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Articles 571 - 600 of 1687

Full-Text Articles in Physical Sciences and Mathematics

Land-Surface Parameters For Spatial Predictive Mapping And Modeling, Aaron E. Maxwell, Charles Shobe Feb 2022

Land-Surface Parameters For Spatial Predictive Mapping And Modeling, Aaron E. Maxwell, Charles Shobe

Faculty & Staff Scholarship

Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and …

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Understanding Deep Learning - Challenges And Prospects, Niha Adnan, Fahad Umer Feb 2022

Understanding Deep Learning - Challenges And Prospects, Niha Adnan, Fahad Umer

Department of Surgery

The developments in Artificial Intelligence have been on the rise since its advent. The advancements in this field have been the innovative research area across a wide range of industries, making its incorporation in dentistry inevitable. Artificial Intelligence techniques are making serious progress in the diagnostic and treatment planning aspects of dental clinical practice. This will ultimately help in the elimination of subjectivity and human error that are often part of radiographic interpretations, and will improve the overall efficiency of the process. The various types of Artificial Intelligence algorithms that exist today make the understanding of their application quite complex. …

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Early Fire Detection: A New Indoor Laboratory Dataset And Data Distribution Analysis, Amril Nazir, Husam Mosleh, Maen Takruri, Abdul Halim Jallad, Hamad Alhebsi Feb 2022

Early Fire Detection: A New Indoor Laboratory Dataset And Data Distribution Analysis, Amril Nazir, Husam Mosleh, Maen Takruri, Abdul Halim Jallad, Hamad Alhebsi

All Works

Fire alarm systems are typically equipped with various sensors such as heat, smoke, and gas detectors. These provide fire alerts and notifications of emergency exits when a fire has been detected. However, such systems do not give early warning in order to allow appropriate action to be taken when an alarm is first triggered, as the fire may have already caused severe damage. This paper analyzes a new dataset gathered from controlled realistic fire experiments conducted in an indoor laboratory environment. The experiments were conducted in a controlled manner by triggering the source of fire using electrical devices and charcoal …

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A Novel Approach To Face Pattern Analysis, Shashi Bhushan, Mohammed Alshehri, Neha Agarwal, Ismail Keshta, Jitendra Rajpurohit, Ahed Abugabah Feb 2022

A Novel Approach To Face Pattern Analysis, Shashi Bhushan, Mohammed Alshehri, Neha Agarwal, Ismail Keshta, Jitendra Rajpurohit, Ahed Abugabah

All Works

Recognizing facial expressions is a major challenge and will be required in the latest fields of research such as the industrial Internet of Things. Currently, the available methods are useful for detecting singular facial images, but they are very hard to extract. The main aim of face detection is to capture an image in real‐time and search for the image in the available dataset. So, by using this biometric feature, one can recognize and verify the person’s image by their facial features. Many researchers have used Principal Component Analysis (PCA), Support Vector Machine (SVM), a combination of PCA and SVM, …

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Automated Reverse Engineering Of Role-Based Access Control Policies Of Web Applications, Ha Thanh Le, Lwin Khin Shar, Domenico Bianculli, Lionel C. Briand, Cu Duy Nguyen Feb 2022

Automated Reverse Engineering Of Role-Based Access Control Policies Of Web Applications, Ha Thanh Le, Lwin Khin Shar, Domenico Bianculli, Lionel C. Briand, Cu Duy Nguyen

Research Collection School Of Computing and Information Systems

Access control (AC) is an important security mechanism used in software systems to restrict access to sensitive resources. Therefore, it is essential to validate the correctness of AC implementations with respect to policy specifications or intended access rights. However, in practice, AC policy specifications are often missing or poorly documented; in some cases, AC policies are hard-coded in business logic implementations. This leads to difficulties in validating the correctness of policy implementations and detecting AC defects.In this paper, we present a semi-automated framework for reverse-engineering of AC policies from Web applications. Our goal is to learn and recover role-based access …

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In Search Of Star Clusters: An Introduction To The K-Means Algorithm, Marcio Nascimento Jan 2022

In Search Of Star Clusters: An Introduction To The K-Means Algorithm, Marcio Nascimento

Journal of Humanistic Mathematics

This article is a gentle introduction to K-means, a mathematical technique of processing data for further classification. We begin with a brief historical introduction, where we find connections with Plato’s Timæus, von Linné’s binomial classification, and the star clustering concept of Mary Sommerville and collaborators. Artificial intelligence algorithms use K-means as a classification methodology to learn about data in a very accurate way, because it is a quantitative procedure based on similarities.

