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Articles 1 - 23 of 23
Full-Text Articles in Computer Sciences
Investigation, Detection And Prevention Of Online Child Sexual Abuse Material: A Comprehensive Survey, Vuong Ngo, Christina Thorpe, Cach N. Dang, Susan Mckeever
Investigation, Detection And Prevention Of Online Child Sexual Abuse Material: A Comprehensive Survey, Vuong Ngo, Christina Thorpe, Cach N. Dang, Susan Mckeever
Conference papers
Child sexual abuse inflicts lifelong devastating consequences for victims and is a growing social concern. In most countries, child sexual abuse material (CSAM) distribution is illegal. As a result, there are many research papers in the literature which proposed technologies to detect and investigate CSAM. In this survey, a comprehensive search of the peer reviewed journal and conference paper databases (including preprints) is conducted to identify high-quality literature. We use the PRISMA methodology to refine our search space to 2,761 papers published by Springer, Elsevier, IEEE and ACM. After iterative reviews of title, abstract and full text for relevance to …
Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth
Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth
Publications
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …
Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel
Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel
SMU Data Science Review
Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model …
A Gpu-Based Machine Learning Approach For Detection Of Botnet Attacks, Michal Motylinski, Áine Macdermott, Farkhund Iqbal, Babar Shah
A Gpu-Based Machine Learning Approach For Detection Of Botnet Attacks, Michal Motylinski, Áine Macdermott, Farkhund Iqbal, Babar Shah
All Works
Rapid development and adaptation of the Internet of Things (IoT) has created new problems for securing these interconnected devices and networks. There are hundreds of thousands of IoT devices with underlying security vulnerabilities, such as insufficient device authentication/authorisation making them vulnerable to malware infection. IoT botnets are designed to grow and compete with one another over unsecure devices and networks. Once infected, the device will monitor a Command-and-Control (C&C) server indicating the target of an attack via Distributed Denial of Service (DDoS) attack. These security issues, coupled with the continued growth of IoT, presents a much larger attack surface for …
Analysis Of Patch And Sample Size Effects For 2d-3d Cnn Models Using Multiplatform Dataset: Hyperspectral Image Classification Of Rosis And Jilin-1 Gp01 Imagery, Taşkin Kavzoğlu, Eli̇f Özlem Yilmaz
Analysis Of Patch And Sample Size Effects For 2d-3d Cnn Models Using Multiplatform Dataset: Hyperspectral Image Classification Of Rosis And Jilin-1 Gp01 Imagery, Taşkin Kavzoğlu, Eli̇f Özlem Yilmaz
Turkish Journal of Electrical Engineering and Computer Sciences
Modern hyperspectral sensors provide a huge volume of data at spectral and spatial domains with high redundancy, which requires robust methods for analysis. In this study, 2D and 3D CNN models were applied to hyperspectral image datasets (ROSIS and Jilin-1 GP01) using varying patch and sample sizes to determine their combined impacts on the performance of deep learning models. Differences in classification performances in relation to particle and sample sizes were statistically analysed using McNemar?s test. According to the findings, raising the patch and sample size enhances the performance of the 2D/3D CNN model and produces more accurate results in …
Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi
Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi
All Works
COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which …
Quertci: A Tool Integrating Github Issue Querying With Comment Classification, Ye Paing, Tatiana Castro Vélez, Raffi T. Khatchadourian
Quertci: A Tool Integrating Github Issue Querying With Comment Classification, Ye Paing, Tatiana Castro Vélez, Raffi T. Khatchadourian
Publications and Research
Empirical Software Engineering (ESE) researchers study (open-source) project issues and the comments and threads within to discover—among others—challenges developers face when incorporating new technologies, platforms, and programming language constructs. However, such threads accumulate, becoming unwieldy and hindering any insight researchers may gain. While existing approaches alleviate this burden by classifying issue thread comments, there is a gap between searching popular open-source software repositories (e.g., those on GitHub) for issues containing particular keywords and feeding the results into a classification model. This paper demonstrates a research infrastructure tool called QuerTCI that bridges this gap by integrating the GitHub issue comment search …
