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A Comprehensive Analysis of Explainable Artificial Intelligence (XAI) Methods in Cardiology and Neuroscience: A Systematic Review
No full textArtificial Intelligence (AI) is established as a vital branch within computer science, representing a technology designed to clone human intelligence for proficient problem-solving capabilities. Over the past decade, AI has quietly become ingrained in everyday life, with many people unaware of its pervasive influence until recently. The introduction of ChatGPT in late 2022 abruptly thrust AI into the forefront of public consciousness, leading to widespread recognition of its applications. Literature surveys have shown a rapid proliferation of publications on AI, particularly with a sharp increase in writings dedicated to ChatGPT in recent months. AI applications have exploded in every possible domain of everyone\u27s life, including medicine and healthcare. The lack of trust and transparency in AI-based healthcare and other applications resulted in the emergence of a new field known as Explainable Artificial intelligence (XAI), where algorithms are developed to provide human-understandable explanations for AI-generated decisions. XAI is a set of processes and methods that explain how the AI model reached its prediction. While many XAI surveys are conducted in the healthcare sector, this paper mainly focuses on recent (2019 -2024) findings of XAI within the domain of cardiology and neuroscience. The primary objective of this study is to assess various XAI methodologies and machine learning models to elucidate the decision-making processes of AI, enabling clinicians to validate and interpret AI-derived insights with confidence relevant to the fields of cardiology and neuroscience. This paper systematically reviews the advancement of XAI by carefully selecting and analyzing the latest research within cardiology and neuroscience. Multiple journal databases were comprehensively searched following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 2020. This review includes 130 articles examining various XAI techniques, including SHAP, LIME, Decision Trees, Decision boundary plots, Perturbation-based methods, and many others. Additionally, the paper explores the potential challenges in the development of XAI, offering insights to guide future research efforts in the field.
Keywords: black-box models, explainable artificial intelligence, cardiology, neuroscienc
RSA Algorithm
No full textI will be presenting about the RSA method in cryptology which is the coding and decoding of messages. My research will focus on proving that the method works and how it is used to communicate secretly
AdviSync: A Dynamic Academic Course Scheduler
Full text linkAcademic advising at universities can be a tedious and disorganized process for both students and advisors. Each advisor may have several dozen advisees to manage each semester, and each individual student has unique sets of classes they need to take to graduate. This might lead to scheduling errors. These errors can put the student behind in their degree, thus extending the time it takes for them to graduate past financial aid periods and delay their entry into the workforce. To address this issue, we create AdviSync. It is a tool for both students and advisors that aims to provide a personalized degree map. AdviSync dynamically suggests classes for each semester left in their degree plan. This application would not only be beneficial for the students to graduate on time, but also eliminate any human error and help with financial repercussions
Multi-Rotor Hexicopter
Full text linkThe Multi-Rotor Hexicopter is an original design for an eighteen motor, hydrogen fuel cell powered drone. After initial success with the hydrogen fuel cell powered drone created by previous students, our goal was take their success and create a much larger drone that could be controlled both from the ground and in the air. Our design was based on a drone created by the company Lift Aircraft. The drone is separated into four components that were designed in Autodesk Inventor. The bottom, known as the sled, is used for the storage of the two hydrogen fuel tanks. The second section, above the sled, holds the two 2.2 kW hydrogen fuel cells. The third section is the cockpit where the drone will be controlled by the pilot and contains the controller and display module. Lastly, the top is where eighteen 40 pound thrust motors are located along with eighteen 18.5 volt batteries. The Hexicopter was designed to be built out of aluminum or titanium tubing and the shell to be overlaid with carbon fiber sheathing. The batteries are positioned below the motors and are used to take the drone off and land it. After takeoff, the hydrogen fuel cells are switched on in order to increase flight time substantially. The goal of this senior design project was to create a design and find the components needed to build the drone with the intention of handing construction over to a group of both electrical and mechanical engineers
Assignment to Advancement: A Deep Dive into TECH 1001 & 1013 Success & Retention
