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حرية الوصول المقاله
1 - Recognizing the Emotional State Changes in Human Utterance by a Learning Statistical Method based on Gaussian Mixture Model
Reza Ashrafidoost Saeed Setayeshi Arash SharifiSpeech is one of the most opulent and instant methods to express emotional characteristics of human beings, which conveys the cognitive and semantic concepts among humans. In this study, a statistical-based method for emotional recognition of speech signals is proposed, أکثرSpeech is one of the most opulent and instant methods to express emotional characteristics of human beings, which conveys the cognitive and semantic concepts among humans. In this study, a statistical-based method for emotional recognition of speech signals is proposed, and a learning approach is introduced, which is based on the statistical model to classify internal feelings of the utterance. This approach analyzes and tracks the emotional state changes trend of speaker during the speech. The proposed method classifies utterance emotions in six standard classes including, boredom, fear, anger, neutral, disgust and sadness. For this purpose, it is applied the renowned speech corpus database, EmoDB, for training phase of the proposed approach. In this process, once the pre-processing tasks are done, the meaningful speech patterns and attributes are extracted by MFCC method, and meticulously selected by SFS method. Then, a statistical classification approach is called and altered to employ as a part of the method. This approach is entitled as the LGMM, which is used to categorize obtained features. Aftermath, with the help of the classification results, it is illustrated the emotional states changes trend to reveal speaker feelings. The proposed model also has been compared with some recent models of emotional speech classification, in which have been used similar methods and materials. Experimental results show an admissible overall recognition rate and stability in classifying the uttered speech in six emotional states, and also the proposed algorithm outperforms the other similar models in classification accuracy rates. تفاصيل المقالة -
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2 - Sports Result Prediction Based on Machine Learning and Computational Intelligence Approaches: A Survey
Milad Keshtkar Langaroudi Mohammadreza YamaghaniIn the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently أکثرIn the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently realized the abundant science available in their data and sought to take advantage of that science through the use of data mining techniques. Sports data mining assists coaches and managers in result prediction, player performance assessment, player injury prediction, sports talent Identification and game strategy evaluation. Predicting the results of sports matches is interesting to many, from fans to punters. It is also interesting as a research problem, in part due to its difficulty: the result of a sports match is dependent on many factors, such as the morale of a team (or a player), skills, coaching strategy, etc. So even for experts, it is very hard to predict the exact results of individual matches. The present study reviews previous research on data mining systems to predict sports results and evaluates the advantages and disadvantages of each system. تفاصيل المقالة -
حرية الوصول المقاله
3 - A Novel Fuzzy-C Means Image Segmentation Model for MRI Brain Tumor Diagnosis
Aref Safari Danial Barazandeh Seyed Ali Khalegh PourAccurate segmentation of brain tumor plays a key role in the diagnosis of brain tumor. Preset and precise diagnosis of Magnetic Resonance Imaging (MRI) brain tumor is enormously significant for medical analysis. During the last years many methods have been proposed. In أکثرAccurate segmentation of brain tumor plays a key role in the diagnosis of brain tumor. Preset and precise diagnosis of Magnetic Resonance Imaging (MRI) brain tumor is enormously significant for medical analysis. During the last years many methods have been proposed. In this research, a novel fuzzy approach has been proposed to classify a given MRI brain image as normal or cancer label and the intensity of the disease. The applied method first employed feature selection algorithms to extract features from images, and then followed by applying a median filter to reduce the dimensions of features. The brain MRI offers a valuable method to perform pre-and-post surgical evaluations, which are keys to define procedures and to verify their effects. The reduced dimension was submitted to a diagnosis algorithm. We retrospectively investigated a total of 19 treatment plans, each of whom has CT simulation and MRI images acquired during pretreatment. The dose distributions of the same treatment plans were calculated on original CT simulation images as ground truth, as well as on pseudo CT images generated from MRI images. The simulation results demonstrate that the proposed algorithm is promising. تفاصيل المقالة -
