Volume 13 (2025)
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.6.5.14. Text processing
Enhancing the Quality of Scientific Writing Using Advanced Language Models: Automated Evaluation and Proofreading

Hamid Hassanpour; Amir Ali Kharazmi

Volume 13, Issue 1 , January 2025, , Pages 11-24

https://doi.org/10.22044/jadm.2025.15482.2661

Abstract
  Advancements in artificial intelligence have produced powerful language models that enhance scientific writing through automated evaluation and proofreading. Effective use of these models relies on prompt engineering—the precise formulation of requests—which directly influences output quality. ...  Read More

H.3.2.6. Games and infotainment
Harnessing Machine Learning for Procedural Content Generation in Gaming: A Comprehensive Review

Shaqayeq Saffari; Morteza Dorrigiv; Farzin Yaghmaee

Volume 12, Issue 4 , November 2024, , Pages 583-597

https://doi.org/10.22044/jadm.2025.15016.2603

Abstract
  Procedural Content Generation (PCG) through automated and algorithmic content generation is an active research field in the gaming industry. Recently, Machine Learning (ML) approaches have played a pivotal role in advancing this area. While recent studies have primarily focused on examining one or a ...  Read More

H.3. Artificial Intelligence
An Intelligent Blockchain-Based System Configuration for Screening, Monitoring, and Tracing of Pandemics

Ali Rebwar Shabrandi; Ali Rajabzadeh Ghatari; Mohammad Dehghan nayeri; Nader Tavakoli; Sahar Mirzaei

Volume 12, Issue 2 , April 2024, , Pages 163-191

https://doi.org/10.22044/jadm.2024.14379.2541

Abstract
  This study proposes a high-level design and configuration for an intelligent dual (hybrid and private) blockchain-based system. The configuration includes the type of network, level of decentralization, nodes, and roles, block structure information, authority control, and smart contracts and intended ...  Read More

H.6.2. Models
Diagnosis and Classification of Tuberculosis Chest X-ray Images of Children Less Than 15 years at Mbarara Regional Referral Hospital Using Deep Learning

Simon Kawuma; Elias Kumbakumba; Vicent Mabirizi; Deborah Nanjebe; Kenneth Mworozi; Adolf Oyesigye Mukama; Lydia Kyasimire

Volume 12, Issue 2 , April 2024, , Pages 315-324

https://doi.org/10.22044/jadm.2024.14270.2530

Abstract
  Tuberculosis (TB) is an underestimated cause of death in children, with only 45% of cases correctly diagnosed and reported. It is estimated that 1.12 million TB cases occurred among newborns, children, and adolescents aged less or equal 14 years. In Uganda, TB prevalence is 8.5% in children and 16.7% ...  Read More

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

H.3. Artificial Intelligence
LSTM Modeling and Optimization of Rice (Oryza sativa L.) Seedling Growth using Intelligent Chamber

Hamid Ghaffari; Hemmatollah Pirdashti; Mohammad Reza Kangavari; Sjoerd Boersma

Volume 11, Issue 4 , November 2023, , Pages 561-571

https://doi.org/10.22044/jadm.2023.13026.2451

Abstract
  An intelligent growth chamber was designed in 2021 to model and optimize rice seedlings' growth. According to this, an experiment was implemented at Sari University of Agricultural Sciences and Natural Resources, Iran, in March, April, and May 2021. The model inputs included radiation, temperature, carbon ...  Read More

H.3. Artificial Intelligence
Fast COVID-19 Infection Prediction with In-House Data Using Machine Learning Classification Algorithms: A Case Study of Iran

Ali Rebwar Shabrandi; Ali Rajabzadeh Ghatari; Nader Tavakoli; Mohammad Dehghan Nayeri; Sahar Mirzaei

Volume 11, Issue 4 , November 2023, , Pages 573-585

https://doi.org/10.22044/jadm.2023.13291.2458

Abstract
  To mitigate COVID-19’s overwhelming burden, a rapid and efficient early screening scheme for COVID-19 in the first-line is required. Much research has utilized laboratory tests, CT scans, and X-ray data, which are obstacles to agile and real-time screening. In this study, we propose a user-friendly ...  Read More

I.3.7. Engineering
Evaluation of liquefaction potential based on CPT results using C4.5 decision tree

A. Ardakani; V. R. Kohestani

Volume 3, Issue 1 , March 2015, , Pages 85-92

https://doi.org/10.5829/idosi.JAIDM.2015.03.01.09

Abstract
  The prediction of liquefaction potential of soil due to an earthquake is an essential task in Civil Engineering. The decision tree is a tree structure consisting of internal and terminal nodes which process the data to ultimately yield a classification. C4.5 is a known algorithm widely used to design ...  Read More