods such as ANFIS and neural networks.

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Question classification is a crucial task for answer selection. Question classification could help define the structure of question sentences generated by features extraction from a sentence, such as who, when, where, and how. In this paper, we proposed a methodology to improve question classification from texts by using feature selection and word embedding techniques. We conducted several experiments to evaluate the performance of the proposed methodology using two different datasets (TREC-6 dataset and Thai sentence dataset) with term frequency and combined term frequency-inverse document frequency including Unigram, Unigram+Bigram, and Unigram + Trigram as features. Machine learning models based on traditional and deep learning classifiers were used. The traditional classification models were Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine. The deep learning techniques were Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNN), and Hybrid model, which combined CNN and BiLSTM model. The experiment results showed that our methodology based on Part-of-Speech (POS) tagging was the best to improve question classification accuracy. The classifying question categories achieved with average micro 𝐹 1 -score of 0.98 when applied SVM model on adding all POS tags in the TREC-6 dataset. The highest average micro 𝐹 1 -score achieved 0.8 when applied GloVe by using CNN model on adding focusing tags in the Thai sentences dataset.

Microarrays are useful biological resource to study living forms at the molecule level. Microarrays usually have only few samples but high dimensionality with many missing values. The consequent downstream analysis becomes less efficiency. This paper proposes a methodology to impute missing values in microarray data. The proposed methodology is a combination of KNN-Based Feature Selection and KNN-based imputation (KNNFS Impute). The KNNFS Impute comprises of two main ideas: feature selection and estimation of new values. A comparative study of the proposed method with traditional KNN and Row average methods has been presented for the estimation of the missing values on three microarray data sets: Lung Tumor, Colon Cancer, and ALL-AML Leukemia dataset. The best estimation results are measured by the minimum Normalized Root Mean Squared Error (NRMSE). The results show that the proposed method has powerful estimation ability on the three data sets with smaller NRMSE than the compared methods. The 3rd Intetnational Conference on Innovative Computing Information and Control (ICICIC'08) 978-0-7695-3161-8/08 $25.00 © 2008 IEEE The 3rd Intetnational Conference on Innovative Computing Information and Control (ICICIC'08) 978-0-7695-3161-8/08 $25.00