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This study explored the hidden biomedical information from knee MR images for osteoarthritis prediction. We have computed the Cartilage Damage Index (CDI) information from 36 informative locations on tibiofemoral compartment from 3D MR imaging reconstruction and used PCA analysis to process the feature set. The processed feature set and original raw feature set were severed as input to four machine...
Financial time series prediction is remains a challenge, due to the nonstationary and fuzziness financial data. In this paper, we propose and achieve a hybrid financial time series model by combining the Maximum Entropy (ME), Support Vector Regression (SVR) and Trend model based on Artificial neural networks (ANNs) for forecasting financial time series. The method contains three steps. The first step...
Diabetic retinopathy (DR) is an eye abnormality caused by long term diabetes and it is the most common cause of blindness before the age of 50. Microaneurysms (MAs), resulting from leakage from retinal blood vessels, are early indicators of DR, yielding a large body of diagnostic work focused on automatic detection of MA. However, automated detection of MAs is difficult because (1) the small size...
Machine learning classifiers help physicians to make near-perfect diagnoses, minimizing costs and time. Since medical data usually contains a high degree of uncertainty and ambiguity, proper ordering and classification require a proper comparative performance analysis of machine learning classifiers. Machine learning classifiers are applied on the Ovarian Cancer Dataset. Ovarian cancer is the fifth...
In Face recognition, a combination of neural network (NN), known as an ensemble of neural network, often outperforms individual ones. This paper is aiming to present a support vector machines (SVM)-ensemble-based efficient face recognition system. The training samples are randomly chosen by means of bootstrap technique to train the different SVM independently. These SVM's are combined together to...
For processing purposes of silver colloidal suspensions in view of specific applications, this study evaluates the suitability of using alginate/lignosulfonate mixtures as an efficient dispersion matrix for the silver nanoparticles. The rheological behavior of the in situ obtained silver nanoparticle suspensions was investigated by rotational measurements performed using cone-plate geometry, considering...
The typical method of entering a password for user authentication is vulnerable to hacking; therefore, various security technologies using bio-signals, such as iris scan, electrocardiography, electromyography (EMG), and fingerprint recognition, are being developed. In this research, an authentication algorithm using an EMG signal is proposed to supplement the weakness of personal certification techniques...
An algorithm that can predict the review rating of a potential business with only existing information about the location and business categories would be an invaluable tool in making investment decisions. Utilizing the Yelp business dataset, we built a model, that can do as such, by classifying whether a potential business belongs to a positively-reviewed class (star ratings greater than or equal...
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...
Plant recognition from their leaves has become a popular area in the machine learning and image processing. In this study 7 different types of apricot trees were determined and classified by using their leaves. At first leaves images were pre-processed. After than each image was scanned by 5×5 overlapping filter and median values of each filter process were recorded to represent the leaves. After...
A variety of methods exists for electroencephalographic (EEG) signals classification. In this paper, we briefly review selected methods developed for such a purpose. First, a short description of the EEG signal characteristics is provided. Then, a comparison between the selected EEG signal classification methods, based on the overview of research studies on this topic, is presented. Examples of methods...
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...
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...
Software Development Effort Estimation (SDEE) plays a primary role in software project management. Among several techniques suggested for estimating software development effort, analogy-based software effort estimation approaches stand out as promising techniques.In this paper, the performance of Fuzzy Analogy is compared with that of six other SDEE techniques (Linear Regression, Support Vector Regression,...
In recent years use of image processing techniques for texture analysis of machined surface is gaining importance in the field of manufacturing. This manuscript addresses texture identification methodology using Wavelet transform and artificial intelligence techniques. Captured images of machined surface using Electric discharge machining, milling, sand blasting and shaping is decomposed in to sub...
Brain Computer Interface is a reliable communication interface between human brain and external world. It translates human brain electrical activity to useful command by extracting meaningful features from Electroencephalogram signals. In present work, feature extraction techniques and classification methods are proposed for implementation of Brain Computer Interface system. Proposed methodology is...
Epileptic seizure is the abnormal synchronous neuronal activity that occurs in human brain. The early detection of epileptic seizure helps in improving patient's mental health. In this work, an Electroencephalogram based methodology of automated epileptic seizure detection using Flexible Analytical Wavelet Transform is presented. In the proposed methodology, Electroencephalogram signals are decomposed...
Convolutional neural network (CNN) is more and more important in pattern recognition. In this work, we adopt label relations and long short-term memory (LSTM) to develop an accurate CNN-based scene classification algorithm. Traditional scene classification algorithms assume that labels are mutually exclusive. However, this is not reasonable when an image has a variety of objects and hence has multiple...
This study investigates the discrimination between calm, exciting positive and exciting negative emotional states using EEG signals. Towards this direction, a publicly available dataset from eNTERFACE Workshop 2006 was used having as stimuli emotionally evocative images. At first, EEG features were extracted based on literature review. Then, a computational framework is proposed using machine learning...
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