Search results for: statistical features

Authors: Zahra Kazemi Saleh, Bénédicte Poulin-Charronnat, Annie Vinter

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This study examines the effects of aging on visual statistical learning, using eye-tracking techniques to investigate this cognitive phenomenon. Visual statistical learning is a fundamental brain function that enables the automatic and implicit recognition, processing, and internalization of environmental patterns over time. Some previous research has suggested the robustness of this learning mechanism throughout the aging process, underscoring its importance in the context of education and rehabilitation for the elderly. The study included three distinct groups of participants, including 21 young adults (Mage: 19.73), 20 young-old adults (Mage: 67.22), and 17 old-old adults (Mage: 79.34). Participants were exposed to a series of 12 arbitrary black shapes organized into 6 pairs, each with different spatial configurations and orientations (horizontal, vertical, and oblique). These pairs were not explicitly revealed to the participants, who were instructed to passively observe 144 grids presented sequentially on the screen for a total duration of 7 min. In the subsequent test phase, participants performed a two-alternative forced-choice task in which they had to identify the most familiar pair from 48 trials, each consisting of a base pair and a non-base pair. Behavioral analysis using t-tests revealed notable findings. The mean score for the first group was significantly above chance, indicating the presence of visual statistical learning. Similarly, the second group also performed significantly above chance, confirming the persistence of visual statistical learning in young-old adults. Conversely, the third group, consisting of old-old adults, showed a mean score that was not significantly above chance. This lack of statistical learning in the old-old adult group suggests a decline in this cognitive ability with age. Preliminary eye-tracking results showed a decrease in the number and duration of fixations during the exposure phase for all groups. The main difference was that older participants focused more often on empty cases than younger participants, likely due to a decline in the ability to ignore irrelevant information, resulting in a decrease in statistical learning performance.

Keywords: aging, eye tracking, implicit learning, visual statistical learning

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Authors: Selim M. Khan

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Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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Authors: Sabariah Mohd Yusoff, Qamaruddin Adzeem Muhamad Murad

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This study investigates the impact of car seat design on driver response time, addressing a critical gap in understanding how ergonomic features influence both performance and safety. Controlled driving experiments were conducted with fourteen participants (11 male, 3 female) across three locations chosen for their varying traffic conditions to account for differences in driver alertness. Participants interacted with various seat designs while performing driving tasks, and objective metrics such as braking and steering response times were meticulously recorded. Advanced statistical methods, including regression analysis and t-tests, were employed to identify design factors that significantly affect driver response times. Subjective feedback was gathered through detailed questionnaires—focused on driving experience and knowledge of response time—and in-depth interviews. This qualitative data was analyzed thematically to provide insights into driver comfort and usability preferences. The study aims to identify key seat design features that impact driver response time and to gain a deeper understanding of driver preferences for comfort and usability. The findings are expected to inform evidence-based guidelines for optimizing car seat design, ultimately enhancing driver performance and safety. The research offers valuable implications for automotive manufacturers and designers, contributing to the development of seats that improve driver response time and overall driving safety.

Keywords: car seat design, driver response time, cognitive driving, ergonomics optimization

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Authors: Sung Yong Kim, Byung Joo Song

