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Full-Text Articles in Physical Sciences and Mathematics

Exploring The Effectiveness Of Multiple-Exemplar Training For Visual Analysis Of Ab-Design Graphs, Verena S. Bethke Jun 2022

Exploring The Effectiveness Of Multiple-Exemplar Training For Visual Analysis Of Ab-Design Graphs, Verena S. Bethke

Dissertations, Theses, and Capstone Projects

In behavior analysis, data are usually analyzed using visual analysis of the graphed data. There are a wide range of methods used to visually analyze data, from a basic ‘textbook’ style approach to the use of visual aids, decision-rubrics, and computer-based approaches. In the literature, there have been some comparisons of the efficacy of different approaches. Visual analysis as a behavior can be taught using a variety of methods, independent of how the skill itself is to be performed. Teaching methods include lecture, online instruction, and equivalence-based instruction. There is not much research on the teaching of visual analysis specifically ...


A Systematic Review On Machine Learning Models For Online Learning And Examination Systems, Sanaa Kaddoura, Daniela Elena Popescu, Jude D. Hemanth May 2022

A Systematic Review On Machine Learning Models For Online Learning And Examination Systems, Sanaa Kaddoura, Daniela Elena Popescu, Jude D. Hemanth

All Works

Examinations or assessments play a vital role in every student’s life; they determine their future and career paths. The COVID pandemic has left adverse impacts in all areas, including the academic field. The regularized classroom learning and face-to-face real-time examinations were not feasible to avoid widespread infection and ensure safety. During these desperate times, technological advancements stepped in to aid students in continuing their education without any academic breaks. Machine learning is a key to this digital transformation of schools or colleges from real-time to online mode. Online learning and examination during lockdown were made possible by Machine learning ...


The Quest For New Music: A Recommendation Algorithm For Spotify Users, Ian Curtis May 2022

The Quest For New Music: A Recommendation Algorithm For Spotify Users, Ian Curtis

Honors Projects

Music is one of the rare forms of communication that can be understood on a profound level by anyone; it has the power to cause significant emotional effects, to spark inspiration, to ignite change, to spread knowledge, and more, even regardless of song language. A popular subject of research in music pertains to recommendations; determining a song a listener would enjoy is not an easy task. Moreover, certain factors may influence a user's satisfaction with recommended songs and their likelihood to continue using a service. Focusing on the major streaming service Spotify, we build a K-Means clustering algorithm to ...


Intraday Algorithmic Trading Using Momentum And Long Short-Term Memory Network Strategies, Andrew R. Whitinger Ii May 2022

Intraday Algorithmic Trading Using Momentum And Long Short-Term Memory Network Strategies, Andrew R. Whitinger Ii

Undergraduate Honors Theses

Intraday stock trading is an infamously difficult and risky strategy. Momentum and reversal strategies and long short-term memory (LSTM) neural networks have been shown to be effective for selecting stocks to buy and sell over time periods of multiple days. To explore whether these strategies can be effective for intraday trading, their implementations were simulated using intraday price data for stocks in the S&P 500 index, collected at 1-second intervals between February 11, 2021 and March 9, 2021 inclusive. The study tested 160 variations of momentum and reversal strategies for profitability in long, short, and market-neutral portfolios, totaling 480 ...


Development Of A Machine Learning-Based Financial Risk Control System, Zhigang Hu May 2022

Development Of A Machine Learning-Based Financial Risk Control System, Zhigang Hu

All Graduate Theses and Dissertations

With the gradual end of the COVID-19 outbreak and the gradual recovery of the economy, more and more individuals and businesses are in need of loans. This demand brings business opportunities to various financial institutions, but also brings new risks. The traditional loan application review is mostly manual and relies on the business experience of the auditor, which has the disadvantages of not being able to process large quantities and being inefficient. Since the traditional audit processing method is no longer suitable some other method of reducing the rate of non-performing loans and detecting fraud in applications is urgently needed ...


