انجمن کامپیوتر ایران

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عنوان مقاله نویسنده(ها) مربوط به کنفرانس چکیده خرید مقاله
Atefeh Torkaman, Nasrollah Moghaddam Charkari, Mahnaz Aghaeipour
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
Classification is a well known task in data mining and machine learning that aims to predict the class of items as accurately as possible. A well planned data classification system makes essential data easy to find. An object is classified into one of the categories called classes according to the features that well separated the classes. Actually, classification maps an object to its classification label. Many researches used different learning algorithms to classify data; neural networks, decision trees, etc. In this paper, a new classification approach based on cooperative game is proposed. Cooperative game is a branch of game theory consists of a set of players and a characteristic function which specifies the value created by different subsets of the players in the game. In order to find classes in classification process, objects can be imagine as the players in a game and according to the values which obtained by these players, classes will be separated. This approach can be used to classify a population according to their contributions. In the other words, it applies equally to different types of data. Through out this paper, a special case in medical diagnosis was studied. 304 samples taken from human leukemia tissue consists of 17 attributes which determine different CD markers related to leukemia were analyzed. These samples collected from different types of leukemia at Iran Blood Transfusion Organization (IBTO). Obtained results demonstrate that cooperative game is very promising to use directly for classification.
S.A. Hosseini Amereii, M.M. Homayounpour
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
Two popular and better performing approaches to language Identification (LID) are Phone Recognition followed by Language Modeling (PRLM) and Parallel PRLM. In this paper, we report several improvements in Phone Recognition which reduces error rate in PRLM and PPRLM based LID systems. In our previous paper, we introduced APRLM approach that reduces error rate for about 1.3% in LID tasks. In this paper, we suggest other solution that overcomes APRLM. This new LID approach is named Generalized PRLM or GPRLM. Several language identification experiments were conducted and the proposed improvements were evaluated using OGI-MLTS corpus. Our results show that GPRLM overcomes PPRLM and APRLM about 2.5% and 1.2% respectively in two language classification tasks.
A. Mehdi, R. R. Ggholam-ali
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
This paper estimates and segments the moving objects based on center of mass model to decrease the search window and to provide a new algorithm, which achieves an accurate and rapid tracking. Furthermore, a novel method is proposed to update the template size adaptively by using estimation and segmentation of moving objects. The estimated results of moving target is transformed to wavelet domain and target tracking is performed in that domain. To improve the algorithm center of mass model is performed in wavelet domain. By using kalman predictor and thresholding method, a new approach is presented for object tracking failure and recovery.
Rezvan Kianifar, Farzad Towhidkhah
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
Human can determine optimal behaviors which depend on the ability to make planned and adaptive decisions. In this paper, we have proposed a predictive structure based on neuropsychological evidences to model human decision making process by concentrating on the role of frontal brain regions which are responsible for predictive control of human behavior. We have considered a model-based reinforcement learning framework to implement the relations between these brain areas. Finally, we have designed an experimental test to compare the function of model with human behavior in a maze task. Our results reveal that there is more than reward and punishment in human behavior, and considering higher cognitive functions such as prediction will help to have more reliable models which could better describe human behavior.
زهرا افصحی
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
مهدی اکرمی, محمدرضا رزازی
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
محمد طاهر پیله‌ور, هشام فیلی
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
نستوه طاهری جوان, آرش نصیری اقبالی
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
فهیمه فتاح‌پور, خشایار یغمایی
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
مریم سنقرزاده, راهبه نیارکی اصل
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
حوا علیزاده نوقابی, فرزانه غیور باغبانی
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
سیدمحمد بیدکی, محمد هادی صدرالدینی, منصور ذوالقدری, نادعلی محمودی کهن
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
جواد محبی نجم‌آباد, هادی آدینه, حسین دلداری
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
اباذر برزگر, مصطفی جهانگیر, محمد حسن بیات
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
مسعود بشیری, سعید شیری قیداری
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
محمد حسین‌زاده مقدم, علیرضا باقری, علی صفری ممقانی
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
سعید احمدی ارزیل, محمد علی جبرئیل جمالی, مصطفی حقی‌فام
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
اسماء شمسی, حسین نظام‌آبادی‌پور, سعید سریزدی, احسان‌اله کبیر
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
معصومه صادقی, نسرین دسترنج ممقانی, فریبرز موسوی
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
احسان وجدانی محمودی, فاطمه سعادتمند, مسعود نیازی ترشیز, قمرناز تدین تبریزی
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
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