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Artykuły w czasopismach na temat „Black-box learning algorithm”

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Data publikacji: 6 września 2023

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1

Hwangbo, Jemin, Christian Gehring, Hannes Sommer, Roland Siegwart, and Jonas Buchli. "Policy Learning with an Efficient Black-Box Optimization Algorithm." International Journal of Humanoid Robotics 12, no. 03 (2015): 1550029. http://dx.doi.org/10.1142/s0219843615500292.

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Robotic learning on real hardware requires an efficient algorithm which minimizes the number of trials needed to learn an optimal policy. Prolonged use of hardware causes wear and tear on the system and demands more attention from an operator. To this end, we present a novel black-box optimization algorithm, Reward Optimization with Compact Kernels and fast natural gradient regression (ROCK⋆). Our algorithm immediately updates knowledge after a single trial and is able to extrapolate in a controlled manner. These features make fast and safe learning on real hardware possible. The performance o

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Kirsch, Louis, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh, and Yutian Chen. "Introducing Symmetries to Black Box Meta Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7202–10. http://dx.doi.org/10.1609/aaai.v36i7.20681.

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Meta reinforcement learning (RL) attempts to discover new RL algorithms automatically from environment interaction. In so-called black-box approaches, the policy and the learning algorithm are jointly represented by a single neural network. These methods are very flexible, but they tend to underperform compared to human-engineered RL algorithms in terms of generalisation to new, unseen environments. In this paper, we explore the role of symmetries in meta-generalisation. We show that a recent successful meta RL approach that meta-learns an objective for backpropagation-based learning exhibits

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Xiang, Fengtao, Jiahui Xu, Wanpeng Zhang, and Weidong Wang. "A Distributed Biased Boundary Attack Method in Black-Box Attack." Applied Sciences 11, no. 21 (2021): 10479. http://dx.doi.org/10.3390/app112110479.

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The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios. Research on black-box attack methods and the generation of adversarial samples is helpful to discover the defects of machine learning models. It can strengthen the robustness of machine learning algorithms models. Such methods require queries frequently, which are less efficient. This paper has made improvements in the initial generation and the search for the most effective adversarial examples. Bes

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LIU, Yanhe, Michael AFNAN, Vincent CONTIZER, et al. "Embryo Selection by “Black-Box” Artificial Intelligence: The Ethical and Epistemic Considerations." Fertility & Reproduction 04, no. 03n04 (2022): 147. http://dx.doi.org/10.1142/s2661318222740590.

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Background: The combination of time-lapse imaging and artificial intelligence (AI) offers novel potential for embryo assessment by allowing a vast quantity of image data to be analysed via machine learning. Most algorithms developed to date have used neural networks which are uninterpretable (“black-box”) and cannot be understood by doctors, embryologists and patients, which raises ethical and epistemic concerns for embryo selection in a clinical setting. Aim: This study aims to discuss ethical and epistemic considerations surrounding clinical implementation of “black-box” based embryo selecti

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Bausch, Johannes. "Fast Black-Box Quantum State Preparation." Quantum 6 (August 4, 2022): 773. http://dx.doi.org/10.22331/q-2022-08-04-773.

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Quantum state preparation is an important ingredient for other higher-level quantum algorithms, such as Hamiltonian simulation, or for loading distributions into a quantum device to be used e.g. in the context of optimization tasks such as machine learning. Starting with a generic "black box" method devised by Grover in 2000, which employs amplitude amplification to load coefficients calculated by an oracle, there has been a long series of results and improvements with various additional conditions on the amplitudes to be loaded, culminating in Sanders et al.'s work which avoids almost all ari

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MIKE, KOBY, and ORIT HAZZAN. "MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH." STATISTICS EDUCATION RESEARCH JOURNAL 21, no. 2 (2022): 10. http://dx.doi.org/10.52041/serj.v21i2.45.

