H.3. Artificial Intelligence
Autoencoder-PCA-based Online Supervised Feature Extraction-Selection Approach

Amir Mehrabinezhad; Mohammad Teshnelab; Arash Sharifi

Volume 11, Issue 4 , November 2023, , Pages 525-534

https://doi.org/10.22044/jadm.2023.12436.2390

Abstract
  Due to the growing number of data-driven approaches, especially in artificial intelligence and machine learning, extracting appropriate information from the gathered data with the best performance is a remarkable challenge. The other important aspect of this issue is storage costs. The principal component ...  Read More

A Multi-objective Approach based on Competitive Optimization Algorithm and its Engineering Applications

Y. Sharafi; M. Teshnelab; M. Ahmadieh Khanesar

Volume 9, Issue 4 , November 2021, , Pages 497-514

https://doi.org/10.22044/jadm.2021.10698.2204

Abstract
  A new multi-objective evolutionary optimization algorithm is presented based on the competitive optimization algorithm (COOA) to solve multi-objective optimization problems (MOPs). Based on nature-inspired competition, the competitive optimization algorithm acts between animals such as birds, cats, bees, ...  Read More

Convolutional Neural Network Equipped with Attention Mechanism and Transfer Learning for Enhancing Performance of Sentiment Analysis

H. Sadr; Mir M. Pedram; M. Teshnehlab

Volume 9, Issue 2 , April 2021, , Pages 141-151

https://doi.org/10.22044/jadm.2021.9618.2100

Abstract
  With the rapid development of textual information on the web, sentiment analysis is changing to an essential analytic tool rather than an academic endeavor and numerous studies have been carried out in recent years to address this issue. By the emergence of deep learning, deep neural networks have attracted ...  Read More

Identification of Multiple Input-multiple Output Non-linear System Cement Rotary Kiln using Stochastic Gradient-based Rough-neural Network

Gh. Ahmadi; M. Teshnelab

Volume 8, Issue 3 , July 2020, , Pages 417-425

https://doi.org/10.22044/jadm.2020.8865.2021

Abstract
  Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism ...  Read More

H.6.2.2. Fuzzy set
Identification of Cement Rotary Kiln in Noisy Condition using Takagi-Sugeno Neuro-fuzzy System

N. Moradkhani; M. Teshnehlab

Volume 7, Issue 3 , July 2019, , Pages 367-375

https://doi.org/10.22044/jadm.2018.5295.1638

Abstract
  Cement rotary kiln is the main part of cement production process that have always attracted many researchers’ attention. But this complex nonlinear system has not been modeled efficiently which can make an appropriate performance specially in noisy condition. In this paper Takagi-Sugeno neuro-fuzzy ...  Read More

F.2.7. Optimization
Non-linear Fractional-Order Chaotic Systems Identification with Approximated Fractional-Order Derivative based on a Hybrid Particle Swarm Optimization-Genetic Algorithm Method

M. Kosari; M. Teshnehlab

Volume 6, Issue 2 , July 2018, , Pages 365-373

https://doi.org/10.22044/jadm.2017.4670.1553

Abstract
  Although many mathematicians have searched on the fractional calculus since many years ago, but its application in engineering, especially in modeling and control, does not have many antecedents. Since there are much freedom in choosing the order of differentiator and integrator in fractional calculus, ...  Read More

A.1. General
Designing stable neural identifier based on Lyapunov method

F. Alibakhshi; M. Teshnehlab; M. Alibakhshi; M. Mansouri

Volume 3, Issue 2 , July 2015, , Pages 141-147

https://doi.org/10.5829/idosi.JAIDM.2015.03.02.03

Abstract
  The stability of learning rate in neural network identifiers and controllers is one of the challenging issues which attracts great interest from researchers of neural networks. This paper suggests adaptive gradient descent algorithm with stable learning laws for modified dynamic neural network (MDNN) ...  Read More