Identifying Functional And Non-Functional Software Requirements From User App Reviews And Requirements Artifacts, Dev Jayant Dave May 2022

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 ...


Machine Learning-Based Device Type Classification For Iot Device Re- And Continuous Authentication, Kaustubh Gupta Apr 2022

Machine Learning-Based Device Type Classification For Iot Device Re- And Continuous Authentication, Kaustubh Gupta

Computer Science and Engineering: Theses, Dissertations, and Student Research

Today, the use of Internet of Things (IoT) devices is higher than ever and it is growing rapidly. Many IoT devices are usually manufactured by home appliance manufacturers where security and privacy are not the foremost concern. When an IoT device is connected to a network, currently there does not exist a strict authentication method that verifies the identity of the device, allowing any rogue IoT device to authenticate to an access point. This thesis addresses the issue by introducing methods for continuous and re-authentication of static and dynamic IoT devices, respectively. We introduce mechanisms and protocols for authenticating a ...


Data-Driven Models For Remaining Useful Life Estimation Of Aircraft Engines And Hard Disk Drives, Austin Coursey Apr 2022

Data-Driven Models For Remaining Useful Life Estimation Of Aircraft Engines And Hard Disk Drives, Austin Coursey

Honors College Theses

Failure of physical devices can cause inconvenience, loss of money, and sometimes even deaths. To improve the reliability of these devices, we need to know the remaining useful life (RUL) of a device at a given point in time. Data-driven approaches use data from a physical device to build a model that can estimate the RUL. They have shown great performance and are often simpler than traditional model-based approaches. Typical statistical and machine learning approaches are often not suited for sequential data prediction. Recurrent Neural Networks are designed to work with sequential data but suffer from the vanishing gradient problem ...


Computational Complexity Reduction Of Deep Neural Networks, Mee Seong Im, Venkat Dasari Apr 2022

Computational Complexity Reduction Of Deep Neural Networks, Mee Seong Im, Venkat Dasari

Mathematica Militaris

Deep neural networks (DNN) have been widely used and play a major role in the field of computer vision and autonomous navigation. However, these DNNs are computationally complex and their deployment over resource-constrained platforms is difficult without additional optimizations and customization.

In this manuscript, we describe an overview of DNN architecture and propose methods to reduce computational complexity in order to accelerate training and inference speeds to fit them on edge computing platforms with low computational resources.


Deapsecure Computational Training For Cybersecurity: Third-Year Improvements And Impacts, Bahador Dodge, Jacob Strother, Rosby Asiamah, Karina Arcaute, Wirawan Purwanto, Masha Sosonkina, Hongyi Wu Apr 2022

Deapsecure Computational Training For Cybersecurity: Third-Year Improvements And Impacts, Bahador Dodge, Jacob Strother, Rosby Asiamah, Karina Arcaute, Wirawan Purwanto, Masha Sosonkina, Hongyi Wu

Modeling, Simulation and Visualization Student Capstone Conference

The Data-Enabled Advanced Training Program for Cybersecurity Research and Education (DeapSECURE) was introduced in 2018 as a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. By its third year, DeapSECURE, like many other educational endeavors, experienced abrupt changes brought by the COVID-19 pandemic. The training had to be retooled to adapt to fully online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper ...


Physics-Guided Machine Learning In Ocean Acoustics Using Fisher Information, Michael Craig Mortenson Apr 2022

Physics-Guided Machine Learning In Ocean Acoustics Using Fisher Information, Michael Craig Mortenson

Theses and Dissertations

Waterborne acoustic signals carry information about the ocean environment. Ocean geoacoustic inversion is the task of estimating environmental parameters from received acoustic signals by matching the measured sound with the predictions of a physics-based model. A lower bound on the uncertainty associated with environmental parameter estimates, the Cramér-Rao bound, can be calculated from the Fisher information, which is dependent on derivatives of a physics-based model. Physics-based preconditioners circumvent the need for variable step sizes when computing numerical derivatives. This work explores the feasibility of using a neural network to perform geoacoustic inversion for environmental parameters and their associated uncertainties from ...


