فهرس المقالات Adaboost

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  1 - Performance Analysis of Hybrid SOM and AdaBoost Classifiers for Diagnosis of Hypertensive Retinopathy
  Wiharto Wiharto Esti Suryani Murdoko Susilo
  10.52547/jist.9.34.79
  The diagnosis of hypertensive retinopathy (CAD-RH) can be made by observing the tortuosity of the retinal vessels. Tortuosity is a feature that is able to show the characteristics of normal or abnormal blood vessels. This study aims to analyze the performance of the CAD أکثر

  The diagnosis of hypertensive retinopathy (CAD-RH) can be made by observing the tortuosity of the retinal vessels. Tortuosity is a feature that is able to show the characteristics of normal or abnormal blood vessels. This study aims to analyze the performance of the CAD-RH system based on feature extraction tortuosity of retinal blood vessels. This study uses a segmentation method based on clustering self-organizing maps (SOM) combined with feature extraction, feature selection, and the ensemble Adaptive Boosting (AdaBoost) classification algorithm. Feature extraction was performed using fractal analysis with the box-counting method, lacunarity with the gliding box method, and invariant moment. Feature selection is done by using the information gain method, to rank all the features that are produced, furthermore, it is selected by referring to the gain value. The best system performance is generated in the number of clusters 2 with fractal dimension, lacunarity with box size 22-29, and invariant moment M1 and M3. Performance in these conditions is able to provide 84% sensitivity, 88% specificity, 7.0 likelihood ratio positive (LR+), and 86% area under the curve (AUC). This model is also better than a number of ensemble algorithms, such as bagging and random forest. Referring to these results, it can be concluded that the use of this model can be an alternative to CAD-RH, where the resulting performance is in a good category. تفاصيل المقالة
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  2 - ارایه یک مدل هوشمند به‌منظور تشخیص چندوجهی شخصیت کاربران با استفاده از روش‌های یادگیری ژرف
  حسین صدر فاطمه محدث دیلمی مرتضی ترخان
  با توجه به رشد قابل‌توجه اطلاعات و داده‌های متنی که توسط انسان‌ها در شبکه‌های ‌مجازی تولید می‌شوند، نیاز به سیستم‌هایی است که بتوان به کمک آن‌ها به‌صورت خودکار به تحلیل داده‌ها پرداخت و اطلاعات مختلفی را از آن‌ها استخراج کرد. یکی از مهم‌ترین داده‌های متنی موجود در سطح و أکثر

  با توجه به رشد قابل‌توجه اطلاعات و داده‌های متنی که توسط انسان‌ها در شبکه‌های ‌مجازی تولید می‌شوند، نیاز به سیستم‌هایی است که بتوان به کمک آن‌ها به‌صورت خودکار به تحلیل داده‌ها پرداخت و اطلاعات مختلفی را از آن‌ها استخراج کرد. یکی از مهم‌ترین داده‌های متنی موجود در سطح وب دید‌گاه‌های افراد نسبت به یک موضوع مشخص است. متن‌های منتشرشده توسط کاربران در فضای مجازی می‌تواند معرف شخصیت آن‌ها باشد. الگوریتم‌های یادگیری ماشین می‌تواند انتخاب مناسبی برای تجزیه‌و‌تحلیل این‌گونه مسائل باشند، اما به‌منظور غلبه بر پیچیدگی و پراکندگی محتوایی و نحوی داده‌ها نیاز به الگوریتم‌های یادگیری ژرف بیش از پیش در این حوزه احساس می‌شود. در این راستا، هدف این مقاله به‌کارگیری الگوریتم‌های یادگیری ژرف به‌منظور دسته‌بندی متون برای پیش‌بینی شخصیت می‌باشد. برای رسیدن به این هدف، شبکه عصبی کانولوشنی با مدل آدابوست به‌منظور دسته‌بندی داده‌ها ترکیب گردید تا بتوان به کمک آن داده‌های آزمایشی که با خطا دسته‌بندی ‌شده‌اند را در مرحله دوم دسته‌بندی با اختصاص ضریب آلفا، با دقت بالاتری دسته‌بندی کرد. مدل پیشنهادی این مقاله روی دو مجموعه داده ایزیس و یوتیوب آزمایش شد و بر اساس نتایج بدست آمده مدل پیشنهادی از دقت بالاتری نسبت به سایر روش‌های موجود روی هر دو مجموعه داده برخودار است. تفاصيل المقالة
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  3 - Ensemble learning of daboosting based on deep weighting for classification of hand-written numbers in Persian
  amir asil hamed Alipour Shahram mojtahedzadeh hasan Asil
  10.61186/jist.41053.12.46.162
  Today, the hand-written data volume is huge, which prohibits these data from being manually converted into electronic files. During the past years, different types of solutions were developed to convert machine learning-based handwritten data. Each method classifies or أکثر

  Today, the hand-written data volume is huge, which prohibits these data from being manually converted into electronic files. During the past years, different types of solutions were developed to convert machine learning-based handwritten data. Each method classifies or clusters the data according to the data type and application. In the present paper, a new approach is presented based on compound methods and deep learning for the classification of Persian handwritten data, where a deeper investigation is made of the data in basic learning by combining the Ada boosting and convolution. The present study aims at providing a new technique for classification of the images of handwritten Persian numbers. The structure of this technique is founded on Ada Boosting, which in turn, is based on weak learning. This technique improves learning by iteration of the weak learning processes and updating weights. In the meantime, the proposed method tried to employ stronger learners and present a stronger algorithm by combining these strong learners. The method was assessed on the standard Hoda dataset containing 60000 training data. The results show that the proposed method has a lower error rate than the previous methods by more than 1%. In the future, by developing basic learner, new mechanisms can be provided to improve the results by new types of learning. – Today, the hand-written data volume is huge, which prohibits these data from being manually converted into electronic files. During the past years, different types of solutions were developed to convert machine learning-based handwritten data. Each method classifies or clusters the data according to the data type and application. In the present paper, a new approach is presented based on compound methods and deep learning for the classification of Persian handwritten data, where a deeper investigation is made of the data in basic learning by combining the Ada boosting and convolution. The method was assessed on the standard Hoda dataset containing 60000 training data. The results showed that the error rate of the method has decreased by more than 1% compared to the previous methods. تفاصيل المقالة