Full text linkThis research investigates the multifaceted implications of student retention, academic performance, and instructional methodologies within our TECH 1001/1013 courses at Arkansas Tech University. The work examines enrollment trends, scrutinizes the academic grades of retained and non-retained students, evaluates overall course performance metrics, assesses the average grading patterns of instructors, and compares the academic outcomes between face-to-face and online instructional formats. By conducting this quantitative analysis study, the research aims to explain the correlations between student retention rates and academic success. The findings of this study hold implications for educators and administrators and offer insights into strategies for improving student retention and optimizing teaching practices to enhance student achievement in higher education
A Comparative Study on the Effects of Meishan and Duroc Teaser Boars on Farrowing Rates of Two individual Swine Operations
Full text linkEffective use of certain teaser boar breeds in artificial insemination, a common assisted reproductive procedure, in swine production provides a surmountable outlet to increase farrowing rates, breeding outcomes, and economic gain within the swine industry. Previous studies have emphasized the importance of boar types in swine breeding outcomes; however, this study delves into the divergent characteristics of Meishan and Duroc boars and how their different attributes affect the farrowing rates of Yorkshire-landrace sows at two individual swine operations, a smaller independent facility, and a larger commercial facility. Data was garnered and contrasted for three-year (2015-2017) time spans from each operation. Statistical analysis was conducted utilizing a 95% confidence interval and a two-sample, two-tailed T-test of equal variances. Furthermore, statistical tests were run in Microsoft Excel 2022, version 2402. The smaller independent operation used Duroc teaser boars and had annual farrowing rates of 90, 93, and 97% in 2015, 2016, and 2017, respectively. Similarly, the larger commercial operation used Meishan teaser boars and had annual farrowing rates of 88, 87, and 87%, respectively in 2021, 2022, and 2023. A 95% confidence interval test yielded lower and upper confidence intervals of –4.01 and 15.67. The two-tailed T-test yielded a no difference (p\u3e0.05) between the farms studied. Both statistical tests suggest no differences between the farrowing rates of each operation. To evaluate the true effects of teaser boar breeds on farrowing rates, more replicated studies comparing boar breeds are needed along with larger sample sizes and a broadened geographic scope for the operations included
Breast Cancer Classification with Machine Learning
Full text linkBreast cancer is one of the foremost causes of death amongst women worldwide. Breast tumours are characteristically classified as either benign (non-cancerous) or malignant (cancerous). Benign tumours do not spread external side of the breast and are not fatal, whereas malignant tumours can metastasize and be incurable if untreated. Rapidly and accurate diagnosis of malignant tumours is significant for efficient treatment and advanced outcomes. In 2022, breast cancer claimed 670 000 lives worldwide. Women without any particular risk factors other than age and sex account for half of all cases of breast cancer. In 157 out of 185 nations, breast cancer was the most frequent cancer among women in 2022. Worldwide, breast cancer affects people in every nation. Men are affected by breast cancer at a rate of 0.5–1% [1]. One prevalent and vigorous machine learning algorithm that has been significantly used for classification undertakings is the Support Vector Machine (SVM). SVM is a supervised learning model that forms an optimal hyperplane or decision boundary to maximize the margin concerning classes. It can handle high-dimensional and non-linear data by using kernel functions to map the data into a higher-dimensional space where it develops linearly separable. By exercising the SVM algorithm, I aim to leveraging its adeptness to efficiently handle high-dimensional data and portray complex, non-linear relationships involving features and class labels. The SVM\u27s flexibility in using different kernel functions acknowledges for modelling diverse decision boundaries, theoretically steering to advanced classification accuracy.
In this study, I will investigate the performance of the SVM classifier on the Wisconsin Breast Cancer dataset and compare it with the formerly reported results using KNN and NB classifiers. Furthermore, I will probe the effect of different kernel functions and hyperparameter tuning on the SVM\u27s performance to enhance its classification capabilities.
The results of this research will provide perceptions into the effectiveness of the SVM algorithm for breast cancer diagnosis and influence on the development of accurate and trustworthy machine learning-based diagnostic tools. Precise classification of benign and malignant tumours can assist healthcare professionals in determining the applicable course of cure and managing for patients, ultimately recovering clinical outcomes. Healthcare professionals in determining the appropriate course of treatment and management for patients
Introductory Sociology
Full text linkReview of OER Sociology textbook by Bill Pelz, available at https://library.achievingthedream.org/herkimerintrosociology
Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD)
Full text linkReview of OER Emergency Management textbook by Candice Carter, et al., available at https://kstatelibraries.pressbooks.pub/drone-delivery
Bad Ideas about Writing
Full text linkReview of OER Composition textbook by Cheryl E. Ball and Drew M. Loewe, available at https://open.umn.edu/opentextbooks/textbooks/bad-ideas-about-writin