Prof Bhekisipho Twala
@tut.ac.za
Deputy Vice-Chancellor (Digital Transformation)
Tshwane University of Technology
Bhekisipho Twala is the deputy vice-chancellor for digital transformation and professor of artificial intelligence and data science at the Tshwane University of Technology in South Africa. Before then he was the Executive Dean of Engineering and Built Environment at the Durban University of Technology (DUT), South Africa. Before joining DUT, he was the Director of the School of Engineering at the University of South Africa and the founder of the Institute for Intelligent Systems at the University of Johannesburg, South Africa. He completed his PhD at the Open University (UK) in 2005. He was a post-doctoral researcher at Brunel University in the UK, mainly focussing on applying machine learning in software engineering. Prof Twala’s current research includes promoting and conducting research in Artificial Intelligence within the Big Data Analytics field and developing novel and innovative solutions to crucial research problems.
EDUCATION
BA (Economics & Statistics)
Post Graduate Certificate (Statistics)
MSc (Statistics)
PhD (Artificial Intelligence & Statistical Science)
RESEARCH, TEACHING, or OTHER INTERESTS
Multidisciplinary, Statistics, Probability and Uncertainty, Statistics and Probability, Electrical and Electronic Engineering
FUTURE PROJECTS
Red Cards and Red Flags: Understanding Domestic Violence in Football Communities
"Red Cards and Red Flags: Understanding Domestic Violence in Football Communities" explores the intricate relationship between football culture and the prevalence of domestic violence. This study investigates how the intense emotions and cultural norms associated with football can contribute to domestic violence incidents, particularly in communities where football is deeply ingrained. Through a combination of qualitative and quantitative research methods, including interviews, surveys, and analysis of domestic violence reports during football seasons, the study seeks to identify patterns and underlying causes. Key findings suggest that heightened emotions during football matches, alcohol consumption, and deeply rooted gender norms within football communities can exacerbate domestic violence. The study also highlights how the competitive and aggressive nature of the sport can spill over into personal relationships, leading to increased tension and conflict at home.
Applications Invited
Masters, PhD students & Collaboration
The Psychological Impact of "Indoda Ayikhayi"
The cultural norm encapsulated in the phrase "Indoda ayikhali" (a man does not cry) exerts a profound psychological impact on men's mental health by promoting emotional repression. This study delves into how societal expectations for men to suppress their emotions contribute to adverse mental health outcomes. Using a combination of psychological research, case studies, and empirical data, we explore the consequences of this cultural expectation on men's mental well-being. The findings reveal that the suppression of emotions, as dictated by the "Indoda ayikhayi" ethos, often leads to increased levels of stress, anxiety, and depression among men. This cultural norm discourages men from seeking emotional support and expressing vulnerability, resulting in a reluctance to engage in mental health services. The study also highlights the role of societal and familial reinforcement in perpetuating this behaviour, creating a cycle of emotional repression that spans generations.
Applications Invited
Masters, PhD students & Collaboration
Utilizing Machine Learning Algorithms to Enhance Climate Models.
Integrating Artificial Intelligence (AI) in optimizing renewable energy systems significantly advances the fight against climate change. This study explores how AI can enhance the efficiency and effectiveness of solar and wind energy systems, which are pivotal to transitioning towards sustainable energy sources. AI algorithms, such as machine learning and neural networks, are employed to predict energy production based on weather patterns, optimize renewable energy infrastructure maintenance schedules, and manage energy storage and distribution in smart grids. By analyzing vast amounts of meteorological data, AI can accurately forecast solar and wind energy outputs, thereby improving the reliability and stability of renewable energy supplies. AI-driven predictive maintenance reduces downtime and prolongs the lifespan of energy systems, ensuring continuous and efficient operation.
