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Articles 9151 - 9180 of 63016

Full-Text Articles in Computer Sciences

Pdf Malware Detection: Toward Machine Learning Modeling With Explainability Analysis, G. M.Sakhawat Hossain, Kaushik Deb, Helge Janicke, Iqbal H. Sarker Jan 2024

Pdf Malware Detection: Toward Machine Learning Modeling With Explainability Analysis, G. M.Sakhawat Hossain, Kaushik Deb, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

The Portable Document Format (PDF) is one of the most widely used file types, thus fraudsters insert harmful code into victims' PDF documents to compromise their equipment. Conventional solutions and identification techniques are often insufficient and may only partially prevent PDF malware because of their versatile character and excessive dependence on a certain typical feature set. The primary goal of this work is to detect PDF malware efficiently in order to alleviate the current difficulties. To accomplish the goal, we first develop a comprehensive dataset of 15958 PDF samples taking into account the non-malevolent, malicious, and evasive behaviors of the …

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A Scoping Review On Mobile Health Technology For Assessment And Intervention Of Upper Limb Motor Function In Children With Motor Impairments, Md Raihan Mia, Sheikh Iqbal Ahamed, Alissa V. Fial, Samuel Nemanich Jan 2024

A Scoping Review On Mobile Health Technology For Assessment And Intervention Of Upper Limb Motor Function In Children With Motor Impairments, Md Raihan Mia, Sheikh Iqbal Ahamed, Alissa V. Fial, Samuel Nemanich

Computer Science Faculty Research and Publications

Upper limb (UL) motor dysfunctions impact residual movement in hands/shoulders and limit participation in play, sports, and leisure activities. Clinical and laboratory assessments of UL movement can be time-intensive, subjective, and/or require specialized equipment and may not optimally capture a child's motor abilities. The restrictions to in-person research experienced during the COVID-19 pandemic have inspired investigators to design inclusive at-home studies with child participants and their families. Relying on the ubiquity of mobile devices, mobile health (mHealth) applications offer solutions for various clinical and research problems. This scoping review article aimed to aggregate and synthesize existing research that used health …

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Accelerating Cavity Fault Prediction Using Deep Learning At Jefferson Laboratory, Md M. Rahman, A. Carpenter, K. Iftekharuddin, C. Tennant Jan 2024

Accelerating Cavity Fault Prediction Using Deep Learning At Jefferson Laboratory, Md M. Rahman, A. Carpenter, K. Iftekharuddin, C. Tennant

Electrical & Computer Engineering Faculty Publications

Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive …

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A Signal Injection Attack Against Zero Involvement Pairing And Authentication For The Internet Of Things, Isaac Ahlgren, Jack West, Kyuin Lee, George K. Thiruvathukal, Neil Klingensmith Jan 2024

A Signal Injection Attack Against Zero Involvement Pairing And Authentication For The Internet Of Things, Isaac Ahlgren, Jack West, Kyuin Lee, George K. Thiruvathukal, Neil Klingensmith

Computer Science: Faculty Publications and Other Works

Zero Involvement Pairing and Authentication (ZIPA) is a promising technique for auto-provisioning large networks of Internet-of-Things (IoT) devices. In this work, we present the first successful signal injection attack on a ZIPA system. Most existing ZIPA systems assume there is a negligible amount of influence from the unsecured outside space on the secured inside space. In reality, environmental signals do leak from adjacent unsecured spaces and influence the environment of the secured space. Our attack takes advantage of this fact to perform a signal injection attack on the popular Schurmann & Sigg algorithm. The keys generated by the adversary with …

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Actionpoint: An App To Combat Cyberbullying By Strengthening Parent-Teen Relationships, Maddie Juarez, Natali Barragan, Deborah Hall, George K. Thiruvathukal, Yasin Silva Jan 2024

Actionpoint: An App To Combat Cyberbullying By Strengthening Parent-Teen Relationships, Maddie Juarez, Natali Barragan, Deborah Hall, George K. Thiruvathukal, Yasin Silva

Computer Science: Faculty Publications and Other Works

Overview: Urgent need for intervention tools to mitigate increase in cyberbullying • ActionPoint is based on parent-teen relationship research • App includes interactive modules to improve family communication and online behavior to combat cyberbullying effects • To be presented in the IEEE World Forum on Public Safety Technology 2024.

