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Articles 18991 - 19020 of 63078

Full-Text Articles in Computer Sciences

Exploring Media Portrayals Of People With Mental Disorders Using Nlp, Swapna Gottipati, Mark Chong, Andrew Wei Kiat Lim, Benny Haryanto Kawidiredjo Feb 2021

Exploring Media Portrayals Of People With Mental Disorders Using Nlp, Swapna Gottipati, Mark Chong, Andrew Wei Kiat Lim, Benny Haryanto Kawidiredjo

Research Collection School Of Computing and Information Systems

Media plays an important role in creating an impact in society. Several studies show that news media and entertainment channels, at times may create overwhelming images of the mental illness that emphasize criminality and dangerousness. The consequences of such negative impact may impact the audience with stigma and on the other hand, they impair the self-esteem and help-seeking behavior of the people with mental disorders. This is the first study to examine the Singapore media’s portrayal of persons with mental disorders (MDs) using text analytics and natural language processing. To date, most studies on media portrayal of people with MDs …

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Revman: Revenue-Aware Multi-Task Online Insurance Recommendation, Yu Li, Yi Zhang, Lu Gan, Gengwei Hong, Zimu Zhou, Qiang Li Feb 2021

Revman: Revenue-Aware Multi-Task Online Insurance Recommendation, Yu Li, Yi Zhang, Lu Gan, Gengwei Hong, Zimu Zhou, Qiang Li

Research Collection School Of Computing and Information Systems

Online insurance is a new type of e-commerce with exponential growth. An effective recommendation model that maximizes the total revenue of insurance products listed in multiple customized sales scenarios is crucial for the success of online insurance business. Prior recommendation models are ineffective because they fail to characterize the complex relatedness of insurance products in multiple sales scenarios and maximize the overall conversion rate rather than the total revenue. Even worse, it is impractical to collect training data online for total revenue maximization due to the business logic of online insurance. We propose RevMan, a Revenue-aware Multi-task Network for online …

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Relative And Absolute Location Embedding For Few-Shot Node Classification On Graph, Zemin Liu, Yuan Fang, Chenghao Liu, Steven C. H. Hoi Feb 2021

Relative And Absolute Location Embedding For Few-Shot Node Classification On Graph, Zemin Liu, Yuan Fang, Chenghao Liu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Node classification is an important problem on graphs. While recent advances in graph neural networks achieve promising performance, they require abundant labeled nodes for training. However, in many practical scenarios there often exist novel classes in which only one or a few labeled nodes are available as supervision, known as few-shot node classification. Although meta-learning has been widely used in vision and language domains to address few-shot learning, its adoption on graphs has been limited. In particular, graph nodes in a few-shot task are not independent and relate to each other. To deal with this, we propose a novel model …

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Mimoa: A Membrane-Inspired Multi-Objective Algorithm For Green Vehicle Routing Problem With Stochastic Demands, Yunyun Niu, Yongpeng Zhang, Zhiguang Cao, Kaizhou Gao, Jianhua Xiao, Wen Song, Fangwei Zhang Feb 2021

Mimoa: A Membrane-Inspired Multi-Objective Algorithm For Green Vehicle Routing Problem With Stochastic Demands, Yunyun Niu, Yongpeng Zhang, Zhiguang Cao, Kaizhou Gao, Jianhua Xiao, Wen Song, Fangwei Zhang

Research Collection School Of Computing and Information Systems

Nowadays, an increasing number of vehicle routing problem with stochastic demands (VRPSD) models have been studied to meet realistic needs in the field of logistics. In this paper, a bi-objective vehicle routing problem with stochastic demands (BO-VRPSD) was investigated, which aims to minimize total cost and customer dissatisfaction. Different from traditional vehicle routing problem (VRP) models, both the uncertainty in customer demands and the nature of multiple objectives make the problem more challenging. To cope with BO-VRPSD, a membrane-inspired multi-objective algorithm (MIMOA) was proposed, which is characterized by a parallel distributed framework with two operation subsystems and one control subsystem, …

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Fine-Grained Generalization Analysis Of Vector-Valued Learning, Liang Wu, Antoine Ledent, Yunwen Lei, Marius Kloft Feb 2021

Fine-Grained Generalization Analysis Of Vector-Valued Learning, Liang Wu, Antoine Ledent, Yunwen Lei, Marius Kloft

