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The aim of this study is to compare some classifiers' performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The readmission process dataset of Diabetic patients, which has been experimented, consists of 47 features...
This paper presents the design of a convolutional neural network architecture using the MatConvNet library for MATLAB in order to achieve the recognition of 2 classes of hand gestures: ”open” and ”closed”. Six architectures were implemented to which their hyperparameters and depth were varied to observe their behavior through the validation error in the training and accuracy in the estimation of each...
This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques,...
In speech recognition system, an improved multi-base neural network speech recognition model is proposed to solve the problem of long learning time and slow convergence rate of deep neural network. However, the improved model introduces a large number of parameters in the training process to make the model over-fitted in the test set, resulting in the deterioration of generalization ability and the...
It is well-known that the precision of data, weight vector, and internal representations employed in learning systems directly impacts their energy, throughput, and latency. The precision requirements for the training algorithm are also important for systems that learn on-the-fly. In this paper, we present analytical lower bounds on the precision requirements for the commonly employed stochastic gradient...
Sorting is an important step in processing and packing lines of pomegranate fruits. Currently pomegranates are sorted into quality categories manually. But manual sorting poses problems such as tediousness, low accuracy, subjectivity etc. Moreover, manual sorting is not recommended for export quality fruits. Hence a machine vision system is required in order to sort the pomegranate fruits. The present...
We propose a new pretreatment for pedestrian detection with convolutional networks. It is widely known that the phenomenon of overlapping feature distribution is common, which leads to overfitting problem. We present a method that divide one category that have overlapping distributed features into multi-subcategories. By this means smooth boundaries can be easily found to separate different subcategories,...
Machine learning based classifiers used quite often for predicting forest cover types, are the Naïve Bayes classifier, the k-Nearest Neighbors classifier, and the Random forest classifier. This paper is directed towards examining all of these classifiers coupled with feature selection and attribute derivation in order to evaluate which one is best suited for forest cover type classification. Numerous...
This paper describes an algorithm that parallelizes support vector machines. The data is split into subsets and optimized separately with multiple SVMs, instead of analyzing the whole training set in one optimization step. The partial results are combined and filtered in a cascade of SVMs. The process terminates when the global optimum is reached. The Cascade SVM is spread over multiple processors...
In this paper we apply particle swarm optimization (PSO) feature selection to enhance Hidden Markov Model (HMM) states and parameters for face recognition systems. Ideal Feature selection for face images based on the idea of collaborative behavior of bird flocking to reduce the feature size and hence recognition time complicity. The framework has been inspected on 400 face pictures of the Olivetti...
Most of the intrusion detection systems analyze all network traffic features to identify intrusions with different classification techniques. Any intrusion detection model developed has to provide maximum accuracy with minimal false alarms. Identifying the optimal feature subset for classification is an important task for improved classification. In this paper, consistency based feature selection...
Lung Cancer is a disease of infection in lung due to uncontrolled cell growth which affects its functionality. It is mostly incurable due to that early detection of Lung Cancer is important. Early detection and treatment may help for patient's survival. Normally, diagnosis of lung cancer includes chest X-ray, ECG, Citi scan, MRI etc. In cancer diagnosis Artificial Neural Network and Fuzzy Min-Max...
This paper presents a review on few notable speech recognition models that are reported in the last decade. Firstly, the models are categorized into sparse models, learning models and domain - specific models. Subsequently, the characteristics of the models have been observed using speech constraints, algorithmic constraints and performance constraints. The performance of these models reported in...
As the attackers nowadays are getting craftier it is deemed important to have a security system which is easy to maintain and economically affordable and gives suitable defense against attacks both known and novel. In this paper, the concept of genetic programming is applied to recreate open network conditions, using records obtained from KDD Cup'99 dataset. Then the newly created records (network...
This paper reviews the comparative performance of Support Vector Machine (SVM) using four different kernels, i.e., Linear, Polynomial, Radial Basis Function (RBF) and Sigmoid. Overall accuracy (OA), Kappa Index Analysis (KIA), Receiver Operating Characteristic (ROC) and Precision (P) have been considered as evaluation parameters in order to assess the predictive accuracy of SVM. Both high resolution...
An Artificial Neural Network (ANN) is a statistical data modeling tool inspired by the functionality and the structure of the biological nervous system. An ANN consists of processing elements known as neurons that are interconnected to each other and work in unison to answer a particular problem. Neural networks can be used in places where detecting trends and extracting patterns are too complex to...
Email is a rapid and cheap communication medium for sending and receiving information where spam is becoming a nuisance for such communication. A good spam filtering cannot only be achieved by high performance accuracy but low false positive is also necessary. This paper presents a combining classifiers approach with committee selection mechanism where the main objective is to combine individual decisions...
Classification is the one of the most important techniques in Datamining for data analysis. In Datamining, different Classification Techniques are available to predict outcome for a given dataset. There are many classification techniques for predicting and estimating accuracy, one such famous technique is Naïve Bayes Classifier. Naïve Bayes is very popular as it is easy to build, not so complex and...
There exists a base classification system for classification of problem tickets in the Enterprise domain. Different deep learning algorithms (Gated Recursive Unit and Long Short Term Memory) were investigated for solving the classification problem. Experiments were conducted for different parameters and layers for these algorithms. Paper brings out the architectures tried, results obtained, our conclusions...
In this present paper, a Radial Basis Function Network (RBFN) based on Modified Optimal Clustering Algorithm (MOCA) have been developed for clear and occluded fingerprint identification. Unlike conventional OCA technique which only considers intra cluster similarity for performing the desired number of clusters, MOCA combines both intra and inter cluster similarity while grouping such that not only...
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