Sharif Digital Repository

Computational intelligence (CI) as an offshoot of artificial intelligence (AI), is becoming more and more widespread nowadays for solving different engineering problems. Especially by embracing Swarm Intelligence techniques such as ant colony optimization (ACO), CI is known as a good alternative to classical AI for dealing with practical problems which are not easy to solve by traditional methods. Besides, electricity load forecasting is one of the most important concerns of power systems, consequently; developing intelligent methods in order to perform accurate forecasts is vital for such systems. This study presents a hybrid CI methodology (called ACO-GA) by integration of ant colony... 

Multiple Attribute Decision Making (MADM) addresses the problem of choosing an optimum choice containing the highest degree of satisfaction from a set of alternatives which are characterized in terms of their attributes. In order to make a decision or choose a best alternative, a decision maker (DM) is often asked to provide his/her preferences either on alternatives or on the relative weights of attributes or on both of them. In this paper some basic principles from data envelopment analysis (DEA) is used in order to extract the necessary information for solving an MADM problem. We will introduce a comprehensive yet efficient approach for accountable and understandable MADM. For obtaining... 

Success in forecasting and analyzing sales for given goods or services can mean the difference between profit and loss for an accounting period and, ultimately, the success or failure of the business itself. Therefore, reliable prediction of sales becomes a very important task. This article presents a novel sales forecasting approach by the integration of genetic fuzzy systems (GFS) and data clustering to construct a sales forecasting expert system. At first, all records of data are categorized into k clusters by using the K-means model. Then, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. In order to evaluate our K-means... 

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all... 

Forecasting stock price time series is very important and challenging in the real world because they are affected by many highly interrelated economic, social, political and even psychological factors, and these factors interact with each other in a very complicated manner. This article presents an approach based on Genetic Fuzzy Systems (GFS) for constructing a stock price forecasting expert system. We use a GFS model with the ability of rule base extraction and data base tuning for next day stock price prediction to extract useful patterns of information with a descriptive rule induction approach. We evaluate capability of the proposed approach by applying it on stock price forecasting... 

Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays, because of their flexibility, symbolic reasoning, and explanation capabilities. Meanwhile, accurate forecasts on tourism demand and study on the pattern of the tourism demand from various origins is essential for the tourism-related industries to formulate efficient and effective strategies on maintaining and boosting tourism industry in a country. In this paper we develop a hybrid AI model to deal with tourist arrival forecasting problems. The hybrid model adopts Adaptive Neuro-Fuzzy Inference System (ANFIS) and... 

The trend of gold price in the market is the most important consideration for the investors of the gold, and serves as the basis of gaining profit, so there are scholars who try to forecast the gold price. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. Besides, nowadays artificial intelligence (AI) techniques are becoming more and more widespread because of their accuracy, symbolic reasoning, flexibility and explanation capabilities. Among these techniques, particle swarm optimization (PSO) is one of the best AI techniques for optimization and parameter estimation. In this study a PSO-based time series model for the gold price... 

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques and combining them to improve forecasting accuracy in different fields. Besides, stock market forecasting has always been a subject of interest for most investors and professional analysts. Stock market forecasting is a tough problem because of the uncertainties involved in the movement of the market. This paper proposes a hybrid artificial intelligence model for stock exchange index forecasting, the model is a combination of genetic algorithms and feedforward neural networks. Actually it evolves neural network weights by using genetic algorithms. We also employ preprocessing... 

Accurate forecasting of outpatient visits aids in decision-making and planning for the future and is the foundation for greater and better utilization of resources and increased levels of outpatient care. It provides the ability to better manage the ways in which outpatient's needs and aspirations are planned and delivered. This study presents a hybrid artificial intelligence (AI) model to develop a Mamdani type fuzzy rule based system to forecast outpatient visits with high accuracy. The hybrid model uses genetic algorithm for evolving knowledge base of fuzzy system. Actually it extracts useful patterns of information with a descriptive rule induction approach based on Genetic Fuzzy Systems... 

Purpose: The purpose of this paper is to estimate long-run elasticities for housing prices in Tehran's (capital of Iran) 20 different zones relative to several explanatory variables available for use such as land price, total substructure area, material price, etc. Moreover, another goal of this paper is to propose a new approach to deal with problems which arise due to a lack of proper data. Design/methodology/approach: The data set is gathered from "The Municipality of Tehran" and "The Central Bank of Islamic Republic of Iran (CBI)". One-way fixed effects and one-way random effects approaches (which are panel data approaches) are applied to model housing price forecasting function in... 

