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In this paper, we assess the extent of environmental pollution in terms of PM2.5 particulate matter and noise in Tikrit University, located in Tikrit City of Iraq. The geographic information systems (GIS) technology was used for data analysis. Moreover, we built two multiple linear regression models (based on two different data inputs) for the prediction of PM2.5 particulate matter, which were based on the explanatory variables of maximum and minimum noise, temperature, and humidity. Furthermore, the maximum prediction coefficient R2 of the best models was 0.82, with a validated (via testing data) coefficient R2 of 0.94. From the actual total distribution of PM2.5 particulate values ranging from 35–58 μg/m3, our best model managed to predict values between 34.9–60.6 μg/m3. At the end of the study, the overall air quality was determined between moderate and harmful. In addition, the overall detected noise ranged from 49.30–85.79 dB, which inevitably designated the study area to be categorized as a noisy zone, despite being an educational institution.
Mohammed Hashim Ameen; Huda Jamal Jumaah; Bahareh Kalantar; Naonori Ueda; Alfian Abdul Halin; Abdullah Saeb Tais; Sarah Jamal Jumaah. Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling. Sustainability 2021, 13, 9571 .
AMA StyleMohammed Hashim Ameen, Huda Jamal Jumaah, Bahareh Kalantar, Naonori Ueda, Alfian Abdul Halin, Abdullah Saeb Tais, Sarah Jamal Jumaah. Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling. Sustainability. 2021; 13 (17):9571.
Chicago/Turabian StyleMohammed Hashim Ameen; Huda Jamal Jumaah; Bahareh Kalantar; Naonori Ueda; Alfian Abdul Halin; Abdullah Saeb Tais; Sarah Jamal Jumaah. 2021. "Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling." Sustainability 13, no. 17: 9571.
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).
Huda Jumaah; Bahareh Kalantar; Alfian Halin; Shattri Mansor; Naonori Ueda; Sarah Jumaah. Development of UAV-Based PM2.5 Monitoring System. Drones 2021, 5, 60 .
AMA StyleHuda Jumaah, Bahareh Kalantar, Alfian Halin, Shattri Mansor, Naonori Ueda, Sarah Jumaah. Development of UAV-Based PM2.5 Monitoring System. Drones. 2021; 5 (3):60.
Chicago/Turabian StyleHuda Jumaah; Bahareh Kalantar; Alfian Halin; Shattri Mansor; Naonori Ueda; Sarah Jumaah. 2021. "Development of UAV-Based PM2.5 Monitoring System." Drones 5, no. 3: 60.
Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted feature sets are created based on their respective contextual explanations. Each feature sets are specifically designed for the sole task of sarcasm detection. The objective is to find the most optimal features. Some sets are good to go even when it is used in independence. Other sets are not really significant without any combination. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features are classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task. Furthermore, result comparison to recent works and the performance of each feature set are also shown as additional information.
Saifullah Razali; Alfian Abdul Halin; Lei Ye; Shyamala Doraisamy; Noris Mohd Norowi. Sarcasm Detection Using Deep Learning With Contextual Features. IEEE Access 2021, 9, 68609 -68618.
AMA StyleSaifullah Razali, Alfian Abdul Halin, Lei Ye, Shyamala Doraisamy, Noris Mohd Norowi. Sarcasm Detection Using Deep Learning With Contextual Features. IEEE Access. 2021; 9 ():68609-68618.
Chicago/Turabian StyleSaifullah Razali; Alfian Abdul Halin; Lei Ye; Shyamala Doraisamy; Noris Mohd Norowi. 2021. "Sarcasm Detection Using Deep Learning With Contextual Features." IEEE Access 9, no. : 68609-68618.
