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In a computer vision system, handwritten digits recognition is a complex task that is central to a variety of emerging applications. It has been widely used by machine learning and computer vision researchers for implementing practical applications like computerized bank check numbers reading. In this study, we implemented a multi-layer fully connected neural network with one hidden layer for handwritten...
Many areas include public transportation, hospitals and shopping mall use the computer technologies for security reasons. The need for integrating the technology with human needs is increased dramatically. Car license plate recognition is one of those technologies that help people to secure themselves from different attacks either attacks that affect their lives or their properties. There is an increasing...
In this study, normal (n), benign (b), and malign (m) stomach image cells have taken from faculty of Medicine the Fırat University with Light Microscope help. Total number of stomach images are 180 which be 60 n, 60 b, and 60 m. 90 of these 180 stomach images have been used for testing purposes and 90 have used for training purposes. The histograms of oriented gradient (HOG) feature vectors have been...
The application of Artificial Neural Networks (ANNs) to discriminate tag actions in UHF-RFID gate is presented in this paper. By exploiting Received Signal Strength Indicator values acquired in a real experimental scenario, a multi-layer perceptron neural network is trained to distinguish among tags incoming, outgoing or passing the RFID gate. A 99% accuracy can be obtained in tag classification by...
Feature selection is addressed an important problem in data mining. To be high dimension of the data obtained from the sources is encountered as an issue in many issues such as computation cost. For this reason, eliminating the unnecessary ones among these data and choosing the appropriate ones makes it possible to evaluate the information correctly. In this study, it is tried to suggest a method...
İn this study, we aim at gathering more scientific information about the musical data by using computer techology, and we are willing, to some extent, figure out the maqam structure of Traditional Turkish Art Music interpreted in the compositions. For this reason, 120 compositions from among Muhayyer Kurdi, Acem Kurdi and Kurdi makams in Traditional Turkish Art Music have been analysed and these compositions...
In the industry, maintenance costs can be reduced by early detection and diagnosis. It can also improve the overall equipment efficiency of the machine system. To diagnose the problem is required a diagnosis system with a particular method. The Hidden Markov Model (HMM) method is used because it can determine the parameters that are hidden from the observable parameters. Then, The specified parameters...
In this paper using a machine with a motor configuration that is connected with 3 discs. Performance of a machine can be known by analyzing the vibrations that occur in the machine. Vibration that occurs on the machine may be normal or abnormal. Abnormal vibrations on a machine can cause severe damage. This abnormal vibration can be caused by the mass distribution of rotation no longer exists in the...
Some of the best current face recognition approaches use feature extraction techniques based on either Principle Component Analysis (PCA), Local Binary Patterns (LBP), Autoencoder (non-linear PCA), etc. While each of these feature techniques works fairly well, we propose to combine multiple feature extractors with deep learning in a system so that the overall face recognition accuracy can be improved...
The main objective of this paper is the time-frequency analysis of the EEG signal captured in a cognitive task (i.e. object recognition) performed by human subjects. We investigate whether the power spectral density of the gamma frequency range can be used to classify the outcome of the object recognition task (i.e. seen, unseen, uncertain). The EEG signals were acquired and analyzed from 128 electrodes...
Every organism emits energy around it which comprises UV-radiation, EM-radiation, infrared and thermal radiation. This energy around human body represents health condition of the subject under study. These energy fields are called as aura of the body under consideration. Several types of equipments are there to capture such energy. Kirlian camera captures the distribution of energy radiation around...
This research describes skin disease recognition by using neural network which based on the texture analysis. There are many skin diseases which have a lot of similarities in their symptoms, such as Measles (rubeola), German measles (rubella), and Chickenpox etc. In general, these diseases have similarities in pattern of infection and symptoms such as redness and rash. Diagnosis and recognition of...
Audio Event Detection (AED) aims to recognize sounds within audio and video recordings. AED employs machine learning algorithms commonly trained and tested on annotated datasets. However, available datasets are limited in number of samples and hence it is difficult to model acoustic diversity. Therefore, we propose combining labeled audio from a dataset and unlabeled audio from the web to improve...
Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma,...
The analysis of the Variability of the Heart Rate (HRV) is coming as an important indicator for different clinical applications like the prediction of arrhythmias, sudden cardiac death, assessing cardiovascular and metabolic illness progression or in sports physiology. In this paper we have developed an algorithm to detect a supraventricular arrhythmia, by processing the heart rate variability (HRV)...
Building recognition from images is a challenging task since pictures can be taken from different angles and under different illumination conditions. Most of the building recognition methods use local and global handcrafted image features and do not consider the rejection scenario, where the method have to be capable of identifying if a given image does not belong to any of the classes of interest...
In Deep Learning, which has become a topic of intense study in recent years, an auto encoder that learns an identity map by a neural network plays an important role to extract features of data. One disadvantage of this method is that it is not always possible to extract appropriate features in the middle layer. In this study, a five-layer auto-encoder based on a sensory integration model proposed...
In this paper we evaluate three state-of-the-art neural-network-based approaches for large-scale video classification, where the computational efficiency of the inference step is of particular importance due to the ever increasing amount of data throughput for video streams. Our evaluation focuses on finding good efficiency vs. accuracy tradeoffs by evaluating different network configurations and...
A stroke occurs when the blood supply to a person's brain is interrupted or reduced. The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. Numerous works have been carried out for predicting various diseases by comparing the performance of predictive data mining technologies. In this work, we compare different methods with our approach for stroke prediction...
This paper presents a novel feature extraction framework for content-based image retrieval (CBIR). Discrete wavelet transform (DWT) based Local tetra pattern (LTrP) is used to obtain the feature map from an input image. Decomposition of DWT up to single level and the features obtained from it would make the CBIR system very sensitive to noise. Therefore, decomposition up to three scales is used to...
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