حمید پروین
دانشگاه آزاد اسلامی واحد نورآباد ممسنی - دانشکده فنی و مهندسی|دانشگاه آزاد اسلامی واحد نورآباد ممسنی - باشگاه پژوهشگران جوان و نخبگان
[ 1 ] - بهبود حافظه برای حل مسئله زمان‌بندی کار کارگاهی پویا
وقتی با یک جهان در حال تغییر مواجه می‌شوید، انسان‌ها نه‌تنها به آینده بلکه به گذشته هم توجه می‌کنند. توجه کردن به راه‌حل‌های مشابه، به ما در تصمیم‌گیری در آینده کمک می‌کند. زمانی‌که با وضعیتی روبرو می‌شویم که قبلاً آن را تجربه کرده باشیم بهتر می‌توانیم با آن روبرو شویم. اگر در حل مسائل بهینهسازی با ماهیتی پویا در هنگام جستجو، از اطلاعات گذشته داخل بهینهسازی و یادگیری استفاده شود، می‌تواند به ف...
[ 2 ] - ارائه روشی مبتنی بر پوشش سراسری و تخمین اتفاق آرا برای بهبود کارایی در شبکه حسگر بی‌سیم
شبکه‌های حسگر بی‌سیم (WSNها) از تعداد زیادی سنسور تشکیل شده است که دارای قابلیت‌هایی مانند حسگری، محاسبات، و برقراری ارتباط می‌باشند. توان باتری یک منبع مهم شبکه حسگر بی‌سیم می‌باشد. بنابراین عملکرد مؤثر شبکه حسگر بی‌سیم بستگی به استفاده بهینه از منبع باتری دارد. شبکه‌های حسگر بی‌سیم به‌طور معمول دارای محدودیت مصرف انرژی هستند. طراحی پروتکل مسیریابی مناسب به‌طور قابل توجهی می‌تواند مصرف انرژی د...
[ 3 ] - الگوریتم ژنتیک آشوب گونه مبتنی بر حافظه و خوشه بندی برای حل مسائل بهینه سازی پویا
چکیده: اکثر مسائل موجود در دنیای واقعی یک مسئله بهینهسازی با ماهیتی پویا هستند، به‌طوری‌که مقدار بهینه سراسری آنها در طول زمان ممکن است تغییر کند، بنابراین برای حل این مسائل الگوریتمهایی نیاز داریم که بتوانند خود را با شرایط این مسائل بهخوبی سازگار نموده و بهینه جدید را برای این مسائل ردیابی نمایند. در این مقاله، یک الگوریتم ژنتیک آشوبگونه مبتنی بر خوشهبندی و حافظه برای حل مسائل پویا ارائ...
[ 4 ] - Optimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines
In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...
[ 5 ] - Fault Identification using end-to-end data by imperialist competitive algorithm
Faults in computer networks may result in millions of dollars in cost. Faults in a network need to be localized and repaired to keep the health of the network. Fault management systems are used to keep today’s complex networks running without significant cost, either by using active techniques or passive techniques. In this paper, we propose a novel approach based on imperialist competitive alg...
[ 6 ] - Classifier Ensemble Framework: a Diversity Based Approach
Pattern recognition systems are widely used in a host of different fields. Due to some reasons such as lack of knowledge about a method based on which the best classifier is detected for any arbitrary problem, and thanks to significant improvement in accuracy, researchers turn to ensemble methods in almost every task of pattern recognition. Classification as a major task in pattern recognition,...
[ 7 ] - Chaotic Genetic Algorithm based on Explicit Memory with a new Strategy for Updating and Retrieval of Memory in Dynamic Environments
Many of the problems considered in optimization and learning assume that solutions exist in a dynamic. Hence, algorithms are required that dynamically adapt with the problem’s conditions and search new conditions. Mostly, utilization of information from the past allows to quickly adapting changes after. This is the idea underlining the use of memory in this field, what involves key design issue...
[ 8 ] - Improvement of Routing Operation Based on Learning with Using Smart Local and Global Agents and with the Help of the Ant Colony Algorithm
Routing in computer networks has played a special role in recent years. The cause of this is the role of routing in a performance of the networks. The quality of service and security is one of the most important challenges in routing due to lack of reliable methods. Routers use routing algorithms to find the best route to a particular destination. When talking about the best path, we consider p...
[ 9 ] - ارائه یک الگوریتم چندجمعیتی مبتنی بر ازدحام ذرات برای حل مسائل بهینهسازی پویا
بسیاری از مسائل بهینهسازی در دنیای واقعی پویا می‌باشند. در این مسائل بهینه سراسری و بهینه‌های محلی در طول زمان تغییر می‌کنند. نشان داده‌شده که استفاده از الگوریتم‌های یادگیر تقلید از طبیعت برای مواجهه با این مسائل مناسب هستند. در میان الگوریتم‌های مختلف بهینهسازی برای محیط‌های پویا در سال‌های اخیر الگوریتم بهینهسازی گروه ذرات توجه زیادی را به خود جلب کرده است. در این مقاله یک الگوریتم مبتنی ...
[ 10 ] - ارائه روشی برای استخراج کلمات کلیدی و وزن‌دهی کلمات برای بهبود طبقه‌بندی متون فارسی
Due to ever-increasing information expansion and existing huge amount of unstructured documents, usage of keywords plays a very important role in information retrieval. Because of a manually-extraction of keywords faces various challenges, their automated extraction seems inevitable. In this research, it has been tried to use a thesaurus, (a structured word-net) to automatically extract them. A...
