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Predictive Insights For Improving The Resilience Of Global Food Security Using Artificial Intelligence, Meng Leong How, Yong Jiet Chan, Sin Mei Cheah

Research Collection Lee Kong Chian School Of Business

Unabated pressures on food systems affect food security on a global scale. A human-centric artificial intelligence-based probabilistic approach is used in this paper to perform a unified analysis of data from the Global Food Security Index (GFSI). The significance of this intuitive probabilistic reasoning approach for predictive forecasting lies in its simplicity and user-friendliness to people who may not be trained in classical computer science or in software programming. In this approach, predictive modeling using a counterfactual probabilistic reasoning analysis of the GFSI dataset can be utilized to reveal the interplay and tensions between the variables that underlie food affordability, …

Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han

School of Public Health Faculty Research

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …

Estimating The Peak Vertical Ground Reaction Force Component And Step Time In Treadmill Running Using Machine Learning - A Pilot Study, Alex Ong Dr, Ian Sujae Harris Dr, Joseph Hamill Dr

ISBS Proceedings Archive

This study aims to investigate the efficacy of a stacking approach to estimate parameters in treadmill running. Nineteen participants ran on a treadmill at self-selected pace. Ground reaction force and kinematic data were collected. Stacking in machine learning was used to estimate the peak vertical ground reaction force and step time. Good agreement was observed in the test data set for predicted and measured values of the peak vertical ground reaction force component and step time where the ICC values were 0.85 and 0.99 respectively. This suggests stacking may be a feasible approach to estimate kinetic and kinematic parameters during …

A Machine Learning Approach To Delineating Neighborhoods From Geocoded Appraisal Data, Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee, Erik Linstead

Engineering Faculty Articles and Research

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information …

Predicting The Long-Term Stability Of Compact Multiplanet Systems, Daniel Tamayo, Miles Cranmer, Samuel Hadden, Hanno Rein, Peter Battaglia, Alysa Obertas, Philip J. Armitage, Shirley Ho, David N. Spergel, Christian Gilbertson, Naireen Hussain, Ari Silburt, Daniel Jontof-Hutter, Kristen Menou

Pacific Faculty Work

We combine analytical understanding of resonant dynamics in two-planet systems with machine-learning techniques to train a model capable of robustly classifying stability in compact multiplanet systems over long timescales of 109 orbits. Our Stability of Planetary Orbital Configurations Klassifier (SPOCK) predicts stability using physically motivated summary statistics measured in integrations of the first 104 orbits, thus achieving speed-ups of up to 105 over full simulations. This computationally opens up the stability-constrained characterization of multiplanet systems. Our model, trained on_100,000 three-planet systems sampled at discrete resonances, generalizes both to a sample spanning a continuous period-ratio range, as well as to a …

Automatic Depression Screening And Depressive Symptom Prediction Using Smartphone Sensing Data, Shweta Ware

Doctoral Dissertations

Depression is a common, yet serious health problem. It has significant detrimental impacts on both physical and psychological functioning. Current diagnosis techniques rely on physician-administered or patient self-administered interview tools, which are burdensome and suffer from recall bias. Additionally, these techniques incur higher medical costs. There is an urgent need for an accurate, objective and easily accessible depression screening tool for mass usage. In this dissertation, we explore the usage of smartphone sensing data, collected directly on smartphones or meta-data collected from a WiFi infrastructure, for automatic depression screening and depressive symptom prediction.

In the first part of the dissertation, …

Automatic Detection Of Dynamic And Static Activities Of The Older Adults Using A Wearable Sensor And Support Vector Machines, Jian Zhang, Rahul Soangra, Thurmon E. Lockhart

Physical Therapy Faculty Articles and Research

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant …

Gep Automatic Clustering Algorithm With Dynamic Penalty Factors, Chen Yan, Kangshun Li, Yang Lei

Journal of System Simulation

Abstract: Various problems such as sensitive selection of initial clustering center, easily falling into local optimal solution, and determining numbers of clusters, still exist in the traditional clustering algorithm. A GEP automatic clustering algorithm with dynamic penalty factors was proposed. This algorithm combines penalty factors and GEP clustering algorithm, and doesn't rely on any priori knowledge of the data set. And a dynamic algorithm was proposed to generate the penalty factors according to the distribution characteristics of different data sets, which is a better solution for the impact of isolated points and noise points. According to four dataset, penalty factors' …

Towards A Data-Driven Financial System: The Impact Of Covid-19, Nydia Remolina

Centre for AI & Data Governance (2019-2025)

