Relevant bibliographies by topics / Architectures and machine learning models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Architectures and machine learning models'

Author: Grafiati
Published: 7 June 2025
Last updated: 9 July 2025

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Journal articles on the topic "Architectures and machine learning models"

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Putra, Muhammad Daffa Arviano, Tawang Sahro Winanto, Retno Hendrowati, Aji Primajaya, and Faisal Dharma Adhinata. "A Comparative Analysis of Transfer Learning Architecture Performance on Convolutional Neural Network Models with Diverse Datasets." Komputika : Jurnal Sistem Komputer 12, no. 1 (2023): 1–11. http://dx.doi.org/10.34010/komputika.v12i1.8626.

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Deep learning is a branch of machine learning with many highly successful applications. One application of deep learning is image classification using the Convolutional Neural Network (CNN) algorithm. Large image data is required to classify images with CNN to obtain satisfactory training results. However, this can be overcome with transfer learning architectural models, even with small image data. With transfer learning, the success rate of a model is likely to be higher. Since there are many transfer learning architecture models, it is necessary to compare each model's performance results to find the best-performing architecture. In this study, we conducted three experiments on different datasets to train models with various transfer learning architectures. We then performed a comprehensive comparative analysis for each experiment. The result is that the DenseNet-121 architecture is the best transfer learning architecture model for various datasets.
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Dr. Pradeep Laxkar and Dr. Nilesh Jain. "A Review of Scalable Machine Learning Architectures in Cloud Environments: Challenges and Innovations." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2907–16. https://doi.org/10.32628/cseit25112764.

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As the demand for machine learning (ML) and data analysis grows across industries, the need for scalable and efficient cloud-based architectures becomes critical. The increase in of data generation, along with the increasing demand for advanced analytics and machine learning (ML), has make necessary the development of scalable architectures in cloud environments. Cloud computing provides a flexible and scalable solution, allowing organizations to efficiently process large datasets and deploy complex ML models without traditional hardware limitations. The review paper explores the various cloud-based machine learning (ML) architectures, highlighting the scalability features of various cloud platforms such as AWS, Azure, and GCP. This study also discusses emerging technologies like serverless computing, automated machine learning AutoMLL), and microservices-based architectures that enhance the scalability of the cloud environment. Furthermore, challenges such as data security, talent gaps, and resource allocation inefficiencies are also considered. The paper concludes by evaluating innovative approaches that drive scalable ML in cloud environments, providing insights into the future landscape of cloud-based machine learning. In conclusion, this scalable cloud-based architecture provides a robust and flexible solution for organizations looking to implement machine learning and data analysis workflows. By leveraging distributed computing, containerization, and serverless technologies, the architecture can efficiently manage large datasets and complex models while maintaining cost-efficiency, security, and adaptability to future needs.
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Journal, of Global Research in Electronics and Communications. "A Review of Scalable Machine Learning Architectures in Cloud Environments: Challenges and Innovations." Journal of Global Research in Electronics and Communications 1, no. 4 (2025): 7–11. https://doi.org/10.5281/zenodo.15115138.

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As the demand for machine learning (ML) and data analysis grows across industries, the need for scalable and efficient cloud-based architectures becomes critical. The increase in of data generation, along with the increasing demand for advanced analytics and machine learning (ML), has make necessary the development of scalable architectures in cloud environments. Cloud computing provides a flexible and scalable solution, allowing organizations to efficiently process large datasets and deploy complex ML models without traditional hardware limitations. The review paper explores the various cloud-based machine learning (ML) architectures, highlighting the scalability features of various cloud platforms such as AWS, Azure, and GCP. This study also discusses emerging technologies like serverless computing, automated machine learning AutoMLL), and microservices-based architectures that enhance the scalability of the cloud environment. Furthermore, challenges such as data security, talent gaps, and resource allocation inefficiencies are also considered. The paper concludes by evaluating innovative approaches that drive scalable ML in cloud environments, providing insights into the future landscape of cloud-based machine learning. In conclusion, this scalable cloud-based architecture provides a robust and flexible solution for organizations looking to implement machine learning and data analysis workflows.  By leveraging distributed computing, containerization, and serverless technologies, the architecture can efficiently manage large datasets and complex models while maintaining cost-efficiency, security, and adaptability to future needs.
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Pukach, Pavlo. "Analysis of framework networks for sign detection in deep learning models." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 12 (December 15, 2022): 169–76. http://dx.doi.org/10.23939/sisn2022.12.169.

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This paper analyzes and compares modern deep learning models for the classification of MRI images of the knee joint. An analysis of modern deep computer vision architectures for feature extraction from MRI images is presented. This analysis was used to create applied architectures of machine learning models. These models are aimed at automating the process of diagnosing knee injuries in medical devices and systems. This work is devoted to different types of feature detection framework networks for machine learning architectures that perform magnetic resonance imaging (MRI) image classification of the knee. The resulting models were evaluated on the MRNet validation dataset, calculating the metrics (ROC-AUC), prediction accuracy, F1 score, and Cohen’s K-Kappa. The results of this work also show that Cohen's Kappa metric is important for evaluating models on the MRNet architecture because it provides a deeper understanding of the classification decisions of each model.
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Meda, Shefqet, and Ervin Domazet. "Advanced computer architecture optimization for machine learning/deep learning." CRJ, no. 5 (July 31, 2024): 28–41. http://dx.doi.org/10.59380/crj.vi5.5108.