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Assessing Feature Representations For Instance-Based Cross-Domain Anomaly Detection In Cloud Services Univariate Time Series Data, Rahul Agrahari, Matthew Nicholson, Clare Conran, Haythem Assem, John D. Kelleher Jan 2022

Assessing Feature Representations For Instance-Based Cross-Domain Anomaly Detection In Cloud Services Univariate Time Series Data, Rahul Agrahari, Matthew Nicholson, Clare Conran, Haythem Assem, John D. Kelleher

Articles

In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best …

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An Empirical Study On The Efficacy Of Evolutionary Algorithms For Automated Neural Architecture Search, Andrew D. Cuccinello Jan 2022

An Empirical Study On The Efficacy Of Evolutionary Algorithms For Automated Neural Architecture Search, Andrew D. Cuccinello

Theses and Dissertations

The configuration and architecture design of neural networks is a time consuming process that has been shown to provide significant training speed and prediction improvements. Traditionally, this process is done manually, but this requires a large amount of expert knowledge and significant investment of labor. As a result it is beneficial to have automated ways to optimize model architectures. In this thesis, we study the use of evolutionary algorithm for neural architecture search (NAS). Moreover, we investigate the effect of integrating evolutionary NAS into deep reinforcement learning to learn control policy for ATARI game playing. Empirical classification results on the …

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Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil Patnaik Jan 2022

Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil Patnaik

Faculty Publications

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of …

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A Keyword-Enhanced Approach To Handle Class Imbalance In Clinical Text Classification, Andrew E. Blanchard, Shang Gao, Hong Jun Yoon, J. Blair Christian, Eric B. Durbin, Xiao Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen M. Schwartz, Charles Wiggins, Linda Coyle, Lynne Penberthy, Georgia D. Tourassi Jan 2022

A Keyword-Enhanced Approach To Handle Class Imbalance In Clinical Text Classification, Andrew E. Blanchard, Shang Gao, Hong Jun Yoon, J. Blair Christian, Eric B. Durbin, Xiao Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen M. Schwartz, Charles Wiggins, Linda Coyle, Lynne Penberthy, Georgia D. Tourassi

School of Public Health Faculty Publications

Recent applications ofdeep learning have shown promising results for classifying unstructured text in the healthcare domain. However, the reliability of models in production settings has been hindered by imbalanced data sets in which a small subset of the classes dominate. In the absence of adequate training data, rare classes necessitate additional model constraints for robust performance. Here, we present a strategy for incorporating short sequences of text (i.e. keywords) into training to boost model accuracy on rare classes. In our approach, we assemble a set of keywords, including short phrases, associated with each class. The keywords are then used as …

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A Predictive Model To Predict Cyberattack Using Self-Normalizing Neural Networks, Oluwapelumi Eniodunmo Jan 2022

A Predictive Model To Predict Cyberattack Using Self-Normalizing Neural Networks, Oluwapelumi Eniodunmo

Theses, Dissertations and Capstones

Cyberattack is a never-ending war that has greatly threatened secured information systems. The development of automated and intelligent systems provides more computing power to hackers to steal information, destroy data or system resources, and has raised global security issues. Statistical and Data mining tools have received continuous research and improvements. These tools have been adopted to create sophisticated intrusion detection systems that help information systems mitigate and defend against cyberattacks. However, the advancement in technology and accessibility of information makes more identifiable elements that can be used to gain unauthorized access to systems and resources. Data mining and classification tools …

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Supervised Machine Learning Techniques Applied To Low-Cost Air Quality Sensor Suites, Peter Wahman Jan 2022

