Computer Sciences Commons^™

A Predictive Model For Diabetes Mellitus Using Machine Learning Techniques (A Study In Nigeria), Abraham Eseoghene Evwiekpaefe, Nafisat Abdulkadir

The African Journal of Information Systems

Diabetes Mellitus (DM) is a metabolic disorder that occurs when the blood sugar level in the body is considered to be high, thereby resulting in inadequate insulin in the body leading to a myriad complications. The World Health Organization in 2021 indicated that in 2019, diabetes was the direct cause of 1.5 million deaths. Though some research has been carried out in the area of DM prediction in high-income countries, not much has been done in middle/low-income countries like Nigeria, using factors that are peculiar to their environment. This paper, therefore, aims to develop a machine learning model that predicts …

Visual Analytics And Modeling Of Materials Property Data, Diwas Bhattarai

LSU Doctoral Dissertations

Due to significant advancements in experimental and computational techniques, materials data are abundant. To facilitate data-driven research, it calls for a system for managing and sharing data and supporting a set of tools for effective data analysis and modeling. Generally, a given material property M can be considered as a multivariate data problem. The dimensions of M are the values of the property itself, the conditions (pressure P, temperature T, and multi-component composition X) that control the concerned property, and relevant metadata I (source, date).

Here we present a comprehensive database considering both experimental and computational sources …

Machine Learning Models Interpretability For Malware Detection Using Model Agnostic Language For Exploration And Explanation, Ikuromor Mabel Ogiriki

Theses and Dissertations

The adoption of the internet as a global platform has birthed a significant rise in cyber-attacks of various forms ranging from Trojans, worms, spyware, ransomware, botnet malware, rootkit, etc. In order to tackle the issue of all these forms of malware, there is a need to understand and detect them. There are various methods of detecting malware which include signature, behavioral, and machine learning. Machine learning methods have proven to be the most efficient of all for malware detection. In this thesis, a system that utilizes both the signature and dynamic behavior-based detection techniques, with the added layer of the …

Adoption Of Artificial Intelligence (Ai) In Local Governments: An Exploratory Study On The Attitudes And Perceptions Of Officials In A Municipal Government In The Philippines, Charmaine Distor, Odkhuu Khaltar, M. Jae Moon

Journal of Public Affairs and Development

Emerging technologies like artificial intelligence (AI) have been instrumental in transforming governments in recent years, which is why several agencies worldwide have integrated them into their governance strategies. One of the countries that have paid attention to the potential of AI is the Philippines, which launched its national AI roadmap in 2021. This study investigated the perceived acceptance and adoption of AI in the Municipality of Carmona located in the Province of Cavite. Following the combined constructs from the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), perception data were gathered from among Carmona’s …

Survey On Outdoor Navigation Applications For People With Visual Impairment, Luis Miralles-Pechuán

Datasets

Outdoor navigation is a very challenging activity for People who suffer from Blindness or Visually Impairment (PBVI). Having examined the current literature, we conclude that there are very few publications providing a nuanced understanding of how PBVI undertake a journey in an outdoor environment and what their main challenges and obstacles are. To throw some light on this gap, we conducted a questionnaire in collaboration with the National Council for the Blind Ireland (NCBI) for 49 PBVI. Our questionnaire gathers information about key aspects related to PBVI outdoor navigation such as support tools/devices, hazards, journey preparation, crossing roads, and understanding …

Towards A Framework For Privacy-Preserving Pedestrian Analysis, Anil Kunchala, Mélanie Bouroche, Bianca Schoen-Phelan

Conference papers

The design of pedestrian-friendly infrastructures plays a crucial role in creating sustainable transportation in urban environments. Analyzing pedestrian behaviour in response to existing infrastructure is pivotal to planning, maintaining, and creating more pedestrian-friendly facilities. Many approaches have been proposed to extract such behaviour by applying deep learning models to video data. Video data, however, includes an broad spectrum of privacy-sensitive information about individuals, such as their location at a given time or who they are with. Most of the existing models use privacy-invasive methodologies to track, detect, and analyse individual or group pedestrian behaviour patterns. As a step towards privacy-preserving …

