Browsing by Author "Aroba, Oluwasegun Julius"

Now showing 1 - 20 of 21

The adoption of an intelligent waste collection system in a smart city
(IEEE, 2023-03) Mthethwa, Simphiwe M.; Xulu, Thuthukani; Msani, Nonsikelelo N.; Mohlakoana, Thuso T.; Ndlovu, Experience E.; Aroba, Oluwasegun Julius
Solid waste management has become a significant concern in environmental issues. This can be a problem, especially in cities where the population is quickly developing, and the sum of waste produced is expanding like never before. Programs for innovative city waste can help raise proficiency, diminish costs, and improve the aesthetics of open places as cities endeavor to oversee waste in public regions effectively. This study enhances intelligent waste systems by developing innovative technologies and software as additional tools for collection. This research demonstrates how the SQERT model, a periodic trend analysis report specific to projects, will be used to assess the intelligent waste management system and the proposed software technology. Furthermore, A software prototype visualization was created to demonstrate and show how the software system will look and its functionalities to improve the waste collection system.
Adoption of smart traffic system to reduce traffic congestion in a smart city
(Springer Nature Switzerland, 2023) Aroba, Oluwasegun Julius; Mabuza, Phumla; Mabaso, Andile; Sibisi, Phethokuhle
Cities across the world suffer significantly from traffic congestion. Governments are trying to harness the power of today's computing, networking, and communication technologies to build system that can improve the efficiency of current road traffic and conditions. The study investigated the purpose efficiencies of intelligent system to assess their performance. Considering the findings, it can be said that traffic flow forecasting (TFF) possibilities are numerous, involve a variety of technologies, and can significantly reduce most traffic issues in smart cities. The studies were later evaluated to find similarities, content, benefits, and disadvantages of traffic congestion. By applying the project management tools such as the performance metrics and SQERT model were used to evaluate and prioritize the state-of-the-art methods. A classical model was proposed to improve upon and determine the traffic dangers that affect road users and aggregate the information about traffic from vehicles, traffic lights, and roadside sensors. These on-road sensors (ORS) performance are used for analyses such are vehicle classification, speed calculations, and vehicle counts.
African women entrepreneurs and COVID-19 : towards achieving the African Union Agenda 2063
(AOSIS, 2022-01-01) Anwana, Emem O.; Aroba, Oluwasegun Julius
Research on the challenges facing African women entrepreneurship and the impact of the coronavirus disease 2019 (COVID-19) pandemic is scant. This article explored the challenges and the impact of COVID-19 on African women-owned businesses and the effect thereof on the 17th goal of the African Union (AU) Agenda 2063. African women entrepreneurs experience many social inequalities, ranging from cultural norms to family to legal and regulatory measures to accessing finance. The COVID-19 pandemic exacerbated these challenges as many African women entrepreneurs have been forced out of business because of measures taken by African governments to halt the spread of the disease. The article reviewed current literature on African women entrepreneurs and the impact of the COVID-19 pandemic using two databases and is based on a total of 104 published articles. The article provided a foundation for post-COVID-19 policy directives in Africa. The authors recommend measures to mitigate these challenges and discuss strategies for policymakers to re-position African women entrepreneurs for the post-COVID-19 era towards achieving AU Agenda 2063 and realising the Africa we all want. Contribution: The study aligns with the United Nations Sustainable Development Goal of ending poverty in Africa and the AU Agenda 2063 goal of achieving gender equality and empowerment of all women and girls to achieve the Africa we all want.
Analysis of road traffic accidents severity using a pruned tree-based model
(International Information and Engineering Technology Association, 2023-06-30) Adeliyi, Timothy T.; Oluwadele, Deborah; Igwe, Kevin; Aroba, Oluwasegun Julius
Traffic accidents are becoming a global issue, causing enormous losses in both human and financial resources. According to a World Health Organization assessment, the severity of road accidents affects between 20 and 50 million people each year. This study intends to examine significant factors that contribute to road traffic accident severity. Seven machine learning models namely, Naive Bayes, KNN, Logistic model tree, Decision Tree, Random Tree, and Logistic Regression machine learning models were compared to the J48 pruned tree model to analyze and predict accident severity in the road traffic accident. To compare the effectiveness of the machine learning models, ten well-known performance evaluation metrics were employed. According to the experimental results, the J48 pruned tree model performed more accurately than the other seven machine learning models. According to the analysis, the number of casualties, the number of vehicles involved in the accident, the weather conditions, and the lighting conditions of the road, is the main determinant of road traffic accident severity.
