Accuracy improvement has been one of the most outstanding issues in the recommender systems research community. Recently, multi-criteria recommender systems that use multiple criteria ratings to estimate overall rating have been receiving considerable attention within the recommender systems research domain. This paper proposes a neural network model for improving the prediction accuracy of multi-criteria recommender systems. The neural network was trained using simulated annealing algorithms and integrated with two samples of single-rating recommender systems. The paper presents the experimental results for each of the two single-rating techniques together with their corresponding neural network-based models. To analyze the performance of the approach, we carried out a comparative analysis of the performance of each single rating-based technique and the proposed multi-criteria model. The experimental findings revealed that the proposed models have by far outperformed the existing techniques.

Interactive learning tools are emerging as effective educational materials in the area of computer science and engineering. It is a research domain that is rapidly expanding because of its positive impacts on motivating and improving students' performance during the learning process. This paper introduces an interactive learning environment for teaching and learning information and communication theory and related courses. The environment integrates several modules to meet the students' different learning styles. It contains a movie-like module, an animated hypertext introductory module that fully explains the fundamental concepts of information and communication theory. Besides those important learning modules, it also contains a self-assessment module that contains a set of interactive tests and examinations. Learners can use the environment as a standalone application or as an applet from within any web browser. Some evaluation experiments and comparative analyses of the results were performed to measure the performance of our environment in the classroom.

Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compression ratio (CR), pick signal-to-noise ratio (PSNR), efficiency, encoding time (ET) and decoding time (DT) in order to measure the performance of the state-of-the-art techniques.

We often make decisions on the things we like, dislike, or even don't care about. However, taking the right decisions becomes relatively difficult from a variety of items from different sources. Recommender systems are intelligent decision support software tools that help users to discover items that might be of interest to them. Various techniques and approaches have been applied to design and implement such systems to generate credible recommendations to users. A multi-criteria recommendation technique is an extended approach for modeling user's preferences based on several characteristics of the items. This research presents genetic algorithm-based approaches for predicting user preferences in multi-criteria recommendation problems. Three genetic algorithms' methods, namely standard genetic algorithm, adaptive genetic algorithm, and multi-heuristic genetic algorithms are used to conduct the experiments using a multi-criteria dataset for movies recommendation. The empirical results of the comparative analysis of their performance are presented in this study.

Background: Volunteers play a significant role in supporting hospice and palliative care in Africa, but little is known about the types of volunteers, their motivations and roles in service delivery. Methods: Palliative care experts from 30 African countries were invited to participate in an online survey, conducted in English and French, that consisted of 58 questions on: socio-demographics, the activities, motivation and coordination of volunteers, and an appraisal of recent developments in volunteering. The questionnaire was pre-tested in Uganda. Quantitative data was analysed descriptively with SPSS v22; answers on open-ended questions were analysed using content analysis. Results: Twenty-five respondents from 21 countries replied to the questionnaire. The typical volunteer was reported to be a female aged between 30 and 50 years. Volunteer roles included, among others: direct patient assistance, providing psychosocial / spiritual support, and assisting patients' families. Respondents considered altruism, civic engagement and personal gain (for a professional career) as volunteers' most significant motivational drivers. One in two respondents noted that recruiting volunteers is easy, and cooperation with the communities was often mentioned as helpful. Trainings mostly occurred before the first assignment, with topics covering the palliative care concept, care, psychosocial support and team work. Half of respondents described recent overall volunteering developments as positive, while the other half described problems primarily with financing and motivation. Most volunteers received transportation allowances or bicycles; some received monetary compensation. Conclusions: The findings show a wide range of volunteering in palliative care. We identified volunteers as typically 30-50 years old, non-professional females, motivated by altruism, a sense of civic engagement and personal gain. Palliative care services benefit from volunteers who take on high workloads and are close to the patients. The main challenges for volunteer programmes are funding and the long-term motivation of volunteers.