In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed Point Theorem to prove the existence of the desired voter scoring function and Normalized Google Distance (NGD) to show closeness between words before an answer is suggested to users. Answers are ranked according to their Fixed-Point Score (FPS) for each question. Thereafter, the highest scored answer is chosen as the FPS Best Answer (BA). For each question asked by user, the system applies NGD to check if similar or related questions with the best answer had been asked and stored in the database. When similar or related questions with the best answer are not found in the database, Brouwer Fixed point is used to calculate the best answer from the pool of answers on a question then the best answer is stored in the NGD data-table for recommendation purpose. The system was implemented using PHP scripting language, MySQL for database management, JQuery, and Apache. The system was evaluated using standard metrics: Reciprocal Rank, Mean Reciprocal Rank (MRR) and Discounted Cumulative Gain (DCG). The system eliminated longer waiting time faced by askers in a community question answering system. The developed system can be used for research and learning purposes.

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In community question answering (CQA) systems, topical comments are very valuable to provide information for users. However, it becomes cumbersome going through all these in order to decipher the correct answers to particular questions.Hence, extracting a particular answer to a question becomes vital to avoid reading every comment in the forum. This paper is an extension of of our previous research work that extracted questions from an online forum to develop a system for answer extraction to questions. This system is based on a graph-based method by building answers for related questions using nKullback-Leibler (KL) divergence to obtain ranked answers to a question. The process of extracting answers to questions involves; question core, building question query, query-based answer extraction (QBAE), pattern-based answer extraction (PBAE), and combined answer extraction. The source data for this work were already existing data from ResearchGate, a socio-academic networking website that provides researchers the platform collaborate, ask question and offer answers to question. The performance for answer extraction for 2786 questions shows that when 80% of patterns and keywords were considered, QBAE and PBAE extracted 2765 and 2766 correct answers respectively, while the QBAE + PBAE method extracted 2782 correct answers. Also, when 90% of patterns and keywords were utilized, QBAE and PBAE extracted 2782 and 2784 correct answers, whereas the QBAE + PBAE method extracted 2786 correct answers. Our method was able to identify 229 questions without answers. Finally, the evaluation of our model reveals high-performance accuracy and precision.

The most important dependencies for life sustainability with the growth of same is Health Care. Mobile technologies offers a tremendous opportunity for health care system for developing countries through provision of remote medical consultations. The challenges in our health system serve as the motivation for this research. This research focused on the development of Mobile Application for Monitoring and Management of Out-Patients that can be used by health providers and patients to provide medical consultations remotely with instant feedback using android mobile phone. The features includes chatting between patients and the health providers. Agile Software development life Cycle is employed. The implementation was carried out using Extensible Mark-Up Language (XML), Java Programming language and Android Studio with Android 7.0 Naugaut for the client-side while MySQL database with Django as the web framework and Anaconda as the interpreter at the server-side. Questionnaire method of data collection was employed, and Descriptive Statistics was used to analyze the data collected. The result of the evaluation shows that the application was rated positively and the average score from the users reached 95% Confidence Value.