Applications Invited
Masters, PhD students & Collaboration
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Shailendra Tiwari, Anita Gehlot, Rajesh Singh, Bhekisipho Twala, and Neeraj Priyadarshi
Elsevier BV
Mlungisi Duma, Bhekisipho Twala, and Tshilidzi Marwala
Elsevier BV
Abhishek Anand, Muhamad Mansor, Kamal Sharma, Amritanshu Shukla, Atul Sharma, Md Irfanul Haque Siddiqui, Kishor Kumar Sadasivuni, Neeraj Priyadarshi, and Bhekisipho Twala
Elsevier BV
Pawan Kumar Pathak, Anil Kumar Yadav, Sanjeevikumar Padmanaban, Bhekisipho Twala, and Innocent Kamwa
Institution of Engineering and Technology (IET)
AbstractThis paper proposes an intelligent battery charging scheme for hybrid electric vehicles (HEVs) with a fuel cell as the primary energy source and solar photovoltaic (PV) and battery as the auxiliary energy sources. While dealing with the PV, a minimized oscillation‐based improved perturb and observe (I‐P&O) maximum power point (MPP) tracking (MPPT) scheme is designed to mitigate the impact of oscillations around MPP and loss of tracking direction. The DC–DC boost and DC–DC buck power converters are connected in a cascade manner to harvest optimal power from PV and as a charging circuit for HEV, respectively. An intelligent fuzzy logic‐based proportional integral derivative (PID) (F‐PID) controller is employed for the buck converter to get the constant voltage and constant current for the effective charging of the battery. The two primary objectives of this work are (1) maximum utilization of the designed PV array via the I‐P&O MPPT scheme to enhance the system efficacy, reduce system cost, and reduce complexity. (2) To obtain minimum battery losses and an enhanced life cycle of HEV. The proposed MPPT scheme provides a maximum 99.80% tracking efficiency of the considered PV array at an insolation level of 1000 W/m2. Moreover, almost nominal voltage and current ripples have appeared in HEV's proposed intelligent battery charging circuit.
Shailendra Tiwari, Anita Gehlot, Rajesh Singh, Bhekisipho Twala, and Neeraj Priyadarshi
Elsevier BV
Vishant Kumar, Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, Amit Kumar Thakur, Ronald Aseer, Neeraj Priyadarshi, and Bhekisipho Twala
Springer Science and Business Media LLC
AbstractBlack ice is responsible for dangerous road-related incidents that can cause collisions and harm vehicle drivers and pedestrians. Visual examination and weather forecasts are two standard traditional methods for detecting black ice on roads, but they are often inaccurate and may not deliver the vehicle driver with up-to-date information on road conditions. The evolution of Industry 4.0 enabling technologies such as wireless sensor network (WSN), Internet of Things (IoT), cloud computing, and machine learning (ML) has been capable of detecting events in real time. This study aims to analyse the integration of the WSN, IoT, ML, and image processing for black ice detection. The qualitative research method is followed in this study, where the problems of black ice detection are studied. Following this, the role of Industry 4.0 enabling technologies is analyzed in detail for black ice detection. According to the study, we can detect black ice using different methods, but some methods need to be refined if we talk about the prediction. By merging different technologies, we can improve the overall architecture and create an algorithm that works with images and physical variables like temperature, humidity, due point, and road temperature, which were responsible for black ice formation, and predict the chances of black ice formation by training the system.
Ashish Singh Chauhan, Rajesh Singh, Neeraj Priyadarshi, Bhekisipho Twala, Surindra Suthar, and Siddharth Swami
Springer Science and Business Media LLC
AbstractThis study explores the practical applications of artificial intelligence (AI) in medical imaging, focusing on machine learning classifiers and deep learning models. The aim is to improve detection processes and diagnose diseases effectively. The study emphasizes the importance of teamwork in harnessing AI’s full potential for image analysis. Collaboration between doctors and AI experts is crucial for developing AI tools that bridge the gap between concepts and practical applications. The study demonstrates the effectiveness of machine learning classifiers, such as forest algorithms and deep learning models, in image analysis. These techniques enhance accuracy and expedite image analysis, aiding in the development of accurate medications. The study evidenced that technologically assisted medical image analysis significantly improves efficiency and accuracy across various imaging modalities, including X-ray, ultrasound, CT scans, MRI, etc. The outcomes were supported by the reduced diagnosis time. The exploration also helps us to understand the ethical considerations related to the privacy and security of data, bias, and fairness in algorithms, as well as the role of medical consultation in ensuring responsible AI use in healthcare.