Development of a prioritized traffic light control system for emergency vehicles
(Institute of Advanced Engineering and Science (IAES), 2024-10-08) Halleluyah Oluwatobi, Aworinde; Abidemi Emmanuel Adeniyi; Segun Adebayo; Adeniji, Faith; Aroba, Oluwasegun Julius
This research presents a model for an adaptive traffic signal control system aimed at improving urban traffic regulation. It dynamically adjusts signal timing based on vehicle volume at intersections, prioritizing emergency vehicles by allowing them immediate passage. Utilizing Arduino coding, the system controls traffic light intensity according to the traffic flow, enhancing road safety and efficiency. This innovative approach not only facilitates faster clearance for emergency services without human intervention but also reduces congestion and accident rates. This research creates a model for a prioritized traffic signal control system. When the vehicular volume at the intersection varies, the signal time alters autonomously. It identifies theambulance/emergency vehicles and allows the green light for emergency vehicles like ambulances, and fire engines. This approach may be used to detect traffic accidents and infractions of automobile spiral motions. When erected on the road, the entire system allows for quick traffic clearing for rescue vehicles without requiring a policeman. The system's design eliminates the need for sensors or radio frequency identification (RFID) tags, simplifying traffic management. Simulations validate that emergency vehicle travel time is significantly reduced, proving the system's effectiveness in streamlining urban traffic flows.
An ERP implementation case study in the South African retail sector
(Springer Nature Switzerland, 2023) Aroba, Oluwasegun Julius; Chinsamy, Kameshni K.; Makwakwa, Tsepo G.
The enterprise resource planning (ERP) is an ever-growing software used globally and in all sectors of business to increase productivity and efficiency, however, the south African market does not show any symptoms that it needs such facilities as we tangle the whys and how’s on this case study. We use previous studies from the literatures that show an ever-thriving sector such as the South African retail can continue to thrive in the absence of ERP and remain relevant and the biggest market contributors as they have been for the past decades. We focus our sources from year 2020 to 2022 to further influence our case to openly clarify the question of the implementation of ERP system. Our studies settle the unanswered question of the implement ability of an ERP system in the retail sector by exploring both functioning and failed installations and how those were resolved, the effectiveness, efficiency and productivity in the absence and presence of ERP system in place in similar economies such as the South African retail sector, both in the past and present times. The south African retail sector has adopted expensive and difficult to maintain ERP systems, which has a drastic increased improvement in the productivity together with the risks of failure. Such risks were witnessed with Shoprite closing doors in Botswana, Nigeria, and Namibia, this has been proof in failure of expensive and fully paid enterprise resource planning that still failed in more than one country. Our solutions consist of methodology contributed an easy to implement solutions to the retail sectors and can be adapted for different purpose, the integration between large retailers and our system would save millions, time and resources.