Supervised Machine Learning Techniques Applied To Low-Cost Air Quality Sensor Suites, Peter Wahman

All Undergraduate Theses and Capstone Projects

Low-cost PM sensors have garnered interest for their ability to reduce the cost of investigating PM concentrations in both indoor and outdoor spaces. They perform well in high concentration lab testing with correlation coefficients greater than 0.9. In real-world applications, the correlation coefficients drop significantly because of sensing floors and adverse ambient conditions. There are plenty of supervised machine learning techniques that aim to correct the measurements ranging from linear regression to more advanced neural networks and random forests. This work aims to use those more complicated techniques to adjust the measurements using other data sets gathered by a sensor …

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Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos Jan 2022

Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos

VMASC Publications

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, …

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Part I - Ai And Data As Medical Devices, W. Nicholson Price Ii Jan 2022

Part I - Ai And Data As Medical Devices, W. Nicholson Price Ii

Other Publications

It may seem counterintuitive to open a book on medical devices with chapters on software and data, but these are the frontiers of new medical device regulation and law. Physical devices are still crucial to medicine, but they – and medical practice as a whole – are embedded in and permeated by networks of software and caches of data. Those software systems are often mindbogglingly complex and largely inscrutable, involving artificial intelligence and machine learning. Ensuring that such software works effectively and safely remains a substantial challenge for regulators and policymakers. Each of the three chapters in this part examines …

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Applications Of A Combined Approach Of Kinetic Monte Carlo Simulations And Machine Learning To Model Atomic Layer Deposition (Ald) Of Metal Oxides, Emily Justus Jan 2022

Applications Of A Combined Approach Of Kinetic Monte Carlo Simulations And Machine Learning To Model Atomic Layer Deposition (Ald) Of Metal Oxides, Emily Justus

MSU Graduate Theses

Metal-oxides such as ZnO or Al2O3 synthesized through Atomic Layer Deposition (ALD) have been of great research interest as the candidate materials for ultra-thin tunnel barriers. In this study, I have applied a 3D on-lattice Kinetic Monte Carlo (kMC) code developed by Timo Weckman’s group to simulate the growth mechanisms of the tunnel barrier layer and to evaluate the role of various experimentally relevant factors in the ALD processes. I have systematically studied the effect of parameters such as the chamber pressure temperature, pulse, and purge times. The database generated from the kMC simulations was subsequently used …

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Incorporating Ontological Information In Biomedical Entity Linking Of Phrases In Clinical Text, Evan French Jan 2022

Incorporating Ontological Information In Biomedical Entity Linking Of Phrases In Clinical Text, Evan French

Theses and Dissertations

Biomedical Entity Linking (BEL) is the task of mapping spans of text within biomedical documents to normalized, unique identifiers within an ontology. Translational application of BEL on clinical notes has enormous potential for augmenting discretely captured data in electronic health records, but the existing paradigm for evaluating BEL systems developed in academia is not well aligned with real-world use cases. In this work, we demonstrate a proof of concept for incorporating ontological similarity into the training and evaluation of BEL systems to begin to rectify this misalignment. This thesis has two primary components: 1) a comprehensive literature review and 2) …

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A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir Jan 2022

A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir

Dissertations and Theses

Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches toward the comprehension of urban flood risk using data science techniques on crowd-sourced data. This is accomplished by developing a series of data-driven models to identify flood factors of significance and localized areas of flood vulnerability in New York City (NYC). First, the infrastructural (catch basin clogs, …

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Finding The Best Predictors For Foot Traffic In Us Seafood Restaurants, Isabel Paige Beaulieu Jan 2022

Finding The Best Predictors For Foot Traffic In Us Seafood Restaurants, Isabel Paige Beaulieu

Honors Theses and Capstones

COVID-19 caused state and nation-wide lockdowns, which altered human foot traffic, especially in restaurants. The seafood sector in particular suffered greatly as there was an increase in illegal fishing, it is made up of perishable goods, it is seasonal in some places, and imports and exports were slowed. Foot traffic data is useful for business owners to have to know how much to order, how many employees to schedule, etc. One issue is that the data is very expensive, hard to get, and not available until months after it is recorded. Our goal is to not only find covariates that …

