Tamer Abuhmed

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Verified

Tamer verified their affiliation via an institutional email.

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• Ph.D.
• Professor (Associate) at Sungkyunkwan University

Efficient extraction of code authorship attributes is key for successful identification. However, the extraction of such attributes is very challenging, due to various programming language specifics, the limited number of available code samples per author, and the average code lines per file, among others. To this end, this work proposes a Deep Lea...

Smartphones have become crucial for our daily life activities and are increasingly loaded with our personal information to perform several sensitive tasks including mobile banking, communication, and are used for storing private photos and files. Therefore, there is a high demand for applying usable authentication techniques that prevent unauthoriz...

Early prediction of Alzheimer’s disease (AD) is crucial for delaying its progression. As a chronic disease, ignoring the temporal dimension of AD data affects the performance of a progression detection and medically unacceptable. Besides, AD patients are represented by heterogeneous, yet complementary, multimodalities. Multitask modeling improves p...

The prevalence of Alzheimer’s disease (AD) in the growing elderly population makes accurately predicting AD progression crucial. Due to AD’s complex etiology and pathogenesis, an effective and medically practical solution is a challenging task. In this paper, we developed and evaluated two novel hybrid deep learning architectures for AD progression...

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model’s decision-making. Thus, the need for eXplainable AI (XAI) methods f...

Recently, automatic disease diagnosis based on medical images has become an integral part of digital pathology packages. To create, develop, evaluate, and compare these systems, we need diverse data sets. One of the key features in the diagnosis of bone diseases is measuring bone mineral density (BMD). Most research in this field uses manual method...

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, me...

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer's Disease Neur...

Federated Learning (FL) offers a privacy-preserving solution by enabling multiple clients to train a shared model collaboratively without centralizing data. However, the decentralized nature of FL presents challenges, particularly regarding security and performance under adversarial conditions. This paper investigates the effects of poisoning attac...

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confiden...

Digital twins (DTs) have emerged as a groundbreaking development in digital technology, providing intricate digital representations of physical entities for remote, real-time monitoring and analysis. This paper provides a survey based on the existing DT literature in the healthcare domain. We outline the key DT attributes and investigate the contem...

The Fitzpatrick scale is a widely used tool in dermatology for categorizing skin types based on melanin levels and sensitivity to ultraviolet light. The primary objective of this study is to enhance the accuracy of Fitzpatrick scale classification by addressing limitations in existing methodologies. Current approaches either rely on custom-designed...

Deep neural network (DNN) models are susceptible to adversarial samples in white-box and opaque environments. Although previous studies have shown high attack success rates, coupling DNN models with interpretation models could offer a sense of security when a human expert is involved. However, in white-box environments, interpretable deep learning...

The current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs. We examine factors that may influence model reliability, including model complexity, training data quality, and hyperparameters; w...

Background: Depression is a pervasive mental health condition, particularly affecting older adults, where early detection and intervention are essential to mitigate its impact. This study presents an explainable multi-layer dynamic ensemble framework designed to detect depression and assess its severity, aiming to improve diagnostic precision and p...

In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several quality degradations resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination. Therefor...

Background and Objective: The importance of early diagnosis of Alzheimer's Disease (AD) is by no means negligible because no cure has been recognized for it rather than some therapies only lowering the pace of progression. The research gap reveals information on the lack of an automatic non-invasive approach toward the diagnosis of AD, in particula...

Sepsis remains a complex, life-threatening condition characterized by an overwhelming immune response to infection, leading to high mortality rates in hospital settings. Rapid and precise diagnosis is crucial to improving survival rates, but current practices lack personalized, predictive tools. The emergence of electronic health records has spurre...

Deep Learning (DL) is rapidly maturing to the point that it can be used in safety- and security-crucial applications, such as self-driving vehicles, surveillance, drones, and robots. However, adversarial samples, which are undetectable to the human eye, pose a serious threat that can cause the model to misbehave and compromise the performance of su...

Rapid advancements of deep learning are accelerating adoption in a wide variety of applications, including safety-critical applications such as self-driving vehicles, drones, robots, and surveillance systems. These advancements include applying variations of sophisticated techniques that improve the performance of models. However, such models are n...

Establishing trust and helping experts debug and understand the inner workings of deep learning models, interpretation methods are increasingly coupled with these models, building interpretable deep learning systems. However, adversarial attacks pose a significant threat to public trust by making interpretations of deep learning models confusing an...

The fusion of multimodal longitudinal data is difficult but crucial for enhancing the accuracy of deep learning models for disease identification and helps provide tailored and patient-centric decisions. This study explores the fusion of multimodal data to detect the progression of Alzheimer’s disease (AD) using ensemble learning. We propose a hete...