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Background: Circulating 25-hydroxyvitamin D (25(OH)D) levels has been considered to be inversely related to breast cancer development, recurrence risk, and mortality. Mean vitamin D levels in Korean population is lower than western countries due to higher incidence of lactose intolerance and lower exposure to sunlight. The purpose of this study was to assess incidence of 25(OH)D deficiency at diagnosis and after adjuvant chemotherapy and to investigate the correlation serum 25(OH)D levels with clinicopathologic features. Methods: From December 2011 to October 2012, 280 breast cancer patients seen at a single tertiary cancer center were enrolled. Serum 25(OH)D was measured at the time of surgery and after completion of adjuvant chemotherapy. Statistical analyses used chi-square test, Fisher's exact test, t-test, and ANOVA. Results: Mean serum 25(OH)D was 18.5 ng/ml. The 25(OH)D levels were deficient (<20 ng/ml) in 190 patients (67.9%), insufficient (20-29 ng/ml) in 51 patients(18.2%), and sufficient (30-150 ng/ml) in 39 patients(13.9%). A notable decrease in 25(OH)D concentration was observed(p<0.001) after chemotherapy but was not related to chemotherapy regimens. It was found significant lower 25(OH)D levels at winter season(from October to March, p=0.030). Subjects with invasive carcinoma (IDC or ILC) had significantly lower circulating levels of 25(OH)D than those with ductal carcinoma in situ(DCIS) (p=0.010). Patients with larger tumor size tends to have lower serum 25(OH)D but there were no statistical significance. Conclusions: Most of the breast cancer patients showed deficient or insufficient serum 25(OH)D concentration. Incidence of vitamin D deficiency was higher in invasive carcinoma than DCIS. Serum 25(OH)D levels were decreased after chemotherapy. Consideration should be given to the supplement of vitamin D to those patients.

Keywords: breast neoplasms, vitamin D, Korean population, breast cancer

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Authors: Kawalpreet Kalra, Alpana Goel

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The behaviour of the rotational bands should be smooth but due to large amount of inertia and decreased pairing it is not so. Many experiments have been done in the last few decades, and a large amount of data is available for comprehensive study in this region. Peculiar features like signature dependence, signature inversion, and signature reversal are observed in many two quasiparticle rotational bands of doubly odd and doubly even nuclei. At high rotational frequencies, signature and parity are the only two good quantum numbers available to label a state. Signature quantum number is denoted by α. Even-angular momentum states of a rotational band have α =0, and the odd-angular momentum states have α =1. It has been observed that the odd-spin members lie lower in energy up to a certain spin Ic; the normal signature dependence is restored afterwards. This anomalous feature is termed as signature inversion. The systematic of signature inversion in high-j orbitals for doubly odd rare earth nuclei have been done. Many unusual features like signature dependence, signature inversion and signature reversal are observed in rotational bands of even-even/odd-odd nuclei. Attempts have been made to understand these phenomena using several models. These features have been analyzed within the framework of the Two Quasiparticle Plus Rotor Model (TQPRM).

Keywords: rotational bands, signature dependence, signature quantum number, two quasiparticle

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Authors: Zhihui Liu, Dongmei Hao, Qian Qiu, Yang An, Lin Yang, Song Zhang, Yimin Yang, Xuwen Li, Dingchang Zheng

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Premature birth, defined as birth before 37 completed weeks of gestation is a leading cause of neonatal morbidity and mortality and has long-term adverse consequences for health. It has recently been reported that the worldwide preterm birth rate is around 10%. The existing measurement techniques for diagnosing preterm delivery include tocodynamometer, ultrasound and fetal fibronectin. However, they are subjective, or suffer from high measurement variability and inaccurate diagnosis and prediction of preterm labor. Electrohysterography (EHG) method based on recording of uterine electrical activity by electrodes attached to maternal abdomen, is a promising method to assess uterine activity and diagnose preterm labor. The purpose of this study is to analyze the difference of EHG signal features between term labor and preterm labor. Free access database was used with 300 signals acquired in two groups of pregnant women who delivered at term (262 cases) and preterm (38 cases). Among them, EHG signals from 38 term labor and 38 preterm labor were preprocessed with band-pass Butterworth filters of 0.08–4Hz. Then, EHG signal features were extracted, which comprised classical time domain description including root mean square and zero-crossing number, spectral parameters including peak frequency, mean frequency and median frequency, wavelet packet coefficients, autoregression (AR) model coefficients, and nonlinear measures including maximal Lyapunov exponent, sample entropy and correlation dimension. Their statistical significance for recognition of two groups of recordings was provided. The results showed that mean frequency of preterm labor was significantly smaller than term labor (p < 0.05). 5 coefficients of AR model showed significant difference between term labor and preterm labor. The maximal Lyapunov exponent of early preterm (time of recording < the 26th week of gestation) was significantly smaller than early term. The sample entropy of late preterm (time of recording > the 26th week of gestation) was significantly smaller than late term. There was no significant difference for other features between the term labor and preterm labor groups. Any future work regarding classification should therefore focus on using multiple techniques, with the mean frequency, AR coefficients, maximal Lyapunov exponent and the sample entropy being among the prime candidates. Even if these methods are not yet useful for clinical practice, they do bring the most promising indicators for the preterm labor.

