H.3. Artificial Intelligence
A New Hybrid Method to Detect Risk of Gastric Cancer using Machine Learning Techniques

Ali Zahmatkesh Zakariaee; Hossein Sadr; Mohamad Reza Yamaghani

Volume 11, Issue 4 , November 2023, , Pages 505-515

https://doi.org/10.22044/jadm.2023.13377.2464

Abstract
  Machine learning (ML) is a popular tool in healthcare while it can help to analyze large amounts of patient data, such as medical records, predict diseases, and identify early signs of cancer. Gastric cancer starts in the cells lining the stomach and is known as the 5th most common cancer worldwide. ...  Read More

Vehicle Type, Color and Speed Detection Implementation by Integrating VGG Neural Network and YOLO algorithm utilizing Raspberry Pi Hardware

Mojtaba Nasehi; Mohsen Ashourian; Hosein Emami

Volume 10, Issue 4 , November 2022, , Pages 579-588

https://doi.org/10.22044/jadm.2022.11915.2338

Abstract
  Vehicle type recognition has been widely used in practical applications such as traffic control, unmanned vehicle control, road taxation, smuggling detection, and so on. In this paper, various techniques such as data augmentation and space filtering have been used to improve and enhance the data. Then, ...  Read More

Classification of Skin Lesions By Tda Alongside Xception Neural Network

N. Elyasi; M. Hosseini Moghadam

Volume 10, Issue 3 , July 2022, , Pages 333-344

https://doi.org/10.22044/jadm.2022.10948.2239

Abstract
  In this paper, we use the topological data analysis (TDA) mapper algorithm alongside a deep convolutional neural network in order to classify some medical images.Deep learning models and convolutional neural networks can capture the Euclidean relation of a data point with its neighbor data points like ...  Read More

H.5. Image Processing and Computer Vision
A Novel Face Detection Method Based on Over-complete Incoherent Dictionary Learning

S. Mavaddati

Volume 7, Issue 2 , April 2019, , Pages 263-278

https://doi.org/10.22044/jadm.2019.7215.1857

Abstract
  In this paper, face detection problem is considered using the concepts of compressive sensing technique. This technique includes dictionary learning procedure and sparse coding method to represent the structural content of input images. In the proposed method, dictionaries are learned in such a way that ...  Read More

H.6.2.4. Neural nets
Intelligent identification of vehicle’s dynamics based on local model network

M. Abtahi

Volume 7, Issue 1 , January 2019, , Pages 161-168

https://doi.org/10.22044/jadm.2018.5334.1642

Abstract
  This paper proposes an intelligent approach for dynamic identification of the vehicles. The proposed approach is based on the data-driven identification and uses a high-performance local model network (LMN) for estimation of the vehicle’s longitudinal velocity, lateral acceleration and yaw rate. ...  Read More

F.2.11. Applications
Estimating scour below inverted siphon structures using stochastic and soft computing approaches

M. Fatahi; B. Lashkar-Ara

Volume 5, Issue 1 , March 2017, , Pages 55-66

https://doi.org/10.22044/jadm.2016.757

Abstract
  This paper uses nonlinear regression, Artificial Neural Network (ANN) and Genetic Programming (GP) approaches for predicting an important tangible issue i.e. scours dimensions downstream of inverted siphon structures. Dimensional analysis and nonlinear regression-based equations was proposed for estimation ...  Read More

G.2. Models and Principles
Governor design for hydropower plants by intelligent sliding mode variable structure control

D. Qian; L. Yu

Volume 4, Issue 1 , March 2016, , Pages 85-92

https://doi.org/10.5829/idosi.JAIDM.2016.04.01.10

Abstract
  This work proposes a neural-fuzzy sliding mode control scheme for a hydro-turbine speed governor system. Considering the assumption of elastic water hammer, a nonlinear mode of the hydro-turbine governor system is established. By linearizing this mode, a sliding mode controller is designed. The linearized ...  Read More