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Journal articles on the topic 'AutoDL'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Bang, Chang Seok, Hyun Lim, Hae Min Jeong, and Sung Hyeon Hwang. "Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study." Journal of Medical Internet Research 23, no. 4 (2021): e25167. http://dx.doi.org/10.2196/25167.

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Background In a previous study, we examined the use of deep learning models to classify the invasion depth (mucosa-confined versus submucosa-invaded) of gastric neoplasms using endoscopic images. The external test accuracy reached 77.3%. However, model establishment is labor intense, requiring high performance. Automated deep learning (AutoDL) models, which enable fast searching of optimal neural architectures and hyperparameters without complex coding, have been developed. Objective The objective of this study was to establish AutoDL models to classify the invasion depth of gastric neoplasms.

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Chen, Yi-Wei, Qingquan Song, and Xia Hu. "Techniques for Automated Machine Learning." ACM SIGKDD Explorations Newsletter 22, no. 2 (2021): 35–50. http://dx.doi.org/10.1145/3447556.3447567.

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Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a problem description, its task type, and datasets. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we portray AutoML as a bi-level optimization problem, where one problem is nested within another to search the optimum in the search space, and review the current developments of AutoML in terms of three categories, auto

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Chien, Ching-Syuan. "AutoDL-based convolutional neural networks for wildfire detection." Applied and Computational Engineering 18, no. 1 (2023): 134–42. http://dx.doi.org/10.54254/2755-2721/18/20230978.

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Wildfires have emerged as a pressing issue in many regions of the world due to the ongoing impact of global warming on the planet. However, a reliable and high-performance detection system is currently lacking. This study strives to introduce a wildfire detection system that is based on neural networks and image recognition. This study utilized Edge Impulse to train a neural network to identify wildfire occurrences in the given pictures. To optimize the performance and adaptability of the model, an extensive dataset was compiled by collating images from two Kaggle projects, resulting in a fina

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Tuggener, Lukas, Mohammadreza Amirian, Fernando Benites, et al. "Design Patterns for Resource-Constrained Automated Deep-Learning Methods." AI 1, no. 4 (2020): 510–38. http://dx.doi.org/10.3390/ai1040031.

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We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accuracy and search efficiency under tight time and computing constraints. We propose structured empirical evaluations as the most promising avenue to obtain design principles for deep-learning systems due to the absence of strong theoretical support. From these evaluations, we distill relevant patterns which give rise to neural network design recom

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Zimmer, Lucas, Marius Lindauer, and Frank Hutter. "Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (2021): 3079–90. http://dx.doi.org/10.1109/tpami.2021.3067763.

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Liu, Zhengying, Adrien Pavao, Zhen Xu, et al. "Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (2021): 3108–25. http://dx.doi.org/10.1109/tpami.2021.3075372.

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Preuveneers, Davy. "AutoFL: Towards AutoML in a Federated Learning Context." Applied Sciences 13, no. 14 (2023): 8019. http://dx.doi.org/10.3390/app13148019.

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Federated learning (FL) is a decentralized machine learning (ML) technique that learns from distributed data by moving the training process from a centralized server towards many clients rather than centralizing the client data, as is common with classical machine learning. The recent literature on federated learning often focuses on domain-specific use cases (e.g., IoT), investigates various privacy concerns (e.g., membership inference), or analyzes the impact of adversarial attacks (e.g., poisoning) and possible countermeasures. In these works, it is common for the server to have already cho

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Wang, Wenbo, and Chengyou Lei. "Training a Minesweeper Agent Using a Convolutional Neural Network." Applied Sciences 15, no. 5 (2025): 2490. https://doi.org/10.3390/app15052490.

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The Minesweeper game is modeled as a sequential decision-making task, for which a neural network architecture, state encoding, and reward function were herein designed. Both a Deep Q-Network (DQN) and supervised learning methods were successfully applied to optimize the training of the game. The experiments were conducted on the AutoDL platform using an NVIDIA RTX 3090 GPU for efficient computation. The results showed that in a 6 × 6 grid with four mines, the DQN model achieved an average win rate of 93.3% (standard deviation: 0.77%), while the supervised learning method achieved 91.2% (standa

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Chen, Xu, and Brett Wujek. "AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3537–44. http://dx.doi.org/10.1609/aaai.v34i04.5759.

