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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...
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...
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,...
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...
The urban classification of PolSAR images is made difficult by the characteristic of a rotated target to exhibit volume scattering. In this paper we use a deep learning technique in conjunction with some statistical parameters to learn to classify urban areas irrespective of the rotation. The learning algorithm was trained to differentiate urban from non-urban areas and was able to achieve a 8.5834%...
A new method of classification of a speaker’s gender based on cumulant coefficients is proposed. The effect of an additive noise and measurement error of classification signs on accuracy of classification is analyzed. The expediency of construction of an adaptive system of classification operating with considering of masking of a speech signal by noise is shown. Comparison of the proposed method of...
machine learning algorithms are widely used in classification problems. Certainly, recognition quality of algorithms is important indicator, but the ability of the algorithm to learn is more significant. In this work the learning curves experiment was performed in order to identify which of the three learning rates occur when training the machine learning algorithms: overfitting, perfect case and...
Research that explores the use of machine learning for automatic security classification of information objects is about to emerge. In this paper we investigate the opportunity to increase the machine learning performance by taking advantage from time information that is "hidden" in the documents of the training set. This paper presents a technique to do so, and confirms that this is a promising...
This paper tackles the Romanian syllabification and stress assignment problems, and proposes an efficient machine learning based solution. We show that by designing the appropriate feature sets for each specific problem, learning algorithms achieve satisfactory accuracy rates for both problems (∼92% for syllabification, ∼85% for stress assignment), even for relatively small training set sizes. We...
With the increasing risk of data leakage, information guards have emerged as a novel concept in the field of security which bears similarity to spam filter that examine the content of the exchanged messages. A guard is defined as a high-assurance device used to control the information flow, typically from a domain with a "high" level of confidentiality, such as a corporate or military network,...
A method for sentiment polarity assignment for textual content written in Polish using supervised machine learning approach with transfer learning scheme is proposed in the paper. It has been shown that performing simple natural language processing steps prior to classification, provides inspiring results without redundant computation overhead. The documents containing subjective opinions were classified...
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...
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...
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,...
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...
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...
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...
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...
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