Papers by Alfian Abdul Halin
International Journal of Advanced Computer Science and Applications, 2023
Extensive research developed assistive technologies (ATs) to improve mobility for people with vis... more Extensive research developed assistive technologies (ATs) to improve mobility for people with vision impairment (PVI). However, a limited number of PVI rely on ATs for mobility. One of the factors contributing to the limited reliability and low acceptance of ATs is the developers' failure to consider PVI mobility traits from the target group's perspective. Many developers and researchers proposed solutions based on their knowledge and experiences, where PVI have been excluded from several studies except for limited involvement in testing phases. Accordingly, this study aims to bridge this gap by providing comprehensive information on PVIs' behaviors, challenges, and requirements for safe and independent outdoor mobility. Therefore, a total of 15 participants with vision impairment were involved in semi-structured interviews and two observation sessions. One key finding highlights the need for AT that complements the conventional cane and overcomes its limitations, not substituting the cane. Moreover, the proposed AT should address instant mobility and future needs simultaneously. Overall, the study contributes to providing comprehensive knowledge on PVI safe and independent mobility traits, which assist AT developers to explore the potential barriers and facilitators of the adoption of ATs among PVIs and leads to develop effective and reliable ATs that meet their needs. For future work, the researchers will develop an AT that complements the conventional cane, supports instant mobility, and enhances cognitive map formation.
Software - Practice and Experience, Jun 21, 2018
Ontologies play a crucial role in multiagent systems (MASs) development, especially for domain kn... more 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.
Sustainability, Aug 25, 2021
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
International journal of technology in teaching and learning, 2016
One pressing issue in resource constrained schools is the lack of access to computer facilities f... more One pressing issue in resource constrained schools is the lack of access to computer facilities for learning. A single display groupware (SDG) is explored in this paper to provide an alternative solution for this limitation where it allows multiple users to work concurrently on a single computer display. This study aims to investigate the feasibility of SDG as an educational approach in resource constrained schools. The prototype SDG system RimbaIlmu, is designed and developed. Quantitative evaluations are carried out where the results indicate its feasibility in students' engagement and learning performance, while overcoming technical constraints. Limitation of the SDG is also presented with the intent of guiding future improvements.
IntechOpen eBooks, Dec 16, 2020
Large scale developmental projects firstly require the selection of one or more cities to be deve... more Large scale developmental projects firstly require the selection of one or more cities to be developed. In Libya, the selection process is done by selected organizations, which is highly influenced by human judgement that can be inconsiderate of socioeconomic and environmental factors. In this study, we propose an automated selection process, which takes into consideration only the important factors for city (cities) selection. Specifically, a geospatial decision-making tool, free of human bias, is proposed based on the fuzzy overlay (FO) and technique for order performance by similarity to ideal solution (TOPSIS) techniques for development projects in Libya. In this work, a dataset of 17 evaluation criteria (GIS factors) across five urban conditioning factors were prepared. The dataset served as input to the FO model to calculate weights (importance) for each criterion. A support vector machine (SVM) classifier was then trained to refine weights from the FO model. TOPSIS was then applied on the refined results to rank the cities for development. Experimental results indicate promising overall accuracy and kappa statistics. Our findings also show that highest and lowest success rates are 0.94 and 0.79, respectively, while highest and lowest prediction rates are 0.884 and 0.673, respectively.
International journal of interactive mobile technologies, Nov 27, 2017
Mobile application development is receiving much attention nowadays. With the enhancement of mobi... more 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 mobilebased 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. Keywords-requirement engineering for mobile application, agent-oriented modelling, functional and non-functional requirement Usability : The quality goal for user satisfaction. Orientation: The quality goal for the layout of the system. Consistency: The quality goal for the appearance of the system. Avoid cognitive overload: The quality goal for the level of user interpretation of the system. Learnability: The quality goal refers for the ease of learnability of the system, with minimum guideline. Accessibility: The quality goal for suitability of the system across different levels of users. Content quality: The quality goal ensuring the good quality of the content.
EJISDC: The Electronic Journal on Information Systems in Developing Countries, Jul 1, 2016
This paper presents the post-mortem report upon completion of the Long Lamai e-commerce developme... more 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.
This study investigates the effectiveness of using groundwater inventory data for groundwater spr... 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...
