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2023

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Articles 691 - 720 of 3503

Full-Text Articles in Computer Sciences

Privacy Harm And Non-Compliance From A Legal Perspective, Suvineetha Herath, Haywood Gelman, Lisa Mckee Oct 2023

Privacy Harm And Non-Compliance From A Legal Perspective, Suvineetha Herath, Haywood Gelman, Lisa Mckee

Journal of Cybersecurity Education, Research and Practice

In today's data-sharing paradigm, personal data has become a valuable resource that intensifies the risk of unauthorized access and data breach. Increased data mining techniques used to analyze big data have posed significant risks to data security and privacy. Consequently, data breaches are a significant threat to individual privacy. Privacy is a multifaceted concept covering many areas, including the right to access, erasure, and rectify personal data. This paper explores the legal aspects of privacy harm and how they transform into legal action. Privacy harm is the negative impact to an individual as a result of the unauthorized release, gathering, …

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What You See Is Not What You Know: Studying Deception In Deepfake Video Manipulation, Cathryn Allen, Bryson R. Payne, Tamirat Abegaz, Chuck Robertson Oct 2023

What You See Is Not What You Know: Studying Deception In Deepfake Video Manipulation, Cathryn Allen, Bryson R. Payne, Tamirat Abegaz, Chuck Robertson

Journal of Cybersecurity Education, Research and Practice

Research indicates that deceitful videos tend to spread rapidly online and influence people’s opinions and ideas. Because of this, video misinformation via deepfake video manipulation poses a significant online threat. This study aims to discover what factors can influence viewers’ capability to distinguish deepfake videos from genuine video footage. This work focuses on exploring deepfake videos’ potential use for deception and misinformation by exploring people’s ability to determine whether videos are deepfakes in a survey consisting of deepfake videos and original unedited videos. The participants viewed a set of four videos and were asked to judge whether the videos shown …

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Adapting The Adapters For Code-Switching In Multilingual Asr, Atharva Kulkarni, Ajinkya Kulkarni, Miguel Couceiro, Hanan Al Darmaki Oct 2023

Adapting The Adapters For Code-Switching In Multilingual Asr, Atharva Kulkarni, Ajinkya Kulkarni, Miguel Couceiro, Hanan Al Darmaki

Natural Language Processing Faculty Publications

Recently, large pre-trained multilingual speech models have shown potential in scaling Automatic Speech Recognition (ASR) to many low-resource languages. Some of these models employ language adapters in their formulation, which helps to improve monolingual performance and avoids some of the drawbacks of multi-lingual modeling on resource-rich languages. However, this formulation restricts the usability of these models on code-switched speech, where two languages are mixed together in the same utterance. In this work, we propose ways to effectively fine-tune such models on code-switched speech, by assimilating information from both language adapters at each language adaptation point in the network. We also …

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Sigite'23 Poster And Extended Abstract: The Proof Of Gold Is Fire: Measuring Stress To Show Impact Of Gender Based Initiatives In Computing Education, Alina Berry, Sarah Jane Delany Oct 2023

Sigite'23 Poster And Extended Abstract: The Proof Of Gold Is Fire: Measuring Stress To Show Impact Of Gender Based Initiatives In Computing Education, Alina Berry, Sarah Jane Delany

Other resources

Many initiatives across the world try to address the issue of gender imbalance in computing education. Evaluation of the impact of these initiatives is not always straightforward. Some interventions are able to demonstrate impact based on recruitment and retention numbers of underrepresented gender groups, in particular, women, often requiring a longer-term study to see impact. Others, especially shorter term interventions and those taking place during the teaching and learning process, frequently use feedback and other instruments as a measure of impact. This work reviews existing evaluation methods from the literature, categorises them as using statistical data, feedback or instruments, grouping …

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Editorial For "Automated Breast Density Assessment In Mri Using Deep Learning And Radiomics: Strategies For Reducing Inter-Observer Variability"., Pegah Khosravi Oct 2023

Editorial For "Automated Breast Density Assessment In Mri Using Deep Learning And Radiomics: Strategies For Reducing Inter-Observer Variability"., Pegah Khosravi

Publications and Research

No abstract provided.