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Automated machine learning (AutoML) strives to establish an appropriate machine learning model for any dataset automatically with minimal human intervention. Although extensive research has been conducted on AutoML, most of it has focused on supervised learning. Research of automated semi-supervised learning and active learning algorithms is still limited. Implementation becomes more challenging when the algorithm is designed for a distributed computing environment. With this as motivation, we propose a novel automated learning system for distributed active learning (AutoDAL) to address these

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Parker-Holder, Jack, Raghu Rajan, Xingyou Song, et al. "Automated Reinforcement Learning (AutoRL): A Survey and Open Problems." Journal of Artificial Intelligence Research 74 (June 1, 2022): 517–68. http://dx.doi.org/10.1613/jair.1.13596.

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The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems and also limits its full potential. In many other areas of machine learning, AutoML has shown that it is possible to automate such design choices, and AutoML has also yielded promising initial results

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Patel, Kush. "AutoML and Automated Data Science by Democratizing AI through End-to-End Automation." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 494–504. http://dx.doi.org/10.22214/ijraset.2024.64555.

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The rise of data-driven decision-making has led to a significant demand for data science and machine learning (ML) solutions across industries. However, developing these solutions requires extensive expertise in data preprocessing, feature engineering, model selection, hyperparameter tuning and evaluation. AutoML (Automated Machine Learning) and Automated Data Science (AutoDS) have emerged as transformative approaches that aim to democratize data science by automating the endto-end ML pipeline. This paper explores the foundational concepts of AutoML, highlighting key techniques and algorithms,

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Cao, Longbing. "Beyond AutoML: Mindful and Actionable AI and AutoAI With Mind and Action." IEEE Intelligent Systems 37, no. 5 (2022): 6–18. http://dx.doi.org/10.1109/mis.2022.3207860.

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Lan, Hai, Yuanjia Zhang, Zhifeng Bao, et al. "AutoDI." Proceedings of the VLDB Endowment 15, no. 12 (2022): 3626–29. http://dx.doi.org/10.14778/3554821.3554860.

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Manual analysis on plan regression is both labor-intensive and inefficient for a large query plan and numerous queries. In this paper, we demonstrate AutoDI, an automatic detection and inference tool that has been developed to investigate why a sub-optimal plan is obtained by analyzing two different plans of the same query. AutoDI consists of two main modules, Difference Finder and Inference. The former aims to find where the two plans are different, and the latter tries to obtain the reasons why the differences come out. In our demonstration, we use a real plan regression in TiDB to show how

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Yakovlev, Anatoly, Hesam Fathi Moghadam, Ali Moharrer, et al. "Oracle AutoML." Proceedings of the VLDB Endowment 13, no. 12 (2020): 3166–80. http://dx.doi.org/10.14778/3415478.3415542.

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Tornede, Tanja, Alexander Tornede, Jonas Hanselle, Felix Mohr, Marcel Wever, and Eyke Hüllermeier. "Towards Green Automated Machine Learning: Status Quo and Future Directions." Journal of Artificial Intelligence Research 77 (June 12, 2023): 427–57. http://dx.doi.org/10.1613/jair.1.14340.

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Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution — a machine learning pipeline — tailored to the learning task (dataset) at hand. Over the last decade, AutoML has developed into an independent research field with hundreds of contributions. At the same time, AutoML is being criticized for its high resource consumption as many approaches rely on the (costly) evaluation of many machine learning pipelines, as well as the expensive large-scale experiments across many datasets and appr

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Thongprayoon, Charat, Pattharawin Pattharanitima, Andrea G. Kattah, et al. "Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury." Journal of Clinical Medicine 11, no. 21 (2022): 6264. http://dx.doi.org/10.3390/jcm11216264.

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Background: We aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI). Methods: Using 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model. We then compared model performance using area under the receiver operat

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Mustafa, Akram, and Mostafa Rahimi Azghadi. "Automated Machine Learning for Healthcare and Clinical Notes Analysis." Computers 10, no. 2 (2021): 24. http://dx.doi.org/10.3390/computers10020024.