حرية الوصول المقاله
4 - چهارچوب استخراج ویژگی سیستم تشخیص محتوای تصاویر بر اساس آنالیز تبدیل چرخشی و اطلاعات مکانی
شاهین شافعی حمید وحدتی توحید صدقی اصغر چرمینیک چارچوب جدید استخراج ویژگی برای بازیابی تصاویر ارائه می‌شود. این سیستم برای آنالیز تبدیل چرخشی طراحی شده است. از توصیفگرهای آماری در دامنه فرکانسی سیگنال استفاده می کند. تبدیل چرخشی توسط یک الگوریتم نیمه نظارتی محاسبه می شود که این الگوریتم بسیار ساده و موثر است. ویژگ أکثریک چارچوب جدید استخراج ویژگی برای بازیابی تصاویر ارائه می‌شود. این سیستم برای آنالیز تبدیل چرخشی طراحی شده است. از توصیفگرهای آماری در دامنه فرکانسی سیگنال استفاده می کند. تبدیل چرخشی توسط یک الگوریتم نیمه نظارتی محاسبه می شود که این الگوریتم بسیار ساده و موثر است. ویژگی های استخراجی انرژی و بردار نرم-1 هستند که از بخش های مختلف صفحه دو قطبی فرکانسی بدست می آید. این ویژگی های بازدهی مناسبی را به ارمغان می اورند. مضافا بر اینکه این مقاله چارچوبی جدید دیگری برای ویژگی های مکانی ارائه می دهد که منجر به افزایش مجدد درصد بازیابی در پایگاه داده تصاویر رنگی می‌شود. طلاعات مکان توسط ماتریس میدان نزولی بدست می آید. ترکیب معنادار ویژگی بافتی با اطلاعات طیفی منجر به ویژگی مقاوم در مقابل چرخش و مقیاس بندی می‌شود. نتایج آزمایشات بر روی پایگاه داده مقیاس بزرگ با 10000 تصویر، تضمین کننده کارایی سیسیتم پیشنهادی است. در بلوک بازیابی جهت اندازه گیری فاصله از فاصله کانبرا و مینکوفسکی استفاده شده است. نتایج نهایی سیستم با چند روش قبلی مقایسه می‌شود که در تمامی کلاسها روش پیشنهادی عملکرد مناسبی دارد که در حدود 10 درصد باعث بهبود نتایج می‌شود. تفاصيل المقالة -
حرية الوصول المقاله
5 - The Effectiveness of the Automatic System of Fuzzy Logic-Based Technical Patterns Recognition: Evidence from Tehran Stock Exchange
Abdolmajid Abdolbaghi Ataabadi Sayyed Mohammad Reza Davoodi Mohammad Salimi BaniThe present research proposes an automatic system based on moving average (MA) and fuzzy logic to recognize technical analysis patterns including head and shoulder patterns, triangle patterns and broadening patterns in the Tehran Stock Exchange. The automatic system was أکثرThe present research proposes an automatic system based on moving average (MA) and fuzzy logic to recognize technical analysis patterns including head and shoulder patterns, triangle patterns and broadening patterns in the Tehran Stock Exchange. The automatic system was used on 38 indicators of Tehran Stock Exchange within the period 2014-2017 in order to evaluate the effectiveness of technical patterns. Having compared the conditional distribution of daily returns under the condition of the discovered patterns and the unconditional distribution of returns at various levels of confidence driven from fuzzy logic with the mean returns of all normalized market indicators, we observed that in the desired period, after recognizing the pattern, all patterns investigated at the confidence level 0.95 with a fuzzy point 0.5 contained useful information, practically leading to abnormal returns. تفاصيل المقالة -
حرية الوصول المقاله
6 - Geochemical pattern recognition for Cu-Au Deposit Based on Self-Organizing Map (SOM) and Fuzzy K-means Clustering (FKMC) in Meshginshahr, NW of Iran
Aynur NasseriMapping the mineralized zones and providing an appropriate distribution pattern of elements for characterizing geochemical system and targeting potentially promising areas of Cu-Au mineralization by utilizing an adequate technique and establishing an optimized explorati أکثرMapping the mineralized zones and providing an appropriate distribution pattern of elements for characterizing geochemical system and targeting potentially promising areas of Cu-Au mineralization by utilizing an adequate technique and establishing an optimized exploration tool is the main object of this study in Meshginshahr, NW of Iran. In this respect 144 stream sediments samples were collected and analyzed for Au, Ba, Bi, Cd, Ce, Co, Cr, Cu, Hg, Mo, Ag, As, Sn, Sb, W and Pb. In this study, self-organizing map (SOM) and Fuzzy K-means clustering (FKMC) approaches with the aim of pattern recognition were employed. The SOM as a dimension reduction approach was introduced to recognize geochemical dispersion patterns with high certainty