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Text classification is one of the key methods used in text mining. Generally, traditional classification algorithms from machine learning field are used in text classification. These algorithms are primarily designed for structured data. In this paper, we propose a new classifier for textual data, called Supervised Meaning Classifier (SMC). The new SMC classifier uses meaning measure, which is based...
This paper presents a novel road/terrain classification system based on the analysis of volunteered geographic information gathered by bikers. By ubiquitous collection of multi-sensor bike data, consisting of visual images, accelerometer information and GPS coordinates of the bikers' smartphone, the proposed system is able to distinguish between 6 different road/terrain types. In order to perform...
Today almost every system or service is dependent on IT systems, and failure of these systems have serious and negative effects on the society. IT incidents are critical for the society as they can stop the function of critical systems and services. Therefore, it is important to analyze these systems for potential risks before becoming dependent on them. Moreover, in a software engineering context...
Experienced pipeline operators utilize Magnetic Flux Leakage (MFL) sensors to probe oil and gas pipelines for the purpose of localizing and sizing different defect types. A large number of sensors is usually used to cover the targeted pipelines. The sensors are equally distributed around the circumference of the pipeline, and every three millimeters the sensors measure MFL signals. Thus, the collected...
Keystroke dynamics authentication is not as widely used compared to other biometric systems. In recent years, keystroke dynamic authentication systems have gained interest because of low cost and integration with existing security systems. Many different methods have been proposed for data collection, feature representation, classification, and performance evaluation. The work presents a detailed...
Artificial neural networks have been investigated for many years as a technique for automated diagnosis of defects causing partial discharge (PD). While good levels of accuracy have been reported, disadvantages include the difficulty of explaining results, and the need to hand-craft appropriate features for standard two-layer networks. Recent advances in the design and training of deep neural networks,...
Recognizing inference in text (RITE) plays an important role in the answer validation modules for a Question Answering (QA) system. The problem of class imbalance has received increased attention in the machine learning community. In recent years, several attempts have been made on the linguistic phenomena analysis, however, little is known about the effects of imbalanced datasets with linguistic...
Deep learning has recently exhibited good performance in many applications. The convolution neural network is an often-used architecture for deep learning and has been widely used in computer vision and audio recognition, and outperformed other related handcraft designed feature in recent years. These techniques compared to other artificial intelligence algorithms and handcraft features need extremely...
The fast growing use of social networking sites among the teens have made them vulnerable to get exposed to bullying. Cyberbullying is the use of computers and mobiles for bullying activities. Comments containing abusive words effect psychology of teens and demoralizes them. In this paper we have devised methods to detect cyberbullying using supervised learning techniques. We present two new hypotheses...
This paper propose a novel learning framework for classification of messages into spam and legit. We introduce a classification method based on feature space segmentation. Naive Bayes (NB) model is a statistical filtering process which uses previously gathered knowledge. Instead of using a single classifier, we propose the use of local and global classifier, based on Bayesian hierarchal framework...
System call analysis is a behavioral malware detection technique that is popular due to its promising detection results and ease of implementation. This study describes a system that uses system call analysis to detect malware that evade traditional defenses. The system monitors executing processes to identify compromised hosts in production environments. Experimental results compare the effectiveness...
In addition to the traditional video surveillance, various audio processing techniques can also be added to the existing CCTV cameras. They can be used as additional features to help in analyzing the scene better and autonomously detecting violence or any unwanted activity in the scene. For this purpose, a deep learning based scream sound detection approach is proposed in this paper. MFCC features...
Occurrence of multiple seizures is a common phenomenon observed in patients with epilepsy: a neurological malfunction that affects approximately 50 million people in the world. Seizure prediction is widely acknowledged as an important problem in the neurological domain, as it holds promise to improve the quality of life for patients with epilepsy. A noticeable number of clinical studies showed evidence...
Malware analysis on the Android platform has been an important issue as the platform became prevalent. The paper proposes a malware detection approach based on static analysis and machine learning techniques. By conducting SVM training on two different feature sets, malicious-preferred features and normal-preferred features, we built a hybrid-model classifier to improve the detection accuracy. With...
Randomized algorithms have good performances for regression and classification problems by using random hidden weights and pseudoinverse computing for the output weights. They have one single hidden layer structure. On the other hand, deep learning techniques have been successfully used for pattern recognition due to their deep structure and effective unsupervised learning. In this paper, the randomized...
Deep Belief Network (DBN) is a classic deep learning model, and it can learn higher feature and do better classification job. We combine DBN's basic component Restricted Boltzmann Machines (RBM) with the statistic distribution of Polarimetric SAR (PolSAR) data. Based on it, we develop a deep learning classification method that is suitable for PolSAR data. To verify the effectiveness of the method,...
Recently, machine-learning based vulnerability prediction models are gaining popularity in web security space, as these models provide a simple and efficient way to handle web application security issues. Existing state-of-art Cross-Site Scripting (XSS) vulnerability prediction approaches do not consider the context of the user-input in output-statement, which is very important to identify context-sensitive...
After lung cancer, breast cancer is known to be the greatest cause for death among females [20]. The improving effectiveness of machine learning approaches is being given a lot of importance by medical practitioners for breast cancer diagnosis. The paper proposes an effective hybridized classifier for breast cancer diagnosis. The classifier is made by combining an unsupervised artificial neural network...
A new method for fuzzy categorization of cursive handwritten text has been addressed in the present work. This is based on input text clustering and subsequent learning of weighted attributes in each subject cluster. The system first employs a new algorithm to detect the letter boundary in each cursive word in the textual sentences. A Modified Optimal Clustering Algorithm (MOCA) and Back Propagation...
Learning a deep architecture involves a tough issue called hyperparameter search. This is especially the case for convolutional neural networks with a large number of hyperparameters. To solve this problem, we propose a tensor completion method to predict the best architecture configurations for convolutional neural networks. This method is based on a hypothesis that the generalization performance...
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