Papers by Jibril Muhammad Adam
Remote sensing, May 21, 2024
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Information Sciences, Aug 1, 2022
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Information Sciences, Oct 1, 2022
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Earth Science Informatics
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Expert Systems With Applications, Dec 1, 2022
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International Journal of Applied Earth Observation and Geoinformation
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Information Sciences
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Remote Sensing, 2020
A point cloud is a set of points defined in a 3D metric space. Point clouds have become one of th... more A point cloud is a set of points defined in a 3D metric space. Point clouds have become one of the most significant data formats for 3D representation and are gaining increased popularity as a result of the increased availability of acquisition devices, as well as seeing increased application in areas such as robotics, autonomous driving, and augmented and virtual reality. Deep learning is now the most powerful tool for data processing in computer vision and is becoming the most preferred technique for tasks such as classification, segmentation, and detection. While deep learning techniques are mainly applied to data with a structured grid, the point cloud, on the other hand, is unstructured. The unstructuredness of point clouds makes the use of deep learning for its direct processing very challenging. This paper contains a review of the recent state-of-the-art deep learning techniques, mainly focusing on raw point cloud data. The initial work on deep learning directly with raw poin...
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Remote Sensing, 2020
A point cloud is a set of points defined in a 3D metric space. Point clouds have become one of th... more A point cloud is a set of points defined in a 3D metric space. Point clouds have become one of the most significant data formats for 3D representation and are gaining increased popularity as a result of the increased availability of acquisition devices, as well as seeing increased application in areas such as robotics, autonomous driving, and augmented and virtual reality. Deep learning is now the most powerful tool for data processing in computer vision and is becoming the most preferred technique for tasks such as classification, segmentation, and detection. While deep learning techniques are mainly applied to data with a structured grid, the point cloud, on the other hand, is unstructured. The unstructuredness of point clouds makes the use of deep learning for its direct processing very challenging. This paper contains a review of the recent state-of-the-art deep learning techniques, mainly focusing on raw point cloud data. The initial work on deep learning directly with raw point cloud data did not model local regions; therefore, subsequent approaches model local regions through sampling and grouping. More recently, several approaches have been proposed that not only model the local regions but also explore the correlation between points in the local regions. From the survey, we conclude that approaches that model local regions and take into account the correlation between points in the local regions perform better. Contrary to existing reviews, this paper provides a general structure for learning with raw point clouds, and various methods were compared based on the general structure. This work also introduces the popular 3D point cloud benchmark datasets and discusses the application of deep learning in popular 3D vision tasks, including classification, segmentation, and detection.
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Expert Systems with Applications
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2020 2nd International Conference on Computer and Information Sciences (ICCIS)
Water pricing and freshwater scarcity is an emerging global issue, a topic of debate among resear... more Water pricing and freshwater scarcity is an emerging global issue, a topic of debate among researchers, households and water utility managers. This is due to the fact that, the process can provide early warning signs as well as assisting water utility managers to make proper decisions on control and management of the scarce water resources through implementing water pricing policies, ensuring proper water allocation, water-use restriction as well as water production. In this paper, we presented a two-step methodology coupled stacked LSTM+GRU models while analyzing their relative performance to our reference models i.e. stacked LSTM and GRU for long term water price Prediction. It is thought that, the coupled Stacked LSTM and GRU models to exploit building of higher level of representation of the input sequence data while creating a higher level of abstraction on the final results. The GRU on the other hand assists in solving the vanishing gradient problems. The experimental results obtained from this research work indicates our coupled (Stacked LSTM+GRU) with supervised learning to significantly outperform our reference models for water price Prediction.
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Information Sciences
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2020 2nd International Conference on Computer and Information Sciences (ICCIS), 2020
Water pricing and freshwater scarcity is an emerging global issue, a topic of debate among resear... more Water pricing and freshwater scarcity is an emerging global issue, a topic of debate among researchers, households and water utility managers. This is due to the fact that, the process can provide early warning signs as well as assisting water utility managers to make proper decisions on control and management of the scarce water resources through implementing water pricing policies, ensuring proper water allocation, water-use restriction as well as water production. In this paper, we presented a two-step methodology coupled stacked LSTM+GRU models while analyzing their relative performance to our reference models i.e. stacked LSTM and GRU for long term water price Prediction. It is thought that, the coupled Stacked LSTM and GRU models to exploit building of higher level of representation of the input sequence data while creating a higher level of abstraction on the final results. The GRU on the other hand assists in solving the vanishing gradient problems. The experimental results ...
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Neurocomputing
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Lecturers spend an awful lot of time and effort to manually mark assignments. When marking assign... more Lecturers spend an awful lot of time and effort to manually mark assignments. When marking assignments, lecturers go through a repetitive process of opening files, working through checklist, calculating grades, recording them, etc. It would be easier for lecturers to follow good-practice in assessment if some of this leg-work was done for them. This onerous task needs to be addressed so lecturers can quickly and easily mark assignments and provide more useful and qualitative feedback. With the advancement of technology, there have been attempts to overcome this burden through eMarking tools. With this in mind, this paper describes the design and implementation of an eMarking tool that will aid at automating the repetitive processes involved in marking so that the heavy load caused by these burdensome activities will be lifted off the shoulders of lecturers.
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Papers by Jibril Muhammad Adam