Assessing Automated Administration, Cary Coglianese, Alicia Lai Apr 2022

Assessing Automated Administration, Cary Coglianese, Alicia Lai

Faculty Scholarship at Penn Law

To fulfill their responsibilities, governments rely on administrators and employees who, simply because they are human, are prone to individual and group decision-making errors. These errors have at times produced both major tragedies and minor inefficiencies. One potential strategy for overcoming cognitive limitations and group fallibilities is to invest in artificial intelligence (AI) tools that allow for the automation of governmental tasks, thereby reducing reliance on human decision-making. Yet as much as AI tools show promise for improving public administration, automation itself can fail or can generate controversy. Public administrators face the question of when exactly they should use automation ...


Using Connections To Make Predictions On Dynamic Networks, Rebecca Dorff Jones Apr 2022

Using Connections To Make Predictions On Dynamic Networks, Rebecca Dorff Jones

Theses and Dissertations

Networks are sets of objects that are connected in some way and appear abundantly in nature, sociology, and technology. For many centuries, network theory focused on static networks, which are networks that do not change. However, since all networks transform over time, static networks have limited applications. By comparison, dynamic networks model how connections between objects change over time. In this work, we will explore how connections in dynamic networks change and how we can leverage these changes to make predictions about future iterations of networks. We will do this by first considering the link prediction problem, using either Katz ...


Advanced Communication And Sensing Protocols Using Twisted Light And Engineered Quantum Statistics, Michelle L. Lollie Apr 2022

Advanced Communication And Sensing Protocols Using Twisted Light And Engineered Quantum Statistics, Michelle L. Lollie

LSU Doctoral Dissertations

Advanced performance of modern technology at a fundamental physical level is driving new innovations in communication, sensing capability, and information processing. Key to this improvement is the ability to harness the power of physical phenomena at the quantum mechanical level, where light and light-matter interactions produce technological advancement not realizable by classical means. Theoretical investigation into quantum computing, sensing capability beyond classical limits, and quantum information has prompted experimental work to bring state-of-the-art quantum systems to the forefront for commercial use. This dissertation contributes to the latter portion of the work. A set of preliminaries is included highlighting pertinent physical ...


Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian Apr 2022

Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian

Northeast Journal of Complex Systems (NEJCS)

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted ...


Preprocessing Of Astronomical Images From The Neowise Survey For Near-Earth Asteroid Detection, Rachel Meyer Apr 2022

Preprocessing Of Astronomical Images From The Neowise Survey For Near-Earth Asteroid Detection, Rachel Meyer

Scholar Week 2016 - present

Asteroid detection is a common field in astronomy for planetary defense which requires observations from survey telescopes to detect and classify different objects. The amount of data collected each night is increasing as better designed telescopes are created each year. This amount is quickly becoming unmanageable and many researchers are looking for ways to better process this data. The dominant solution is to implement computer algorithms to automatically detect these sources and to use Machine Learning in order to create a more efficient and accurate classifier. In the past there has been a focus on larger asteroids that create streaks ...


A Machine Learning Approach To Denoising Particle Detector Observations In Nuclear Physics, Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian, Nikos Chrisochoides Apr 2022

A Machine Learning Approach To Denoising Particle Detector Observations In Nuclear Physics, Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian, Nikos Chrisochoides

College of Sciences Posters

With the evolution in detector technologies and electronic components used in the Nuclear Physics field, experimental setups become larger and more complex. Faster electronics enable particle accelerator experiments to run with higher beam intensity, providing more interactions per time and more particles per interaction. However, the increased beam intensities present a challenge to particle detectors because of the higher amount of noise and uncorrelated signals. Higher noise levels lead to a more challenging particle reconstruction process by increasing the number of combinatorics to analyze and background signals to eliminate. On the other hand, increasing the beam intensity can provide physics ...


