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Journal articles on the topic 'Adaptive machine learning'

To see the other types of publications on this topic, follow the link: Adaptive machine learning.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Wilson, Craig, Yuheng Bu, and Venugopal V. Veeravalli. "Adaptive sequential machine learning." Sequential Analysis 38, no. 4 (October 2, 2019): 545–68. http://dx.doi.org/10.1080/07474946.2019.1686889.

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Wang, Gai-Ge, Mei Lu, Yong-Quan Dong, and Xiang-Jun Zhao. "Self-adaptive extreme learning machine." Neural Computing and Applications 27, no. 2 (March 21, 2015): 291–303. http://dx.doi.org/10.1007/s00521-015-1874-3.

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3

Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (April 1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.

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SUMMARYToday's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space. A learning control scheme is presented that utilizes the sensory information to enhance machine performance in the next trial. An adaptive scheme is proposed for the modification of learning gain matrices, and is implemented on an industrial robot. Experimental results verify the potentials of the proposed adaptive learning scheme, and illustrate how it can be used for improvement of different manufacturing processes.

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Mundhe, Shivani. "Image Segmentation using Adaptive Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (May 31, 2021): 948–50. http://dx.doi.org/10.22214/ijraset.2021.34383.

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Hie, Brian L., and Kevin K. Yang. "Adaptive machine learning for protein engineering." Current Opinion in Structural Biology 72 (February 2022): 145–52. http://dx.doi.org/10.1016/j.sbi.2021.11.002.

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Majumdar, Somshubra, Ishaan Jain, and Kunal Kukreja. "AdaSort: Adaptive Sorting using Machine Learning." International Journal of Computer Applications 145, no. 12 (July 15, 2016): 12–17. http://dx.doi.org/10.5120/ijca2016910726.

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Stange, Renata Luiza, and Joao Jose. "Applying Adaptive Technology in Machine Learning." IEEE Latin America Transactions 12, no. 7 (October 2014): 1298–306. http://dx.doi.org/10.1109/tla.2014.6948866.

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Cao, Jiuwen, Zhiping Lin, and Guang-Bin Huang. "Self-Adaptive Evolutionary Extreme Learning Machine." Neural Processing Letters 36, no. 3 (July 18, 2012): 285–305. http://dx.doi.org/10.1007/s11063-012-9236-y.

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Hasan, Sajib. "Adaptive Fitts for Adaptive Interface." AIUB Journal of Science and Engineering (AJSE) 17, no. 2 (July 31, 2018): 51–58. http://dx.doi.org/10.53799/ajse.v17i2.9.

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Adaptive interface would enable Human Computer Interaction apply machine learning to cope with human carelessness (mistakes), understand user performance level and provide an interaction interface accordingly. This study tends to translate the theoretical issues of human task into working model by investigating and implementing the predicting equation of human psychomotor behavior to a rapid and aimed movement, developed by Paul Fitt in 1954. The study finds logarithmic speed-accuracy trade-off and predict user performance in a common task “point-select” using common input device mouse. The performance of user is visualized as an evidence and this visualization make a valuable step toward understanding the change required in user interface to make the interface adaptive and consistent. It proposed a method of calculating the amount of change required through learning; add extension to the theory of machine intelligence and increase knowledge of Fitts applicability in terms of machine learning.

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Chabaud, Ulysse, Damian Markham, and Adel Sohbi. "Quantum machine learning with adaptive linear optics." Quantum 5 (July 5, 2021): 496. http://dx.doi.org/10.22331/q-2021-07-05-496.

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We study supervised learning algorithms in which a quantum device is used to perform a computational subroutine – either for prediction via probability estimation, or to compute a kernel via estimation of quantum states overlap. We design implementations of these quantum subroutines using Boson Sampling architectures in linear optics, supplemented by adaptive measurements. We then challenge these quantum algorithms by deriving classical simulation algorithms for the tasks of output probability estimation and overlap estimation. We obtain different classical simulability regimes for these two computational tasks in terms of the number of adaptive measurements and input photons. In both cases, our results set explicit limits to the range of parameters for which a quantum advantage can be envisaged with adaptive linear optics compared to classical machine learning algorithms: we show that the number of input photons and the number of adaptive measurements cannot be simultaneously small compared to the number of modes. Interestingly, our analysis leaves open the possibility of a near-term quantum advantage with a single adaptive measurement.

