No abstract

Image recognition process could be plagued by many problems including noise, overlap, distortion, errors in the outcomes of segmentation, and impediment of objects within the image. Based on feature selection and combination theory between major extracted features, this study attempts to establish a system that could recognize fish object within the image utilizing texture, anchor points, and statistical measurements. Then, a generic fish classification is executed with the application of an innovative classification evaluation through a meta-heuristic algorithm known as Memetic Algorithm (Genetic Algorithm with Simulated Annealing) with back-propagation algorithm (MA-B Classifier). Here, images of dangerous and non-dangerous fish are recognized. Images of dangerous fish are further recognized as Predatory or Poison fish family, whereas families of non-dangerous fish are classified into garden and food family.  A total of 24 fish families were used in testing the proposed prototype, whereby each family encompasses different number of species. The process of classification was successfully undertaken by the proposed prototype, whereby 400 distinct fish images were used in the experimental tests. Of these fish images, 250 were used for training phase while 150 were used for testing phase. The back-propagation algorithm and the proposed MA-B Classifier produced a general accuracy recognition rate of 82.25 and 90% respectively.

No abstract

Hotelling's T 2 chart is a popular tool for monitoring statistical process control. However, this chart is sensitive in the presence of outliers. To alleviate the problem, this paper proposed alternative Hotelling's T 2 charts for individual observations using robust location and scale matrix instead of the usual mean vector and the covariance matrix, respectively. The usual mean vector in the Hotelling T 2 chart is replaced by the winsorized modified one-step M-estimator (MOM) whereas the usual covariance matrix is replaced by the winsorized covariance matrix. MOM empirically trims the data based on the shape of the data distribution. This study also investigated on the different trimming criteria used in MOM. Two robust scale estimators with highest breakdown point, namely S n and T n were selected to suit the criteria. The upper control limits for the proposed robust charts were calculated based on simulated data. The performance of each control chart is based on the false alarm and the probability of outlier's detection. In general, the performance of an alternative robust Hotelling's T 2 charts is better than the performance of the traditional Hotelling's T 2 chart.

Background: Low- and middle-income countries (LMICs) have been consistently under-represented in the pool of contributors to academic journals on health. For the past two decades, prominent voices within the psychiatric profession have called for better representation of LMICs in the interest of advancing the understanding of mental health globally and benefiting health systems in these countries. Objective: To investigate the absolute and relative representation of authors affiliated to institutes from LMICs in the most influential journals on mental health in 2019. Method: Thirty top-ranking journals on mental health based on Scimago Journal Rank were selected, and all papers other than correspondence and letters to the editor published in those journals in 2019 were examined to extract the country of affiliation of each of their authors and their position (corresponding author, first author, second author). Results: Of the 4022 articles examined, 3720 articles (92.5%) were written exclusively by authors from high-income countries (HICs); 302 (7.5%) featured one or more authors from a LMIC along with those from HICs; 91 (2.2%) featured authors only from one LMIC; and only 3 (0.07%) featured authors from more than one LMICs but without any co-author from a HIC. The ratio of articles by contributors from LMICs to all the articles published in 2019 in a given journal ranged from 0% to 19%. Of 1855 individual contributors from 45 LMICs, 1050 (56%) were from China. Conclusion: Despite the growth of the global health movement and frequent calls for academic inclusivity, LMICs were significantly under-represented among the authors of papers published in top-ranking journals on mental health in 2019.