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Recent Advances In Crispr/Cas9-Assisted Gene Therapy, Apeksha Srivastava, Shikha Chauhan, Vishal Ahuja Oct 2023

Recent Advances In Crispr/Cas9-Assisted Gene Therapy, Apeksha Srivastava, Shikha Chauhan, Vishal Ahuja

Karbala International Journal of Modern Science

CRISPR/Cas9 (Clustered regularly interspaced short palindromic repeats) is an exponentially growing tool with wide-spread applications in therapeutics like gene modifications that focus on altering the hereditary material to repair or eliminate any defective gene-causing diseases like cancer, AIDS (Acquired immunodeficiency syndrome), etc. It also includes the identification of the target sequence with the help of sgRNA followed by the substitution of a malfunction-ing gene with a normal version. It offers high efficiency, specificity, and post-gene-editing efficacy, but have also some off-target impressions, and immunogenic effects. The contribution of CRISPR/Cas9 has already been proved primarily in in-vitro studies using animal germ …

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Optical, Structural, And Electrochemical Properties Of P3ht:Y6 Photoactive Layer For Organic Photovoltaics, Hassan Tarikhum B., Furqan Almyahi, Basil Ali Oct 2023

Optical, Structural, And Electrochemical Properties Of P3ht:Y6 Photoactive Layer For Organic Photovoltaics, Hassan Tarikhum B., Furqan Almyahi, Basil Ali

Karbala International Journal of Modern Science

Recently, organic photovoltaics (OPV) have emerged as a promising technology for environmentally friendly energy production. Achieving high performance in OPV requires the discovery of novel compounds. This article aims to study the optical, structural, electrochemical, and electrical properties of a P3HT:Y6 blend. The UV-visible spectra of the blend provided insight into how composition affects the optoelectronic properties of OPV devices. The optimised P3HT:Y6 ratio (0.6:1) resulted in the largest redshift and the highest absorption intensity. Raman and X-ray diffraction tests showed low aggregation and low crystallinity of the polymer P3HT as Y6 content increased. In the OPV devices, the best …

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Estimation Of Water Quality Of Dikhu River Of Nagaland Through A Combination Of Water Quality Index And Principal Component Analysis Techniques., Lanuyanger Longchar, Wati Temjen, Khikeya Semy, Wati Lemla Oct 2023

Estimation Of Water Quality Of Dikhu River Of Nagaland Through A Combination Of Water Quality Index And Principal Component Analysis Techniques., Lanuyanger Longchar, Wati Temjen, Khikeya Semy, Wati Lemla

Karbala International Journal of Modern Science

Monitoring the physico-chemical attributes of water is essential for determining water quality. The present study explores the effect of various anthropogenic factors on the quality of Dikhu River, Nagaland. Water samples from three sampling stations were compared against three standards: Indian Council of Medical Research, Bureau of Indian Standards, and World Health Organization. Although all parameters were within the permissible limits, the Water Quality Index categorized all sites during the rainy season as "poor quality", highlighting anthropogenic impacts. A principal component analysis created a Minimum Data Set (MDS) explaining 100%, 93.27%, and 96.26% of the total variance for the three …

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Cybervandalism Detection In Wikipedia Using Light Architecture Of 1d-Cnn, Azha Talal Mohammed Ali, Huda Hallawi, Noor D. Al-Shakarchy Oct 2023

Cybervandalism Detection In Wikipedia Using Light Architecture Of 1d-Cnn, Azha Talal Mohammed Ali, Huda Hallawi, Noor D. Al-Shakarchy

Karbala International Journal of Modern Science

The rapid expansion of human-software-agent interaction has come with new issues. Accordingly, different engage-ments are necessary to adapt to changing human needs in dynamic socio-technical systems. Generally, cybervandalism is the act of leaving any negative impact on any piece of writing in an attempt to modify it. In Wikipedia, vandalism is any attempt to modify an article in a way that negatively affects the article's quality. Recently, several automatic detec-tion techniques and related features have been developed to address this issue. This work introduces a deep learning model with a new and light architecture to detect vandalism in Wikipedia articles. …