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jun 4, 2019
Multisource remote sensing image data provides synthesized information to support many applicatio... more Multisource remote sensing image data provides synthesized information to support many applications including land cover mapping, urban planning, water resource management, and GIS modelling. Effectively utilizing such images however requires proper image registration, which in turn highly relies on accurate ground control points (GCP) selection. This study evaluates the performance of the interest point descriptor SURF (Speeded-Up Robust Features) for GCPs selection from UAV and LiDAR images. The main motivation for using SURF is due to it being invariant to scaling, blur and illumination, and partially invariant to rotation and view point changes. We also consider features generated by the Sobel and Canny edge detectors as complements to potentially increase the accuracy of feature matching between the UAV and LiDAR images. From our experiments, the red channel (Band-3) produces the most accurate and practical results in terms of registration, while adding the edge features seems to produce lacklustre results.
IEEE Access, 2023
Iris segmentation is a significant phase in the iris recognition process because segmentation err... more Iris segmentation is a significant phase in the iris recognition process because segmentation errors cascade into all subsequent phases. Therefore, it is important that errors in iris segmentation are minimised. The U-Net architecture that uses a deep learning approach was previously adopted for this task, but its performance was affected by the deformation of iris images caused by various noise factors in unconstrained (non-ideal) environments. Scratches, blurriness, dirt, specular reflections and other noise factors are some of the challenges faced in unconstrained environments when eyeglasses are present in the original images. Additionally, the performance of the iris segmentation was degraded due to problems of exploding gradient or vanishing gradient and the loss of information. This paper proposes a multisegmentation network called MS-Net, based on a deep learning approach, that aims to capture high-level semantic features while maintaining spatial information to improve the accuracy of iris segmentation. MS-Net consists of three principal segments: a feature encoder network, a multi-scale context feature extractor network (MSCFE-Net) and a feature decoder network. MSCFE-Net a multi-scale context feature extractor network is constructed from a dilated residual multi-convolutional network module and a pyramid pooling residual model based on an attention convolutional module. In addition, the proposed MS-Net contains dense connections within the feature decoder network to decrease training difficulty, by using only a few training samples. The accuracy of MS-Net was evaluated on the CASIA-Iris.V4-1000 and UBIRIS.V2 databases. The performance of our proposed MS-Net method on the CASIA-Iris.V4-1000 and UBIRIS.V2 databases achieved an overall accuracy of 97.11% and 96.128%, respectively. Experiment results show that MS-Net is able to achieve better results compared to earlier methods used for the same purpose. INDEX TERMS Iris recognition, iris segmentation, traditional techniques, U-Net architecture, convolutional neural network (CNN) techniques, dilated convolution (DC), residual network (ResNet), deep learning (DL) techniques.
Journal of electronic systems, Jun 1, 2019
The advancement of mobile technology with reasonable cost has indulge the mobile phone users to p... more The advancement of mobile technology with reasonable cost has indulge the mobile phone users to photograph and share their foods in social media. Since that, food recognition has become emerging research area in image processing and machine learning. Food recognition provides an automatic identification of the types of foods from an image. Further analysis of food images can be carried out to approximate the calories and nutrients that can be used for health-care purposes as well as the other application domains. The interest region-based detector by using Maximally Stable Extremal Region (MSER) provides distinctive interest points by representing the arbitrary shape of foods in parallelogram especially the food images with strong mixture of ingredients. However, the classification performance on food categories with less diverse texture food images by using MSER are obviously was not up to par with the rest of food categories that have more noticeable texture. The respective food objects were suffered from low volume extremal regions (ER) detection that were associated with the condition of food images that have visually texture-less objects, low contrast and brightness as well as small image pixel dimensions. Therefore, this paper proposed an adaptive interest regions detection by using MSER (aMSER) that provide an automatic MSER parameter configuration to increase the density of interest points on the targeted food images. The features are described by using Speeded-up Robust Feature Transform (SURF) and encoded by using Bag of Features (BoF) model. The classification is performed by using Linear Support Vector Machine and yield 84.20% classification rate on UEC100-Food dataset with competitive volume of ER and extraction time efficiency.