Keywords: electrohysterogram, feature, preterm labor, term labor

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Authors: Paola Bordon, Breno Braga

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This paper investigates whether firms use university prestige to statistically discriminate among college graduates. The test is based on the employer learning literature which suggests that if firms use a characteristic for statistical discrimination, this variable should become less important for earnings as a worker gains labor market experience. In this framework, we use a regression discontinuity design to estimate a 19% wage premium for recent graduates of two of the most selective universities in Chile. However, we find that this premium decreases by 3 percentage points per year of labor market experience. These results suggest that employers use college selectivity as a signal of workers' quality when they leave school. However, as workers reveal their productivity throughout their careers, they become rewarded based on their true quality rather than the prestige of their college.

Keywords: employer learning, statistical discrimination, college returns, college selectivity

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Authors: Huijie Wang, Bin Zhang

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In China, the middle reaches of the Yangtze River are well-developed, boasting a wealth of different types of watershed landscape. In this regard, landscape character assessment (LCA) can serve as a basis for protection, management and planning of trans-regional watershed landscape types. For this study, we chose the middle reaches of the Yangtze River in Wuhan metropolitan area as our study site, wherein the water system consists of rich variety in landscape types. We analyzed trans-regional data to cluster and identify types of landscape characteristics at two levels. 55 basins were analyzed as variables with topography, land cover and river system features in order to identify the watershed landscape character types. For watershed landscape, drainage density and degree of curvature were specified as special variables to directly reflect the regional differences of river system features. Then, we used the principal component analysis (PCA) method and hierarchical clustering algorithm based on the geographic information system (GIS) and statistical products and services solution (SPSS) to obtain results for clusters of watershed landscape which were divided into 8 characteristic groups. These groups highlighted watershed landscape characteristics of different river systems as well as key landscape characteristics that can serve as a basis for targeted protection of watershed landscape characteristics, thus helping to rationally develop multi-value landscape resources and promote coordinated development of trans-regions.

Keywords: GIS, hierarchical clustering, landscape character, landscape typology, principal component analysis, watershed

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Authors: Angad Arora

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In production operations/manufacturing, a cell or line is typically a bunch of similar machines (computer numerical control (CNCs), advanced cutting, 3D printing or special purpose machines. For qualifying a typical manufacturing line /cell / new process, Ideally, we need a sample of parts that can be flown through the process and then we make a judgment on the health of the line/cell. However, with huge volumes and mass production scope, such as in the mobile phone industry, for example, the actual cells or lines can go in thousands and to qualify each one of them with statistical confidence means utilizing samples that are very large and eventually add to product /manufacturing cost + huge waste if the parts are not intended to be customer shipped. To solve this, we come up with 2 steps statistical approach. We start with a small sample size and then objectively evaluate whether the process needs additional samples or not. For example, if a process is producing bad parts and we saw those samples early, then there is a high chance that the process will not meet the desired yield and there is no point in keeping adding more samples. We used this hypothesis and came up with 2 steps binomial testing approach. Further, we also prove through results that we can achieve an 18-25% reduction in samples while keeping the same statistical confidence.