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Actionpoint: An App To Combat Cyberbullying Via The Strengthening Of Parent-Teen Relationships, Maddie Juarez, Natali Barragan, Deborah Hall, George K. Thiruvathukal, Yasin N. Silva Jan 2024

Actionpoint: An App To Combat Cyberbullying Via The Strengthening Of Parent-Teen Relationships, Maddie Juarez, Natali Barragan, Deborah Hall, George K. Thiruvathukal, Yasin N. Silva

Computer Science: Faculty Publications and Other Works

Due to the increased prevalence of cyberbullying and the detrimental impact it can have on adolescents, there is a critical need for tools to help combat cyberbullying. This paper introduces the ActionPoint app, a mobile application based on empirical work highlighting the importance of strong parent-teen relationships for reducing cyberbullying risk. The app is designed to help families improve their communication skills, set healthy boundaries for social media use, identify instances of cyberbullying and cyberbullying risk, and, ultimately, decrease the negative outcomes associated with cyberbullying. The app guides parents and teens through a series of interactive modules that engage them …

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Advancing Temporal Sepsis Biomarking: Covariate Vascular Endothelial Growth Factor A And B Gene Expression Profiling In A Murine Model Of Sars-Cov Infection, Asrar Rashid, Feras Al-Obeidat, Kesava Ramakrishnan, Wael Hafez, Nouran Hamza, Zainab A. Malik, Raziya Kadwa, Muneir Gador, Govind Benakatti, Rayaz A. Malik, Ibrahim Elbialy, Hekmieh Manad, Guftar Shaikh, Ahmed Al-Dubai, Amir Hussain Jan 2024

Advancing Temporal Sepsis Biomarking: Covariate Vascular Endothelial Growth Factor A And B Gene Expression Profiling In A Murine Model Of Sars-Cov Infection, Asrar Rashid, Feras Al-Obeidat, Kesava Ramakrishnan, Wael Hafez, Nouran Hamza, Zainab A. Malik, Raziya Kadwa, Muneir Gador, Govind Benakatti, Rayaz A. Malik, Ibrahim Elbialy, Hekmieh Manad, Guftar Shaikh, Ahmed Al-Dubai, Amir Hussain

All Works

The limited specificity of standard inflammatory biomarkers poses a challenge for the diagnosis and monitoring of sepsis. The differential gene expression patterns of Vascular Endothelial Growth Factor A and B (VEGF-A and B) are promising candidates. This study aimed to elucidate variations in VEGF-A/B gene expression following SARS-CoV MA15 disease initiation. Biomarker tracking was examined in a murine C57BL wild-type (WT) genotype MA15 (SARS-CoV) nasal instillation model. In [GSE40824], the expression of TNF and VEGF-A significantly differed between the groups (p = 1.53e-07, and 0.0043) and over time. In [GSE40827], [GSE51386], [GSE51387], and [GSE40840], the expression of TNF, VEGF-A, and …

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Unlocking The Potential Of Simulated Hyperspectral Imaging In Agro Environmental Analysis: A Comprehensive Study Of Algorithmic Approaches, Shafaq Khan, Munir Majdalawieh, Boubakeur Boufama, Yajan Sharma, Ashwitha Basani Jan 2024

Unlocking The Potential Of Simulated Hyperspectral Imaging In Agro Environmental Analysis: A Comprehensive Study Of Algorithmic Approaches, Shafaq Khan, Munir Majdalawieh, Boubakeur Boufama, Yajan Sharma, Ashwitha Basani

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This study focuses on identifying and evaluating the severity of powdery mildew disease in tomato plants. The uniqueness of this work lies in combining the imaging and advanced deep learning methods to develop a technique that transforms Red Green Blue (RGB) images into Simulated Hyperspectral Images (SHSI) to perform spectral and spatial analysis for precise detection and assessment of powdery mildew severity, thereby enhancing disease management. Furthermore, this research evaluates three advanced pre-trained VGG16 models, ResNet50 and EfficientNet-B7 algorithms for image preprocessing and feature extraction. Extracted features are passed to a neural network generator model to convert RGB image features …

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Fraud Detection In Medical Insurance Claims Using Majority Voting Of Multiple Unsupervised Algorithms, Mohamed Ahmed Abo El-Enen, Dina Tbaishat, Ahmed T. Sahlol, Amril Nazir, Khalid Almaymun, Mustafa Abdulrazek, Reem Muhammad, Fatima Adlan, Ravishankar Sharma Jan 2024

