Volume 12 (2024)
Volume 11 (2023)
Volume 10 (2022)
Volume 9 (2021)
Volume 8 (2020)
Volume 7 (2019)
Volume 6 (2018)
Volume 5 (2017)
Volume 4 (2016)
Volume 3 (2015)
Volume 2 (2014)
Volume 1 (2013)
H.3.9. Problem Solving, Control Methods, and Search
Event-Triggered Optimal Adaptive Leader-Follower Consensus Control for Unknown Input-Constrained Discrete-Time Nonlinear Systems

Zahra Jahan; Abbas Dideban; Farzaneh Tatari

Volume 12, Issue 2 , April 2024, , Pages 149-161

https://doi.org/10.22044/jadm.2024.13779.2500

Abstract
  This paper introduces an adaptive optimal distributed algorithm based on event-triggered control to solve multi-agent discrete-time zero-sum graphical games for unknown nonlinear constrained-input systems with external disturbances. Based on the value iteration heuristic dynamic programming, the proposed ...  Read More

B.3. Communication/Networking and Information Technology
Intrusion Detection for IoT Network Security with Deep learning

Roya Morshedi; S. Mojtaba Matinkhah; Mohammad Taghi Sadeghi

Volume 12, Issue 1 , January 2024, , Pages 37-55

https://doi.org/10.22044/jadm.2023.13539.2471

Abstract
  IoT devices has witnessed a substantial increase due to the growing demand for smart devices. Intrusion Detection Systems (IDS) are critical components for safeguarding IoT networks against cyber threats. This study presents an advanced approach to IoT network intrusion detection, leveraging deep learning ...  Read More

FEEM: A Flexible Model based on Artificial Intelligence for Software Effort Estimation

Amin Moradbeiky

Volume 11, Issue 1 , January 2023, , Pages 39-51

https://doi.org/10.22044/jadm.2022.11161.2265

Abstract
  Managing software projects due to its intangible nature is full of challenges when predicting the effort needed for development. Accordingly, there exist many studies with the attempt to devise models to estimate efforts necessary in developing software. According to the literature, the accuracy of estimator ...  Read More

Learning a Nonlinear Combination of Generalized Heterogeneous Classifiers

M. Rahimi; A. A. Taheri; H. Mashayekhi

Volume 11, Issue 1 , January 2023, , Pages 77-93

https://doi.org/10.22044/jadm.2022.12403.2387

Abstract
  Finding an effective way to combine the base learners is an essential part of constructing a heterogeneous ensemble of classifiers. In this paper, we propose a framework for heterogeneous ensembles, which investigates using an artificial neural network to learn a nonlinear combination of the base classifiers. ...  Read More

H.3. Artificial Intelligence
Forecasting Gold Price using Data Mining Techniques by Considering New Factors

A.R. Hatamlou; M. Deljavan

Volume 7, Issue 3 , July 2019, , Pages 411-420

https://doi.org/10.22044/jadm.2018.6114.1727

Abstract
  Gold price forecast is of great importance. Many models were presented by researchers to forecast gold price. It seems that although different models could forecast gold price under different conditions, the new factors affecting gold price forecast have a significant importance and effect on the increase ...  Read More