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Globally, the selection of tertiary programmes for higher education in a university by prospective applicants is a daunting task. Different universities offer a wide range of programmes using different education delivery modes for teaching and learning. This creates information overload in the context of tertiary programmes. To tackle the information overload problem of tertiary programmes in the context of higher education institutions (HEIs), this paper, therefore, proposes a novel recommendation model called Collaborative and Social-Personality Aware Recommendation of Programmes (CoSPARP) for tertiary programme selection. CoSPARP utilizes a hybrid filtering system that incorporates the computation of similarities relating to the CF, personality traits, and the tie strength of users (prospective applicants) to generate effective programme recommendations for a tertiary programme applicant (TPA). The proposed CoSPARP recommendation method employs the above recommendation entities to create profiles of the TPAs as a basis of profile similarity for tertiary programme recommendations. Results of benchmarking experiments showed that CoSPARP overcomes cold-start due to the proposed (innovative) hybridization process. Additionally, using a relevant real-world dataset and suitable evaluation metrics such as precision, recall, and F-measure, CoSPARP produces more favourable outcomes in comparison to other state-of-the-art methods.

In developing nations such as Ghana, traditional face-to-face (F2F) mode of education is challenged when physical classroom (academic) resources are not available for all students in a particular tertiary institution. Globally, education modes have improved through learning management systems (LMSs) as a result of technological advancements. Accra Technical University (ATU) in Ghana is currently facing the problem of turning away qualified applicants due to the fact that the academic resources in ATU are not enough to accommodate all qualified applicants. Using a quantitative research instrument (questionnaire) in accordance to the five-point Likert scale and components of the technology acceptance model (TAM) framework, this paper tackled the problem above by proposing and developing an LMS to support the education of students in ATU. Analytical results of data responses from 200 lecturers and 16 students in ATU, showed that majority of these stakeholders are willing to embrace technology and the developed LMS in ATU.

This paper assesses the performance of electricity generation using wind/hydrogen/fuel-cell technology. The intermittency of renewables, especially wind, and the need for storage of excess energy make them unattractive for continuous generation of electricity. This paper focuses on the wind resource of Anloga (Ghana) and the potential of hydrogen production from water electrolysis. The assessment of this system covers three main areas including the potential energy generation, environmental impacts, and economic impacts. The paper adopted analytical models of energy generation of fuel cell and hydrogen technologies and further performs their assessment using HOMER software. It was revealed that the annual electricity production from the hydrogen fuel cell is 25,999kW/yr, with an annual capacity shortage of 392kW/yr representing a 10% capacity shortage. The levelized cost of electricity was 0.602$/kWh and the emissions have been completely minimized as compared to diesel generation plants.

Even though many universities across the world have incorporated internet-based educational and academic systems, the success of their implementation requires an extensive understanding of the end user acceptance process Access to academic resources for teaching and learning using technology (electronic library) has become a popular approach within higher education institutions due to the continuous growth of internet innovations and technologies This case study research, conducted at Accra Technical University uses the technology acceptance model (TAM) as a theoretical framework The study investigates the effects of the system characteristics such as appropriate user interface (UI), software design, and relevance towards the perceived ease of use and perceived usefulness on intention to use the proposed e-library system Two hundred (200) students and sixteen (16) lecturers participated in this quantitative study Results show that improvement of the existing e-library system in ATU will enable students to utilize digital learning resources for effective teaching and learning, especially during the current global COVID-19 pandemic Copyright © 2021, IGI Global Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited

Globally, the superfluity of scholarly research conferences in varying disciplines has introduced the issue of scholarly big data and information overload related to both research papers and conference proceedings/sessions. This evident scholarly expansion in different disciplines has increased the collaborative importance of conferences. Consequently, the problem regarding attendees selecting the right conference session(s) to attend in academic conferences requires further and urgent attention. Using a smart conference scenario, this paper aims to address the problem above by proposing an improved venue recommender algorithm called Socially-Aware Recommendation of Venues and Environments-2 ( SARVE-2 ). Using a closeness centrality approach, SARVE-2 initially employs Breadth First Search (BFS) and Depth First Search (DFS) strategies to search for relevant presenters for a target attendee. Then, the tie strength of the (searched) presenter and target attendee is computed to generate reliable social (conference session) recommendations for the target attendee. Through the utilization of a relevant (real-world) dataset, our benchmark experiments reveal that, in comparison with other contemporary methods, SARVE-2 exhibits better performance in terms of effective social recommendation search, as well as social recommendation quality, coverage and accuracy.