The COVID-19 outbreak has a growing impact on the global economy and the financial sector, which plays a critical role in mitigating the unprecedented macroeconomic and financial shock caused by the pandemic. Given the unprecedented nature of the current crisis, financial regulators and supervisors, central banks, along with governments and legislatures face challenges to maintain financial stability, preserve the well-functioning core markets, and ensure the flow of credit to the real economy. Even though the COVID-19 has slowed down our daily lives and stopped the operation of many industries, it did not have the same effect in the data-driven finance …

A Nonlinear Distortion Removal Based On Deep Neural Network For Underwater Acoustic Ofdm Communication With The Mitigation Of Peak To Average Power Ratio, Xuefei Ma, Waleed Raza, Zhiqiang Wu, Muhammad Bilal, Ziqi Zhou, Amir Ali

Electrical Engineering Faculty Publications

Machine learning and deep learning algorithms have proved to be a powerful tool for developing data-driven signal processing algorithms for challenging engineering problems. This paper studies the modern machine learning algorithm for modeling nonlinear devices like power amplifiers (PAs) for underwater acoustic (UWA) orthogonal frequency divisional multiplexing (OFDM) communication. The OFDM system has a high peak to average power ratio (PAPR) in the time domain because the subcarriers are added coherently via inverse fast Fourier transform (IFFT). This causes a higher bit error rate (BER) and degrades the performance of the PAs; hence, it reduces the power efficiency. For long-range …

Generic Modeling Of Differential Striplines Using Machine Learning Based Regression Analysis, Srinath Penugonda, Shaohui Yong, Anna Gao, Kevin Cai, Bidyut Sen, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a generic model for a differential strip line is created using machine learning (ML) based regression analysis. A recursive approach of creating various inputs is adapted instead of traditional design of experiments (DoE) approach. This leads to reduction of number of simulations as well as control the data points required for performing simulations. The generic model is developed using 48 simulations. It is comparable to the linear regression model, which is obtained using 1152 simulations. Additionally, a tabular W-element model of a differential strip line is used to take into consideration the frequency-dependent dielectric loss. In order …

An Enhanced Deep Reinforcement Learning Algorithm For Decoupling Capacitor Selection In Power Distribution Network Design, Ling Zhang, Wenchang Huang, Jack Juang, Hank Lin, Bin Chyi Tseng, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

The selection of decoupling capacitors (decap) is a critical but tedious process in power distribution network (PDN) design. In this paper, an improved decap-selection algorithm based on deep reinforcement learning (DRL), which seeks the minimum number of decaps through a self-exploration training to satisfy a given target impedance, is presented. Compared with the previous relevant work: the calculation speed of PDN impedance is significantly increased by adopting an impedance matrix reduction method; also, the enhanced algorithm performs a better convergence by utilizing the techniques of double Q-learning and prioritized experience replay; furthermore, a well-designed reward is proposed to facilitate long-term …

A Data-Driven Approach For Winter Precipitation Classification Using Weather Radar And Nwp Data, Bong Chul Seo

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This study describes a framework that provides qualitative weather information on winter precipitation types using a data-driven approach. The framework incorporates the data retrieved from weather radars and the numerical weather prediction (NWP) model to account for relevant precipitation microphysics. To enable multimodel-based ensemble classification, we selected six supervised machine learning models: k-nearest neighbors, logistic regression, support vector machine, decision tree, random forest, and multi-layer perceptron. Our model training and cross-validation results based on Monte Carlo Simulation (MCS) showed that all the models performed better than our baseline method, which applies two thresholds (surface temperature and atmospheric layer thickness) for …

Application Of Predictive Modelling To Improve The Discharge Process In Hospitals, Sayed Hisham, Shahina Abdul Rasheed, Brayal Dsouza

Open Access archive

Objectives: To find out the factors influencing discharge process turnaround time (TAT) and to accurately predict the discharge process TAT. Methods: The discharge process of cardiology department inpatients in a tertiary care hospital was mapped over a month. The likely factors influencing discharge TAT were tested for significance by ANOVA. Multiple linear regression (MLR) was used to predict the TAT. The sample was divided into testing and training sets for regression. A model was generated using the training set and compared with the testing set for accuracy. Results: After a process map was plot-ted, the significant factors influencing the TAT …

Rethinking The Weakness Of Stream Ciphers And Its Application To Encrypted Malware Detection, William T. Stone, Junggab Son

Master of Science in Computer Science Theses

Encryption key use is a critical component to the security of a stream cipher: because many implementations simply consist of a key scheduling algorithm and logical exclusive or (XOR), an attacker can completely break the cipher by XORing two ciphertexts encrypted under the same key, revealing the original plaintexts and the key itself. The research presented in this paper reinterprets this phenomenon, using repeated-key cryptanalysis for stream cipher identification. It has been found that a stream cipher executed under a fixed key generates patterns in each character of the ciphertexts it produces and that these patterns can be used to …

