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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Plasmidic Expression of nemA and yafC * Increased Resistance of Ethanologenic Escherichia coli LY180 to Nonvolatile Side Products from Dilute Acid Treatment of Sugarcane Bagasse and Artificial Hydrolysate

Hydrolysate-resistant Escherichia coli SL100 was previously isolated from ethanologenic LY180 after sequential transfers in AM1 medium containing a dilute acid hydrolysate of sugarcane bagasse and was used as a source of resistance genes. Many genes that affect tolerance to furfural, the most abundant inhibitor, have been described previously. To identify genes associated with inhibitors other than furfural, plasmid clones were selected in an artificial hydrolysate that had been treated with a vacuum to remove furfural. Two new resistance genes were discovered from Sau3A1 libraries of SL100 genomic DNA: nemA (N-ethylmaleimide reductase) and a putative regulatory gene containing a mutation in the coding region, yafC *. The presence of these mutations in SL100 was confirmed by sequencing. A single mutation was found in the upstream regulatory region of nemR ( nemRA operon) in SL100. This mutation increased nemA activity 20-fold over that of the parent organism (LY180) in AM1 medium without hydrolysate and increased nemA mRNA levels >200-fold. Addition of hydrolysates induced nemA expression (mRNA and activity), in agreement with transcriptional control. NemA activity was stable in cell extracts (9 h, 37°C), eliminating a role for proteinase in regulation. LY180 with a plasmid expressing nemA or yafC * was more resistant to a vacuum-treated sugarcane bagasse hydrolysate and to a vacuum-treated artificial hydrolysate than LY180 with an empty-vector control. Neither gene affected furfural tolerance. The vacuum-treated hydrolysates inhibited the reduction of N -ethylmaleimide by NemA while also serving as substrates. In conclusion, expression of the nemA or yafC * plasmid in LY180 doubled the rate of ethanol production from the vacuum-treated sugarcane bagasse hydrolysate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Results From Invoking Artificial Neural Networks to Measure Insider Threat Detection & Mitigation

Advances on differentiating between malicious intent and natural “organizational evolution” to explain observed anomalies in operational workplace patterns suggest benefit from evaluating collective behaviors observed in the facilities to improve insider threat detection and mitigation (ITDM). Advances in artificial neural networks (ANN) provide more robust pathways for capturing, analyzing, and collating disparate data signals into quantitative descriptions of operational workplace patterns. In response, a joint study by Sandia National Laboratories and the University of Texas at Austin explored the effectiveness of commercial artificial neural network (ANN) software to improve ITDM. Overall, this research demonstrates the benefit of learning patterns of organizational behaviors, detecting off-normal (or anomalous) deviations from these patterns, and alerting when certain types, frequencies, or quantities of deviations emerge for improving ITDM. Evaluating nearly 33,000 access control data points and over 1,600 intrusion sensor data points collected over a nearly twelve-month period, this study's results demonstrated the ANN could recognize operational patterns at the Nuclear Engineering Teaching Laboratory (NETL) and detect off-normal behaviors—suggesting that ANNs can be used to support a data-analytic approach to ITDM. Several representative experiments were conducted to further evaluate these conclusions, with the resultant insights supporting collective behavior-based analytical approaches to quantitatively describe insider threat detection and mitigation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

Resilience Measurement Framework For Post-deployment Artificial Intelligence (ai) Integrated Systems