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Data science is a new field of research, with growing interest in recent years, that focuses on extracting knowledge and value from data. New data science education programs, which are being launched at a growing rate, are designed for multiple levels, beginning with elementary school pupils. Machine learning is an important element of data science that requires an extensive background in mathematics. While it is possible to teach the principles of machine learning as a black box, it might be difficult to improve algorithm performance without a white box understanding of the underlaying learni

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García, Javier, Roberto Iglesias, Miguel A. Rodríguez, and Carlos V. Regueiro. "Directed Exploration in Black-Box Optimization for Multi-Objective Reinforcement Learning." International Journal of Information Technology & Decision Making 18, no. 03 (2019): 1045–82. http://dx.doi.org/10.1142/s0219622019500093.

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Usually, real-world problems involve the optimization of multiple, possibly conflicting, objectives. These problems may be addressed by Multi-objective Reinforcement learning (MORL) techniques. MORL is a generalization of standard Reinforcement Learning (RL) where the single reward signal is extended to multiple signals, in particular, one for each objective. MORL is the process of learning policies that optimize multiple objectives simultaneously. In these problems, the use of directional/gradient information can be useful to guide the exploration to better and better behaviors. However, trad

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Mayr, Franz, Sergio Yovine, and Ramiro Visca. "Property Checking with Interpretable Error Characterization for Recurrent Neural Networks." Machine Learning and Knowledge Extraction 3, no. 1 (2021): 205–27. http://dx.doi.org/10.3390/make3010010.

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This paper presents a novel on-the-fly, black-box, property-checking through learning approach as a means for verifying requirements of recurrent neural networks (RNN) in the context of sequence classification. Our technique steps on a tool for learning probably approximately correct (PAC) deterministic finite automata (DFA). The sequence classifier inside the black-box consists of a Boolean combination of several components, including the RNN under analysis together with requirements to be checked, possibly modeled as RNN themselves. On one hand, if the output of the algorithm is an empty DFA

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Anđelić, Nikola, Ivan Lorencin, Matko Glučina, and Zlatan Car. "Mean Phase Voltages and Duty Cycles Estimation of a Three-Phase Inverter in a Drive System Using Machine Learning Algorithms." Electronics 11, no. 16 (2022): 2623. http://dx.doi.org/10.3390/electronics11162623.

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To achieve an accurate, efficient, and high dynamic control performance of electric motor drives, precise phase voltage information is required. However, measuring the phase voltages of electrical motor drives online is expensive and potentially contains measurement errors, so they are estimated by inverter models. In this paper, the idea is to investigate if various machine learning (ML) algorithms could be used to estimate the mean phase voltages and duty cycles of the black-box inverter model and black-box inverter compensation scheme with high accuracy using a publicly available dataset. I

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Veugen, Thijs, Bart Kamphorst, and Michiel Marcus. "Privacy-Preserving Contrastive Explanations with Local Foil Trees." Cryptography 6, no. 4 (2022): 54. http://dx.doi.org/10.3390/cryptography6040054.

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We present the first algorithm that combines privacy-preserving technologies and state-of-the-art explainable AI to enable privacy-friendly explanations of black-box AI models. We provide a secure algorithm for contrastive explanations of black-box machine learning models that securely trains and uses local foil trees. Our work shows that the quality of these explanations can be upheld whilst ensuring the privacy of both the training data and the model itself.

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Pulatov, Damir, and Lars Kotthoff. "Opening the Black Box: Automatically Characterizing Software for Algorithm Selection (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13899–900. http://dx.doi.org/10.1609/aaai.v34i10.7222.

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Meta-algorithmics, the field of leveraging machine learning to use algorithms more efficiently, has achieved impressive performance improvements in many areas of AI. It treats the algorithms to improve on as black boxes – nothing is known about their inner workings. This allows meta-algorithmic techniques to be deployed in many applications, but leaves potential performance improvements untapped by ignoring information that the algorithms could provide. In this paper, we open the black box without sacrificing the universal applicability of meta-algorithmic techniques by automatically analyzing

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BALL, NICHOLAS M., and ROBERT J. BRUNNER. "DATA MINING AND MACHINE LEARNING IN ASTRONOMY." International Journal of Modern Physics D 19, no. 07 (2010): 1049–106. http://dx.doi.org/10.1142/s0218271810017160.

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We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process

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Yu, Wen, and Francisco Vega. "Nonlinear system modeling using the takagi-sugeno fuzzy model and long-short term memory cells." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 4547–56. http://dx.doi.org/10.3233/jifs-200491.

