Dr. Umar Yahya
@iuiu.ac.ug
Senior Lecturer, Computer Science.
Islamic University in Uganda
EDUCATION
PhD Computer Science & MSc Computing and New Media (UBD,Brunei), BSc. Computer Science and IT (IUT, Bangladesh)
RESEARCH INTERESTS
Computational Biomechanics, IoT, Computational Intelligence, Intelligent Systems
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Kassim Kalinaki, Umar Yahya, Owais Ahmed Malik, and Daphne Teck Ching Lai
IGI Global
Globally, the industrial landscape is witnessing a significant transformation with the emergence of Industry 5.0, marking a new era characterized by seamless convergence of digital technologies, physical systems, and human expertise. This shift hinges on the dynamic interplay between big data analytics (BDA) and artificial intelligence (AI), becoming the cornerstone of intelligent decision-making in Industry 5.0. Accordingly, this study explores the profound impact of integrating BDA and AI in Industry 5.0, emphasizing the pivotal roles of data acquisition, storage, and processing. Additionally, it examines how AI improves human decision-making across various industrial sectors like manufacturing, retail, automotive, energy grid management, and healthcare, showcasing real-world case studies. Moreover, the chapter addresses the challenges associated with managing large-scale data and offers innovative solutions. It concludes by looking ahead, outlining promising areas for future research at the intersection of BDA and AI to foster well-informed decision-making in Industry 5.0.
Kassim Kalinaki, Owais Ahmed Malik, Umar Yahya, and Daphne Teck Ching Lai
Elsevier
Chour Singh Rajpoot, Gajanand Sharma, Praveen Gupta, Pankaj Dadheech, Umar Yahya, and Nagender Aneja
Informa UK Limited
Guoxu Wang, Andrew Leonce, E.A. Edirisinghe, Tamer Khafaga, Gregory Simkins, Umar Yahya, and Moayyed Sher Shah
IEEE
The Ghaf is a drought-resilient tree native to some parts of Asia and the Indian Subcontinent, including the United Arab Emirates (UAE). To the UAE, the Ghaf is a national tree, and it is regarded as a symbol of stability and peace due to its historical and cultural importance. Due to increased urbanization and infrastructure development in the UAE, the Ghaf is currently considered an endangered tree, requiring protection. Utilization of modern-day aerial surveillance technologies in combination with Artificial Intelligence (AI) can particularly be useful in keeping count of the Ghaf trees in a particular area, as well as continuously monitoring unauthorized use to feed animals and to monitor their health status, thereby aiding in their preservation. In this paper, we utilize one of the best Convolutional Neural Networks (CNN), YOLO-V5, based model to effectively detect Ghaf trees in images taken by cameras onboard light-weight, Unmanned Aircraft Vehicles (UAV), i.e. drones, in some areas of the UAE. We utilize a dataset of over 3200 drone captured images partitioned into data-subsets to be used for training (60%), validation (20%), and testing (20%). Four versions of YOLO-V5 CNN architecture are trained using the training data subset. The validation data subset was used to fine tune the trained models in order to realize the best Ghaf tree detection accuracy. The trained models are finally evaluated on the reserved test data subset not utilized during training. The object detection results of the Ghaf tree detection models obtained by the use of four different sub-versions of YOLO-V5 are compared quantitatively and qualitatively. YOLO-V5x model produced the highest average detection accuracy of 81.1%. In addition, YOLO-V5x can detect and locate Ghaf trees of different sizes moreover in complex natural environments and in areas with sparse distributions of Ghaf trees. The promising results presented in this work offer fundamental grounds for AI-driven UAV applications to be used for monitoring the Ghaf tree in real-time, and thus aiding in its preservation.
