Data Mining - Concepts and Techniques.

Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

Stock market or equity market have a profound impact in today's economy. A rise or fall in the share price has an important role in determining the investor's gain. The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the daily closing price. The proposed method is a model independent approach. Here we are not fitting the data to a specific model, rather we are identifying the latent dynamics existing in the data using deep learning architectures. In this work we use three different deep learning architectures for the price prediction of NSE listed companies and compares their performance. We are applying a sliding window approach for predicting future values on a short term basis. The performance of the models were quantified using percentage error.

Stock price prediction using LSTM, RNN and CNN-sliding window model

Mexico City : México

Reviews Environmental Contamination and Toxicology

Outliers detection and classification in wireless sensor networks

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange.

A Machine Learning Model for Stock Market Prediction

Finding frequent itemsets is one of the most important fields of data mining. Apriori algorithm is the most established algorithm for finding frequent itemsets from a transactional dataset; however, it needs to scan the dataset many times and to generate many candidate itemsets. Unfortunately, when the dataset size is huge, both memory use and computational cost can still be very expensive. In addition, single processor’s memory and CPU resources are very limited, which make the algorithm performance inefficient. Parallel and distributed computing are effective strategies for accelerating algorithms performance. In this paper, we have implemented an efficient MapReduce Apriori algorithm (MRApriori) based on HadoopMapReduce model which needs only two phases (MapReduce Jobs) to find all frequent k-itemsets, and compared our proposed MRApriori algorithm with current two existed algorithms which need either one or k phases (k is maximum length of frequent itemsets) to find the same frequent k-itemsets. Experimental results showed that the proposed MRApriori algorithm outperforms the other two algorithms.

/pdf/an-efficient-implementation-of-apriori-algorithm-based-on-1j4x008nb6.pdf

An efficient implementation of apriori algorithm based on hadoop-mapreduce model

In this Paper, five recent natural inspired algorithms are proposed to optimize and train Least Square- Support Vector Machine (LS-SVM). These algorithms are namely, Flower Pollination Algorithm (FPA), Bat algorithm (BA), Modified Cuckoo Search (MCS), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO). These algorithms are proposed to automatically select best free parameters combination for LSSVM. Six financial technical indicators derived from stock historical data are used as inputs to proposed models. Standard LS-SVM and ANN are used as benchmarks for comparison with proposed models. Proposed models tested with six datasets representing different sectors in S&P 500 stock market. Proposed models were used to predict daily, weekly, and monthly stock prices. Results presented in this paper showed that the proposed models have quick convergence rate at early stages of the iterations. They achieved better accuracy than compared methods in price and trend prediction. They also overcame over fitting and local minima problems found in ANN and standard LS-SVM.

https://accentsjournals.org/PaperDirectory/Journal/IJACR/2015/3/5.pdf

Comparative Study between FPA, BA, MCS, ABC, and PSO Algorithms in Training and Optimizing of LS-SVM for Stock Market Prediction

This article presents an automatic system for assessing water quality based on fish gills microscopic images. As fish gills are a good biomarker for assessing water quality, the proposed system uses fish gills microscopic images in order to detect water pollution. The proposed system consists of three phases; namely pre-processing, feature extraction, and classification phases. Since the shape is the main characteristic of fish gills microscopic images, the proposed system uses shape feature based on edge detection and wavelets transform for classifying the water-quality degree. Furthermore, it implemented Principal Components Analysis (PCA) along with Support Vector Machines (SVMs) algorithms for feature extraction and water quality degree classification. The datasets used for experiments were constructed based on real sample images for fish gills. Training dataset is divided into four classes representing the different histopathological changes and the corresponding water quality degrees. Experimental results showed that the proposed classification system has obtained water quality classification accuracy of 95.41%, using the SVMs linear kernel function and 10-fold cross validation with 37 images per class for training.

Osman Hegazy

Papers

Outliers detection and classification in wireless sensor networks

A Machine Learning Model for Stock Market Prediction

An efficient implementation of apriori algorithm based on hadoop-mapreduce model

Comparative Study between FPA, BA, MCS, ABC, and PSO Algorithms in Training and Optimizing of LS-SVM for Stock Market Prediction

Water Pollution Detection System Based on Fish Gills as a Biomarker