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Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML is to provide seamless integration of ML in various industries, which will facilitate better outcomes in everyday tasks. In healthcare, AutoML has been already applied to easier settings with structured data such as tabular lab data. However, there is still a need for applying AutoML for interpreting medical t

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Schlicher, Max, and Klaus Möller. "AutoML im Controlling." Controlling 34, no. 2 (2022): 39–42. http://dx.doi.org/10.15358/0935-0381-2022-2-39.

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Mit der Demokratisierung von künstlicher Intelligenz bekommen auch Controller die Möglichkeit, automatisiertes Maschinelles Lernen einzusetzen. AutoML-Systeme (Automated Machine Learning) können in endnutzer- und expertenorientierte Systeme unterteilt werden. Als Startpunkt für das Controlling eignen sich endnutzerorientierte AutoML-Systeme am besten.

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Zender, Alexander, and Bernhard G. Humm. "Benchmarking Meta AutoML." Procedia Computer Science 256 (2025): 130–41. https://doi.org/10.1016/j.procs.2025.02.105.

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Thirunavukarasu, Arun James, Kabilan Elangovan, Laura Gutierrez, et al. "Clinical performance of automated machine learning: A systematic review." Annals of the Academy of Medicine, Singapore 53, no. 3 - Correct DOI (2024): 187–207. http://dx.doi.org/10.47102/annals-acadmedsg.2023113.

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Introduction: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other. Method: This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427). The Cochrane Library, Embase, MEDLINE and Scopus were searched from inception to

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Thirunavukarasu, Arun James, Kabilan Elangovan, Laura Gutierrez, et al. "Clinical performance of automated machine learning: A systematic review." Annals of the Academy of Medicine, Singapore 53, no. 3 (2024): 187–207. http://dx.doi.org/10.47102/https://doi.org/10.47102/annals-acadmedsg.2023113.

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Introduction: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other. Method: This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427). The Cochrane Library, Embase, MEDLINE and Scopus were searched from inception to

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Zöller, Marc-André, and Marco F. Huber. "Benchmark and Survey of Automated Machine Learning Frameworks." Journal of Artificial Intelligence Research 70 (January 27, 2021): 409–72. http://dx.doi.org/10.1613/jair.1.11854.

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Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks f

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Lazebnik, Teddy, Tzach Fleischer, and Amit Yaniv-Rosenfeld. "Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks." Sustainability 15, no. 14 (2023): 11232. http://dx.doi.org/10.3390/su151411232.

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Data-driven economic tasks have gained significant attention in economics, allowing researchers and policymakers to make better decisions and design efficient policies. Recently, with the advancement of machine learning (ML) and other artificial intelligence (AI) methods, researchers can now solve complex economic tasks with previously unseen performance and ease. However, to use such methods, one is required to have a non-trivial level of expertise in ML or AI, which currently is not standard knowledge in economics. In order to bridge this gap, automatic machine learning (AutoML) models have

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Shujaat, Sohaib. "Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions." Diagnostics 15, no. 3 (2025): 273. https://doi.org/10.3390/diagnostics15030273.

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The adoption of automated machine learning (AutoML) in dentistry is transforming clinical practices by enabling clinicians to harness machine learning (ML) models without requiring extensive technical expertise. This narrative review aims to explore the impact of autoML in dental applications. A comprehensive search of PubMed, Scopus, and Google Scholar was conducted without time and language restrictions. Inclusion criteria focused on studies evaluating autoML applications and performance for dental tasks. Exclusion criteria included non-dental studies, single-case reports, and conference abs

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Tian, Junchi, and Chang Che. "Automated Machine Learning: A Survey of Tools and Techniques." Journal of Industrial Engineering and Applied Science 2, no. 6 (2024): 71–76. https://doi.org/10.70393/6a69656173.323336.

