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)
Technical Paper H.3. Artificial Intelligence
Improving the Hierarchical Classification of Protein Families and Model Interpretation with the Grad-CAM Method and Transformers

Naeimeh Mohammad Karimi; Mehdi Rezaeian

Volume 13, Issue 3 , July 2025, Pages 261-273

https://doi.org/10.22044/jadm.2025.15493.2667

Abstract
  In the era of massive data, analyzing bioinformatics fields and discovering its functions are very important. The rate of sequence generation using sequence generation techniques is increasing rapidly, and researchers are faced with many unknown functions. One of the essential operations in bioinformatics ...  Read More

Original/Review Paper H.5. Image Processing and Computer Vision
A Phenology Based Paddy Rice Mapping by Correlation Analysis on Sentinel-1/2 Imagery in Fragmented Lands Using GEE Platform

Fateme Namazi; Mehdi Ezoji; Ebadat Ghanbari Parmehr

Volume 13, Issue 3 , July 2025, Pages 275-291

https://doi.org/10.22044/jadm.2025.15379.2639

Abstract
  Paddy fields in the north of Iran are highly fragmented, leading to challenges in accurately mapping them using remote sensing techniques. Cloudy weather often degrades image quality or renders images unusable, further complicating monitoring efforts. This paper presents a novel paddy rice mapping method ...  Read More

Applied Article H.5.9. Scene Analysis
Attention-HAR: Advanced Human Activity Recognition Using a Deep Learning Model with an Integrated Attention Mechanism

Navid Raisi; Mahdi Rezaei; Behrooz Masoumi

Volume 13, Issue 3 , July 2025, Pages 293-304

https://doi.org/10.22044/jadm.2025.15658.2683

Abstract
  Human Activity Recognition (HAR) using computer vision is an ‎expanding field with diverse applications, including healthcare, ‎transportation, and human-computer interaction. While classical ‎approaches such as Support Vector Machines (SVM), Histogram ‎of Oriented Gradients (HOG), and ...  Read More

Technical Paper B.3. Communication/Networking and Information Technology
Anomaly Detection in IoT Traffic in the Presence of Gaussian Noise Using Deep Neural Networks

Roya Morshedi; S. Mojtaba Matinkhah

Volume 13, Issue 3 , July 2025, Pages 305-319

https://doi.org/10.22044/jadm.2025.15672.2684

Abstract
  The Internet of Things (IoT) is a rapidly growing domain essential for modern smart services. However, resource limitations in IoT nodes create significant security vulnerabilities, making them prone to cyberattacks. Deep learning models have emerged as effective tools for detecting anomalies in IoT ...  Read More

Original/Review Paper H.3.8. Natural Language Processing
ClusQPP: A Clustering-Based Framework for Query Performance Prediction

Mozhgan Akaberi; Maryam Khodabakhsh; Seyedehfatemeh Karimi; Hoda Mashayekhi

Volume 13, Issue 3 , July 2025, Pages 321-336

https://doi.org/10.22044/jadm.2025.15835.2698

Abstract
  The exponential growth of digital information has increased the demand for robust and efficient Information Retrieval (IR) systems. Query Performance Prediction (QPP) is a critical task for identifying difficult queries and enhancing retrieval strategies. However, existing QPP methods suffer from several ...  Read More

Technical Paper H.3. Artificial Intelligence
Robust Persian Digit Recognition in Noisy Environments Using Hybrid CNN-BiGRU Model

Ali Nasr-Esfahani; Mehdi Bekrani; Roozbeh Rajabi

Volume 13, Issue 3 , July 2025, Pages 337-345

https://doi.org/10.22044/jadm.2025.15932.2707

Abstract
  Artificial intelligence (AI) has significantly advanced speech recognition applications. However, many existing neural network-based methods struggle with noise, reducing accuracy in real-world environments. This study addresses isolated spoken Persian digit recognition (zero to nine) under noisy conditions, ...  Read More

Original/Review Paper H.3. Artificial Intelligence
Discrete Rotated Isolation Forest in High Dimensions

Vahideh Monemizadeh; Kourosh Kiani

Volume 13, Issue 3 , July 2025, Pages 347-358

https://doi.org/10.22044/jadm.2025.15883.2704

Abstract
  Anomaly detection is becoming increasingly crucial across various fields, including cybersecurity, financial risk management, and health monitoring. However, it faces significant challenges when dealing with large-scale, high-dimensional, and unlabeled datasets. This study focuses on decision tree-based ...  Read More

Review Article H.3. Artificial Intelligence
Attention Mechanisms in Transformers: A General Survey

Rasoul Hosseinzadeh; Mahdi Sadeghzadeh

Volume 13, Issue 3 , July 2025, Pages 359-368

https://doi.org/10.22044/jadm.2025.15584.2679

Abstract
  The attention mechanisms have significantly advanced the field of machine learning and deep learning across various domains, including natural language processing, computer vision, and multimodal systems. This paper presents a comprehensive survey of attention mechanisms in Transformer architectures, ...  Read More

Original/Review Paper H.6.5.13. Signal processing
Valvular Heart Disease Classification through Hierarchical Decomposition via Matrix Factorization of Scalogram-Based Phonocardiogram Representations

Samira Moghani; Hossein Marvi; Zeynab Mohammadpoory

Volume 13, Issue 3 , July 2025, Pages 369-378

https://doi.org/10.22044/jadm.2025.16092.2726

Abstract
  This study introduces a novel classification framework based on Deep Orthogonal Non-Negative Matrix Factorization (Deep ONMF), which leverages scalogram representations of phonocardiogram (PCG) signals to hierarchically extract structural features crucial for detecting valvular heart diseases (VHDs). ...  Read More

Original/Review Paper H.6.5.2. Computer vision
Image Inpainting Enhancement by Replacing the Original Mask with a Self-attended Region from the Input Image

Kourosh Kiani; Razieh Rastgoo; Alireza Chaji; Sergio Escalera

Volume 13, Issue 3 , July 2025, Pages 379-391

https://doi.org/10.22044/jadm.2025.15970.2713

Abstract
  Image inpainting, the process of restoring missing or corrupted regions of an image by reconstructing pixel information, has recently seen considerable advancements through deep learning-based approaches. Aiming to tackle the complex spatial relationships within an image, in this paper, we introduce ...  Read More