Hitesh Bhatt, Rajesh Bahuguna, Siddharth Swami, Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, Lovi Raj Gupta, Amit Kumar Thakur, Neeraj Priyadarshi, and Bhekisipho Twala
Springer Science and Business Media LLC
AbstractThe judiciary is a foundation of democracy, upholding the rule of law and protecting rights. Efficient court administration is vital for public trust, justice, and timely proceedings. Currently, courts face challenges such as inconsistencies, adjournments, and absence of transparency, undermining the justice system. Traditional manual processes and paper-based documentation cause significant backlogs, slow resolutions, and limited public access. As case volumes and complexities rise, modernizing court administration through digital transformation is progressively critical. Currently, many countries are integrating technologies in the courts for its administration and other activities. In recent years, courts and judges have been subjected to pressure to improve performance, uplifting judicial effectiveness to a top priority. Subsequently, several countries have integrated simplification and digitization strategies in judicial initiatives to enhance court efficiency. Switzerland’s Justitia 4.0 and Brazil’s PJE are notable initiatives that focused to strengthen court administration through digitalization. These aspects motivated this study to examine the detailed integration of industry 4.0 technologies such as the Internet of things, cloud computing, blockchain, big data analytics, robotics, drones, Metaverse, digital twins, and artificial intelligence for court administration with digitalized infrastructure. According to the study, integrating these technologies in less complex cases helps minimize expenditures and save time, making to resolve cases conveniently, efficiently, and superiorly. The study also identified the challenges and issues associated with industry 4.0 technologies such as evidence gathering, evidence preservation, robot judges for pre-judgment analysis, and judgment delivery, which future studies need to be explored.
G. Gopichand, T. Sarath, Ankur Dumka, Himanshu Rai Goyal, Rajesh Singh, Anita Gehlot, Lovi Raj Gupta, Amit Kumar Thakur, Neeraj Priyadarshi, and Bhekisipho Twala
Springer Science and Business Media LLC
Vikrant Pachouri, Awadhesh Chandramauli, Rajesh Singh, Anita Gehlot, Neeraj Priyadarshi, and Bhekisipho Twala
Springer Science and Business Media LLC
AbstractAccording to the United Nations, Sustainable Development Goals (SDG) goal 6 and goal 14 seeks to ensure the sustainable management of water present over the earth for all. Urban cities saw a lot of expansion in terms of population and the number of industries established there. Water quality analysis becomes a huge requirement of today’s scenario due to the impurities present in water which harms the livelihood of society. Some of the hazardous impurities like heavy metals destruct the surroundings. In this study, the removal of heavy metals from wastewater with an efficient technique i.e. Bioremediation is represented with the analysis and evaluation of results recorded in the laboratory testing. Two samples were taken from two different sites which are being analyzed with the implementation methodology represented in the present article. The inclusion of the ANOVA model for the optimization of the outcome generated is evaluated and received the P-value and F-crit value. Two algae Chlorella Minutissima & Chlorella Singularis were evaluated based on their impurity removal efficiency as well an analysis of the biological treatment method over other chemical methods has been examined. The results were analyzed and represented in the form of a table as well and the variation in the value of WQP is shown in the form of graphs. The observation shows that the variation in WQP after the integration of algae lies under the permissible limit. Alkalinity is estimated in the range of 20–40 mg/l, Hardness lies in the range of 0–60mg/l, and pH comes approximately in a range of 6.5–8. The results of the ANOVA model is also depicted in graphical form highlighting the P-value and F-crit value of different result generated. Finally, the summary of the proposed work is illustrated with the challenges faced and future recommendations have been provided. Based on the evaluation, the framework is generated for the efficient technique used for heavy metal removal i.e. Bioremediation which provides a great advancement in the efficacy of removal of heavy metals.
Dumisani Selby Nkambule, Bhekisipho Twala, and Jan Harm Christiaan Pretorius
MDPI AG
Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit risk. Data are vital at the core of the credit decision-making processes. Decision-making depends heavily on accurate, complete data, and failure to harness high-quality data would impact credit lenders when assessing the loan applicants’ risk profiles. In this paper, an empirical comparison of the robustness of seven machine learning algorithms to credit risk, namely support vector machines (SVMs), naïve base, decision trees (DT), random forest (RF), gradient boosting (GB), K-nearest neighbour (K-NN), and logistic regression (LR), is carried out using the Lending Club credit data from Kaggle. This task uses seven performance measures, including the F1 Score (recall, accuracy, and precision), ROC-AUC, and HL and MCC metrics. Then, the harnessing of generative adversarial networks (GANs) simulation to enhance the robustness of the single machine learning classifiers for predicting credit risk is proposed. The results show that when GANs imputation is incorporated, the decision tree is the best-performing classifier with an accuracy rate of 93.01%, followed by random forest (92.92%), gradient boosting (92.33%), support vector machine (90.83%), logistic regression (90.76%), and naïve Bayes (89.29%), respectively. The classifier is the worst-performing method with a k-NN (88.68%) accuracy rate. Subsequently, when GANs are optimised, the accuracy rate of the naïve Bayes classifier improves significantly to (90%) accuracy rate. Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. In summary, most individual classifiers are more robust to missing data when GANs are used as an imputation technique. The differences in performance of all seven machine learning algorithms are significant at the 95% level.
