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Haidi Ibrahim
School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Malaysia

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Digital Image Processing
deep learning for image processing
deep learning for Signal processing
Deep learning for medical data

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Journal article
Published: 06 August 2021 in Sensors
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Proliferative Diabetic Retinopathy (PDR) is a severe retinal disease that threatens diabetic patients. It is characterized by neovascularization in the retina and the optic disk. PDR clinical features contain highly intense retinal neovascularization and fibrous spreads, leading to visual distortion if not controlled. Different image processing techniques have been proposed to detect and diagnose neovascularization from fundus images. Recently, deep learning methods are getting popular in neovascularization detection due to artificial intelligence advancement in biomedical image processing. This paper presents a semantic segmentation convolutional neural network architecture for neovascularization detection. First, image pre-processing steps were applied to enhance the fundus images. Then, the images were divided into small patches, forming a training set, a validation set, and a testing set. A semantic segmentation convolutional neural network was designed and trained to detect the neovascularization regions on the images. Finally, the network was tested using the testing set for performance evaluation. The proposed model is entirely automated in detecting and localizing neovascularization lesions, which is not possible with previously published methods. Evaluation results showed that the model could achieve accuracy, sensitivity, specificity, precision, Jaccard similarity, and Dice similarity of 0.9948, 0.8772, 0.9976, 0.8696, 0.7643, and 0.8466, respectively. We demonstrated that this model could outperform other convolutional neural network models in neovascularization detection.

ACS Style

Michael Tang; Soo Teoh; Haidi Ibrahim; Zunaina Embong. Neovascularization Detection and Localization in Fundus Images Using Deep Learning. Sensors 2021, 21, 5327 .

AMA Style

Michael Tang, Soo Teoh, Haidi Ibrahim, Zunaina Embong. Neovascularization Detection and Localization in Fundus Images Using Deep Learning. Sensors. 2021; 21 (16):5327.

Chicago/Turabian Style

Michael Tang; Soo Teoh; Haidi Ibrahim; Zunaina Embong. 2021. "Neovascularization Detection and Localization in Fundus Images Using Deep Learning." Sensors 21, no. 16: 5327.

Journal article
Published: 14 June 2021 in IEEE Access
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A segmentation process is usually required in order to analyze an image. One of the available segmentation approaches is by detecting the edges on the image. Up to now, there are many edge detection algorithms that researchers have proposed. Thus, the purpose of this systematic literature review is to investigate the available quality assessment methods that researchers have utilized to evaluate the performance of the edge detection algorithms. Due to the vast number of available literature in this area, we limit our search to only open-access publications. A systematic search in five publisher websites (i.e., IEEExplore, IET digital library, Wiley, MDPI, and Hindawi) and Scopus database was carried out to gather resources that are related to the edge detection algorithms. Seventy-three publications that are about developing or comparing edge detection algorithms have been chosen. From these publication samples, we have identified 17 quality assessment methods used by researchers. Among the popular quality assessment methods are visual inspection, processing time, confusion-matrix based measures, mean square error (MSE)-based measures, and figure of merit (FOM). This survey also indicates that although most of the researchers only use a small number of test images (i.e., less than 10 test images), there are available datasets with a larger number of images for digital image segmentation that researchers can utilize.

ACS Style

Nazish Tariq; Rostam Affendi Hamzah; Theam Foo Ng; Shir Li Wang; Haidi Ibrahim. Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review. IEEE Access 2021, 9, 1 -1.

AMA Style

Nazish Tariq, Rostam Affendi Hamzah, Theam Foo Ng, Shir Li Wang, Haidi Ibrahim. Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

Nazish Tariq; Rostam Affendi Hamzah; Theam Foo Ng; Shir Li Wang; Haidi Ibrahim. 2021. "Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review." IEEE Access 9, no. : 1-1.

Review
Published: 06 April 2021 in Electronics
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Image super-resolution has become an important technology recently, especially in the medical and industrial fields. As such, much effort has been given to develop image super-resolution algorithms. A recent method used was convolutional neural network (CNN) based algorithms. super-resolution convolutional neural network (SRCNN) was the pioneer of CNN-based algorithms, and it continued being improved till today through different techniques. The techniques included the type of loss functions used, upsampling module deployed, and the adopted network design strategies. In this paper, a total of 18 articles were selected through the PRISMA standard. A total of 19 algorithms were found in the selected articles and were reviewed. A few aspects are reviewed and compared, including datasets used, loss functions used, evaluation metrics applied, upsampling module deployed, and adopted design techniques. For each upsampling module and design techniques, their respective advantages and disadvantages were also summarized.