Industrial Digital Twins At The Nexus Of Nextg Wireless Networks And Computational Intelligence: A Survey, Shah Zeb, Aamir Mahmood, Syed Ali Hassan, Md. Jalil Piran, Mikael Gidlund, Mohsen Guizani Apr 2022

Industrial Digital Twins At The Nexus Of Nextg Wireless Networks And Computational Intelligence: A Survey, Shah Zeb, Aamir Mahmood, Syed Ali Hassan, Md. Jalil Piran, Mikael Gidlund, Mohsen Guizani

Machine Learning Faculty Publications

By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry 4.0 promotes integrating cyber–physical worlds through cyber–physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT enables interaction with the digital image of the industrial physical objects/processes to simulate, analyze, and control their real-time operation. DT is rapidly diffusing in numerous industries with the interdisciplinary advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and ...


Reinforcement Learning With Deep Q-Networks, Caleb Cassady Apr 2022

Reinforcement Learning With Deep Q-Networks, Caleb Cassady

Masters Theses & Specialist Projects

In the past decade, machine learning strategies centered on the use of Deep Neural Networks (DNNs) have caught the interest of researchers due to their success in complicated classification and prediction problems. More recently, these DNNs have been applied to reinforcement learning tasks with state of- the-art results using Deep Q-Networks (DQNs) based on the Q-Learning algorithm. However, the DQN training process is different from standard DNNs and poses significant challenges for certain reinforcement learning environments. This paper examines some of these challenges, compares proposed solutions, and offers novel solutions based on previous research. Experiment implementation available at https://github ...


Application Of Machine Learning To Predict The Performance Of An Emipg Reactor Using Data From Numerical Simulations, Owen Sedej, Eric G. Mbonimpa, Trevor Sleight, Jeremy Slagley Mar 2022

Application Of Machine Learning To Predict The Performance Of An Emipg Reactor Using Data From Numerical Simulations, Owen Sedej, Eric G. Mbonimpa, Trevor Sleight, Jeremy Slagley

Faculty Publications

Microwave-driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A ...


Rowan-Bms Collaboration, Vasil Hnatyshin Mar 2022

Rowan-Bms Collaboration, Vasil Hnatyshin

Faculty Scholarship for the College of Science & Mathematics

No abstract provided.


Incremental Non-Greedy Clustering At Scale, Nicholas Monath Mar 2022

Incremental Non-Greedy Clustering At Scale, Nicholas Monath

Doctoral Dissertations

Clustering is the task of organizing data into meaningful groups. Modern clustering applications such as entity resolution put several demands on clustering algorithms: (1) scalability to massive numbers of points as well as clusters, (2) incremental additions of data, (3) support for any user-specified similarity functions.

Hierarchical clusterings are often desired as they represent multiple alternative flat clusterings (e.g., at different granularity levels). These tree-structured clusterings provide for both fine-grained clusters as well as uncertainty in the presence of newly arriving data. Previous work on hierarchical clustering does not fully address all three of the aforementioned desiderata. Work on ...


Artificial Intelligence For Satellite Communication: A Review, Fares Fourati, Mohamed-Slim Alouini Mar 2022

Artificial Intelligence For Satellite Communication: A Review, Fares Fourati, Mohamed-Slim Alouini

Intelligent and Converged Networks

Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects has demonstrated ...


A High Precision Machine Learning-Enabled System For Predicting Idiopathic Ventricular Arrhythmia Origins, Jianwei Zheng, Guohua Fu, Daniele Struppa, Islam Abudayyeh, Tahmeed Contractor, Kyle Anderson, Huimin Chu, Cyril Rakovski Mar 2022

A High Precision Machine Learning-Enabled System For Predicting Idiopathic Ventricular Arrhythmia Origins, Jianwei Zheng, Guohua Fu, Daniele Struppa, Islam Abudayyeh, Tahmeed Contractor, Kyle Anderson, Huimin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Background: Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy.