Resilience is largely defined as the ability to adapt or recover from adverse conditions, stresses, attacks, or compromises on systems that use or are enabled by digital resources. In Artificial Intelligence Management and Research for Advanced Networked Testbed Hub (AMARANTH), resilience is measured in the amount of time it took from the beginning of a testing period for the model to reach predictions outside of the original 95% confidence interval or using the Kullback-Leibler (KL) divergence theorem, the Population Stability Index (PSI), and traditional methods such as root mean squared error (RMSE) threshold. Artificial Intelligence (AI) model drift is of significant concern when deploying AI-integrated systems into critical and/or secure environments. Drift can impact resilience of the AI-integrated system post-deployment and requires consistent maintenance and upkeep to ensure the model is accurate and precise. To quantify model drift and predict the point when a model's drift becomes unacceptable, we describe using Kullback-Leibler (KL) divergence, Population Stability Index (PSI) and/or confidence interval width estimations to determine the point of failure and time to failure of a model post-deployment. Through simple code functions, the KL-divergence, PSI, confidence interval, and root mean squared (RMSE) point of failures can be used to derive when a model needs to be maintained as well as the impact of adversarial action through statistical means.

Yockey, Patience [Idaho National Laboratory (INL),↗
DOE OSTI2025

Evaluating the Trustworthiness of Explainable Artificial Intelligence (XAI) Methods Applied to Regression Predictions of Arctic Sea Ice Motion

Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance Statement Explainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere.

Hoffman, Lauren [Scripps Institution of Oceanograp↗
DOE OSTI2025

The Artificial Intelligence Ontology: LLM-Assisted Construction of AI Concept Hierarchies

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).

Joachimiak, Marcin P. [Biosystems Data Science Dep↗
DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗
DOE OSTI2024

Decision-making based on Markov decision process in integrated artificial reasoning framework—Part I: Theory

This paper presents a decision-making framework based on an integrated artificial reasoning framework and Markov decision process (MDP). The integrated artificial reasoning framework provides a physics-based approach that converts system information into state transition models, and the analysis result will be represented by the transition probabilities that can be used with an MDP to find a traceable and explainable optimal pathway. A dynamic Bayesian network (DBN) is well suited for representing the structure of an MDP. The causality information among process variables (or among subsystems) is mathematically represented in a DBN by the conditional probabilities of the node’s states provided different probabilities of the parent node’s states. To define node states in a physically understandable manner, we used multilevel flow modeling (MFM). An MFM follows the fundamental energy and mass conservation laws and supports the selection of process variables that represent the system of interest so that causal relations among process variables are properly captured. An MFM-based DBN supports developing state transition models in an MDP to capture the effect of process variables of system having physical relations. The operators of the target system can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from component degradation or random failures. We analyzed a simplified exemplary system to illustrate an optimal operational policy using the suggested approach.

Markov decision process↗
DOE OSTI2021

Artificial Reasoning System for Symptom-Based Conditional Failure Probability Estimation Using Bayesian Network

Advances in nuclear power technologies require enhanced capabilities for operator advice and autonomous control. One of the first tasks in the development of such capabilities is the formulation of symptom-based conditional failure probabilities for structures, systems, and components (SSCs) of interest, for which the primary goal is to aid plant personnel in deducing the probabilistic performance status of the monitored SSCs and in detecting impending faults/failure. The task of conditional failure probability estimation is a bidirectional inference problem and shall be logically tackled by the Bayesian network (BN) approach. As a knowledge-based artificial intelligence tool and a probabilistic graphical model, BN offers the capability of reasoning under uncertainty and graphical representation emulating the physical behavior of the target SSC. This paper provides a systematic overview of the BN technique and the software tools for handling implementation of BN models, along with the associated knowledge representation and reasoning paradigm. Both operational data and expert judgement can be readily incorporated into the knowledge base of a BN model. The challenges with data availability are highlighted, and the general approach to target SSC identification is presented. Our focus is upon failure-prone and risk-important balance of plant assets, especially cases having strong operator involvement. An exemplary case study on the failure of a motor-driven centrifugal pump is also conducted to demonstrate the usefulness and technical feasibility of the proposed artificial reasoning system using an expert system shell.