ACS Style

Yoong Ooi; Haidi Ibrahim. Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review. Electronics 2021, 10, 867 .

AMA Style

Yoong Ooi, Haidi Ibrahim. Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review. Electronics. 2021; 10 (7):867.

Chicago/Turabian Style

Yoong Ooi; Haidi Ibrahim. 2021. "Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review." Electronics 10, no. 7: 867.

Journal article
Published: 03 February 2021 in IEEE Access
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A sudden blow or jolt to the human brain called traumatic brain injury (TBI) is one of the most common injuries recorded in the health insurance claim. Generally, computed tomography (CT) or magnetic resonance imaging (MRI) is required to identify the trauma’s severity. Unfortunately, CT and MRI equipment are bulky, expensive, and not always available, limiting their use in TBI detection. Therefore, as an alternative, this study presents a novel classification architecture that can classify non-severe TBI patients from healthy subjects by using resting-state electroencephalogram (EEG) as the input. The proposed architecture employs a convolutional neural network (CNN), and error-correcting output codes support vector machine (ECOC-SVM) to perform automated feature extraction and multi-class classification. In this architecture, complex feature selection and extraction steps are avoided. The proposed architecture attained a high-performance classification accuracy of 99.76%, potentially being used as a classification approach to preventing healthcare insurance fraud. The proposed method is compared to existing studies in the literature. The outcome from the comparisons indicates that the proposed method has outperformed the benchmarked methods by presenting the highest classification accuracy and precision.

ACS Style

Chi Qin Lai; Haidi Ibrahim; Jafri Malin Abdullah; Azlinda Azman; Mohd Zaid Abdullah. Convolutional Neural Network Utilizing Error-Correcting Output Codes Support Vector Machine for Classification of Non-Severe Traumatic Brain Injury From Electroencephalogram Signal. IEEE Access 2021, 9, 24946 -24964.

AMA Style

Chi Qin Lai, Haidi Ibrahim, Jafri Malin Abdullah, Azlinda Azman, Mohd Zaid Abdullah. Convolutional Neural Network Utilizing Error-Correcting Output Codes Support Vector Machine for Classification of Non-Severe Traumatic Brain Injury From Electroencephalogram Signal. IEEE Access. 2021; 9 ():24946-24964.

Chicago/Turabian Style

Chi Qin Lai; Haidi Ibrahim; Jafri Malin Abdullah; Azlinda Azman; Mohd Zaid Abdullah. 2021. "Convolutional Neural Network Utilizing Error-Correcting Output Codes Support Vector Machine for Classification of Non-Severe Traumatic Brain Injury From Electroencephalogram Signal." IEEE Access 9, no. : 24946-24964.

Journal article
Published: 06 January 2021 in Electronics
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Face detection by electronic systems has been leveraged by private and government establishments to enhance the effectiveness of a wide range of applications in our day to day activities, security, and businesses. Most face detection algorithms that can reduce the problems posed by constrained and unconstrained environmental conditions such as unbalanced illumination, weather condition, distance from the camera, and background variations, are highly computationally intensive. Therefore, they are primarily unemployable in real-time applications. This paper developed face detectors by utilizing selected Haar-like and local binary pattern features, based on their number of uses at each stage of training using MATLAB’s trainCascadeObjectDetector function. We used 2577 positive face samples and 37,206 negative samples to train Haar-like and LBP face detectors for a range of False Alarm Rate (FAR) values (i.e., 0.01, 0.05, and 0.1). However, the study shows that the Haar cascade face detector at a low stage (i.e., at six stages) for 0.1 FAR value is the most efficient when tested on a set of classroom images dataset with 100% True Positive Rate (TPR) face detection accuracy. Though, deep learning ResNet101 and ResNet50 outperformed the average performance of Haar cascade by 9.09% and 0.76% based on TPR, respectively. The simplicity and relatively low computational time used by our approach (i.e., 1.09 s) gives it an edge over deep learning (139.5 s), in online classroom applications. The TPR of the proposed algorithm is 92.71% when tested on images in the synthetic Labeled Faces in the Wild (LFW) dataset and 98.55% for images in MUCT face dataset “a”, resulting in a little improvement in average TPR over the conventional face identification system.

