85 research outputs found
Automated detection of depression from brain structural magnetic resonance imaging (sMRI) scans
Automated sMRI-based depression detection system is developed whose components include acquisition and preprocessing, feature extraction, feature selection, and classification. The core focus of the research is on the establishment of a new feature selection algorithm that quantifies the most relevant brain volumetric feature for depression detection at an individual level
Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection
level using structural magnetic resonance imaging (sMRI)
remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depressio
An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans
To be diagnostically effective, structural magnetic resonance imaging (sMRI) must reliably distinguish a depressed individual from a healthy individual at individual scans level. One of the tasks in the automated diagnosis of depression from brain sMRI is the classification. It determines the class to which a sample belongs (i.e., depressed/not depressed, remitted/not-remitted depression) based on the values of its features. Thus far, very limited works have been reported for identification of a suitable classification algorithm for depression detection. In this paper, different types of classification algorithms are compared for effective diagnosis of depression. Ten independent classification schemas are applied and a comparative study is carried out. The algorithms are: Naïve Bayes, Support Vector Machines (SVM) with Radial Basis Function (RBF), SVM Sigmoid, J48, Random Forest, Random Tree, Voting Feature Intervals (VFI), LogitBoost, Simple KMeans Classification Via Clustering (KMeans) and Classification Via Clustering Expectation Minimization (EM) respectively. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. A classification accuracy evaluation method was employed for evaluation and comparison of the performance of the examined classifiers
Inquiry-based Assessment â Transforming Wonder into Knowledge
Transformative or transformation learning is one of the learning theory which focuses on enhancing the studentâs ability to learn by initiating their critical thinking based on new information [1]. Through transformative learning, the educators can create active learning [2], where the students or learners can thrive. There are many approaches
to perform transformation learning. As for Digital Signal Processing (DSP) course, an inquiry-based assessment is designed for the students. It is based on real-inquiry-based problems to measure the targeted Learning Outcome (LO). The assessment is initiated and adapted based on the
structured inquiry formation to attain the breadth and depth on the specific knowledge and information. This is then supported by the related evidence and facts gathered using and during the investigative processes
Classification of metamorphic virus using n-grams signatures
Metamorphic virus has a capability to change, translate, and rewrite
its own code once infected the system to bypass detection. The computer
system then can be seriously damage by this undetected metamorphic virus.
Due to this, it is very vital to design a metamorphic virus classification model
that can detect this virus. This paper focused on detection of metamorphic virus
using Term Frequency Inverse Document Frequency (TF-IDF) technique. This
research was conducted using Second Generation virus dataset. The first step is
the classification model to cluster the metamorphic virus using TF-IDF
technique. Then, the virus cluster is evaluated using NaÃŊve Bayes algorithm in
terms of accuracy using performance metric. The types of virus classes and
features are extracted from bi-gram assembly language. The result shows that
the proposed model was able to classify metamorphic virus using TF-IDF with
optimal number of virus class with average accuracy of 94.2%
Sago Palm Detection and its Maturity Identification Based on Improved Convolution Neural Network
Sago palms are mainly cultivated in Sarawak, especially in the Mukah and Betong division,
for consumption and export purposes. The starches produced from the sago are mostly
for food products such as noodles, traditional food such as tebaloi, and animal feeds.
Nowadays, the sago palm and its maturity detection are done manually, and it is crucial to
ensure the productivity of starch. The existing detection methods are very laborious and
time-consuming since the plantation areas are vast. The improved CNN model has been
developed in this paper to detect the maturity of the sago palm. The detection is done by
using drone photos based on the shape of the sago palm canopy. The model is developed by
combining the architecture of three existing CNN models, AlexNet, Xception, and ResNet.
