Search results for: Simon Fong

Items from 1 to 20 out of 157 results

article

Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation

Ying Wang, Qun Wu, Nilanjan Dey, Simon Fong, more

Biocybernetics and Biomedical Engineering > 2020 > Vol. 40, no. 3 > 987--1001

Autonomous rehabilitation training for assisted patients with injured upper-limbs promotes the regenerative communication between muscle signals and brain consciousness. Surface electromyographic (sEMG) is a type of electrical signals of neuromuscular activity recorded by electrodes on the surface of the human body, which is widely applied for detecting gestures and stimuli reactions. Experimental...

article

Quantifying breast milk intake by term and preterm infants for input into paediatric physiologically based pharmacokinetic models

Cindy H.T. Yeung, Simon Fong, Paul R.V. Malik, Andrea N. Edginton

Maternal & Child Nutrition > 16 > 2 > n/a - n/a

Despite the many benefits of breast milk, mothers taking medication are often uncertain about the risks of drug exposure to their infants and decide not to breastfeed. Physiologically based pharmacokinetic models can contribute to drug‐in‐milk safety assessments by predicting the infant exposure and subsequently, risk for toxic effects that would result from continuous breastfeeding. This review aimed...

article

Development of interventions for an intelligent and individualized mobile health care system to promote healthy diet and physical activity: using an intervention mapping framework

Yuling Chen, Fangqin Wu, Ying Wu, Jia Li, more

BMC Public Health > 2019 > 19 > 1 > 1-16

Background The mortality of coronary heart disease can be largely reduced by modifying unhealthy lifestyles. However, the long-term effectiveness of interventions for modifying unhealthy diet and physical inactivity of patients with coronary heart disease remain unsatisfactory worldwide. This study aims to systematically design a set of theory-based and evidence-based, individualized, and intelligent...

article

Enhanced ground segmentation method for Lidar point clouds in human-centric autonomous robot systems

Phuong Minh Chu, Seoungjae Cho, Jisun Park, Simon Fong, more

Human-centric Computing and Information Sciences > 2019 > 9 > 1 > 1-14

Ground segmentation is an important step for any autonomous and remote-controlled systems. After separating ground and nonground parts, many works such as object tracking and 3D reconstruction can be performed. In this paper, we propose an efficient method for segmenting the ground data of point clouds acquired from multi-channel Lidar sensors. The goal of this study is to completely separate ground...

article

Multiaspect‐based opinion classification model for tourist reviews

Muhammad Afzaal, Muhammad Usman, Alvis C.M. Fong, Simon Fong

Expert Systems > 36 > 2 > n/a - n/a

Tourist reviews on social media websites reflect the tourist's opinions concerning various aspects of a tourist place or service (e.g., “comfortable room” and “terrible service” in hotel reviews). Extracting these aspects from reviews is a challenging task in opinion mining. Therefore, aspect‐based opinion mining has emerged as a new area of social review mining. Existing approaches in this area focus...

article

3D object recognition method with multiple feature extraction from LiDAR point clouds

Yifei Tian, Wei Song, Su Sun, Simon Fong, more

The Journal of Supercomputing > 2019 > 75 > 8 > 4430-4442

During autonomous driving, fast and accurate object recognition supports environment perception for local path planning of unmanned ground vehicles. Feature extraction and object recognition from large-scale 3D point clouds incur massive computational and time costs. To implement fast environment perception, this paper proposes a 3D recognition system with multiple feature extraction from light detection...

article

Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in X-ray image datasets

Jinyan Li, Simon Fong, Lian-sheng Liu, Nilanjan Dey, more

Multimedia Tools and Applications > 2019 > 78 > 15 > 20913- 20933

The imbalance and multi-dimension are two common problems in the medical image datasets, which affect the performances of the image processing procedures. The traditional methods to solve these two problems are notoriously difficult. Accordingly, this work employed metaheuristic methods to optimize the rebalancing process of the imbalanced class distribution for further use in the feature selection...