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Abstract The recent progress in Machine Learning (Géron, 2022) and particularly Deep Learning (Goodfellow, 2016) models exposed the limitations of traditional computer architectures. Modern algorithms demonstrate highly increased computational demands and data requirements that most existing architectures cannot handle efficiently. These demands result in training speed, inference latency, and power consumption bottlenecks, which is why advanced methods of computer architecture optimization are required to enable the development of ML/DL-dedicated efficient hardware platforms (Engineers, 2019). The optimization of computer architecture for applications of ML/DL becomes critical, due to the tremendous demand for efficient execution of complex computations by Neural Networks (Goodfellow, 2016). This paper reviewed the numerous approaches and methods utilized to optimize computer architecture for ML/DL workloads. The following sections contain substantial discussion concerning the hardware-level optimizations, enhancements of traditional software frameworks and their unique versions, and innovative explorations of architectures. In particular, we discussed hardware including specialized accelerators, which can improve the performance and efficiency of a computation system using various techniques, specifically describing accelerators like CPUs (multicore) (Hennessy, 2017), GPUs (Hwu, 2015) and TPUs (Contributors, 2017), parallelism in multicore architectures, data movement in hardware systems, especially techniques such as caching and sparsity, compression, and quantization, other special techniques and configurations, such as using specialized data formats, and measurement sparsity. Moreover, this paper provided a comprehensive analysis of current trends in software frameworks, Data Movement optimization strategies (A.Bienz, 2021), sparsity, quantization and compression methods, using ML for architecture exploration, and, DVFS (Hennessy, 2017),, which provides strategies for maximizing hardware utilization and power consumption during training, machine learning, dynamic voltage, and frequency scaling, runtime systems. Finally, the paper discussed research opportunity directions and the possibilities of computer architecture optimization influence in various industrial and academic areas of ML/DL technologies. The objective of implementing these optimization techniques is to largely minimize the current gap between the computational needs of ML/DL algorithms and the current hardware’s capability. This will lead to significant improvements in training times, enable real-time inference for various applications, and ultimately unlock the full potential of cutting-edge machine learning algorithms.
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Airlangga, Gregorius. "A Hybrid CNN-RNN Model for Enhanced Anemia Diagnosis: A Comparative Study of Machine Learning and Deep Learning Techniques." Indonesian Journal of Artificial Intelligence and Data Mining 7, no. 2 (2024): 366. http://dx.doi.org/10.24014/ijaidm.v7i2.29898.

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This study proposes a hybrid Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model for the accurate diagnosis of anemia types, leveraging the strengths of both architectures in capturing spatial and temporal patterns in Complete Blood Count (CBC) data. The research involves the development and evaluation of various models of single-architecture deep learning (DL) models, specifically Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Fully Convolutional Network (FCN). The models are trained and validated using stratified k-fold cross-validation to ensure robust performance. Key metrics such as test accuracy are utilized to provide a comprehensive assessment of each model's performance. The hybrid CNN-RNN model achieved the highest test accuracy of 90.27%, surpassing the CNN (89.88%), FCN (85.60%), MLP (79.77%), and RNN (73.54%) models. The hybrid model also demonstrated superior performance in cross-validation, with an accuracy of 87.31% ± 1.77%. Comparative analysis highlights the hybrid model's advantages over single-architecture DL models, particularly in handling imbalanced data and providing reliable classifications across all anemia types. The results underscore the potential of advanced DL architectures in medical diagnostics and suggest pathways for further refinements, such as incorporating attention mechanisms or additional feature engineering, to enhance model performance. This study contributes to the growing body of knowledge on AI-driven medical diagnostics and presents a viable tool for clinical decision support in anemia diagnosis
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Praveen, Kumar Sridhar. "A Case Study on the Diminishing Popularity of Encoder-Only Architectures in Machine Learning Models." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 13, no. 4 (2024): 22–27. https://doi.org/10.35940/ijitee.D9827.13040324.

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<strong>Abstract:</strong> This paper examines the shift from encoder-only to decoder and encoder-decoder models in machine learning, highlighting the decline in popularity of encoder-only architectures. It explores the reasons behind this trend, such as the advancements in decoder models that offer superior generative capabilities, flexibility across various domains, and enhancements in unsupervised learning techniques. The study also discusses the role of prompting techniques in simplifying model architectures and enhancing model versatility. By analyzing the evolution, applications, and shifting preferences within the research community and industry, this paper aims to provide insights into the changing landscape of machine learning model architectures.
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Walid, Abdullah, and Salah Ahmad. "A novel hybrid deep learning model for price prediction." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 3420–31. https://doi.org/10.11591/ijece.v13i3.pp3420-3431.

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Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several models&rsquo; architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNNLSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.
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Babhulkar, Mr Shubham. "Application of Machine Learning for Emotion Classification." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1567–72. http://dx.doi.org/10.22214/ijraset.2021.36459.

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In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creat- ing a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture. After presenting the details of the training pro- cedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also introduced the very recent real-time enabled guided back- propagation visualization technique. Guided back-propagation uncovers the dynamics of the weight changes and evaluates the learned features. We argue that the careful implementation of modern CNN architectures, the use of the current regu- larization methods and the visualization of previously hidden features are necessary in order to reduce the gap between slow performances and real-time architectures. Our system has been validated by its deployment on a Care-O-bot 3 robot used during RoboCup@Home competitions. All our code, demos and pre- trained architectures have been released under an open-source license in our public repository.
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Siddesh, Kumar B., and Naduvinamani Onkarappa. "Machine Learning in Power Electronics: Focusing on Convolutional Neural Networks." International Journal of Computational Engineering and Management (IJCEM), A Peer Reviewed Refereed Multidisciplinary Research Journal 9, no. 1 (2021): 112–17. https://doi.org/10.5281/zenodo.14899610.