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. Since RS data revolves around imagery, CNNs can arguably be most effective at automatically discovering relevant features, avoiding the need for feature engineering based on expert knowledge. In this work, we focus on RS imageries from orthophoto imageries for damaged-building detection, specifically for (i) background, (ii) no damage, (iii) minor damage, and (iv) debris classifications. The gist is to uncover the CNN architecture that will work best for this purpose. To this end, three CNN models, namely the twin model, fusion model, and composite model, are applied to the pre- and post-orthophoto imageries collected from the 2016 Kumamoto earthquake, Japan. The robustness of the models was evaluated using four evaluation metrics, namely overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and F1 score. According to the obtained results, the twin model achieved higher accuracy (OA = 76.86%; F1 score = 0.761) compare to the fusion model (OA = 72.27%; F1 score = 0.714) and composite (OA = 69.24%; F1 score = 0.682) models.
Bahareh Kalantar; Naonori Ueda; Husam A. H. Al-Najjar; Alfian Abdul Halin. Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images. Remote Sensing 2020, 12, 3529 .
AMA StyleBahareh Kalantar, Naonori Ueda, Husam A. H. Al-Najjar, Alfian Abdul Halin. Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images. Remote Sensing. 2020; 12 (21):3529.
Chicago/Turabian StyleBahareh Kalantar; Naonori Ueda; Husam A. H. Al-Najjar; Alfian Abdul Halin. 2020. "Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images." Remote Sensing 12, no. 21: 3529.
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.
Mohammed W. Ashour; Fatimah Khalid; Alfian Abdul Halin; Samy H. Darwish; M. M. Abdulrazzaq. A Review on Steel Surface Image Features Extraction and Representation Methods. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020, 239 -250.
AMA StyleMohammed W. Ashour, Fatimah Khalid, Alfian Abdul Halin, Samy H. Darwish, M. M. Abdulrazzaq. A Review on Steel Surface Image Features Extraction and Representation Methods. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2020; ():239-250.
Chicago/Turabian StyleMohammed W. Ashour; Fatimah Khalid; Alfian Abdul Halin; Samy H. Darwish; M. M. Abdulrazzaq. 2020. "A Review on Steel Surface Image Features Extraction and Representation Methods." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 239-250.
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 improvements. The dataset used to train and test all the algorithms consists of a landslide inventory map of 227 landslide locations. From these sources, 13 conditioning factors are extracted to be used in the models. Experimental evaluations are made based on True Skill Statistic (TSS), the Receiver Operation characteristic (ROC) curve and kappa index. The results show that the best TSS (0.6986), ROC (0.904) and kappa (0.6915) were obtained by the ensemble model. FDA on its own seems effective at modelling landslide susceptibility from multiple data sources, with performance comparable to GLM. However, it slightly underperforms when compared to GBM (BRT) and RF. RF seems most capable compared to GBM, GLM, and FDA, when dealing with all conditioning factors.
Bahareh Kalantar; Naonori Ueda; Vahideh Saeidi; Kourosh Ahmadi; Alfian Abdul Halin; Farzin Shabani. Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data. Remote Sensing 2020, 12, 1737 .
AMA StyleBahareh Kalantar, Naonori Ueda, Vahideh Saeidi, Kourosh Ahmadi, Alfian Abdul Halin, Farzin Shabani. Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data. Remote Sensing. 2020; 12 (11):1737.
Chicago/Turabian StyleBahareh Kalantar; Naonori Ueda; Vahideh Saeidi; Kourosh Ahmadi; Alfian Abdul Halin; Farzin Shabani. 2020. "Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data." Remote Sensing 12, no. 11: 1737.
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 been successfully applied for soil erosion and landslide mapping, but has not yet been fully explored for groundwater potential mapping (GPM). Although other discriminant methods, such as LDA, exist, MDA is worth exploring due to its capability to model multivariate nonlinear relationships between variables; it also undertakes a mixture of unobserved subclasses with regularization of non-linear decision boundaries, which could potentially provide more accurate classification. For the validation, areas under Receiver Operating Characteristics (ROC) curves (AUC) were calculated for the three algorithms. RF performed better with AUC value of 84.4%, while MDA and LDA yielded 75.2% and 74.9%, respectively. Although MDA performance is lower than RF, the result is satisfactory, because it is within the acceptable standard of environmental modeling. The outcome of factor analysis and groundwater maps emphasizes on optimization of multicolinearity factors for faster spatial modeling and provides valuable information for government agencies and private sectors to effectively manage groundwater in the region.
