Human activity discovery aims to distinguish the activities performed by humans, without any prior information of what defines each activity. Most methods presented in human activity recognition are supervised, where there are labeled inputs to train the system. In reality, it is difficult to label data because of its huge volume and the variety of activities performed by humans. In this paper, a novel unsupervised approach is proposed to perform human activity discovery in 3D skeleton sequences. First, important frames are selected based on kinetic energy. Next, the displacement of joints, set of statistical, angles, and orientation features are extracted to represent the activities information. Since not all extracted features have useful information, the dimension of features is reduced using PCA. Most human activity discovery proposed are not fully unsupervised. They use pre-segmented videos before categorizing activities. To deal with this, we used the fragmented sliding time window method to segment the time series of activities with some overlapping. Then, activities are discovered by a novel hybrid particle swarm optimization with a Gaussian mutation algorithm to avoid getting stuck in the local optimum. Finally, k-means is applied to the outcome centroids to overcome the slow rate of PSO. Experiments on three datasets have been presented and the results show the proposed method has superior perfor- * Corresponding author.

Background: Colorectal cancer (CRC) is the third most common cancer in both men and women. In most Asian countries, both the incidence and mortality rates of CRC are gradually increasing. In Brunei Darussalam, CRC ranks first and second in lifetime risk among men and women respectively. This study aims to report the overall survival rates and associated factors of CRC in Brunei Darussalam. Methods: This is a retrospective study examining CRC data for the period 2007 to 2017 retrieved from a population based cancer registry in Brunei Darussalam. A total of 728 patients were included in the analysis. Kaplan Meier method was used to estimate survival rates. Univariate analysis using log-rank test was used to examine the differences in survival between groups. Multivariate analysis using Cox PH regression was used to estimate hazard of death and obtain significant predictors that influence CRC patients' survival. Results: The median survival time for colorectal, colon and rectal cancer patients were 57.0, 85.8 and 40.0 months respectively. The overall 1-, 3-and 5-year survival rates for CRC patients were 78.0%, 57.7% and 49.6% respectively. In univariate analysis, age at diagnosis, ethnicity, cancer stage, tumour location and histology were found to have significant difference in CRC patients' survival. In the Cox PH analysis, older age (≥70 years), cancer stage, ethnicity and other histological type were determined as associated factors of CRC patients' survival. Conclusion: This study found the overall 5-year survival rate of CRC in Brunei Darussalam is similar to that in some Asian countries such as Singapore and Malaysia. However, more efforts need to be carried out in order to raise awareness of CRC and improve the survival of CRC patients.

This article provides a cross-sectional weighted measurement of noncommunicable diseases (NCDs) and risk factors prevalence among Brunei adult population using WHO STEPS methodology. A 2-staged randomized sampling was conducted during August 2015 to April 2016. Three-step surveillance included (1) interview using standardized questionnaire, (2) blood pressure and anthropometric measurements, and (3) biochemistry tests. Data weighting was applied. A total of 3808 adults aged 18 to 69 years participated in step 1; 2082 completed steps 2 and 3 measurements. Adult smoking prevalence was 19.9%, obesity 28.2%, hypertension 28.0%, diabetes 9.7%, prediabetes 2.1%, and 51.3% had fasting cholesterol level ≥5 mmol/L. Inadequate consumption of fruits and vegetables prevalence was high at 91.7%. Among those aged 40 to 69 years, 8.9% had a 10-year cardiovascular disease (CVD) risk ≥30%, or with existing CVD. Population strategies and targeted group interventions are required to control the NCD risk factors and morbidities.

Despite many advances in human activity recognition, most existing works are conducted with supervision. Supervised methods rely on labeled training data. However, obtaining labeled data is difficult, costly, and time-consuming. In this paper, we introduce an automatic multi-objective particle swarm optimization clustering based on Gaussian mutation and game theory (MOPGMGT) to tackle the problem of human activity discovery fully unsupervised and map the multi-objective clustering problem to game theory to get the best optimal solution. The proposed algorithm does not require any prior knowledge of the number of activities to be discovered and can find the optimal number. Multi-objective optimization problems typically cannot have a single optimal solution. To solve this issue, Nash Equilibrium (NE) is applied to the pareto front to choose the best solution. NE does not just look for the best solution, but tries to optimize the final solution by considering the effect of choosing each of the solutions as the best solution on the other solutions and one with the best impact is chosen. Moreover, a Gaussian mutation is applied on the pareto front to avoid premature convergence. Experiments on five challenging datasets demonstrate that the proposed approach is the most efficient to achieve better accuracy in human activity discovery and also can determine the optimal number of clusters.

Abstract-In previous work, six clinically novel and useful subgroups of breast cancer were identified using rules and clinicians' expertise to combine solutions from three different clustering algorithms on a database of biomarkers. The motivation for the present work is to reproduce this classification using a single clustering method. In the long term, we hope to produce a clinically useful classification using fewer features (biomarkers), reducing the time and cost of running complex and expensive clinical tests. Hence, the aim of this paper is to investigate the use of feature selection in combination with ssFCM to reduce the number of features while maintaining accuracy (defined as agreement with the previous classification), both on our breast cancer biomarker data and on other benchmark datasets. We show experimental results using four feature selection techniques, exploring with 10, 15 and 17 selected features out of the original 25 biomarkers for breast cancer. We experimented with varying amounts of labelled data (10% -60% of the training data) and we evaluate classification accuracy using cross-validation. It was found that classification accuracy increased using 15 or 17 breast cancer biomarkers. Using SVM-RFE and CFS, improved classification accuracy was found on three UCI datasets, Arrhythmia, Cardiotocography and Yeast.

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This research aims to study and assess state-of-the-art physics-informed neural networks (PINNs) from different researchers’ perspectives. The PRISMA framework was used for a systematic literature review, and 120 research articles from the computational sciences and engineering domain were specifically classified through a well-defined keyword search in Scopus and Web of Science databases. Through bibliometric analyses, we have identified journal sources with the most publications, authors with high citations, and countries with many publications on PINNs. Some newly improved techniques developed to enhance PINN performance and reduce high training costs and slowness, among other limitations, have been highlighted. Different approaches have been introduced to overcome the limitations of PINNs. In this review, we categorized the newly proposed PINN methods into Extended PINNs, Hybrid PINNs, and Minimized Loss techniques. Various potential future research directions are outlined based on the limitations of the proposed solutions.