The proposed model, CraunNet, gives 85.7% accuracy with 11 minutes of learning time
based on five-fold-validation. Meanwhile, the training time of the CraunNet is almost two
times faster than the existing models, ResNet and Xception. It shows that the computation
cost in the CraunNet is much faster than the established mode
Feature Extraction Algorithms of Retinal Microvasculature for Cost-Effective Medical Device
At present, chronic diseases such as stroke and diabetes mellitus continues to increase. In such medical conditions, if inappropriately treated, complications will easily occur such as visual morbidity, including blindness. According to the World Health Organization, as of 2010 worldwide, there are 39 million (13.6%) blind people due to visual morbidity related to chronic diseases. Therefore, this represent the magnitude of urgency needed to come up with technologies capable of preventing the unwanted complication (Mariotti, 2010). Digital image processing is one of the most remarkable advancing disciplines of computer visual image technology which is being widely employed in the modern biomedical imaging systems with increasing accuracy. This includes growing contributions of digital image processing in modern ophthalmic diagnostic systems. The human retina is the only location where blood vessels can be directly visualized non-invasively in vivo
Visual Odometry Based Vehicle Lane-changing Detection
Lane-changing detection is necessary for accurate
positioning, to allow vehicle navigation system to generate more
specific path planning. Lane-changing detection method in this
paper is more of a deterministic task, proposed based on curve
analysis obtained from visual odometry. From the visual
odometry trajectory, we have the estimation of vehicle
lateral/longitudinal position, yaw, and speed. We also used the
road lane information from digital map provided by
OpenStreetMap to narrow the lane-changing event possibility.
The analysis is conducted on sequences from KITTI dataset that
contains lane-changing scenarios to study the potential of lanechanging detection by using visual odometry trajectory curve.
Cumulative sum and curve fitting methods were utilized for the
lane-changing detection from visual odometry curve. The
detection was conducted on several visual odometry approaches
for comparison and system feasibility. Our analysis shows that
trajectory generated by visual odometry is highly potential for a
low-cost and effective lane-changing detection with 90.9%
precision and 93.8% recall accuracy to complement more
accurate routing service and safety application in Advanced
Driver Assistance System
Severity Estimation of Plant Leaf Diseases Using Segmentation Method
Plants have assumed a significant role in the history of humankind, for the most part as a source of nourishment
for human and animals. However, plants typically powerless to different sort of diseases such as leaf blight, gray
spot and rust. It will cause a great loss to farmers and ranchers. Therefore, an appropriate method to estimate
the severity of diseases in plant leaf is needed to overcome the problem. This paper presents the fusions of the
Fuzzy C-Means segmentation method with four different colour spaces namely RGB, HSV, L*a*b and YCbCr
to estimate plant leaf disease severity. The percentage of performance of proposed algorithms are recorded and
compared with the previous method which are K-Means and Otsuâs thresholding. The best severity estimation
algorithm and colour space used to estimate the diseases severity of plant leaf is the combination of Fuzzy
C-Means and YCbCr color space. The average performance of Fuzzy C-Means is 91.08% while the average
performance of YCbCr is 83.74%. Combination of Fuzzy C-Means and YCbCr produce 96.81% accuracy. This
algorithm is more effective than other algorithms in terms of not only better segmentation performance but also
low time complexity that is 34.75s in average with 0.2697s standard deviation.N/
The preliminary results on the push factors for the elderly to move to retirement villages in Malaysia
Many countries are witnessing a rise in the ageing population, which has become a global phenomenon that all nations must address. As the population of greying people is expected to increase in Malaysia, the demand for senior citizen accommodation is predicted to have experienced a major rise by 2030. However, although studies related to retirement villages (RV) are highly important to understand how to provide a better ambience for the elderly, research on the development of retirement villages in Malaysia is yet to gather pace fully. Thus, this paper aims to explore the potential of the retirement village in Malaysia by focusing on the push factors for the elderly to move to retirement villages in the local Malaysian context. The outcome of this paper presents the initial findings derived from a literature review and pilot survey. Eight potential push factors were identified after questions were posed to potential respondents through a pilot survey questionnaire. The research revealed that the main potential reason why the elderly relocate to retirement villages was related to social factors, with the elderly preferring better access to healthcare and support due to their unique requirements. The findings of this study are relevant to Chapter 11, as underlined in the Sustainable Development Goals (SDGs), which call on all governments to offer access to a secure, green environment for everyone, especially the elderly. Theoretically, this research provides the first findings on the elements that encourage the elderly to relocate to an RV when they retire in Malaysia
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