Bahareh Kalantar; Husam A. H. Al-Najjar; Biswajeet Pradhan; Vahideh Saeidi; Alfian Abdul Halin; Naonori Ueda; Seyed Amir Naghibi; Al- Najjar; Ueda. Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping. Water 2019, 11, 1909 .
AMA StyleBahareh Kalantar, Husam A. H. Al-Najjar, Biswajeet Pradhan, Vahideh Saeidi, Alfian Abdul Halin, Naonori Ueda, Seyed Amir Naghibi, Al- Najjar, Ueda. Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping. Water. 2019; 11 (9):1909.
Chicago/Turabian StyleBahareh Kalantar; Husam A. H. Al-Najjar; Biswajeet Pradhan; Vahideh Saeidi; Alfian Abdul Halin; Naonori Ueda; Seyed Amir Naghibi; Al- Najjar; Ueda. 2019. "Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping." Water 11, no. 9: 1909.
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 data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of ~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants were dense.
Husam A. H. Al-Najjar; Bahareh Kalantar; Biswajeet Pradhan; Vahideh Saeidi; Alfian Abdul Halin; Naonori Ueda; Shattri Mansor. Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks. Remote Sensing 2019, 11, 1461 .
AMA StyleHusam A. H. Al-Najjar, Bahareh Kalantar, Biswajeet Pradhan, Vahideh Saeidi, Alfian Abdul Halin, Naonori Ueda, Shattri Mansor. Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks. Remote Sensing. 2019; 11 (12):1461.
Chicago/Turabian StyleHusam A. H. Al-Najjar; Bahareh Kalantar; Biswajeet Pradhan; Vahideh Saeidi; Alfian Abdul Halin; Naonori Ueda; Shattri Mansor. 2019. "Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks." Remote Sensing 11, no. 12: 1461.
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 as a multi-graph matching task. This allows correspondences to be tracked more reliably across frames, which does not necessarily have to be limited to frame pairs. Building upon this, all detected objects and candidate objects are reanalyzed where a graph-coloring algorithm performs occlusion detection by considering multiple frames. The proposed framework was evaluated against a public dataset and a self-captured dataset. Precision and recall are calculated to evaluate and validate overall MOD performance. The proposed approach is also compared with Support vector machine (SVM), linear SVM classifier, and Canny edge detector detection algorithms. Experimental results show promising results with precision and recall at 94% and 89%, respectively.
Bahareh Kalantar; Naonori Ueda; Shattri Mansor; Alfian Abdul Halin; Helmi Zulhaidi Mohd Shafri; Mohsen Zand. A graph-based approach for moving objects detection from UAV videos. Image and Signal Processing for Remote Sensing XXIV 2018, 10789, 107891Y .
AMA StyleBahareh Kalantar, Naonori Ueda, Shattri Mansor, Alfian Abdul Halin, Helmi Zulhaidi Mohd Shafri, Mohsen Zand. A graph-based approach for moving objects detection from UAV videos. Image and Signal Processing for Remote Sensing XXIV. 2018; 10789 ():107891Y.
Chicago/Turabian StyleBahareh Kalantar; Naonori Ueda; Shattri Mansor; Alfian Abdul Halin; Helmi Zulhaidi Mohd Shafri; Mohsen Zand. 2018. "A graph-based approach for moving objects detection from UAV videos." Image and Signal Processing for Remote Sensing XXIV 10789, no. : 107891Y.
Ontologies play a crucial role in multiagent systems (MASs) development, especially for domain knowledge modeling, interaction specifications, and behavioral aspect representation. Domain‐specific ontologies can be developed in an ad hoc or systematic manner through the incorporation of ontology development steps on the basis of agent‐oriented methodologies. Developing such ontologies, however, is challenging because of the extensive amounts of knowledge and experience required. Moreover, since many ontologies cater for very specific domains, the question arises of whether some can be reused for faster systems development. This paper attempts to answer this question by proposing an ontology pattern classification scheme to allow the reuse of existing ontology knowledge for MAS development. Specifically, ontology patterns relevant to the design problem at hand are identified through the pattern classification scheme. These patterns are then reused and shared among agent software communities during the system development phase. The effectiveness of the proposed approach is validated using a restaurant‐finder MAS case study. Our findings suggest that utilization of the classified ontology patterns reduces development time and complexity when dealing with domain‐specific applications. The scheme also seems useful for software practitioners, where searching and reusing the patterns can easily be done during the analysis, design, and implementation of MAS development.