Keywords: statistics, data science, manufacturing process qualification, production planning

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Authors: Dilek Yalız Solmaz

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Meta-cognition is an important factor for educating conscious individuals who are aware of their cognitive processes. With this respect, the purposes of this article is to find out the perceived metacognition level of Physical Education and Sports School students at Anadolu University and to identify whether metacognition levels display significant differences in terms of various variables. 416 Anadolu University Physical Education and Sports School students were formed the research universe. "The Meta-Cognitions Questionnaire (MCQ-30)" developed by Cartwright-Hatton and Wells and later developed the 30-item short form (MCQ-30) was used. The MCQ-30 which was adapted into Turkish by Tosun and Irak is a four-point agreement scale. In the data analysis, arithmethic mean, standard deviation, t-test and ANOVA were used. There is no statistical difference between mean scores of uncontrollableness and danger, cognitive awareness, cognitive confidence and the positive beliefs of girls and boys students. There is a statistical difference between mean scores of the need to control thinking. There is no statistical difference according to departments of students between mean scores of uncontrollableness and danger, cognitive awareness, cognitive confidence, need to control thinking and the positive beliefs. There is no statistical difference according to grade level of students between mean scores of the positive beliefs, cognitive confidence and need to control thinking. There is a statistical difference between mean scores of uncontrollableness and danger and cognitive awareness.

Keywords: meta cognition, physical education, sports school students, thinking

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Authors: Muhammet Baldan, Emel Timuçin

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Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, random forest, molecular descriptors, maccs keys

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Authors: Jung-Hong Hong, Jing-Cen Yang, Cai-Yu Ou

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The analysis of geographic inequality heavily relies on the use of location-enabled statistical data and quantitative measures to present the spatial patterns of the selected phenomena and analyze their differences. To protect the privacy of individual instance and link to administrative units, point-based datasets are spatially aggregated to area-based statistical datasets, where only the overall status for the selected levels of spatial units is used for decision making. The partition of the spatial units thus has dominant influence on the outcomes of the analyzed results, well known as the Modifiable Areal Unit Problem (MAUP). A new spatial reference framework, the Taiwan Geographical Statistical Classification (TGSC), was recently introduced in Taiwan based on the spatial partition principles of homogeneous consideration of the number of population and households. Comparing to the outcomes of the traditional township units, TGSC provides additional levels of spatial units with finer granularity for presenting spatial phenomena and enables domain experts to select appropriate dissemination level for publishing statistical data. This paper compares the results of respectively using TGSC and township unit on the mortality data and examines the spatial characteristics of their outcomes. For the mortality data between the period of January 1^st, 2008 and December 31^st, 2010 of the Taitung County, the all-cause age-standardized death rate (ASDR) ranges from 571 to 1757 per 100,000 persons, whereas the 2^nd dissemination area (TGSC) shows greater variation, ranged from 0 to 2222 per 100,000. The finer granularity of spatial units of TGSC clearly provides better outcomes for identifying and evaluating the geographic inequality and can be further analyzed with the statistical measures from other perspectives (e.g., population, area, environment.). The management and analysis of the statistical data referring to the TGSC in this research is strongly supported by the use of Geographic Information System (GIS) technology. An integrated workflow that consists of the tasks of the processing of death certificates, the geocoding of street address, the quality assurance of geocoded results, the automatic calculation of statistic measures, the standardized encoding of measures and the geo-visualization of statistical outcomes is developed. This paper also introduces a set of auxiliary measures from a geographic distribution perspective to further examine the hidden spatial characteristics of mortality data and justify the analyzed results. With the common statistical area framework like TGSC, the preliminary results demonstrate promising potential for developing a web-based statistical service that can effectively access domain statistical data and present the analyzed outcomes in meaningful ways to avoid wrong decision making.