Although in Accra Technical University (ATU), Ghana there exists a traditional face-to-face (F2F) mode of education already in place, the implementation of mobile learning (m-learning) through ICT in education will solve problems such as small classroom size, inappropriate time schedule for lectures, and provision technological resources needed to run successful classroom education. In order to validate successful implementation of m-learning in ATU, this paper employed a questionnaire research instrument with reference to the technology acceptance model (TAM-2) as a theoretical framework. Closed-ended questionnaires were administered to a sample size of 160 students and 15 lecturers in the Faculty of Applied Sciences (FAS) in ATU. Based on the responses received, the authors established positive technological acceptance of respondents, which paved the way to propose and develop a suitable m-learning system for ATU. It is envisaged that successful implementation of the m-learning system proposed in this paper will practically increase the use of ICT in education by both lecturers and students in ATU.

The rapid growth of mobile television (TV), smart TV, and Internet Protocol Television (IPTV) content due to the convergence of broadcasting and the Internet requires effective recommendation methods to select appropriate TV programs/channels. Many previous methods have been proposed to address this issue. However, imperative factors such as the utilization of personality traits and social properties to recommend programs for TV viewers remain a challenge. Consequently, in this paper, we propose a recommender algorithm called Recommendation of Programs via Personality and Social Awareness (ROPPSA) for TV viewers. ROPPSA utilizes normalization and folksonomy procedures to generate group recommendations for TV viewers who have common similarities in terms of personality traits and tie strength with a Target TV Viewer (TTV). Therefore, ROPPSA improves TV viewer cold-start and data sparsity situations by utilizing their personality traits and tie strengths. We conducted extensive experiments on a relevant dataset using standard evaluation metrics to substantiate our ROPPSA recommendation method. Results of our experimentation procedure depict the advantage, recommendation accuracy, and outperformance of ROPPSA in comparison with other contemporary methods in terms of precision, recall, f-measure (F1), and arithmetic mean (AM).

Although in Accra Technical University (ATU), Ghana, there exists a traditional Face-to-Face (F2F) mode of education already in place, the implementation of electronic learning (e-learning) through ICT in education will increase the number of students admitted yearly. Lack of academic facilities makes e-learning an attractive alternative. Consequently, this article employed a questionnaire as a research instrument with reference to the Technology Acceptance Model (TAM-2) as a theoretical framework. In all, questionnaires were administered to 190 students and 14 lecturers in the Faculty of Applied Sciences in Accra Technical University (ATU). Based on the responses received, various types of e-learning systems were exploited and a SWOT (Strength Weaknesses Opportunities and Threats) analysis was conducted to validate the sustainability of proposing and developing a suitable e-learning system for ATU. It is envisaged that successful implementation of the e-learning system proposed in this article will practically increase the use of ICT in education by both lecturers and students in ATU.