Changrong Chen, E.A. Edirisinghe, Andrew Leonce, Gregory Simkins, Tamer Khafaga, Moayyed Sher Shah, and Umar Yahya
IEEE
There is a growing interest among the research community in the search for possible technology-driven strategies for the conservation of the much-needed, historically rich and culturally important, desert life. In this work, we investigate the use of one of the best available Deep Neural Networks, YOLO Version-5 (v5), to enable offline detection, identification and classification of three popular desert animals (i.e Camels, Oryxes, and Gazelles) in a Drone Imagery Dataset captured by the Dubai Desert Conservation Reserve (DDCR), United Arab Emirates. The dataset contains over 1200 images, which were partitioned into training, validation, and testing data sub-sets in a 8:1:1 ratio, respectively. We trained three multi-class models, animal classification models, based on YOLO v5 Small(S), Medium(M) and Large(L), representing increasingly deep and complex architectures, to simultaneously detect and label the 3 kinds of animals. Models' performance was compared on the basis of classification accuracy (F1-Measure), The multi-class detector models generated were also compared with the single animal detector models created using the same network architectures, to assess the trained network's robustness against detecting more than one class of object. YOLO v5 L achieved the highest multi-class average classification accuracy of 96.71 percent (95.39 - 98.98). In comparison with the single animal detector models, the multi-class models exhibited the ability to correctly detect the target objects even for cases where the objects are located close to each other. We show that the promising results achieved in this work provide a promising foundation for the development of real-time multiclass identification and classification applications utilizing UAV imagery, to aid in the conservation efforts of fauna, particularly in the urbanized modern-day deserts and semi-desert places, such as the DDCR. We provide comprehensive test results and an analysis of results to demonstrate the effectiveness of the proposed models.
Kavita Sheoran, Arpit Bajgoti, Rishik Gupta, Nishtha Jatana, Geetika Dhand, Charu Gupta, Pankaj Dadheech, Umar Yahya, and Nagender Aneja
Institute of Electrical and Electronics Engineers (IEEE)
Ajay Kumar Bansal, Virendra Swaroop Sangtani, Pankaj Dadheech, Nagender Aneja, and Umar Yahya
Informa UK Limited
Owais A. Malik, Nazrul Ismail, Burhan R. Hussein, and Umar Yahya
MDPI AG
The identification of plant species is fundamental for the effective study and management of biodiversity. In a manual identification process, different characteristics of plants are measured as identification keys which are examined sequentially and adaptively to identify plant species. However, the manual process is laborious and time-consuming. Recently, technological development has called for more efficient methods to meet species’ identification requirements, such as developing digital-image-processing and pattern-recognition techniques. Despite several existing studies, there are still challenges in automating the identification of plant species accurately. This study proposed designing and developing an automated real-time plant species identification system of medicinal plants found across the Borneo region. The system is composed of a computer vision system that is used for training and testing a deep learning model, a knowledge base that acts as a dynamic database for storing plant images, together with auxiliary data, and a front-end mobile application as a user interface to the identification and feedback system. For the plant species identification task, an EfficientNet-B1-based deep learning model was adapted and trained/tested on a combined public and private plant species dataset. The proposed model achieved 87% and 84% Top-1 accuracies on a test set for the private and public datasets, respectively, which is more than a 10% accuracy improvement compared to the baseline model. During real-time system testing on the actual samples, using our mobile application, the accuracy slightly dropped to 78.5% (Top-1) and 82.6% (Top-5), which may be related to training data and testing conditions variability. A unique feature of the study is the provision of crowdsourcing feedback and geo-mapping of the species in the Borneo region, with the help of the mobile application. Nevertheless, the proposed system showed a promising direction toward real-time plant species identification system.
Muhammad Adam, Mugerwa Derrick, Umar Yahya, Abdal Kasule, Mwaka Lucky, Pembe Fahad, Kasagga Usama, Hamisi Ramadhan Mubarak, and Kasule Moses
IEEE
Greenhouse farming enables all year-round cultivation of crops outside of typical seasons. Moreover, increased global concerns of climate change and food security continue to popularize greenhouses as a viable alternative to open field farming. However, maintaining the required greenhouse conditions is still a demanding task yet crucial to achieving the desired high yield farming. This work presents a successful implementation of a semi-autonomous solar-powered framework for managing a greenhouse. Soil moisture and ambient temperature are continuously recorded, and accordingly, irrigation or aeration is automatically initiated when the desired threshold values are violated. The continuously captured sensor data (soil moisture, and ambient temperature) as well as the timestamps (start time, and stop time) for automatic actuations (irrigation and aeration) are transmitted to a remote database to enable real-time visualization of the greenhouse's condition. This framework could therefore facilitate high yield farming as it enables semi-autonomous management of greenhouses as well as real-time remote visualization.
Umar Yahya, S. M. N. Arosha Senanayake, and A. G. Naim
Springer Science and Business Media LLC
Umar Yahya, S M N Arosha Senanayake, and A G Naim
IEEE
Umar Yahya, S M N Arosha Senanayake, and A G Naim
IEEE
Umar Yahya, S. M. N. Arosha Senanayake, and Daphne Lai
Springer Berlin Heidelberg