Explanipedia

RxSafeBench: Identifying Medication Safety Issues of Large Language Models in Simulated Consultation Open

Jiahao Zhao, L. Xu, Minghuan Tan, Lichao Zhang, Ahmadreza Argha , et al. · 2025

Numerous medical systems powered by Large Language Models (LLMs) have achieved remarkable progress in diverse healthcare tasks. However, research on their medication safety remains limited due to the lack of real world datasets, constraine…

Structuring Reasoning for Complex Rules Beyond Flat Representations Open

Zhihao Yang, A. Xu, Jingpeng Li, Yan Liang, Jian-hua Zhou , et al. · 2025

Large language models (LLMs) face significant challenges when processing complex rule systems, as they typically treat interdependent rules as unstructured textual data rather than as logically organized frameworks. This limitation results…

Gradient surgery: A necessity for robust test-time adaptation for detecting casting defects Open

Afshar Shamsi, Rejisa Becirovic, Hamid Alinejad‐Rokny, Arash Mohammadi, Ahmadreza Argha · 2025

MultiECGNet: A novel deep learning-based multi-format ensemble method for image-based electrocardiographic diagnosis of atrial fibrillation Open

J. Phan, George Parker, Nigel H. Lovell, Rajesh Subbiah, Jamie I. Vandenberg , et al. · 2025

Aim To evaluate the performance of an ensemble classifier, MultiECGNet, using multi-format electrocardiographic (ECG) images for the diagnosis of atrial fibrillation (AF), and to compare its performance with a signal-based deep learning mo…

SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics Open

Roxana Zahedi, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Youqiong Ye , et al. · 2025

Spatial transcriptomics (ST) technologies enable gene expression profiling with spatial resolution, offering unprecedented insights into tissue organization and disease heterogeneity. However, current analysis methods often struggle with n…

STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation Open

Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang , et al. · 2025

Stories are central to human culture, serving to share ideas, preserve traditions, and foster connections. Automatic story generation, a key advancement in artificial intelligence (AI), offers new possibilities for creating personalized co…

Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation Open

Dingwei Chen, Ziqiang Liu, Feiteng Fang, Chak Tou Leong, Shiwen Ni , et al. · 2025

Large Language Models (LLMs) demonstrate remarkable capabilities in text understanding and generation. However, their tendency to produce factually inconsistent outputs, commonly referred to as ''hallucinations'', remains a critical challe…

Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking Open

Alireza Sadeghi, Farshid Hajati, Ahmadreza Argha, Nigel H. Lovell, Min Yang , et al. · 2025

Integrating heterogeneous biomedical data including imaging, omics, and clinical records supports accurate diagnosis and personalised care. Graph-based models fuse such non-Euclidean data by capturing spatial and relational structure, yet …

Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review Open

Seng Hansun, Ahmadreza Argha, Ivan Bakhshayeshi, Arya Wicaksana, Hamid Alinejad‐Rokny , et al. · 2025

Background Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of the various TB diagnostic tools introduced is deemed sufficient on its own for t…

xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreaking Open

Sunbowen Lee, Shiwen Ni, Chi Wei, Shuaimin Li, Liyang Fan , et al. · 2025

Safety alignment mechanism are essential for preventing large language models (LLMs) from generating harmful information or unethical content. However, cleverly crafted prompts can bypass these safety measures without accessing the model's…

Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation Open

Dingwei Chen, Ziqiang Liu, Feiteng Fang, Chak Tou Leong, Shiwen Ni , et al. · 2025

STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation Open

Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang , et al. · 2025

A New Ensemble Transfer Learning Approach With Rejection Mechanism for Tuberculosis Disease Detection Open

Seng Hansun, Ahmadreza Argha, Hamid Alinejad‐Rokny, Roohallah Alizadehsani, J. M. Górriz , et al. · 2024

Transfer Learning (TL) is a strategic solution to handle vast data volume requirements in Deep Learning (DL). It transfers knowledge learned from a large base dataset, as a Pre-Trained Model (PTM), to a new domain. In this study, we introd…

ETAGE: Enhanced Test Time Adaptation with Integrated Entropy and Gradient Norms for Robust Model Performance Open

Afshar Shamsi, Rejisa Becirovic, Ahmadreza Argha, Ehsan Abbasnejad, Hamid Alinejad‐Rokny , et al. · 2024

Test time adaptation (TTA) equips deep learning models to handle unseen test data that deviates from the training distribution, even when source data is inaccessible. While traditional TTA methods often rely on entropy as a confidence metr…

Measuring blood pressure from Korotkoff sounds as the brachial cuff inflates on average provides higher values than when the cuff deflates Open

Branko G. Celler, Ahmadreza Argha · 2024

Objectives . In this study, we test the hypothesis that if, as demonstrated in a previous study, brachial arteries exhibit hysteresis as the occluding cuff is deflated and fail to open until cuff pressure (CP) is well below true intra-arte…