[ 11 ] - ارائه روشی جدید برای شاخص‌گذاری خودکار و استخراج کلمات کلیدی برای بازیابی اطلاعات و خوشهبندی متون
Persian words in writing with a diverse and cover all modes of grammatical words with the recruitment of a series of specific rules because it is impossible to extract keywords automatically from Persian texts difficult and complex. This thesis has attempted to use linguistic information and thesaurus, keywords Mnatry be provided. Using the symbol system is structured network can be keywords, i...
[ 12 ] - خوشهبندی ترکیبی مبتنی بر زیرمجموعه‌ای از خوشه‌های اولیه
Most of the recent studies have tried to create diversity in primary results and then applied a consensus function over all the obtained results to combine the weak partitions. In this paper a clustering ensemble method is proposed which is based on a subset of primary clusters. The main idea behind this method is using more stable clusters in the ensemble. The stability is applied as a goodnes...
[ 13 ] - Optimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines
In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...
[ 14 ] - Fault Identification using end-to-end data by imperialist competitive algorithm
Faults in computer networks may result in millions of dollars in cost. Faults in a network need to be localized and repaired to keep the health of the network. Fault management systems are used to keep today’s complex networks running without significant cost, either by using active techniques or passive techniques. In this paper, we propose a novel approach based on imperialist competitive alg...
[ 15 ] - Improvement of Routing Operation Based on Learning with Using Smart Local and Global Agents and with the Help of the Ant Colony Algorithm
Routing in computer networks has played a special role in recent years. The cause of this is the role of routing in a performance of the networks. The quality of service and security is one of the most important challenges in routing due to lack of reliable methods. Routers use routing algorithms to find the best route to a particular destination. When talking about the best path, we consider p...
[ 16 ] - Introducing a new meta-heuristic algorithm based on See-See Partridge Chicks Optimization to solve dynamic optimization problems
The SSPCO (See-See Particle Chicks Optimization) is a type of swarm intelligence algorithm derived from the behavior of See-See Partridge. Although efficiency of this algorithm has been proven for solving static optimization problems, it has not yet been tested to solve dynamic optimization problems. Due to the nature of NP-Hard dynamic problems, this algorithm alone is not able to solve such o...
[ 17 ] - پیش بینی کوتاه مدت بار استان چهارمحال و بختیاری با استفاده از اجماع شبکه های عصبی
پیشبینی کوتاه مدت بار در بازار برق اهمیت زیادی دارد. از طرفی عوامل مهم تأثیرگذار بر پیشبینی کوتاه مدت بار به ویژگیهای بار الکتریکی و آب و هوایی هر منطقه بستگی دارد، بنابراین با استفاده از دادههای واقعی استان چهارمحال و بختیاری-شامل بار و دما- به پیشبینی کوتاه مدت بار الکتریکی استان پرداختهایم. بدین منظور با استفاده از چهار روش مختلف شبکه عصبی پرسپترون (MLp < /strong>)، مجمعی از شبکه عصبی ...
[ 18 ] - A new ensemble clustering method based on fuzzy cmeans clustering while maintaining diversity in ensemble
An ensemble clustering has been considered as one of the research approaches in data mining, pattern recognition, machine learning and artificial intelligence over the last decade. In clustering, the combination first produces several bases clustering, and then, for their aggregation, a function is used to create a final cluster that is as similar as possible to all the cluster bundles. The inp...
[ 19 ] - Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization
So far, various optimization methods have been proposed, and swarm intelligence algorithms have gathered a lot of attention by academia. However, most of the recent optimization problems in the real world have a dynamic nature. Thus, an optimization algorithm is required to solve the problems in dynamic environments well. In this paper, a novel collective optimization algorithm, namely the Clus...
[ 20 ] - A New WordNet Enriched Content-Collaborative Recommender System
The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommen...
[ 21 ] - An ontological hybrid recommender system for dealing with cold start problem
Recommender Systems ( ) are expected to suggest the accurate goods to the consumers. Cold start is the most important challenge for RSs. Recent hybrid s combine and . We introduce an ontological hybrid RS where the ontology has been employed in its part while improving the ontology structure by its part. In this paper, a new hybrid approach is proposed based on the combination of demog...
[ 22 ] - Predicting cardiac arrhythmia on ECG signal using an ensemble of optimal multicore support vector machines
The use of artificial intelligence in the process of diagnosing heart disease has been considered by researchers for many years. In this paper, an efficient method for selecting appropriate features extracted from electrocardiogram (ECG) signals, based on a genetic algorithm for use in an ensemble multi-kernel support vector machine classifiers, each of which is based on an optimized genetic al...
[ 23 ] - Hybrid Recommender System Based on Variance Item Rating
K-nearest neighbors (KNN) based recommender systems (KRS) are among the most successful recent available recommender systems. These methods involve in predicting the rating of an item based on the mean of ratings given to similar items, with the similarity defined by considering the mean rating given to each item as its feature. This paper presents a KRS developed by combining the following app...
[ 24 ] - The ensemble clustering with maximize diversity using evolutionary optimization algorithms
Data clustering is one of the main steps in data mining, which is responsible for exploring hidden patterns in non-tagged data. Due to the complexity of the problem and the weakness of the basic clustering methods, most studies today are guided by clustering ensemble methods. Diversity in primary results is one of the most important factors that can affect the quality of the final results. Also...
نویسندگان همکار
M. Sina 1