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Diabetes Mellitus is a dreadful disease characterized by increased levels of glucose in the blood, termed as the condition of hyperglycemia. As this disease is prominent among the tropical countries like India, an intense research is being carried out to deliver a machine learning model that could learn from previous patient records in order to deliver smart diagnosis. This research work aims to improve...
In view of textual remote sensing image classification, a classification approach based on Extreme Learning Machine (ELM) in introduced. As the performance of ELM is mainly affected by the value of input weights and hidden biases genetic algorithm (GA) and particle swarm optimization algorithm (PSO) have been used to learn these parameters for ELM in order to improve the stability of extreme learning...
In real world, the datasets are having varying dimensions which incorporates noisy, irrelevant and redundant data which is hard to analyze. Feature selection is a preprocessing step used for selecting the significant information. The selection of optimal feature subset is an optimization problem which has been solved by several versions of metaheuristic algorithms. The metaheuristic optimization algorithm...
Feature selection is a key step in data analysis. However, most of the existing feature selection techniques are serial and inefficient to be applied to massive data sets. We propose a feature selection method based on a multi-population weighted intelligent genetic algorithm to enhance the reliability of diagnoses in e-Health applications. The proposed approach, called PIGAS, utilizes a weighted...
Feature selection is an important preprocessing in data mining, it aims to reduce the computational complexity of learning algorithm, and to improve the performance of data mining algorithms by removing irrelevant and redundant features. In the framework of discrete-valued feature selection, this paper experimentally compares two feature selection methods which are based on generic algorithm. The...
In the recent years, online reviews are one of most important source of customer opinion. Nowadays consumer can gain knowledge about the products and service from online review resources, using which they can make decisions. This may lead to Opinion Spam, where spammers may manipulate and fake reviews to promote artificially or devalue the products and other services. Opinion spam detection is done...
Diabetic Retinopathy (DR) is one of the leading causes of blindness amongst the working age population. The presence of microaneurysms (MA) in retinal images is a pathognomonic sign of DR. In this work we have presented a novel combination of algorithms applied to a public dataset for automated detection of MA in colour fundus images of the retina. The proposed technique first detects an initial set...
In this paper a method for multiple human detection in the image has been presented. This method uses differential evolution (DE) algorithm to improve window position detection speed and HOG-LBP algorithm for feature extraction. Fitness function for DE algorithm is SVM and in the final state, a postprocessing on detected windows by DE algorithm is performed. This method has been tested on INRIA datasets...
This paper will investigate viability of a screening application that could be used to identify individuals with Dysarthria from among a larger population using sentence-level speech data. This task presents a number of challenged particularly if we aim to identify the disorder in the earlier stages before the more significant symptoms have begun to manifest themselves. A principal challenge in this...
Feature selection is an important preprocessing technique for data analysis and data mining. One of main challenge for feature selection is to overcome the curse of dimensionality. Bacterial algorithms, like Bacterial Foraging Optimization (BFO), have been well-exploited as the metaheuristics for addressing the optimization problems. In this paper, an extended bacterial algorithm named as Bacterial-Inspired...
Processing of the movement related task under planning by artificial means provides a means to those people whose natural modality of performing the task is bottlenecked by physical disability or neuro-motor disorders. Electroencephalography (EEG) based Brain-Computer Interfacing (BCI) systems can be defined to be a non-muscular pathway to operate rehabilitative devices using motor imagery signals...
The paper presents the comparison of two genetic methods that can be used for feature selection, NSGA (Nondominated Sorting Genetic Algorithm) and GAAM (genetic algorithm with aggressive mutation). While the first method is very popular for optimizing multi-objective functions, the second one is a new method that was introduced just two years ago. The comparison was made with a benchmark file from...
Identifying defects and classifying them according to some predefined classes is common in many manufacturing processes. The basis of such approach depends on a set of features extracted from all the classes and using them to train a classifier and then use it to determine the class to which the unseen data belongs to, with a reasonable accuracy. Hence the performance of the classifier depends on...
Text feature acquiring is the key to construct the classifier to classify the text, According to the problem that the text dimension of the original feature vector is reduced and accurate, put forward a text feature acquiring algorithm based on co evolution, the algorithm uses genetic algorithm optimization performance and co evolution can implement multiple population mutual evaluation and competition,...
Image classification system with machine learning is significant technique that is important in various fields such as face recognition in biometrics and inspection system in product factory. In this paper, we use the three steps image classification model as following. (1)Image filtering as preprocessing. (2)Extraction of features from the image. (3)Classification using the extracted features. Manual...
This paper presents a review of some of the most recent evolutionary algorithms used for solving feature selection based upon previously published research on feature selection. In addition, we discuss various research issues relating to each of the presented evolutionary algorithm. Evolutionary algorithms present several advantages over traditional search such as they require less domain-specific...
In order to obtain the higher classification accuracy in specific categories for the different feature subset, a hierarchical classification algorithm based on Feature Selection is proposed, and is used for synthetic aperture radar (SAR) image classification, and feature selection is achieved by Genetic algorithm. The algorithm has two main characteristics: one is hierarchical classification which...
In Stream data classification intrusion detection happens when a completely new kind of attack occurs in the traffic. Novel class detection approach solves the problem of intrusion detection based on ensemble technique of clustering and classification on feature evaluation technique. Feature evolution process faced a problem of exact selection of cluster midpoint for the process of clusters which...
In this paper, intelligent algorithms for intrusion detection are proposed which detect the network attacks as normal or anomaly based attacks by performing effective preprocessing and classification. This system uses a new genetic algorithm approach for pre-processing and Modified J48 classification algorithm to identify the intended activities. The new genetic based feature selection algorithm proposed...
Author identification is the process of recognizing an author based on a sample of text. Feature selection is the process of selecting the most salient features required for recognition. In many cases, this results in an increase in recognition accuracy. In this paper, we apply Genetic and Evolutionary Feature Selection with Machine Learning (GEFeSML) to author identification. We then introduce Genetic...
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