Cheah Wai Shiang; Fu Swee Tee; Alfian Abdul Halin; Ng Keng Yap; Puah Chin Hong. Ontology reuse for multiagent system development through pattern classification. Software: Practice and Experience 2018, 1 .
AMA StyleCheah Wai Shiang, Fu Swee Tee, Alfian Abdul Halin, Ng Keng Yap, Puah Chin Hong. Ontology reuse for multiagent system development through pattern classification. Software: Practice and Experience. 2018; ():1.
Chicago/Turabian StyleCheah Wai Shiang; Fu Swee Tee; Alfian Abdul Halin; Ng Keng Yap; Puah Chin Hong. 2018. "Ontology reuse for multiagent system development through pattern classification." Software: Practice and Experience , no. : 1.
In this paper, a method combining the use of discrete shearlet transform (DST) and the gray-level co-occurrence matrix (GLCM) is presented to classify surface defects of hot-rolled steel strips into the six classes of rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. Feature extraction involves the extraction of multi-directional shearlet features from each input image followed by GLCM calculations from all extracted sub-bands, from which a set of statistical features is extracted. The resultant high-dimensional feature vectors are then reduced using principal component analysis. A supervised support vector machine classifier is finally trained to classify the surface defects. The proposed feature set is compared against the Gabor, wavelets and the original GLCM in order to evaluate and validate its robustness. Experiments were conducted on a database of hot-rolled steel strips consisting of 1800 grayscale images whose defects exhibit high inter-class similarity as well as high intra-class appearance variations. Results indicate that the proposed DST–GLCM method is superior to other methods and achieves classification rates of 96.00%.
Mohammed Waleed Ashour; Fatimah Khalid; Alfian Abdul Halin; Lili Nurliyana Abdullah; Samy Hassan Darwish. Surface Defects Classification of Hot-Rolled Steel Strips Using Multi-directional Shearlet Features. Arabian Journal for Science and Engineering 2018, 44, 2925 -2932.
AMA StyleMohammed Waleed Ashour, Fatimah Khalid, Alfian Abdul Halin, Lili Nurliyana Abdullah, Samy Hassan Darwish. Surface Defects Classification of Hot-Rolled Steel Strips Using Multi-directional Shearlet Features. Arabian Journal for Science and Engineering. 2018; 44 (4):2925-2932.
Chicago/Turabian StyleMohammed Waleed Ashour; Fatimah Khalid; Alfian Abdul Halin; Lili Nurliyana Abdullah; Samy Hassan Darwish. 2018. "Surface Defects Classification of Hot-Rolled Steel Strips Using Multi-directional Shearlet Features." Arabian Journal for Science and Engineering 44, no. 4: 2925-2932.
Sentiment analysis is the computational study of opinions given by the users of online media platforms e.g. Twitter, Facebook, Instagram. The output will be in the form of polarity: positive, negative or indifferent. The field has become very useful for the industry as it can feed them the information of what is sought after by their customers in a given time. It has also rapidly became a topic of interest in the research world, for its importance and subjectivity. One of the most challenging issue in sentiment analysis is sarcasm. The existence of sarcasm is mostly ignored by the researchers in the field of sentiment analysis as it is considered to be too complex. Sarcasm is what most researchers regarded as a subset of irony. It is the utterance of positive statement with negative intent. Intent is hard to detect not only for computers but also for humans. The listener is deemed to have a certain degree of background knowledge or context of what the speaker is saying to understand sarcasm. The researches that takes sarcasm into account or solely focuses on sarcasm is in the trend of using context outside the target word for sarcasm detection, and the most popular approach is deep learning. However, both deep learning and context need a lot of features. In this paper, we will look at some researches that focuses on sarcasm detection and their agreement that more than text is needed to properly detect sarcasm. Also in this paper is the trends undergone by sarcasm detection researchers and their proposed techniques.