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Performance Analysis And Comparison Of Nonideal Wireless Pbft And Raft Consensus Networks In 6g Communications, Haoxiang Luo, Xiangyue Yang, Hongfang Yu, Gang Sun, Bo Lei, Mohsen Guizani Oct 2023

Performance Analysis And Comparison Of Nonideal Wireless Pbft And Raft Consensus Networks In 6g Communications, Haoxiang Luo, Xiangyue Yang, Hongfang Yu, Gang Sun, Bo Lei, Mohsen Guizani

Machine Learning Faculty Publications

Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine Fault Tolerance (PBFT) and RAFT are seen as the most applicable consensus mechanisms (CMs) in blockchain-enabled wireless networks. However, previous studies on PBFT and RAFT rarely consider the channel performance of the physical layer, such as path loss and channel fading, resulting in research results that are far from real networks. Additionally, 6G communications will widely deploy high-frequency signals such as terahertz (THz) and millimeter wave (mmWave), while performances of PBFT and RAFT are still unknown when these signals are …

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Service-Oriented Framework For Developing Interoperable E-Health Systems In A Low-Income Country, Bonface Abima, Agnes Nakakawa, Geoffrey Mayoka Kituyi Oct 2023

Service-Oriented Framework For Developing Interoperable E-Health Systems In A Low-Income Country, Bonface Abima, Agnes Nakakawa, Geoffrey Mayoka Kituyi

The African Journal of Information Systems

e-Health solutions in low-income countries are fragmented, address institution-specific needs, and do little to address the strategic need for inter-institutional exchange of health data. Although various e-health interoperability frameworks exist, contextual factors often hinder their effective adoption in low-income countries. This underlines the need to investigate such factors and to use findings to adapt existing e-health interoperability models. Following a design science approach, this research involved conducting an exploratory survey among 90 medical and Information Technology personnel from 67 health facilities in Uganda. Findings were used to derive requirements for e-health interoperability, and to orchestrate elements of a service oriented …

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Contemporary Art Authentication With Large-Scale Classification, Todd Dobbs, Abdullah-Al-Raihan Nayeem, Isaac Cho, Zbigniew Ras Oct 2023

Contemporary Art Authentication With Large-Scale Classification, Todd Dobbs, Abdullah-Al-Raihan Nayeem, Isaac Cho, Zbigniew Ras

Computer Science Faculty and Staff Publications

Art authentication is the process of identifying the artist who created a piece of artwork and is manifested through events of provenance, such as art gallery exhibitions and financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning (ML) algorithm on painting images. Art authentication is not always possible …

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Malfe—Malware Feature Engineering Generation Platform, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter Oct 2023

Malfe—Malware Feature Engineering Generation Platform, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter

All Works

The growing sophistication of malware has resulted in diverse challenges, especially among security researchers who are expected to develop mechanisms to thwart these malicious attacks. While security researchers have turned to machine learning to combat this surge in malware attacks and enhance detection and prevention methods, they often encounter limitations when it comes to sourcing malware binaries. This limitation places the burden on malware researchers to create context-specific datasets and detection mechanisms, a time-consuming and intricate process that involves a series of experiments. The lack of accessible analysis reports and a centralized platform for sharing and verifying findings has resulted …

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Enhancing Exploration-Exploitation In Harmony Search For Airborne Hyperspectral Imaging Band Selection (E3hs), Mohammed Abdulmajeed Moharram, Divya Meena Sundaram Oct 2023

Enhancing Exploration-Exploitation In Harmony Search For Airborne Hyperspectral Imaging Band Selection (E3hs), Mohammed Abdulmajeed Moharram, Divya Meena Sundaram

Turkish Journal of Electrical Engineering and Computer Sciences

Hyperspectral imaging has emerged as a prominent area of research in the field of remote sensing science. However, hyperspectral images (HSIs) pose a notable challenge due to the presence of numerous irrelevant and redundant spectral bands exhibiting high correlation. Therefore, it is necessary to enhance the classification performance for HSI processing by selecting the most relevant discriminative spectral bands. To this end, this paper introduces a metaheuristic search method called enhancing exploration-exploitation in harmony search (E3HS). The standard harmony search suffers from many weaknesses, such as premature convergence and falling easily into the local optimum. Consequently, E3HS was proposed to …