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Deep Neural Networks (DNNs) have revolutionized various fields, but their resource-intensive nature poses significant challenges for deployment, especially on edge devices with limited power and area budgets. This dissertation focuses on the development of efficient and low-power Very-Large-Scale Integration (VLSI) architectures for DNN accelerators, addressing the key bottlenecks in DNN hardware implementation. One of the major challenges in DNN hardware is the high computational cost associated with Multiply-Accumulate (MAC) operations and non-linear Activation Functions (AFs). While CORDIC-based architectures offer area and power efficiency, they suffer from low throughput. To address this, we propose a pipelined CORDIC-based design for MAC and AF operations, aiming to significantly improve performance without compromising area and power efficiency. To optimize resource utilization and maintain accuracy, we investigate the concept of mutual exclusivity among CORDIC stages. By carefully analyzing the impact of reduced pipeline stages on accuracy, we identify the optimal trade-off between performance and precision. This research contributes to the advancement of DNN hardware acceleration by providing innovative solutions to the challenges of power consumption, area efficiency, and throughput. The proposed architectures enable the deployment of DNN models on resource-constrained devices, paving the way for a new era of AI-powered applications. &nbsp;
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Dissertations / Theses on the topic "Architectures and machine learning models"

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Aihe, David. "A REINFORCEMENT LEARNING TECHNIQUE FOR ENHANCING HUMAN BEHAVIOR MODELS IN A CONTEXT-BASED ARCHITECTURE." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2408.

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A reinforcement-learning technique for enhancing human behavior models in a context-based learning architecture is presented. Prior to the introduction of this technique, human models built and developed in a Context-Based reasoning framework lacked learning capabilities. As such, their performance and quality of behavior was always limited by what the subject matter expert whose knowledge is modeled was able to articulate or demonstrate. Results from experiments performed show that subject matter experts are prone to making errors and at times they lack information on situations that are inherently necessary for the human models to behave appropriately and optimally in those situations. The benefits of the technique presented is two fold; 1) It shows how human models built in a context-based framework can be modified to correctly reflect the knowledge learnt in a simulator; and 2) It presents a way for subject matter experts to verify and validate the knowledge they share. The results obtained from this research show that behavior models built in a context-based framework can be enhanced by learning and reflecting the constraints in the environment. From the results obtained, it was shown that after the models are enhanced, the agents performed better based on the metrics evaluated. Furthermore, after learning, the agent was shown to recognize unknown situations and behave appropriately in previously unknown situations. The overall performance and quality of behavior of the agent improved significantly.<br>Ph.D.<br>School of Electrical Engineering and Computer Science<br>Engineering and Computer Science<br>Computer Engineering PhD
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Faccin, João Guilherme. "Preference and context-based BDI plan selection using machine learning : from models to code generation." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/138209.

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A tecnologia de agentes surge como uma solução que fornece flexibilidade e robustez para lidar com domínios dinâmicos e complexos. Tal flexibilidade pode ser alcançada através da adoção de abordagens já existentes baseadas em agentes, como a arquitetura BDI, que provê agentes com características mentais de crenças, desejos e intenções. Essa arquitetura é altamente personalizável, deixando lacunas a serem preenchidas de acordo com aplicações específicas. Uma dessas lacunas é o algoritmo de seleção de planos, responsável por selecionar um plano para ser executado pelo agente buscando atingir um objetivo, e tendo grande influência no desempenho geral do agente. Grande parte das abordagens existentes requerem considerável esforço para personalização e ajuste a fim de serem utilizadas em aplicações específicas. Nessa dissertação, propomos uma abordagem para seleção de planos apta a aprender quais planos possivelmente terão os melhores resultados, baseando-se no contexto atual e nas preferências do agente. Nossa abordagem é composta por um meta-modelo, que deve ser instanciado a fim de especificar metadados de planos, e uma técnica que usa tais metadados para aprender e predizer resultados da execução destes planos. Avaliamos nossa abordagem experimentalmente e os resultados indicam que ela é efetiva. Adicionalmente, fornecemos uma ferramenta para apoiar o processo de desenvolvimento de agentes de software baseados em nosso trabalho. Essa ferramenta permite que desenvolvedores modelem e gerem código-fonte para agentes BDI com capacidades de aprendizado. Um estudo com usuários foi realizado para avaliar os benefícios de um método de desenvolvimento baseado em agentes BDI auxiliado por ferramenta. Evidências sugerem que nossa ferramenta pode auxiliar desenvolvedores que não sejam especialistas ou que não estejam familiarizados com a tecnologia de agentes.<br>Agent technology arises as a solution that provides flexibility and robustness to deal with dynamic and complex domains. Such flexibility can be achieved by the adoption of existing agent-based approaches, such as the BDI architecture, which provides agents with the mental attitudes of beliefs, desires and intentions. This architecture is highly customisable, leaving gaps to be fulfilled in particular applications. One of these gaps is the plan selection algorithm that is responsible for selecting a plan to be executed by an agent to achieve a goal, having an important influence on the overall agent performance. Most existing approaches require considerable effort for customisation and adjustment to be used in particular applications. In this dissertation, we propose a plan selection approach that is able to learn plans that provide possibly best outcomes, based on current context and agent’s preferences. Our approach is composed of a meta-model, which must be instantiated to specify plan metadata, and a technique that uses such metadata to learn and predict plan outcomes. We evaluated our approach experimentally, and results indicate it is effective. Additionally, we provide a tool to support the development process of software agents based on our work. This tool allows developers to model and generate source code for BDI agents with learning capabilities. A user study was performed to assess the improvements of a tool-supported BDI-agent-based development method, and evidences suggest that our tool can help developers that are not experts or are unfamiliar with the agent technology.
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Templeton, Julian. "Designing Robust Trust Establishment Models with a Generalized Architecture and a Cluster-Based Improvement Methodology." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42556.