Theoretical Investigation Of The Biomass Conversion On Transition Metal Surfaces Based On Density Functional Theory Calculations And Machine Learning, Wenqiang Yang

Theses and Dissertations

During the past decades, heterogenous catalyzed conversion of biomass to hydrocarbons with similar or identical properties to conventional fossil fuels has gained significantly academic and industrial interest. However, the conventional heterogeneous catalysts such as sulfided NiMo/Al₂O₃ and CoMo/Al₂O₃ used have various drawbacks, such as short catalyst lifetime and high sulfur content of product. To overcome the limitations of the conventional sulfided catalysts, new catalysts must be developed, which requires a better understanding of the reaction mechanism of the biomass conversion. Based on density functional theory, in this thesis, we reported a computational calculation study …

Evaluation Of Temporal Damage Progression In Concrete Structures Affected By Asr Using Data-Driven Methods, Vafa Soltangharaei

Theses and Dissertations

Alkali-silica reaction (ASR) is a chemical reaction, which causes damage in concrete structures such as bridges, dams, and nuclear containments and powerplant structures. The ASR-induced damage may endanger the integrity and serviceability of structures. Several methods such as visual inspection, petrographic analysis, demountable mechanical strain gauges, and cracking index have been utilized for study the effect of ASR on structures, which are not always efficient in early damage detection and some are destructive and prohibited in nuclear structures. Nondestructive methods and structural health monitoring techniques can be alternatives for the condition assessment of structures. Among the nondestructive methods, acoustic emission …

Dipole Source Reconstruction By Convolutional Neural Networks, Jiayi He, Qiaolei Huang, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

Equivalent dipole moments are widely used for noise source reconstruction in radio frequency interference (RFI) study. The equivalent dipole sources are usually extracted from measured near-field pattern. This paper introduces a machine learning based method to extract the dipole moments. A convolutional neural network is trained to perform a multi-label classification to determine the type of dipole moments. The locations of the dipole moments are obtained from the global averaging pooling layer. Then the magnitude and phase of the dipoles can be calculated from least square (LSQ) optimization. The proposed method is tested on simulated near-field patterns. The comparison between …

A Study Of The Thermodynamics Of Small Systems And Phase Transition In Bulk Square Well-Hard Disk Binary Mixture, Gulce Kalyoncu

Graduate Theses and Dissertations

Under the umbrella of statistical mechanics and particle-based simulations, two distinct problems have been discussed in this study. The first part included systems of finite clusters of three and 13 particles, where the particles are interacting via Lennard Jones potential. A machine learning technique, Diffusion Maps (DMap), has been employed to the large datasets of thermodynamically small systems from Monte Carlo simulations in order to identify the structural and energetic changes in these systems. DMap suggests at most three dimensions are required to describe and identify the systems with 9 (N = 3) and 39 (N = 13) dimensions. At …

Identification Of Microscopy Cell Images By Using Convolutional Neural Network Application, Ajis Norfatin Farisya

Student Works (2020-2029)

Breast cancer has been the major factor of cancer death and the second main cause of women’s deaths in the world. The false positive results of this cancer cell detection during the screening test leads to false treatment and emotional disturbance of the patients. Thus, breast cancer cell lines (MCF7) is used as the microscopy image samples together with the Human Bone Osteosarcoma Epithelial Cells (U2OS), and Human Hepatocyte as control to study the effectiveness of convolutional neural network (CNN) as a method of image recognition. The objectives of this study are to determine the ability of convolutional neural network …

Objsim: Efficient Testing Of Cyber-Physical Systems, Jun Sun, Zijiang Yang

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPSs) play a critical role in automating public infrastructure and thus attract wide range of attacks. Assessing the effectiveness of defense mechanisms is challenging as realistic sets of attacks to test them against are not always available. In this short paper, we briefly describe smart fuzzing, an automated, machine learning guided technique for systematically producing test suites of CPS network attacks. Our approach uses predictive ma- chine learning models and meta-heuristic search algorithms to guide the fuzzing of actuators so as to drive the CPS into different unsafe physical states. The approach has been proven effective on two …

Moving Targets: Addressing Concept Drift In Supervised Models For Hacker Communication Detection, Susan Mckeever, Brian Keegan, Andrei Quieroz

Conference papers

Abstract—In this paper, we are investigating the presence of concept drift in machine learning models for detection of hacker communications posted in social media and hacker forums. The supervised models in this experiment are analysed in terms of performance over time by different sources of data (Surface web and Deep web). Additionally, to simulate real-world situations, these models are evaluated using time-stamped messages from our datasets, posted over time on social media platforms. We have found that models applied to hacker forums (deep web) presents an accuracy deterioration in less than a 1-year period, whereas models applied to Twitter (surface …