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Trcaptionnet: A Novel And Accurate Deep Turkish Image Captioning Model With Vision Transformer Based Image Encoders And Deep Linguistic Text Decoders, Serdar Yildiz, Abbas Memi̇ş, Songül Varli Oct 2023

Trcaptionnet: A Novel And Accurate Deep Turkish Image Captioning Model With Vision Transformer Based Image Encoders And Deep Linguistic Text Decoders, Serdar Yildiz, Abbas Memi̇ş, Songül Varli

Turkish Journal of Electrical Engineering and Computer Sciences

Image captioning is known as a fundamental computer vision task aiming to figure out and describe what is happening in an image or image region. Through an image captioning process, it is ensured to describe and define the actions and the relations of the objects within the images. In this manner, the contents of the images can be understood and interpreted automatically by visual computing systems. In this paper, we proposed the TRCaptionNet a novel deep learning-based Turkish image captioning (TIC) model for the automatic generation of Turkish captions. The model we propose essentially consists of a basic image encoder, …

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Cccd: Corner Detection And Curve Reconstruction For Improved 3d Surface Reconstruction From 2d Medical Images, Mriganka Sarmah, Arambam Neelima Oct 2023

Cccd: Corner Detection And Curve Reconstruction For Improved 3d Surface Reconstruction From 2d Medical Images, Mriganka Sarmah, Arambam Neelima

Turkish Journal of Electrical Engineering and Computer Sciences

The conventional approach to creating 3D surfaces from 2D medical images is the marching cube algorithm, but it often results in rough surfaces. On the other hand, B-spline curves and nonuniform rational B-splines (NURBSs) offer a smoother alternative for 3D surface reconstruction. However, NURBSs use control points (CTPs) to define the object shape and corners play an important role in defining the boundary shape as well. Thus, in order to fill the research gap in applying corner detection (CD) methods to generate the most favorable CTPs, in this paper corner points are identified to predict organ shape. However, CTPs must …

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Focal Modulation Network For Lung Segmentation In Chest X-Ray Images, Şaban Öztürk, Tolga Çukur Oct 2023

Focal Modulation Network For Lung Segmentation In Chest X-Ray Images, Şaban Öztürk, Tolga Çukur

Turkish Journal of Electrical Engineering and Computer Sciences

Segmentation of lung regions is of key importance for the automatic analysis of Chest X-Ray (CXR) images, which have a vital role in the detection of various pulmonary diseases. Precise identification of lung regions is the basic prerequisite for disease diagnosis and treatment planning. However, achieving precise lung segmentation poses significant challenges due to factors such as variations in anatomical shape and size, the presence of strong edges at the rib cage and clavicle, and overlapping anatomical structures resulting from diverse diseases. Although commonly considered as the de-facto standard in medical image segmentation, the convolutional UNet architecture and its variants …

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Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi Oct 2023

Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi

Turkish Journal of Electrical Engineering and Computer Sciences

Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice …

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Feature Distillation From Vision-Language Model For Semisupervised Action Classification, Asli Çeli̇k, Ayhan Küçükmani̇sa, Oğuzhan Urhan Oct 2023

Feature Distillation From Vision-Language Model For Semisupervised Action Classification, Asli Çeli̇k, Ayhan Küçükmani̇sa, Oğuzhan Urhan

Turkish Journal of Electrical Engineering and Computer Sciences

The training of supervised machine learning approaches is critically dependent on annotating large-scale datasets. Semisupervised learning approaches aim to achieve compatible performance with supervised methods using relatively less annotation without sacrificing good generalization capacity. In line with this objective, ways of leveraging unlabeled data have been the subject of intense research. However, semisupervised video action recognition has received relatively less attention compared to image domain implementations. Existing semisupervised video action recognition methods trained from scratch rely heavily on augmentation techniques, complex architectures, and/or the use of other modalities while distillation-based methods use models that have only been trained for 2D …

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Multi-View Brain Tumor Segmentation (Mvbts): An Ensemble Of Planar And Triplanar Attention Unets, Snehal Rajput, Rupal Kapdi, Mehul Raval, Mohendra Roy Oct 2023