Determining Knowledge From Student Performance Prediction Using Machine Learning, Wala El Rashied Mohamed
Determining Knowledge From Student Performance Prediction Using Machine Learning, Wala El Rashied Mohamed
Theses
Recent years have seen a rapid development in the field of educational data mining (EDM), enhancing the ability to trace student knowledge. Data from intelligent tutoring systems (ITS) have been analyzed and interpreted by multiple researchers seeking to measure students’ knowledge as it evolves. Human nature, as well as other factors, makes it difficult to determine whether or not students are knowledgeable. This thesis sets out to examine the level of students’ knowledge by predicting their current and future academic performance based on records of their historical interactions. By restructuring data and considering a student perspective, we can gain insight …
A Few-Shot Learning Model Based On A Triplet Network For The Prediction Of Energy Coincident Peak Days, Jinxiang Liu, Laura Brown
A Few-Shot Learning Model Based On A Triplet Network For The Prediction Of Energy Coincident Peak Days, Jinxiang Liu, Laura Brown
Michigan Tech Publications
In an electricity system, a coincident peak (CP) is defined as the highest daily power demand in a year, which plays an important role in keeping the balance between power supply and its demand. Advanced information about the time of coincident peaks would be helpful for both utility companies and their customers. This work addresses the prediction of the five coincident peak days (5CP) in a year. We present a few-shot learning model to classify a day as a 5CP day or a non-5CP day 24-hours ahead. A triplet network is implemented for the 2-way-5-shot classifications on six different historical …
Identifying Functional And Non-Functional Software Requirements From User App Reviews And Requirements Artifacts, Dev Jayant Dave
Identifying Functional And Non-Functional Software Requirements From User App Reviews And Requirements Artifacts, Dev Jayant Dave
Theses, Dissertations and Culminating Projects
This thesis proposes and evaluates Machine Learning (ML) based data models to identify and isolate software requirements from datasets containing user app review statements. The ML models classify user app review statements into Functional Requirements (FRs), Non-Functional Requirements (NFRs), and Non-Requirements (NRs). This proposed approach consisted of creating a novel hybrid dataset that contains software requirements from Software Requirements Specification (SRS) documents and user app reviews. The Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), and Random Forest (RF) ML algorithms combined with the term frequency-inverse document frequency (TF-IDF) natural language processing (NLP) technique were implemented on the hybrid dataset. …
Classification And Phenological Staging Of Crops From In Situ Image Sequences By Deep Learning, Uluğ Bayazit, Deni̇z Turgay Altilar, Ni̇lgün Güler Bayazit
Classification And Phenological Staging Of Crops From In Situ Image Sequences By Deep Learning, Uluğ Bayazit, Deni̇z Turgay Altilar, Ni̇lgün Güler Bayazit
Turkish Journal of Electrical Engineering and Computer Sciences
Accurate knowledge of crop type information is not only valuable for verifying the declaration of farmers to obtain subsidy or insurance for the grown crop, but also for generating crop type maps that serve a variety of purposes in land monitoring and policy. On the other hand, accurate knowledge of crop phenological stage can help farm personnel apply fertilization and irrigation regimes on a timely basis. Although deep learning based networks have been applied in the past to classify the type and predict the phenological stage of crops from in situ images of fields, more advanced deep learning based networks, …
Studying The Role Of Cerebrovascular Changes In Different Compartments In Human Brains In Hypertension Prediction, Heba Kandil, Ahmed Soliman, Nada Elsaid, Ahmed Saied, Norah Saleh Alghamdi, Ali Mahmoud, Fatma Taher, Ayman El-Baz
Studying The Role Of Cerebrovascular Changes In Different Compartments In Human Brains In Hypertension Prediction, Heba Kandil, Ahmed Soliman, Nada Elsaid, Ahmed Saied, Norah Saleh Alghamdi, Ali Mahmoud, Fatma Taher, Ayman El-Baz
All Works