Research Collection School Of Computing and Information Systems

Many fundamental machine learning tasks can be formulated as a problem of learning with vector-valued functions, where we learn multiple scalar-valued functions together. Although there is some generalization analysis on different specific algorithms under the empirical risk minimization principle, a unifying analysis of vector-valued learning under a regularization framework is still lacking. In this paper, we initiate the generalization analysis of regularized vector-valued learning algorithms by presenting bounds with a mild dependency on the output dimension and a fast rate on the sample size. Our discussions relax the existing assumptions on the restrictive constraint of hypothesis spaces, smoothness of loss …

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Model Uncertainty Guides Visual Object Tracking, Lijun Zhou, Antoine Ledent, Qintao Hu, Ting Liu, Jianlin Zhang, Marius Kloft Feb 2021

Model Uncertainty Guides Visual Object Tracking, Lijun Zhou, Antoine Ledent, Qintao Hu, Ting Liu, Jianlin Zhang, Marius Kloft

Research Collection School Of Computing and Information Systems

Model object trackers largely rely on the online learning of a discriminative classifier from potentially diverse sample frames. However, noisy or insufficient amounts of samples can deteriorate the classifiers' performance and cause tracking drift. Furthermore, alterations such as occlusion and blurring can cause the target to be lost. In this paper, we make several improvements aimed at tackling uncertainty and improving robustness in object tracking. Our first and most important contribution is to propose a sampling method for the online learning of object trackers based on uncertainty adjustment: our method effectively selects representative sample frames to feed the discriminative branch …

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Portfolio Diversification Using Shape-Based Clustering, Tristan Lim, Chin Sin Ong Feb 2021

Portfolio Diversification Using Shape-Based Clustering, Tristan Lim, Chin Sin Ong

Research Collection School Of Computing and Information Systems

Portfolio diversification involves lowering the correlation between portfolio assets to achieve improved risk–return exposure. It is reasonable to infer from the classic Anscombe quartet that relying on descriptive statistics, and specifically, correlation, to achieve portfolio diversification may not derive the most optimal multiperiod portfolio risk-adjusted return because stocks in a portfolio can exhibit different price trends over time, even with the same computed pairwise correlation. This research applied a shape-based time-series clustering technique of agglomerative hierarchical clustering using dynamic time-series warping as a distance measure to aggregate stocks into like-trending clusters across time as a portfolio diversification tool. Results support …

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Evidence Aware Neural Pornographic Text Identification For Child Protection, Kaisong Song, Yangyang Kang, Wei Gao, Zhe Gao, Changlong Sun, Xiaozhong Liu Feb 2021

Evidence Aware Neural Pornographic Text Identification For Child Protection, Kaisong Song, Yangyang Kang, Wei Gao, Zhe Gao, Changlong Sun, Xiaozhong Liu

Research Collection School Of Computing and Information Systems

Identifying pornographic text online is practically useful to protect children from access to such adult content. However, some authors may intentionally avoid using sensitive words in their pornographic texts to take advantage of the lack of human audits. Without prior knowledge guidance, real semantics of such pornographic text is difficult to understand by existing methods due to its high context-sensitivity and heavy usage of figurative language, which brings huge challenges to the porn detection systems used in social media platforms. In this paper, we approach to the problem as a document-level porn identification task by locating and integrating sentence-level evidence …

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Qlens: Visual Analytics Of Multi-Step Problem-Solving Behaviors For Improving Question Design, Meng Xia, Reshika P. Velumani, Yong Wang, Huamin Qu, Xiaojuan Ma Feb 2021

Qlens: Visual Analytics Of Multi-Step Problem-Solving Behaviors For Improving Question Design, Meng Xia, Reshika P. Velumani, Yong Wang, Huamin Qu, Xiaojuan Ma

Research Collection School Of Computing and Information Systems

With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students’ problem-solving processes unfold step by step to infer whether students’ problem-solving logic matches their design intent. They also need to compare the behaviors of different groups (e.g., students from different grades) to distribute questions to students with the right level of knowledge. The availability of fine-grained interaction data, such as mouse movement trajectories from …

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Zynq System-On-Chip Dma Messaging For Processor Monitoring, Daniel F. Koranek, Douglas D. Hodson, Scott R. Graham Feb 2021

Zynq System-On-Chip Dma Messaging For Processor Monitoring, Daniel F. Koranek, Douglas D. Hodson, Scott R. Graham