ACS Style

Sirajdin Adeshina; Haidi Ibrahim; Soo Teoh; Seng Hoo. Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons. Electronics 2021, 10, 102 .

AMA Style

Sirajdin Adeshina, Haidi Ibrahim, Soo Teoh, Seng Hoo. Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons. Electronics. 2021; 10 (2):102.

Chicago/Turabian Style

Sirajdin Adeshina; Haidi Ibrahim; Soo Teoh; Seng Hoo. 2021. "Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons." Electronics 10, no. 2: 102.

Journal article
Published: 01 December 2020 in Electronics
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It is essential to restore digital images corrupted by noise to make them more useful. Many approaches have been proposed to restore images affected by fixed value impulse noise, but they still do not perform well at high noise density. This paper presents a new method to improve the detection and removal of fixed value impulse noise from digital images. The proposed method consists of two stages. The first stage is the noise detection stage, where the difference values between the pixels and their surrounding pixels are computed to decide whether they are noisy pixels or not. The second stage is the image denoising stage. In this stage, the original intensity value of the noisy pixels is estimated using only their first-order and second-order neighborhood pixels. These neighboring orders are based on the Euclidean distance between the noisy pixel and its neighboring pixels. The proposed method was evaluated by comparing it with some of the recent methods using 50 images at 18 noise densities. The experimental results confirm that the proposed method outperforms the existing filters, excelling in noise removal capability with structure and edge information preservation.

ACS Style

Ali Mursal; Haidi Ibrahim. Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images. Electronics 2020, 9, 2034 .

AMA Style

Ali Mursal, Haidi Ibrahim. Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images. Electronics. 2020; 9 (12):2034.

Chicago/Turabian Style

Ali Mursal; Haidi Ibrahim. 2020. "Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images." Electronics 9, no. 12: 2034.

Journal article
Published: 18 September 2020 in IEEE Access
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A multi-camera system combines features from different cameras to exploit a scene of an event to increase the output image quality. The combination of two or more cameras requires prior settings in terms of calibration and architecture. Therefore, this paper surveys the available literature in terms of multi-camera systems’ physical arrangements, calibrations, algorithms, and their advantages and disadvantages. We also survey the recent developments and advancements in four areas of multi-camera system applications, which are surveillance, sports, education, and mobile phones. In the surveillance system, the combination of multiple heterogeneous cameras and the discovery of Pan-Tilt-Zoom (PTZ) and smart cameras have brought tremendous achievements in the area of multi-camera control and coordination. Different approaches have been proposed to facilitate effective collaboration and monitoring among the camera network. Furthermore, the application of multi-cameras in sports has made the games more interesting in the aspect of analyses and transparency. The application of the multi-camera system in education has taken education beyond the four walls of the class. The method of teaching, student attendance enrollment, determination of students’ attention, teacher and student assessment can now be determined with ease, and all forms of proxy and manipulation in education can be reduced by using a multi-camera system. Besides, the number of cameras featuring on smartphones is gaining noticeable recognition. However, most of these cameras serve different purposes, from zooming, telephoto, and wider Field of View (FOV). Therefore, future smartphones should be expecting more cameras or the development would be in a different direction.

ACS Style

Adeshina Sirajdin Olagoke; Haidi Ibrahim; Soo Siang Teoh. Literature Survey on Multi-Camera System and Its Application. IEEE Access 2020, 8, 172892 -172922.

AMA Style

Adeshina Sirajdin Olagoke, Haidi Ibrahim, Soo Siang Teoh. Literature Survey on Multi-Camera System and Its Application. IEEE Access. 2020; 8 (99):172892-172922.

Chicago/Turabian Style

Adeshina Sirajdin Olagoke; Haidi Ibrahim; Soo Siang Teoh. 2020. "Literature Survey on Multi-Camera System and Its Application." IEEE Access 8, no. 99: 172892-172922.

Journal article
Published: 14 September 2020 in Sensors
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Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision.

ACS Style

Chi Lai; Haidi Ibrahim; Aini Abd Hamid; Jafri Abdullah. Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM. Sensors 2020, 20, 5234 .