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Few-Shot Malware Detection Using A Novel Adversarial Reprogramming Model, Ekula Praveen Kumar Jan 2022

Few-Shot Malware Detection Using A Novel Adversarial Reprogramming Model, Ekula Praveen Kumar

Browse all Theses and Dissertations

The increasing sophistication of malware has made detecting and defending against new strains a major challenge for cybersecurity. One promising approach to this problem is using machine learning techniques that extract representative features and train classification models to detect malware in an early stage. However, training such machine learning-based malware detection models represents a significant challenge that requires a large number of high-quality labeled data samples while it is very costly to obtain them in real-world scenarios. In other words, training machine learning models for malware detection requires the capability to learn from only a few labeled examples. To address …

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Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning, Jaiteg Singh, Gaurav Goyal, Farman Ali, Babar Shah, Sangheon Pack Jan 2022

Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning, Jaiteg Singh, Gaurav Goyal, Farman Ali, Babar Shah, Sangheon Pack

All Works

Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm …

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Developing And Validating A Machine Learning-Based Student Attentiveness Tracking System, Andrew L. Sanders Jan 2022

Developing And Validating A Machine Learning-Based Student Attentiveness Tracking System, Andrew L. Sanders

Electronic Theses and Dissertations

Academic instructors and institutions desire the ability to accurately and autonomously measure the attentiveness of students in the classroom. Generally, college departments use unreliable direct communication from students (i.e. emails, phone calls), distracting and Hawthorne effect-inducing observational sit-ins, and end-of-semester surveys to collect feedback regarding their courses. Each of these methods of collecting feedback is useful but does not provide automatic feedback regarding the pace and direction of lectures. Young et al. discuss that attention levels during passive classroom lectures generally drop after about ten to thirty minutes and can be restored to normal levels with regular breaks, novel activities, …

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A Validity-Based Approach For Feature Selection In Intrusion Detection Systems, Eljilani Hmouda Jan 2022

A Validity-Based Approach For Feature Selection In Intrusion Detection Systems, Eljilani Hmouda

CCE Theses and Dissertations

Intrusion detection systems are tools that detect and remedy the presence of malicious activities. Intrusion detection systems face many challenges in terms of accurate analysis and evaluation. One such challenge is the involvement of many features during analysis, which leads to high data volume and ultimately excessive computational overhead. This research surrounds the development of a new intrusion detection system by employing an entropy-based measure called v-measure to select significant features and reduce dimensionality. After the development of the intrusion detection system, this feature reduction technique was tested on public datasets by applying machine learning classifiers such as Decision Tree, …

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Establishing A Machine Learning Framework For Discovering Novel Phononic Crystal Designs, Drew Feltner Jan 2022

Establishing A Machine Learning Framework For Discovering Novel Phononic Crystal Designs, Drew Feltner

Browse all Theses and Dissertations

A phonon is a discrete unit of vibrational motion that occurs in a crystal lattice. Phonons and the frequency at which they propagate play a significant role in the thermal, optical, and electronic properties of a material. A phononic material/device is similar to a photonic material/device, except that it is fabricated to manipulate certain bands of acoustic waves instead of electromagnetic waves. Phononic materials and devices have been studied much less than their photonic analogues and as such current materials exhibit control over a smaller range of frequencies. This study aims to test the viability of machine learning, specifically neural …

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Fake Review Detection, Michael Husk, Faryaneh Poursardar (Mentor) Jan 2022

Fake Review Detection, Michael Husk, Faryaneh Poursardar (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

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Multi-Modality Automatic Lung Tumor Segmentation Method Using Deep Learning And Radiomics, Siqiu Wang Jan 2022

Multi-Modality Automatic Lung Tumor Segmentation Method Using Deep Learning And Radiomics, Siqiu Wang