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The data driven black-box or gray-box models like neural networks and fuzzy systems have some disadvantages, such as the high and uncertain dimensions and complex learning process. In this paper, we combine the Takagi-Sugeno fuzzy model with long-short term memory cells to overcome these disadvantages. This novel model takes the advantages of the interpretability of the fuzzy system and the good approximation ability of the long-short term memory cell. We propose a fast and stable learning algorithm for this model. Comparisons with others similar black-box and grey-box models are made, in orde

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Muñoz, Mario Andrés, and Michael Kirley. "Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization." Algorithms 14, no. 1 (2021): 19. http://dx.doi.org/10.3390/a14010019.

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In this paper, we investigate how systemic errors due to random sampling impact on automated algorithm selection for bound-constrained, single-objective, continuous black-box optimization. We construct a machine learning-based algorithm selector, which uses exploratory landscape analysis features as inputs. We test the accuracy of the recommendations experimentally using resampling techniques and the hold-one-instance-out and hold-one-problem-out validation methods. The results demonstrate that the selector remains accurate even with sampling noise, although not without trade-offs.

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Muñoz, Mario Andrés, and Michael Kirley. "Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization." Algorithms 14, no. 1 (2021): 19. http://dx.doi.org/10.3390/a14010019.

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In this paper, we investigate how systemic errors due to random sampling impact on automated algorithm selection for bound-constrained, single-objective, continuous black-box optimization. We construct a machine learning-based algorithm selector, which uses exploratory landscape analysis features as inputs. We test the accuracy of the recommendations experimentally using resampling techniques and the hold-one-instance-out and hold-one-problem-out validation methods. The results demonstrate that the selector remains accurate even with sampling noise, although not without trade-offs.

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Žlahtič, Bojan, Jernej Završnik, Helena Blažun Vošner, Peter Kokol, David Šuran, and Tadej Završnik. "Agile Machine Learning Model Development Using Data Canyons in Medicine: A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement." Applied Sciences 13, no. 14 (2023): 8329. http://dx.doi.org/10.3390/app13148329.

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Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of th

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HOLZINGER, ANDREAS, MARKUS PLASS, KATHARINA HOLZINGER, GLORIA CERASELA CRIS¸AN, CAMELIA-M. PINTEA, and VASILE PALADE. "A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop." Creative Mathematics and Informatics 28, no. 2 (2019): 121–34. http://dx.doi.org/10.37193/cmi.2019.02.04.

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The ultimate goal of the Machine Learning (ML) community is to develop algorithms that can automatically learn from data, to extract knowledge and to make decisions without any human intervention. Specifically, automatic Machine Learning (aML) approaches show impressive success, e.g. in speech/image recognition or autonomous drive and smart car industry. Recent results even demonstrate intriguingly that deep learning applied for automatic classification of skin lesions is on par with the performance of dermatologists, yet outperforms the average human efficiency. As human perception is inheren

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Saokani, Ukan, Mohamad Irfan, Dian Sa'adillah Maylawati, Rachmat Jaenal Abidin, Ichsan Taufik, and Riyan Naufal Hay's. "Comparison of the Fisher-Yates Shuffle and the Linear Congruent Algorithm for Randomizing Questions in Nahwu Learning Multimedia." Khazanah Journal of Religion and Technology 1, no. 1 (2023): 10–14. http://dx.doi.org/10.15575/kjrt.v1i1.159.

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Nahwu Quiz is a basic Arabic learning application that can be played by the public over the age of 12 years. In the question practice menu, there are questions and 4 multiple choice questions. The user only needs to choose one of the multiple choices that the user thinks is correct/matches the question at hand. In one game, there are 5 questions. After answering all these questions, you will immediately see the score. The purpose of developing this application apart from being a medium of entertainment as well as a medium of learning and memory training for game users (users). To make this Nah

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Wongvibulsin, Shannon, Katherine C. Wu, and Scott L. Zeger. "Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation." JMIR Medical Informatics 8, no. 6 (2020): e15791. http://dx.doi.org/10.2196/15791.