Fraud Detection In Medical Insurance Claims Using Majority Voting Of Multiple Unsupervised Algorithms, Mohamed Ahmed Abo El-Enen, Dina Tbaishat, Ahmed T. Sahlol, Amril Nazir, Khalid Almaymun, Mustafa Abdulrazek, Reem Muhammad, Fatima Adlan, Ravishankar Sharma

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This paper addresses the critical challenge of fraud detection in medical insurance claims, a pervasive issue causing significant financial losses in healthcare. The primary goal is to develop an advanced fraud detection approach by integrating multiple unsupervised machines learning algorithms, leveraging their collective strengths through a majority voting mechanism, where labelling of data is unavailable. Central to this approach is the ensemble of 18 novel unsupervised algorithms, specifically, anomaly detection models. The novelty lies in the majority voting system employed to aggregate the decisions from these diverse algorithms, enhancing the reliability and accuracy of fraud detection. To validate the effectiveness …

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Enhancing Medication Adherence With Chronic Diseases Through Iot Technology: A Novel Approach, Nadia Dahmani, Suja A. Alex Jan 2024

Enhancing Medication Adherence With Chronic Diseases Through Iot Technology: A Novel Approach, Nadia Dahmani, Suja A. Alex

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This paper proposes a novel IoT-based Medication Adherence System to combat the pervasive issue of non-adherence among chronic disease patients. This system leverages real-time monitoring and timely reminders to improve medication intake and, consequently, patient outcomes. We delve into the factors behind non-adherence and explore how IoT technology can empower patient education and alleviate medication anxieties. The study emphasizes the significance of proactive interventions in fostering adherence and ultimately improving health for those with chronic conditions. Advocating for a holistic approach that merges patient education, behavioral modifications, and technological advancements, this research proposes a transformative model for chronic disease management. …

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Potato Leaf Disease Detection Approach Based On Transfer Learning With Spatial Attention, Rima Grati, Emna Abdallah, Khouloud Boukadi, Ahmed Smaoui Jan 2024

Potato Leaf Disease Detection Approach Based On Transfer Learning With Spatial Attention, Rima Grati, Emna Abdallah, Khouloud Boukadi, Ahmed Smaoui

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No abstract provided.

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Mixed Criticality Reward-Based Systems Using Resource Reservation, Amjad Ali, Shah Zeb, Madallah Alruwaili, Asad Masood Khattak, Bashir Hayat, Ki Il Kim Jan 2024

Mixed Criticality Reward-Based Systems Using Resource Reservation, Amjad Ali, Shah Zeb, Madallah Alruwaili, Asad Masood Khattak, Bashir Hayat, Ki Il Kim

All Works

Real-time systems mostly interact with the external world and each input operation must meet predetermined deadlines to be useful. However, in many real-time applications, a partial result is also acceptable. We developed a reward-based mixed criticality system based on the resource reservation approach to address the problem of ensuring the effective execution of low- and high-criticality tasks in both low- and high modes, even under heavy workloads. Using dedicated servers with pessimistic resource allocation for each high criticality task ensured their execution in both modes unaffected by low criticality tasks. The surplus resources are reclaimed and assigned to low critical …

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Diagnostic Performance Of Ai-Based Models Versus Physicians Among Patients With Hepatocellular Carcinoma: A Systematic Review And Meta-Analysis, Feras Al-Obeidat, Wael Hafez, Muneir Gador, Nesma Ahmed, Marwa Muhammed Abdeljawad, Antesh Yadav, Asrar Rashed Jan 2024

Diagnostic Performance Of Ai-Based Models Versus Physicians Among Patients With Hepatocellular Carcinoma: A Systematic Review And Meta-Analysis, Feras Al-Obeidat, Wael Hafez, Muneir Gador, Nesma Ahmed, Marwa Muhammed Abdeljawad, Antesh Yadav, Asrar Rashed

All Works

Background: Hepatocellular carcinoma (HCC) is a common primary liver cancer that requires early diagnosis due to its poor prognosis. Recent advances in artificial intelligence (AI) have facilitated hepatocellular carcinoma detection using multiple AI models; however, their performance is still uncertain. Aim: This meta-analysis aimed to compare the diagnostic performance of different AI models with that of clinicians in the detection of hepatocellular carcinoma. Methods: We searched the PubMed, Scopus, Cochrane Library, and Web of Science databases for eligible studies. The R package was used to synthesize the results. The outcomes of various studies were aggregated using fixed-effect and random-effects models. …