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Automated Machine Learning (AutoML) has revolutionized the field of machine learning by automating complex and time-intensive tasks such as data preprocessing, model selection, and hyperparameter tuning. This study explores the capabilities, limitations, and practical applications of six widely used AutoML tools: Auto-sklearn, TPOT, H2O.ai, Google Cloud AutoML, Microsoft Azure AutoML, and Amazon SageMaker Autopilot. By evaluating these tools across diverse datasets—spanning tabular data, time series, image classification, and text sentiment analysis—the research highlights their predictive per

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Rosário, Albérico Travassos, and Anna Carolina Boechat. "How Automated Machine Learning Can Improve Business." Applied Sciences 14, no. 19 (2024): 8749. http://dx.doi.org/10.3390/app14198749.

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Automated Machine Learning (AutoML) is revolutionizing how businesses utilize data, but there seems to be a lack of clarity and a holistic view regarding all its advantages, especially concerning the benefits of AutoML for companies. In order to deeply understand how AutoML can improve businesses, a systematic review examined the bibliometric literature of this field, analyzing 74 academic and scientific documents from the Scopus database. Results showed that AutoML (i) reduces the time and resources needed to develop and deploy machine learning models, (ii) accelerates decision-making and ena

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KADIOGLU, Muhammet Ali. "End-to-End AutoML Implementation Framework." Eurasia Proceedings of Science Technology Engineering and Mathematics 19 (December 14, 2022): 35–40. http://dx.doi.org/10.55549/epstem.1218713.

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Automated machine learning (AutoML) has been an active research area in recent years. Researchers investigate the potential of AutoML as more stakeholders want to maximize the value of their data. The methods are designed to increase the effectiveness of machine learning (ML), accelerate model development processes, and make it accessible for domain experts that are not ML professionals. The systems without the aid of humans are feasible with AutoML, an area that has been increasingly studied recently. Even though efficiency and automation are two of AutoML's key points, a number of critical s

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Lazebnik, Teddy, Amit Somech, and Abraham Itzhak Weinberg. "SubStrat." Proceedings of the VLDB Endowment 16, no. 4 (2022): 772–80. http://dx.doi.org/10.14778/3574245.3574261.

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Automated machine learning (AutoML) frameworks have become important tools in the data scientist's arsenal, as they dramatically reduce the manual work devoted to the construction of ML pipelines. Such frameworks intelligently search among millions of possible ML pipelines - typically containing feature engineering, model selection, and hyper parameters tuning steps - and finally output an optimal pipeline in terms of predictive accuracy. However, when the dataset is large, each individual configuration takes longer to execute, therefore the overall AutoML running times become increasingly hig

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Liu, Sijia, Parikshit Ram, Deepak Vijaykeerthy, et al. "An ADMM Based Framework for AutoML Pipeline Configuration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4892–99. http://dx.doi.org/10.1609/aaai.v34i04.5926.

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We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed va

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Li, Yang, Yu Shen, Wentao Zhang, et al. "VolcanoML." Proceedings of the VLDB Endowment 14, no. 11 (2021): 2167–76. http://dx.doi.org/10.14778/3476249.3476270.

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End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existing AutoML systems, however, suffer from scalability issues when applying to application domains with large, high-dimensional search spaces. We present VOLCANOML, a scalable and extensible framework that facilitates systematic exploration of large AutoML search spaces. VOLCANOML introduces and implements basic building blocks that decompose a large search space in

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TOPSAKAL, Oguzhan, and Tahir Cetin AKINCI. "Classification and Regression Using Automatic Machine Learning (AutoML) – Open Source Code for Quick Adaptation and Comparison." Balkan Journal of Electrical and Computer Engineering 11, no. 3 (2023): 257–61. http://dx.doi.org/10.17694/bajece.1312764.

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This paper presents a comprehensive exploration of automatic machine learning (AutoML) tools in the context of classification and regression tasks. The focus lies on understanding and illustrating the potential of these tools to accelerate and optimize the process of machine learning, thereby making it more accessible to non-experts. Specifically, we delve into multiple popular open-source AutoML tools and provide illustrative examples of their application. We first discuss the fundamental principles of AutoML, including its key features such as automated data preprocessing, feature engineerin

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Weerts, Hilde, Florian Pfisterer, Matthias Feurer, et al. "Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML." Journal of Artificial Intelligence Research 79 (February 17, 2024): 639–77. http://dx.doi.org/10.1613/jair.1.14747.

