- Faculty of Computer Science & Information Technology, 43400 Universiti Putra Malaysia, Serdang, Selangor
- +60389471798
- Image Processing, Applications of Machine Learning, Machine Learning, Pattern Recognition, Computer Vision, Computer Science, and 6 moreElectrical Engineering, Electrical and Computer Engineering (ECE), Image and Video Processing, Video Processing, Feature Extraction, and Multimedia Indexing and Searchingedit
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This study investigates the effectiveness of using groundwater inventory data for groundwater spring potential mapping in the Haraz watershed located in Norther Iran. From a total of 917 groundwater inventory dataset, six random inventory... more
This study investigates the effectiveness of using groundwater inventory data for groundwater spring potential mapping in the Haraz watershed located in Norther Iran. From a total of 917 groundwater inventory dataset, six random inventory scenarios of 917, 690, 450, 230, 92, and 46 were generated. We trained two learning classifiers, namely the Support Vector Machine (SVM) and Random Forest (RF) based on each scenario to determine which one(s) would be more suitable for spring potential mapping. In each of the scenarios, 70% of the dataset was used for training whereas 30% was used for testing. The end results (classified maps) for each classifier and their respective dataset were quantitatively assessed based on the Area under Curve (AUC) metric. The prediction accuracies for the spring potential maps being produced for each scenario ranged from 0.693 to 0.736 using the SVM, and 0.608 to 0.895 for RF. Our findings indicate that 46 random points of inventory data did not produce a d...
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This paper discusses a framework used to detect sarcasm in relation to time. It uses a set of deep learning extracted features (deep features) combined with a set of handcrafted features. The results of the experiments are positive in... more
This paper discusses a framework used to detect sarcasm in relation to time. It uses a set of deep learning extracted features (deep features) combined with a set of handcrafted features. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features is classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task with an accuracy of 89%. Furthermore, result comparison to recent works and the performance of each feature set are also shown as additional information.
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The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture combined with carefully handcrafted... more
The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture combined with carefully handcrafted contextual features. All of these will be discussed in detail in this paper. It is also found that a few sets performed well when they are used independently, but the others not so much. However, even the latter sets become very useful after the combination process with the former sets. Finally, the combined feature sets undergo the classification using well-known machine learning classification algorithms. It is decided that all the five algorithms are used for the purpose of comparison. The best algorithm for this task is found to be Support Vector Machine (SVM). The outcome of all the experiments are good in all the metrics used. Furthermore, result comparison in terms of F1-measure to existing works in the same domain is compiled and stated in this...
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Forest conservation is crucial for the maintenance of a healthy and thriving ecosystem. The field of remote sensing (RS) has been integral with the wide adoption of computer vision and sensor technologies for forest land observation. One... more
Forest conservation is crucial for the maintenance of a healthy and thriving ecosystem. The field of remote sensing (RS) has been integral with the wide adoption of computer vision and sensor technologies for forest land observation. One critical area of interest is the detection of active forest fires. A forest fire, which occurs naturally or manually induced, can quickly sweep through vast amounts of land, leaving behind unfathomable damage and loss of lives. Automatic detection of active forest fires (and burning biomass) is hence an important area to pursue to avoid unwanted catastrophes. Early fire detection can also be useful for decision makers to plan mitigation strategies as well as extinguishing efforts. In this paper, we present a deep learning framework called Fire-Net, that is trained on Landsat-8 imagery for the detection of active fires and burning biomass. Specifically, we fuse the optical (Red, Green, and Blue) and thermal modalities from the images for a more effec...
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Research Interests: Computer Vision, Multimedia Learning, Video Processing, Video Indexing, Semantic Video Analysis, and 8 moreSemantic Video Annotation, Image and Video Processing, Audio and Video Broadcasting, Multimedia Applications, Multimedia Computing, Sports Videos, Event Detection In Soccer Video, and Soccer Goal Event Detection Technique
Tremendous number of f food images in the social media services can be exploited by using food recognition for healthcare benefits and food industry marketing. The main challenges in food recognition are the large variability of food... more
Tremendous number of f food images in the social media services can be exploited by using food recognition for healthcare benefits and food industry marketing. The main challenges in food recognition are the large variability of food appearance that often generates a highly diverse and ambiguous descriptions of local feature. Ironically, the ambiguous descriptions of local feature have triggered information loss in visual dictionary constructions from the hard assignment practices. The current method based on hard assignment and Fisher vector approach to construct visual dictionary have unexpectedly cause errors from the uncertainty problem during visual word assignation. This research proposes a method of combination in soft assignment technique by using fuzzy encoding approach and maximum pooling technique to aggregate the features to produce a highly discriminative and robust visual dictionary across various local features and machine learning classifiers. The local features by u...