Saifullah Razali; Alfian Abdul Halin; Noris Mohd Norowi; Shyamala Doraisamy. The importance of multimodality in sarcasm detection for sentiment analysis. 2017 IEEE 15th Student Conference on Research and Development (SCOReD) 2017, 56 -60.
AMA StyleSaifullah Razali, Alfian Abdul Halin, Noris Mohd Norowi, Shyamala Doraisamy. The importance of multimodality in sarcasm detection for sentiment analysis. 2017 IEEE 15th Student Conference on Research and Development (SCOReD). 2017; ():56-60.
Chicago/Turabian StyleSaifullah Razali; Alfian Abdul Halin; Noris Mohd Norowi; Shyamala Doraisamy. 2017. "The importance of multimodality in sarcasm detection for sentiment analysis." 2017 IEEE 15th Student Conference on Research and Development (SCOReD) , no. : 56-60.
Mobile application development is receiving much attention nowadays. With the enhancement of mobile application tools like an Android studio, etc. and kinds of online support, the development of the mobile application is getting easier. Indeed, mobile application development is not a trivial task. When given a particular problem, a novice mobile programmer will commonly sketch the mobile interface followed by coding. The rapid prototyping technique and trial from errors have led to issues such as poor domain understanding. We argue that a complete understanding of the domain is needed for mobile application development. Hence, requirements engineering is an important phase. This paper introduces a technique to assist mobile application development through Agent-Oriented Requirements Engineering (AORE). AORE consists of goal modelling to analyse and understand a mobile-based project. With goal modelling, AORE allows a modeller to identify and analyse the functionalities and non-functionalities of the system and present a holistic view of the proposed system. It showcases the services, operations and constraints of the proposed system. AORE is a useful part of the development phase and can complement current steps in mobile application development lifecycle.
Cheah Waishiang; Aida Shafreena Bt Ahmad Puad; Puah Chin Hong; Alfian Abdul Halin. Agent-Oriented Requirement Engineering for Mobile Application Development. International Journal of Interactive Mobile Technologies (iJIM) 2017, 11, 32 -48.
AMA StyleCheah Waishiang, Aida Shafreena Bt Ahmad Puad, Puah Chin Hong, Alfian Abdul Halin. Agent-Oriented Requirement Engineering for Mobile Application Development. International Journal of Interactive Mobile Technologies (iJIM). 2017; 11 (6):32-48.
Chicago/Turabian StyleCheah Waishiang; Aida Shafreena Bt Ahmad Puad; Puah Chin Hong; Alfian Abdul Halin. 2017. "Agent-Oriented Requirement Engineering for Mobile Application Development." International Journal of Interactive Mobile Technologies (iJIM) 11, no. 6: 32-48.
Food object recognition has gained popularity in recent years. This can perhaps be attributed to its potential applications in fields such as nutrition and fitness. Recognizing food images however is a challenging task since various foods come in many shapes and sizes. Besides having unexpected deformities and texture, food images are also captured in differing lighting conditions and camera viewpoints. From a computer vision perspective, using global image features to train a supervised classifier might be unsuitable due to the complex nature of the food images. Local features on the other hand seem the better alternative since they are able to capture minute intricacies such as interest points and other intricate information. In this paper, two local features namely SURF (Speeded- Up Robust Feature) and MSER (Maximally Stable Extremal Regions) are investigated for food object recognition. Both features are computationally inexpensive and have shown to be effective local descriptors for complex images. Specifically, each feature is firstly evaluated separately. This is followed by feature fusion to observe whether a combined representation could better represent food images. Experimental evaluations using a Support Vector Machine classifier shows that feature fusion generates better recognition accuracy at 86.6%.
Mohd Norhisham Razali; Noridayu Manshor; Alfian Abdul Halin; Razali Yaakob; Norwati Mustapha. Food Category Recognition Using SURF and MSER Local Feature Representation. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 10645, 212 -223.