Prafful Negi, Ashish Pathani, Bhuvan Chandra Bhatt, Siddharth Swami, Rajesh Singh, Anita Gehlot, Amit Kumar Thakur, Lovi Raj Gupta, Neeraj Priyadarshi, Bhekisipho Twala,et al.
MDPI AG
The incorporation of Industry 4.0 has integrated various innovations into fire safety management, thus changing the mode of identifying, assessing, and controlling fire risks. This review aims at how emerging technologies like IoT, AI, cloud technology, and BIM are making changes to fire safety in structural structures. With IoT-enabled sensors, data, and analytics coupled with predictive algorithms for real-time scenarios, fire safety systems have become dynamic systems where early detection, quick response, and risk management can be achieved. In addition, cloud web-based solutions improve the storage of information while providing the predictive aspect for certainty of safety measures. This paper also largely focuses on such activities through the likes of ISO/IEC 30141 and IEEE 802.15.4, thus making a critical role in maintaining effective connectivity between IoT devices, which is necessary for the effective performance of fire safety systems. Furthermore, the implementation issues, including the high costs, the difficulty in scaling up the projects, and the cybersecurity concerns, are considered and compared to the possible solutions, which include upgrading in stages and the possibility of subsidies from the government. The review also points out areas for further study, such as the creation of small cell networks with lower latency, the use of AI to carry out the maintenance of IoTs, and the enhancement of protection mechanisms of systems that are based on the IoTs. In general, this paper highlights the vast possibilities offered by Industry 4.0 technologies to support organizational fire safety management or decrease fire fatalities and improve built environment fire safety.
Gopal Krishna, Rajesh Singh, Anita Gehlot, Vaseem Akram Shaik, Bhekisipho Twala, and Neeraj Priyadarshi
Elsevier BV
Prafful Negi, Gaurav Thakur, Rajesh Singh, Anita Gehlot, Amit Kumar Thakur, Lovi Raj Gupta, Neeraj Priyadarshi, and Bhekisipho Twala
Elsevier BV
Sibusiso Reuben Bakana, Yongfei Zhang, and Bhekisipho Twala
Elsevier BV
Yashwant Singh Bisht, Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, Amit Kumar Thakur, Neeraj Priyadarshi, and Bhekisipho Twala
Research and Development Academy
Prototyping technology is becoming vital in the business as a means of cutting costs and manufacturing time. At present, reverse engineering and rapid prototyping are important technologies that enhance prototype development. The traditional approaches require various intricate processes, such as selective heat sintering (SHS), digital-light-processing printer (DLP), remote distributed rapid prototyping model (RDRP), Stereo Lithography (STL) models, and reconstructing computer-aided design (CAD) models from scanned point data and these approaches has limitations in terms of time-consuming and expert knowledge required for automation. This study aims to explore the significance of Industry 4.0 and its impact on rapid prototyping. The study also addresses rapid prototyping in computer network architecture; digital-light-processing printers (DLP) in rapid prototyping, and software-defined network (SDN) networks in the context of rapid prototyping. Along with this powder bed fusion (PBF) method and electron beam melting (EBM) are included in the manuscript. Based on our exploration, the study suggested vital recommendations for the advancement in rapid prototyping using Industry 4.0
Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, Amit Kumar Thakur, Lovi Raj Gupta, Neeraj Priyadarshi, and Bhekisipho Twala
Research and Development Academy
The potential assimilation of Industry 4.0 technologies across diverse sectors unlocks the pathways to achieve sustainability through innovative infrastructure with sustainable approaches. The World Travel & Tourism Council’s (WTTC) 2023 report emphasizes the impact of the hospitality industry contributes $9.5 trillion to the gross domestic product (GDP) and provides a workforce of 320 million globally and also strives towards meeting sustainability. Driven by the facts above, this study conducted a review to explore the potentiality of Industry 4.0 technologies specifically focused on meeting sustainability. Along with the review, the study has proposed a scalable-based architecture with the assimilation of many Industry 4.0 technologies. Further, this study has analyzed the real-world examples of Industry 4.0 technologies adoption in the hospitality industry with an objective of innovation and sustainable practices. Finally, the articles discussed the recommendations that can empower the establishment of resilient infrastructure through Industry 4.0 technologies.