AMA Style

Chi Lai, Haidi Ibrahim, Aini Abd Hamid, Jafri Abdullah. Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM. Sensors. 2020; 20 (18):5234.

Chicago/Turabian Style

Chi Lai; Haidi Ibrahim; Aini Abd Hamid; Jafri Abdullah. 2020. "Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM." Sensors 20, no. 18: 5234.

Journal article
Published: 03 September 2020 in IEEE Transactions on Components, Packaging and Manufacturing Technology
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This paper presents improved formulations for the latency insertion method (LIM) for diodes and MOSFETs utilizing the voltage-in-current framework. LIM is a fast circuit simulation algorithm which computes the solutions to the voltages and currents in a circuit in a leapfrog manner, instead of a simultaneous matrix solution typically performed in the modified nodal analysis formalism. This allows LIM to have a far superior scaling with respect to the dimensions of the circuit. The formulations presented here have the advantage of not being limited by the stability condition in LIM and thus allow the simulations to be performed at larger time steps. Numerical examples are presented which illustrate the improved performances of the developed formulations.

ACS Style

Wei Chun Chin; Andrei Pashkovich; Kostas Malinauskas; Jose E. Schutt-Aine; Haidi Ibrahim; Nur Syazreen Ahmad; Patrick Goh. Voltage-in-Current Latency Insertion Method for Diodes and MOSFETs. IEEE Transactions on Components, Packaging and Manufacturing Technology 2020, 10, 1708 -1720.

AMA Style

Wei Chun Chin, Andrei Pashkovich, Kostas Malinauskas, Jose E. Schutt-Aine, Haidi Ibrahim, Nur Syazreen Ahmad, Patrick Goh. Voltage-in-Current Latency Insertion Method for Diodes and MOSFETs. IEEE Transactions on Components, Packaging and Manufacturing Technology. 2020; 10 (10):1708-1720.

Chicago/Turabian Style

Wei Chun Chin; Andrei Pashkovich; Kostas Malinauskas; Jose E. Schutt-Aine; Haidi Ibrahim; Nur Syazreen Ahmad; Patrick Goh. 2020. "Voltage-in-Current Latency Insertion Method for Diodes and MOSFETs." IEEE Transactions on Components, Packaging and Manufacturing Technology 10, no. 10: 1708-1720.

Review
Published: 01 June 2020 in IEEE Access
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Recent developments in the field of machine learning (ML) have led to a renewed interest in the use of electroencephalography (EEG) to predict the outcome after traumatic brain injury (TBI). This systematic review aims to determine how previous studies have taken into consideration the important modeling issues for quantitative EEG (qEEG) predictors in developing prognostic models. A systematic search in the PubMed and Google Scholar databases was performed to identify all predictive models for the extended Glasgow outcome scale (GOSE) and Glasgow outcome scale (GOS) based on EEG data. Fourteen studies were identified that evaluated ML algorithms using qEEG predictors to predict outcome in patients with moderate to severe TBI. In each model, a maximum of five qEEG predictors were selected to determine the association between these parameters, and favorable or unfavorable predicted outcomes. The most common ML technique used was logistic regression, but the algorithms varied depending on the types and numbers of qEEG predictors selected in each model. The qEEG variability for the relative and absolute band powers were the most common qEEG predictors included in the models (46%) followed by total EEG power of all frequency bands (31%), EEG-reactivity (31%) and coherence (15%). Model performance was often quantified by the area under the receiving-operating characteristic curve (AUROC) rather than by accuracy rate. Various ML models have demonstrated great potential, especially using qEEG predictors, to predict outcome in patients with moderate to severe TBI.

ACS Style

Nor Safira Elaina Mohd Noor; Haidi Ibrahim. Machine Learning Algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review. IEEE Access 2020, 8, 102075 -102092.

AMA Style

Nor Safira Elaina Mohd Noor, Haidi Ibrahim. Machine Learning Algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review. IEEE Access. 2020; 8 (99):102075-102092.

Chicago/Turabian Style

Nor Safira Elaina Mohd Noor; Haidi Ibrahim. 2020. "Machine Learning Algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review." IEEE Access 8, no. 99: 102075-102092.

Research article
Published: 11 March 2020 in Computational Intelligence and Neuroscience
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Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.