Multi-View Brain Tumor Segmentation (Mvbts): An Ensemble Of Planar And Triplanar Attention Unets, Snehal Rajput, Rupal Kapdi, Mehul Raval, Mohendra Roy

Turkish Journal of Electrical Engineering and Computer Sciences

3D UNet has achieved high brain tumor segmentation performance but requires high computation, large memory, abundant training data, and has limited interpretability. As an alternative, the paper explores using 2D triplanar (2.5D) processing, which allows images to be examined individually along axial, sagittal, and coronal planes or together. The individual plane captures spatial relationships, and combined planes capture contextual (depth) information. The paper proposes and analyzes an ensemble of uniplanar and triplanar UNets combined with channel and spatial attention for brain tumor segmentation. It investigates the significance of each plane and analyzes the impact of uniplanar and triplanar ensembles with …

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Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha Oct 2023

Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha

Turkish Journal of Electrical Engineering and Computer Sciences

Chronic kidney diseases proliferate due to hypertension, diabetes, anemia, obesity, smoking etc. Patients with such conditions are sometimes unaware of first symptoms, complicating disease diagnosis. This paper presents chronic kidney disease (CKD) prediction model to classify CKD patients from NCKD (Non-CKD). The proposed study has two main stages. First, we found the odds ratio through logistic regression and comparison test to identify early risk factors from kidneys? MRI and differentiate CKD from NCKD subjects. In stage 2, LR, LDA, MLP classifiers were applied to predict CKD and NCKD by extracting features from MRI. The odds ratio of blood glucose random …

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A Unique Hybrid Domain Hand-Crafted Feature To Classify Colorectal Tissue Histopathological Images Using Multiheaded Cnn, Anurodh Kumar, Amit Vishwakarma, Varun Bajaj Oct 2023

A Unique Hybrid Domain Hand-Crafted Feature To Classify Colorectal Tissue Histopathological Images Using Multiheaded Cnn, Anurodh Kumar, Amit Vishwakarma, Varun Bajaj

Turkish Journal of Electrical Engineering and Computer Sciences

Early diagnosis of colorectal cancer lengthens human life and is helpful in efforts to cure the illness. Histopathological inspection is a routinely utilized technique to diagnose it. Visual assessment of histopathological images takes more investigation time, and the decision is based on the individual perceptions of clinicians. The existing methods for colorectal cancer classification use only spatial information. However, studies on the spectral domains of information are lacking in the literature. Therefore, the performance of the existing techniques is moderate. To improve the performance of colorectal cancer classification, this work proposes a unique hybrid domain hand-crafted feature formulated using scale-invariant …

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Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni Oct 2023

Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni

Turkish Journal of Electrical Engineering and Computer Sciences

Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …

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Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson Oct 2023

Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson

Turkish Journal of Electrical Engineering and Computer Sciences

Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep …

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Yolo And Lsh-Based Video Stream Analytics Landscape For Short-Term Traffic Density Surveillance At Road Networks, Lavanya K, Stuti Tiwari, Rahul Anand, Jude Hemanth Oct 2023

Yolo And Lsh-Based Video Stream Analytics Landscape For Short-Term Traffic Density Surveillance At Road Networks, Lavanya K, Stuti Tiwari, Rahul Anand, Jude Hemanth

Turkish Journal of Electrical Engineering and Computer Sciences

The duty of monitoring traffic during rush hour is difficult due to the fact that modern roadways are getting more crowded every day. The automated solutions that have already been created in this area are ineffective at processing enormous amounts of data in a short amount of time, leading to ineffectiveness and inconsistent results. The YOLO (you only look once) and LSH (locality sensitive hashing) algorithms are combined with the Kafka architecture in this study to create a method for assessing traffic density in real-time scenarios. Our concept, which is specifically designed for vehicular networks, predicts the traffic density in …

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Deep Feature Extraction, Dimensionality Reduction, And Classification Of Medical Images Using Combined Deep Learning Architectures, Autoencoder, And Multiple Machine Learning Models, Ahmet Hi̇dayet Ki̇raz, Fatime Oumar Djibrillah, Mehmet Emi̇n Yüksel Oct 2023