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The work in this paper deals with moving object detection (MOD) for single/multiple moving objects from unmanned aerial vehicles (UAV). The proposed technique aims to overcome limitations of traditional pairwise image registrationbased... more
The work in this paper deals with moving object detection (MOD) for single/multiple moving objects from unmanned aerial vehicles (UAV). The proposed technique aims to overcome limitations of traditional pairwise image registrationbased MOD approaches. The first limitation relates to how potential objects are detected by discovering corresponding regions between two consecutive frames. The commonly used gray level distance-based similarity measures might not cater well for the dynamic spatio-temporal differences of the camera and moving objects. The second limitation relates to object occlusion. Traditionally, when only frame-pairs are considered, some objects might disappear between two frames. However, such objects were actually occluded and reappear in a later frame and are not detected. This work attempts to address both issues by firstly converting each frame into a graph representation with nodes being segmented superpixel regions. Through this, object detection can be treated ...
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In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was... more
In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are considered as typical methods to produce building damage maps using RS images. The visual technique, however, is time-consuming due to manual sampling. The automatic method is able to detect the damaged building by extracting the defect features. However, various design methods and widely changing real-world conditions, such as shadow and light changes, cause challenges to the extensive appointing of automatic methods. As a potential solution for such challenges, this research proposes the adaption of deep learning (DL), specifically convolutional neural networks (CNN), which has a high ability to learn features automatically, to identify damaged buildings from pre- and post-event RS imageries....
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Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis... more
Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods—Variance Inflation Factor (VIF), Chi-Square Factor Optimization, and Gini Importance—to measure the significance of GCFs. From a total of 15 frequently used GCFs, 11 most effective ones (i.e., altitude, slope angle, plan curvature, profile curvature, topographic wetness index, distance from river, distance from fault, river density, fault density, land use, and lithology) were finally selected. In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). The resultant trained models were then applied for groundwater potential prediction and mapping in the Haraz basin of Mazandaran province, Iran. MDA has...
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This paper presents exploratory work looking into the effectiveness of attention mechanisms (AMs) in improving the task of building segmentation based on convolutional neural network (CNN) backbones. Firstly, we evaluate the effectiveness... more
This paper presents exploratory work looking into the effectiveness of attention mechanisms (AMs) in improving the task of building segmentation based on convolutional neural network (CNN) backbones. Firstly, we evaluate the effectiveness of CNN-based architectures with and without AMs. Secondly, we attempt to interpret the results produced by the CNNs using explainable artificial intelligence (XAI) methods. We compare CNNs with and without (vanilla) AMs for buildings detection. Five metrics are calculated, namely F1-score, precision, recall, intersection over union (IoU) and overall accuracy (OA). For the XAI portion of this work, the methods of Layer Gradient X activation and Layer DeepLIFT are used to explore the internal AMs and their overall effects on the network. Qualitative evaluation is based on color-coded value attribution to assess how the AMs facilitate the CNNs in performing buildings classification. We look at the effects of employing five AM algorithms, namely (i) sq...
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Building damage maps can be generated from either optical or Light Detection and Ranging (Lidar) datasets. In the wake of a disaster such as an earthquake, a timely and detailed map is a critical reference for disaster teams in order to... more
Building damage maps can be generated from either optical or Light Detection and Ranging (Lidar) datasets. In the wake of a disaster such as an earthquake, a timely and detailed map is a critical reference for disaster teams in order to plan and perform rescue and evacuation missions. Recent studies have shown that, instead of being used individually, optical and Lidar data can potentially be fused to obtain greater detail. In this study, we explore this fusion potential, which incorporates deep learning. The overall framework involves a novel End-to-End convolutional neural network (CNN) that performs building damage detection. Specifically, our building damage detection network (BDD-Net) utilizes three deep feature streams (through a multi-scale residual depth-wise convolution block) that are fused at different levels of the network. This is unlike other fusion networks that only perform fusion at the first and the last levels. The performance of BDD-Net is evaluated under three d...
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In recent years complex food security issues caused by climatic changes, limitations in human labour, and increasing production costs require a strategic approach in addressing problems. The emergence of artificial intelligence due to the... more
In recent years complex food security issues caused by climatic changes, limitations in human labour, and increasing production costs require a strategic approach in addressing problems. The emergence of artificial intelligence due to the capability of recent advances in computing architectures could become a new alternative to existing solutions. Deep learning algorithms in computer vision for image classification and object detection can facilitate the agriculture industry, especially in paddy cultivation, to alleviate human efforts in laborious, burdensome, and repetitive tasks. Optimal planting density is a crucial factor for paddy cultivation as it will influence the quality and quantity of production. There have been several studies involving planting density using computer vision and remote sensing approaches. While most of the studies have shown promising results, they have disadvantages and show room for improvement. One of the disadvantages is that the studies aim to detec...
