ABSTRACT Requirements engineering is one of the most crucial parts of the lifecycle of software engineering. Many programs fail annually due to deficiencies in requirements engineering. Requirements engineering documents are written in natural languages, which can lead to ambiguities. The presence of ambiguity in natural language causes misunderstandings. Accurate and timely identification of these requirements is vital for the development process. However, manual classification is time‐consuming and necessitates automation. Today, with the rapid advancement of technology, machine learning and deep learning are being used to detect these ambiguities in requirement specification documents. The BERT word embedding technique and the Bi‐LSTM algorithm were used in this research. We have used meta‐heuristic algorithms to choose the best value of hyperparameters of our deep learning algorithm. The publicly available Fault‐Prone SRS dataset was utilized to train the models. This dataset was also used to evaluate the performance of the proposed algorithm in terms of F1‐score, accuracy, and other statistical metrics. The BERT‐BiLSTM model outperformed other models in classifying and detecting ambiguities in requirement specification documents, achieving an F1‐score and higher than 81% accuracy.

Abstract Big data analytics is important for identifying and analyzing different patterns, relations, and trends within a large volume of data such as crime prediction. In this paper, we propose a new model for predicting crimes using neural‐fuzzy model. For this purpose, time of occurrence and longitude and latitude of the crime scene are used. We use k‐means algorithms to cluster crime records based on their locations. The crime pattern of each cluster was learnt by its own neural‐fuzzy network, in addition using shuffled frog leaping algorithm to determine the optimal cluster radius. The accuracy and Mean Absolute Error and Root Mean Squared Error of this method were compared with: Gaussian Process Regression, Classification and Regression Tree, and Artificial Neural Network. The experiment results showed that the proposed model was the most accurate model and had the least error to predict the occurrence of crimes so can be used as an effective tool in real‐world applications.

Abstract Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and it is considered as one of the most important risk factor for death, stroke, hospitalization, and heart failure. It is possible to detect AF by analyzing electrocardiogram (ECG) of patients. To work on clean signals and reduce errors resulted from noise, we have used Butterworth filter. The short‐term Fourier transform was used to analyze ECG segments to obtain ECG spectrogram images. Convolutional neural network (CNN) models have been proposed for improving automatic detection of AF. The number of convolutional layers varies in different CNN models, and as the model become deeper, more hyper parameters are added. So in this article, variable length genetic algorithm was used in order to optimize hyper parameters of CNN. The results of experiments that performed on the MIT‐BIH AF database showed that the proposed method achieved 100%, 98.90%, and 99.95% for the sensitivity, specificity, and accuracy, respectively, so the proposed method outperforms the deep CNNs. Hence, the proposed method is an accurate and efficient method for detection of AF.

Abstract Matrix factorization (MF) is one of the most important regression analysis methods used in recommendation systems. Max margin matrix factorization (MMMF) is a variant of MF which transforms the regression analysis problem into a single multi‐class classification problem, and then learns a multi‐class max margin classifier to achieve to a better error rate. One drawback of multi‐class MMMF is its bias towards class with small sample size. Therefore, hierarchical MMMF (HMF) which uses some two‐class MMMF problems in a hierarchical manner for multi‐class classification was proposed. Each two‐class MMMF of HMF is learned on the basis of thresholded training data which is too imbalanced for some two‐class MMMFs. Meanwhile, all training data is used in each two‐class MMMF. In the test phase of HMF, an imbalanced tree is used to estimate rating. Each node of this tree is a learned two‐class MMMF. In this paper, we propose a balanced HMF, which constructs a balanced tree with minimum depth. Each node of this tree is a learned two‐class MMMF on the basis of a part of data which is selected such that to be more balanced than that of the traditional HMF. Moreover, each part of data in our proposed balanced HMF does not have overlap with all previous parts of data. Therefore, the overall training data used in each step of balanced HMF is smaller than that of in the traditional HMF. Experimental results on real datasets show that training time, test time and error rate of our proposed balanced HMF is better than those of the traditional HMF.