AMA StyleMohd Norhisham Razali, Noridayu Manshor, Alfian Abdul Halin, Razali Yaakob, Norwati Mustapha. Food Category Recognition Using SURF and MSER Local Feature Representation. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; 10645 ():212-223.
Chicago/Turabian StyleMohd Norhisham Razali; Noridayu Manshor; Alfian Abdul Halin; Razali Yaakob; Norwati Mustapha. 2017. "Food Category Recognition Using SURF and MSER Local Feature Representation." Transactions on Petri Nets and Other Models of Concurrency XV 10645, no. : 212-223.
Over the past two decades, the use of computer vision methods for enabling machines to recognize human action from a sequence of images, has grown as information technologies advance, and hardware availability such as cameras (especially closed circuit television) has increased. From the latter part of the 1980s till recently, computer vision has been employed for human action recognition research. Due to the volume of existing academic studies, it would be impractical to review all researches. This paper presents a brief analysis regarding the body of knowledge in Human Activity Recognition from 1987 to 2015. Bibliometric techniques based on the Science Citation Index (SCI) databases of the Web of Science are employed where 1,172 articles are critically analysed on the various aspects of publication characteristics such as authorship, countries, institutions, number of citations, and keywords. The pace of publishing in this field has shown to increase rapidly over last 20 years. By identifying the global trends in HAR research, this study is beneficial for researchers, for example, in the selection of future research topics. Similarly, policy makers can also benefit from the findings for a better understanding of how HAR develops over time.
Alihossein Aryanfar; Alfian Abdul Halin; Razali Yaakob; Nasir Sulaiman; Leila Mohammadpour. A Bibliometric Analysis of Human Action Recognition. Inventive Computation and Information Technologies 2017, 15, 419 -427.
AMA StyleAlihossein Aryanfar, Alfian Abdul Halin, Razali Yaakob, Nasir Sulaiman, Leila Mohammadpour. A Bibliometric Analysis of Human Action Recognition. Inventive Computation and Information Technologies. 2017; 15 ():419-427.
Chicago/Turabian StyleAlihossein Aryanfar; Alfian Abdul Halin; Razali Yaakob; Nasir Sulaiman; Leila Mohammadpour. 2017. "A Bibliometric Analysis of Human Action Recognition." Inventive Computation and Information Technologies 15, no. : 419-427.
Image registration has been long used as a basis for the detection of moving objects. Registration techniques attempt to discover correspondences between consecutive frame pairs based on image appearances under rigid and affine transformations. However, spatial information is often ignored, and different motions from multiple moving objects cannot be efficiently modeled. Moreover, image registration is not well suited to handle occlusion that can result in potential object misses. This paper proposes a novel approach to address these problems. First, segmented video frames from unmanned aerial vehicle captured video sequences are represented using region adjacency graphs of visual appearance and geometric properties. Correspondence matching (for visible and occluded regions) is then performed between graph sequences by using multigraph matching. After matching, region labeling is achieved by a proposed graph coloring algorithm which assigns a background or foreground label to the respective region. The intuition of the algorithm is that background scene and foreground moving objects exhibit different motion characteristics in a sequence, and hence, their spatial distances are expected to be varying with time. Experiments conducted on several DARPA VIVID video sequences as well as self-captured videos show that the proposed method is robust to unknown transformations, with significant improvements in overall precision and recall compared to existing works.
Bahareh Kalantar; Shattri Bin Mansor; Alfian Abdul Halin; Helmi Zulhaidi Mohd Shafri; Mohsen Zand. Multiple Moving Object Detection From UAV Videos Using Trajectories of Matched Regional Adjacency Graphs. IEEE Transactions on Geoscience and Remote Sensing 2017, 55, 5198 -5213.
AMA StyleBahareh Kalantar, Shattri Bin Mansor, Alfian Abdul Halin, Helmi Zulhaidi Mohd Shafri, Mohsen Zand. Multiple Moving Object Detection From UAV Videos Using Trajectories of Matched Regional Adjacency Graphs. IEEE Transactions on Geoscience and Remote Sensing. 2017; 55 (9):5198-5213.