Method: A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into ...


Moving Toward Personalized Law, Cary Coglianese Mar 2022

Moving Toward Personalized Law, Cary Coglianese

Faculty Scholarship at Penn Law

Rules operate as a tool of governance by making generalizations, thereby cutting down on government officials’ need to make individual determinations. But because they are generalizations, rules can result in inefficient or perverse outcomes due to their over- and under-inclusiveness. With the aid of advances in machine-learning algorithms, however, it is becoming increasingly possible to imagine governments shifting away from a predominant reliance on general rules and instead moving toward increased reliance on precise individual determinations—or on “personalized law,” to use the term Omri Ben-Shahar and Ariel Porat use in the title of their 2021 book. Among the various ...


A Novel Expert System To Assist High School Students In Selecting Their Appropriate University Program: A Case Study Of Hebron University, Aseel Alnajjar, Nabil Hasasneh, Mario Macido Mar 2022

A Novel Expert System To Assist High School Students In Selecting Their Appropriate University Program: A Case Study Of Hebron University, Aseel Alnajjar, Nabil Hasasneh, Mario Macido

Hebron University Research Journal-A (Natural Sciences) - (مجلة جامعة الخليل للبحوث- أ (العلوم الطبيعيه

Information and Communication Technology (ICT) became a measure of the level of progress of an organization and shows its ability to compete. There is no doubt that the applications of Artificial Intelligence (AI) have contributed to a technological progress in various fields among which is expert systems, which is defined simply as the system that replaces or assists a human expert in a complex task that requires specialized knowledge. The fundamental purpose of the present study is to propose and develop an expert system to guide high school students in choosing the appropriate university major at Hebron University as a ...


The Role Of 3d Ct Imaging In The Accurate Diagnosis Of Lung Function In Coronavirus Patients, Ibrahim Shawky Farahat, Ahmed Sharafeldeen, Mohamed Elsharkawy, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Fatma Taher, Maha Bilal, Ahmed Abdel Khalek Abdel Razek, Waleed Aladrousy, Samir Elmougy, Ahmed Elsaid Tolba, Moumen El-Melegy, Ayman El-Baz Mar 2022

The Role Of 3d Ct Imaging In The Accurate Diagnosis Of Lung Function In Coronavirus Patients, Ibrahim Shawky Farahat, Ahmed Sharafeldeen, Mohamed Elsharkawy, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Fatma Taher, Maha Bilal, Ahmed Abdel Khalek Abdel Razek, Waleed Aladrousy, Samir Elmougy, Ahmed Elsaid Tolba, Moumen El-Melegy, Ayman El-Baz

All Works

Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov–Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials ...


Landslide Detection In The Himalayas Using Machine Learning Algorithms And U-Net, Sansar Raj Meena, Lucas Pedrosa Soares, Carlos H. Grohmann, Cees Van Westen, Kushanav Bhuyan, Ramesh P. Singh, Mario Floris, Filippo Catani Feb 2022

Landslide Detection In The Himalayas Using Machine Learning Algorithms And U-Net, Sansar Raj Meena, Lucas Pedrosa Soares, Carlos H. Grohmann, Cees Van Westen, Kushanav Bhuyan, Ramesh P. Singh, Mario Floris, Filippo Catani

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Event-based landslide inventories are essential sources to broaden our understanding of the causal relationship between triggering events and the occurring landslides. Moreover, detailed inventories are crucial for the succeeding phases of landslide risk studies like susceptibility and hazard assessment. The openly available inventories differ in the quality and completeness levels. Event-based landslide inventories are created based on manual interpretation, and there can be significant differences in the mapping preferences among interpreters. To address this issue, we used two different datasets to analyze the potential of U-Net and machine learning approaches for automated landslide detection in the Himalayas. Dataset-1 is composed ...


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 ...


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 ...