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Sentiment analysis is an application of natural language processing (NLP) that requires a machine learning algorithm and a dataset. In some cases, the dataset availability is scarce, particularly with Arabic dialects, precisely the Bahraini ones, which necessitates using an approach such as translation, where a rich source language is exploited to create the target language dataset. In this study, a dataset of Amazon product reviews in Bahraini dialects is presented. This dataset was generated using two cascading stages of translation—a machine translation followed by a manual one. Machine translation was applied using Google Translate to translate English Amazon product reviews into Standard Arabic. In contrast, the manual approach was applied to translate the resulting Arabic reviews into Bahraini ones by qualified native speakers utilizing constructed customized forms. The resulting parallel dataset of English, Standard Arabic, and Bahraini dialects is called English_Modern Standard Arabic_Bahraini Dialects product reviews for sentiment analysis “E_MSA_BDs-PR-SA”. The dataset is balanced, composed of 2500 positive and 2500 negative reviews. The sentiment analysis process was implemented using a stacked LSTM deep learning model. The Bahraini dialect product dataset can be utilized in the transfer learning process for sentimentally analyzing another dataset in Bahraini dialects.

Background Virtual reality is utilised in exergames to help patients with disabilities improve on the movement of their limbs. Exergame settings, such as the game difficulty, play important roles in the rehabilitation outcome. Similarly, suboptimal exergames’ settings may adversely affect the accuracy of the results obtained. As such, the improvement in patients’ movement performances falls below the desired expectations. In this paper, a recommender system is incorporated to suggest the most preferred movement setting for each patient, based on the movement history of the patient. Method The proposed recommender system (ResComS) suggests the most suitable setting necessary to optimally improve patients’ rehabilitation performances. In the course of developing the recommender system, three methods are proposed and compared: ReComS (K-nearest neighbours and collaborative filtering algorithms), ReComS+ (k-means, K-nearest neighbours, and collaborative filtering algorithms) and ReComS++ (bacterial foraging optimisation, k-means, K-nearest neighbours, and collaborative filtering algorithms). The experimental datasets are collected using the Medical Interactive Recovery Assistant (MIRA) software platform. Result Experimental results, validated by the patients’ exergame performances, reveal that the ReComS++ approach predicts the best exergame settings for patients with 85.76% accuracy.

Abstract The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed multi-task learning (MTL) to exploit commonalities in drug targets and assays. We used datasets containing curated records about the activity of specific compounds on drug targets provided by ChEMBL. Totally, 1091 assays have been analysed. As a baseline, a single task learning approach that trains random forest to predict drug activity for each drug target individually was considered. We then carried out feature-based and instance-based MTL to predict drug activities. We introduced a natural metric of evolutionary distance between drug targets as a measure of tasks relatedness. Instance-based MTL significantly outperformed both, feature-based MTL and the base learner, on 741 drug targets out of 1091. Feature-based MTL won on 179 occasions and the base learner performed best on 171 drug targets. We conclude that MTL QSAR is improved by incorporating the evolutionary distance between targets. These results indicate that QSAR learning can be performed effectively, even if little data is available for specific drug targets, by leveraging what is known about similar drug targets.

In this article, we present a dataset that comprises different physical rehabilitation movements. The dataset was captured as part of a research project intended to provide automatic feedback on the execution of rehabilitation exercises, even in the absence of a physiotherapist. A Kinect motion sensor camera was used to record gestures. The dataset contains repetitions of nine gestures performed by 29 subjects, out of which 15 were patients and 14 were healthy controls. The data are presented in an easily accessible format, provided as 3D coordinates of 25 body joints along with the corresponding depth map for each frame. Each movement was annotated with the gesture type, the position of the person performing the gesture (sitting or standing) as well as a correctness label. The data are publicly available and were released with to provide a comprehensive dataset that can be used for assessing the performance of different patients while performing simple movements in a rehabilitation setting and for comparing these movements with a control group of healthy individuals.

Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used.We propose an optimization approach for the feature selection problem that considers a "chaotic" version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature. The balance between exploration of the search space and exploitation of the best solutions is a challenge in multi-objective optimization. The exploration/exploitation rate is controlled by the parameter I that limits the random walk range of the ants/prey. This variable is increased iteratively in a quasi-linear manner to decrease the exploration rate as the optimization progresses. The quasi-linear decrease in the variable I may lead to immature convergence in some cases and trapping in local minima in other cases. The chaotic system proposed here attempts to improve the tradeoff between exploration and exploitation. The methodology is evaluated using different chaotic maps on a number of feature selection datasets. To ensure generality, we used ten biological datasets, but we also used other types of data from various sources. The results are compared with the particle swarm optimizer and with genetic algorithm variants for feature selection using a set of quality metrics.

Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP), multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR). The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE) of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven.

Component-based Software Engineering (CBSE) uses components to construct systems, being a means to increase productivity by promoting software reuse and increasing software quality. The process of assembling component is called component composition. Components are themselves compositions of components. This give rise to the idea of composition levels, where a component on level i may be decomposed (using more components) at level i+1 or compositions at level i+1 serves as component at level i. We are approaching the multilevel component composition problem. We formulate the problem as multiobjective, involving 4 objectives. The approach used is an evolutionary computation technique.

The study of social networks and the spread of disease on networks has recently attracted considerable attention and there is a huge amount of work dealing with this matter. The current work presents and develops a social network model based on the multigraph idea in order to simulate the spread of an epidemic. The paper deals with a real example which refers to the spread and evolution of A/H1N1 virus in Romania. The simulations obtained by applying the proposed model approximate very well the real evolution of the virus studied over a period of 10 months.