ACS Style

Chi Qin Lai; Haidi Ibrahim; Aini Ismafairus Abd. Hamid; Mohd Zaid Abdullah; Azlinda Azman; Jafri Malin Abdullah. Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography. Computational Intelligence and Neuroscience 2020, 2020, 8923906 -10.

AMA Style

Chi Qin Lai, Haidi Ibrahim, Aini Ismafairus Abd. Hamid, Mohd Zaid Abdullah, Azlinda Azman, Jafri Malin Abdullah. Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography. Computational Intelligence and Neuroscience. 2020; 2020 ():8923906-10.

Chicago/Turabian Style

Chi Qin Lai; Haidi Ibrahim; Aini Ismafairus Abd. Hamid; Mohd Zaid Abdullah; Azlinda Azman; Jafri Malin Abdullah. 2020. "Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography." Computational Intelligence and Neuroscience 2020, no. : 8923906-10.

Review article
Published: 15 September 2019 in Journal of Sensors
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The application of biometric recognition in personal authentication enables the growth of this technology to be employed in various domains. The implementation of biometric recognition systems can be based on physical or behavioral characteristics, such as the iris, voice, fingerprint, and face. Currently, the attendance tracking system based on biometric recognition for education sectors is still underutilized, thus providing a good opportunity to carry out interesting research in this area. As evidenced in a typical classroom, educators tend to take the attendance of their students by using conventional methods such as by calling out names or signing off an attendance sheet. Yet, these types of methods are proved to be time consuming and tedious, and sometimes, fraud occurs. As a result, significant progress had been made to mark attendance automatically by making use of biometric recognition. This progress enables a new and more advanced biometric-based attendance system being developed over the past ten years. The setting-up of biometric-based attendance systems requires both software and hardware components. Since the software and hardware sections are too broad to be discussed in one paper, this literature survey only provides an overview of the types of hardware used. Emphasis is then placed on the microcontroller platform, biometric sensor, communication channel, database storage, and other components in order to assist future researchers in designing the hardware part of biometric-based attendance systems.

ACS Style

Seng Chun Hoo; Haidi Ibrahim. Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements. Journal of Sensors 2019, 2019, 1 -25.

AMA Style

Seng Chun Hoo, Haidi Ibrahim. Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements. Journal of Sensors. 2019; 2019 ():1-25.

Chicago/Turabian Style

Seng Chun Hoo; Haidi Ibrahim. 2019. "Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements." Journal of Sensors 2019, no. : 1-25.

Conference paper
Published: 04 July 2019 in Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020)
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Traumatic brain injury (TBI) is defined as blunt and penetrating injury to the head and/or brain caused by an external force that leads to temporary or permanent impairments to the brain function. Accurate measurement of prediction for the outcomes of affected individual is highly desirable to plan and optimize treatment decision. The clinical experts predict the outcomes of brain injury patients with a high degree of accuracy based on their experience and the standardized Glasgow Outcome Scale (GOS). The GOS has been used over the past 40 years and it plays an important role in developing the understanding of brain injury. Recent developments in Artificial Intelligence (AI) have heightened the need for developing predictive models using machine learning (ML) methods especially for TBI patients who require life-saving interventions. ML is a subfield of AI which allows the computer algorithms to learn patterns by studying data directly without being explicitly programmed. This paper compares the different ways in which predictive models evaluate the potential of ML for TBI outcome prediction. A literature survey of latest articles from 2016 to 2018 reveals that the predictions of existing predictive models compute different prediction performances in terms of accuracy, sensitivity, specificity and area under receiving operator characteristic (ROC) curve (AUC). Depending on the specific prediction task evaluated and the type of input features included, Artificial Neural Network (ANN) creates a powerful model to predict outcomes of TBI with profound accuracy compared to other ML models. Although ANNs are considered as “black-box” in computational models, their benefits in clinical medicine have infinite potentials in evidence-based medicine practice because ANNs can be trained on new patient information. Moreover, the existing predictive models show that ML can be leveraged to more accurately predict the outcomes of TBI patients. Most importantly, predictive models can provide real-time clinical utilization that leads to greater accuracy and higher predictive value for patients suffered from traumatic brain injury.

ACS Style

Nor Safira Elaina Mohd Noor; Haidi Ibrahim. Predicting Outcomes in Patients with Traumatic Brain Injury Using Machine Learning Models. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) 2019, 12 -20.