Hypertension is a major cause of mortality of millions of people worldwide. Cerebral vascular changes are clinically observed to precede the onset of hypertension. The early detection and quantification of these cerebral changes would help greatly in the early prediction of the disease. Hence, preparing appropriate medical plans to avoid the disease and mitigate any adverse events. This study aims to investigate whether studying the cerebral changes in specific regions of human brains (specifically, the anterior, and the posterior compartments) separately, would increase the accuracy of hypertension prediction compared to studying the vascular changes occurring over the entire brain’s vasculature. …
Afnd: Arabic Fake News Dataset For The Detection And Classification Of Articles Credibility, Ashwaq Khalil, Moath Jarrah, Monther Aldwairi, Manar Jaradat
Afnd: Arabic Fake News Dataset For The Detection And Classification Of Articles Credibility, Ashwaq Khalil, Moath Jarrah, Monther Aldwairi, Manar Jaradat
All Works
The news credibility detection task has started to gain more attention recently due to the rapid increase of news on different social media platforms. This article provides a large, labeled, and diverse Arabic Fake News Dataset (AFND) that is collected from public Arabic news websites. This dataset enables the research community to use supervised and unsupervised machine learning algorithms to classify the credibility of Arabic news articles. AFND consists of 606912 public news articles that were scraped from 134 public news websites of 19 different Arab countries over a 6-month period using Python scripts. The Arabic fact-check platform, Misbar, is …
A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun
A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun
FIU Electronic Theses and Dissertations
Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.
However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.
Traditional approaches for biomarker discovery calculate the fold change for each …
Split Classification Model For Complex Clustered Data, Katherine Gerot
Split Classification Model For Complex Clustered Data, Katherine Gerot
Honors Theses
Classification in high-dimensional data has generated tremendous interest in a multitude of fields. Data in higher dimensions often tend to reside in non-Euclidean metric space. This prevents Euclidean-based classification methodologies, such as regression, from reliably modeling the data. Many proposed models rely on computationally-complex embedding to convert the data to a more usable format. Others, namely the Support Vector Machine, rely on kernel manipulation to implicitly describe the "feature space" to arrive at a non-linear decision boundary. The proposed methodology in this paper seeks to classify complex data in a relatively computationally-simple and explainable manner.
Efficient Search Of Live-Coding Screencasts From Online Videos, Chengran Yang, Ferdian Thung, David Lo
Efficient Search Of Live-Coding Screencasts From Online Videos, Chengran Yang, Ferdian Thung, David Lo
Research Collection School Of Computing and Information Systems
Programming videos on the Internet are valuable resources for learning programming skills. To find relevant videos, developers typically search online video platforms (e.g., YouTube) with keywords on topics they wish to learn. Developers often look for live-coding screencasts, in which the videos’ authors perform live coding. Yet, not all programming videos are livecoding screencasts. In this work, we develop a tool named PSFinder to identify live-coding screencasts. PSFinder leverages a classifier to identify whether a video frame contains an IDE window. It uses a sampling strategy to pick a number of frames from an input video, runs the classifer on …
The Analysis And Optimization Of Cnn Hyperparameters With Fuzzy Tree Modelfor Image Classification, Kübra Uyar, Şaki̇r Taşdemi̇r, İlker Ali̇ Özkan
The Analysis And Optimization Of Cnn Hyperparameters With Fuzzy Tree Modelfor Image Classification, Kübra Uyar, Şaki̇r Taşdemi̇r, İlker Ali̇ Özkan
Turkish Journal of Electrical Engineering and Computer Sciences
The meaningful performance of convolutional neural network (CNN) has enabled the solution of various state-of-the-art problems. Although CNNs achieve satisfactory results in computer-vision problems, they still have some difficulties. As the designed CNN models are deepened to achieve much better accuracy, computational cost and complexity increase. It is significant to train CNNs with suitable topology and training hyperparameters that include initial learning rate, minibatch size, epoch number, filter size, number of filters, etc. because the initialization of hyperparameters affects classification results. On the other hand, it is not possible to make a definite inference for the hyperparameter initialization and there …
