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Classification of lung cancer using a low population, high dimensional dataset is challenging due to insufficient samples to learn an accurate mapping among features and class labels. Current literature usually handles this task through hand-crafted feature creation and selection. In recent years, deep learning is found to be able to identify the underlying structure of data through the use of autoencoders...
Extreme Learning machines (ELM) and Support Vector Machines have become two of the most widely used machine learning techniques for both classification and regression problems of recent. However the comparison of both ELM and SVM for classification and regression problems has often caught the attention of several researchers. In this work, an attempt has been made at investigating how SVM and ELM...
Feature fusion methods have been demonstrated to be effective for many computer vision based applications. These methods generally use multiple hand-crafted features. However, in recent days, features extracted through transfer leaning procedures have been proved to be robust than the hand-crafted features in myriad applications, such as object classification and recognition. The transfer learning...
This article is devoted to improving previously developed texture classifier that performs on noisy images. The basic principle of this classifier is to join several simple local parameters using some fuzzy logic system (support vector machine or neural network). It is shown that aggregating procedure applied on the classifier's input can result in significant improvement of its efficiency.
This paper develops an advanced method to classify load-pull contours for the design of a broadband high-efficiency power amplifiers (PA) using the technique of a support vector machine (SVM). The classifier models for load-pull contours are verified through their accuracy in test and design validation. Comparisons reveal that the proposed method significantly outperforms commercial electronic design...
Facial expression recognition (FER) has been applied for human-robot interaction (HRI). An assistant robot having a close interaction with human being should be able to recognize human facial expression. FER is a non-trivial problem because each individual has his own way to reveal his emotion and the facial expressions of two different persons may not be totally identical. Facial expression can be...
This paper introduces automatic framework brain tumor detection, which detects and classify brain tumor in MR imaging. The proposed framework brain tumor detection is an important tool to detect the tumor and differentiate between patients that diagnosis as certain brain tumor and probable brain tumor due to its ability to measure regional changes features in the brain that reflect disease progression...
Breast cancer is an life threatening disease in USA and UK. It is also one of the major diseases that has greater death rate. Cancer is the erratic growth of cells that originate in the blood tissue and Tumours may be malignant or benign. Early detection increases the chances of survival and reduces the death rate. This paper compares the approach to classify the mammogram based on the features extracted...
The incredible popularity of the Android mobile operating system has resulted in a massive influx of malicious applications for the platform. This malware can come from a number of sources as Google allows the installation of Android App Packages (APKs) from third parties. Even within its own Google Play storefront, however, malicious software can be found.
This research presents framework for real time face recognition and face emotion detection system based on facial features and their actions. The key elements of Face are considered for prediction of face emotions and the user. The variations in each facial feature are used to determine the different emotions of face. Machine learning algorithms are used for recognition and classification of different...
With the successive increase in usage of vehicles, severe traffic congestion is on the rise. This in turn leads to increase in environmental pollution and accidents which ultimately affects the safety, time consumed and money spent of the transport users. The solution to this critical problem is traffic flow prediction depending on which traffic control measures and traffic management can be done...
This paper presents a process of knowledge Discovery from Data (KDD) applied on 2D Inverse Synthetic Aperture Radar (ISAR) images. This process is based on four crucial steps which are data acquisition, data pre-processing, data representation and data classification to make the final decision. We propose a new method for data representation based on combining mathematical morphology top-Hat, Thresholding...
Power system has appeared as a complex interconnected network due to competitive business environment. Power producers and consumers obligate for a precise price forecasting, as this information is an important part of decision making process. Decisions, regarding optimal scheduling of generators, bidding tactics and demand side organizations are based on price forecast. In recent years, development...
Word2vec is a neural network language model which can convert words and phrases into a high-quality distributed vector (called word embedding) with semantic word relationships, so it offers a unique perspective to the text classification and other natural language processing (NLP) tasks. In this paper, we propose to combine improved tfidf algorithm and word embedding as a way to represent documents...
A number of important applied problems in engineering, finance and medicine can be formulated as a problem of anomaly detection. A classical approach to the problem is to describe a normal state using a one-class support vector machine. Then to detect anomalies we quantify a distance from a new observation to the constructed description of the normal class. In this paper we present a new approach...
Considering the fact that the underlying structural information in the training data within classes is vital for a good classifier in real-world classification problems, Structural Nonparallel Support Vector Machine (or SNPSVM, for short) has been proposed. By combining the structural information with nonparallel support vector machine (NPSVM), SNPSVM can fully exploit prior knowledge to directly...
With the development of the IoT market, collectable data is increasing exponentially. Recently, various methods for big data analysis are being suggested. Existing general research on data analysis has some problem that if the size of data is getting bigger, the processing speed is rapidly slow. In this paper, we find out the optimal algorithm that efficiently manage the energy data based on Big data...
Electrical load monitoring, by means of a smart meter, is getting more and more popular these days. Power demand information from smart meters is drawing attention among researchers, since it could be applied for power demand control. Providing attractive services with smart meters encourage electricity retailers to utilize demand side management, which could be a solution for energy-related problems...
In this paper, we try to make an author identification of two ancient Arabic religious books dating from the 6th century: The holy Quran and the Hadith. The authorship identification process is achieved through four phases which are: documents collection, text preprocessing, features extraction and classification model building. Thus, two series of experiments are undergone and commented. The first...
Support Vector Machines (SVMs) are powerful classification tools. However, the model training is very time-consuming when meeting large scale data sets. Some efforts have been devoted to screening out non-support vectors (non-SVs) to accelerate the training. But their processes rely on prior knowledge of other classifiers with different parameters to screen out non-SVs. In this paper, we propose Directional...
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