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The automatic analysis of radargrams acquired by radar sounder instruments is an important task, as it has been often highlighted by the scientific community. In particular, an automatic classification technique enables a fast and objective extraction of ice subsurface target properties on wide areas. The main drawback of the available classification techniques is the use of fixed size windows for...
In this paper, a novel iterative clustering based active learning (ICAL) method for hyperspectral image classification is proposed. On the one hand, the extreme learning machine is combined with the Markov random field (ELM-MRF) for label assignment, to exploit both spectral and spatial information to boost classification result. On the other hand, an iterative clustering based sample selection strategy...
These days, a lot number of elderly people need health care which may cause huge financial costs, especially in formal case. Machine Learning and the profound achievements in sensing technology provide the opportunities to monitor people living independently at home and can detect a distress situation affordably. Although there are some approaches to do recognize activities for this purpose, but there...
This paper proposes a method for the problem of processing high-dimensional data. When one has thousands of features (attributes) in a dataset, it is hard to achieve an efficient feature selection. To cope with this problem, we propose the use of a binary particle swarm optimization algorithm combined with the C4.5 as a classifier in the fitness function for the selection of informative attributes...
Tuberculosis is one of the top ten causes of death worldwide. Although this disease is curable and preventable, yet many new tuberculosis cases still occur especially in developing countries. Many low-income families cannot afford the medical diagnosis for tuberculosis. Therefore, this paper proposes an initial screening for tuberculosis infection using a data mining approach. In this paper, the initial...
Leaf can be one of the many different parameters on the basis of which a plant can be uniquely identified. Many plants types are on the verge of extinction and can be taken care of, if identified correctly. The proposed method discusses an automated image processing system for leaf classification. The leaf pixels from the image are segmented and termed as region of interest (ROI). A set of geometrical,...
Diabetic Retinopathy and Diabetic Macular Edema are diseases that affect vision and eventually may lead to blindness. Early detection is a must to prevent the progression of the disease imploring the need for effective computer-aided diagnostic techniques. In the following research paper, a robust method has been proposed to segment hard exudates from digital, color fundus images using anisotropic...
The discrimination of the preictal state in EEG signals is of great importance in neuroscience and the epileptic seizure prediction field has yet to provide conclusive evidence. In this study, three different classification approaches, including the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm, Support Vector Machines (SVMs) and Neural Networks (NNs), are investigated...
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...
With the evolution of technology and the major role that technology now plays in the diagnosis and identification of disorders and difficulties, improving the accuracy of diagnostic systems is paramount. Improving and evaluating the way in which patterns of results are identified and classified may help uncover answers that are not always obvious. This paper attempts to discover such patterns found...
In this paper we explore how some parameters, less investigated in the literature, can be selected in a Histogram of oriented gradients (HOG)-based car detection system. The main goal is to find ways for reducing computation complexity while maintaining a good classification performance, in order to make possible real-time, embedded implementations. We analyze the effect of cell size, of binary representation...
Yield estimation is becoming a challenging task for circuits that are replicated in millions of instances on a large design (High Replication Circuits, HRC) such as SRAMs and flip flops. This is because a rare event in a circuit cell may have a large impact on the system yield. To achieve high yield in HRC, the failure probability of the individual cell is requested to be very small. Thus the number...
Identifying hand configuration is a critical feature of sign language translation. In this paper, we describe our approach to recognize hand configurations in real time with the purpose of providing accurate predictions to be used in automatic sign language translation. To capture the hand configuration we rely on data gloves with 14 sensors that measure finger joints bending. These inputs are sampled...
The loyalty and retention of students in educational institutions has become one of the greatest challenges for the management area of these institutions. A promising solution to achieve this goal is the use of educational data mining to identify patterns that aid in decision making. This paper presents a proposal for the creation of temporal attributes with the purpose of helping to predict the avoidance...
Parametric statistical tests (e.g., t-tests) can sometimes return highly significant results in cases that would be considered uninformative, such as when the individuals’ accuracies are just above chance. This paper demonstrates that permutation tests can produce the expected non-significant results in these datasets. The properties of null distributions underlying this difference in significance...
In this work, a simple method for separation between normal and abnormal heart sounds (Phonocardiogram) is presented. Mel-Frequency Cepstral Coefficients (MFCC) are extracted from two different datasets of heartbeats. Several Classifiers, such as, Support Vectors Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Classification Tree (CT) and discriminative analysis (DA), are used. Simulation...
Traditional methods for hyperspectral image classification typically use raw spectral signatures without considering spatial characteristics. In this work, a classification algorithm based on Gabor features and decision fusion is proposed. First, the adjacent and high correlated spectral bands are intelligently grouped by coefficient correlation matrix. Following that, Gabor features in each group...
This paper presents a simple wearable, non-intrusive affordable mobile framework allowing physiotherapy sessions, helping doctors and physiotherapists monitoring gait rehabilitation of the patient who may be assisted remotely too. The system includes a set of IMU, Bluetooth compatible from Shimmer, an Android smartphone for primary processing of the data and data storage in a local database. Low computational...
Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is help to make predictions on medical data. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. The methods strongly...
In this paper, an effective feature selection method to extract high-dimensional feature of breast cancer was proposed, and designed a geometric mean KNN (K-Nearest Neighbor) membership function and L-KMOD kernel function FSVM (Fuzzy Support Vector Machine) to recognize breast cancer gene, when selecting 60 feature genes, the classification accuracy can achieve 98.9%.
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