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The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices. However, decisions derived from ML models can reproduce, amplify, or even introduce unfairness in our societies, causing harm to (groups of) individuals. In response, researchers have started to propose AutoML systems that jointly optimize fairness and predictive performance to mitigate fairness-related harm. However, fairness is a complex and inherently interdisciplinary subject, and solely p

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Castellanos-Nieves, Dagoberto, and Luis García-Forte. "Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence." Applied Sciences 14, no. 14 (2024): 6196. http://dx.doi.org/10.3390/app14146196.

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Automated machine learning (AutoML) is recognized for its efficiency in facilitating model development due to its ability to perform tasks autonomously, without constant human intervention. AutoML automates the development and optimization of machine learning models, leading to high energy consumption due to the large amount of calculations involved. Hyperparameter optimization algorithms, central to AutoML, can significantly impact its carbon footprint. This work introduces and investigates energy efficiency metrics for advanced hyperparameter optimization algorithms within AutoML. These metr

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Helali, Mossad, Essam Mansour, Ibrahim Abdelaziz, Julian Dolby, and Kavitha Srinivas. "A scalable AutoML approach based on graph neural networks." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2428–36. http://dx.doi.org/10.14778/3551793.3551804.

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AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner. Many AutoML systems use meta-learning to guide search for optimal pipelines. In this work, we present a novel meta-learning system called KGpip which (1) builds a database of datasets and corresponding pipelines by mining thousands of scripts with program analysis, (2) uses dataset embeddings to find similar datasets in the database based on its content instead of metadata-based features, (3) models AutoML pipe

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Paladino, Lauren M., Alexander Hughes, Alexander Perera, Oguzhan Topsakal, and Tahir Cetin Akinci. "Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction." AI 4, no. 4 (2023): 1036–58. http://dx.doi.org/10.3390/ai4040053.

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Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing machine learning (ML) techniques in diagnosing and predicting heart conditions. While applying ML demands a certain level of computer science expertise—often a barrier for healthcare professionals—automated machine learning (AutoML) tools significantly lower this barrier. They enable users to construct the most effective ML models without in-depth

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Jin, Zhengyang. "Exploring the Advancements and Challenges of Automated Machine Learning." Applied and Computational Engineering 8, no. 1 (2023): 732–37. http://dx.doi.org/10.54254/2755-2721/8/20230095.

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Automl,a rapidly growing field which is aiming to apply the machine to solve problems that human cant easily deal with . This includes tasks such as feature selection, model selection, and hyperparameter tuning. One of the many advantages about Auto Ml is that it can greatly shorten the cost of researchs and resources cost by applying machine learning to a problem. This makes it accessible to a wider range of users, including those without a background in computer science or statistics.In spite of some advantages of AutoML, many challenges are waiting to be addressed. The main challenge is tha

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Thirunagalingam, Arunkumar. "Transforming Real-Time Data Processing: The Impact of AutoML on Dynamic Data Pipelines." FMDB Transactions on Sustainable Intelligent Networks 1, no. 2 (2024): 110–19. http://dx.doi.org/10.69888/ftsin.2024.000213.

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Modern data-driven applications demand efficient real-time data processing, driving innovation across sectors like e-commerce, banking, and healthcare. However, the manual construction and optimization of data pipelines struggle to meet the challenges of today’s high-velocity, dynamic data environments. Automated machine learning (AutoML) emerges as a transformative technology by automating and enhancing the creation, optimization, and management of data pipelines. This study explores the profound impact of AutoML on dynamic data pipelines, highlighting its role in improving efficiency, adapta

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Swapna Reddy Anugu. "Democratizing AI: How AutoML is transforming enterprise cloud strategies." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 701–8. https://doi.org/10.30574/wjaets.2025.15.1.0275.