An ERP SAP implementation case study of the South African Small Medium Enterprise sectors
(Step Academic, 2023-06-01) Aroba, Oluwasegun Julius
Purpose – This research examines the enterprise resource planning system (ERP) SAP implementation in SMEs. Small and Medium Enterprises (SMEs) are challenged by their use of manual working style and operations preparations. Human errors in the preparation and execution of important documents such as financial statements, invoices, purchase orders, and internal sales orders have been overcome by business Intelligence processes that computerize every document, efficiently store, Retrieved, and quickly send or receive copies. Methodology – This research proposed solution is an ERP theoretical proposed solution and information evaluation pertinent to the investigation of how to continue and monitor the performance of the ERP SAP procurement process in SMEs. This research technique examines difficulties and suggests solutions. It also includes the stages of the procurement process and the benefit of the enterprise resource planning process, use cases and tables were used to represent the findings generated. Other elements contributing to the ERP procurement process are examined, including the challenges and solutions SMEs face. Furthermore, research on enterprise resource procurement and business intelligence (ERPBI) is conducted to gain in-depth knowledge on how the performance of ERP procurement, including ERP SAP, has been monitored in the existence of SMEs. Findings - ERP implementation and SMEs' outcomes were studied using the explorative approach. To validate the model being created, eight individuals from Business and academia with experience in ERP systems were contacted during the development of the tools. The overall findings offer helpful hints on how organizational and operational elements interact to ensure the successful implementation of ERP. Implementing ERP systems is considered both from the process and organizational learning and knowledge management perspectives. Practical Implications – ERP companies concentrate more on SMEs due to the nearsaturation of ERP adoptions in Large Enterprises (LE). Additionally, many SMEs are implementing ERP systems due to globalization, collaboration, value networks, and the massive information flow between and within SAMEs today. Adoption risks stem from SMEs' limited resources and unique qualities that set them apart from Large Enterprises (LE). This article's primary goal is to highlight the aspect of the ERP in the SME domain that needs further research, suggest future research directions, and give the most recent research findings that could help buyers, suppliers, and SMEs when starting ERP projects. More so, there are advantages to implementing an ERP system successfully. However, there is already evidence of substantial failure risks in ERP implementation projects. The non-technical parts of the implementation project are frequently neglected or given less attention by the project managers, who, instead, concentrate primarily on the technical and financial aspects of the project. Therefore, the study of ERP deployment success is one of the key research topics in ERP systems today. Research Limitations – This study contributes to the knowledge of SMEs' ERP intelligence and procurement process implementation.
Higher education enterprise resource planning system transformation of supply chain management processes
(Springer Nature Singapore, 2023) Aroba, Oluwasegun Julius; Chisita, Collence Takaingenhamo; Buthelezi, Ndumiso; Mthethwa, Nompumelelo; Yang, X.; Sherratt, R.S.; Dey, N.; Joshi, A.
The goal of this study was to outline the impact of the enterprise resource planning (ERP) system digital transformation of supply chain management (SCM) processes in higher education by using the desk research technique to gather information from other sources that we reviewed to build our study and identify gaps that were detailed in the discussions and results. This study concentrated on higher education, and observation was made that ERP systems do not fully cover all business operations, including supply chain management procedures such as price fixing, bid rigging, and collusion between employees and suppliers; yet the study satisfied all three research objectives by providing a recommended key methodology to enhance the ERP system of SCM integration in higher education.
A hyper-heuristic heterogeneous multisensor node scheme for energy efficiency in larger wireless sensor networks using DEEC-Gaussian algorithm
(Hindawi Limited, 2021-02-15) Aroba, Oluwasegun Julius; Naicker, Nalindren; Adeliyi, Timothy
A wireless sensor network (WSN) is an intellect-sustainable network that comprises multiple spatially distributed sensor nodes and several sink nodes that collect data from sensors. WSNs remain an active research area in the literature due to challenging factors such as the selection of sensor location according to a given premise, finding optimal routing algorithm, and ensuring energy efficiency and consumption. Minimizing energy and prolonging the network lifetime in the WSNs are the focus of this research work. In the literature, a clustering approach is used in grouping sensor nodes into clusters and is seen as an effective technique used in optimizing energy consumption in WSNs. Hence, in this paper, we put forward a novel clustering-based approach by amalgamating the Gaussian elimination method with the Distributed Energy-Efficient Clustering to produce DEEC_Gaussian (DEEC_Gaus) to stabilize energy efficiency optimization in WSNs. We took the advantages of DEEC and Gaussian elimination algorithms to resolve energy efficiency problems in WSNs. DEEC presents attributes such as increased heterogeneity performance level, clustering stability in operation, and energy efficiency which helps to prolong network lifetime while the Gaussian elimination algorithm added an additional advantage to improve and optimize energy efficiency, to aggregate packets of operations performed in the network lifestyle of energy efficiency in WSNs. The simulations were carried out using MATLAB software with 1000 to 1500 nodes. The performance of the proposed work was compared with state-of-the-art algorithms such as DEEC, DDEEC, and EDEEC_E. The simulated results presented show that the proposed DEEC-Gauss outperformed the three other conventional algorithms in terms of network lifetime, first node dead, tenth node dead, alive nodes, and the overall timing of the packets received at the base station. The results showed that the proposed hyper-heuristic heterogeneous multisensor DEEC-Gauss algorithm presented an average percentage of 3.0% improvement for the tenth node dead (TND) and further improvement of 4.8% for the first node dead (FND). When the performance was compared to the state-of-the-art algorithms in larger networks, the overall delivery was greatly improved and optimized.