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Food has become one of the most photographed objects since the inceptions of smart phones and social media services. Recently, the analysis of food images using object recognition techniques have been investigated to recognize food... more
Food has become one of the most photographed objects since the inceptions of smart phones and social media services. Recently, the analysis of food images using object recognition techniques have been investigated to recognize food categories. It is a part of a framework to accomplish the tasks of estimating food nutrition and calories for health-care purposes. The initial stage of food recognition pipeline is to extract the features in order to capture the food characteristics. A local feature by using SURF is among the efficient image detector and descriptor. It is using fast hessian detector to locate interest points and haar wavelet for descriptions. Despite the fast computation of SURF extraction, the detector seems ineffective as it obviously detects quite a small volume of interest points on the food objects with monotonous appearance. It occurs due to 1) food has texture-less surface 2) image has small pixel dimensions, and 3) image has low contrast and brightness. As a resu...
This paper extensively explores and highlights the main issues, concepts and trends related to steel surface image features extraction and representation methods. These methods are widely used in the past years to identify the surface... more
This paper extensively explores and highlights the main issues, concepts and trends related to steel surface image features extraction and representation methods. These methods are widely used in the past years to identify the surface texture and surface detects in several industrial fields. The different analysis techniques used to extract features from steel surface images for the purpose of classification are also explored. Furthermore, this study aims to identify the research gap in steel surface inspection domain by reviewing the previous related works of visual inspection methods and exploring their main outcomes, limitations and how they are solved in their fields.
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High rate of urbanization coupled with population growth has led to unexpected land use and land cover changes in Hilla city, which is located in the Babylon governorate of Iraq. Understanding and quantifying the spatiotemporal dynamics... more
High rate of urbanization coupled with population growth has led to unexpected land use and land cover changes in Hilla city, which is located in the Babylon governorate of Iraq. Understanding and quantifying the spatiotemporal dynamics of the urban land use and land cover changes, as well as the driving factors behind them, are therefore vital in order to design appropriate policies and monitoring mechanisms to govern urban growth. This study analyzes land use and land cover changes over Hilla city through remote sensing and GIS (Geographical Information System) techniques. IKONOS satellite imagery from years 2000, 2005, and 2011 was collected and pre-processed using ENVI and ArcGIS, which then goes through an object-based supervised image classification stage to generate land use and land cover maps. The classification is performed using the statistical machine learning algorithm, SVM (Support Vector Machine). The confusion matrix and kappa coefficients are used to evaluate the overall accuracy of the results. The statistical results obtained enable assessment of class changes from years 2000 to 2011 and also identify the gain and loss of the built-up areas in relation to other land cover classes. The results also allow assessment of the spatial trend of these built-up areas. Ultimately, forecasts can be made to predict expected future class changes in 2026 and 2036. Generally, the results of this study show increased expansions of built-up areas, i.e., from 8.14% in 2000 to 14.53% in 2005 and up to 18.36% in 2011. All this was at the expense of bare land areas. Simultaneously, there was an increased expansion of vegetation/agricultural land area, specifically from 36.14% in 2000 to 41.71% in 2005 and 45.13% in 2011. The spatial trend also shows that the growth of built-up areas is focused in the southwestern part of Hilla city. In all, we foresee that the findings of this study can provide a good visual resource for decision-makers to perform more efficient urban planning.
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This paper proposes a UAV-based PM2.5 air quality and temperature-humidity monitoring system. The system includes an air quality detector comprising four Arduino sensor modules. Specifically, it includes a dust (DSM501A) sensor and a... more
This paper proposes a UAV-based PM2.5 air quality and temperature-humidity monitoring system. The system includes an air quality detector comprising four Arduino sensor modules. Specifically, it includes a dust (DSM501A) sensor and a temperature and humidity (DHT11) sensor. The NEO-6M GPS module and DS3231 real-time module are also included for input visualization. A DIY SD card logging shield and memory module is also available for data recording purposes. The Arduino-based board houses multiple sensors and all are programmable using the Arduino integrated development environment (IDE) coding tool. Measurements conducted in a vertical flight path show promise where comparisons with ground truth references data showed good similarity. Overall, the results point to the idea that a light-weight and portable system can be used for accurate and reliable remote sensing data collection (in this case, PM2.5 concentration data and environmental data).
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Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility... more
Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the identification of landslide prone areas. Computational modelling techniques have been successful in related disaster scenarios, which motivate this work to explore such modelling for LSM. In this research, the potential of supervised machine learning and ensemble learning is investigated. Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvemen...
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In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently,... more
In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)–digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image ...