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An Enhanced Deep Autoencoder For Flight Delay Prediction, Desmond B. Bisandu, Dan Andrei Soviani-Sitoiu, Irene Moulitsas Jan 2024

An Enhanced Deep Autoencoder For Flight Delay Prediction, Desmond B. Bisandu, Dan Andrei Soviani-Sitoiu, Irene Moulitsas

Journal of Aviation/Aerospace Education & Research

Accurate and timely flight delay prediction cannot be overemphasized because of the ever-increasing demand for air travel and its importance in deploying intelligent transportation systems. Nonetheless, there has not been a universal solution to the problem, as more intelligent flight decision systems are required for the aviation industry's future growth. Existing flight delay classification and prediction approaches are mainly shallow traffic models and do not satisfy many applications in the real world. Our motivation to rethink the deep architecture model for predicting flight delays emanates from the problem. In this research, we proposed a technique that modified stacked autoencoder architecture …

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Sub-Band Backdoor Attack In Remote Sensing Imagery, Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu, Jiang Li Jan 2024

Sub-Band Backdoor Attack In Remote Sensing Imagery, Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in …

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Different Visions From Biosview: A Brief Report, Lucas N. Potter, Xavier-Lewis Palmer Jan 2024

Different Visions From Biosview: A Brief Report, Lucas N. Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

In this collaborative research endeavor at the intersection of biological safety and cybersecurity for BiosView labs, the authors highlight their engagement with a diverse student cohort. The chapter delves into the motivation behind collaborations extending beyond traditional academic research environments, emphasizing inclusivity. The meticulous examination of student demographics, including gender, self-reported ethnicity, and national origin, is detailed in the methodology. A student-centric approach is central to the exploration, focusing on aligning teaching and management styles with unique student needs. The chapter elaborates on effective teaching methodologies and management practices tailored for BiosView labs. A dedicated section emphasizes the purpose of …

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Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin Jan 2024

Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …

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Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen Jan 2024

Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …

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Deapsecure Computational Training For Cybersecurity: Progress Toward Widespread Community Adoption, Wirawan Purwanto, Bahador Dodge, Karina Arcaute, Masha Sosonkina, Hongyi Wu Jan 2024

Deapsecure Computational Training For Cybersecurity: Progress Toward Widespread Community Adoption, Wirawan Purwanto, Bahador Dodge, Karina Arcaute, Masha Sosonkina, Hongyi Wu

Electrical & Computer Engineering Faculty Publications

The Data-Enabled Advanced Computational Training Program for Cybersecurity Research and Education (DeapSECURE) is a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. Since 2020, these lesson modules have been updated and retooled to suit fully-online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, we summarize the four years of the project comparing in-person and on-line only instruction methods as well as outlining …

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Beyond Binary: Revealing Variations In Islamophobic Content With Hierarchical Multi-Class Classification, Esraa Aldreabi, Khawlah M. Harahsheh, Mukul Dev Chhangani, Chung-Hao Chen, Jeremy Blackburn Jan 2024

Beyond Binary: Revealing Variations In Islamophobic Content With Hierarchical Multi-Class Classification, Esraa Aldreabi, Khawlah M. Harahsheh, Mukul Dev Chhangani, Chung-Hao Chen, Jeremy Blackburn

Electrical & Computer Engineering Faculty Publications

In the digital age, the rise of Islamophobia-marked by an irrational fear or discrimination against Islam and Muslims-has emerged as a pressing issue, especially on social media platforms. In this paper we employs a multi-class classification system, moving beyond traditional binary models. We categorize Islamophobic content into three main classes and various subclasses, covering a range from subtle biases to explicit incitement. Comparative analysis of data from Reddit and Twitter illuminates the distinct prevalence and types of Islamophobic content specific to each platform. This paper deepens our understanding of digital Islamophobia and provides insights for crafting targeted online counter strategies. …

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Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim Jan 2024

Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim

Electrical & Computer Engineering Faculty Publications

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …

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Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen Jan 2024

Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In this paper, we implement a comprehensive three-class system to categorize social media discussions about Islam and Muslims, enhancing the typical binary approach. These classes are: I) General Discourse About Islam and Muslims, II) Criticism of Islamic Teachings and Figures, and III) Comments Against Muslims. These categories are designed to balance the nuances of free speech while protecting diverse groups like Muslims, ex-Muslims, LGBTQ+ communities, and atheists. By utilizing machine learning and employing transformer-based models, we analyze the distribution and characteristics of these classes in social media content. Our findings reveal distinct patterns of user engagement with topics related to …

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Ensemble Learning With Sleep Mode Management To Enhance Anomaly Detection In Iot Environment, Khawlah Harahsheh, Rami Al-Naimat, Malek Alzaqebah, Salam Shreem, Esraa Aldreabi, Chung-Hao Chen Jan 2024

Ensemble Learning With Sleep Mode Management To Enhance Anomaly Detection In Iot Environment, Khawlah Harahsheh, Rami Al-Naimat, Malek Alzaqebah, Salam Shreem, Esraa Aldreabi, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid proliferation of Internet of Things (IoT) devices has underscored the critical need for energy-efficient cybersecurity measures. This presents the dual challenge of maintaining robust security while minimizing power consumption. Thus, this paper proposes enhancing the machine learning performance through Ensemble Techniques with Sleep Mode Management (ELSM) approach for IoT Intrusion Detection Systems (IDS). The main challenge lies in the high-power consumption attributed to continuous monitoring in traditional IDS setups. ELSM addresses this challenge by introducing a sophisticated sleep-awake mechanism, activating the IDS system only during anomaly detection events, effectively minimizing energy expenditure during periods of normal network operation. …

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Skipresnet: Crop And Weed Recognition Based On The Improved Resnet, Wenyi Hu, Tian Chen, Chunjie Lan, Shan Liu, Lirong Yin Jan 2024

Skipresnet: Crop And Weed Recognition Based On The Improved Resnet, Wenyi Hu, Tian Chen, Chunjie Lan, Shan Liu, Lirong Yin

Electrical & Computer Engineering Faculty Publications

Weeds have a detrimental effect on crop yield. However, the prevailing chemical weed control methods cause pollution of the ecosystem and land. Therefore, it has become a trend to reduce dependence on herbicides; realize a sustainable, intelligent weed control method; and protect the land. In order to realize intelligent weeding, efficient and accurate crop and weed recognition is necessary. Convolutional neural networks (CNNs) are widely applied for weed and crop recognition due to their high speed and efficiency. In this paper, a multi-path input skip-residual network (SkipResNet) was put forward to upgrade the classification function of weeds and crops. It …

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Predictions Of Lattice Parameters In Niti High-Entropy Shape-Memory Alloys Using Different Machine Learning Models, Tu-Ngoc Lam, Jiajun Jiang, Min-Cheng Hsu, Shr-Ruei Tsai, Mao-Yuan Luo, Shuo-Ting Hsu, Wen-Jay Lee, Chung-Hao Chen, E-Wen Huang Jan 2024

Predictions Of Lattice Parameters In Niti High-Entropy Shape-Memory Alloys Using Different Machine Learning Models, Tu-Ngoc Lam, Jiajun Jiang, Min-Cheng Hsu, Shr-Ruei Tsai, Mao-Yuan Luo, Shuo-Ting Hsu, Wen-Jay Lee, Chung-Hao Chen, E-Wen Huang

Electrical & Computer Engineering Faculty Publications

This work applied three machine learning (ML) models—linear regression (LR), random forest (RF), and support vector regression (SVR)—to predict the lattice parameters of the monoclinic B19′ phase in two distinct training datasets: previously published ZrO₂-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed that LR provided the most accurate predictions for ac, am, bm, and cm in NiTi-based HESMAs, while RF excelled in computing βm for both datasets. SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets. A combination approach …

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Scene Classification Of Remote Sensing Image Based On Multi-Path Reconfigurable Neural Network, Wenyi Hu, Chunjie Lan, Tian Chen, Shan Liu, Lirong Yin, Lei Wang Jan 2024

Scene Classification Of Remote Sensing Image Based On Multi-Path Reconfigurable Neural Network, Wenyi Hu, Chunjie Lan, Tian Chen, Shan Liu, Lirong Yin, Lei Wang

Electrical & Computer Engineering Faculty Publications

Land image recognition and classification and land environment detection are important research fields in remote sensing applications. Because of the diversity and complexity of different tasks of land environment recognition and classification, it is difficult for researchers to use a single model to achieve the best performance in scene classification of multiple remote sensing land images. Therefore, to determine which model is the best for the current recognition classification tasks, it is often necessary to select and experiment with many different models. However, finding the optimal model is accompanied by an increase in trial-and-error costs and is a waste of …