The implementation of augmented reality on the Internet of Things for virtual learning in higher education
(Step Academic, 2024-01-28) Aroba, Oluwasegun Julius; Prof. Bringula, Rex P.
Purpose–This article investigates the potential of augmented reality (AR) for virtual learning in higher education. This review discusses the advantages as well as disadvantages of virtual learning, as well as the advantages and functions of augmented reality in digital literacy on innovative education. With the launch of electronic literacy about two years ago during the COVID-19 epidemic, considerable changes in literacy and tuition methods in higher education have previously occurred. It has become clear that virtual literacy issues thereafter worse than actual literacy issues. To meet the needs of today's scholars and establish novel tutoring approaches, educational institutions must implement new literacy technology, such as augmented reality. By implementing nascent literacy technology, this investigation hopes to lead to a better comprehension of stoked reality in virtual literacy for advanced education researchers. Method–The goal of this essay is to investigatethe use of augmented reality in higher education for virtual learning. The moderate category of this technology will also be investigated. Head-mounted displays are occasionally used in conjunction with real-world environments or props, such as when simulating takeoff on a motion platform; however, augmented reality makes reading and teaching methods far more accessible.Results–Similarly, a use case was created to demonstrate the student journey using stoked reality software on the mobile device to fantasize, comprehend, and make learning more accessible for students to engage with their environment. Conclusion–Augmented reality has the potential to identify educational surroundings as far more accurate, acceptable, more unifying than digital illiteracy. Increased reality technology affects literacy and the higher education system. It possesses the possibility of increasing the approachability and accessibility of literacy sources in team and personal study. Recommendations–To successfully integrate augmented reality into the Internet of Things for virtual learning in higher education, organizations need to put a high priority on staff development, make significant infrastructure investments, and foster cross-disciplinary collaboration. Practical Implications–Higher education institutions should prioritize data security and ethical issues while simultaneously investing in faculty development and AR-IoT infrastructure.
An implementation of SAP enterprise resource planning : a case study of the South African revenue services and taxation sectors
(Informa UK Limited, 2023) Aroba, Oluwasegun Julius; Abayomi, Abdultaofeek
A SAP enterprise resource planning (ERP) is a software system that assists organizations in automating and managing fundamental business processes for ideal performance. This research study aims to ameliorate the business operation problems of the South African Revenue Services (SARS) and Taxation sectors. Involving tax sectors in the preliminary stages of SAP ERP design and implementation saves SARS’ clients some resources such as money and time while also allowing various departments to improve their tax technology ecosystem. To address the associated financial, operational, technical, and compliance challenges, an ERP implementation requiring significant support from strategy execution is suggested. This proposed model for designing and implementing an ERP with a case study of the South African Revenue Services (SARS) and other taxation sectors, the benefits of ERP system within the taxation sector, implementation challenges, and proposed solutions are presented in this article while utilising data from a survey that was conducted for 50 SARS employees and taxpayers outside the organization. The proposed ERP system will enhance the connectivity of all operations within the SARS with a central access to all departments rather than having silos of business operations. From our analysis, the Cronbach report of 0.85 obtained, which is greater than 0.7 minimum, shows that it fits the proposed solution of a SAP ERP mobile app for inclusivity in the operational processes for both SARS employees and other taxpayers.