Journal of Telecommunication, Electronic and Computer Engineering, Sep 15, 2017
The social media services such as Facebook, Instagram and Twitter has attracted millions of food ... more The social media services such as Facebook, Instagram and Twitter has attracted millions of food photos to be uploaded every day since its inception. Automatic analysis on food images are beneficial from health, cultural and marketing aspects. Hence, recognizing food objects using image processing and machine learning techniques has become emerging research topic. However, to represent the key features of foods has become a hassle from the immaturity of current feature representation techniques in handling the complex appearances, high deformation and large variation of foods. To employ many kinds of feature types are also infeasible as it inquire much pre-processing and computational resources for segmentation, feature representation and classification. Motivated from these drawbacks, we proposed the integration on two kinds of local feature namely Speeded-Up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) to represent the features large variation food objects. Local invariant features have shown to be successful in describing object appearances for image classification tasks. Such features are robust towards occlusion and clutter and are also invariant against scale and orientation changes. This makes them suitable for classification tasks with little inter-class similarity and large intra-class difference. The Bag of Features (BOF) approach is employed to enhance the discriminative ability of the local features. Experimental results demonstrate impressive overall recognition at 82.38% classification accuracy from the local feature integration based on the challenging UEC-Food100 dataset. Then, we provide depth analysis on SURF and SIFT implementation to highlight the problems towards recognizing foods that need to be rectified in the future research.
Lecture Notes in Computer Science, 2017
Food object recognition has gained popularity in recent years. This can perhaps be attributed to ... more 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%.
Nucleation and Atmospheric Aerosols, 2017
Local invariant features have shown to be successful in describing object appearances for image c... more Local invariant features have shown to be successful in describing object appearances for image classification tasks. Such features are robust towards occlusion and clutter and are also invariant against scale and orientation changes. This makes them suitable for classification tasks with little inter-class similarity and large intra-class difference. In this paper, we propose an integrated representation of the Speeded-Up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors, using late fusion strategy. The proposed representation is used for food recognition from a dataset of food images with complex appearance variations. The Bag of Features (BOF) approach is employed to enhance the discriminative ability of the local features. Firstly, the individual local features are extracted to construct two kinds of visual vocabularies, representing SURF and SIFT. The visual vocabularies are then concatenated and fed into a Linear Support Vector Machine (SVM) to classify the respective food categories. Experimental results demonstrate impressive overall recognition at 82.38% classification accuracy based on the challenging UEC-Food100 dataset. RESEARCH BACKROUND Feature representation to describe key features in an image is very crucial for reliable object recognition. A variety of low-level invariant features are available in the literature comprising global and local features. Local invariant features, such as the Scale Invariant Feature Transform(SIFT) 1 provides a powerful descriptor due its stability under different scale and orientation changes as well as being robust to occlusion and clutter 1,2. On the other hand, a variant of SIFT, namely Speeded-up Robust Feature Transform(SURF) 3 , is more computationally efficient than its counterpart while also being able to detect and derive meaningful local descriptors. However, due to complex appearances of real images, using a single descriptor in isolation might not be sufficient to effectively represent the huge variations of an object. Therefore, using local features in combination might prove beneficial. In this paper, we propose to feature representation that integrates both SIFT and SURF using late fusion strategy. Bag-of-Feature(BOF) 4 approach is used to tokenize the key-points into two visual vocabularies that are small in size. The proposed method is used for the recognition of food objects from the UEC-Food100 dataset, whose images have complex appearances, non-rigid deformation, fine-grained as well extremely huge in variations 5-9. The rest sections of this paper explain the related works in feature representation methods of food recognition, the experimental procedure undertaken to evaluate of the proposed method and the discussions from the experimental results.
2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
International Journal of Advanced Computer Science and Applications, 2022
Requirement specifications (RS) are essential and fundamental artefacts in system development. RS... more Requirement specifications (RS) are essential and fundamental artefacts in system development. RS is the primary reference in software development and is commonly written in natural language. Bad requirement quality, such as requirement smells, may lead to project delay, cost overrun, and failure. Focusing on requirement quality in the Malaysian government, this paper investigates the methods for preparing Malay RS and personnel competencies to identify the root cause of this issue. We conducted semi-structured interviews that involved 17 respondents from eight critical Malaysian public sector agencies. This study found that ambiguity, incompleteness, and inconsistency are the top three requirement smells that cause project delays and failures. Furthermore, based on our static analysis, we collected the initial Malay RS documents from various Malaysian public sector agencies; we found that 30% of the RS were ambiguous. Our analysis also found that respondents with more than 10 years of experience could manually identify the smells in RS. Most respondents chose the Public Sector Application Systems Engineering (KRISA) handbook as a guideline for preparing Malay RS documents. Respondents acknowledged a correlation between the quality of RS and project delays and failures.
2022 International Conference on Digital Transformation and Intelligence (ICDI)
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Papers by Alfian Abdul Halin