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Background Despite the promise of machine learning (ML) to inform individualized medical care, the clinical utility of ML in medicine has been limited by the minimal interpretability and black box nature of these algorithms. Objective The study aimed to demonstrate a general and simple framework for generating clinically relevant and interpretable visualizations of black box predictions to aid in the clinical translation of ML. Methods To obtain improved transparency of ML, simplified models and visual displays can be generated using common methods from clinical practice such as decision trees

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Lu, Li, Yizhong Wu, Qi Zhang, and Ping Qiao. "A Transformation-Based Improved Kriging Method for the Black Box Problem in Reliability-Based Design Optimization." Mathematics 11, no. 1 (2023): 218. http://dx.doi.org/10.3390/math11010218.

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In order to overcome the drawbacks of expensive function evaluation in the practical reliability-based design optimization (RBDO) problem, researchers have proposed the black box-based RBDO method. The algorithm flow of the commonly employed RBDO method for the black box problem consists of the outer construction loop of the surrogate model of the constraint function and the inner surrogate model-based solving loop. To improve the solving ability of the black box RBDO problem, this paper proposes a transformation-based improved kriging method to increase the effectiveness of the two loops iden

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Kerschke, Pascal, and Heike Trautmann. "Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning." Evolutionary Computation 27, no. 1 (2019): 99–127. http://dx.doi.org/10.1162/evco_a_00236.

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In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that, compared to the portfolio's single best solver, on average requires less than half of the resources

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Possatto, André Bina. "Painting the black box white: Interpreting an algorithm-based trading strategy." Brazilian Review of Finance 20, no. 3 (2022): 105–38. http://dx.doi.org/10.12660/rbfin.v20n3.2022.81999.

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Difficulty understanding how a black box model makes predictions undermines machine learning's success in financial markets. We show how to employ model-agnostic methods to carry out machine learning stock market predictions that are more transparent to a human investor. We create long-short investment strategies using a tree-based fundamental analysis. We apply the models to the Brazilian stock market, achieving an out-of-sample expected annual return of 26.4% with a Sharpe ratio of 0.50. Ensembles between the long and short legs improve Sharpe ratio up to 1.26. Our strategy has low asset tur

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Verma, Pulkit, Shashank Rao Marpally, and Siddharth Srivastava. "Asking the Right Questions: Learning Interpretable Action Models Through Query Answering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 12024–33. http://dx.doi.org/10.1609/aaai.v35i13.17428.

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This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act. Our main contributions are a new paradigm for estimating such models using a rudimentary query interface with the agent and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent's internal model in a user-interpretable vocabulary. Empirical evaluation of our approach shows that despite the intractable search space of possible agent models, our approach allows correct and scalable estimation of interpretable agent

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Zhu, Mingzhe, Jie Cheng, Tao Lei, et al. "C-RISE: A Post-Hoc Interpretation Method of Black-Box Models for SAR ATR." Remote Sensing 15, no. 12 (2023): 3103. http://dx.doi.org/10.3390/rs15123103.

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The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field of radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to the limited visual interpretation of their internal feature representation and parameter organization. In this paper, we propose an innovative approach named C-RISE, which builds upon the RISE algorithm to provide a post-hoc interpretation technique for black-box models used in SAR Images Target

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Sudry, Matan, and Erez Karpas. "Learning to Estimate Search Progress Using Sequence of States." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 362–70. http://dx.doi.org/10.1609/icaps.v32i1.19821.

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Many problems of interest can be solved using heuristic search algorithms. When solving a heuristic search problem, we are often interested in estimating search progress, that is, how much longer until we have a solution. Previous work on search progress estimation derived formulas based on some relevant features that can be observed from the behavior of the search algorithm. In this paper, rather than manually deriving such formulas we leverage machine learning to learn more accurate search progress predictors automatically. We train a Long Short-Term Memory (LSTM) network, which takes as inp

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Englert, Peter, and Marc Toussaint. "Learning manipulation skills from a single demonstration." International Journal of Robotics Research 37, no. 1 (2017): 137–54. http://dx.doi.org/10.1177/0278364917743795.