Dust pollution poses significant risks to human health, air quality, and food safety, necessitating the identification of dust occurrence and the development of dust susceptibility maps (DSMs) to mitigate its effects. This research aims to detect dust occurrence using satellite images and prepare a DSM for Bushehr province, Iran, by enhancing the a...

Traditional one-time authentication mechanisms cannot authenticate smartphone users’ identities throughout the session – the concept of using behavioral-based biometrics captured by the built-in motion sensors and touch data is a candidate to solve this issue. Many studies proposed solutions for behavioral-based continuous authentication; however,...

Urban gas pipelines pose significant risks to public safety and infrastructure integrity, necessitating thorough risk assessment methodologies to mitigate potential hazards. This study investigates the dynamics of population distribution, demographic characteristics, and building structures to assess the risk associated with gas pipelines. Using ge...

The challenge of making flexible, standard, and early medical diagnoses is significant. However, some limitations are not fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secon...

Ensuring precise and reliable classification effectiveness holds paramount importance in essential sectors such as medicine, industry, and healthcare. Machine learning (ML) techniques have evolved in recent years to address the performance, efficiency, and robustness of the applied models. Recent advancements in Machine Learning (ML) techniques hav...

Deep learning methods have gained increasing attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems...

Alzheimer’s disease (AD) is a complex chronic neurodegenerative disease that propagates over time. Deep learning (DL) models can be used to learn time series data to extract deep temporal features and make robust decisions. The fusion of multimodal time series data has been proven to enhance model performance. For instance, cognitive scores (CSs) i...

Artificial intelligence (AI)-based diagnostic systems provide less error-prone and safer support to clinicians, enhancing the medical decision-making process. This study presents a smart and reliable healthcare framework for detecting Alzheimer's disease (AD) progression. Early detection of AD before the onset of clinical symptoms is the most cruci...

Alzheimer’s disease (AD) is the most common form of dementia. Early and accurate detection of AD is crucial to plan for disease modifying therapies that could prevent or delay the conversion to sever stages of the disease. As a chronic disease, patient’s multivariate time series data including neuroimaging, genetics, cognitive scores, and neuropsyc...

The digitalization of music has led to increased availability of music globally, and this spread has further raised the possibility of plagiarism. Numerous methods have been proposed to analyze the similarity between two pieces of music. However, these traditional methods are either focused on good processing speed at the expense of accuracy or the...

Deep learning has been rapidly employed in many applications revolutionizing many industries, but it is known to be vulnerable to adversarial attacks. Such attacks pose a serious threat to deep learning-based systems compromising their integrity, reliability, and trust. Interpretable Deep Learning Systems (IDLSes) are designed to make the system mo...

Deep learning models are susceptible to adversarial samples in white and black-box environments. Although previous studies have shown high attack success rates, coupling DNN models with interpretation models could offer a sense of security when a human expert is involved, who can identify whether a given sample is benign or malicious. However, in w...

In this paper, we present a novel Single-class target-specific Adversarial attack called SingleADV. The goal of SingleADV is to generate a universal perturbation that deceives the target model into confusing a specific category of objects with a target category while ensuring highly relevant and accurate interpretations. The universal perturbation...

In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several types of quality degradation resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination....

The challenge of making flexible, standard, and early medical diagnoses is significant. It has some limitations that it hasn't fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances....

Recurring wildfires pose a critical global issue as they undermine social and economic stability and jeopardize human lives. To effectively manage disasters and bolster community resilience, the development of wildfire susceptibility maps (WFSMs) has emerged as a crucial undertaking in recent years. In this research endeavor, two deep learning algo...

This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several algorithms are utilized such as k-nearest neighbor, Random Forest (RF), C4.5, Artificial Neural Net...

Spatial intelligence (SI) is the leverage that helps students to have a deeper understanding of concepts of science, technology, engineering and mathematics (STEM) and obtain outstanding academic achievement in STEM. The main objective of this research is to find effective factors in SI. Then, based on those factors, a machine learning (ML) model i...

A new probabilistic framework is proposed for providing a distribution-free prediction interval (PI) of seismic responses required for various earthquake engineering applications. The framework overcomes the limitation of point prediction models and avoids the complexity of traditional probabilistic methods. The framework utilizes a few assumptions...

The precise diagnosis of Alzheimer's disease is critical for patient treatment, especially at the early stage, because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage occurs. It is possible to gain a holistic view of Alzheimer's disease staging by combining multiple data modal...

Background and Objectives: Parkinson’s Disease (PD) is a devastating chronic neurological condition. Machine learning (ML) techniques have been used in the early prediction of PD progression. Fusion of heterogeneous data modalities proved its capability to improve the performance of ML models. Time series data fusion supports the tracking of the di...