Sibusiso Reuben Bakana, Yongfei Zhang, and Bhekisipho Twala
IEEE
Lightweight and efficient wildlife monitoring algorithms, including pose estimation, that can operate on edge devices with limited computational resources are urgently needed for wildlife studies and protections. To reduce computational costs while maintaining accuracy in animal pose estimation, this paper introduces WildPose, an HRNet-w32 based model, designed, which integrates Efficient Channel Attention (ECA) to enhance important feature representations without complex operations, Non-Local Blocks (NLB) to capture long-range contextual information and handle occlusions, and Depthwise Convolutions (DWConv) to reduce computational complexity and parameters. Additionally, the Online Hard Keypoint Mining with Mean Squared Error (OHKMSE) loss function is employed to improve accuracy for occluded keypoints detection by focusing on harder keypoints. When evaluated on the largest wildlife dataset of Animal Kingdom, WildPose demonstrated a nice trade-off between accuracy and efficiency, 75% reduction in parameters and 65% reduction in GFLOPs, while achieving an increased Percentage of Correct Keypoints (PCK) for occluded keypoints such as the hip, ankle, and tail. Therefore, WildPose can serve as a practical solution for real-time wildlife monitoring and artificial intelligence-based ecological studies, especially in resource-constrained environments.
Rakesh Kumar, Rajesh singh, Richa Goel, Tilottama Singh, Neeraj Priyadarshi, and Bhekisipho Twala
F1000 Research Ltd
Future viability depends on ensuring a sustainable society because green energy methods may efficiently reduce greenhouse gas emissions. Nevertheless, stakeholders, consumers, and developers continue to be notably ignorant of the financial incentives connected to green technology. Moreover, there is still a dearth of studies on the range of financial incentives offered by different authorities in India. Monetary incentives, such as tax breaks, indirect tax exemptions, and refunds, are crucial in encouraging the use of green technology in the modern world. This study explores the importance of financial incentives for green building technologies in India, which also looks at the wide range of incentives provided by federal, state, and local governments. Furthermore, the study highlights various state government programs such as goods subsidies, exemptions from local taxes, and fee waivers. Notably, several incentives aimed at consumers, developers, and other stakeholders have been implemented by the Indian Green Building Council (IGBC). This review study emphasizes the effectiveness of financial incentives in green construction projects and draws attention to a clear knowledge gap regarding the adoption of green technology. This study also provides insights into potential future directions. Studies and research results emphasize the importance of spreading the word about financial incentives as a key factor in determining the adoption of green technologies. Many parties, including governmental organizations, municipal governments, developers, and clients engaged in green building technology projects, stand to gain increased awareness.
Ishteyaaq Ahmad, Sonal Sharma, Rajesh Singh, Anita Gehlot, Lovi Raj Gupta, Amit Kumar Thakur, Neeraj Priyadarshi, and Bhekisipho Twala
Informa UK Limited
Bhekisipho Twala and Eamon Molloy
Springer Science and Business Media LLC
AbstractAn ensemble of classifiers combines several single classifiers to deliver a final prediction or classification decision. An increasingly provoking question is whether such an ensemble can outperform the single best classifier. If so, what form of ensemble learning system (also known as multiple classifier learning systems) yields the most significant benefits in the size or diversity of the ensemble? In this paper, the ability of ensemble learning to predict and identify factors that influence or contribute to autism spectrum disorder therapy (ASDT) for intervention purposes is investigated. Given that most interventions are typically short-term in nature, henceforth, developing a robotic system that will provide the best outcome and measurement of ASDT therapy has never been so critical. In this paper, the performance of five single classifiers against several multiple classifier learning systems in exploring and predicting ASDT is investigated using a dataset of behavioural data and robot-enhanced therapy against standard human treatment based on 3000 sessions and 300 h, recorded from 61 autistic children. Experimental results show statistically significant differences in performance among the single classifiers for ASDT prediction with decision trees as the more accurate classifier. The results further show multiple classifier learning systems (MCLS) achieving better performance for ASDT prediction (especially those ensembles with three core classifiers). Additionally, the results show bagging and boosting ensemble learning as robust when predicting ASDT with multi-stage design as the most dominant architecture. It also appears that eye contact and social interaction are the most critical contributing factors to the ASDT problem among children.