Improving node localization and energy efficiency for wireless sensor networks using hyper-heuristic optimization algorithms
(2022-04-08) Aroba, Oluwasegun Julius; Naicker, N.; Adeliyi, Timothy Temitope
Within the growing Internet of Things (IoT) paradigm, a Wireless Sensor Network (WSN) is a critical component. In a WSN, sensor node localization is typically utilized to identify the target node’s current location at the sink node (SN). This allows local data to be analysed, making it more meaningful. However, there exists an intrinsic problem with node localization and energy efficiency, as identified in the literature, which has led to poor performance, namely, poor estimation, transmission, and detection of the network. This intrinsic problem also directly affects energy efficiency in a WSN, resulting in energy loss and poor node distribution in the WSN. There seems to be no lasting and reliable solution to this intrinsic node localization problem in WSNs. Hence, this research study proposed hyper-heuristic optimization algorithms to improve node localization and energy efficiency in WSNs. This research adopts the Design Research (DR) methodology and the Theory of Modelling and Simulation as the theoretical frameworks of the study. The hyper-heuristic model designed, was considered the conceptual framework of the study. To validate the novel technique, different sizes of sensor networks, namely: - 100 sensor nodes; 100 to 1 500 nodes and 200 to 450 sensor nodes with 20 anchor nodes were simulated in an area measuring 100m x 100m. The novel hyper-heuristic model was implemented in a MATLAB R2020a environment. The hyper-heuristic optimization algorithm’s substantial simulated experiment results were benchmarked utilizing state-of-the-art (modern) techniques to solve challenges related to node localization error, total energy consumed, average consumed packet energy, network throughput, shortest path, dead nodes, packets dispatched to the base station (BS), and the probability of error within the entire network dependent on size. The Data Energy Efficiency Clustering-Gaussian (DEEC-GAUSS) method was used to provide solutions to challenges related to energy efficiency in WSNs. In addition, this research study explored the use of the novel DEEC-GAUSS Gradient Distance Elimination Algorithm (DGGDEA) as the hyper-heuristic optimisation model for localization of nodes in WSNs. DEEC-GAUSS and DGGDEA were valuable additions to the body of knowledge. The results showed that the novel DEEC-GAUSS was the most energy efficient algorithm for 100 sensor nodes and 1000 to 1500 sensor nodes when compared to other stateof-the-art algorithms. Furthermore, the results showed that the novel DGGDEA was able to drastically minimize the node estimation error for sensor nodes. Reliability, accuracy and convergence using hyper-heuristic models to enhance the communication within WSNs has been simulated with evidence that DEEC-GAUSS and DGGDEA has outperformed other stateof-the-art approaches.
Improving supply chain management processes through digital transformation of ERP systems in the oil and gas industry
(2023-08-03) Aroba, Oluwasegun Julius; Prinavin, Govender
This research investigates the changes caused by cloud computing Enterprise Resource Planning (ERP) systems in the oil and gas industry at different levels of organisational structure, processes, supply chain management, and employee management processes across an entire organisation. This research improved on the use of digital transformation and Enterprise Resource Planning (ERP) and its impact on supply chain management with a desktop study approach. This study aims to enhance how ERP integrated software package for the oil and gas industry is implemented in supply chain management operations. The study uses a qualitative approach to establish high-performance operational processes and administers questionnaires to oil and gas companies. Data collection was carried out through questionnaires. The researchers implemented cloud ERP after giving survey questions to managers in the supply chain of oil and gas firms and assessing their responses. The results show a positive impact that will come with cloud ERP implementation in oil and gas companies. Since the result is higher than 0.70, as evidenced by the analysis's 0.81 Cronbach Alpha reliability co-efficient score, the qualitative analysis' output is homogeneous. Oil and gas firms may maintain complete control over storage costs while instantly satisfying consumer demands thanks to the supply chain management system.
An innovative hyperheuristic, gaussian clustering scheme for energy-efficient optimization in wireless sensor networks
(Hindawi Limited, 2021-02-11) Aroba, Oluwasegun Julius; Naicker, Nalindren; Adeliyi, Timothy
Energy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC-Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC-Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy-efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC-Gauss). The results show that the proposed DEEC-Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.
Meta analysis of heuristic approaches for optimizing node localization and energy efficiency in wireless sensor networks
(Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, 2020-10) Aroba, Oluwasegun Julius; Naicker, Nalindren; Adeliyi, Timothy T.; Ogunsakin, Ropo E.