Medical applications of Artificial Intelligence (AI) have consistently shown remarkable performance in providing medical professionals and patients with support for complex tasks. Nevertheless, the use of these applications in sensitive clinical domains where high-stakes decisions are involved could be much more extensive if patients, medical profe...

Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of m...

Cameras are becoming more pervasive and ubiquitous. The daily activities of individuals are being captured by millions of cameras in public spaces, while individuals are obtaining massive amounts of egocentric videos by employing wearable cameras intended for life-logging. However, recording devices are inexpensive, highly computational, and inconv...

In today’s world, technology has assumed an indispensable role in everyday life. However, though developments in the Internet of Things (IoT) have laid a foundation achieving different educational purposes, IoT is not being employed to its full potential. A separate issue is that the topic of geometry has been neglected or received only limited att...

Alzheimer's disease (AD) is a neurological illness that causes cognitive impairment and has no known treatment. The premise for delivering timely therapy is the early diagnosis of AD before clinical symptoms appear. Mild cognitive impairment is an intermediate stage in which cognitively normal patients can be distinguished from those with AD. In th...

Deep learning methods have gained increased attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems....

With the emergence of the metaverse, the popularity of augmented reality (AR) is increasing; accessing concise, accurate, and precise information in this field is becoming challenging on the world wide web. In regard to accessing the right information through search engines, semantic information retrieval via a semantic analysis delivers more relev...

Robust and rabid mortality prediction is crucial in intensive care units because it is considered one of the critical steps for treating patients with serious conditions. Combining mortality prediction with the length of stay (LoS) prediction adds another level of importance to these models. No studies in the literature predict such tasks for neona...

Predicting Alzheimer’s disease (AD) progression is crucial for improving the management of this chronic disease. Usually, data from AD patients are multimodal and time series in nature. This study proposes a novel ensemble learning framework for AD progression incorporating heterogeneous base learners into an integrated model using the stacking tec...

In a hospital, accurate and rapid mortality prediction of Length of Stay (LOS) is essential since it is one of the essential measures in treating patients with severe diseases. When predictions of patient mortality and readmission are combined, these models gain a new level of significance. The likelihood of a patient being readmitted to the hospit...

Despite the benefits of the Internet of Things (IoT), the growing influx of IoT-specific malware coordinating large-scale cyberattacks via infected IoT devices has created a substantial threat to the internet ecosystem. Assessing IoT systems’ security and developing mitigation measures to prevent the spread of IoT malware is therefore critical. Fur...

Cutaneous leishmaniasis is a complex infection that is caused by different species of Leishmania and affects more than 2 million people in 88 countries. Identifying the environmental factors affecting the occurrence of cutaneous leishmaniasis and preparing a risk map is one of the basic tools to control and manage this disease. The aim of this stud...

Most countries and territories worldwide are affected by coronavirus disease 2019 (COVID-19), and some cities have become known as epicenters owing to high outbreaks. Because of the changeable and unknown nature of the virus, managers of different cities could learn from the experiences of cities that have been successful in controlling COVID-19 in...

During the outbreak of the COVID-19 pandemic, social networks became the preeminent medium for communication, social discussion, and entertainment. Social network users are regularly expressing their opinions about the impacts of the coronavirus pandemic. Therefore, social networks serve as a reliable source for studying the topics, emotions, and a...

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by thinking, behavioral and memory impairments. Early prediction of conversion from mild cognitive impairment (MCI) to AD is still a challenging task. No study has been able to predict the exact conversion time of MCI patients. In addition, most studies have achieve...

In recent years, the use of wearable sensors and social networking in the healthcare industry has been rapidly increasing. Wearable sensors are utilized to continuously monitor a patient's body internally and externally to detect chronic diseases, such as Alzheimer's disease (AD) and heart disease. Social network data are utilized to identify vario...

In this study, isothermal compression tests of highly ductile AZ31-0.5Ca Mg alloys were conducted at different strain rates (0.001–0.1 s⁻¹) and temperatures (423–523 K) along with extruded direction. The flow stress characteristics were evaluated at elevated temperatures. In addition, a strain-dependent constitutive model based on the Arrhenius equ...

This project presents the source code of a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original article introducing this algorithm is: Rezaei, F.; Safavi, H.R.; Abd Elaziz, M.; El-Sappagh, S.H.A.; Al-Betar, M.A.; Abuhmed, T. An Enhanced Grey Wolf Optimizer with a Velocity-Aided...

This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into...

Deep Neural Networks (DNNs) have achieved state-of-the-art performance in various applications. It is crucial to verify that the high accuracy prediction for a given task is derived from the correct problem representation and not from the misuse of artifacts in the data. Hence, interpretation models have become a key ingredient in developing deep l...