Faculty Publications

Xilinx Zynq-7000 System-on-Chip architectures combine an ARM Cortex-A9 core with an FPGA fabric. One benefit of this hybrid architecture is that it allows fast prototyping of designs where the security of either the processing system (PS) is monitored by the programmable logic (PL) or vice versa. The choice of implementing a design in the PS or PL is driven by cost-to-benefit analysis across many factors. This effort examines the design process required to construct security monitoring designs that use both the PS and PL. For background, this effort reviews similar security monitoring projects. For the effort, a PL peripheral was …

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Project Coolbit: Can Your Watch Predict Heat Stress And Thermal Comfort Sensation?, Negin Nazarian, Sijie Liu, Manon Kohler, Jason Lee, Clayton Miller, Winston T. L. Chow, S. B. B. Alhadad, Alberto Martilli, Matias Quintana, Lindsey Sunden, Lindsey Norford Feb 2021

Project Coolbit: Can Your Watch Predict Heat Stress And Thermal Comfort Sensation?, Negin Nazarian, Sijie Liu, Manon Kohler, Jason Lee, Clayton Miller, Winston T. L. Chow, S. B. B. Alhadad, Alberto Martilli, Matias Quintana, Lindsey Sunden, Lindsey Norford

Research Collection School of Social Sciences

Global climate is changing as a result of anthropogenic warming, leading to higher daily excursions of temperature in cities. Such elevated temperatures have great implications on human thermal comfort and heat stress, which should be closely monitored. Current methods for heat exposure assessments (surveys, microclimate measurements, and laboratory experiments), however, present several limitations: measurements are scattered in time and space and data gathered on outdoor thermal stress and comfort often does not include physiological and behavioral parameters. To address these shortcomings, Project Coolbit aims to introduce a human-centric approach to thermal comfort assessments. In this study, we propose and evaluate …

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Evoking Empathy: A Framework For Describing Empathy Tools, Sydney Pratte, Anthony Tang, Lora Oehlberg Feb 2021

Evoking Empathy: A Framework For Describing Empathy Tools, Sydney Pratte, Anthony Tang, Lora Oehlberg

Research Collection School Of Computing and Information Systems

Empathy tools are experiences designed to evoke empathetic responses by placing the user in another’s lived and felt experience. The problem is that designers do not have a common vocabulary to describe empathy tool experiences; consequently, it is difficult to compare/contrast empathy tool designs or to think about their efficacy. To address this problem, we analyzed 26 publications on empathy tools to develop a descriptive framework for designers of empathy tools. Based on our analysis, we found that empathy tools can be described along three dimensions: (i) the amount of agency the tool allows, (ii) the user’s perspective while using …

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Visual Analysis Of Discrimination In Machine Learning, Qianwen Wang, Zhenghua Xu, Zhutian Chen, Yong Wang, Shixia Liu, Huamin Qu Feb 2021

Visual Analysis Of Discrimination In Machine Learning, Qianwen Wang, Zhenghua Xu, Zhutian Chen, Yong Wang, Shixia Liu, Huamin Qu

Research Collection School Of Computing and Information Systems

The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set …

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Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning Via Importance Sampling, Yugang Ji, Mingyang Yin, Hongxia Yang, Jingren Zhou, Vincent W. Zheng, Chuan Shi, Yuan Fang Feb 2021

Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning Via Importance Sampling, Yugang Ji, Mingyang Yin, Hongxia Yang, Jingren Zhou, Vincent W. Zheng, Chuan Shi, Yuan Fang

Research Collection School Of Computing and Information Systems

In real-world problems, heterogeneous entities are often related to each other through multiple interactions, forming a Heterogeneous Interaction Graph (HIG in short). While modeling HIGs to deal with fundamental tasks, graph neural networks present an attractive opportunity that can make full use of the heterogeneity and rich semantic information by aggregating and propagating information from different types of neighborhoods. However, learning on such complex graphs, often with millions or billions of nodes, edges, and various attributes, could suffer from expensive time cost and high memory consumption. In this paper, we attempt to accelerate representation learning on large-scale HIGs by adopting …

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Computational Thinking In Mathematics And Computer Science: What Programming Does To Your Head, Al Cuoco, E. Paul Goldenberg Jan 2021

Computational Thinking In Mathematics And Computer Science: What Programming Does To Your Head, Al Cuoco, E. Paul Goldenberg

Journal of Humanistic Mathematics

How you think about a phenomenon certainly influences how you create a program to model it. The main point of this essay is that the influence goes both ways: creating programs influences how you think. The programs we are talking about are not just the ones we write for a computer. Programs can be implemented on a computer or with physical devices or in your mind. The implementation can bring your ideas to life. Often, though, the implementation and the ideas develop in tandem, each acting as a mirror on the other. We describe an example of how programming and …