Keywords: mortality map, spatial patterns, statistical area, variation

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Authors: Jalel Khelil

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In order to improve reliability of Gas Turbine machine especially its generator equipment, a fault diagnosis system based on fuzzy approach is proposed. Three various methods namely K-NN (K-nearest neighbors), F-KNN (Fuzzy K-nearest neighbors) and FNM (Fuzzy nearest mean) are adopted to provide the measurement of relative strength of vibration defaults. Both applications consist of two major steps: Feature extraction and default classification. 09 statistical features are extracted from vibration signals. 03 different classes are used in this study which describes vibrations condition: Normal, unbalance defect, and misalignment defect. The use of the fuzzy approaches and the classification results are discussed. Results show that these approaches yield high successful rates of vibration default classification.

Keywords: fault diagnosis, fuzzy classification k-nearest neighbor, vibration

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Authors: Benyamin Ahmadnia, Javier Serrano

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In this paper we compare two different approaches for translating from Persian to Spanish, as a language pair with scarce parallel corpus. The first approach involves direct transfer using an statistical machine translation system, which is available for this language pair. The second approach involves translation through English, as a pivot language, which has more translation resources and more advanced translation systems available. The results show that, it is possible to achieve better translation quality using English as a pivot language in either approach outperforms direct translation from Persian to Spanish. Our best result is the pivot system which scores higher than direct translation by (1.12) BLEU points.

Keywords: statistical machine translation, direct translation approach, pivot language translation approach, parallel corpus

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Authors: Yuanchao Sang, Zhihao Gong, Longsheng Chen, Long Chen

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Clothing fashion represents a human’s aesthetic appreciation towards everyday outfits and appetite for fashion, and it reflects the development of status in society, humanity, and economics. However, modelling fashion by machine is extremely challenging because fashion is too abstract to be efficiently described by machines. Even human beings can hardly reach a consensus about fashion. In this paper, we are dedicated to answering a fundamental fashion-related problem: what image feature best describes clothing fashion? To address this issue, we have designed and evaluated various image features, ranging from traditional low-level hand-crafted features to mid-level style awareness features to various current popular deep neural network-based features, which have shown state-of-the-art performance in various vision tasks. In summary, we tested the following 9 feature representations: color, texture, shape, style, convolutional neural networks (CNNs), CNNs with distance metric learning (CNNs&DML), AutoEncoder, CNNs with multiple layer combination (CNNs&MLC) and CNNs with dynamic feature clustering (CNNs&DFC). Finally, we validated the performance of these features on two publicly available datasets. Quantitative and qualitative experimental results on both intra-domain and inter-domain fashion clothing image retrieval showed that deep learning based feature representations far outweigh traditional hand-crafted feature representation. Additionally, among all deep learning based methods, CNNs with explicit feature clustering performs best, which shows feature clustering is essential for discriminative fashion feature representation.

Keywords: convolutional neural network, feature representation, image processing, machine modelling

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: So-Young Lee, Myoung-Won Oh, Soon-Cheol Eom, Yeon-Won Suh

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Buildings produce more than one thirds of the total energy consumption and CO₂ emissions. Korean government agency pronounced and initiated Zero Energy Buildings policy for construction as of 2025. The net zero energy design features include passive (daylight, layout, materials, insulation, finishes, etc.) and active (renewable energy sources) elements. The Zero Energy House recently built in Nowon-gu, Korea is provided for 121 households as a public rental housing complex. However most of public rental housing did not include sustainable features which can reduce housing maintaining cost significantly including energy cost. It is necessary to implement net zero design features to the obsolete public rental housing during the remodeling procedure since it can reduce housing cost in long term. The purpose of this study is to investigate sustainable design elements implemented in Net Zero Energy House in Korea and passive and active housing design features in order to apply the sustainable features to the case public rental apartment for remodeling. Housing complex cases in this study are Nowan zero Energy house, Gangnam Bogemjari House, and public rental housings built in more than 20 years in Seoul areas. As results, energy consumption in public rental housing built in 5-years can be improved by exterior surfaces. Energy optimizing in case housing built in more than 20 years can be enhanced by renovated materials, insulation, replacement of windows, exterior finishes, lightings, gardening, water, renewable energy installation, Green IT except for sunlight and layout of buildings. Further life costing analysis is needed for energy optimizing for case housing alternatives.