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In Multi-Agent Systems consisting of intelligent agents that interact with one another, where the agents are software entities which represent individuals or organizations, it is important for the agents to be equipped with trust evaluation models which allow the agents to evaluate the trustworthiness of other agents when dishonest agents may exist in an environment. Evaluating trust allows agents to find and select reliable interaction partners in an environment. Thus, the cost incurred by an agent for establishing trust in an environment can be compensated if this improved trustworthiness leads to an increased number of profitable transactions. Therefore, it is equally important to design effective trust establishment models which allow an agent to generate trust among other agents in an environment. This thesis focuses on providing improvements to the designs of existing and future trust establishment models. Robust trust establishment models, such as the Integrated Trust Establishment (ITE) model, may use dynamically updated variables to adjust the predicted importance of a task’s criteria for specific trustors. This thesis proposes a cluster-based approach to update these dynamic variables more accurately to achieve improved trust establishment performance. Rather than sharing these dynamic variables globally, a model can learn to adjust a trustee’s behaviours more accurately to trustor needs by storing the variables locally for each trustor and by updating groups of these variables together by using data from a corresponding group of similar trustors. This work also presents a generalized trust establishment model architecture to help models be easier to design and be more modular. This architecture introduces a new transaction-level preprocessing module to help improve a model’s performance and defines a trustor-level postprocessing module to encapsulate the designs of existing models. The preprocessing module allows a model to fine-tune the resources that an agent will provide during a transaction before it occurs. A trust establishment model, named the Generalized Trust Establishment Model (GTEM), is designed to showcase the benefits of using the preprocessing module. Simulated comparisons between a cluster-based version of ITE and ITE indicate that the cluster-based approach helps trustees better meet the expectations of trustors while minimizing the cost of doing so. Comparing GTEM to itself without the preprocessing module and to two existing models in simulated tests exhibits that the preprocessing module improves a trustee’s trustworthiness and better meets trustor desires at a faster rate than without using preprocessing.
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González, Marcos Tulio Amarís. "Performance prediction of application executed on GPUs using a simple analytical model and machine learning techniques." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-06092018-213258/.

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The parallel and distributed platforms of High Performance Computing available today have became more and more heterogeneous (CPUs, GPUs, FPGAs, etc). Graphics Processing Units (GPU) are specialized co-processor to accelerate and improve the performance of parallel vector operations. GPUs have a high degree of parallelism and can execute thousands or millions of threads concurrently and hide the latency of the scheduler. GPUs have a deep hierarchical memory of different types as well as different configurations of these memories. Performance prediction of applications executed on these devices is a great challenge and is essential for the efficient use of resources in machines with these co-processors. There are different approaches for these predictions, such as analytical modeling and machine learning techniques. In this thesis, we present an analysis and characterization of the performance of applications executed on GPUs. We propose a simple and intuitive BSP-based model for predicting the CUDA application execution times on different GPUs. The model is based on the number of computations and memory accesses of the GPU, with additional information on cache usage obtained from profiling. We also compare three different Machine Learning (ML) approaches: Linear Regression, Support Vector Machines and Random Forests with BSP-based analytical model. This comparison is made in two contexts, first, data input or features for ML techniques were the same than analytical model, and, second, using a process of feature extraction, using correlation analysis and hierarchical clustering. We show that GPU applications that scale regularly can be predicted with simple analytical models, and an adjusting parameter. This parameter can be used to predict these applications in other GPUs. We also demonstrate that ML approaches provide reasonable predictions for different cases and ML techniques required no detailed knowledge of application code, hardware characteristics or explicit modeling. Consequently, whenever a large data set with information about similar applications are available or it can be created, ML techniques can be useful for deploying automated on-line performance prediction for scheduling applications on heterogeneous architectures with GPUs.<br>As plataformas paralelas e distribuídas de computação de alto desempenho disponíveis hoje se tornaram mais e mais heterogêneas (CPUs, GPUs, FPGAs, etc). As Unidades de processamento gráfico são co-processadores especializados para acelerar operações vetoriais em paralelo. As GPUs têm um alto grau de paralelismo e conseguem executar milhares ou milhões de threads concorrentemente e ocultar a latência do escalonador. Elas têm uma profunda hierarquia de memória de diferentes tipos e também uma profunda configuração da memória hierárquica. A predição de desempenho de aplicações executadas nesses dispositivos é um grande desafio e é essencial para o uso eficiente dos recursos computacionais de máquinas com esses co-processadores. Existem diferentes abordagens para fazer essa predição, como técnicas de modelagem analítica e aprendizado de máquina. Nesta tese, nós apresentamos uma análise e caracterização do desempenho de aplicações executadas em Unidades de Processamento Gráfico de propósito geral. Nós propomos um modelo simples e intuitivo fundamentado no modelo BSP para predizer a execução de funções kernels de CUDA sobre diferentes GPUs. O modelo está baseado no número de computações e acessos à memória da GPU, com informação adicional do uso das memórias cachês obtidas do processo de profiling. Nós também comparamos três diferentes enfoques de aprendizado de máquina (ML): Regressão Linear, Máquinas de Vetores de Suporte e Florestas Aleatórias com o nosso modelo analítico proposto. Esta comparação é feita em dois diferentes contextos, primeiro, dados de entrada ou features para as técnicas de aprendizado de máquinas eram as mesmas que no modelo analítico, e, segundo, usando um processo de extração de features, usando análise de correlação e clustering hierarquizado. Nós mostramos que aplicações executadas em GPUs que escalam regularmente podem ser preditas com modelos analíticos simples e um parâmetro de ajuste. Esse parâmetro pode ser usado para predizer essas aplicações em outras GPUs. Nós também demonstramos que abordagens de ML proveem predições aceitáveis para diferentes casos e essas abordagens não exigem um conhecimento detalhado do código da aplicação, características de hardware ou modelagens explícita. Consequentemente, sempre e quando um banco de dados com informação de \\textit esteja disponível ou possa ser gerado, técnicas de ML podem ser úteis para aplicar uma predição automatizada de desempenho para escalonadores de aplicações em arquiteturas heterogêneas contendo GPUs.
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Kundu, Sajib. "Improving Resource Management in Virtualized Data Centers using Application Performance Models." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/874.

