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In this work a machine learning approach is proposed for prediction of volatile substance abuse. Machine learning technique used in this work is artificial neural networks (ANN). Two ANN modules are designed, ANN-D to predict whether a person is using VSA or not and ANN-C to predict the time of use. Input features used are age, gender, country, ethnicity, education, neuroticism, openness to experience,...
We develop an intelligent credit rating system that can provide debtors' rating information without involving credit rating agencies. Several models are used for credit scoring in our work, including the Duffie's model, logistic regression, and random forest. We compare the performance of these models and build an in-depth understanding of the evaluation of credit rating. Furthermore, we propose a...
The amount of data produced every day on the internet increases every day and with the increasing popularity of the social networks the number of published photos are huge, and those pictures contain several implicit or explicit brand logos. Detecting this logos in natural images can provide information about how widespread is a brand, discover unwanted copyright distribution, analyze marketing campaigns,...
In this paper, we try to recognize negative emotions (sadness and disgust) of human affecting driving by using physiological signals that are commonly used to deal with human emotions. To do this, emotional stimuli are used to induce sadness and disgust, and emotion recognition is performed based on the feature vector extracted from the physiological signals collected on the induced emotion by a stimulus...
Nowadays, deep learning is very popular in a variety of research field due to its outperformance over the existing machine learning methods and its high generality over raw inputs. According to recent surveys, deep learning can give high performance in visual object recognition system. Human Action Recognition (HAR) is a promising research area over the computer vision research field due to its enormous...
New and unseen network attacks pose a great threat to the signature-based detection systems. Consequently, machine learning-based approaches are designed to detect attacks, which rely on features extracted from network data. The problem is caused by different distribution of features in the training and testing datasets, which affects the performance of the learned models. Moreover, generating labeled...
There is a desire to detect and assess unmanned aerial systems (UAS) with a high probability of detection and low nuisance alarm rates in numerous fields of security. Currently available solutions rely upon exploiting electronic signals emitted from the UAS. While these methods may enable some degree of security, they fail to address the emerging domain of autonomous UAS that do not transmit or receive...
Action recognition is still a challenging problem. In order to catch effective compact representation of the action sequences, the discriminative dictionaries could be learned by sparse coding. But sparse coding is needed in both the training and testing phases of the classifier framework. And it is also time consuming for the adoption of 1-norm sparsity constraint on the representation coefficients...
Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when...
The SFSVC (Super Fast Support Vector Classifier) architecture is implemented to a computational mobile platform and its performances are evaluated against its implementation on a classic machine (personal computer). The aim of this article is to prove that the SFSVC architecture can have good performances on an environment with very limited resources by taking advantages of its compact structure and...
In this paper, we propose a classification model for learning state based on individual biometric data. In particular, we use the pupil size as a biometric data and the data has been collected from 72 participants. We also deploy the support vector machine (SVM) in conjunction with k-fold validation as an analysis tool. In order to improve the performance of the SVM, the we remove outliers from the...
A novel framework in image sparse representation based on ensemble learning is proposed in this paper. Due to the random extraction of training patches and the variation of single optimal processing result, the proposed scheme develops classical dictionary learning algorithms in compressed sensing with ensemble learning theory to improve the performance of sparse representation. The analysis of computational...
Infrared thermography has been widely used today for nondestructive evaluation and testing of materials and other qualitative approaches. However, the field of thermography is much less developed. Most of the existing research uses a relatively simple model, while more realistic models are currently in development. One interesting scenario for thermography is determining the material composition of...
This paper presents the DeepCD framework which learns a pair of complementary descriptors jointly for image patch representation by employing deep learning techniques. It can be achieved by taking any descriptor learning architecture for learning a leading descriptor and augmenting the architecture with an additional network stream for learning a complementary descriptor. To enforce the complementary...
An image-driven, model-free approach to design control systems for a large class of industrial process is proposed. A mathematical model of the process is replaced by sequences of subsequent images which play the role of the process (plant) states. The length of this sequences depends on the speed of the process dynamics and on the frame rate. Firstly, a learning sequence of the system states is collected...
Programmers produce code clones when developing software. By copying and pasting code with or without modification, developers reuse existing code to improve programming productivity. However, code clones present challenges to software maintenance: they may require consistent application of the same or similar bug fixes or program changes to multiple code locations. To simplify the maintenance process,...
One of the key tasks of Outdoor-type mobile robotics is traversability estimation of underlying surfaces in a in a priori of an unknown heterogeneous environment. The paper presents practical realization of traversability estimation system based on group method of data handling (GMDH). This method is classical technique of data mining and one of the first techniques of Deep Learning. The results of...
Coreference resolution plays a significant role in natural language processing systems. It is the method of figuring out all the noun phrases that refer back to the identical real world entity. Several researches have been done in noun phrase coreference resolution by using certain machine learning techniques. Our paper proposes a machine learning approach using support vector machines (SVM) towards...
In recent years, analysis of remote sensing imagery is imperatives in the domain of environmental and climate monitoring primarily for the application of detecting and managing a natural disaster. Satellite imagery or aerial imagery is beneficial because it can widely capture the condition of the surface ground and provides a massive amount of information in a piece of satellite imagery. Since obtaining...
Fraud, waste, and abuse in medical insurance contributes to significant increases in costs for providers and patients. One way to reduce costs is through the detection of abnormal medical practices that could indicate possible fraud. In this paper, we expand upon our previous research into medical specialty anomaly detection by validating the efficacy of our model using real-world fraud cases, and...
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