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Prediction stock price is considered the most challenging and important financial topic. Thus, its complexity, nonlinearity and much other characteristic, single method could not optimize a good result. Hence, this paper proposes a hybrid ensemble model based on BP neural network and EEMD to predict FTSE100 closing price. In this paper there are five hybrid prediction models, EEMD-NN, EEMD-Bagging-NN,...
Human activity recognition (HAR) is the basis for many real world applications concerning health care, sports and gaming industry. Different methodological perspectives have been proposed to perform HAR. One appealing methodology is to take an advantage of data that are collected from inertial sensors which are embedded in the individual's smartphone. These data contain rich amount of information...
This study investigates the evaluation of machine learning models based on multiple criteria. The criteria included are: predictive model accuracy, model complexity, and algorithmic complexity (related to the learning/adaptation algorithm and prediction delivery) captured by monitoring the execution time. Furthermore, it compares the models generated from optimising the criteria using two approaches...
Skin color is a robust cue in human skin detection. It has been widely used in various human-related image processing applications. Although many researches have been carried out for skin color detection, there is no consensus on which color space is the most appropriate for skin color detection because many researchers do not provide strict justification of their color space choice. In this paper,...
The traditional method for detecting the tumor diseases in the human MRI brain images is done manually by physicians. Automatic classification of tumors of MRI images requires high accuracy, since the non-accurate diagnosis and postponing delivery of the precise diagnosis would lead to increase the prevalence of more serious diseases. To avoid that, an automatic classification system is proposed for...
This paper discusses the effort of discriminating plaque psoriasis skin lesions using Artificial Neural Network (ANN) for dermatological early diagnosis based on color representations. For any digital acquired images, colors can be identified numerically, for example, with respect to the unique RGB, HSV and YCbCr pixel indices. Previous work have produced intelligent identification models for selected...
Satellite remote sensing is an important tool in the detection and short range forecasting (now-casting) of fog events. Fog over land develops primarily during the late-night and pre-dawn hours, infrared remote sensing is indispensable in observing fog formation, while visible imagery helps to monitor the extent and density of fog after sunrise. On average there are more than forty five fog occurrences...
We propose and evaluate a framework for detection of plant leaf/stem diseases. Studies show that relying on pure naked-eye observation of experts to detect such diseases can be prohibitively expensive, especially in developing countries. Providing fast, automatic, cheap and accurate image-processing-based solutions for that task can be of great realistic significance. The proposed framework is image-processing-based...
In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN with the aim of further reducing data in terms of both size and dimensionality. Indeed, the use of SVD makes data more amenable to classification and the convergence training process faster. Specifically, the effectiveness...
The paper presents an upgrading process of rubber tree seed clones identification model using image processing techniques. Sample of rubber tree seeds are captured using digital camera where the RGB color image are processed involving segmentation algorithm which includes thresholding and morphological technique. Texture patterns from seed clones images are then analysed through wavelet's Daubechies...
In algorithms design, one of the important aspects is to consider efficiency. Many algorithm design paradigms are existed and used in order to enhance algorithms' efficiency. Opposition-based Learning (OBL) paradigm was recently introduced as a new way of thinking during the design of algorithms. The concepts of opposition have already been used and applied in several applications. These applications...
The drastic increase of residential load consumption in recent years result in over loading feeder lines and transformers for the Iraqi northern area distribution system especially in the city of Mosul. Solution for this problem requires up to date research consumers load study to find the proper solution to stop excess overload in the transformers and the feeders. This paper include the regional...
This paper describes research work in developing an intelligent model for classifying selected rubber tree series clones based on shape features using image processing techniques. Sample of rubber tree seeds are captured using digital camera where the RGB color image are processed involving segmentation algorithm which includes thresholding and morphological technique. Shape features such as area,...
In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area...
This paper presents the classification of benign and malignant breast tumor based on fine needle aspiration cytology (FNAC) and probabilistic neural network (PNN). Five hundred and sixty nine sets of cell nuclei characteristics obtained by applying image analysis techniques to microscopic slides of FNAC samples of breast biopsy have been used in this study. These data were obtained from the University...
Neural network is one of the successful methods for protein secondary structure prediction. Day to day this technology is modified, improved, even other methods also combined with it to get better result. In this paper we trained feed-forward neural network with trans-membrane protein for helix prediction. Using Java object oriented neural engine (JOONE) our achieved accuracy is 71%. This paper is...
Intrusion detection (ID) is an interesting approach that could be used to improve the security of network systems. IDS detects suspected patterns of network traffic on the remaining open parts through monitoring user activities (runtime gathering of data from system operations), and the subsequent analysis of these activities. The purpose of this work is to contribute ideas of finding a solution to...
This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation...
In the following paper we present a complete analytical model that predicts the band-gap (Eg) of single-walled carbon nanotubes (SWCNTs) directly from their diameter (d) and chiral angle (thetas). The proposed analytical model is based on two mathematical expressions that have been derived by curve-fitting the outcome generated from the third-nearest-neighbor tight-binding (TB) method in conjunction...
Features from the Saudi Stock Market (SSM) have been examined to attempt to predict the direction of daily price changes. Backpropagation neural network has been applied to predict the direction of price changes for the listed stocks in SSM. The price change in SSM ranges between -10% and 10%. The target has a representation of three classes 1, -1 and 0 that respectively represent the increase, decrease...
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