Accurate forecasts of tourist arrivals and study of the tourist arrival patterns are essential for the tourism-related industries to formulate efficient and effective strategies on maintaining and boosting tourism industry in a country. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. This study presents a hybrid artificial intelligence (AI) model to develop a Mamdani-type fuzzy rule-based system to forecast tourist arrivals with high accuracy. The hybrid model uses genetic algorithm for learning rule base and tuning data base of fuzzy system. Actually it extracts useful information patterns with a descriptive rule induction approach based... 

In vivo dose-dependent effects of nanoscale graphene oxide (NGO) sheets on reproduction capability of Balb/C mice were investigated. Biodistribution study of the NGO sheets (intravenously injected into male mice at dose of ∼2000 μg/mL or 4 mg/kg of body weight) showed a high graphene uptake in testis. Hence, in vivo effects of the NGO sheets on important characteristics of spermatozoa (including their viability, morphology, kinetics, DNA damage and chromosomal aberration) were evaluated. Significant in vivo effects was found at the injected concentrations ≥200 μg/mL after (e.g., ∼45% reduction in sperm viability and motility at 2000 μg/mL). Observation of remarkable DNA fragmentations and... 

Asian red ginseng was used for green reduction of chemically exfoliated graphene oxide (GO) into reduced graphene oxide (rGO). The reduction level and electrical conductivity of the ginseng-rGO sheets were comparable to those of hydrazine-rGO ones. Reduction by ginseng resulted in repairing the sp 2 graphitic structure of the rGO, while hydrazine-rGO showed more defects and/or smaller aromatic domains. The ginseng-rGO sheets presented a better stability against aggregation than the hydrazine-rGO ones in an aqueous suspension. Whilst the hydrophobic hydrazine-rGO films exhibited no toxicity against human neural stem cells (hNSCs), the hydrophilic GO and ginseng-rGO films (as more... 

Vigorous debates have taken place within and between many countries about regulatory policy regimes covering the assessment and approval of genetically modified (GM) crops. In Iran, a very vigorous and hotly contested policy debate concerning legislation covering GM crops occurred between 2004 and 2009, but it was confined within government circles with almost no public discussion. This paper analyses the Iranian policy-making process in the period 2006-9. It explains how and why a stalemate arose in disputes between ministries and departments. The chosen analytical framework draws mainly on the regulation of technological risks and the analysis of public policy-making. It deploys the... 

A μ-way latin trade of volume s is a set of μ partial latin rectangles (of inconsequential size) containing exactly the same s filled cells, such that if cell (i, j) is filled, it contains a different entry in each of the μ partial latin rectangles, and such that row i in each of the μ partial latin rectangles contains, set-wise, the same symbols and column j, likewise. In this paper we show that all μ-way latin trades with sufficiently large volumes exist, and state some theorems on the non-existence of μ-way latin trades of certain volumes. We also find the set of possible volumes (that is, the volume spectrum) of μ-way latin trades for μ = 4 and 5. (The case μ = 2 was dealt with by Fu,... 

The set of integers k for which there exist three latin squares of order n having precisely k cells identical, with their remaining n2 -k cells different in all three latin squares, denoted by I3[n], is determined here for all orders n. In particular, it is shown that I3[n] = {0,.,n2 -15}U [n2 - 12,n2-9,n2], for n ≫8. ©2002 Eisevier Science B.V. All rights reserved  

Coronary angiography is a vital instrument to detect the prevailing of vascular diseases, and accurate vascular segmentation acts a crucial role for proper quantitative analysis of the vascular tree morphological features. Level set methods are popular for segmenting the coronary arteries, but their performance is related to suitable start-up and optimum setting of regulating parameters, essentially done manually. This research presents a novel fuzzy level set procedure with the objective of segmentation of the coronary artery tree in 2-D X-ray angiography as automatically. It is clever to clearly develop from the early segmentation with spatial fuzzy grouping. The adjusting parameters of... 

Visual attributes are great means of describing images or scenes, in a way both humans and computers understand. In order to establish a correspondence between images and to be able to compare the strength of each property between images, relative attributes were introduced. However, since their introduction, hand-crafted and engineered features were used to learn increasingly complex models for the problem of relative attributes. This limits the applicability of those methods for more realistic cases. We introduce a deep neural network architecture for the task of relative attribute prediction. A convolutional neural network (ConvNet) is adopted to learn the features by including an... 

We present the first public code for the calculation of the astrometric centroid shift occurring during microlensing events. The computation is based on the contour integration scheme and covers single and binary lensing of finite sources with arbitrary limb darkening profiles. This allows for general detailed investigations of the impact of finite source size in astrometric binary microlensing. The new code is embedded in version 3.0 of vbbinarylensing, which offers a powerful computational tool for extensive studies of microlensing data from current surveys and future space missions. © 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society