AMA Style

Nor Safira Elaina Mohd Noor, Haidi Ibrahim. Predicting Outcomes in Patients with Traumatic Brain Injury Using Machine Learning Models. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020). 2019; ():12-20.

Chicago/Turabian Style

Nor Safira Elaina Mohd Noor; Haidi Ibrahim. 2019. "Predicting Outcomes in Patients with Traumatic Brain Injury Using Machine Learning Models." Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) , no. : 12-20.

Conference paper
Published: 04 July 2019 in Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020)
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Digital images may suffer from impulse noise, including salt-and-pepper noise. One of the common methods to deal with this noise is by using median filter, which is a type of non-linear filter. Standard median filter includes noisy pixels in calculating the median value for the restoration process. However, this will lead to an inaccurate result, where the noisy pixel values may be selected for the restoration. Another approach is by using recursive median filter, where the calculation for the median value is also based on the previous outputs. Therefore, in this paper, we investigate the feasibility of improving the performance of recursive median filter, by adapting it to switching and adaptive approaches. This scheme is called as Recursive Switching Adaptive Median Filter. As the switching median filter is used, the method is divided into two stages, which are noise detection and noise restoration stages. In the noise detection stage, salt-and-pepper pixel candidates are identified. Then, in the restoration stage, an adaptive method is used for the restoration. The size of the filter is expanding until there are at least eight noise-free pixel candidates defined by the window. As the recursive method is used, the noise mask is updated every time the restoration is done. The experimental results show that this scheme has good performance in terms of mean square error and structural similarity index measure, as compared to six other median filtering approaches. However, the scheme does not perform well at high level of corruption, especially when the level of corruption is more than 80%.

ACS Style

Aina Qistina Md. Taha; Haidi Ibrahim. Reduction of Salt-and-Pepper Noise from Digital Grayscale Image by Using Recursive Switching Adaptive Median Filter. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) 2019, 32 -47.

AMA Style

Aina Qistina Md. Taha, Haidi Ibrahim. Reduction of Salt-and-Pepper Noise from Digital Grayscale Image by Using Recursive Switching Adaptive Median Filter. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020). 2019; ():32-47.

Chicago/Turabian Style

Aina Qistina Md. Taha; Haidi Ibrahim. 2019. "Reduction of Salt-and-Pepper Noise from Digital Grayscale Image by Using Recursive Switching Adaptive Median Filter." Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) , no. : 32-47.

Research article
Published: 02 June 2019 in Computational Intelligence and Neuroscience
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Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is able to distinct between two individuals. The literature reviews have shown that convolutional neural network (CNN) is one of the classification approaches that can avoid the complex stages of preprocessing, feature extraction, and feature selection. Therefore, CNN is suggested to be one of the efficient classifiers for biometric identification. Conventionally, input to CNN can be in image or matrix form. The objective of this paper is to explore the arrangement of EEG for CNN input to investigate the most suitable input arrangement of EEG towards the performance of EEG-based identification. EEG datasets that are used in this paper are resting state eyes open (REO) and resting state eyes close (REC) EEG. Six types of data arrangement are compared in this paper. They are matrix of amplitude versus time, matrix of energy versus time, matrix of amplitude versus time for rearranged channels, image of amplitude versus time, image of energy versus time, and image of amplitude versus time for rearranged channels. It was found that the matrix of amplitude versus time for each rearranged channels using the combination of REC and REO performed the best for biometric identification, achieving validation accuracy and test accuracy of 83.21% and 79.08%, respectively.

ACS Style

Chi Qin Lai; Haidi Ibrahim; Mohd Zaid Abdullah; Jafri Malin Abdullah; Shahrel Azmin Suandi; Azlinda Azman. Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification. Computational Intelligence and Neuroscience 2019, 2019, 7895924 -10.

AMA Style

Chi Qin Lai, Haidi Ibrahim, Mohd Zaid Abdullah, Jafri Malin Abdullah, Shahrel Azmin Suandi, Azlinda Azman. Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification. Computational Intelligence and Neuroscience. 2019; 2019 ():7895924-10.

Chicago/Turabian Style

Chi Qin Lai; Haidi Ibrahim; Mohd Zaid Abdullah; Jafri Malin Abdullah; Shahrel Azmin Suandi; Azlinda Azman. 2019. "Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification." Computational Intelligence and Neuroscience 2019, no. : 7895924-10.