Leveraging A Machine Learning Based Predictive Framework To Study Brain-Phenotype Relationships, Sage Hahn

Graduate College Dissertations and Theses

An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the …

Improving Developers' Understanding Of Regex Denial Of Service Tools Through Anti-Patterns And Fix Strategies, Sk Adnan Hassan, Zainab Aamir, Dongyoon Lee, James C. Davis, Francisco Servant

Department of Electrical and Computer Engineering Faculty Publications

Regular expressions are used for diverse purposes, including input validation and firewalls. Unfortunately, they can also lead to a security vulnerability called ReDoS (Regular Expression Denial of Service), caused by a super-linear worst-case execution time during regex matching. Due to the severity and prevalence of ReDoS, past work proposed automatic tools to detect and fix regexes. Although these tools were evaluated in automatic experiments, their usability has not yet been studied; usability has not been a focus of prior work. Our insight is that the usability of existing tools to detect and fix regexes will improve if we complement them …

Observing Human Mobility Internationally During Covid-19, Shane Allcroft, Mohammed Metwaly, Zachery Berg, Isha Ghodgaonkar, Fischer Bordwell, Xinxin Zhao, Xinglei Liu, Jiahao Xu, Subhankar Chakraborty, Vishnu Banna, Akhil Chinnakotla, Abhinav Goel, Caleb Tung, Gore Kao, Wei Zakharov, David A. Shoham, George K. Thiruvathukal, Yung-Hsiang Lu

Computer Science: Faculty Publications and Other Works

This article analyzes visual data captured from five countries and three U.S. states to evaluate the effectiveness of lockdown policies for reducing the spread of COVID-19. The main challenge is the scale: nearly six million images are analyzed to observe how people respond to the policy changes.

Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Systems Science Faculty Publications and Presentations

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Research@Smu: Sustainable Living, Singapore Management University

Research Collection Office of Research & Tech Transfer

Sustainable Living is one of the three key priorities of the SMU 2025 Strategy, and the University is committed to develop it into an area of cross-disciplinary strength. The articles in this booklet highlight impactful sustainability research accomplishments at SMU, which spans five broad pillars: Sustainable Business Operations; Sustainable Finance and Impact Assessment; Sustainable Ageing and Wellness; Sustainable Urban Infrastructure; and Sustainable Agro-business and Food Consumption.

Contents:

Sustainable Business Operations

• Managing the Load on Loading Bays
• Going the Last-mile
• Feeding a Growing World
• Pooling the Benefits of Sharing a Ride

Sustainable Finance and Impact Assessment

• When Going Green Becomes a …

Reasoning About The Conant Gasket, M. Douglas Mcilroy

Computer Science Technical Reports

Previously conjectured properties of the Conant gasket, a particular non-periodic tiling of the non-negative integer grid, are proved using new recurrences. A slabwise periodicity property is identified and proved. Further fractal properties are conjectured.

Self-Omics: A Self-Supervised Learning Framework For Multi-Omics Cancer Data, Sayed Hashim, Karthik Nandakumar, Mohammad Yaqub

Computer Vision Faculty Publications

We have gained access to vast amounts of multi-omics data thanks to Next Generation Sequencing. However, it is challenging to analyse this data due to its high dimensionality and much of it not being annotated. Lack of annotated data is a significant problem in machine learning, and Self-Supervised Learning (SSL) methods are typically used to deal with limited labelled data. However, there is a lack of studies that use SSL methods to exploit inter-omics relationships on unlabelled multi-omics data. In this work, we develop a novel and efficient pre-training paradigm that consists of various SSL components, including but not limited …

E-Learning Course Recommender System Using Collaborative Filtering Models, Kalyan Kumar Jena, Sourav Kumar Bhoi, Tushar Kanta Malik, Kshira Sagar Sahoo, N. Z. Jhanjhi, Sajal Bhatia Ed., Fathi Amsaad

School of Computer Science & Engineering Faculty Publications

e-Learning is a sought-after option for learners during pandemic situations. In e-Learning platforms, there are many courses available, and the user needs to select the best option for them. Thus, recommender systems play an important role to provide better automation services to users in making course choices. It makes recommendations for users in selecting the desired option based on their preferences. This system can use machine intelligence (MI)-based techniques to carry out the recommendation mechanism. Based on the preferences and history, this system is able to know what the users like most. In this work, a recommender system is proposed …