article

Classifying 3D objects in LiDAR point clouds with a back-propagation neural network

Wei Song, Shuanghui Zou, Yifei Tian, Simon Fong, more

Human-centric Computing and Information Sciences > 2018 > 8 > 1 > 1-12

Due to object recognition accuracy limitations, unmanned ground vehicles (UGVs) must perceive their environments for local path planning and object avoidance. To gather high-precision information about the UGV’s surroundings, Light Detection and Ranging (LiDAR) is frequently used to collect large-scale point clouds. However, the complex spatial features of these clouds, such as being unstructured,...

article

Medical data mining in sentiment analysis based on optimized swarm search feature selection

Daohui Zeng, Jidong Peng, Simon Fong, Yining Qiu, more

Australasian Physical & Engineering Sciences in Medicine > 2018 > 41 > 4 > 1087-1100

In this paper, we propose a novel technique termed as optimized swarm search-based feature selection (OS-FS), which is a swarm-type of searching function that selects an ideal subset of features for enhanced classification accuracy. In terms of gaining insights from unstructured medical based texts, sentiment prediction is becoming an increasingly crucial machine learning technique. In fact, due to...

article

Clustering big IoT data by metaheuristic optimized mini-batch and parallel partition-based DGC in Hadoop

Rui Tang, Simon Fong

Future Generation Computer Systems > 2018 > 86 > C > 1395-1412

Clustering algorithms are an important branch of data mining family which has been applied widely in IoT applications such as finding similar sensing patterns, detecting outliers, and segmenting large behavioral groups in real-time. Traditional full batch k -means for clustering IoT big data is confronted by large scaled storage and high computational complexity problems. In order to overcome the...

article

Vitality-based elephant search algorithm

Zhonghuan Tian, Simon Fong, Suash Deb, Rui Tang, more

Operational Research > 2018 > 18 > 3 > 841-863

Elephant search algorithm (ESA) is one of the contemporary meta-heuristic search algorithms recently proposed. The male elephants are responsible for global exploration, roaming to new dimensions of search space. The female elephants focus on doing local search, for finding the optimal solution. A lifespan mechanism is designed to control the birth and death that all agents will have an increasing...

article

A suite of swarm dynamic multi-objective algorithms for rebalancing extremely imbalanced datasets

Jinyan Li, Simon Fong, Raymond K. Wong, Sabah Mohammed, more

Applied Soft Computing > 2018 > 69 > C > 784-805

Imbalanced datasets can be found in a number of fields; they are commonly regarded as big data because of their sheer volume and high attribute dimensions. As the name suggests, imbalanced big datasets come with an extremely imbalanced ratio between the amount of major class and minority class samples. Traditionalmethods: have been attempted but still cannot fully, effectively, and reliably solve...

article

A Survey of Data Mining and Deep Learning in Bioinformatics

Kun Lan, Dan-tong Wang, Simon Fong, Lian-sheng Liu, more

Journal of Medical Systems > 2018 > 42 > 8 > 1-20

The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One...

article

Elephant search algorithm applied to data clustering

Suash Deb, Zhonghuan Tian, Simon Fong, Raymond Wong, more

Soft Computing > 2018 > 22 > 18 > 6035-6046

Data clustering is one of the most popular branches of machine learning and data analysis. Partitioning-based type of clustering algorithms, such as K-means, is prone to the problem of producing a set of clusters that is far from perfect due to its probabilistic nature. The clustering process starts with some random partitions at the beginning, and then it attempts to improve the partitions progressively...

article

Solving permutation flow-shop scheduling problem by rhinoceros search algorithm

Suash Deb, Zhonghuan Tian, Simon Fong, Rui Tang, more

Soft Computing > 2018 > 22 > 18 > 6025-6034

In this paper, a novel meta-heuristic search algorithm inspired by rhinoceros’ natural behaviour is proposed, namely rhinoceros search algorithm (RSA). Similar to our earlier version called elephant search algorithm, RSA simplifies certain habitual characteristics of rhinoceros and stream-lines the search operations, thereby reducing the number of operational parameters required to configure the model...