Prediction Of Feed Utilization Performance In Clarias Gariepinus Using Multiple Linear Regression In Machine Learning, Adekunle Oluwatosin Familusi

Journal of Bioresource Management

Machine learning models can be used to make predictions about nutrient utilization performance index using available proximate analysis data on feed composition. Data from similar experiments on nutrient utilization performance was used to fit a multiple linear regression model for the prediction of four performance indexes. The Specific Growth Rate and percentage inclusion with strength of 0.57 was noted along with a negative relationship between protein efficiency and protein content. A negative relationship between Nitrogen Free Extract (NFE) and Protein Efficiency Ratio (PER) at NFE content ≥25 % was observed. PER was predicted with 85 % accuracy, while Weight Gain …

The Calculation Methods Of Goat Trunk’S Segmentation Trajectory Based On Machine Vision And Machine Learning, Li Zhen-Qiang, Wang Shu-Cai, Zhao Shi-Da, Wang Yu-Quan

Food and Machinery

An automated segmentation method of goat trunk with machine vision and machine learning was proposed. 396 images of goat trunk were acquired randomly in a goat slaughtering plant by using two industrial cameras. 24 sets of feature parameters were extracte

Data-Driven And Model-Based Methods With Physics-Guided Machine Learning For Damage Identification, Zhiming Zhang

LSU Doctoral Dissertations

Structural health monitoring (SHM) has been widely used for structural damage diagnosis and prognosis of a wide range of civil, mechanical, and aerospace structures. SHM methods are generally divided into two categories: (1) model-based methods; (2) data-driven methods. Compared with data-driven SHM, model-based methods provide an updated physics-based numerical model that can be used for damage prognosis when long-term data is available. However, the performance of model-based methods is susceptible to modeling error in establishing the numerical model, which is usually unavoidable due to model simplification and omission. The major challenge of data-driven SHM methods lies in data insufficiency, e.g., …

Exploiting Earth Observation Data To Impute Groundwater Level Measurements With An Extreme Learning Machine, Steven Evans, Gustavious P. Williams, Norman L. Jones, Daniel P. Ames, E. James Nelson

Faculty Publications

Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture …

In Search Of Trustworthy And Transparent Intelligent Systems With Human-Like Cognitive And Reasoning Capabilities, Nikhil R. Pal

Journal Articles

At present we are witnessing a tremendous interest in Artificial Intelligence (AI), particularly in Deep Learning (DL)/Deep Neural Networks (DNNs). One of the reasons appears to be the unmatched performance achieved by such systems. This has resulted in an enormous hope on such techniques and often these are viewed as all—cure solutions. But most of these systems cannot explain why a particular decision is made (black box) and sometimes miserably fail in cases where other systems would not. Consequently, in critical applications such as healthcare and defense practitioners do not like to trust such systems. Although an AI system is …

Literature Review: How U.S. Government Documents Are Addressing The Increasing National Security Implications Of Artificial Intelligence, Bert Chapman

Libraries Faculty and Staff Scholarship and Research

This article emphasizes the increasing importance of artificial intelligence (AI) in military and national security policy making. It seeks to inform interested individuals about the proliferation of publicly accessible U.S. government and military literature on this multifaceted topic. An additional objective of this endeavor is encouraging greater public awareness of and participation in emerging public policy debate on AI's moral and national security implications..

Towards Cooperating In Repeated Interactions Without Repeating Structure, Huy Pham

Theses and Dissertations

A big challenge in artificial intelligence (AI) is creating autonomous agents that can interact well with other agents over extended periods of time. Most previously developed algorithms have been designed in the context of Repeated Games, environments in which the agents interact in the same scenario repeatedly. However, in most real-world interactions, relationships between people and autonomous agents consist of sequences of distinct encounters with different incentives and payoff structures. Therefore, in this thesis, we consider Interaction Games, which model interactions in which the scenario changes from encounter to encounter, often in ways that are unanticipated by the players. For …

A Convolutional Neural Network For Fast Fluence Estimation In Complex Tissues, Nicholas Blasey, Geoffrey P. Luke

ENGS 88 Honors Thesis (AB Students)

Photoacoustic (PA) imaging is a non-invasive diagnostic imaging technique that gives images of photoabsorbers based on their absorption of optical energy. These optical absorption properties can then be linked to important tissue properties. For the method to be quantitative, however, it is necessary to have an accurate estimation of the light fluence in the tissue. The current gold standard in addressing the fluence estimation problem, a Monte Carlo Simulation, is costly in time and computation. In this work, we developed a deep neural network to quickly and accurately estimate light fluence in arbitrary tissue types and geometries. The network was …