Chicago/Turabian StyleBahareh Kalantar; Shattri Bin Mansor; Alfian Abdul Halin; Helmi Zulhaidi Mohd Shafri; Mohsen Zand. 2017. "Multiple Moving Object Detection From UAV Videos Using Trajectories of Matched Regional Adjacency Graphs." IEEE Transactions on Geoscience and Remote Sensing 55, no. 9: 5198-5213.
Mohd Norhisham Bin Razali; Noridayu Manshor; Alfian Abdul Halin; Norwati Mustapha; Razali Yaakob. An integration of minimum local feature representation methods to recognize large variation of foods. THE 2ND INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY 2017 (ICAST’17) 2017, 1891, 20123 .
AMA StyleMohd Norhisham Bin Razali, Noridayu Manshor, Alfian Abdul Halin, Norwati Mustapha, Razali Yaakob. An integration of minimum local feature representation methods to recognize large variation of foods. THE 2ND INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY 2017 (ICAST’17). 2017; 1891 ():20123.
Chicago/Turabian StyleMohd Norhisham Bin Razali; Noridayu Manshor; Alfian Abdul Halin; Norwati Mustapha; Razali Yaakob. 2017. "An integration of minimum local feature representation methods to recognize large variation of foods." THE 2ND INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY 2017 (ICAST’17) 1891, no. : 20123.
Motorcyclists are particularly vulnerable to injury in crashes with heavy vehicles due to substantial differences in vehicle mass, the degree of protection and speed. There is a considerable difference in height between motorcycles and trucks; motorcycles are viewed by truck drivers from downward angles, and shorter distances between them mean steeper downward angles. Hence, we anticipated that the effects of motorcycle conspicuity treatments would be different for truck drivers. Therefore, this study aims to evaluate the effects of motorcycle conspicuity treatments on the identification and detection of motorcycles by truck drivers. Two complementary experiments were performed; the first experiment assessed the impact of motorcycle sensory conspicuity on the ability of un-alerted truck drivers to detect motorcycles, and the second experiment assessed the motorcycle cognitive conspicuity to alerted truck drivers. The sensory conspicuity was measured in terms of motorcycle detection rates by un-alerted truck drivers when they were not anticipating a motorcycle within a realistic driving scene, while the cognitive conspicuity was determined by the time taken by alerted truck drivers to actively search for a motorcycle. In the first experiment, the participants were presented with 10 pictures and were instructed to report the kinds of vehicles that were presented in the pictures. Each picture was shown to the participants for 600ms. In the second experiment, the participants were presented with the same set of pictures and were instructed to respond by clicking the right button on a mouse as soon as they detected a motorcycle in the picture. The results indicate that the motorcycle detection rate increases, and the response time to search for a motorcycle decreases, as the distance between the targeted motorcycle and the viewer decreases. This is true regardless of the type of conspicuity treatment used. The use of daytime running headlights (DRH) was found to increase the detection rate and the identification of a motorcycle by a truck driver at a farther distance, but effect deteriorates as the distance decreases. The results show that the detection rate and the identification of a motorcyclist wearing a black helmet with a reflective sticker increases as the distance between the motorcycle and the truck decreases. We also found that a motorcyclist wearing a white helmet and a white outfit is more identifiable and detectable at both shorter and longer distances. In conclusion, although this study provides evidence that the use of appropriate conspicuity treatments enhances motorcycle conspicuity to truck drivers, we suggest that more attention should be paid to the effect of background environment on motorcycle conspicuity.
Teik Hua Law; Mahshid Ghanbari; Hussain Hamid; Alfian Abdul-Halin; Choy Peng Ng. Role of sensory and cognitive conspicuity in the prevention of collisions between motorcycles and trucks at T-intersections. Accident Analysis & Prevention 2016, 96, 64 -70.