article

Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall

Simon Fong, Jiaxue Li, Wei Song, Yifei Tian, more

Journal of Ambient Intelligence and Humanized Computing > 2018 > 9 > 4 > 1197-1221

With the popularity and affordability of ZigBee wireless sensor technology, IoT-based smart controlling system for home appliances becomes prevalent for smart home applications. From the data analytics point of view, one important objective from analyzing such IoT data is to gain insights from the energy consumption patterns, thereby trying to fine-tune the energy efficiency of the appliance usage...

article

Adaptive multi-objective swarm fusion for imbalanced data classification

Jinyan Li, Simon Fong, Raymond K. Wong, Victor W. Chu

Information Fusion > 2018 > 39 > C > 1-24

Learning a classifier from an imbalanced dataset is an important problem in data mining and machine learning. Since there is more information from the majority classes than the minorities in an imbalanced dataset, the classifier would become over-fitted to the former and under-fitted to the latter classes. Previous attempts to address the problem have been focusing on increasing the learning sensitivity...

article

Emerging Service Orchestration Discovery and Monitoring

Victor W. Chu, Raymond K. Wong, Simon Fong, Chi-Hung Chi

IEEE Transactions on Services Computing > 2017 > 10 > 6 > 889 - 901

Due to the popularity of web services on the Internet, it is important to have a clear view of their utilization behaviors. Despite asynchronous service invocations and distributed executions can provide better user experience, our views are blurred by out-of-order and fragmented service logs. Researchers have been trying various methods to reveal emerging service orchestration patterns, but nearly...

article

Simulation framework of ubiquitous network environments for designing diverse network robots

Seoungjae Cho, Simon Fong, Yong Woon Park, Kyungeun Cho

Future Generation Computer Systems > 2017 > 76 > C > 468-473

Smart homes provide residents with services that offer convenience using sensor networks and a variety of ubiquitous instruments. Network robots based on such networks can perform direct services for these residents. Information from various ubiquitous instruments and sensors located in smart homes is shared with network robots. These robots effectively help residents in their daily routine by accessing...

article

An adaptive meta-heuristic search for the internet of things

Mohammad Ebrahimi, Elaheh ShafieiBavani, Raymond K. Wong, Simon Fong, more

Future Generation Computer Systems > 2017 > 76 > C > 486-494

The number of sensors deployed around the world is growing at a rapid pace when we are moving towards the Internet of Things (IoT). The widespread deployment of these sensors represents significant financial investment and technical achievement. These sensors continuously generate enormous amounts of data which is capable of supporting an almost unlimited set of high value proposition applications...

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INFONA - science communication portal

Search results for: Simon Fong

Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation

Quantifying breast milk intake by term and preterm infants for input into paediatric physiologically based pharmacokinetic models

Development of interventions for an intelligent and individualized mobile health care system to promote healthy diet and physical activity: using an intervention mapping framework

Enhanced ground segmentation method for Lidar point clouds in human-centric autonomous robot systems

Multiaspect‐based opinion classification model for tourist reviews

3D object recognition method with multiple feature extraction from LiDAR point clouds

Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in X-ray image datasets

Classifying 3D objects in LiDAR point clouds with a back-propagation neural network

Medical data mining in sentiment analysis based on optimized swarm search feature selection

Clustering big IoT data by metaheuristic optimized mini-batch and parallel partition-based DGC in Hadoop

Vitality-based elephant search algorithm

A suite of swarm dynamic multi-objective algorithms for rebalancing extremely imbalanced datasets

A Survey of Data Mining and Deep Learning in Bioinformatics

Elephant search algorithm applied to data clustering

Solving permutation flow-shop scheduling problem by rhinoceros search algorithm

Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall

Adaptive multi-objective swarm fusion for imbalanced data classification

Emerging Service Orchestration Discovery and Monitoring

Simulation framework of ubiquitous network environments for designing diverse network robots

An adaptive meta-heuristic search for the internet of things

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