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The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.
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Evgeniou, Theodoros K. (Theodoros Kostantinos) 1974. "Learning with kernel machine architectures." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86442.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (p. 99-106).<br>by Theodoros K. Evgeniou.<br>Ph.D.
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de, la Rúa Martínez Javier. "Scalable Architecture for Automating Machine Learning Model Monitoring." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280345.

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Last years, due to the advent of more sophisticated tools for exploratory data analysis, data management, Machine Learning (ML) model training and model serving into production, the concept of MLOps has gained more popularity. As an effort to bring DevOps processes to the ML lifecycle, MLOps aims at more automation in the execution of diverse and repetitive tasks along the cycle and at smoother interoperability between teams and tools involved. In this context, the main cloud providers have built their own ML platforms [4, 34, 61], offered as services in their cloud solutions. Moreover, multiple frameworks have emerged to solve concrete problems such as data testing, data labelling, distributed training or prediction interpretability, and new monitoring approaches have been proposed [32, 33, 65]. Among all the stages in the ML lifecycle, one of the most commonly overlooked although relevant is model monitoring. Recently, cloud providers have presented their own tools to use within their platforms [4, 61] while work is ongoing to integrate existent frameworks [72] into open-source model serving solutions [38]. Most of these frameworks are either built as an extension of an existent platform (i.e lack portability), follow a scheduled batch processing approach at a minimum rate of hours, or present limitations for certain outliers and drift algorithms due to the platform architecture design in which they are integrated. In this work, a scalable automated cloudnative architecture is designed and evaluated for ML model monitoring in a streaming approach. An experimentation conducted on a 7-node cluster with 250.000 requests at different concurrency rates shows maximum latencies of 5.9, 29.92 and 30.86 seconds after request time for 75% of distance-based outliers detection, windowed statistics and distribution-based data drift detection, respectively, using windows of 15 seconds length and 6 seconds of watermark delay.<br>Under de senaste åren har konceptet MLOps blivit alltmer populärt på grund av tillkomsten av mer sofistikerade verktyg för explorativ dataanalys, datahantering, modell-träning och model serving som tjänstgör i produktion. Som ett försök att föra DevOps processer till Machine Learning (ML)-livscykeln, siktar MLOps på mer automatisering i utförandet av mångfaldiga och repetitiva uppgifter längs cykeln samt på smidigare interoperabilitet mellan team och verktyg inblandade. I det här sammanhanget har de största molnleverantörerna byggt sina egna ML-plattformar [4, 34, 61], vilka erbjuds som tjänster i deras molnlösningar. Dessutom har flera ramar tagits fram för att lösa konkreta problem såsom datatestning, datamärkning, distribuerad träning eller tolkning av förutsägelse, och nya övervakningsmetoder har föreslagits [32, 33, 65]. Av alla stadier i ML-livscykeln förbises ofta modellövervakning trots att det är relevant. På senare tid har molnleverantörer presenterat sina egna verktyg att kunna användas inom sina plattformar [4, 61] medan arbetet pågår för att integrera befintliga ramverk [72] med lösningar för modellplatformer med öppen källkod [38]. De flesta av dessa ramverk är antingen byggda som ett tillägg till en befintlig plattform (dvs. saknar portabilitet), följer en schemalagd batchbearbetningsmetod med en lägsta hastighet av ett antal timmar, eller innebär begränsningar för vissa extremvärden och drivalgoritmer på grund av plattformsarkitekturens design där de är integrerade. I det här arbetet utformas och utvärderas en skalbar automatiserad molnbaserad arkitektur för MLmodellövervakning i en streaming-metod. Ett experiment som utförts på ett 7nodskluster med 250.000 förfrågningar vid olika samtidigheter visar maximala latenser på 5,9, 29,92 respektive 30,86 sekunder efter tid för förfrågningen för 75% av avståndsbaserad detektering av extremvärden, windowed statistics och distributionsbaserad datadriftdetektering, med hjälp av windows med 15 sekunders längd och 6 sekunders fördröjning av vattenstämpel.
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Fox, Sean. "Specialised Architectures and Arithmetic for Machine Learning." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26893.

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Machine learning has risen to prominence in recent years thanks to advancements in computer technology, the abundance of data, and numerous breakthroughs in a broad range of applications. Unfortunately, as the demand for machine learning has grown, so too has the amount of computation required for training. Combine this trend with declines observed in performance scaling of standard computer architectures, and it has become increasingly difficult to support machine learning training at increased speed and scale, especially in embedded devices which are smaller and have stricter constraints. Research points towards the development of purpose-built hardware accelerators to overcome the computing challenge, and this thesis explains how specialised hardware architectures and specialised computer arithmetic can achieve performance not possible with standard technology, e.g. Graphics Processing Units (GPUs) and floating-point arithmetic. Based on the implementation of kernel methods and deep neural network (DNN) algorithms using Field Programmable Gate Arrays (FPGAs), this thesis shows how specialised arithmetic is crucial for accurately training large models with less memory, while specialised architectures are needed to increase computational parallelism and reduce off-chip memory transfers. These outcomes are an important step towards moving more machine intelligence into e.g. mobile phones, video cameras, radios, and satellites.
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Moss, Duncan J. M. "FPGA Architectures for Low Precision Machine Learning." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/18182.