Background: In the literature node localization and energy efficiency are intrinsic problems often experienced in wireless sensor networks (WSNs). Consequently, various heuristic approaches have been proposed to allay the challenges faced by WSNs. However, there is little to nothing in the literature to support which of the heuristic approaches is best in optimizing node localization and energy efficiency problems in WSN. The aim of this paper is to assess the best heuristic approach to date on resolving the node localization and energy efficiency in WSNs. Method: The extraction of the relevant articles was designed following the technique of preferred reporting items for systematic reviews and meta-analyses (PRISMA). All the included research articles were searched from the widely used databases of Google Scholar and Web of Science. All statistical analysis was performed with the fixed-effects model and the random-effects model implementation in RStudio. The overall pooled global estimate and categorization of performance for the heuristic approaches were presented in forest plots. Results: A total of 18 studies were included in this meta-analysis and the overall pooled estimated categorization of the heuristic approaches was 35% (95% CI (13%, 67%)). According to subgroup analysis the pooled estimation of heuristic approach with hyper-heuristic was 71% (95% CI: 6% to 99%), I2 = 100%) while the hybrid heuristic, was 31% (95% CI: 3% to 87%, I2 = 100%) and metaheuristic was 21%(95% CI: 9% to 41%, I2 = 100%). Conclusion: It can be concluded based on the experimental results that hyper-heuristic approach outclassed the hybrid heuristic and metaheuristic approaches in optimizing node localization and energy efficiency in WSNs.
Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm
(Elsevier BV, 2023-03) Aroba, Oluwasegun Julius; Naicker, Nalindren; Adeliyi, Timothy T.
In the recent age of technological advancements, wireless sensor networks are an important application for smart modernized environments. In WSNs, node localization has been an issue for over a decade in the research community. One of the goals of localization in a wireless sensor network is to localize sensor nodes in a two-dimensional plane. Localization in wireless sensor networks helps to supply information to aid decision-making from the aggregated data that are sent from packets to base stations. Internet of Things with the use of Global Positioning Systems for tracking sensor zones is not a cost-effective means of solution. In the extant literature, there have been a variety of algorithms to identify unknown sensor locations in wireless sensor networks. This research paper aims to address the problem of determining the location of the sensor node at the base station with minimum localization error when the data between the nodes is transmitted wirelessly. To detect the location of an unknown sensor node packets sent to the destinations, the total number of anchor nodes, location error and distance estimation error were considered. The DEEC-Gauss Gradient Distance Algorithm has a lower localization error than the Weighted Centroid Localizations algorithm, Compensation Coefficient algorithm, DV-Hop algorithm, Weighted Hyperbolic algorithm and Weighted Centroid algorithm for the same ratio of anchor nodes and WSN configuration. According to the study's findings, the DGGDEA has an average localization error of 11% for anchor nodes (20-80), and an average localization error of 11.3% for anchor nodes 200-450. Hence, the DEEC-Gaussian Gradient Distance Elimination Algorithm (DGGDEA) showed higher accuracy with comparison to the modern-day approaches.
Professional leadership investigation in big data and computer-mediated communication in relation to the 11th Sustainable Development Goals (SDG) Global Blueprint global blueprint
(Step Academic, 2024-02-08) Aroba, Oluwasegun Julius; Dr. Bringula, Rex
Methodology–The method adopted here is a research design and with the use of a quantitative researchdesign approachto carry out the analysis, thestudy investigated the connection between leadership influence, communication satisfaction, and job satisfaction in the context of CMC. Convenience sampling was used to gather the data, and structured questionnaires were used to protect the privacy and identity of the participants. The association between employee engagement and CMC was examined using multiple regression analysis. Findings–The questionnaireused in this study also evaluated participants' perceptions of workplace leadership, their satisfaction with communication, and their job satisfaction with the 11thSustainable Development Goals. The results showed a gender distribution with a slight female preponderance among the 103 participants (48 men and 55 women). Furthermore, they indicated that computer-mediated communication (CMC) channels account for 65.4% of organizational communication. These results demonstrate the widespread adoption and utilization of information and communication technologies (ICTs) within the surveyed organizations. Practical Implication–The substantial presence of internet-based communication channels, representing 65.4% of organizationalcommunication, emphasizes these channels' crucial role in facilitating effective communication within these organizations. Overall, the study analyses the effects of big data and CMC on professionalism and provides insights into gender distribution among participants. Research Limitations–Encouraging inclusive, safe, resilient, and sustainable cities and human settlements is the focus of Sustainable Development Goal (SDG) 11. Although it focuses on housing and urban challenges, its theoretical implications can be applied to several different sectors, such as professional leadership in big data. Theoretically, SDG 11 may have the following effects on Big Data-related professional leadership investigations.