Review
Published: 02 April 2019 in Lecture Notes in Electrical Engineering
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Convolution neural network (CNN) presents high robustness in computer vision applications. In state-of-the-art methods, CNN is being used in EEG processing for various classification and problem solving. To enable EEG to fit in the CNN architecture, data conversion of EEG has to be done. The ways of data conversion need to be investigated in order to fully utilize the information. From the study, it was found that the simplest way of re-arranging the signal is by creating a two dimensional matrix of channels versus time points. There are approaches that compute Pearson correlation coefficients and fit them into a two dimensional matrix to represent the input signal. There are also methods which extract frequency components and fit them in matrix structure as channels versus frequency components, such as power spectral density. Other approaches includes graph representation and wavelet components.

ACS Style

Chi Qin Lai; Haidi Ibrahim; Mohd Zaid Abdullah; Jafri Malin Abdullah; Shahrel Azmin Suandi; Azlinda Azman. A Literature Review on Data Conversion Methods on EEG for Convolution Neural Network Applications. Lecture Notes in Electrical Engineering 2019, 521 -527.

AMA Style

Chi Qin Lai, Haidi Ibrahim, Mohd Zaid Abdullah, Jafri Malin Abdullah, Shahrel Azmin Suandi, Azlinda Azman. A Literature Review on Data Conversion Methods on EEG for Convolution Neural Network Applications. Lecture Notes in Electrical Engineering. 2019; ():521-527.

Chicago/Turabian Style

Chi Qin Lai; Haidi Ibrahim; Mohd Zaid Abdullah; Jafri Malin Abdullah; Shahrel Azmin Suandi; Azlinda Azman. 2019. "A Literature Review on Data Conversion Methods on EEG for Convolution Neural Network Applications." Lecture Notes in Electrical Engineering , no. : 521-527.

Conference paper
Published: 02 April 2019 in Lecture Notes in Electrical Engineering
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The world’s population is increasing since year 1950 till now. The large number of population with age between 25 and 29 implied the importance of health care services to maintain this population’s good health. However, there are a lot of health measuring devices only measure one health parameter from each person. This is very inconvenience to most of the users. Another problem encountered is that there is a large number of health data that are not analysed by the system. Therefore, the purpose of this research is to develop a data acquisition system that consists of three sensors, which are temperature sensor, pulse oximeter sensor and heart rate sensor. Besides, this project also develops Support Vector Machine (SVM) based machine learning algorithm to monitor health condition. All the sensors will measure respective reading and read by Arduino microcontroller. The reading will then transfer to Raspberry Pi 3 via serial communication for health prediction using machine learning. A classification model is derived from 240 training data and tested with 60 testing data. The classification model gives an overall accuracy of 93.33%. While looking at user’s accuracy on each class, all class except two classes give 100% accuracy. However, both ROC of these two classes are 0.998, which are still high. Therefore, the classification model is good and can be used to predict health condition.

ACS Style

Yoong Khang Ooi; Haidi Ibrahim. Development of Health Monitoring System with Support Vector Machine Based Machine Learning. Lecture Notes in Electrical Engineering 2019, 115 -121.

AMA Style

Yoong Khang Ooi, Haidi Ibrahim. Development of Health Monitoring System with Support Vector Machine Based Machine Learning. Lecture Notes in Electrical Engineering. 2019; ():115-121.

Chicago/Turabian Style

Yoong Khang Ooi; Haidi Ibrahim. 2019. "Development of Health Monitoring System with Support Vector Machine Based Machine Learning." Lecture Notes in Electrical Engineering , no. : 115-121.

Research article
Published: 22 January 2019 in Journal of Sensors
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Palmprint has become one of the biometric modalities that can be used for personal identification. This modality contains critical identification features such as minutiae, ridges, wrinkles, and creases. In this research, feature from creases will be our focus. Feature from creases is a special salient feature of palmprint. It is worth noting that currently, the creases-based identification is still not common. In this research, we proposed a method to extract crease features from two regions. The first region of interest (ROI) is in the hypothenar region, whereas another ROI is in the interdigital region. To speed up the extraction, most of the processes involved are based on the processing of the image that has been a downsampled image by using a factor of 10. The method involved segmentations through thresholding, morphological operations, and the usage of the Hough line transform. Based on 101 palmprint input images, experimental results show that the proposed method successfully extracts the ROIs from both regions. The method has achieved an average sensitivity, specificity, and accuracy of 0.8159, 0.9975, and 0.9951, respectively.