Keywords: affordable housing, remodeling, sustainable design, zero-energy house

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: clusters of microcalcifications, ductal carcinoma in situ, least-square support vector machine, particle swarm optimization

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

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This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems.

Keywords: isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW

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Authors: Ramy Hammady, Sylvester Arnab

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Significant attention has been directed to adopt game interventions practically to change certain behaviours in many disciplines such as health, education, psychology through many years. That’s due to the intrinsic motivation that games can cause and the substantial impact the games can leave on the player. Many review papers were induced to highlight and measure the effectiveness of the game’s interventions on changing behaviours; however, most of these studies neglected the game design process itself and the game features and elements that can stimuli changing behaviours. Therefore, this paper aims to identify the most game design mechanics and features that are the most influencing on changing behaviour during or after games interventions. This paper also sheds light on the theories of changing behaviours that clearly can led the game design process. This study gives directions to game designers to spot the most influential game features and mechanics for changing behaviour games in order to exploit it on the same manner.

Keywords: behaviour change, game design, serious gaming, gamification, review

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Lavanya Srinivasan

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Biometric identification is to identify unique features in a person like fingerprints, iris, ear, and voice recognition that need the subject's permission and physical contact. Gait biometric is used to identify the unique gait of the person by extracting moving features. The main advantage of gait biometric to identify the gait of a person at a distance, without any physical contact. In this work, the gait biometric is used for person re-identification. The person walking naturally compared with the same person walking with bag, coat, and case recorded using longwave infrared, short wave infrared, medium wave infrared, and visible cameras. The videos are recorded in rural and in urban environments. The pre-processing technique includes human identified using YOLO, background subtraction, silhouettes extraction, and synthesis Gait Entropy Image by averaging the silhouettes. The moving features are extracted from the Gait Entropy Energy Image. The extracted features are dimensionality reduced by the principal component analysis and recognised using different classifiers. The comparative results with the different classifier show that linear discriminant analysis outperforms other classifiers with 95.8% for visible in the rural dataset and 94.8% for longwave infrared in the urban dataset.

Keywords: biometric, gait, silhouettes, YOLO

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Authors: Boudemagh Naime

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Automatic radar detection requires some methods of adapting to variations in the background clutter in order to control their false alarm rate. The problem becomes more complicated in non-Gaussian environment. In fact, the conventional approach in real time applications requires a complex statistical modeling and much computational operations. To overcome these constraints, we propose another approach based on artificial neural network (ANN-CMLD-CFAR) using a Back Propagation (BP) training algorithm. The considered environment follows a log-normal distribution in the presence of multiple Rayleigh-targets. To evaluate the performances of the considered detector, several situations, such as scale parameter and the number of interferes targets, have been investigated. The simulation results show that the ANN-CMLD-CFAR processor outperforms the conventional statistical one.

Keywords: radat detection, ANN-CMLD-CFAR, log-normal clutter, statistical modelling

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Authors: Fatih Sulak, Betül A. Özdemir, Beyza Bozdemir

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In order to improve symmetric key research, several competitions had been arranged by organizations like National Institute of Standards and Technology (NIST) and International Association for Cryptologic Research (IACR). In recent years, the importance of authenticated encryption has rapidly increased because of the necessity of simultaneously enabling integrity, confidentiality and authenticity. Therefore, at January 2013, IACR announced the Competition for Authenticated Encryption: Security, Applicability, and Robustness (CAESAR Competition) which will select secure and efficient algorithms for authenticated encryption. Cryptographic algorithms are anticipated to behave like random mappings; hence, it is important to apply statistical randomness tests to the outputs of the algorithms. In this work, the statistical randomness tests in the NIST Test Suite and the other recently designed randomness tests are applied to six second round algorithms of the CAESAR Competition. It is observed that AEGIS achieves randomness after 3 rounds, Ascon permutation function achieves randomness after 1 round, Joltik encryption function achieves randomness after 9 rounds, Morus state update function achieves randomness after 3 rounds, Pi-cipher achieves randomness after 1 round, and Tiaoxin achieves randomness after 1 round.