Divyanjali, Gaurav Thakur, Priyanka, Rajesh Singh, Anita Gehlot, Bhekisipho Twala, Neeraj Priyadarshi, and Shaik Vaseem Akram
Springer Science and Business Media LLC
AbstractHuman activities have degraded lakes in Uttarakhand, endangering their vital role in urban sustainability, which includes providing essential services like water supply, flood mitigation, agriculture support, and biodiversity conservation in the Himalayan region. This study focused on Nainital district lakes, utilizing remote sensing and GIS techniques to assess their condition. Time series Landsat 8 satellite imageries acquire by USGS earth explorer from 2017 and 2022 were captured, pre-processed, and subjected to spectral-based classification algorithms in ArcGIS software to calculate Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), and Normalised Difference Built-up Index (NDBI) indices to assess changes in vegetation, water bodies, and build-up area in and around the lakes respectively. The results indicate a decrease in built-up areas for Nainital district lakes from 2017 to 2022: Naini Lake (1.42%), Bhimtal Lake (1.83%), Naukuchiatal Lake (1.45%), Sattal Lake (2.18%), Khurpatal Lake (2.25%), and Sariyatal Lake (1.3%). Additionally, Bhimtal, Naukuchiatal, and Khurpatal lakes exhibited reductions in shrub and grass vegetation by approximately 12%, 16%, and 0% over the five-year period. Notably, Sattal and Khurapatal lakes demonstrated significant decreases in built-up areas, likely attributed to restoration efforts or landslides. Findings emphasize the need for conservation, sustainable land-use practices, and effective management to protect lake ecosystems.
Satish Kumar Mahariya, Awaneesh Kumar, Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, Bhekisipho Twala, Mohammed Ismail Iqbal, and Neeraj Priyadarshi
Akademia Baru Publishing
According to the United Nations, global sustainability in terms of social, economic, and environmental issues must be achieved by 2030. SDGs 4 and 9 are related to education and strengthen the attainment of quality education and infrastructure innovation. Resilient infrastructure plays a significant role in strengthening the campus in terms of education, management, placement and environment. These all aspects come under the smart campus. Smart campus 4.0 is the amalgamation of multitude industry 4.0 enabling technologies for delivering smart and innovative facilities with the aspect of sustainability. The previous studies have proved that the sustainable development goals (SDGs) can be achieved with the amalgamation of industry 4.0 enabling technologies in the campus such as cloud computing, artificial intelligence (AI), Internet of things (IoT), edge/fog computing, blockchain, robot process automation (RPA), drones, augmented reality (AR), virtual reality (VR), big data, digital twin, and metaverse. The main objective of this study to provide the detailed discussion of all industry 4.0 enabling technologies in single research related to smart campus. The findings observed are IoT-Based Drone system is intended to ground patrolling, and a cloud server to develop a smart campus energy monitoring system. AI for campus placement prediction model; cloud and Edge computing architecture to build an intelligent air-quality monitoring system. The novelty of the study, it has discussed all industry 4.0 enabling technologies for a smart campus with challenges, recommendations, and future directions.
Sibusiso Mdletshe, Oupa Steven Motshweneng, Marcus Oliveira, and Bhekisipho Twala
Elsevier BV
Sanjay Chauhan, Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, Bhekisipho Twala, and Neeraj Priyadarshi
MDPI AG
Supply chain management is one of the most prominent areas that needs to incorporate sustainability to achieve responsible consumption and production (SDG 11).It has been identified that there are limited studies that have presented the significance of different Industry 4.0 technologies from the perspective of sustainable SCM. The purpose of this study is to discuss the role of Industry 4.0 technologies in the context of sustainable SCM, as well as to identify important areas for future research. The PRISM framework is followed to discuss the role and significance of sustainable SCM and the integration of Industry 4.0-enabling technologies such as the Internet of Things (IoT), cloud computing, big data, artificial intelligence (AI), blockchain, and digital twin for sustainable SCM. The findings of the study reveal that there are limited empirical studies for developing countries and the majority are emphasized in case studies. Additionally, a few studies have focused on operational aspects, economics, and automation in SCM. The current study is able to contribute to the significance and application of IoT, cloud computing, big data, AI, blockchain, and digital twin in achieving sustainable SCM in the future. The current study can be expanded to discuss the Industry 4.0-enabling technologies in analyzing sustainability performance in any organization using environmental, social, and governance (ESG) metrics.