Theses and Dissertations

Delineation of the tumor volume is the initial and fundamental step in the radiotherapy planning process. The current clinical practice of manual delineation is time-consuming and suffers from observer variability. This work seeks to develop an effective automatic framework to produce clinically usable lung tumor segmentations. First, to facilitate the development and validation of our methodology, an expansive database of planning CTs, diagnostic PETs, and manual tumor segmentations was curated, and an image registration and preprocessing pipeline was established. Then a deep learning neural network was constructed and optimized to utilize dual-modality PET and CT images for lung tumor segmentation. …

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Algorithm Vs. Algorithm, Cary Coglianese, Alicia Lai Jan 2022

Algorithm Vs. Algorithm, Cary Coglianese, Alicia Lai

All Faculty Scholarship

Critics raise alarm bells about governmental use of digital algorithms, charging that they are too complex, inscrutable, and prone to bias. A realistic assessment of digital algorithms, though, must acknowledge that government is already driven by algorithms of arguably greater complexity and potential for abuse: the algorithms implicit in human decision-making. The human brain operates algorithmically through complex neural networks. And when humans make collective decisions, they operate via algorithms too—those reflected in legislative, judicial, and administrative processes. Yet these human algorithms undeniably fail and are far from transparent. On an individual level, human decision-making suffers from memory limitations, fatigue, …

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Antitrust By Algorithm, Cary Coglianese, Alicia Lai Jan 2022

Antitrust By Algorithm, Cary Coglianese, Alicia Lai

All Faculty Scholarship

Technological innovation is changing private markets around the world. New advances in digital technology have created new opportunities for subtle and evasive forms of anticompetitive behavior by private firms. But some of these same technological advances could also help antitrust regulators improve their performance in detecting and responding to unlawful private conduct. We foresee that the growing digital complexity of the marketplace will necessitate that antitrust authorities increasingly rely on machine-learning algorithms to oversee market behavior. In making this transition, authorities will need to meet several key institutional challenges—building organizational capacity, avoiding legal pitfalls, and establishing public trust—to ensure successful …

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From Negative To Positive Algorithm Rights, Cary Coglianese, Kat Hefter Jan 2022

From Negative To Positive Algorithm Rights, Cary Coglianese, Kat Hefter

All Faculty Scholarship

Artificial intelligence, or “AI,” is raising alarm bells. Advocates and scholars propose policies to constrain or even prohibit certain AI uses by governmental entities. These efforts to establish a negative right to be free from AI stem from an understandable motivation to protect the public from arbitrary, biased, or unjust applications of algorithms. This movement to enshrine protective rights follows a familiar pattern of suspicion that has accompanied the introduction of other technologies into governmental processes. Sometimes this initial suspicion of a new technology later transforms into widespread acceptance and even a demand for its use. In this paper, we …

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Temporal Bagging: A New Method For Time-Based Ensemble Learning, Göksu Tüysüzoğlu, Derya Bi̇rant, Volkan Kiranoğlu Jan 2022

Temporal Bagging: A New Method For Time-Based Ensemble Learning, Göksu Tüysüzoğlu, Derya Bi̇rant, Volkan Kiranoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

One of the main problems associated with the bagging technique in ensemble learning is its random sample selection in which all samples are treated with the same chance of being selected. However, in time-varying dynamic systems, the samples in the training set have not equal importance, where the recent samples contain more useful and accurate information than the former ones. To overcome this problem, this paper proposes a new time-based ensemble learning method, called temporal bagging (T-Bagging). The significant advantage of our method is that it assigns larger weights to more recent samples with respect to older ones, so it …

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Predictive Models In Software Engineering: Challenges And Opportunities, Yanming Yang, Xin Xia, David Lo, Tingting Bi, John C. Grundy, Xiaohu Yang Jan 2022

Predictive Models In Software Engineering: Challenges And Opportunities, Yanming Yang, Xin Xia, David Lo, Tingting Bi, John C. Grundy, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. There have been a large number of primary studies that apply predictive models and that present well-performed studies in various research domains, including software requirements, software design and development, testing and debugging, and software maintenance. This article is a first attempt to systematically organize knowledge in this area by surveying a body of 421 papers on predictive models published between 2009 and 2020. We describe the key models and approaches used, classify the different models, summarize the range of key application …

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