Keywords: authenticated encryption, CAESAR competition, NIST test suite, statistical randomness tests

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Authors: Zin Mar Lwin

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Brain Computer Interface (BCI) Systems have developed for people who suffer from severe motor disabilities and challenging to communicate with their environment. BCI allows them for communication by a non-muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which is recorded from the human„s brain by means of an electrode. The electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human‟s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Dataset from “Purdue University.” Independent Component Analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non-stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been carried out for both 1-against-1 and 1-against-all methods.

Keywords: BCI, EEG, ICA, SVM

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Authors: Masoud Sheidai

Abstract:

Biologists with a special field of population genetics and phylogeny have different research tasks such as populations’ genetic variability and divergence, species relatedness, the evolution of genetic and morphological characters, and identification of DNA SNPs with adaptive potential. To tackle these problems and reach a concise conclusion, they must use the proper and efficient statistical and bioinformatic methods as well as suitable genetic and morphological characteristics. In recent years application of different bioinformatic and statistical methods, which are based on various well-documented assumptions, are the proper analytical tools in the hands of researchers. The species delineation is usually carried out with the use of different clustering methods like K-means clustering based on proper distance measures according to the studied features of organisms. A well-defined species are assumed to be separated from the other taxa by molecular barcodes. The species relationships are studied by using molecular markers, which are analyzed by different analytical methods like multidimensional scaling (MDS) and principal coordinate analysis (PCoA). The species population structuring and genetic divergence are usually investigated by PCoA and PCA methods and a network diagram. These are based on bootstrapping of data. The Association of different genes and DNA sequences to ecological and geographical variables is determined by LFMM (Latent factor mixed model) and redundancy analysis (RDA), which are based on Bayesian and distance methods. Molecular and morphological differentiating characters in the studied species may be identified by linear discriminant analysis (DA) and discriminant analysis of principal components (DAPC). We shall illustrate these methods and related conclusions by giving examples from different edible and medicinal plant species.

Keywords: GWAS analysis, K-Means clustering, LFMM, multidimensional scaling, redundancy analysis

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Authors: Bushra Zafar, Usman Qamar

Abstract:

Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: P. Selyshchev

Abstract:

We consider fluctuations of defects density taking into account their interaction. Stochastic field of displacement generation rate gives random defect distribution. We determinate statistical characteristics (mean and dispersion) of random field of point defect distribution as function of defect generation parameters, temperature and properties of irradiated crystal.

Keywords: irradiation, primary defects, interaction, fluctuations

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Authors: Hassan Sabo

Abstract:

Language is a system of conventionally spoken, manual and written symbols by human beings that members of a certain social group and participants in its culture express themselves. Communication, expression of identity and imaginative expression are among the functions of language. Dialect is a form of language, or a regional variety of language that is spoken in a particular geographical setting by a particular group of people. Hausa is one of the major languages in Africa, in terms of large number of people for whom it is the first language. Hausa is one of the western Chadic groups of languages. It constitutes one of the five or six branches of Afro-Asiatic family. The predominant Hausa speakers are in Nigeria and they live in different geographical locations which resulted to variety of dialects within the Hausa language apart of the standard Hausa language, the Hausa language has a variety of dialect that distinguish from one another by such features as phonology, grammar and vocabulary. This study intends to examine such features that serve as means of identification among Hausa speakers who are set off from others, geographically or socially.

Keywords: dialect, features, geographical location, Hausa language

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Authors: Khaled M. Alhawiti

Abstract:

This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: speech recognition, acoustic phonetic, artificial intelligence, hidden markov models (HMM), statistical models of speech recognition, human machine performance

Procedia PDF Downloads 478