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An Enhanced Real-Time Intrusion Detection Framework Using Federated Transfer Learning In Large-Scale Iot Networks, Khawlah Harahsheh, Malek Alzaqebah, Chung-Hao Chen Jan 2024

An Enhanced Real-Time Intrusion Detection Framework Using Federated Transfer Learning In Large-Scale Iot Networks, Khawlah Harahsheh, Malek Alzaqebah, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The exponential growth of Internet of Things (IoT) devices has introduced critical security challenges, particularly in scalability, privacy, and resource constraints. Traditional centralized intrusion detection systems (IDS) struggle to address these issues effectively. To overcome these limitations, this study proposes a novel Federated Transfer Learning (FTL)-based intrusion detection framework tailored for large-scale IoT networks. By integrating Federated Learning (FL) with Transfer Learning (TL), the framework enhances detection capabilities while ensuring data privacy and reducing communication overhead. The hybrid model incorporates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), attention mechanisms, and ensemble learning. To address the class imbalance, Synthetic …

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Advancing Chronic Kidney Disease Prediction Through Machine Learning And Deep Learning With Feature Analysis, Shiddarth Dey Tusar, S. M. Ahad Ali Chowdhury, Md. Jalal Uddin Chowdhury, Rana M. Pir, H. M. Nur A. Alam, Muhammad Rezaur Rahman, Md. Nural Absar Siddiky, Muhammad Enayetur Rahman Jan 2024

Advancing Chronic Kidney Disease Prediction Through Machine Learning And Deep Learning With Feature Analysis, Shiddarth Dey Tusar, S. M. Ahad Ali Chowdhury, Md. Jalal Uddin Chowdhury, Rana M. Pir, H. M. Nur A. Alam, Muhammad Rezaur Rahman, Md. Nural Absar Siddiky, Muhammad Enayetur Rahman

Electrical & Computer Engineering Faculty Publications

Chronic Kidney Diesease (CKD) is a significant health issue, ranking as the fourth leading cause of mortality worldwide. The traditional diagnosis and treatment process, reliant on medical experts, is time-consuming. Therefore, thereis an urgent need for more efficient diagnostic methods to improve patient outcomes and reduce mortality rates. In this study, we employ Machine Learning (ML) and Deep Learning (DL) techniques to predict CKD based on important features. Feature analysis was performed using a correlation matrix and the LASSO algo-rithm to identify the most relevant features for model training. We evaluated several ML and DL classifiers, including Logistic Regression (LR), …

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Comparative Analysis Of Machine Learning Models For Predicting Healthcare Traffic: Insights For Optimized Emergency Response, Shadman Mahmood Khan Pathan, Sakan Binte Imran, M. M. Shabab Iqbal, Muhammad Enayetur Rahman, Md. Nurul Absar Siddiky, Muhammad Rezaur Rahman, Md Rafid Hasan, Nondon Lal Dey, Md Sobuj Hossain Jan 2024

Comparative Analysis Of Machine Learning Models For Predicting Healthcare Traffic: Insights For Optimized Emergency Response, Shadman Mahmood Khan Pathan, Sakan Binte Imran, M. M. Shabab Iqbal, Muhammad Enayetur Rahman, Md. Nurul Absar Siddiky, Muhammad Rezaur Rahman, Md Rafid Hasan, Nondon Lal Dey, Md Sobuj Hossain

Electrical & Computer Engineering Faculty Publications

Efficient management of healthcare traffic is crucial for ensuring timely access to medical services, particularly in emergency situations where delays can have severe consequences. This study presents a comparative analysis of three widely used machine learning models—Linear Regression, Decision Trees, and Random Forests—aimed at predicting healthcare-related traffic volumes. A large dataset from a metropolitan traffic system was used to train and evaluate the models based on key performance indicators, including Mean Squared Error (MSE), R² Score, and computational efficiency. The results reveal that the Random Forest model offers the best performance, achieving higher predictive accuracy and faster execution times compared …

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Integrated Machine Learning And Deep Learning Models For Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran Jan 2024

Integrated Machine Learning And Deep Learning Models For Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran

Electrical & Computer Engineering Faculty Publications

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the …

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