Efficient Feature Selection Method using Binary Teaching-learning-based Optimization Algorithm

S. Hosseini; M. Khorashadizade

Volume 11, Issue 1 , January 2023, , Pages 29-37


  High dimensionality is the biggest problem when working with large datasets. Feature selection is a procedure for reducing the dimensionality of datasets by removing additional and irrelevant features; the most effective features in the dataset will remain, increasing the algorithms’ performance. ...  Read More

A Novel Classification and Diagnosis of Multiple Sclerosis Method using Artificial Neural Networks and Improved Multi-Level Adaptive Conditional Random Fields

Seyedeh R. Mahmudi Nezhad Dezfouli; Y. Kyani; Seyed A. Mahmoudinejad Dezfouli

Volume 10, Issue 3 , July 2022, , Pages 361-372


  Due to the small size, low contrast, variable position, shape, and texture of multiple sclerosis lesions, one of the challenges of medical image processing is the automatic diagnosis and segmentation of multiple sclerosis lesions in Magnetic resonance images. Early diagnosis of these lesions in the first ...  Read More

Estimating Pier Scour Depth: Comparison of Empirical Formulations with ANNs, GMDH, MARS, and Kriging

M. Zarbazoo Siahkali; A.A. Ghaderi; Abdol H. Bahrpeyma; M. Rashki; N. Safaeian Hamzehkolaei

Volume 9, Issue 1 , January 2021, , Pages 109-128


  Scouring, occurring when the water flow erodes the bed materials around the bridge pier structure, is a serious safety assessment problem for which there are many equations and models in the literature to estimate the approximate scour depth. This research is aimed to study how surrogate models estimate ...  Read More

H.5. Image Processing and Computer Vision
Pedestrian Detection in Infrared Outdoor Images Based on Atmospheric Situation Estimation

Seyed M. Ghazali; Y. Baleghi

Volume 7, Issue 1 , January 2019, , Pages 1-16


  Observation in absolute darkness and daytime under every atmospheric situation is one of the advantages of thermal imaging systems. In spite of increasing trend of using these systems, there are still lots of difficulties in analysing thermal images due to the variable features of pedestrians and atmospheric ...  Read More

I.3.7. Engineering
Artificial neural networks, genetic algorithm and response surface methods: The energy consumption of food and beverage industries in Iran

B. Hosseinzadeh Samani; H. HouriJafari; H. Zareiforoush

Volume 5, Issue 1 , March 2017, , Pages 79-88


  In this study, the energy consumption in the food and beverage industries of Iran was investigated. The energy consumption in this sector was modeled using artificial neural network (ANN), response surface methodology (RSM) and genetic algorithm (GA). First, the input data to the model were calculated ...  Read More

F.4.4. Experimental design
Application of statistical techniques and artificial neural network to estimate force from sEMG signals

V. Khoshdel; A. R Akbarzadeh

Volume 4, Issue 2 , July 2016, , Pages 135-141


  This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There ...  Read More

Yarn tenacity modeling using artificial neural networks and development of a decision support system based on genetic algorithms

Mohaddeseh Dashti; Vali Derhami; Esfandiar Ekhtiyari

Volume 2, Issue 1 , March 2014, , Pages 73-78


  Yarn tenacity is one of the most important properties in yarn production. This paper addresses modeling of yarn tenacity as well as optimally determining the amounts of the effective inputs to produce yarn with desired tenacity. The artificial neural network is used as a suitable structure for tenacity ...  Read More