Distributed generation presents the potential solution to the power deficit problem faced in developing counties by encouraging local generation with renewable energy resources that are abundantly available in some part of the world, especially in Ghana. Distributed generations on the other hand are closely linked to Smart Grid (SG) that uses information technology to manage electricity networks. A plethora of problems relating to safety, reliability and affordability of Smart grid solutions remains challenging today. In Africa, especially in Ghana, the adoption of smart grid has been very slow, poorly managed, and almost inexistent in some countries despite the crucial need for electricity provision. This slow adoption can be attributed to many limiting factors that demand a thorough investigation. This paper aims at investigating the barriers to the adoption of smart grids in Africa and also develops and evaluates countermeasures to hasten the adoption process. A stratified sampling approach was used to collect comprehensive data in Ghana that were handled with SmartPLS software. The evaluation of factors was done through inferential statistics. The impact of the proposed measures was tested using a partial least structural equation model analysis. Findings revealed some factors that strongly influence the adoption of SG including cost, education, government policies; other factors like culture, Grid stability, had slight influence on the adoption process, nevertheless, societal perception had a low impact on the adoption process and therefore attracts less attention in fostering the SG acceptance in Ghana. Regarding SG prospects, the study identified several factors that can foster the prospect of smart grid adoption in Ghana namely education on smart grid technology, government policies and consumers behaviour. It is believed that, if careful attention is given to the factors listed above, the adoption of SG in Ghana will move at a faster speed.

Globally, the right and appropriate selection of tertiary programmes by potential students in education corroborates every nation's development progress. In order to explore the effect of career counseling and development in high schools in Ghana with a focus on some selected senior high school (SHS) students, this paper utilized a quantitative (questionnaire) research instrument to corroborate the development a web-based expert system for tertiary programme selection. An analytical summary of questionnaire responses received from the selected SHS students showed that due to limited career assessment processes, SHS students in Accra, Ghana arbitrary select tertiary programmes without realizing how such selections can affect their future careers. In terms of user acceptance testing (UAT), 80% of the selected SHS students (100) found our proposed system to be very useful. Such a system will therefore solve and improve career guidance, counselling, and development problems of SHS students in Ghana.

A health system is described as a logically organized collection of resources, agents, and institutions that offer healthcare to a specific population based on the finance, regulation, and delivery of health services. Many health centres have been established in Accra, the capital city of Ghana, due to the importance of good health. People in other developed nations can seek adequate healthcare, since information about relevant health centres is readily available. However, there is a paucity of information about the services provided by existing health institutions in Ghana, particularly in Accra. The majority of patients commute to either Korle-Bu Teaching Hospital or Greater Accra Regional Hospital, putting a considerable medical strain on these facilities. In this study, we use a Geographic Information System (GIS) to establish a database for all of Accra’s health centres and categorize them according to the services they provide. This research tackled the previously mentioned problem by proposing and developing a web-based map called Geohealth for the classification of public health centres in Accra using GIS to assist users in accessing information and locating health centres. We utilized a mixed-method approach consisting of quantitative as well as Build Computer Science Research Methods. Results of our study show that the majority of the participants and stakeholders in our research are eager to embrace Geohealth. Furthermore, in comparison with existing techniques such as Google Maps, our proposed approach, Geohealth, takes less time to obtain information and locate public health centres in Accra, Ghana.

Globally, choosing the right tertiary programme for university (higher) education is quite a difficult task for students. A wide range of programmes are offered by the individual universities which differ in terms of delivery modes and entry requirements. Technology inclusion in the 21st century has paved the way for the proliferation of electronic/computing systems such as electronic counseling (e-counseling) and electronic learning (e-learning). By employing a quantitative research instrument (questionnaire) to ascertain technology acceptance of Senior High School (SHS) in Ghana, this chapter proposes a web-based (e-counseling) expert system which will match students' backgrounds with the right tertiary programme towards career development. Evaluation of our proposed approach suggests that majority of the selected students (80%) out of 100 who used the system accepted and embraced it. Such a system will therefore solve and improve career guidance, counseling, and development problems of SHS students in Ghana.

This paper seeks to propose the implementation of a mobile agriculture (m-agriculture) system for Poms Poultry Farm (PPF) to solve problems such as low patronage of farm products and customer relationship management among farmers and customers. To corroborate the successful implementation of an m-agriculture system at PPF, we employed a questionnaire research instrument concerning the Technology Acceptance Model (TAM) model as a theoretical framework. Closed-ended questionnaires were administered to a sample size of 130 PPF customers. Based on the responses received, we established optimistic responses towards technological acceptance which paved the way for us to propose and develop a suitable m-agriculture application for PPF. It is envisaged that successful implementation of our proposed system will practically increase productivity and help PPF reach out to their target customers.

