In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.

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Error Correction Schemes (ECSs) significantly contribute to enhancing reliability and energy efficiency of Wireless Sensor Networks (WSNs). This review paper offers an overview of the different types of ECS used in communication systems and a synopsis of the standards for WSN. We also discuss channels and network models for WSN as they are crucial for efficient ECS design and implementation. The literature review conducted on the proposed energy consumption and efficiency models for WSN indicates that existing research work has not considered Single Hop Asymmetric Structure (SHAS) with high performing Error Correcting Codes (ECCs). We present a review on proposed ECS for WSN based on three criteria: Forward Error Correction (FEC), adaptive error correction techniques, and other techniques. Based on our review work, we found that there are limited works on ECS design on a realistic network model i.e., a modified multi-hop WSN model. Finally, we offer future research challenges and opportunities on ECS design and implementation for WSN.Keywords: error control scheme (ECS); energy efficiency; forward error correction (FEC); hybrid automatic repeat request (HARQ); wireless sensor network (WSN)

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Location prediction in an indoor environment is a challenge, and this has been a research trend for recent years, with many potential applications. In this paper, machine-learning-based regression algorithms and Received Signal Strength Indicator (RSSI) fingerprint data from Wireless Access Points (WAPs) with dual Service set IDentifiers (SSIDs) are used, and positioning prediction and location accuracy are compared with single SSIDs. It is found that using Wi-Fi RSSI data from dual-frequency SSIDs improves the location prediction accuracy by up to 19%. It is also found that Support Vector Regression (SVR) gives the best prediction among classical machine-learning algorithms, followed by K-Nearest Neighbour (KNN) and Linear Regression (LR). Moreover, we analyse the effect of fingerprint grid size, coverage of the Reference Points (RPs) and location of the Test Points (TPs) on the positioning prediction and location accuracy using these three best algorithms. It is found that the prediction accuracy depends upon the fingerprint grid size and the boundary of the RPs. Experimental results demonstrates that reducing fingerprint grid size improves the positioning prediction and location accuracy. Further, the result also shows that when all the TPs are inside the boundary of RPs, the prediction accuracy increases.