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Machine learning is fast becoming a cornerstone in many data analytic, image processing and scientific computing applications. Depending on the deployment scale, these tasks can either be performed on embedded devices, or larger cloud computing platforms. However, one key trend is an exponential increase in the required compute power as data is collected and processed at a previously unprecedented scale. In an effort to reduce the computational complexity there has been significant work on reduced precision representations. Unlike Central Processing Units, Graphical Processing Units and Applications Specific Integrated Circuits which have fixed datapaths, Field Programmable Gate Arrays (FPGA) are flexible and uniquely positioned to take advantage of reduced precision representations. This thesis presents FPGA architectures for low precision machine learning algorithms, considering three distinct levels: the application, the framework and the operator. Firstly, a spectral anomaly detection application is presented, designed for low latency and real-time processing of radio signals. Two types of detector are explored, a neural network autoencoder and least squares bitmap detector. Secondly, a generalised matrix multiplication framework for the Intel HARPv2 is outlined. The framework was designed specifically for machine learning applications; containing runtime configurable optimisations for reduced precision deep learning. Finally, a new machine learning specific operator is presented. A bit-dependent multiplication algorithm designed to conditionally add only the relevant parts of the operands and arbitrarily skip over redundant computation. Demonstrating optimisations on all three levels; the application, the framework and the operator, illustrates that FPGAs can achieve state-of-the-art performance in important machine learning workloads where high performance is critical; while simultaneously reducing implementation complexity.
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Lounici, Sofiane. "Watermarking machine learning models." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS282.pdf.

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La protection de la propriété intellectuelle des modèles d’apprentissage automatique apparaît de plus en plus nécessaire, au vu des investissements et de leur impact sur la société. Dans cette thèse, nous proposons d’étudier le tatouage de modèles d’apprentissage automatique. Nous fournissons un état de l’art sur les techniques de tatouage actuelles, puis nous le complétons en considérant le tatouage de modèles au-delà des tâches de classification d’images. Nous définissons ensuite les attaques de contrefaçon contre le tatouage pour les plateformes d’hébergement de modèles, et nous présentons une nouvelle technique de tatouages par biais algorithmique. De plus, nous proposons une implémentation des techniques présentées<br>The protection of the intellectual property of machine learning models appears to be increasingly necessary, given the investments and their impact on society. In this thesis, we propose to study the watermarking of machine learning models. We provide a state of the art on current watermarking techniques, and then complement it by considering watermarking beyond image classification tasks. We then define forging attacks against watermarking for model hosting platforms and present a new fairness-based watermarking technique. In addition, we propose an implementation of the presented techniques
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Books on the topic "Architectures and machine learning models"

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Nandi, Anirban, and Aditya Kumar Pal. Interpreting Machine Learning Models. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7802-4.

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Kang, Mingu, Sujan Gonugondla, and Naresh R. Shanbhag. Deep In-memory Architectures for Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35971-3.

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Bolc, Leonard. Computational Models of Learning. Springer Berlin Heidelberg, 1987.

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Galindez Olascoaga, Laura Isabel, Wannes Meert, and Marian Verhelst. Hardware-Aware Probabilistic Machine Learning Models. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74042-9.

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Singh, Pramod. Deploy Machine Learning Models to Production. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6546-8.

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Zhang, Zhihua. Statistical Machine Learning: Foundations, Methodologies and Models. John Wiley & Sons, Limited, 2017.

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Rendell, Larry. Representations and models for concept learning. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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Ehteram, Mohammad, Zohreh Sheikh Khozani, Saeed Soltani-Mohammadi, and Maliheh Abbaszadeh. Estimating Ore Grade Using Evolutionary Machine Learning Models. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8106-7.

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Zhang, Le, Chen Chen, Zeju Li, and Greg Slabaugh, eds. Generative Machine Learning Models in Medical Image Computing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80965-1.

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Bisong, Ekaba. Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8.

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Book chapters on the topic "Architectures and machine learning models"

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Lin, Xiaotong, Jiaxi Wu, and Yi Tang. "Generating Misleading Labels in Machine Learning Models." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05054-2_12.

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Das, Susmita, Amara Tariq, Thiago Santos, Sai Sandeep Kantareddy, and Imon Banerjee. "Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research." In Machine Learning for Brain Disorders. Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_4.

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AbstractRecurrent neural networks (RNNs) are neural network architectures with hidden state and which use feedback loops to process a sequence of data that ultimately informs the final output. Therefore, RNN models can recognize sequential characteristics in the data and help to predict the next likely data point in the data sequence. Leveraging the power of sequential data processing, RNN use cases tend to be connected to either language models or time-series data analysis. However, multiple popular RNN architectures have been introduced in the field, starting from SimpleRNN and LSTM to deep RNN, and applied in different experimental settings. In this chapter, we will present six distinct RNN architectures and will highlight the pros and cons of each model. Afterward, we will discuss real-life tips and tricks for training the RNN models. Finally, we will present four popular language modeling applications of the RNN models –text classification, summarization, machine translation, and image-to-text translation– thereby demonstrating influential research in the field.
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Wenzel, Markus. "Generative Adversarial Networks and Other Generative Models." In Machine Learning for Brain Disorders. Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_5.

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AbstractGenerative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially been meant not to be an image analysis tool but to produce naturally looking images. The adversarial training paradigm has been proposed to stabilize generative methods and has proven to be highly successful—though by no means from the first attempt.This chapter gives a basic introduction into the motivation for generative adversarial networks (GANs) and traces the path of their success by abstracting the basic task and working mechanism and deriving the difficulty of early practical approaches. Methods for a more stable training will be shown, as well as typical signs for poor convergence and their reasons.Though this chapter focuses on GANs that are meant for image generation and image analysis, the adversarial training paradigm itself is not specific to images and also generalizes to tasks in image analysis. Examples of architectures for image semantic segmentation and abnormality detection will be acclaimed, before contrasting GANs with further generative modeling approaches lately entering the scene. This will allow a contextualized view on the limits but also benefits of GANs.
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Eickhoff, Patrick, Matthias Möller, Theresa Pekarek Rosin, Johannes Twiefel, and Stefan Wermter. "Bring the Noise: Introducing Noise Robustness to Pretrained Automatic Speech Recognition." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44195-0_31.

