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Open access

Insu Song, John Vong, Nguwi Yok Yen, Joahchim Diederich and Peter Yellowlees

Abstract

In this study, we propose to diagnose facial nerve palsy using Support Vector Machines (SVMs) and Emergent Self-Organizing Map (ESOM). This research seeks to analyze facial palsy domain using facial features and grade the degree of nerve damage based on the House-Brackmann score. Traditional diagnostic approaches involve a medical doctor recording a thorough history of a patient and determining the onset of paralysis, rate of progression and so on. The most important step is to assess the degree of voluntary movement of the facial nerves and document the grade of facial paralysis using House- Brackmann score. The significance of the work is the attempt to understand the diagnosis and grading processes using semi-supervised learning with the aim of automating the process. The value of the research is in identifying and documenting the limited literature seen in this area. The use of automated diagnosis and grading greatly reduces the duration of medical examination and increases the consistency, because many palsy images are stored to provide benchmark references for comparative purposes. The proposed automated diagnosis and grading are computationally efficient. This automated process makes it ideal for remote diagnosis and examination of facial palsy. The profiling of a large number of facial images are captured using mobile phones and digital cameras.

Open access

Md Wasiur Rahman, Fatema Tuz Zohra and Marina L. Gavrilova

Abstract

Computational intelligence firmly made its way into the areas of consumer applications, banking, education, social networks, and security. Among all the applications, biometric systems play a significant role in ensuring an uncompromised and secure access to resources and facilities. This article presents a first multimodal biometric system that combines KINECT gait modality with KINECT face modality utilizing the rank level and the score level fusion. For the KINECT gait modality, a new approach is proposed based on the skeletal information processing. The gait cycle is calculated using three consecutive local minima computed for the distance between left and right ankles. The feature distance vectors are calculated for each person’s gait cycle, which allows extracting the biometric features such as the mean and the variance of the feature distance vector. For Kinect face recognition, a novel method based on HOG features has been developed. Then, K-nearest neighbors feature matching algorithm is applied as feature classification for both gait and face biometrics. Two fusion algorithms are implemented. The combination of Borda count and logistic regression approaches are used in the rank level fusion. The weighted sum method is used for score level fusion. The recognition accuracy obtained for multi-modal biometric recognition system tested on KINECT Gait and KINECT Eurocom Face datasets is 93.33% for Borda count rank level fusion, 96.67% for logistic regression rank-level fusion and 96.6% for score level fusion.

Open access

Yoichi Hayashi, Yuki Tanaka, Tomohiro Takagi, Takamichi Saito, Hideaki Iiduka, Hiroaki Kikuchi, Guido Bologna and Sushmita Mitra

References [1] Marqus, A.I., Garca, V., & Snchez, J.S. On the suitability of resampling techniques for the class imbalance problem in credit scoring. Journal of the Operational Research Society 64, pp. 1060–1070, 2013. [2] Zhao Z., Xu, S., Kang, B. H., Kabir, M. M. J., & Liu, Y. Investigation and improvement of multilayer perceptron neural networks for credit scoring Expert Systems with Applications 42, pp. 3508–3516, 2015 [3] Finlay, S. M. Multiple classifier architectures and their applications to credit risk assessment. European Journal of

Open access

Attila Bölcskei, András Zsolt Kovács and Domen Kušar

Abstract

Spatial ability development is of paramount importance in engineering training, especially for architects. The paper aims to compare results achieved by the world-wide Mental Rotation Test (MRT) at the University of Ljubljana and at the Ybl Faculty of SzIU in Budapest, with respect to total scores, improvement and hand preferences. The paper concludes that the mental rotation aspect of spatial intelligence can significantly be developed by Descriptive Geometry courses in both countries. Sophisticated statistical analysis, however, leads to new ideas in scoring MRT. The main goal of the paper is to present an alternative scoring system, which seems to be fairer and provides the expected statistical behavior of samples.

