The study was conducted to determine the status of the current stocks of the S. varius in Lianga Bay, Surigao del Sur from July 2016 to August 2019. Some variables in population dynamics, such as asymptotic length (L∞), growth coefficient (K), mortality rate (Z, F and M) and the exploitation rate of S. varius were estimated from length frequencies analyses by means of FAO-ICLARM Stock Assessment Tools (FISAT). Results showed that the growth performance index (φ) was 3.79, asymptotic length (L∞) was 202.65mm and growth coefficient (K) was predicted to be 0.15yr-1. Total mortality (Z) for S. varius was 1.04yr-1 with fishing mortality (F) and natural mortality (M) of 0.74yr-1 and 1.90yr-1, respectively. The increased of fishing mortality over natural mortality, decreased of size of the catch, inflated current exploitation rates (E) of 0.7 which was higher than the optimum level of exploitation (E= 0.50). The Emax, and E10 were 1.29, and 1.55 respectively which interpreted as overexploited. It is therefore to recommend for immediate action in ecological, economical and legal aspects in the fishery of S. varius in Lianga Bay Surigao del Sur, Philippines.
I thought this was Renee's website! (Maybe hers is becomingdatascience.com?) Most machine learning (ML) models use samples / examples observations as input. This data lacks any time dimension. Time-series forecasting models are...
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G. Singh. (2013)By comparing historical data of trading like daily Open, High, Low, Close, Volume, Number of Trades, Turnover, Delivery percentage etc. of a particular stock with its Peer Group companies and Non Peer Group companies stocks for a particular period, we can find some unusual observations which are also known as outliers. In this paper we have tried to detect the observations, which are very different from the other observations using a Data Mining Technique for Outlier Detection-“Multiple Linear Regression Analysis”..
G. Singh. (2012)Fraud Detection is of great importance to financial institutions. In this paper we have tried to study the Outlier Analysis in Stock Market Fraud Detection. Outlier Analysis is a fundamental issue in Data Mining, specifically in Fraud Detection. While observing the Indian Stock Market, we could detect that some of the Trading Entities have suspicious trading patterns that give rise to a doubt of having some malpractices in stock transactions within Indian Stock Market. All the facts are presented on the basis of data obtained from the official sites of BSE (Bombay Stock Exchange), NSE (National Stock Exchange) and SEBI (Securities and Exchange Board of India)..