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AbstractIn recent research, in the domain of speech processing, large End-to-End (E2E) systems for Automatic Speech Recognition (ASR) have reported state-of-the-art performance on various benchmarks. These systems intrinsically learn how to handle and remove noise conditions from speech. Previous research has shown, that it is possible to extract the denoising capabilities of these models into a preprocessor network, which can be used as a frontend for downstream ASR models. However, the proposed methods were limited to specific fully convolutional architectures. In this work, we propose a novel method to extract the denoising capabilities, that can be applied to any encoder-decoder architecture. We propose the Cleancoder preprocessor architecture that extracts hidden activations from the Conformer ASR model and feeds them to a decoder to predict denoised spectrograms. We train our preprocessor on the Noisy Speech Database (NSD) to reconstruct denoised spectrograms from noisy inputs. Then, we evaluate our model as a frontend to a pretrained Conformer ASR model as well as a frontend to train smaller Conformer ASR models from scratch. We show that the Cleancoder is able to filter noise from speech and that it improves the total Word Error Rate (WER) of the downstream model in noisy conditions for both applications.
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Mohamed, Khaled Salah. "Comparisons, Hybrid Solutions, Hardware Architectures, and New Directions." In Machine Learning for Model Order Reduction. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75714-8_7.

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Nandini, Chinthakindi, Patil Ambika, Rithika Pagadala, Ravi Boda, and B. Mohan Rao. "Sign language detection and recognition using machine learning (ML) architectures." In Security Issues in Communication Devices, Networks and Computing Models. CRC Press, 2025. https://doi.org/10.1201/9781003591788-22.

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Młodzianowski, Patryk. "Weather Classification with Transfer Learning - InceptionV3, MobileNetV2 and ResNet50." In Digital Interaction and Machine Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_1.

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AbstractWeather recognition is a common problem for many branches of industry. For example self-driving cars need to precisely evaluate weather in order to adjust their driving style. Modern agriculture is also based on the analysis of current meteorological conditions. One of the solutions may be a system detecting weather from image. Because any special sensors are needed, the system should be really cheap. Thanks to transfer learning it is possible to create image classification solutions using a small dataset. In this paper three weather recognition models are proposed. These models are based on InceptionV3, MobileNetV2 and ResNet50 architectures. Their efficiency is compared and described.
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Daw, Arka, R. Quinn Thomas, Cayelan C. Carey, Jordan S. Read, Alison P. Appling, and Anuj Karpatne. "Physics-Guided Architecture (PGA) of LSTM Models for Uncertainty Quantification in Lake Temperature Modeling." In Knowledge-Guided Machine Learning. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-17.

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Purpura, Alberto, Karolina Buchner, Gianmaria Silvello, and Gian Antonio Susto. "Neural Feature Selection for Learning to Rank." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72240-1_34.

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AbstractLEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items. In the past few years, neural LETOR approaches have become a competitive alternative to traditional ones like LambdaMART. However, neural architectures performance grew proportionally to their complexity and size. This can be an obstacle for their adoption in large-scale search systems where a model size impacts latency and update time. For this reason, we propose an architecture-agnostic approach based on a neural LETOR model to reduce the size of its input by up to 60% without affecting the system performance. This approach also allows to reduce a LETOR model complexity and, therefore, its training and inference time up to 50%.
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Backhaus, Andreas, Andreas Herzog, Simon Adler, and Daniel Jachmann. "Deployment architecture for the local delivery of ML-Models to the industrial shop floor." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_4.

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AbstractInformation processing systems with some form of machine-learned component are making their way into the industrial application and offer high potentials for increasing productivity and machine utilization. However, the systematic engineering approach to integrate and manage these machine-learned components is still not standardized and no reference architecture exist. In this paper we will present the building block of such an architecture which is developed with the ML4P project by Fraunhofer IFF.
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Conference papers on the topic "Architectures and machine learning models"

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Poursiami, Hamed, Ihsen Alouani, and Maryam Parsa. "BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00102.

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Pavlitska, Svetlana, Enrico Eisen, and J. Marius Zöllner. "Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00226.

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Karmakar, Saurav, and Julia Kamps. "Tracing architecture of machine learning models through their mentions in scholarly articles." In 2024 7th International Conference on Data Science and Information Technology (DSIT). IEEE, 2024. https://doi.org/10.1109/dsit61374.2024.10881057.

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Ursan, Mihai-Eronim-Octavian, Cătălin Daniel Căleanu, and Marian Bucos. "An Architecture of a Web Application for Deploying Machine Learning Models in Healthcare Domain." In 2024 International Symposium on Electronics and Telecommunications (ISETC). IEEE, 2024. https://doi.org/10.1109/isetc63109.2024.10797433.

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AlShriaf, Abdullatif, Hans-Martin Heyn, and Eric Knauss. "Automated Configuration Synthesis for Machine Learning Models: A Git-Based Requirement and Architecture Management System." In 2024 IEEE 32nd International Requirements Engineering Conference (RE). IEEE, 2024. http://dx.doi.org/10.1109/re59067.2024.00058.

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Schmidt, Fabian, Maximilian Georg Kurzawski, Karin Hammerfald, Henrik Haaland Jahren, Ole André Solbakken, and Vladimir Vlassov. "A Scalable System Architecture for Composition and Deployment of Machine Learning Models in Cognitive Behavioral Therapy." In 2024 IEEE International Conference on Digital Health (ICDH). IEEE, 2024. http://dx.doi.org/10.1109/icdh62654.2024.00024.

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Silva, Publio, Carla I. M. Bezerra, Rafael Lima, and Ivan Machado. "Classifying Feature Models Maintainability based on Machine Learning Algorithms." In SBCARS '20: 14th Brazilian Symposium on Software Components, Architectures, and Reuse. ACM, 2020. http://dx.doi.org/10.1145/3425269.3425276.