Sepsis is a life-threatening disease that is associated with organ dysfunction. It occurs due to the body’s dysregulated response to infection. It is difficult to identify sepsis in its early stages, this delay in identification has a dramatic effect on mortality rate. Developing prognostic tools for sepsis prediction has been the focus of various...

In this work, a machine learning (ML) model was created to predict intrinsic hardness of various compounds using their crystal chemistry. For this purpose, an initial dataset, containing the hardness values of 270 compounds and counterpart applied loads, was employed in the learning process. Based on various features generated using crystal informa...

Early detection of Alzheimer’s disease (AD) progression is crucial for proper disease management. Most studies concentrate on neuroimaging data analysis of baseline visits only. They ignore the fact that AD is a chronic disease and patient’s data are naturally longitudinal. In addition, there are no studies that examine the effect of dementia medic...

In the present work, multi-objective evolutionary (MOE) algorithm and machine learning (ML) techniques were employed to predict the age-hardening behavior of aluminum (Al) alloys in a wide range of processing conditions. For this purpose, data containing hardness, information on alloy compositions, and aging conditions (aging time and temperature)...

Alzheimer’s disease (AD) is a very complex disease that causes brain failure, then eventually, dementia ensues. It is a global health problem. 99% of clinical trials have failed to limit the progression of this disease. The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms. Therapies...

In recent years, the rapid improvement in computing facilities combined with that achieved in algorithms and the immense amount of available data led to a great interest in machine learning (ML), which is a subset of artificial intelligence. Nowadays, the ML technique is used mostly in all applications for various purposes, whereby ML will be possi...

Successful software authorship de-anonymization has both software forensics applications and privacy implications. However, the process requires an efficient extraction of authorship attributes. The extraction of such attributes is very challenging, due to various software code formats from executable binaries with different toolchain provenance to...

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artifi...

The super packed functionalities and artificial intelligence (AI)-powered applications have made the Android operating system a big player in the market. Android smartphones have become an integral part of life and users are reliant on their smart devices for making calls, sending text messages, navigation, games, and financial transactions to name...

During the outbreak of the COVID-19 pandemic, social networks become the preeminent medium for communication, social discussion, and entertainment. Social network users are regularly expressing their opinions about the impacts of the coronavirus pandemic. Therefore, social networks serve as a reliable source for studying the topics, emotions, and a...

Alzheimer’s disease (AD) is a severe neurodegenerative disease. The identification of patients at high risk of conversion from mild cognitive impairment to AD via earlier close monitoring, targeted investigations, and appropriate management is crucial. Recently, several machine learning (ML) algorithms have been used for AD progression detection. M...

Multi-label classification (MLC) is considered an essential research subject in the computer vision field, principally in medical image analysis. For this merit, we derive benefits from MLC to diagnose multiple grades of diabetic retinopathy (DR) from various colored fundus images, especially from multi-label (ML) datasets. Therefore, ophthalmologi...

In this work, various machine learning (ML) techniques were employed to accelerate the designing of aluminum (Al) alloys with improved performance based on the age hardening concept. For this purpose, data of Al-Cu-Mg-x (x: Zn, Zr, etc.) alloys, including composition, aging condition (time and temperature), important physical and chemical propertie...

Surface electromyogram (sEMG) processing and classification can assist neurophysiological standardization and evaluation and provide habitational detection. The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals. Understanding muscle activation timing allows identification of muscle locati...

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high...

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder. Early prediction of Alzheimer’s progression is a crucial process for the patients and their families. As a chronic disease, AD data are multimodal and time series in nature. Building a deep learning model to optimize multi-objective cost function produces a more stable and accurate m...

Spatial modulation (SM) is a multiple-input multiple-output (MIMO) technique that achieves a MIMO capacity by conveying information through antenna indices, while keeping the transmitter as simple as that of a single-input system. Quadrature SM (QSM) expands the spatial dimension of the SM into in-phase and quadrature dimensions, which are used to...

The intensive care unit (ICU) admits the most seriously ill patients requiring extensive monitoring. Early ICU mortality prediction is crucial for identifying patients who are at great risk of dying and for providing suitable interventions to save their lives. Accordingly, early prediction of patients at high mortality risk will enable their provis...

Most authorship identification schemes assume that code samples are written by a single author. However, real software projects are typically the result of a team effort, making it essential to consider a finegrained multi-author identification in a single code sample, which we address with Multi-χ. Multi-χ leverages a deep learning-based approach...

Most authorship identification schemes assume that code samples are written by a single author. However, real software projects are typically the result of a team effort, making it essential to consider a fine-grained multi-author identification in a single code sample , which we address with Multi-χ. Multi-χ leverages a deep learning-based approac...