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We consider the scenario where a robot is demonstrated a manipulation skill once and should then use only a few trials on its own to learn to reproduce, optimize, and generalize that same skill. A manipulation skill is generally a high-dimensional policy. To achieve the desired sample efficiency, we need to exploit the inherent structure in this problem. With our approach, we propose to decompose the problem into analytically known objectives, such as motion smoothness, and black-box objectives, such as trial success or reward, depending on the interaction with the environment. The decompositi

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Yuan, Mu, Lan Zhang, and Xiang-Yang Li. "MLink: Linking Black-Box Models for Collaborative Multi-Model Inference." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9475–83. http://dx.doi.org/10.1609/aaai.v36i9.21180.

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The cost efficiency of model inference is critical to real-world machine learning (ML) applications, especially for delay-sensitive tasks and resource-limited devices. A typical dilemma is: in order to provide complex intelligent services (e.g. smart city), we need inference results of multiple ML models, but the cost budget (e.g. GPU memory) is not enough to run all of them. In this work, we study underlying relationships among black-box ML models and propose a novel learning task: model linking. Model linking aims to bridge the knowledge of different black-box models by learning mappings (du

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Wang, Fangwei, Yuanyuan Lu, Changguang Wang, and Qingru Li. "Binary Black-Box Adversarial Attacks with Evolutionary Learning against IoT Malware Detection." Wireless Communications and Mobile Computing 2021 (August 30, 2021): 1–9. http://dx.doi.org/10.1155/2021/8736946.

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5G is about to open Pandora’s box of security threats to the Internet of Things (IoT). Key technologies, such as network function virtualization and edge computing introduced by the 5G network, bring new security threats and risks to the Internet infrastructure. Therefore, higher detection and defense against malware are required. Nowadays, deep learning (DL) is widely used in malware detection. Recently, research has demonstrated that adversarial attacks have posed a hazard to DL-based models. The key issue of enhancing the antiattack performance of malware detection systems that are used to

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Cretu, Andrei. "Learning the Ashby Box: an experiment in second order cybernetic modeling." Kybernetes 49, no. 8 (2019): 2073–90. http://dx.doi.org/10.1108/k-06-2019-0439.

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Purpose W. Ross Ashby’s elementary non-trivial machine, known in the cybernetic literature as the “Ashby Box,” has been described as the prototypical example of a black box system. As far as it can be ascertained from Ashby’s journal, the intended purpose of this device may have been to exemplify the environment where an “artificial brain” may operate. This paper describes the construction of an elementary observer/controller for the class of systems exemplified by the Ashby Box – variable structure black box systems with parallel input. Design/methodology/approach Starting from a formalizatio

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Li, Zun, and Michael Wellman. "Structure Learning for Approximate Solution of Many-Player Games." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 2119–27. http://dx.doi.org/10.1609/aaai.v34i02.5586.

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Games with many players are difficult to solve or even specify without adopting structural assumptions that enable representation in compact form. Such structure is generally not given and will not hold exactly for particular games of interest. We introduce an iterative structure-learning approach to search for approximate solutions of many-player games, assuming only black-box simulation access to noisy payoff samples. Our first algorithm, K-Roles, exploits symmetry by learning a role assignment for players of the game through unsupervised learning (clustering) methods. Our second algorithm,

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Shahpouri, Saeid, Armin Norouzi, Christopher Hayduk, Reza Rezaei, Mahdi Shahbakhti, and Charles Robert Koch. "Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines." Energies 14, no. 23 (2021): 7865. http://dx.doi.org/10.3390/en14237865.

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The standards for emissions from diesel engines are becoming more stringent and accurate emission modeling is crucial in order to control the engine to meet these standards. Soot emissions are formed through a complex process and are challenging to model. A comprehensive analysis of diesel engine soot emissions modeling for control applications is presented in this paper. Physical, black-box, and gray-box models are developed for soot emissions prediction. Additionally, different feature sets based on the least absolute shrinkage and selection operator (LASSO) feature selection method and phys

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Bizzo, Bernardo C., Shadi Ebrahimian, Mark E. Walters, et al. "Validation pipeline for machine learning algorithm assessment for multiple vendors." PLOS ONE 17, no. 4 (2022): e0267213. http://dx.doi.org/10.1371/journal.pone.0267213.