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Forecasting Optimal Parameters Of The Broken Wing Butterfly Option Strategy Using Differential Evolution, David Munoz Constantine Jan 2021

Forecasting Optimal Parameters Of The Broken Wing Butterfly Option Strategy Using Differential Evolution, David Munoz Constantine

Dissertations and Theses

Obtaining an edge in financial markets has been the objective of many hedge funds, investors, and market participants. Even with today's abundance of data and computing power, few individuals achieve a consistent edge over an extended time. To obtain this edge, investors usually use options strategies. The Broken Wing Butterfly (BWB) is an options strategy that has increased in popularity among traders. Profit is generated primarily by exploiting option value time decay. In this thesis, the selection of entry and exit BWB parameters, such as profit and loss targets, are optimized for an in-sample period. Afterward, they are used to …

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Deep Learning For Multi-Tissue Cancer Classification Of Gene Expressions, Tarek Khorshed Jan 2021

Deep Learning For Multi-Tissue Cancer Classification Of Gene Expressions, Tarek Khorshed

Theses and Dissertations

We contribute in saving the lives of cancer patients through early detection and diagnosis, since one of the major challenges in cancer treatment is that patients are diagnosed at very late stages when appropriate medical interventions become less effective and full curative treatment is no longer achievable. Cancer classification using gene expressions is extremely challenging given the complexity and high dimensionality of the data. Current classification methods typically rely on samples collected from a single tissue type and perform a prerequisite of gene feature selection to avoid processing the full set of genes. These methods fall short in taking advantage …

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Efficient Cnn Building Blocks For Encrypted Data, Nayna Jain, Karthik Nandakumar, Nalini K. Ratha, Sharath U. Pankanti, Uttam Kumar Jan 2021

Efficient Cnn Building Blocks For Encrypted Data, Nayna Jain, Karthik Nandakumar, Nalini K. Ratha, Sharath U. Pankanti, Uttam Kumar

Computer Vision Faculty Publications

Machine learning on encrypted data can address the concerns related to privacy and legality of sharing sensitive data with untrustworthy service providers, while leveraging their resources to facilitate extraction of valuable insights from otherwise non-shareable data. Fully Homomorphic Encryption (FHE) is a promising technique to enable machine learning and inferencing while providing strict guarantees against information leakage. Since deep convolutional neural networks (CNNs) have become the machine learning tool of choice in several applications, several attempts have been made to harness CNNs to extract insights from encrypted data. However, existing works focus only on ensuring data security and ignore security …

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Generative Multi-Label Zero-Shot Learning, Akshita Gupta, Sanath Narayan, Salman Khan, Fahad Shahbaz Khan, Ling Shao, Joost Van De Weijer Jan 2021

Generative Multi-Label Zero-Shot Learning, Akshita Gupta, Sanath Narayan, Salman Khan, Fahad Shahbaz Khan, Ling Shao, Joost Van De Weijer

Computer Vision Faculty Publications

Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of …

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Delineating Knowledge Domains In Scientific Domains In Scientific Literature Using Machine Learning (Ml), Abhay Maurya, Smarajit Paul Choudhury Mr., Kshitij Jaiswal Mr. Jan 2021

Delineating Knowledge Domains In Scientific Domains In Scientific Literature Using Machine Learning (Ml), Abhay Maurya, Smarajit Paul Choudhury Mr., Kshitij Jaiswal Mr.

Library Philosophy and Practice (e-journal)

The recent years have witnessed an upsurge in the number of published documents. Organizations are showing an increased interest in text classification for effective use of the information. Manual procedures for text classification can be fruitful for a handful of documents, but the same lack in credibility when the number of documents increases besides being laborious and time-consuming. Text mining techniques facilitate assigning text strings to categories rendering the process of classification fast, accurate, and hence reliable. This paper classifies chemistry documents using machine learning and statistical methods. The procedure of text classification has been described in chronological order like …

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Automated Test Generation For Validating Systemc Designs, Bin Lin Jan 2021

