Roohallah Alizadehsani

• Doctor of Engineering
• Alfred Deakin Postdoctoral Research Fellow, IISRI, Deakin University at Deakin University

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which...

Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this paper introduces an innovative approach that combines active learning and off-policy Deep Rei...

Machine learning (ML) offers precise predictions and could improve patient care, potentially replacing traditional scoring systems. A retrospective study at Emtiaz Hospital analyzed 3,180 traumatic brain injury (TBI) patients. Nineteen variables were assessed using ML algorithms to predict outcomes. Data preparation addressed missing values and bal...

Premature coronary artery disease (PCAD) refers to the early onset of the disease, usually before the age of 55 for men and 65 for women. Coronary Artery Disease (CAD) develops when coronary arteries, the major blood vessels supplying the heart with blood, oxygen, and nutrients, become clogged or diseased. This is often due to many risk factors, in...

Accurate 3D object detection is critical for safe autonomous driving and smart mobility applications. Existing fusion methods that combine camera and LiDAR data face inefficiencies, including disjointed feature extraction, spatial misalignment, and issues with detecting smaller objects due to road vibrations and clock synchronization errors. Furthe...

Brain aging is a complex and dynamic process, leading to functional and structural changes in the brain. These changes could lead to the increased risk of neurodegenerative diseases and cognitive decline. Accurate brain-age estimation utilizing neuroimaging data has become necessary for detecting initial signs of neurodegeneration. Here, we propose...

Monitoring fatigue is essential for improving safety, particularly for people who work long shifts or in high-demand workplaces. The development of wearable technologies, such as fitness trackers and smartwatches, has made it possible to continuously analyze physiological signals in real-time to determine a person level of exhaustion. This has allo...

Genetic Network Programming (GNP) is an evolutionary algorithm that extends Genetic Programming (GP). It is typically used in agent control problems. In contrast to GP, which employs a tree structure, GNP utilizes a directed graph structure. During the evolutionary process, the connections between nodes change to discover the optimal strategy. Due...

EM) presents significant challenges in securely managing and exchanging information. This study introduces a blockchain-based platform, BAIoT-EMS, designed to enhance security and efficiency in AIoT-enabled EM systems. The platform leverages a consortium network and InterPlanetary File Storage (IPFS) for secure storage and transaction management, s...

For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) has started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to significantly improve clinical outcomes. Ho...

Background: Lung cancer remains a significant health concern, and the effectiveness of early detection significantly enhances patient survival rates. Identifying lung tumors with high precision is a challenge due to the complex nature of tumor structures and the surrounding lung tissues. Methods: To address these hurdles, this paper presents an inn...

Cardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial intelligence (AI) methods—in particular, deep learning (DL)—has been on the rise lately for the analysis of different CVD‐related topics. The use of fundus images and optical coherence tomography angiography (OCTA) in the diagnosis of retinal diseases has...

Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is assessed by analyzing signal data such as electroencephalography (EEG), which can offer scientists valuable information about diseases and human-computer interaction. One of the difficulties researchers confront while evaluating these signals is the existence of...

In an era marked by pervasive digital connectivity, cybersecurity concerns have escalated. The rapid evolution of technology has led to a spectrum of cyber threats, including sophisticated zero-day attacks. This research addresses the challenge of existing intrusion detection systems in identifying zero-day attacks using the CIC-MalMem-2022 dataset...

The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining combinations of basic emotions expressed, their interconnections, and the dynamic variations. Through the utilization of advanced algorithms, CERS provides profound insights into emotional dynamics, facilitating a nuanced understanding and customized respo...

Explainable Artificial Intelligence (XAI) encompasses the strategies and methodologies used in constructing AI systems that enable end-users to comprehend and interpret the outputs and predictions made by AI models. The increasing deployment of opaque AI applications in high-stakes fields, particularly healthcare, has amplified the need for clarity...

Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and...

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addresse...

As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments can be used to identify Alzheimer's disease. A number of database...

Background: Gliomas are the most common primary intracranial tumors. Overall, there are 23 different glioma types, and the grading and prognosis of each type are determined by their specific mutations. Several identified mutations are IDH gene status, CDKN2A/B homozygote delegation, PTEN mutation, p53 mutation, TERT promotor mutation, H3K27M mutati...

e15070 Background: Integrating oncology and artificial intelligence (AI) shows significant potential in improving treatments and early detection of ocular tumors. This systematic review aims to strengthen existing evidence regarding the application of AI in diagnosing and treating ocular tumors. Methods: We systematically searched PubMed, Embase, W...

Despite the absence of a standardized clinical definition, morning stress is widely recognized as the stress experienced upon waking. Given its established link to various diseases prevalent in modern society, the accurate measurement and effective management of stress are paramount for maintaining optimal health. In this study, we present a novel...

Objective. Myocarditis poses a significant health risk, often precipitated by viral infections like Coronavirus disease (COVID-19), and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, Cardiac Magnetic Reso...