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A standardized objective evaluation method is needed to compare machine learning (ML) algorithms as these tools become available for clinical use. Therefore, we designed, built, and tested an evaluation pipeline with the goal of normalizing performance measurement of independently developed algorithms, using a common test dataset of our clinical imaging. Three vendor applications for detecting solid, part-solid, and groundglass lung nodules in chest CT examinations were assessed in this retrospective study using our data-preprocessing and algorithm assessment chain. The pipeline included tools

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McTavish, Hayden, Chudi Zhong, Reto Achermann, et al. "Fast Sparse Decision Tree Optimization via Reference Ensembles." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9604–13. http://dx.doi.org/10.1609/aaai.v36i9.21194.

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Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady effort since the 1960's, breakthroughs have been made on the problem only within the past few years, primarily on the problem of finding optimal sparse decision trees. However, current state-of-the-art algorithms often require impractical amounts of computation time and memory to find optimal or near-optimal trees for some real-world datasets, part

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Van Calster, Ben, Laure Wynants, Dirk Timmerman, Ewout W. Steyerberg, and Gary S. Collins. "Predictive analytics in health care: how can we know it works?" Journal of the American Medical Informatics Association 26, no. 12 (2019): 1651–54. http://dx.doi.org/10.1093/jamia/ocz130.

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Abstract There is increasing awareness that the methodology and findings of research should be transparent. This includes studies using artificial intelligence to develop predictive algorithms that make individualized diagnostic or prognostic risk predictions. We argue that it is paramount to make the algorithm behind any prediction publicly available. This allows independent external validation, assessment of performance heterogeneity across settings and over time, and algorithm refinement or updating. Online calculators and apps may aid uptake if accompanied with sufficient information. For

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Yin, Yiqiao, and Yash Bingi. "Using Machine Learning to Classify Human Fetal Health and Analyze Feature Importance." BioMedInformatics 3, no. 2 (2023): 280–98. http://dx.doi.org/10.3390/biomedinformatics3020019.

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The reduction of childhood mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time consuming and inefficient, e

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Aslam, Nida, Irfan Ullah Khan, Samiha Mirza, et al. "Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)." Sustainability 14, no. 12 (2022): 7375. http://dx.doi.org/10.3390/su14127375.

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With the expansion of the internet, a major threat has emerged involving the spread of malicious domains intended by attackers to perform illegal activities aiming to target governments, violating privacy of organizations, and even manipulating everyday users. Therefore, detecting these harmful domains is necessary to combat the growing network attacks. Machine Learning (ML) models have shown significant outcomes towards the detection of malicious domains. However, the “black box” nature of the complex ML models obstructs their wide-ranging acceptance in some of the fields. The emergence of Ex

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Soucha, Michal, and Kirill Bogdanov. "Observation Tree Approach: Active Learning Relying on Testing." Computer Journal 63, no. 9 (2019): 1298–310. http://dx.doi.org/10.1093/comjnl/bxz056.

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Abstract The correspondence of active learning and testing of finite-state machines (FSMs) has been known for a while; however, it was not utilized in the learning. We propose a new framework called the observation tree approach that allows one to use the testing theory to improve the performance of active learning. The improvement is demonstrated on three novel learning algorithms that implement the observation tree approach. They outperform the standard learning algorithms, such as the L* algorithm, in the setting where a minimally adequate teacher provides counterexamples. Moreover, they ca

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Patil, Vishakha, Ganesh Ghalme, Vineet Nair, and Y. Narahari. "Achieving Fairness in the Stochastic Multi-Armed Bandit Problem." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5379–86. http://dx.doi.org/10.1609/aaai.v34i04.5986.

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We study an interesting variant of the stochastic multi-armed bandit problem, which we call the Fair-MAB problem, where, in addition to the objective of maximizing the sum of expected rewards, the algorithm also needs to ensure that at any time, each arm is pulled at least a pre-specified fraction of times. We investigate the interplay between learning and fairness in terms of a pre-specified vector denoting the fractions of guaranteed pulls. We define a fairness-aware regret, which we call r-Regret, that takes into account the above fairness constraints and extends the conventional notion of

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Li, Yuancheng, and Yimeng Wang. "Defense Against Adversarial Attacks in Deep Learning." Applied Sciences 9, no. 1 (2018): 76. http://dx.doi.org/10.3390/app9010076.