Open access

Christina Brester, Eugene Semenkin and Maxim Sidorov

, E. Gaussier, A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. ECIR’05 Proceedings of the 27th European conference on Advances in Information Retrieval Research, 2005, pp. 345-359.

Open access

G. Genta, A. Astolf, P. Bottalico, G. Barbato and R. Levi

Speech intelligibility is a subjective performance index defined as the percentage of a message understood correctly. Often the results of speech intelligibility tests would suggest that conditions are acceptable, with Intelligibility Score (IS) of the order of 90% or more, while speech transmission performance may not be satisfactory. Subjective ratings of the Listening Easiness Score (LES), based on a discrete questionnaire, provide an alternative approach. A total of 239 primary school pupils, aged 7 to 11, evenly distributed among the grades, participated in the survey. The objective indicator Speech Transmission Index (STI) was also measured for each test setting in seven different positions in the laboratory classroom used for the test. Both IS and LES are inherently bounded, and their data distributions exhibit a significant accumulation of scores in the upper and lower parts. The resulting truncation problem has been addressed with a method based on the normal probability plot, enabling identification of mathematical models relating IS and LES to STI, as well as the estimation of related uncertainties. IS and LES exhibit substantially similar metrological capabilities, as, for both, model relative uncertainty does not exceed 4% and uncertainties in prediction of new observations are about twice as large.

Open access

Daniel Hagan and Martin Hagan

References [1] J. D. Durrant and J. A. McCammon, NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein/Ligand Complexes, J. Chem. Inf. Model, 50, 2010, 1865-1871. [2] Oleg Trott and Arthur J. Olson, AutoDock Vina: Improving the speed and accuracy of docking wih a new scoring function, efficient optimization, and multithreading, J. Computational Chemistry, 31, 2009, 455-461. [3] R. Wang and X. Fang and Y. Lu and S. Wang, The PDBbind Database: Collection of Binding Affinities for Protein-Ligand Complexes with Known

Open access

S. Seyedtabaii

Abstract

Pulse-echo ultrasonic signal is used to detect weld defects with high probability. However, utilizing echo signal for defects classification is another issue that has attracted attention of many researchers who have devised algorithms and tested them against their own databases. In this paper, a study is conducted to score the performance of various algorithms against a single echo signal database. Algorithms tested the use of Wavelet Transform (WT), Fast Fourier Transform (FFT) and time domain echo signal features and employed several NN’s architectures such as Multi-Layer Perceptron Neural Network (MLP), Self Organizing Map (SOM) and others known to be good classifiers. The average performance of all can be viewed fair (90%) while some algorithms render success rate of about 94%. It seems that acquiring higher success rates out of a single fixed angle probe pulseecho set up needs new arrangements of data collection, which is under investigation.

Open access

Kai Lan and Kosuke Sekiyama

Abstract

In this paper, we propose an optimal viewpoint selection system for monitoring robots to search for the optimal viewpoint of a scene with the highest aesthetic property. Using the information of the targets, we propose a novel method for predicting human aesthetic sense for a scene. We construct evaluation functions based on certain known composition rules using three factors, namely, target size, visual balance, and composition fitting value. Then a score, which is a reflection of human evaluation, will be obtained using these functions. The optimal viewpoint will be selected from a number of candidates around the target group, by evaluating the aesthetic properties of scenes for each candidate viewpoint. Finally, once the optimal viewpoint is confirmed, path planning and path following controls are implemented for the robots during the moving process.

Open access

Zuzana Rošt’áková and Roman Rosipal

R eferences [1] Rechtschaffen, A., Kales, A. (1968). A Manual of Standardized Terminology Techniques and Scoring System for Sleep Stages of Human Subjects . Public Health Service, U.S. Government Printing Office, Washington, D.C. [2] Lewandowski, A., Rosipal, R., Dorffner, G. (2012). Extracting more information from EEG recordings for a better description of sleep. Computer Method and Programs in Biomedicine , 108 (3), 961–972. [3] Gervini, D., Gasser, T. (2004). Self-modeling warping functions. Journal of the Royal Statistical Society