Objective Hypertension is one of the most important and preventable causes of cardiovascular disease (CVD), stroke, chronic kidney disease, and dementia which caused approximately 8.5 million deaths in 2015, in low & middle income countries. Hypertension depends on well known risk factors such as age, gender, family history, smoking, alcohol consum...

Thyroid cancer is an increasing global health concern that requires advanced diagnostic methods. The application of AI and radiomics to thyroid cancer diagnosis is examined in this review. A review of multiple databases was conducted in compliance with PRISMA guidelines until October 2023. A combination of keywords led to the discovery of an Englis...

Myocarditis is a significant public health concern because of its potential to cause heart failure and sudden death. The standard invasive diagnostic method, endomyocardial biopsy, is typically reserved for cases with severe complications, limiting its widespread use. Conversely, non‐invasive cardiac magnetic resonance (CMR) imaging presents a prom...

Diabetes mellitus (DM) predisposes patients to vascular complications. Retinal images and vasculature reflect the body's micro-and macrovascular health. They can be used to diagnose DM complications, including diabetic retinopathy (DR), neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as forecast the risk of cardiovascul...

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated. It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction. Early detection is crucial for successful treatment, and cardiac magnetic resonance imaging (CMR) is a valuable tool for identifying this co...

Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. While semi-automated systems have been used in the past to diagnose ROP-related plus disease by quantifying retinal vessel features, traditional machine learning (ML) model...

With the increasing growth of information through smart devices, enhancing the quality of human life necessitates the adoption of various computational paradigms including, cloud, fog, and edge in the Internet of Things (IoT) network. Among these paradigms, cloud computing, as an emerging technology, extends cloud layer services to the network edge...

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 gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate...

Automatic facial expression recognition is a big challenge in human–computer interaction. Analyzing the changes in the face during a facial expression can be used for this purpose. In this paper, these changes are extracted as a number of motion vectors. These motion vectors are extracted using an optical flow algorithm. Then, they are used to anal...

The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a nove...

Researchers are turning to nanofluids in PV/T hybrid systems for enhanced efficiency due to nanoparticle dispersion, improving thermal and optical properties over conventional fluids. Three different concentrations of formulated soybean oil based MXene nanofluids are considered 0.025, 0.075 and 0.125 wt.%. Maximum specific heat capacity nanofluids...

In an era dominated by digital communication, the vast amounts of data generated from social media and financial markets present unique opportunities and challenges for forecasting stock market prices. This paper proposes an innovative approach that harnesses the power of social media sentiment analysis combined with stock market data to predict st...

Budget allocation across multiple advertising channels involves periodically dividing a fixed total budget among various channels. Yet, the challenge of making sequential decisions to optimize long-term benefits rather than short-term gains is often overlooked. Additionally, more apparent connections must be made between actions taken on one advert...

Human Activity Recognition (HAR) is becoming increasingly important in the fast-evolving landscapes of wearable sensors, smart applications, and the Internet of Things (IoT) paradigms. HAR is rapidly gaining importance, especially in health monitoring, elderly and infant care, fitness tracking, and security. Machine learning (ML) and Deep Learning...

Nurses are essential in managing the healthcare of older adults, particularly those over 65, who often face multiple chronic conditions. This group requires comprehensive physical, mental, and functional care. Recent advancements in artificial intelligence (AI) have significantly improved nursing capabilities by enabling real-time health monitoring...

Online social networks, especially Twitter, have become focal points for illicit activities, providing unique criminal investigation opportunities. This paper introduces an innovative methodology that uses social media sentiment analysis to predict criminal activities. One major challenge in sentiment analysis is the uneven distribution of sentimen...

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 introduce an ensemble of classifiers trained on features extracted from some intermediate layers of a PTM for Tubercu...

In recent years, color Local Binary Pattern (LBP) based descriptors have garnered substantial attention in computer vision and image analysis. This study presents a comprehensive review of color LBP-based descriptors developed over the past decade, focusing on their performance in image retrieval tasks. The research compares these descriptors based...

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caus...

Objective. Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events. Approach. The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized data...

We applied machine learning to study associations between regional body fat distribution and diabetes mellitus in a population of community adults in order to investigate the predictive capability. We retrospectively analyzed a subset of data from the published Fasa cohort study using individual standard classifiers as well as ensemble learning alg...

Background: Artificial intelligence (AI)-based medical devices and digital health technologies such as medical sensors, wearable health trackers, telemedicine, mobile (m) Health, large language models (LLMs), and digital care twins (DCTs) have a substantial influence on the process of clinical decision support systems (CDSS) in healthcare and medic...

Background: Artificial intelligence (AI)-based medical devices and digital health technologies such as medical sensors, wearable health trackers, telemedicine, mobile (m) Health, large language models (LLMs), and digital care twins (DCTs) have a substantial influence on the process of clinical decision support systems (CDSS) in healthcare and medic...

Background: Cardiovascular diseases (CVDs) continue to be the leading cause of mortality on a global scale. In recent years, the application of artificial intelligence (AI) techniques, particularly deep learning (DL), has gained considerable popularity for evaluating the various aspects of CVDs. Moreover, using fundus images and optical coherence t...