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Neural networks are very vulnerable to adversarial examples, which threaten their application in security systems, such as face recognition, and autopilot. In response to this problem, we propose a new defensive strategy. In our strategy, we propose a new deep denoising neural network, which is called UDDN, to remove the noise on adversarial samples. The standard denoiser suffers from the amplification effect, in which the small residual adversarial noise gradually increases and leads to misclassification. The proposed denoiser overcomes this problem by using a special loss function, which is

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Samaras, Agorastos-Dimitrios, Serafeim Moustakidis, Ioannis D. Apostolopoulos, Elpiniki Papageorgiou, and Nikolaos Papandrianos. "Uncovering the Black Box of Coronary Artery Disease Diagnosis: The Significance of Explainability in Predictive Models." Applied Sciences 13, no. 14 (2023): 8120. http://dx.doi.org/10.3390/app13148120.

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In recent times, coronary artery disease (CAD) prediction and diagnosis have been the subject of many Medical decision support systems (MDSS) that make use of machine learning (ML) and deep learning (DL) algorithms. The common ground of most of these applications is that they function as black boxes. They reach a conclusion/diagnosis using multiple features as input; however, the user is oftentimes oblivious to the prediction process and the feature weights leading to the eventual prediction. The primary objective of this study is to enhance the transparency and comprehensibility of a black-bo

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Rutten, Daan, and Debankur Mukherjee. "Capacity Scaling Augmented With Unreliable Machine Learning Predictions." ACM SIGMETRICS Performance Evaluation Review 49, no. 2 (2022): 24–26. http://dx.doi.org/10.1145/3512798.3512808.

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Modern data centers suffer from immense power consumption. As a result, data center operators have heavily invested in capacity scaling solutions, which dynamically deactivate servers if the demand is low and activate them again when the workload increases. We analyze a continuoustime model for capacity scaling, where the goal is to minimize the weighted sum of flow-time, switching cost, and power consumption in an online fashion. We propose a novel algorithm, called the Adaptive Balanced Capacity Scaling (ABCS) algorithm, that has access to black-box machine learning predictions. ABCS aims to

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Ott, Simon, Adriano Barbosa-Silva, and Matthias Samwald. "LinkExplorer: predicting, explaining and exploring links in large biomedical knowledge graphs." Bioinformatics 38, no. 8 (2022): 2371–73. http://dx.doi.org/10.1093/bioinformatics/btac068.

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Abstract Summary Machine learning algorithms for link prediction can be valuable tools for hypothesis generation. However, many current algorithms are black boxes or lack good user interfaces that could facilitate insight into why predictions are made. We present LinkExplorer, a software suite for predicting, explaining and exploring links in large biomedical knowledge graphs. LinkExplorer integrates our novel, rule-based link prediction engine SAFRAN, which was recently shown to outcompete other explainable algorithms and established black-box algorithms. Here, we demonstrate highly competiti

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Wang, Yanan, Xuebing Han, Languang Lu, Yangquan Chen, and Minggao Ouyang. "Sensitivity of Fractional-Order Recurrent Neural Network with Encoded Physics-Informed Battery Knowledge." Fractal and Fractional 6, no. 11 (2022): 640. http://dx.doi.org/10.3390/fractalfract6110640.

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In the field of state estimation for the lithium-ion battery (LIB), model-based methods (white box) have been developed to explain battery mechanism and data-driven methods (black box) have been designed to learn battery statistics. Both white box methods and black box methods have drawn much attention recently. As the combination of white box and black box, physics-informed machine learning has been investigated by embedding physic laws. For LIB state estimation, this work proposes a fractional-order recurrent neural network (FORNN) encoded with physics-informed battery knowledge. Three aspec

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Kammüller, Florian, and Dimpy Satija. "Explanation of Student Attendance AI Prediction with the Isabelle Infrastructure Framework." Information 14, no. 8 (2023): 453. http://dx.doi.org/10.3390/info14080453.

