Waste management is one of the challenges facing countries globally, leading to the need for innovative ways to design and operationalize smart waste bins for effective waste collection and management. The inability of extant waste bins to facilitate sorting of solid waste at the point of collection and the attendant impact on waste management process is the motivation for this study. The South African University of Technology (SAUoT) is used as a case study because solid waste management is an aspect where SAUoT is exerting an impact by leveraging emerging technologies. In this article, a convolutional neural network (CNN) based model called You-Only-Look-Once (YOLO) is employed as the object detection algorithm to facilitate the classification of waste according to various categories at the point of waste collection. Additionally, a nature-inspired search method is used as learning rate for the CNN model. The custom YOLO model was developed for waste object detection, trained with different weights and backbones, namely darknet53.conv.74, darknet19_448.conv.23, Yolov4.conv.137 and Yolov4-tiny.conv.29, respectively, for Yolov3, Yolov3-tiny, Yolov4 and Yolov4-tiny models. Eight (8) classes of waste and a total of 3171 waste images are used. The performance of YOLO models is considered in terms of accuracy of prediction (Average Precision—AP) and speed of prediction measured in milliseconds. A lower loss value out of a percentage shows a higher performance of prediction and a lower value on speed of prediction. The results of the experiment show that Yolov3 has better accuracy of prediction as compared with Yolov3-tiny, Yolov4 and Yolov4-tiny. Although the Yolov3-tiny is quick at predicting waste objects, the accuracy of its prediction is limited. The mean AP (%) for each trained version of YOLO models is Yolov3 (80%), Yolov4-tiny (74%), Yolov3-tiny (57%) and Yolov4 (41%). This result of mAP (%) indicates that the Yolov3 model produces the best performance results (80%). In this regard, it is useful to implement a model that ensures accurate prediction to develop a smart waste bin system at the institution. The experimental results show the combination of KSA learning rate parameter of 0.0007 and Yolov3 is identified as the accurate model for waste object detection and classification. The use of nature-inspired search methods, such as the Kestrel-based Search Algorithm (KSA), has shown future prospect in terms of learning rate parameter determination in waste object detection and classification. Consequently, it is imperative for an EdgeIoT-enabled system to be equipped with Yolov3 for waste object detection and classification, thereby facilitating effective waste collection.

In Wireless Sensor Networks (WSNs), an efficient clustering technique is critical in optimizing the energy level of networked sensors and prolonging the network lifetime. While the traditional bee colony optimization technique has been widely used as a clustering technique in WSN, it mostly suffers from energy efficiency and network performance. This study proposes a Bee Colony Optimization that synergistically combines K-mean algorithms (referred to as K-BCO) for efficient clustering in heterogeneous sensor networks. This is to develop a robust and efficient clustering algorithm that addresses the challenges of energy consumption and network performance in WSNs. The K-BCO algorithm outperformed comparative clustering algorithms such as H-LEACH, DBCP, and ABC-ACO in average error rate (AER), average data delivery rate (ADDR), and average energy consumption (AEC) for transmitting data packets from sensors to cluster heads. The K-BCO outperformed other algorithms in terms of ADDR at 95.00% against H-LEACH (75.86%), DBCP (72.07%) and ABC-ACO (90.08%). The findings indicate that the K-BCO not only optimizes energy consumption but also guarantees more stable and robust solutions, thereby extending the network lifetime of WSNs. Thus, K-BCO is recommended to practitioners in wireless sensor networks as it paves the way for more efficient and sustainable wireless communication.

In response to the surging COVID-19 pandemic, the government of Ghana shut down all schools on March 16th, 2020. This compelled many higher education institutions globally and in Ghana to adopt emergency remote teaching and learning (ERTL) to ensure continuity of academic work. This chapter chronicles the experiences of Ghana Communication Technology University (GCTU) with the ERTL from March 2020 to date. Specifically, the chapter discusses what they did as an institution in terms of ERTL from the perspective of management, faculty, and support services. In this chapter, the authors provide a chronology of experiences with ERTL, the rationale behind actions taken, the mode of implementation, as well as the results of ERTL for faculty and students. Based on these experiences, the chapter advocates for the adoption of the pragmatism, accessibility, content, context, and empathy (PACCE) framework as a useful heuristic tool in ensuring equity and effectiveness of ERTL.

Based on revelations in the literature that effective learner support services contribute in improving student achievement whilst reducing dropout rates, particularly in online distance learning courses, and also a discovery that the research literature largely ignore the question of learner support services to blended learners, this study explored students' access, use and perceptions of learner support services offered to blended learners at a higher education institution in Ghana. The objective was to help improve such services, and also contribute towards the evolution of best-practice strategies for providing timely and efficient learner support services in the blended learning environment, particularly under developing world conditions. 254 students participated in the study by responding to an online questionnaire. Data were analysed using simple descriptive statistics Findings reveal that students did not regularly patronize the services on offer, and those who did, hold the perceptions that such services only averagely addressed their needs, hence they felt moderately satisfied with the channels available for accessing and receiving learner support services at the institution. The implications of these findings are discussed.