Deep Feature Extraction, Dimensionality Reduction, And Classification Of Medical Images Using Combined Deep Learning Architectures, Autoencoder, And Multiple Machine Learning Models, Ahmet Hi̇dayet Ki̇raz, Fatime Oumar Djibrillah, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

Accurate analysis and classification of medical images are essential factors in clinical decision-making and patient care. A novel comparative approach for medical image classification is proposed in this study. This new approach involves several steps: deep feature extraction, which extracts the informative features from medical images; concatenation, which concatenates the extracted deep features to form a robust feature vector; dimensionality reduction with autoencoder, which reduces the dimensionality of the feature vector by transforming it into a different feature space with a lower dimension; and finally, these features obtained from all these steps were fed into multiple machine learning classifiers (SVM, …

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Geospatial Data Integration Middleware For Exploratory Analytics Addressing Regional Natural Resource Grand Challenges In The Us Mountain West, Shannon Albeke, Nicholas Case, Samantha Ewers, Jeffrey Hamerlinck, William Kirkpatrick, Jerod Merkle, Luke Todd Oct 2023

Geospatial Data Integration Middleware For Exploratory Analytics Addressing Regional Natural Resource Grand Challenges In The Us Mountain West, Shannon Albeke, Nicholas Case, Samantha Ewers, Jeffrey Hamerlinck, William Kirkpatrick, Jerod Merkle, Luke Todd

I-GUIDE Forum

This paper describes CyberGIS-based research and development aimed at improving geospatial data integration and visual analytics to better understand the impact of regional climate change on water availability in the U.S. Rocky Mountains. Two Web computing applications are presented. DEVISE - Derived Environmental Variability Indices Spatial Extractor, streamlines utilization of environmental data for better-informed wildlife decisions by biologists and game managers. The WY-Adapt platform aims to enhance predictive understanding of climate change impacts on water availability through two modules: “Current Conditions” and “Future Scenarios”. It integrates high-resolution models of the biophysical environment and human interactions, providing a robust framework for …

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Graph Transformer Network For Flood Forecasting With Heterogeneous Covariates, Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan Oct 2023

Graph Transformer Network For Flood Forecasting With Heterogeneous Covariates, Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan

I-GUIDE Forum

Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of extreme weather events, resulting in elevated and frequent flood risk. Therefore, accurate and timely flood forecasting in coastal river systems is critical to facilitate good flood management. However, the computational tools currently used are either slow or inaccurate. In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems. More specifically, FloodGTN learns the spatio-temporal dependencies of water levels at different monitoring stations using Graph Neural Networks (GNNs) …

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Semantic Lung Segmentation From Chest X-Ray Images Using Seg-Net Deep Cnn Model, Dathar Abas Hasan, Umed Hayder Jader Oct 2023

Semantic Lung Segmentation From Chest X-Ray Images Using Seg-Net Deep Cnn Model, Dathar Abas Hasan, Umed Hayder Jader

Polytechnic Journal

Implementing an accurate image segmentation to extract the lung shape from X-ray images is a vital step in designing a CAD system that diagnoses various types of chest diseases. Lung segmentation is a complex process due to the blurred regions that separate the lung area and the rest of the image. The conventional image segmentation techniques do not meet the ambitions to achieve precise lung segmentation. In this paper, we utilized the Seg-Net semantic segmentation model as a practical approach to distinguish the lung region pixels in X-ray images. The model involves an encoder network that extracts the data from …

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Solving Geospatial Problems Under Extreme Time Constraints: A Call For Inclusive Geocomputational Education, Coline C. Dony Oct 2023

Solving Geospatial Problems Under Extreme Time Constraints: A Call For Inclusive Geocomputational Education, Coline C. Dony

I-GUIDE Forum

To prepare our next generation to face geospatial problems that have extreme time constraints (e.g., disasters, climate change) we need to create educational pathways that help students develop their geocomputational thinking skills. First, educators are central in helping us create those pathways, therefore, we need to clearly convey to them why and in which contexts this thinking is necessary. For that purpose, a new definition for geocomputational thinking is suggested that makes it clear that this thinking is needed for geospatial problems that have extreme time constraints. Secondly, we can not further burden educators with more demands, rather we should …

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