The Asian Fisheries Society (AFS) Year Book - 2019

The Asian Fisheries Society (AFS), during last 35 years of its glorious past, has proved to be a most vibrant International Scientific Society which stood tall in steering the fisheries and aquaculture development in the Asia-Pacific. The efforts of the Society in providing appropriate platforms for scientific deliberations and suggesting pragmatic roadmaps have been exemplary. The organisation of Asian Fisheries and Aquaculture Forum (AFAF) in every three years in different countries uninterruptedly in previous 11 occasions and several other events have provided unique opportunities for the scientific communities and all other stakeholders associated with the sector for scientific deliberations in various strategic areas. As you are aware the 11th Asian Fisheries and Aquaculture Forum (11AFAF) was held at Bangkok, Thailand during August 3-7, 2016, which was highly successful in several fronts. The effort of Dr. C. Virapat, Director General, Network of Aquaculture Centres in Asia-Pacific (NACA) and Team NACA for their efforts in organisation 11AFAF was highly appreciable. The contribution of Chulalongkorn University and BITEC for organisation of the event was also praiseworthy

2018 Scholarly Productivity Report

https://scholarsmine.mst.edu/care-scholarly_productivity_reports/1001/thumbnail.jp

Degrees of Membership \u3e 1 and \u3c 0 of the Elements with Respect to a Neutrosophic OffSet

We have defined the Neutrosophic Over- /Under-/Off-Set and -Logic for the first time in 1995 and published in 2007. During 1995-2016 we presented them to various national and international conferences and seminars ([16]-[37]) and did more publishing during 2007-2016 ([1]-[15]). These new notions are totally different from other sets/logics/probabilities. We extended the neutrosophic set respectively to Neutrosophic Overset {when some neutrosophic component is \u3e 1}, to Neutrosophic Underset {when some neutrosophic component is \u3c 0}, and to Neutrosophic Offset {when some neutrosophic components are off the interval [0, 1], i.e. some neutrosophic component \u3e 1 and other neutrosophic component \u3c 0}. This is no surprise since our real-world has numerous examples and applications of over-/under-/off-neutrosophic components

2016 Scholarly Productivity Report

https://scholarsmine.mst.edu/care-scholarly_productivity_reports/1003/thumbnail.jp

Trends in Russian research output indexed in Scopus and Web of Science

Trends are analysed in the annual number of documents published by Russian institutions and indexed in Scopus and Web of Science, giving special attention to the time period starting in the year 2013 in which the Project 5-100 was launched by the Russian Government. Numbers are broken down by document type, publication language, type of source, research discipline, country and source. It is concluded that Russian publication counts strongly depend upon the database used, and upon changes in database coverage, and that one should be cautious when using indicators derived from WoS, and especially from Scopus, as tools in the measurement of research performance and international orientation of the Russian science system.Comment: Author copy of a manuscript accepted for publication in the journal Scientometrics, May 201

Early Detection of Disease using Electronic Health Records and Fisher\u27s Wishart Discriminant Analysis

Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and linear inseparability of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher\u27s Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of potential inverse covariance matrix estimates using a Wishart distribution estimated from the training data. Then, FWDA samples a group of inverse covariance matrices from the Wishart distribution, predicts using LDA classifiers based on the sampled inverse covariance matrices, and weighted-averages the prediction results via Bayesian Voting scheme. The weights for voting are optimally updated to adapt each new input data, so as to enable the nonlinear classification