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Gomes, Diogo, Julian Gutierrez, and Mathieu Laurière. "Machine Learning Architectures for Price Formation Models with Common Noise." In 2023 62nd IEEE Conference on Decision and Control (CDC). IEEE, 2023. http://dx.doi.org/10.1109/cdc49753.2023.10383244.

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Izmailov, Rauf, Sridhar Venkatesan, Achyut Reddy, Ritu Chadha, Michael De Lucia, and Alina Oprea. "Poisoning attacks on machine learning models in cyber systems and mitigation strategies." In Security, Robustness, and Trust in Artificial Intelligence and Distributed Architectures, edited by Misty Blowers, Russell D. Hall, and Venkateswara R. Dasari. SPIE, 2022. http://dx.doi.org/10.1117/12.2622112.

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Hsieh, Chihcheng. "Human-Centred Multimodal Deep Learning Models for Chest X-Ray Diagnosis." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/817.

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My thesis consists of investigating how chest X-ray images, radiologists' eye movements and patients' clinical data can be used to teach a machine how radiologists read and classify images with the goal of creating human-centric AI architectures that can (1) capture radiologists' search behavioural patterns using their eye-movements in order to improve classification in DL systems, and (2) automatically detect lesions in medical images using clinical data and eye tracking data. Heterogeneous data sources such as chest X-rays, radiologists' eye movements, and patients' clinical data can contribute to novel multimodal DL architectures that, instead of learning directly from images' pixels, will learn human classification patterns encoded in both the eye movements of the images' regions and patients' medical history. In addition to a quantitative evaluation, I plan to conduct questionnaires with expert radiologists to understand the effectiveness of the proposed multimodal DL architecture.
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Reports on the topic "Architectures and machine learning models"

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, et al. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.
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Goulet Coulombe, Philippe, Massimiliano Marcellino, and Dalibor Stevanovic. Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables. CIRANO, 2025. https://doi.org/10.54932/qgja3449.

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We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models are better suited to handle cross-sectional nonlinearities. Results show that predictive improvements are broad-based and that even a few high frequency state indicators contribute substantially to forecast accuracy. Our findings highlight the complementarity between flexible modeling and cross-sectional pooling, making panel neural networks a powerful tool for timely and accurate fiscal monitoring in heterogeneous settings.
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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maintaining required levels of accuracy. The growing availability of high-performance computing has improved this analysis by providing the ability to evaluate higher order numerical models. However, more complex models of the seismic response of various civil structures demand increasing amounts of computing power. In addition, computational cost greatly increases with numerous iterations to account for optimization and stochastic loading (e.g., Monte Carlo simulations or Incremental Dynamic Analysis). To address the large computational burden, simpler models are desired for seismic assessment with fragility analysis. Physics reinforced Machine Learning integrates physics knowledge (e.g., scientific principles, laws of physics) into the traditional machine learning architectures, offering physically bounded, interpretable models that require less data than traditional methods. This research introduces a PrML framework to develop fragility curves using the combination of neural networks of domain knowledge. The first aim involves clustering and selecting ground motions for nonlinear response analysis of archetype buildings, ensuring that selected ground motions will include as few ground motions as possible while still expressing all the key representative events the structure will probabilistically experience in its lifetime. The second aim constructs structural PrML metamodels to capture the nonlinear behavior of these buildings utilizing the nonlinear Equation of Motion (EOM). Embedding physical principles, like the general form of the EOM, into the learning process will inform the system to stay within known physical bounds, resulting in interpretable results, robust inferencing, and the capability of dealing with incomplete and scarce data. The third and final aim applies the metamodels to probabilistic seismic response prediction, fragility analysis, and seismic performance factor development. The efficiency and accuracy of this approach are evaluated against existing physics-based fragility analysis methods.
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum kernel methods, while analyzing their impact on neural networks, generative models, and reinforcement learning. Hybrid quantum-classical AI architectures, which combine quantum subroutines with classical deep learning models, are examined for their ability to provide computational advantages in optimization and large-scale data processing. Despite the promise of quantum AI, challenges such as qubit noise, error correction, and hardware scalability remain barriers to full-scale implementation. This study provides an in-depth evaluation of quantum-enhanced AI, highlighting existing applications, ongoing research, and future directions in quantum deep learning, autonomous systems, and scientific computing. The findings contribute to the development of scalable quantum machine learning frameworks, offering novel solutions for next-generation AI systems across finance, healthcare, cybersecurity, and robotics. Keywords Quantum machine learning, quantum computing, artificial intelligence, quantum neural networks, quantum kernel methods, hybrid quantum-classical AI, variational quantum algorithms, quantum generative models, reinforcement learning, quantum optimization, quantum advantage, deep learning, quantum circuits, quantum-enhanced AI, quantum deep learning, error correction, quantum-inspired algorithms, quantum annealing, probabilistic computing.
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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.
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Hovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, 2022. http://dx.doi.org/10.36501/0197-9191/22-016.

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Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust adaptive control. By leveraging data shared over a cloud network available to all entities, vehicles proactively adapted to new environments on the proactive level, thus coping with large-scale environmental changes. On the reactive level, control-barrier-function-based robust adaptive control with machine learning improved the performance around nominal models, providing performance and control certificates. The proposed research shaped a robust foundation for autonomous driving on cloud-connected highways of the future.
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Skryzalin, Jacek, Kenneth Goss, and Benjamin Jackson. Securing machine learning models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1661020.

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Ang, James A., Richard Frederick Barrett, Benner, Robert E.,, et al. Abstract Machine Models and Proxy Architectures for Exascale Computing. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1561498.

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Martinez, Carianne, Jessica Jones, Drew Levin, Nathaniel Trask, and Patrick Finley. Physics-Informed Machine Learning for Epidemiological Models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1706217.

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Lavender, Samantha, and Trent Tinker, eds. Testbed-19: Machine Learning Models Engineering Report. Open Geospatial Consortium, Inc., 2024. http://dx.doi.org/10.62973/23-033.

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