Online learning platforms such as Massive Open Online Course (MOOC), Virtual Learning Environments (VLEs), and Learning Management Systems (LMS) facilitate thousands or even millions of students to learn according to their interests without spatial and temporal constraints. Besides many advantages, online learning platforms face several challenges such as students’ lack of interest, high dropouts, low engagement, students’ self-regulated behavior, and compelling students to take responsibility for settings their own goals. In this study, we propose a predictive model that analyzes the problems faced by at-risk students, subsequently, facilitating instructors for timely intervention to persuade students to increase their study engagements and improve their study performance. The predictive model is trained and tested using various machine learning (ML) and deep learning (DL) algorithms to characterize the learning behavior of students according to their study variables. The performance of various ML algorithms is compared by using accuracy, precision, support, and f-score. The ML algorithm that gives the best result in terms of accuracy, precision, recall, support, and f-score metric is ultimately selected for creating the predictive model at different percentages of course length. The predictive model can help instructors in identifying at-risk students early in the course for timely intervention thus avoiding student dropouts. Our results showed that students’ assessment scores, engagement intensity i.e. clickstream data, and time-dependent variables are important factors in online learning. The experimental results revealed that the predictive model trained using Random Forest (RF) gives the best results with averaged precision =0.60%, 0.79%, 0.84%, 0.88%, 0.90%, 0.92%, averaged recall =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, averaged F-score =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, and average accuracy =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91% at 0%, 20%, 40%, 60%, 80% and 100% of course length.

Context: Recently, research community of certain domain showing their eagerness towards the use of social media networks to gain constructive knowledge in decision making and automation, such as aid to perform software development activities, crypto-currencies usage, network community detection and recommendation and so on. Recently, besides other domains of eHealth, the use of social media and big data analytics has become hot topic to predict the patient of mental illness involved in either depression, schizophrenia, eating disorders, anxiety or addictive behaviors. Problem: Traditional methods either need enough historic data or to keep the regular monitoring on patient activities for identification of a patient associated with a mental illness disease. Method: In order to address this issue, we propose a methodology to classify the patients associated with chronic mental illness diseases (i.e. Anxiety, Depression, Bipolar, and ADHD (Attention Deficit Hyperactivity Disorder) based on the data extracted from the Reddit, a well-known network community platform. The proposed method is employed through Co-training (type of semi-supervised learning approach) technique by incorporating the discriminative power of widely used classifiers namely Random Forrest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). We used Reddit API to download posts and top five associated comments for construction of a feature space. Results: The experimental results indicate the effectiveness of Co-training based classification rather than the state of the art classifiers by a margin of 3% on average in par with every state of art technique. In future, the proposed method could be employed to investigate any classification problem of any domain by extracting date from the social media.

Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle.

Automatic incident detection (AID) plays a vital role among all the safety‐critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers and other stakeholders (hospitals and rescue, police, and insurance departments) in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems (TIMSs) and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type‐specific, i.e., either they are designed for the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification (E‐IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E‐IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E‐IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents.

Sentiment Analysis is a modern discipline at the crossroads of data mining and natural language processing. It is concerned with the computational treatment of public moods shared in the form of text over social networking websites. Social media users express their feelings in conversations through cross-lingual terms, intensifiers, enhancers, reducers, symbols, and Net Lingo. However, the generic Sentiment Analysis (SA) research lacks comprehensive coverage about such abstruseness. In particular, they are inapt in the semantic orientation of Crosslingual based code switching, capitalization and accentuation of opinionative text due to the lack of annotated corpora, computational resources, linguistic processing and inefficient machine translation. This study proposes a Heuristic Framework for Crosslingual Sentiment Analysis (HF-CSA) and takes into consideration the NetLingua, code switching, opinion intensifiers, enhancers and reducers in order to cope with intrinsic linguistic peculiarities. The performance of proposed HF-CSA is examined on Twitter dataset and robustness of system is assessed on SemEval-2020 task9. The results show that HF-CSA outperformed the existing systems and reached to 71.6% and 76.18% of average accuracy on Clift and SemEval-2020 datasets respectively.