Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students’ achievement, university’s performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students’ academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students’ graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naïve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students’ graduation time.

Open data initiatives are a crucial aspect of effective e-governance strategy. They embody aspirations towards sociopolitical values of transparency, trust, confidence, and accountability, pertaining to the relationship between a government and its citizens. The importance of such initiatives is especially important for an emerging economy such as Saudi Arabia which is undergoing rapid social changes directed by a contemporary national vision. The effectiveness of open data initiatives depends strongly on (a) the quality of the data available, (b) the soundness of the methodologies and suitability of platforms used to prepare and present the data, and (c) the ability of the data to facilitate the kinds of insights and social-action that are sought from that data to ensure successful e-governance. This paper investigates the feasibility of current Saudi government open data initiatives in this regard. It assesses existing approaches to improve the effectiveness of open government data through transforming it into linked-open data (using the Resource Description Framework [RDF]) by connecting disparate sources of structured data therein. It proposes to improve existing approaches by suggesting a framework for automating the linking sub-process of existing approaches and organizing the data to be queried through SPARQL. Moreover, it evaluates the potential benefit of this proposal by discussing the kinds of policy insights this could generate which would be difficult without it.

With the advent of user-generated content, usability, and interoperability of web platforms, people are today more eager to express and share their opinions on the web regarding both daily activities and global issues. Cancer is often undetected, leading to serious issues which continue to affect a person's life and his surroundings. Recently Twitter has been very popular to be used to predict and monitor real-world outcomes as well as health-related concerns. Nowadays people are using social media in any situation. Even cancer patients, their friends, and family are increasingly sharing their experience in social media, which has increased the ability of patients to find others similar to their conditions to discuss treatment options, suggest lifestyle changes, and to offer support. Our work targets to link patients with a particular illness (cancer) together and to provide researchers with enriched patient data that might be very useful for future analysis of this disease. We wanted to create a meeting point for the healthcare sector and social media through our work. Our target was to collect Twitter data from different continents of the world and analyze them. We scraped tweets from over the last two years from all around the world. Then clean the data using a regular expression and then process it to prepare our own dataset. We used sentiment analysis and natural language processing to classify them into positive, negative and neutral tweets to determine which of the tweet means to have cancer and which don't. We then analyzed the prepared dataset and visualized and compared them with veritable cancer-related information to ascertain if people's tweets are allied with actual cancer situation.

Opinion Mining or Sentiment Analysis is the process of mining emotions, attitudes, and opinions automatically from speech, text, and database sources through Natural Language Processing (NLP). Opinions can be given on anything. It may be a product, feature of a product or any sentiment view on a product. In this research, Mobile phone products reviews, fetched from Amazon.com, are mined to predict customer rating of the product based on its user reviews. This is performed by the sentiment classification of unlocked mobile reviews for the sake of opinion mining. Different opinion mining algorithms are used to identify the sentiments hidden in the reviews and comments for a specific unlocked mobile. Moreover, a performance analysis of Sentiment Classification algorithms is performed on the data set of mobile phone reviews. Results yields from this research provide the comparative analysis of eight different classifiers on the evaluation parameters of accuracy, recall, precision and F-measure. The Random Forest Classifiers offers more accurate predictions than others but LSTM and CNN also give better accuracy.

Educational data mining (EDM) is an emerging interdisciplinary research area concerned with analyzing and studying data from academic databases to better understand the students and the educational settings. In most of the Asian countries, it is a challenging task to perform EDM due to the diverse characteristics of the educational data. In this study, we have performed students’ educational performance prediction, pattern analysis and proposed a generalized framework to perform rigorous educational analytics. To validate our proposed framework, we have also conducted extensive experiments on a real-world dataset that has been prepared by the transcript data of the students from the Marketing department of a renowned university in Bangladesh. We have applied six state-of-the-art classification algorithms on our dataset for the prediction task where the Random Forest model outperforms the other models with accuracy 94.1%. For pattern analysis, a tree diagram has been generated from the Decision Tree model.

The skills of artificial intelligence (AI) and robotics provide a wide window of job opportunities for the following professionals: computer scientists, mechanical engineers, system engineers, computer engineers, biomedical engineers, and electrical engineers. Additionally, other professionals benefiting from AI and robotics’ job opportunities are information technologists, informatic engineers, electronic engineers, data scientists, industrial engineers, big data engineers, and related specialists in the dynamic field of engineering robotics. Therefore, the purpose of this research was to study the effort of teaching AI and robotics to undergraduate systems engineering students at the Polytechnic University Institute “Santiago Mariño” in Barinas, Venezuela. Consequently, the methodology used in this academic research was the case study approach, which included three phases, namely Initiation Phase, Fieldwork Phase, and Closing Phase. In that sense, the design of research adopted in this study was based on the development of an exploratory single case study method. As a result, the Theoretical Framework created as a cornerstone of this research highlighted the following three research variables: (1) Robotic Applications, (2) Mechanics of Robotic Manipulation and Computer Vision, and (3) Object-oriented Analysis and Design (OOAD) and Object-oriented (OO) High-level Programming Languages. In conclusion, two nondirectional null hypotheses were tested, leading to the positive answers of the following research questions: (1) “Can undergraduate systems engineering students apply OOAD and OO High-level Programming Languages to analyze, design, and develop Robotic Applications?” and (2) “Can undergraduate systems engineering students use Mechanics of Robotic Manipulation and Computer Vision to analyze, design, and develop Robotic Applications?” as stated in this case study.