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Automated Machine Learning (AutoML) is transforming how enterprises develop and implement AI solutions by democratizing access to advanced machine learning capabilities. This paradigm shift enables organizations to overcome traditional barriers to AI adoption by automating complex processes throughout the machine learning lifecycle, from data preprocessing to model deployment and monitoring. By reducing technical complexity and accelerating development cycles, AutoML allows domain experts without specialized data science knowledge to build effective AI solutions that address specific business

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Kang, Sungmin, Gabin An, and Shin Yoo. "A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization." Proceedings of the ACM on Software Engineering 1, FSE (2024): 1424–46. http://dx.doi.org/10.1145/3660771.

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Fault Localization (FL), in which a developer seeks to identify which part of the code is malfunctioning and needs to be fixed, is a recurring challenge in debugging. To reduce developer burden, many automated FL techniques have been proposed. However, prior work has noted that existing techniques fail to provide rationales for the suggested locations, hindering developer adoption of these techniques. With this in mind, we propose AutoFL, a Large Language Model (LLM)-based FL technique that generates an explanation of the bug along with a suggested fault location. AutoFL prompts an LLM to use

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Yu, Chenyan, Yao Li, Minyue Yin, et al. "Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis." Journal of Personalized Medicine 12, no. 11 (2022): 1930. http://dx.doi.org/10.3390/jpm12111930.

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Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training and validation datasets at a ratio of 8.5:1.5. Models were developed on the H2O AutoML platform in the training dataset, and then were evaluated in the validation dataset by area under receiver operating characteristic curves (AUC). The best AutoML model was interpreted by SHapley Ad

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Bodini, Matteo. "Daily Streamflow Forecasting Using AutoML and Remote-Sensing-Estimated Rainfall Datasets in the Amazon Biomes." Signals 5, no. 4 (2024): 659–89. http://dx.doi.org/10.3390/signals5040037.

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Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning (AutoML) models to forecast daily streamflow in the area of the upper Teles Pires River basin, located in the region of the Amazon biomes. The latter area is characterized by extensive water-resource utilization, mostly for power generation through HPPs, a

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Durmaz Engin, Ceren, Mahmut Ozan Gokkan, Seher Koksaldi, et al. "Comparing Auto-Machine Learning and Expert-Designed Models in Diagnosing Vitreomacular Interface Disorders." Journal of Clinical Medicine 14, no. 8 (2025): 2774. https://doi.org/10.3390/jcm14082774.

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Background: The vitreomacular interface (VMI) encompasses a group of retinal disorders that significantly impact vision, requiring accurate classification for effective management. This study aims to compare the effectiveness of an expert-designed custom deep learning (DL) model and a code free Auto Machine Learning (ML) model in classifying optical coherence tomography (OCT) images of VMI disorders. Materials and Methods: A balanced dataset of OCT images across five classes—normal, epiretinal membrane (ERM), idiopathic full-thickness macular hole (FTMH), lamellar macular hole (LMH), and vitre

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Koh, Joshua C. O., German Spangenberg, and Surya Kant. "Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping." Remote Sensing 13, no. 5 (2021): 858. http://dx.doi.org/10.3390/rs13050858.

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Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high-performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in knowledge, we examined the application of AutoML for image-based plant phenotyping using wheat lodging assessment with unmanned aerial vehicle (UAV) imagery as an example. The performance of an open

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Lenkala, Swetha, Revathi Marry, Susmitha Reddy Gopovaram, Tahir Cetin Akinci, and Oguzhan Topsakal. "Comparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG)." Computers 12, no. 10 (2023): 197. http://dx.doi.org/10.3390/computers12100197.

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Epilepsy is a neurological disease characterized by recurrent seizures caused by abnormal electrical activity in the brain. One of the methods used to diagnose epilepsy is through electroencephalogram (EEG) analysis. EEG is a non-invasive medical test for quantifying electrical activity in the brain. Applying machine learning (ML) to EEG data for epilepsy diagnosis has the potential to be more accurate and efficient. However, expert knowledge is required to set up the ML model with correct hyperparameters. Automated machine learning (AutoML) tools aim to make ML more accessible to non-experts

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Bosch, Nigel. "AutoML Feature Engineering for Student Modeling Yields High Accuracy, but Limited Interpretability." Journal of Educational Data Mining 13, no. 2 (2021): 55–79. https://doi.org/10.5281/zenodo.5275315.