Measuring Semantic Similarity Of Documents By Using Named Entity Recognition Methods, David Efraín Muñoz Morales
Measuring Semantic Similarity Of Documents By Using Named Entity Recognition Methods, David Efraín Muñoz Morales
Masters
The work presented in this thesis was born from the desire to map documents with similar semantic concepts between them. We decided to address this problem as a named entity recognition task, where we have identified key concepts in the texts we use, and we have categorized them. So, we can apply named entity recognition techniques and automatically recognize these key concepts inside other documents. However, we propose the use of a classification method based on the recognition of named entities or key phrases, where the method can detect similarities between key concepts of the texts to be analyzed, and …
Eeg Signals Classification Using Lstm-Based Models And Majority Logic, James A. Orgeron
Eeg Signals Classification Using Lstm-Based Models And Majority Logic, James A. Orgeron
Electronic Theses and Dissertations
The study of elecroencephalograms (EEGs) has gained enormous interest in the last decade with the increase of computational power and availability of EEG signals collected from various human activities or produced during medical tests. The applicability of analyzing EEG signals ranges from helping impaired people communicate or move (using appropriate medical equipment) to understanding people's feelings and detecting diseases.
We proposed new methodology and models for analyzing and classifying EEG signals collected from individuals observing visual stimuli. Our models rely on powerful Long-Short Term Memory (LSTM) Neural Network models, which are currently the state of the art models for performing …
Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng
Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng
Engineering Management & Systems Engineering Faculty Publications
A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords …
Optimized Cancer Detection On Various Magnified Histopathological Colon Imagesbased On Dwt Features And Fcm Clustering, Tina Babu, Tripty Singh, Deepa Gupta, Shahin Hameed
Optimized Cancer Detection On Various Magnified Histopathological Colon Imagesbased On Dwt Features And Fcm Clustering, Tina Babu, Tripty Singh, Deepa Gupta, Shahin Hameed
Turkish Journal of Electrical Engineering and Computer Sciences
Due to the morphological characteristics and other biological aspects in histopathological images, the computerized diagnosis of colon cancer in histopathology images has gained popularity. The images acquired using the histopathology microscope may differ for greater visibility by magnifications. This causes a change in morphological traits leading to intra and inter-observer variability. An automatic colon cancer diagnosis system for various magnification is therefore crucial. This work proposes a magnification independent segmentation approach based on the connected component area and double density dual tree DWT (discrete wavelet transform) coefficients are derived from the segmented region. The derived features are reduced further shortened …
Classifying Blood Glucose Levels Through Noninvasive Features, Rishi Reddy
Classifying Blood Glucose Levels Through Noninvasive Features, Rishi Reddy
Graduate Theses, Dissertations, and Problem Reports
Blood glucose monitoring is a key process in the prevention and management of certain chronic diseases, such as diabetes. Currently, glucose monitoring for those interested in their blood glucose levels are confronted with options that are primarily invasive and relatively costly. A growing topic of note is the development of non-invasive monitoring methods for blood glucose. This development holds a significant promise for improvement to the quality of life of a significant portion of the population and is overall met with great enthusiasm from the scientific community as well as commercial interest. This work aims to develop a potential pipeline …
Smart Covid-3d-Scnn: A Novel Method To Classify X-Ray Images Of Covid-19, Ahed Abugabah, Atif Mehmood, Ahmad Ali Al Zubi, Louis Sanzogni
Smart Covid-3d-Scnn: A Novel Method To Classify X-Ray Images Of Covid-19, Ahed Abugabah, Atif Mehmood, Ahmad Ali Al Zubi, Louis Sanzogni
All Works
The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. …