Zhao, Xingang↗
DOE OSTI2020

Varroa destructor mites vector and transmit pathogenic honey bee viruses acquired from an artificial diet

The ectoparasitic mite Varroa destructor is one of the most destructive pests of the honey bee (Apis mellifera) and the primary biotic cause of colony collapse in many regions of the world. These mites inflict physical injury on their honey bee hosts from feeding on host hemolymph and fat body cells/cellular components, and serve as the vector for deadly honey bee viruses, including Deformed wing virus (DWV) and the related Varroa destructor virus-1 (VDV-1) (i.e., DWV-like viruses). Studies focused on elucidating the dynamics of Varroa-mediated vectoring and transmission of DWV-like viruses may be confounded by viruses present in ingested host tissues or the mites themselves. Here we describe a system that includes an artificial diet free of insect tissue-derived components for maintaining Varroa mites for in vitro experimentation. Using this system, together with the novel engineered cDNA clone-derived genetically tagged VDV-1 and wild-type DWV, we demonstrated for the first time that Varroa mites provided an artificial diet supplemented with engineered viruses for 36 hours could acquire and transmit sufficient numbers of virus particles to establish an infection in virus-naïve hosts. While the in vitro system described herein provides for only up to five days of mite survival, precluding study of the long-term impacts of viruses on mite health, the system allows for extensive insights into the dynamics of Varroa-mediated vectoring and transmission of honey bee viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Artificial Graphene in Undoped Semiconductor Heterostructures

The linear energy-momentum dispersion which arises from graphene’s underlying honeycomb lattice gives graphene its unique electronic properties unfound in conventional semiconductors. Theoretically speaking, when an electrostatic potential with hexagonal or honeycomb symmetry is imposed onto a two-dimensional electron/hole system, the band structure is modified in a way that the same linear energy-momentum dispersion could exist. Experimentally, there has not been any evidence from transport demonstrating the so-called “artificial graphene”. In this project, we attempt to create an artificial superlattice potential with hexagonal symmetry for two dimensional carriers in an undoped SiGe heterostructure by patterning a nanoscale hole array in a metallic gate. Using undoped heterostructures allows us to access a very wide density range, which covers the magic densities at which the Dirac points are expected. A process flow for fabricating such field-effect-transistor devices with a lattice constant as small as 90 nm is reported. Magneto-transport measurements performed at 0.3 K show that the superlattice potential in the quantum well in which the two-dimensional system resides is indeed modulated by the gates. However, no signature of the sought-after linear dispersion is observed in the transport data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Shear Behavior of Artificial Clay Seams within Bedded Salt Structures

Bedded salt contains thin layers of clay, also known as clay seams, in-between far thicker layers of salt. These inhomogeneities are thought to have first-order effects on the closure of nearby drifts and potential roof collapses. Despite their importance, characterizations of the peak shear strength and residual shear strength of clay seams in salt are extremely rare in the published literature. A previous paper reported results from laboratory direct shear experiments on clay seam samples from the Permian Basin in New Mexico. These clay seams behaved similar to intact salt, which was attributed to the abundance of salt crystals intersecting the clay seams. None of those specimens contained a distinct ¼" - ½" (6 -12 mm) thick clay seam, as has been observed in drifts at the Waste Isolation Pilot Plant (WIPP). Due to the difficulty in obtaining WIPP samples with these types of clay seams, artificial seams of bentonite and brine sandwiched between sections of salt were created and shear tested. Eight 4" diameter samples were created with either a ¼" or ½" a thick seam and then consolidated at 3000 psi prior to shear testing. The direct shear tests on these samples were performed at nominal normal stresses representative of expected WIPP in-situ conditions (500 to 1500 psi). The resulting shear stress vs. shear displacement curves exhibited a peak followed by a gradual decay of shear strength. The shear stress never transitioned to a true residual shear stress plateau, so the final shear strength at the end of each test (0.75" of shear displacement) was analyzed instead. Both the peak shear strength and the final shear strength conformed to Mohr- Coulomb behavior with friction angles and cohesion strengths consistent with a saturated, highly consolidated, clay. These new artificial clay seam results and the previous clay-interspersed-with-salt results likely bound the expected shear behavior of WIPP clay seams.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Emergent Collective Phenomena in Artificial Spin Ice (Final Technical Report)