Automated Test Generation For Validating Systemc Designs, Bin Lin

Dissertations and Theses

Modern system design involves integration of all components of a system on a single chip, namely System-on-a-Chip (SoC). The ever-increasing complexity of SoCs and rapidly decreasing time-to-market have pushed the design abstraction to the electronic system level (ESL), in order to increase design productivity. SystemC is a widely used ESL modeling language that plays a central role in modern SoCs design process. ESL SystemC designs usually serve as executable specifications for the subsequent SoCs design flow. Therefore, undetected bugs in ESL SystemC designs may propagate to low-level implementations or even final silicon products. In addition, modern SoCs design often involves …

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Modulatory And Toxicological Perspectives On The Effects Of The Small Molecule Kinetin, Eman M. Othman, Moustafa Fathy, Amany Abdlrehim Bekhit, Abdel-Razik H. Abdel-Razik, Arshad Jamal, Yousef Nazzal, Shabana Shams, Thomas Dandekar, Muhammad Naseem Jan 2021

Modulatory And Toxicological Perspectives On The Effects Of The Small Molecule Kinetin, Eman M. Othman, Moustafa Fathy, Amany Abdlrehim Bekhit, Abdel-Razik H. Abdel-Razik, Arshad Jamal, Yousef Nazzal, Shabana Shams, Thomas Dandekar, Muhammad Naseem

All Works

Plant hormones are small regulatory molecules that exert pharmacological actions in mammalian cells such as anti-oxidative and pro-metabolic effects. Kinetin belongs to the group of plant hormones cytokinin and has been associated with modulatory functions in mammalian cells. The mammalian adenosine receptor (A2a-R) is known to modulate multiple physiological responses in animal cells. Here, we describe that kinetin binds to the adenosine receptor (A2a-R) through the Asn253 residue in an adenosine dependent manner. To harness the beneficial effects of kinetin for future human use, we assess its acute toxicity by analyzing different biochemical and histological markers in rats. Kinetin at …

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Seva: A Food Donation App For Smart Living, Christina Varghese, Drashti Pathak, Aparna S. Varde Jan 2021

Seva: A Food Donation App For Smart Living, Christina Varghese, Drashti Pathak, Aparna S. Varde

Department of Computer Science Faculty Scholarship and Creative Works

An important goal in our world today is to eliminate food waste by reutilizing available food sources within local communities: leftover food items in restaurants, stores and food distribution centers that may be approaching expiration; and any perishable items not used in entirety within their desired period. This is highly significant, particularly during crises such as the COVID-19 pandemic. This paper focuses on creating an interesting mobile application (app) called SeVa that provides a ubiquitous platform wherein users can visualize available food resources in their local area and consequently gain access to food, thereby tackling two major issues, i.e. hunger …

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Introduction To Computers And Programming Using Python: A Project-Based Approach, Esma Yildirim, Daniel Garbin, Mathieu Sassolas, Kwang Hyun Kim Jan 2021

Introduction To Computers And Programming Using Python: A Project-Based Approach, Esma Yildirim, Daniel Garbin, Mathieu Sassolas, Kwang Hyun Kim

Open Educational Resources

Welcome to the “Introduction to Computers and Programming using Python: A Project-based Approach”. This book is designed to teach basic programming skills to students who are new to the field of computing using a project-based learning approach. It has been designed to give freedom to the instructor, both in format and topics ultimately used throughout the course. While we provide 13 turnkey projects, it is only expected that 3 or 4 are used over the course of a semester, and all projects are provided both as textual instructions (the student version of this OER) and Jupyter Notebooks (one with and …

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Zooming Into The Pandemic! A Forensic Analysis Of The Zoom Application, Andrew Mahr, Meghan Cichon, Sophia Mateo, Cinthya Grajeda, Ibrahim Baggili Jan 2021

Zooming Into The Pandemic! A Forensic Analysis Of The Zoom Application, Andrew Mahr, Meghan Cichon, Sophia Mateo, Cinthya Grajeda, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

The global pandemic of COVID-19 has turned the spotlight on video conferencing applications like never before. In this critical time, applications such as Zoom have experienced a surge in its user base jump over the 300 million daily mark (ZoomBlog, 2020). The increase in use has led malicious actors to exploit the application, and in many cases perform Zoom Bombings. Therefore forensically examining Zoom is inevitable. Our work details the primary disk, network, and memory forensic analysis of the Zoom video conferencing application. Results demonstrate it is possible to find users' critical information in plain text and/or encrypted/encoded, such …

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Simulation Research On Abandonment Rate Of Contact Center Based On Patience Threshold, Junxiang Li, Wanbin Zang Jan 2021

Simulation Research On Abandonment Rate Of Contact Center Based On Patience Threshold, Junxiang Li, Wanbin Zang