Mining Health Informatics Job Advertisements: Insights For Higher Education Programs And Job Seekers, Ahmed El Noshokaty, Mohammad A. Al-Ramahi, Omar El-Gayar, Abdullah Wahbeh, Tareq Nasralah

Computer Information Systems Faculty Publications

This paper used web scraping and data mining to analyze 831 health informatics job advertisements on indeed.com. Results showed that 87% of jobs explicitly required a college degree in a related field, 41% of jobs preferred a graduate degree, while 29% preferred or required professional certification. The analysis showed that preferred skills were analytics problem solving, communication skills, oral communication, interpersonal skills, project management, statistics, and critical thinking. The analysis also showed that college degrees, certifications, and the above-mentioned skill set are in high demand for working in the field of health informatics, especially in states with large populations and …

Conversational Agents For Mental Health And Well-Being: Discovering Design Recommendations Using Text Mining, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Ahmed El Noshokaty, Tareq Nasralah

Computer Information Systems Faculty Publications

Conversational agents are increasingly being used by the general population due to shortages in healthcare providers and specialists, and limited access to treatments. They are also used by people to deal with loneliness and lack of companionship. As these apps are increasingly replacing real humans, there is a need to explore their design features and limitations for better design of conversational apps. Using text mining and topic modeling, this study analyzed a total of 126,610 reviews about Replika, a popular and well-established conversational agent mobile app. Our results emphasized current practices for designing conversational apps while at the same time …

Dynamic Function Learning Through Control Of Ensemble Systems, Wei Zhang, Vignesh Narayanan, Jr-Shin Li

Publications

Learning tasks involving function approximation are preva- lent in numerous domains of science and engineering. The underlying idea is to design a learning algorithm that gener- ates a sequence of functions converging to the desired target function with arbitrary accuracy by using the available data samples. In this paper, we present a novel interpretation of iterative function learning through the lens of ensemble dy- namical systems, with an emphasis on establishing the equiv- alence between convergence of function learning algorithms and asymptotic behavior of ensemble systems. In particular, given a set of observation data in a function learning task, we …

Neighborhood Retail Amenities And Taxi Trip Behavior: A Natural Experiment In Singapore, Kwan Ok Lee, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

While a small change in land use planning in existing neighborhoods may significantly reduce private vehicle trips, we do not have a great understanding of the magnitude of the project- and shock-based causal change in travel behaviors, especially for the retail purpose. We analyze the impact of newly developed malls on the retail trip behavior of nearby residents for shopping, dining or services. Using the difference-in-differences approach and big data from a major taxi company in Singapore, we find that households residing within 800 m from a new mall are significantly less likely to take taxis to other retail destinations …

Watching The Watchmen: An Ethical Evaluation Of The Behavior Of Modern Software Applications, Joshua Graves

University Honors Program Senior Projects

Software has become a ubiquitous element of modern life around the world. An unprecedented amount of power is bestowed upon the companies that own and operate that software. The obvious question arises: “Do these companies operate in an ethical manner regarding their software?” We derive an ethical code via synthesizing the ethical codes of both the IEEE and the ACM, disregarding principles that cannot be examined by an outside observer. We utilize this ethical code to examine five leaders in the software industry, namely Facebook, Google, Microsoft, Twitter, and Amazon. For each company, we examine four incidents in which they …

Deep Learning-Based Classification Of Chaotic Systems Over Phase Portraits, Sezgi̇n Kaçar, Süleyman Uzun, Burak Aricioğlu

Turkish Journal of Electrical Engineering and Computer Sciences

This study performed a deep learning-based classification of chaotic systems over their phase portraits. To the best of the authors' knowledge, such classification studies over phase portraits have not been conducted in the literature. To that end, a dataset consisting of the phase portraits of the most known two chaotic systems, namely Lorenz and Chen, is generated for different values of the parameters, initial conditions, step size, and time length. Then, a classification with high accuracy is carried out employing transfer learning methods. The transfer learning methods used in the study are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet, and …