AMA StyleTeik Hua Law, Mahshid Ghanbari, Hussain Hamid, Alfian Abdul-Halin, Choy Peng Ng. Role of sensory and cognitive conspicuity in the prevention of collisions between motorcycles and trucks at T-intersections. Accident Analysis & Prevention. 2016; 96 ():64-70.
Chicago/Turabian StyleTeik Hua Law; Mahshid Ghanbari; Hussain Hamid; Alfian Abdul-Halin; Choy Peng Ng. 2016. "Role of sensory and cognitive conspicuity in the prevention of collisions between motorcycles and trucks at T-intersections." Accident Analysis & Prevention 96, no. : 64-70.
This paper presents the post-mortem report upon completion of the Long Lamai e-commerce development project. Some weaknesses with regards to the current software modelling approach are identified and an alternative role-based approach is proposed. We argue that the existing software modelling technique is not suitable for modelling, making it difficult to establish a good contract between stakeholders causing delays in the project delivery. The role-based approach is able to explicitly highlight the responsibilities among stakeholders, while also forming the contract agreement among them leading towards sustainable ICT4D.
Cheah Wai Shiang; Alfian Abdul Halin; Marlene Lu; Gary CheeWhye. Long Lamai Community ICT4D E-Commerce System Modelling: An Agent Oriented Role-Based Approach. The Electronic Journal of Information Systems in Developing Countries 2016, 75, 1 -22.
AMA StyleCheah Wai Shiang, Alfian Abdul Halin, Marlene Lu, Gary CheeWhye. Long Lamai Community ICT4D E-Commerce System Modelling: An Agent Oriented Role-Based Approach. The Electronic Journal of Information Systems in Developing Countries. 2016; 75 (1):1-22.
Chicago/Turabian StyleCheah Wai Shiang; Alfian Abdul Halin; Marlene Lu; Gary CheeWhye. 2016. "Long Lamai Community ICT4D E-Commerce System Modelling: An Agent Oriented Role-Based Approach." The Electronic Journal of Information Systems in Developing Countries 75, no. 1: 1-22.
Automatic image annotation enables efficient indexing and retrieval of the images in the large-scale image collections, where manual image labeling is an expensive and labor intensive task. This paper proposes a novel approach to automatically annotate images by coherent semantic concepts learned from image contents. It exploits sub-visual distributions from each visually complex semantic class, disambiguates visual descriptors in a visual context space, and assigns image annotations by modeling image semantic context. The sub-visual distributions are discovered through a clustering algorithm, and probabilistically associated with semantic classes using mixture models. The clustering algorithm can handle the inner-category visual diversity of the semantic concepts with the curse of dimensionality of the image descriptors. Hence, mixture models that formulate the sub-visual distributions assign relevant semantic classes to local descriptors. To capture non-ambiguous and visual-consistent local descriptors, the visual context is learned by a probabilistic Latent Semantic Analysis (pLSA) model that ties up images and their visual contents. In order to maximize the annotation consistency for each image, another context model characterizes the contextual relationships between semantic concepts using a concept graph. Therefore, image labels are finally specialized for each image in a scene-centric view, where images are considered as unified entities. In this way, highly consistent annotations are probabilistically assigned to images, which are closely correlated with the visual contents and true semantics of the images. Experimental validation on several datasets shows that this method outperforms state-of-the-art annotation algorithms, while effectively captures consistent labels for each image.
Mohsen Zand; Shyamala Doraisamy; Alfian Abdul Halin; Mas Rina Mustaffa. Visual and semantic context modeling for scene-centric image annotation. Multimedia Tools and Applications 2016, 76, 8547 -8571.
AMA StyleMohsen Zand, Shyamala Doraisamy, Alfian Abdul Halin, Mas Rina Mustaffa. Visual and semantic context modeling for scene-centric image annotation. Multimedia Tools and Applications. 2016; 76 (6):8547-8571.
Chicago/Turabian StyleMohsen Zand; Shyamala Doraisamy; Alfian Abdul Halin; Mas Rina Mustaffa. 2016. "Visual and semantic context modeling for scene-centric image annotation." Multimedia Tools and Applications 76, no. 6: 8547-8571.