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Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The methods we compare, Featuretools and TSFRESH (Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests), have rarely been applied in the context of student interaction log data. Thus, we address research questions regarding the accuracy of mod

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Castellanos-Nieves, Dagoberto, and Luis García-Forte. "Improving Automated Machine-Learning Systems through Green AI." Applied Sciences 13, no. 20 (2023): 11583. http://dx.doi.org/10.3390/app132011583.

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Automated machine learning (AutoML), which aims to facilitate the design and optimization of machine-learning models with reduced human effort and expertise, is a research field with significant potential to drive the development of artificial intelligence in science and industry. However, AutoML also poses challenges due to its resource and energy consumption and environmental impact, aspects that have often been overlooked. This paper predominantly centers on the sustainability implications arising from computational processes within the realm of AutoML. Within this study, a proof of concept

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S. Vadar, Dr Parashuram, Dr Tejashree T. Moharekar, and Dr Urmila R. Pol. "COMPARATIVE ANALYSIS OF AUTOMATED MACHINE LEARNING LIBRARIES: PYCARET, H2O, TPOT, AUTO-SKLEARN, AND FLAML." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–8. http://dx.doi.org/10.55041/ijsrem39119.

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Automated Machine Learning (AutoML) frameworks have gained significant popularity in recent years, making machine learning accessible to a broader audience by automating many of the tasks traditionally performed by data scientists and domain experts. This paper presents a comparative analysis of five prominent AutoML libraries: PyCaret, H2O.ai, TPOT, Auto-sklearn, and FLAML. Each of these libraries provides automated solutions for model selection, hyperparameter tuning, and other machine learning tasks. The goal of this study is to assess their performance, ease of use, flexibility, and suitab

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Liam Connor, Rebecca Kelly, Saoirse Murphy, Eoghan McCarthy, and Edward Murray. "Analysis of AutoML Tools in the World of Automated Deep Learning." Fusion of Multidisciplinary Research, An International Journal 5, no. 1 (2024): 541–55. https://doi.org/10.63995/fxpc8243.

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In-depth analysis of AutoML (Automated Machine Learning) tools in the context of automated deep learning, a rapidly evolving field aimed at simplifying and optimizing the development of deep learning models. AutoML tools automate various stages of the machine learning pipeline, including data preprocessing, feature selection, model selection, hyperparameter tuning, and model evaluation, making them accessible to non-experts and enhancing productivity for experts. The article reviews prominent AutoML tools, examining their methodologies, strengths, limitations, and performance across different

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Casapu, Cristina-Ioana, and Simona Moldovanu. "Classification of Microorganism Using Convolutional Neural Network and H2O AutoML." SYSTEM THEORY, CONTROL AND COMPUTING JOURNAL 4, no. 1 (2024): 15–21. http://dx.doi.org/10.52846/stccj.2024.4.1.60.

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The study advances microorganism image classification through a hybrid approach that integrates a Convolutional Neural Network (CNN), modified from the VGG19 architecture, with an ensemble model powered by H2O AutoML. Employing data augmentation and feature extraction, the approach enhances performance on a dataset encapsulating a broad spectrum of microorganism classes. The CNN model shows significant accuracy enhancements in complex bacteria classes, as depicted by the confusion matrix. Concurrently, the AutoML ensemble delivers comparable accuracy, notably in some classes where CNNs struggl

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Singpai, Bodin, and Desheng Wu. "Using a DEA–AutoML Approach to Track SDG Achievements." Sustainability 12, no. 23 (2020): 10124. http://dx.doi.org/10.3390/su122310124.

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Each country needs to monitor progress on their Sustainable Development Goals (SDGs) to develop strategies that meet the expectations of the United Nations. Data envelope analysis (DEA) can help identify best practices for SDGs by setting goals to compete against. Automated machine learning (AutoML) simplifies machine learning for researchers who need less time and manpower to predict future situations. This work introduces an integrative method that integrates DEA and AutoML to assess and predict performance in SDGs. There are two experiments with different data properties in their interval a

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