Journal of System Simulation

Abstract: Customer abandonment rate is an important indicator to measure the service level of contact center. Customers in a traditional call center follow the principle of first come, first served, but some customers will be lost in the queuing process because of their limited patience. In this regard, by setting the customer's patience threshold and considering adding a specific agent channel to the member customers, or adjusting the number of agent channels under the original staffing, a new contact center queuing model is established based on the traditional call center model. The ProModel simulation software is used to study the …

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Joint Low Rank And Sparsity-Based Channel Estimation For Fdd Massive Mimo, Zhou Jin Jan 2021

Joint Low Rank And Sparsity-Based Channel Estimation For Fdd Massive Mimo, Zhou Jin

Journal of System Simulation

Abstract: Channel estimation of millimeter wave communication needs large system load. In order to reduce the load, a low-rank and sparse feature of the wireless channel is combined, and a channel estimation algorithm framework based on non-convex low-rank approximation is proposed. Aiming at the large computation of the channel model-based dictionary learning algorithm, a dictionary learning algorithm for deep neural network channel feature classification is designed. The simulation shows that the average square error of the proposed method is better than the channel model-based dictionary learning method, the channel estimation method under the Bayesian framework, and the compressed …

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Research On Precision Parking Control Method For Emu Inbound Process, Zhongqi Li, Yueshuang Xing Jan 2021

Research On Precision Parking Control Method For Emu Inbound Process, Zhongqi Li, Yueshuang Xing

Journal of System Simulation

Abstract: By analyzing the relationship between braking force and speed during the stop-stop process of high-speed EMU(Electric Multiple Units), the multi-point dynamics model of EMU is constructed. The Smith predictor is introduced due to the delay effect on the system caused by the braking force generated during the late braking, and the RBF(Radial Basis Function) neural network-based PID control strategy is combined with the Smith predictor to achieve tracking control of a given speed during braking. Simulation analysis shows that the error of train speed and set speed controlled by RBF neural network PID-Smith controller is less than ±1 …

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A Predictive Model For Diabetes Using Machine Learning Techniques (A Case Studyof Some Selected Hospitals In Kaduna Metropolis), A E. Evwiekpaefe, Nafisat Abdulkadir Jan 2021

A Predictive Model For Diabetes Using Machine Learning Techniques (A Case Studyof Some Selected Hospitals In Kaduna Metropolis), A E. Evwiekpaefe, Nafisat Abdulkadir

Master of Science in Computer Science Theses

Diabetes Mellitus (DM) which refers to a metabolic disorder that occurs when the level of blood sugar in the body is considered high, which could be a resulting effect of inadequate availability of insulin in the body. It is a chronic disease which may lead to myriads of complications in the body system. Statistics by the World Health Organization (WHO) in 2013, indicated that DM was the cause of death of over 1.5 million people around the world and in 2016, 8.5% of adults within age seventeen (17) and above were reported to be diabetic and diabetic patients have continued …

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Comparison Of Left Ventricular Mechanical Dyssynchrony Parameters Between Exercise And Adenosine Triphosphate Stress Tests Using Gated Single-Photon Emission Computed Tomography Myocardial Perfusion Imaging, Jianzhou Shi, Yanli Zhou, Priyaranjan Kata, Sheilabi Seeburun, Cheng Wang, Dianfu Li, Zhixin Jiang, Weihua Zhou Jan 2021

Comparison Of Left Ventricular Mechanical Dyssynchrony Parameters Between Exercise And Adenosine Triphosphate Stress Tests Using Gated Single-Photon Emission Computed Tomography Myocardial Perfusion Imaging, Jianzhou Shi, Yanli Zhou, Priyaranjan Kata, Sheilabi Seeburun, Cheng Wang, Dianfu Li, Zhixin Jiang, Weihua Zhou

Michigan Tech Publications, Part 1

Background Left ventricular mechanical dyssynchrony (LVMD) can be induced after stress test. However, no studies have compared the influence of different stress-inducing methods on LVMD parameters. aims The aim of the study was to determine whether there is a difference between exercise and adenosine triphosphate (ATP) stress tests in terms of changes in LVMD parameters assessed using gated single-photon emission computed tomography myocardial perfusion imaging (GSPECT MPI). methods A total of 190 patients who underwent 99mTc-sestamibi GSPECT MPI were consecutively enrolled. Treadmill exercise and ATP stress tests were performed in 95 patients each. Normal myocardial perfusion was defined as the …

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