This program encompassed studies of lithographically fabricated “artificial spin ice” arrays of nanometer-scale single-domain ferromagnetic islands in which the array geometry results in frustration of the magnetostatic interactions between the islands. Artificial spin ice offers a wide range of opportunities for studying the mechanism by which nature accommodates frustration and accesses physics associated with frustration in ferromagnetic nanostructures. Since the arrays are created lithographically, we can easily vary the array characteristics, including the geometry of the lattice and the level and type of lattice disorder. We can probe both the local properties of the arrays by imaging individual moments, and we can also probe thousands of moments simultaneously, allowing us to gain insight into the collective properties of the system. The research funded by this program included studies of unusual frustrated lattices, studies of magnetotransport in these systems, studies of perpendicular anisotropy moments, and a range of other related experimental measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Artificial Intelligence for Data Center Operations (AI Ops)

HPC data centers such as the one at NREL's ESIF will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence (AI) and machine learning (ML) approaches provide the means to improve HPC data center efficiency (energy, operational, and managerial efficiency) and resiliency by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. The goal of coupled improvement of data center resiliency and energy efficiency through automated data collection and AI has led to a multi-year, multi-staged collaboration between NREL and Hewlett-Packard Enterprise's Advanced Technology Group, referred to as Artificial Intelligence for Data Center Operations (AIOps). The extended efforts within the AIOps project include a common goal of building capabilities for an advanced smart facility and demonstration of data collection and AI modeling techniques in the ESIF data center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

How Artificial Intelligence and Machine Learning Transform the Human Condition

Our July 2021 symposium, “How Artificial Intelligence and Machine Learning Transform the Human Condition,” was hosted through a partnership between Los Alamos National Laboratory and the National Academies of Sciences, Engineering, and Medicine’s Committee on Science, Technology, and Law. The symposium is part of a broader initiative focusing on harnessing transformative technologies, and builds on our September 2020 symposium titled, “COVID-19: Harnessing a Transformational Pandemic.” Topics such as systems biology and artificial intelligence not only represent compelling research frontiers but also highlight national security challenges with social, ethical, and legal implications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

In October 2021, the U.S. Department of Energy (DOE) welcomed participants to the Artificial Intelligence for Earth System Predictability (AI4ESP) Workshop, hosted by the Office of Biological and Environmental Research (BER)—Advanced Scientific Computing Research (ASCR). The workshop is part of BER-ASCR’s ambition to more radically and aggressively advance prediction capabilities in the climate, Earth, and environmental sciences through the use of modern data analytics and artificial intelligence (AI). Advances in these capabilities are needed to improve predictions of climate change and extreme events that provide actionable information for planning and building resilience to their impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Artificial Intelligence for the Methane Cycle

This report derives from the March 2023 Artificial Intelligence for the Methane Cycle (AI4CH 4 ) virtual work shop, co-organized by staff from the Earth and Environmental Systems Sciences Division (EESSD), within the U.S. Department of Energy Biological and Environmental Research program (BER), and computational ecologist Dr. Pamela Weisenhorn from Argonne National Laboratory. AI4CH 4 provides a follow-up to the 2021 Artificial Intelligence for Earth System Predictability workshop series (ai4esp.org) co-organized by two DOE programs—BER and Advanced Scientific Computing Research (ASCR).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessment of Data-Management Infrastructure Needs for Production Use of Advanced Machine Learning and Artificial Intelligence: Tri-Lab Level II Milestone (8554)

A robust data-management infrastructure is a key enabler for National Security Enterprise (NSE) capabilities in artificial intelligence and machine learning. This document describes efforts from a team of researchers at Sandia National Laboratories, Los Alamos National Laboratory, and Livermore National Laboratory to complete ASC Level II milestone #8854 “Assessment of Data-Management Infrastructure Needs for Production use of Advanced Machine learning and Artificial Intelligence.”

97 MATHEMATICS AND COMPUTING↗