Ad-hoc Social Network (ASNET), which explores social connectivity between users of mobile devices, is becoming one of the most important forms of today's internet. In this context, maximum bandwidth utilization of intermediate nodes in resource scarce environments is one of the challenging tasks. Traditional Transport Control Protocol (TCP) uses the round trip time mechanism for sharing bandwidth resources between users. However, it does not explore socially-aware properties between nodes and cannot differentiate effectively between various types of packet losses in wireless networks. In this paper, a socially-aware congestion avoidance protocol, namely TIBIAS, which takes advantage of similarity matching social properties among intermediate nodes, is proposed to improve the resource efficiency of ASNETs. TIBIAS performs efficient data transfer over TCP. During the course of bandwidth resource allocation, it gives high priority for maximally matched interest similarity between different TCP connections on ASNET links. TIBIAS does not require any modification at lower layers or on receiver nodes. Experimental results show that TIBIAS performs better as compared against existing protocols, in terms of link utilization, unnecessary reduction of the congestion window, throughput and retransmission ratio., 10 pages, 10 figures

Globally, the current coronavirus disease 2019 (COVID-19) pandemic is resulting in high fatality rates. Consequently, the prevention of further transmission is very vital. Until vaccines are widely available, the only available infection prevention methods include the following: contact tracing, case isolation and quarantine, social (physical) distancing, and hygiene measures (washing of hands with soap and water and using alcohol-based hand sanitizers). Contact tracing, which is key in preventing the spread of COVID-19, refers to the process of finding unreported people who maybe infected by using a verified case to trace back possible infections of contacts. Consequently, the wide and fast spread of COVID-19 requires computational approaches which utilize innovative algorithms that build a memory of proximity contacts of cases that are positive. In this paper, a recommender algorithm called socially aware recommendation of people probably infected with COVID-19 (SARPPIC) is proposed. SARPPIC initially utilizes betweenness centrality in a social network to measure the number of target contact points (nodes/users) who have come into contact with an infected contact point (COVID-19 patient). Then, using contact durations and contact frequencies, tie strengths of the same contact points above are also computed. Finally, the above algorithmic computations are hybridized through profile integration to generate results for effective contact tracing recommendations of possible COVID-19-infected patients who will require testing in a healthcare facility. Benchmarking experimental results in the paper demonstrate that, using two interconnected relevant real-world datasets, SARPPIC outperforms other relevant methods in terms of suitable evaluation metrics such as precision, recall, and F-measure.

As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants. Recent research has shown that the personality traits of users can be used as innovative entities for effective recommendations. Nevertheless, subjective perceptions involving the personality of participants at smart conferences are quite rare and haven't gained much attention. Inspired by the personality and social characteristics of users, we present an algorithm called Socially and Personality Aware Recommendation of Participants (SPARP). Our recommendation methodology hybridizes the computations of similar interpersonal relationships and personality traits among participants. SPARP models the personality and social characteristic profiles of participants at a smart conference. By combining the above recommendation entities, SPARP then recommends participants to each other for effective collaborations. We evaluate SPARP using a relevant dataset. Experimental results confirm that SPARP is reliable and outperforms other state-of-the-art methods., Comment: 12 pages, 13 figures

This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.

In order to innovatively solve cold-start problems, research involving trust and socially aware recommender systems is currently proliferating. The relative importance of academic conferences has led to the necessity of recommender systems that seek to generate recommendations for conference attendees. In this paper, we aim to improve the recommendation accuracy of socially-aware recommender systems by proposing a linear hybrid recommender algorithm called Personality and Socially-Aware Recommender ( PerSAR ). PerSAR hybridizes the social and personality behaviours of smart conference attendees. Our recommendation methodology mainly aims to employ an algorithmic framework that computes the personality similarities and tie strengths of conference attendees so that effective and reliable recommendations can be generated for them using a relevant dataset. The experimental results substantiate that our proposed recommendation method is favorable and outperforms other related and contemporary recommendation methods and techniques.