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In this paper, we propose a two-stage phone recognition system using articulatory and spectral features. In the first stage, articulatory features are predicted from spectral features using FeedForward Neural Networks (FFNNs). In the second stage, phone recognition is carried out using the predicted articulatory features and spectral features together. FFNNs and Hidden Markov Models are explored for...
In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data. Having palmprint as a reliable and unique characteristic of every person, we extract and use its features based on its geometry, lines and angles. There are countless ways to define measures for the recognition task. To analyze a new point of view, we extracted textural features...
In this paper, we propose a robust proximal classifier via absolute value inequalities (AVIPC) for pattern classification. AVIPC determines K proximal planes by solving K optimization problems with absolute value inequalities. In AVIPC, each proximal plane is closer to one class and far away from the others. By using the absolute value inequalities, AVIPC is more robust and sparse than traditional...
The current trend of growth of information reveals that it is inevitable that large-scale learning problems become the norm. In this paper, we propose and analyze a novel Low-density Cut based tree Decomposition method for large-scale SVM problems, called LCD-SVM. The basic idea here is divide and conquer: use a decision tree to decompose the data space and train SVMs on the decomposed regions. Specifically,...
This paper presents iNNE (isolation using Nearest Neighbour Ensemble), an efficient nearest neighbour-based anomaly detection method by isolation. Inne runs significantly faster than existing nearest neighbour-based methods such as Local Outlier Factor, especially in data sets having thousands of dimensions or millions of instances. This is because the proposed method has linear time complexity and...
Effective machine-learning handles large datasets efficiently. One key feature of handling large data is the use of databases such as MySQL. The freeware fuzzy decision tree induction tool, FDT, is a scalable supervised-classification software tool implementing fuzzy decision trees. It is based on an optimized fuzzy ID3 (FID3) algorithm. FDT 2.0 improves upon FDT 1.0 by bridging the gap between data...
In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data. Having palmprint as a reliable and unique characteristic of every person, we extract and use its features based on its geometry, lines and angles. There are countless ways to define measures for the recognition task. To analyze a new point of view, we extracted textural features...
Wrapper based gene selection methods tend to obtain better classification accuracy than filter methods, while it is much more time consuming. Accelerating this process without degrading the high accuracy is of great value for researchers to better analyze gene expression profiles. In this paper, we explore to reduce the time complexity of wrapper based gene selection method with K-Nearest-Neighbor...
Big data is a set of very large and complex data that is hard to load on computers. The main challenge in big data world is related to their search, categorize and analyze specially, when they are unbalanced. Despite, there are a lot of works in the field of big data but analyzing unbalanced big data is still a fundamental challenge in this area. In this paper we try to solve the problem of RSIO-LFCM...
The objective of the present paper is to demonstrate the potential of Computational Intelligence in applications pertaining to the automatic identification - categorisation of Cardiotocograms using Machine Learning Algorithms and Artificial Neural Networks whose purpose is to distinguish between healthy or pathological cases leading to mortality during birth or fetal cerebral palsy. Interest is also...
Grammar Induction (GI) is the problem of extracting hidden regularities and syntactic patterns in languages. Not only the manner of extraction is intricate but also the definition of meaningful patterns is a challenge. Alignment Based Learning (ABL) is one of the research endeavors targeting such challenges in GI. Our present research on applying ABL to POS sequences in English, Persian and Arabic...
In a previous paper, we reported on the initial development of KneeRobo, which replicates knee joint troubles experienced by patients in order to enable students studying to become physical or occupational therapists to gain practical training/testing virtually. We also developed a control algorithm that enabled KneeRobo to realize involuntary internal/external rotation during knee flexion and extension...
Rules inferring the memberships to single decision classes have been induced in rough set approaches and used to build a classifier system. Rules inferring the memberships to unions of multiple decision classes can be also induced in the same manner. In this paper, we show the classifier system with rules about the union of multiple decision classes has an advantage in the accuracy of classification...
A method is proposed to distinguish patients with depression from healthy persons using data measured by Functional Near Infrared Spectroscopy (FNIRS) during a cognitive task. Firstly, General Linear Model (GLM) is used to extract features from 52-channel FNIRS data of patients with depression and normal healthy persons. Then a Support Vector Machine (SVM) classifier is designed for classification...
GPS performs considerably well in the outdoor LoS conditions. However, its performance rapidly deteriorates when it is used in non-LoS indoor environments. This paper analyzes the performance of Wi-Fi based positioning in the outdoor environments. Our tests reveal that scene analysis can achieve high accuracies even in the outdoor setups. All observations reported in this work are based on experimental...
The challenge of designing an active video game that is an effective means by which to promote physical activity while at the same time is an engaging video game has been met with varied success. The key to overcoming this challenge is to approach design from the perspective that active video games are a unique combination of traditional video games and sports. By combining models of engagement —...
Fingerprinting is the prevailing positioning method for location based service (LBS) and indoor positioning applications when compared with other methods such as cell of origin (CoO) and trilateration. It is especially more suitable for complicated indoor environments. However, higher positioning accuracy is still expected for it to match the capabilities of other mature techniques such as GPS. This...
Nowadays, online credit card transactions has become a hot spot for frauds. The amount spent by a person using his credit card can vary and there is never a particular pattern of a person's expenses. Due to this characteristic of credit card use and its fraud, traditional algorithms cannot be implemented for credit card fraud detection. We required some trial and error methods which also gained experience...
Feature selection and classification are important tasks in medical data mining. However, different misclassifications of medical cases could lead to different losses. This paper proposes a framework for medical data classification and relevant feature selection by the combination of the trace ratio criterion and a novel cost-sensitive linear discriminant analysis classifier approach. The proposed...
For classification of hyperspectral images, particularly using limited training samples, supervised feature extraction is an approach for reduction of dimensionality, overcoming the Hughes phenomenon and increasing the classification accuracies. Classic and popular feature extraction methods such as linear discriminant analysis (LDA) have not good efficiency in small sample size situation because...
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