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Right from the beginning, attendance has played an important role in the education systems, not only in student success but in the overall interest of the matter. Although all schools try to accentuate good attendance, still some schools find it hard to achieve the required level (96% in UK) of average attendance. The most productive way of increasing the pupils′ attendance rate is to predict when it is going to go down, understand the reasons—why it happened—and act on the affecting factors so as to prevent it. Artificial intelligence (AI) is an automated machine learning solution for differe

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Salih, Dhiadeen Mohammed, Samsul Bahari Mohd Noor, Mohammad Hamiruce Merhaban, and Raja Mohd Kamil. "Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification." Advances in Artificial Intelligence 2015 (September 20, 2015): 1–10. http://dx.doi.org/10.1155/2015/184318.

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A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN) is introduced with OSELM for

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Luong, Ngoc Hoang, Han La Poutré, and Peter A. N. Bosman. "Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning." Evolutionary Computation 26, no. 3 (2018): 471–505. http://dx.doi.org/10.1162/evco_a_00209.

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This article tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the future power demands. Because of many real-world details involved, the structure of the problem is not exploited easily using mathematical programming techniques, for which reason we consider solving this problem with evolutionary algorithms (EAs). We compare three types of EAs for optimizing expansion plans: the classic genetic algorithm (GA), the esti

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Yiğit, Tuncay, Nilgün Şengöz, Özlem Özmen, Jude Hemanth, and Ali Hakan Işık. "Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning." Traitement du Signal 39, no. 3 (2022): 863–69. http://dx.doi.org/10.18280/ts.390311.

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Artificial intelligence holds great promise in medical imaging, especially histopathological imaging. However, artificial intelligence algorithms cannot fully explain the thought processes during decision-making. This situation has brought the problem of explainability, i.e., the black box problem, of artificial intelligence applications to the agenda: an algorithm simply responds without stating the reasons for the given images. To overcome the problem and improve the explainability, explainable artificial intelligence (XAI) has come to the fore, and piqued the interest of many researchers. A

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Du, Xiaohu, Jie Yu, Zibo Yi, et al. "A Hybrid Adversarial Attack for Different Application Scenarios." Applied Sciences 10, no. 10 (2020): 3559. http://dx.doi.org/10.3390/app10103559.

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Adversarial attack against natural language has been a hot topic in the field of artificial intelligence security in recent years. It is mainly to study the methods and implementation of generating adversarial examples. The purpose is to better deal with the vulnerability and security of deep learning systems. According to whether the attacker understands the deep learning model structure, the adversarial attack is divided into black-box attack and white-box attack. In this paper, we propose a hybrid adversarial attack for different application scenarios. Firstly, we propose a novel black-box

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Saleem, Sobia, Marcus Gallagher, and Ian Wood. "Direct Feature Evaluation in Black-Box Optimization Using Problem Transformations." Evolutionary Computation 27, no. 1 (2019): 75–98. http://dx.doi.org/10.1162/evco_a_00247.

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Exploratory Landscape Analysis provides sample-based methods to calculate features of black-box optimization problems in a quantitative and measurable way. Many problem features have been proposed in the literature in an attempt to provide insights into the structure of problem landscapes and to use in selecting an effective algorithm for a given optimization problem. While there has been some success, evaluating the utility of problem features in practice presents some significant challenges. Machine learning models have been employed as part of the evaluation process, but they may require ad

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Barkalov, Konstantin, Ilya Lebedev, and Evgeny Kozinov. "Acceleration of Global Optimization Algorithm by Detecting Local Extrema Based on Machine Learning." Entropy 23, no. 10 (2021): 1272. http://dx.doi.org/10.3390/e23101272.

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This paper features the study of global optimization problems and numerical methods of their solution. Such problems are computationally expensive since the objective function can be multi-extremal, nondifferentiable, and, as a rule, given in the form of a “black box”. This study used a deterministic algorithm for finding the global extremum. This algorithm is based neither on the concept of multistart, nor nature-inspired algorithms. The article provides computational rules of the one-dimensional algorithm and the nested optimization scheme which could be applied for solving multidimensional

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