A review : the bibliometric analysis of emerging node localization in wireless sensor network
(2023-07-31) Aroba, Oluwasegun Julius; Nalindren, Naicker; Timothy, Adeliyi; Avintha, Gupthar; Khadija, Karodia
As research in Node localization in WSN becomes ubiquitous, there is a dire need to interpret and map the increasing scientific knowledge and evolutionary trends so that a firm foundation can be laid for identifying knowledge gaps and advancing the domain. There is a critical need to interpret and map the expanding body of scientific knowledge and evolutionary trends as Node localization research in WSN spreads widely to establish a solid foundation for identifying knowledge gaps and developing the domain. Hence, this study aims to undertake a bibliometric analysis of node localization approaches. The Scopus central assemblage database was searched for titles that included "node localization", "wireless sensor network," and "WSN". A total of 1618 documents were published within the nineteen-study period (2003 - 2022). Microsoft Excel 365, R Bibliometric and Biblioshiny packages were implored for statistical analysis of approved published research articles. This study highlights the trends and current state of node localization research in WSN. It can aid researchers in gaining a thorough understanding of the most recent node localization techniques used in WSN
An SAP enterprise resource planning implementation using a case study of hospital management system for inclusion of digital transformation
(2023-07-31) Aroba, Oluwasegun Julius; Adefemi Oluwaniyi, Owoputi; Temitayo, Mathew Fagbola
Enterprise resource planning (ERP) system implementation necessitates substantial organizational and technological changes. These will have an impact on system stakeholders with various viewpoints and interests. It is crucial to analyze stakeholders in these situations and others like them to comprehend their attitudes and expectations toward the system. This experience report discusses problems with a medical institution's regular SAP ERP setup. This report includes insights and suggestions based on traditional system experience regarding a project to adopt SAP ERP at a healthcare facility. It ought to be a beneficial resource for all parties participating in the ERP installation process in the public healthcare sector. Many hospitals struggle to implement system analysis programs (SAP) and enterprise system programs (ERP) to assist in their business processes. The SAP ERP System is an integrated and consolidated way of easily flowing information within the organization's department. The authors identified hospitals' failure to implement a suitable SAP ERP system that works under their operations, leading to inefficiencies in their supply chain management process. This study addresses significant operational issues and productivity of the hospital management processes by administering 50 questionnaires and using Cronbach's alpha to analyze the responses. The Cronbach alpha is considered acceptable if the result is above 0.70. Our Cronbach result is 0.77. The benefits and difficulties of using SAP ERP provide a comprehensive review of the operations of Hospital and Healthcare Centre SAP ERP system digital transformations in supply chain management. Furthermore, the authors developed a framework to assist in choosing the proper tracking and transferring of information within the hospital technology that we named hospitec
Smart face masks for COVID-19 pandemic management : a concise review of emerging architectures, challenges and future research directions
(Institute of Electrical and Electronics Engineers (IEEE), 2023-01-15) Fagbola, Temitayo Matthew; Fagbola, Funmilola Ikeolu; Aroba, Oluwasegun Julius; Doshi, Ruchi; Hiran, Kamal Kant; Thakur, Surendra Colin
Smart sensing technology has been playing tremendous roles in digital healthcare management over time with great impacts. Lately, smart sensing has awoken the world by the advent of smart face masks (SFMs) in the global fight against the deadly Coronavirus (Covid-19) pandemic. In turn, a number of research studies on innovative SFM architectures and designs are emerging. However, there is currently no study that has systematically been conducted to identify and comparatively analyze the emerging architectures and designs of SFMs, their contributions, socio-technological implications, and current challenges. In this article, we investigate the emerging SFMs in response to Covid-19 pandemic and provide a concise review of their key features and characteristics, design, smart technologies, and architectures. We also highlight and discuss the socio-technological opportunities posed by the use of SFMs and finally present directions for future research. Our findings reveal four key features that can be used to evaluate SFMs to include reusability, self-power generation ability, energy awareness and aerosol filtration efficiency. We discover that SFM has potential for effective use in human tracking, contact tracing, disease detection and diagnosis or in monitoring asymptotic populations in future pandemics. Some SFMs have also been carefully designed to provide comfort and safety when used by patients with other respiratory diseases or comorbidities. However, some identified challenges include standards and quality control, ethical, security and privacy concerns.