Automatic segmentation solution is the process of detecting and extracting information to simplify the representation of Cardiac Magnetic Resonance images (CMRI) of Left Ventricle (LV) contour. This segmented information, using CMR images, helps to reduce the segmentation error between expert and automatic segmented contours. The error represents missing region values calculated in percentages after segmenting a cardiac LV contour. This review paper will discuss the major three segmentation approaches, namely manual approach, semi-automatic, and fully automatic, along with the segmentation models, namely image-based models, region-based models, edge-based models, deformable-based models, active shape-based models (ASM), active contour-based models (ACM), level set-based models (LSM), and Variational LSM (VLSM). The review deeply explains the performance of segmentation models using different techniques. Furthermore, the review compares 122 studies on segmentation model approaches, i.e., 16 from 2004 to 2010, 40 from 2011 to 2016, and 63 from 2017 to 2021, and 3 other related studies were conducted LV contour segmentation, cardiac function, area-at-risk (AAR) identification, scar tissue classification, oedema tissue classification, and identification via presence, size, and location. Given the large number of articles on CMR-LV images that have been published, this review conducted a critical analysis and found a gap for researchers in the areas of LV localization, LV contour segmentation, cardiac function, and oedoema tissue classification and segmentation. Regarding critical analysis, this paper summrised a research gap and made useful suggestions for new CMR-LV researchers. Although a timely reviewed study can lead to cardiac segmentation challenges, which will be discussed in each review section.

Oil palm leaves diseases is caused by various plant pathogens and micronutrient deficiency, and genetic disorders. This problem, if not identified and treated quickly could lead to losses in yield and profitability. The disease on leaves is currently being identified through the different colours, shapes, and forms. Other signs of an infected plant can be seen based on the discolouration on the leaves. In this paper, we present an approach to automatically identify the morphological features of leave diseases in category of healthy to non-healthy based on region of interest of discolouration on young oil palm leaves. Raw leaf images are captured using a built-in digital camera. Pre-processing was done on each of the non-uniform illumination condition raw data images. We tested the colour feature method using RGB (Red, Green Blue) colour filtering in the identification of the leaf region of interest. Next, further segmentation method using HSV (Hue, Saturation, Values) colour filtering approach is employed to remove shadows and to identify the different level of regions of discolouration. The results highlighted that the infected area on the leaves can be identified by 100% based on the discoloured in the region of interest. These regions can be categorised in three different groups -healthy leaves (20% of the discolouration region) to heavily infected (70% of the discolouration region) of the leaves -based on analysis of the pre-processing results. In top of that, the HSV colour feature method could also remove shadow and noise. The results of the detected discolouration will be used oil palm leaves datasets for further classification and recognition research work.

Image search is a challenging process in the field of Content Based Image Retrieval (CBIR). Image search-by-example, search-bykeyword and search-by-sketch methods seldom provide user interface that allows user to accurately formulate their search intent easily. To overcome such issue, a novel image search interface-Semantic Visual Query Builder (SeVQer) is proposed as a non-verbal interface which allows user to drag and drop from the image data provided to formulate user query. The drag and drop mechanism minimizes the difficulty of verbalizing query image into keywords or sketching a correct drawing of the query image. SeVQer was implemented and compared with 3 image search methods (search-by-example, search-by-keyword and search-by-sketch) in terms of task completion time and user satisfaction using traffic images. SeVQer achieved statistically significant lower task completion time with an average of 28 sec, a promising 50% reduction than search-by-sketch (average of 56 sec). The significance of this work is two-fold: the SeVQer user interface allows user to easily formulate intent specific query, while the novel architecture and methodology reduces the semantic gap in general.

Sign languages are one of those mediums for hearing-impaired people. These languages transmit meaning by visual-manual treatment, or more simply, hand movement. Currently, there are only 95 sign language interpreters registered with the Malaysian Federation of the Deaf as of 2020, compared to 40,389 hearing-impaired individuals with disabilities registered with the welfare department which is a problem. Therefore, with the use of deep-learning technology, this paper proposes to alleviate the scarcity of Malaysian Sign Language interpreters for the benefit of hearing-impaired persons. The paper aims to test and report a sequenced 3D keypoint hand pose estimation model for Malaysian Sign Language Recognition and evaluate the implementation of action model in decoding basic poses of Malaysian Sign Language. According to the findings, the detecting of 3D keypoints and incorporating into LSTM models using deep learning machine learning platform and framework like TensorFlow and MediaPipe enables the detection of Malaysian sign language 3D hand posture estimation. The results demonstrated that 3D hand posture estimation may be utilised to estimate sign language in real time, providing for a better interpretation approach for the deaf community.

No abstract

An optimized approach aiming to improve classification accuracy of wrist movements via electromyography (EMG) signals is presented here. EMG signals of the different types of wrist movements are obtained from the NINAPRO database. Useful features are extracted from the EMG signals via the waveform length method. The developed optimized classification system contains two main modules, known here as (i) optimized neural network module and (ii) movement prediction module. The optimized neural network module is made up of multiple 2-class neural networks. During Stage 1 Classification, a group of neural network (named NNG_S1) is formed after analyzing the sensitivity computed from the training outcomes of each neural network. A new group of neural network (named NNG_S2) is later formed in Stage 2 Classification after initial elimination via Stage 1 Classification. Further analysis is performed via the movement prediction module to predict the final outcome of the classification. The overall average classification accuracy achieved via the optimized classification system is 8.3% higher than the conventional neural network. The results validate that the optimized classification system performs better than the conventional neural network, providing more accurate signals for manipulating of exoskeleton for rehabilitation purposes.

No abstract

Mandarin is difficult for a couple of reasons, such as the complexity of writing system, Chinese characters, and tone. It can be excruciatingly hard to learn Mandarin without motivation and immediate positive feedback. With the growth of technology, games now play an essential role in language learning. Games enable learners to actively participate in activities, and to strengthen their affective reactions such as interest and motivation. There are various language learning games available on the market but most of them are using similar ways such as flashcards which is very repetitive. A firstperson view gameplay will be developed in this project to explore the possibility of immersive game-based learning and to provide an entertaining learning environment that motivates learners. The knowledge of vocabulary words for three topics which are numbers, colours, and direction are covered. Players are recruited to participate in our experiments. A paired sample t-test, t (14) = 27.4, p < .001 showed that there is improvement in the Mandarin learning achievement of players before and after playing the game. The average mean value of 4.55 is achieved using the RIMMS survey showed promising result in perceived motivation of the tested gameplay.