A personalized e-learning system is effective in imparting enhanced learning to its users. As compared to a conventional e-learning system, which provides similar contents to each learner, a personalized learning system provides specific learning contents and assessments to the learners. Personalization is based on Artificial Intelligence (AI) based techniques in which appropriate contents for each learner are determined using the level of comprehension of the learner and the preferred modes of learning. This paper presents requirements and challenges for a personalized e-learning system. The paper is focused in elaborating four research questions, which are related to identifying key factors of personalized education, elaborating on state of the art research in the domain, utilizing benefits of AI in personalized education, and determining future research directions. The paper utilizes an in-depth survey of current research papers in answering these questions. It provides a comprehensive review of existing solutions in offering personalized e-learning solutions. It also elaborates on different learning models and learning theories, which are significant in providing personalized education. It proposes an efficient framework, which can offer personalized e-learning to each learner. The proposed framework includes five modules i.e Data Module, Adaptive Learning Module, Adaptable Learning Module, Recommender Module, Content and Assessment Delivery Module. Our work also identifies significant directions for future research. The paper is beneficial for academicians and researchers in understanding the requirements of such a system, comprehending its methodologies, and identifying challenges which are needed to be addressed.

Bug reports facilitate software development teams in improving the quality of software. These reports include significant information related to problems encountered within a software, possible enhancement suggestions, and other potential issues. Bug reports are typically complex and are too detailed; hence a lot of resources are required to analyze and process them manually. Moreover, it leads to delays in the resolution of high priority bugs. Accurate and timely processing of bug reports based on their category and priority plays a significant role in improving the quality of software maintenance. Therefore, an automated process of categorization and prioritization of bug reports is needed to address the aforementioned issues. Automated categorization and prioritization of bug reports have been explored recently by many researchers; however, limited progress has been made in this regard. In this research, we present a novel framework, titled CaPBug, for automated categorization and prioritization of bug reports. The framework is implemented using Natural Language Processing (NLP) and supervised machine learning algorithms. A baseline corpus is built with six categories and five prioritization levels by analyzing more than 2000 bug reports of Mozilla and Eclipse repository. Four classification algorithms i.e., Naive Bayes, Random Forest, Decision Tree, and Logistic Regression have been used to categorize and prioritize bug reports. We demonstrate that the CaPBug framework achieved an accuracy of 88.78% by using a Random Forest classifier with a textual feature for predicting the category. Similarly, using the CaPBug framework, an accuracy of 90.43% was achieved in predicting the priority of bug reports. Synthetic Minority Over-Sampling Technique (SMOTE) has been applied to address the class imbalance issue in priority classes.

Stock market prediction is a challenging task as it requires deep insights for extraction of news events, analysis of historic data, and impact of news events on stock price trends. The challenge is further exacerbated due to the high volatility of stock price trends. However, a detailed overview that discusses the overall context of stock prediction is elusive in literature. To address this research gap, this paper presents a detailed survey. All key terms and phases of generic stock prediction methodology along with challenges, are described. A detailed literature review that covers data preprocessing techniques, feature extraction techniques, prediction techniques, and future directions is presented for news sensitive stock prediction. This work investigates the significance of using structured text features rather than unstructured and shallow text features. It also discusses the use of opinion extraction techniques. In addition, it emphasizes the use of domain knowledge with both approaches of textual feature extraction. Furthermore, it highlights the significance of deep neural network based prediction techniques to capture the hidden relationship between textual and numerical data. This survey is significant and novel as it elaborates a comprehensive framework for stock market prediction and highlights the strengths and weaknesses of existing approaches. It presents a wide range of open issues and research directions that are beneficial for the research community.

In the Social Internet of Things (SIoT), trust refers to the decision-making process used by the trustor (Service Requesters (SRs) or Service Consumers (SCs)) to decide whether or not to entrust the trustee (Service Providers (SPs)) with specific services. Trust is the key factor in SIoT domain. The designing of a two-way, two-stage parameterized feedback-based, service-driven, attacks-resistant trust and reputation system for SIoT accompanied by a penalty mechanism for dishonest SPs and SRs is our main contribution that mitigates the trust-related issues occurring during service provisioning and service acquisition amongst various entities (SPs or SRs) and enhances trust amongst them. Our proposed methodology examines a SP’s local trust, global trust, and reputation by taking into account “Social Trust” and “Quality of Service (QoS)” factors”. Two—Stage Parameterized feedback” is incorporated in our proposed strategy to better manage “intention” and “ability” of SRs and provides early identification of suspicious SRs. This feature compels SRs to act honestly and rate the corresponding SPs in a more accurate way. Our recommended paradigm sorts SPs into three SP status lists (White List, Grey List, and Black List) based on reputation values where each list has a threshold with respect to the maximum service fee that can be charged. SPs in White List charge the most per service. SPs in other lists have a lower selection probability. Every feedback updates the SP’s trust and reputation value. Sorting SPs increases resistance against On Off Attack, Discriminatory Attack, Opportunistic Service Attack, and Selective Behavior Attacks. SPs must operate honestly and offer the complete scope of stated services since their reputation value relies on all their global trust values (Tglobal) for various services. Service requests may be accepted or denied by SPs. “Temporarily banned” SRs can only request unblocked services. SRs lose all privileges once on a “permanently banned” list. If local and global trust values differ by more than the threshold, the SR is banned. Our method also provides resistance against Bad Mouthing Attack, Ballot Stuffing Attack. Good Mouthing Attack/Self—Propagating Attack. Experiments indicate our trust and reputation management system recognizes and bans fraudulent SRs. “Dishonest SPs” are “blacklisted,” which affects their reputation, trust, and service charges.