Mohsen Rahmanian

Mohsen Rahmanian
Jahrom University | JAHROM · Department of Computer Engineering

PhD Candidate

About

14
Publications
11,001
Reads
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42
Citations
Citations since 2016
8 Research Items
38 Citations
201620172018201920202021202202468
201620172018201920202021202202468
201620172018201920202021202202468
201620172018201920202021202202468
Additional affiliations
March 2015 - March 2016
jahrom university, iran, jahrom
Position
  • Head of School
January 2014 - September 2015
jahrom university, Jahrom, Iran
Position
  • Head of School
January 2011 - July 2016
jahrom university, Jahrom, Iran
Position
  • Faculty Member

Publications

Publications (14)
Article
Gene expression data analysis has always been challenging due to complex and high-dimensional samples and genes. Generally, the number of samples is much smaller than the number of genes in microarray gene expression data. Handling this imbalance data as machine learning tasks have the risk of generating an over-fitted learning model, reducing pred...
Article
The data readability, complexity reduction of learning algorithms, and enhancing predictability are the most important reasons for using feature selection methods, especially when there exist lots of features. In recent years, unsupervised feature selection techniques are well explored. Among the various methods of feature selection, algorithms bas...
Conference Paper
The data readability, complexity reduction of learning algorithms and increase predictability are the most important reasons for using feature selection methods, especially when there exist lots of features. In recent years, unsupervised feature selection techniques are well explored. In this paper, we proposed an unsupervised feature selection alg...
Article
Full-text available
The sensitivity of graphene quantum dots towards toxic heavy metals (THMs; Cd, Hg, Pb) can be improved through doping with nitrogen at the vacant site defects. Using density functional theory, we investigate the adsorption of THMs on the graphene quantum dots (GQDs) and nitrogen-coordinated defective GQDs (GQD@1N, GQD@2N, GQD@3N and GQD@4N) surface...
Conference Paper
Full-text available
تشخیص حالت قطعات موسیقی برای علاقه‌مندان به موسیقی همواره موضوعی جالب و در عین حال پیچیده بوده که به دلیل کاربردهای فراوان آن در زمینه‌های آموزشی، آهنگ‌سازی و بازنوازی سلیقه‌ای آهنگ‌های معروف، امروزه از اهمیت بالایی برخوردار است. پیچیده‌تر از تشخیص حالت یک قطعه، تبدیل بین حالت‌های آن است که صرفاً با گوش‌دادن به آن قطعه میسر نمی‌گردد و تنها اساتید ک...
Article
Full-text available
Introduction: Alzheimer’s disease (AD) and cognitive impairment are age-related disorders. Nonetheless, it is possible to maintain Alzheimer’s patients’ quality of life by meeting their needs and reducing the unfavorable consequences. This study introduces an Alzheimer’s Intelligence Care System (AICS), which can meet various needs of such patients...
Conference Paper
Full-text available
Getting unwanted SMS or spam messages by users in high volume is one of the problems that, along with all the benefits of mobile technology, can lead to user discontent. Most mobile operators offer solutions for spamming, but most of these methods limit the filtering of advertisement SMS messages from specific numbers. In scientific literature, the...
Book
Full-text available
This book introducing the problem solving methods and Python programming language. The book is written in Persian.
Article
Full-text available
The classification of power quality (PQ) disturbances to improve the PQ is an important issue in utilities and industrial factories. In this paper, an approach to classify PQ disturbances is presented. First, a signal containing one of the PQ disturbances, like sag, swell, flicker, interruption, transient, or harmonics, is evaluated using the propo...
Article
Full-text available
Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the...
Article
Full-text available
This paper proposes a novel method for studying transmission and generation expansion planning considering load ability limit of power system. Artificial neural network (ANN) technique is employed to evaluate load ability limit of the power system because of its sensitivity characteristic. Power system restructuring and separation of decision-makin...
Article
Full-text available
In order to fully study a Stirling engine based solar power generation system, a detailed model that considers all thermal, mechanical, and electrical aspects of the system should be used. Research in the area of Stirling engine systems has been performed either without considering the electrical parts, or with a simple model for the electrical par...

Questions

Question (1)
Question
I work on Dendritic Cell Algorithm (DCA) for classification of binary data, but I do not understand some of its parts:
I'm going to use DCA for detecting spam SMS. So, I generate signals from the output of two ML algorithms (for example Naive Bayes and SVM) with confidence for each SMS. After that, run DCA for select DC that migrate. (Training phase)
Well, now, how do I determine the type of new SMS? (Test phase) Suppose the new SMS is not like any previous one.
I need an implementation in Python, C++ or Java.
Jason Brownlee provides an example of the Dendritic Cell Algorithm implemented in the Ruby Programming Language. The problem is a contrived anomaly-detection problem with ordinal inputs x range [0,50), where values that divide by 10 with no remainder are considered anomalies. Probabilistic safe and danger signal functions are provided, suggesting danger signals correctly with P(danger)=0.70, and safe signals correctly with P(safe)=0.95. (http://www.cleveralgorithms.com/nature-inspired/immune/dca.html), But my previous ambiguity has not been resolved yet. 
When he wants to test its implementation, it uses the same data as previously trained the system. This is a mistake when new data is entered.
Can anybody explain this situation for me?
Thanks

Network

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Projects

Projects (2)
Project
This study contains three innovations than related researches. The first one is transmission content with the least usage of Internet traffic between OSNs which are not supported by one company. The next innovation is a caching software system to reduce processes of extracting contents from source OSN’s network. The third item is transmission delay time decreased sharply. As a result, the trial version has served users and managers of pages and channels, including the official Telegram channel of a TV program named “90” and a news feed named “CANNews” (the analytical aviation industry news feed). Overall, this method not only has 98.81% correct action, but also contents are fitted into the post format of the destination OSN.
Project
Design an intelligent system for Alzheimer's disease that can live better. Design an intelligent system for Alzheimer's disease that can ٰdo their daily tasks at least help.