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 – Transforming Wonder into Knowledge

Transformative or transformation learning is one of the learning theory which focuses on enhancing the student’s 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 Naïve 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 Otsu’s 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/