Non-invasive parameters of autonomic function using beat-to-beat cardiovascular variations and arterial stiffness in hypertensive individuals: a systematic review Open

Jia Hui Ooi, Renly Lim, H. Cheng Seng, Maw Pin Tan, Choon Hian Goh , et al. · 2024

Deep learning in spatially resolved transcriptomics: a comprehensive technical view Open

Roxana Zahedi, Mohammad Reza Eftekhariyan Ghamsari, Ahmadreza Argha, Callum Macphillamy, Amin Beheshti , et al. · 2024

Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate…

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review Open

Seng Hansun, Ahmadreza Argha, Siaw‐Teng Liaw, Branko G. Celler, Guy B. Marks · 2023

Background Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional e…

Revolutionizing Genomics with Reinforcement Learning Techniques Open

Mohsen Karami, Roohallah Alizadehsani, Khadijeh, Jahanian, Ahmadreza Argha , et al. · 2023

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the…

A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants Open

Mahdieh Labani, Amin Beheshti, Ahmadreza Argha, Hamid Alinejad‐Rokny · 2023

Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and prog…

An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer Open

Mahdieh Labani, Amin Beheshti, Ahmadreza Argha, Hamid Agha-Alinejad · 2023

Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies showed that genomic alterations represent the most common mechanism for molecular alterations that cause the development and progression o…

Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View Open

Roxana Zahedi Nasab, Mohammad Reza Eftekhariyan Ghamsari, Ahmadreza Argha, Callum Macphillamy, Amin Beheshti , et al. · 2022

Spatially resolved transcriptomics (SRT) has evolved rapidly through various technologies, enabling scientists to investigate both morphological contexts and gene expression profiling at single-cell resolution in parallel. SRT data are com…

Blood Pressure Estimation From Korotkoff Sound Signals Using an End-to-End Deep-Learning-Based Algorithm Open

Ahmadreza Argha, Branko G. Celler, Hamid Alinejad‐Rokny, Nigel H. Lovell · 2022

While measurement of blood pressure (BP) is now widely carried out by automated non-invasive BP (NIBP) monitoring devices, as they do not require skilled clinicians and do not carry risk of complications, their accuracy is in doubt. A nove…

Human Motion Intent Description Based on Bumpless Switching Mechanism for Rehabilitation Robot Open

Yao Huang, Rong Song, Ahmadreza Argha, Branko G. Celler, Andrey V. Savkin , et al. · 2021

This paper aims to improve the performance of an electromyography (EMG) decoder based on a switching mechanism in controlling a rehabilitation robot for assisting human-robot cooperation arm movements. For a complex arm movement, the major…

Nonparametric Model Prediction for Intelligent Regulation of Human Cardiorespiratory System to Prescribed Exercise Medicine Open

Hairong Yu, Yi Zhang, Lin Ye, Hamzah M. Alqudah, Kairui Guo , et al. · 2020

Intelligent regulation for human exercise behaviors becomes significantly necessary for exercise medicine after the COVID-19 epidemic. The key issue of exercise regulation and its potential development for intelligent exercise is to descri…

Blood Pressure Estimation From Beat-by-Beat Time-Domain Features of Oscillometric Waveforms Using Deep-Neural-Network Classification Models Open

Ahmadreza Argha, Ji Wu, Steven W. Su, Branko G. Celler · 2019

In general, existing machine learning based approaches, developed for systolic and diastolic blood pressure (SBP and DBP) estimation from oscillometric waveforms (OWs), employ features extracted from the OW envelope (OWE) alone and ignore …

Optimal sparse output feedback for networked systems with parametric uncertainties Open

Ahmadreza Argha, Steven W. Su, Lin Ye, Branko G. Celler · 2019

This paper investigates the design of block row/column-sparse distributed static output ${H}_2$ feedback control for interconnected systems with polytopic uncertainties. The proposed approach is applicable to the networked systems with pub…

Static output feedback fault tolerant control using control allocation scheme Open

Ahmadreza Argha, Steven W. Su, Branko G. Celler · 2018

Summary This paper describes two novel schemes for fault tolerant control using robust suboptimal static output feedback design methods. These schemes can also be employed as actuator redundancy management for overactuated uncertain linear…

Novel methods of testing and calibration of oscillometric blood pressure monitors Open

Branko G. Celler, Ahmadreza Argha, Phu Ngoc Le, Eliathamby Ambikairajah · 2018

We present a robust method for testing and calibrating the performance of oscillometric non-invasive blood pressure (NIBP) monitors, using an industry standard NIBP simulator to determine the characteristic ratios used, and to explore diff…

Advances in Discrete-Time Sliding Mode Control: Theory and Applications Open

Ahmadreza Argha, Steven W. Su, Li Li, Hung T. Nguyen, Branko G. Celler · 2018

While a large number of investigations in the control systems literature focus on the analysis of continuous-time systems, more and more practising control engineers implement the control laws using micro-processors. The controllers can ei…

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