while preserving the originality of data.. During data processing, SOM appropriate structure with a pattern including six clusters was selected and the related elements distribution model was extracted. Results represent two significant sets of elements in clusters for anticipating the mechanism of distribution. In this target pattern, copper and pertaining trace elements formation are localized in the north of the area. Also, Au Anomalies and its associated elements are mostly elongated from NW to SW of the area. To evaluate the SOM results, a comparative study was carried out with the results obtained from Fuzzy K-means clustering (FKMC). FKMC performance showed the proper compliance with the SOM results with respect to the relationship between the elements and their corresponding membership’s probabilities in different clusters. The results illustrated higher performance of the approaches in characterizing geochemical pattern and detecting the element paragenetic sequence in the area for locating the exploration targets.. تفاصيل المقالة -
حرية الوصول المقاله
7 - A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting
Inyeneobong Ekoi Edem Sunday Ayoola Oke Kazeem Adekunle AdebiyiIndustrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions. The scope of this research domain continues to expand due to the continuous knowledge ignition motivated by scholars in th أکثرIndustrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions. The scope of this research domain continues to expand due to the continuous knowledge ignition motivated by scholars in the area. So, more intelligent and intellectual contributions on current research issues in the accident domain will potentially spark more lively academic, value-added discussions that will be of practical significance to members of the safety community. In this communication, a new grey–fuzzy–Markov time series model, developed from nondifferential grey interval analytical framework has been presented for the first time. This instrument forecasts future accident occurrences under time-invariance assumption. The actual contribution made in the article is to recognise accident occurrence patterns and decompose them into grey state principal pattern components. The architectural framework of the developed grey–fuzzy–Markov pattern recognition (GFMAPR) model has four stages: fuzzification, smoothening, defuzzification and whitenisation. The results of application of the developed novel model signify that forecasting could be effectively carried out under uncertain conditions and hence, positions the model as a distinctly superior tool for accident forecasting investigations. The novelty of the work lies in the capability of the model in making highly accurate predictions and forecasts based on the availability of small or incomplete accident data. تفاصيل المقالة -
حرية الوصول المقاله
8 - Presenting a novel approach for estimation the compressive strength of high strength concrete using ANN & GEP
Seyed Azim HosseiniIn this article, the application of artificial neural networks in predicting the degree of concrete compressive strength of High Strength Concrete (HSC) was investigated. For this purpose, use was made of the pattern recognition neural network and the obtained data from أکثرIn this article, the application of artificial neural networks in predicting the degree of concrete compressive strength of High Strength Concrete (HSC) was investigated. For this purpose, use was made of the pattern recognition neural network and the obtained data from the experimental tests for predicting the compressive strength degree of HSC. Five inputs from the HSC mix design were utilized for predicting the degree of compressive strength, by application of the scaled conjugate gradient backpropagation algorithm in neural network. The outputs were classified into 5 strength groups of M1, M2, M3, M4 and M5. The simulation results shows 97.9% accuracy in classifying the different predefined degrees of HSC using the confusion matrix diagram. Moreover, the cross-entropy error obtained from testing the neural network (NN) model and correlation coefficient (R2) of GEP for predicting compressive strength of the HSC were evaluated at 0.042096 and 0.9795, respectively, indicating high accuracy of the model. Application of this model could greatly help the persons, companies and research centers in terms of preparation and making of HSC with desired compressive strength, that are in need of this type of concrete. تفاصيل المقالة
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