Plagiarism detection (PD) in natural language processing involves locating sim- ilar words in two distinct sources. The paper introduces a new approach to plagiarism detection utilizing bidirectional encoder representations from trans- formers (BERT)-generated embedding, an enhanced artificial bee colony (ABC) optimization algorithm for pre-trainin...

Introduction: Congenital malformation of mitral valve could encounter in isolation or with other congenital heart diseases. Objectives: Here, we report a case of congenital hypoplastic anterior mitral valve leaflet (AMVL). Methods: A 30-year-old male with a history of diabetes mellitus was referred with pleuritic chest pain since the previous day....

This study presents finite-time synchronization of chaotic fractional order systems with disturbance uncertainty and unknown time delay. First, a PID sliding surface is presented. Then, for the finite-time synchronization of the master and slave systems, a robust sliding-adaptive control method is provided. Update rules have been retrieved to estim...

The accuracy of coronary artery disease (CAD) diagnosis is dependent on a variety of factors, including demographic, symptom, and medical examination, ECG, and echocardiography data, among others. In this context, artificial intelligence (AI) can help clinicians identify high-risk patients early in the diagnostic process, by synthesizing informatio...

A control method for the robust synchronization of a class of chaotic systems with unknown time delay, unknown uncertainty, and unknown disturbance is presented. The robust controller was designed using a nonlinear fractional order PID sliding surface. The Lyapunov method was used to determine the update laws, prove the stability of the proposed me...

Skin cancer, primarily resulting from the abnormal growth of skin cells, is among the most common cancer types. In recent decades, the incidence of skin cancer cases worldwide has risen significantly (one in every three newly diagnosed cancer cases is a skin cancer). Such an increase can be attributed to changes in our social and lifestyle habits c...

Citation: Zareiamand, H.; Darroudi, A.; Mohammadi, I.; Moravvej, S.V.; Danaei, S.; Alizadehsani, R. Cardiac Magnetic Resonance Imaging (CMRI) Applications in Patients with Chest Pain in the Emergency Department: A Narrative Review. Diagnostics 2023, 13(16), 2667. https://doi.org/10.3390/diagnostics13162667 Abstract: CMRI is the exclusive imaging te...

COVID-19 is most commonly diagnosed using a testing kit but chest X-rays and computed tomography (CT) scan images have a potential role in COVID-19 diagnosis. Currently, CT diagnosis systems based on Artificial intelligence (AI) models have been used in some countries. Previous research studies used complex neural networks, which led to difficulty...

The COVID-19 pandemic has led to the emergence of social media platforms as crucial channels for the dissemination of information and public opinion. Comprehending the sentiment conveyed in tweets on COVID-19 is of paramount importance for individuals involved in policymaking, crisis management, and public health administration. This study seeks to...

Background: Endourologic or percutaneous interventional treatments in genitourinary (GU) is a growing field of science with the advent of improved imaging techniques and smaller catheters. Hereby, we report case series of endovascular GU angioembolization procedures performed in our center. Methods: In this study, we report cases who underwent succ...

Purpose This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring. Design/methodology/approach A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augment...

Background: Equipment to assess muscle mass is not available in all health services. Yet we have limited understanding of whether applying the Global Leadership Initiative on Malnutrition (GLIM) criteria without an assessment of muscle mass affects the ability to predict adverse outcomes. This study used machine learning to determine which combina...

The accuracy of coronary artery disease (CAD) diagnosis is dependent on a variety of factors, including demographic, symptom, and medical examination, ECG, and echocardiography data, among others. In this context, artificial intelligence (AI) can help clinicians identify high-risk patients early in the diagnostic process, by synthesizing informatio...

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However,...

Three‐dimensional (3D) image reconstruction of tumours can visualise their structures with precision and high resolution. In this article, GAN‐LSTM‐3D method is proposed for 3D reconstruction of lung cancer tumours from 2D CT images. Our method consists of three phases: lung segmentation, tumour segmentation, and tumour 3D reconstruction. Lung segm...

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary...

We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic...

Researchers have proposed several approaches for neural network (NN) based uncertainty quantification (UQ). However, most of the approaches are developed considering strong assumptions. Uncertainty quantification algorithms often perform poorly in an input domain and the reason for poor performance remains unknown. Therefore, we present a neural ne...

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences....

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its assoc...

Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis but is expensive and associated with certain risks. Machine learning (ML) using clinical and noninvasive imaging parameters can be used for CAD diagnosis to avoid the side effects and cost of angiography. However, ML methods require labeled samples for effici...

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 capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algor...

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder (NDD) that is caused by genetic, epigenetic, and environmental factors. Recent advances in genomic analysis have uncovered numerous candidate genes with common and/or rare mutations that increase susceptibility to ASD. In addition, there is increasing evidence that copy n...

This paper proposes a novel approach for analyzing the stability of polynomial fractional-order systems using the frequency-distributed fractional integrator model. There are two types of frequency and temporal stabilization methods for fractional-order systems that global and semi-global stability conditions attain using the sum-of-squares (SOS) m...

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless...

Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients a...