ACS Style

Roszaharah Yaacob; Chok Dong Ooi; Haidi Ibrahim; Nik Fakhuruddin Nik Hassan; Puwira Jaya Othman; Helmi Hadi. Automatic Extraction of Two Regions of Creases from Palmprint Images for Biometric Identification. Journal of Sensors 2019, 2019, 1 -12.

AMA Style

Roszaharah Yaacob, Chok Dong Ooi, Haidi Ibrahim, Nik Fakhuruddin Nik Hassan, Puwira Jaya Othman, Helmi Hadi. Automatic Extraction of Two Regions of Creases from Palmprint Images for Biometric Identification. Journal of Sensors. 2019; 2019 ():1-12.

Chicago/Turabian Style

Roszaharah Yaacob; Chok Dong Ooi; Haidi Ibrahim; Nik Fakhuruddin Nik Hassan; Puwira Jaya Othman; Helmi Hadi. 2019. "Automatic Extraction of Two Regions of Creases from Palmprint Images for Biometric Identification." Journal of Sensors 2019, no. : 1-12.

Conference paper
Published: 27 September 2018 in AIP Conference Proceedings
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By using instrument known as electroencephalograph, electroencephalogram (EEG) of the human brain can be obtained. EEG is a record of postsynaptic potentials, generated by neurons. Usually, EEG is used to study the activities inside the human brain. This study gives significant assistant in medical field, especially in diagnosing and planning treatment for the brain related diseases. With the advancement of technology, the usage of EEG is now not only limited within the medical field. EEG has been used for brain-machine-interface (BCT) and neuromarketing. Therefore, the aim of this literature survey is to see the current trend of the applications of EEG. This survey is done by observing the research articles published in two well-known databases, which are IEEExplore and ScienceDirect. From this literature survey, it is found that the researches on EEG are still growing, with the area of applications is expanding.

ACS Style

Chi Qin Lai; Haidi Ibrahim; Mohd Zaid Abdullah; Jafri Malin Abdullah; Shahrel Azmin Suandi; Azlinda Azman. Literature survey on applications of electroencephalography (EEG). AIP Conference Proceedings 2018, 2016, 020070 .

AMA Style

Chi Qin Lai, Haidi Ibrahim, Mohd Zaid Abdullah, Jafri Malin Abdullah, Shahrel Azmin Suandi, Azlinda Azman. Literature survey on applications of electroencephalography (EEG). AIP Conference Proceedings. 2018; 2016 (1):020070.

Chicago/Turabian Style

Chi Qin Lai; Haidi Ibrahim; Mohd Zaid Abdullah; Jafri Malin Abdullah; Shahrel Azmin Suandi; Azlinda Azman. 2018. "Literature survey on applications of electroencephalography (EEG)." AIP Conference Proceedings 2016, no. 1: 020070.

Journal article
Published: 08 May 2018 in Current Medical Imaging Formerly Current Medical Imaging Reviews
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ACS Style

Mohd Hafidz Abdullah; Haidi Ibrahim; Jafri Malin Abdullah; Mohd Zaid Abdullah. Improved Seizure Prediction Using Discrete Hidden Markov Model and Wilks’ Lambda Analysis of the Electroencephalographic Signals. Current Medical Imaging Formerly Current Medical Imaging Reviews 2018, 14, 407 -415.

AMA Style

Mohd Hafidz Abdullah, Haidi Ibrahim, Jafri Malin Abdullah, Mohd Zaid Abdullah. Improved Seizure Prediction Using Discrete Hidden Markov Model and Wilks’ Lambda Analysis of the Electroencephalographic Signals. Current Medical Imaging Formerly Current Medical Imaging Reviews. 2018; 14 (3):407-415.

Chicago/Turabian Style

Mohd Hafidz Abdullah; Haidi Ibrahim; Jafri Malin Abdullah; Mohd Zaid Abdullah. 2018. "Improved Seizure Prediction Using Discrete Hidden Markov Model and Wilks’ Lambda Analysis of the Electroencephalographic Signals." Current Medical Imaging Formerly Current Medical Imaging Reviews 14, no. 3: 407-415.