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Zhang, Yuao, Qingbiao Wu, and Jueliang Hu. "An Adaptive Learning Algorithm for Regularized Extreme Learning Machine." IEEE Access 9 (2021): 20736–45. http://dx.doi.org/10.1109/access.2021.3054483.

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Timothy, Adeboje Olawale, Isiaka Abdulwab, Jimoh Ibraheem Temitope, and Joda Shade. "Reviews on Machine Learning based Adaptive Mobile Learning System." International Journal of Computer Applications 176, no. 38 (July 15, 2020): 1–6. http://dx.doi.org/10.5120/ijca2020920498.

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Balachandran, Prasanna V. "Adaptive machine learning for efficient materials design." MRS Bulletin 45, no. 7 (July 2020): 579–86. http://dx.doi.org/10.1557/mrs.2020.163.

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K, Dr Vijayakumar, Rahul M M, Dhamodara Prasath G, and Aravinth V. "Adaptive Air Quality Sensing using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 423–30. http://dx.doi.org/10.22214/ijraset.2022.42186.

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Abstract: Air pollution is a worldwide problem having impacts on both local and global scales. According to the World Health Organization (WHO), air pollution causes 7 million deaths every year, with 4.2 million attributed to exposure to outdoor air pollution. Compared to the reference methods defined in the Air Quality Directive, the use of low-cost air quality sensors for monitoring ambient air pollution would reduce air pollution monitoring costs and would also allow larger spatial coverage especially in remote areas where monitoring with traditional facilities is uneasy. Theses multi-sensors were either calibrated against standard gas mixtures or using artificial neural network under field conditions. The later method resulted in mixed results either satisfactory for short periods or generally weak for longer data series. This project mainly focuses on the adaptive calibration of the low-cost sensors using the trained machine learning model. The performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques will be compared. A cluster of ozone, nitrogen dioxide, nitrogen monoxide, carbon monoxide and carbon dioxide sensors will be operated. Subsequently, the accuracy of the predicted values will be evaluated for about a period. These predicted values are fed through the training model and the model will run accordingly and it will adapt all the error situations. This will be useful to the automobile industry in the current situation for smoke emission control and also for many refinery industries in which low-cost sensors can be used with the following modelling for the higher accuracy. This would reduce the cost and also yields more accuracy beyond the varying situation. Keywords: 1) Low-Cost Sensors (LCS) 2) Internet of Things (IoT)

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Gheibi, Omid, Danny Weyns, and Federico Quin. "Applying Machine Learning in Self-adaptive Systems." ACM Transactions on Autonomous and Adaptive Systems 15, no. 3 (September 30, 2020): 1–37. http://dx.doi.org/10.1145/3469440.

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Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analyzing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such an overview is important for researchers to understand the state of the art and direct future research efforts. This article reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6,709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression, and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review, we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.

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Ma, Jun, and Chao Yuan. "Adaptive Safe Semi-Supervised Extreme Machine Learning." IEEE Access 7 (2019): 76176–84. http://dx.doi.org/10.1109/access.2019.2922385.

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Horowitz, Roberto. "Learning Control of Robot Manipulators." Journal of Dynamic Systems, Measurement, and Control 115, no. 2B (June 1, 1993): 402–11. http://dx.doi.org/10.1115/1.2899080.

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Learning control encompasses a class of control algorithms for programmable machines such as robots which attain, through an iterative process, the motor dexterity that enables the machine to execute complex tasks. In this paper we discuss the use of function identification and adaptive control algorithms in learning controllers for robot manipulators. In particular, we discuss the similarities and differences between betterment learning schemes, repetitive controllers and adaptive learning schemes based on integral transforms. The stability and convergence properties of adaptive learning algorithms based on integral transforms are highlighted and experimental results illustrating some of these properties are presented.

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Halimovich, Yuldashev Abdusamat, and Ermatov Axror Baxtiyorjon Ogli. "Increasing Learning Efficiency Using Adaptive Testing Technology." American Journal of Engineering And Techonology 03, no. 02 (February 17, 2021): 31–41. http://dx.doi.org/10.37547/tajet/volume03issue02-05.

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Purpose: The article describes a set of software developed for adaptive testing technology in the implementation of an objective assessment of students' knowledge. There is also information about the possibility of computerizing education, reducing the unproductive live work of teachers, preserving the methodological potential of experienced professors, installing computer software for management. Methods: It is noted that the experiments were carried out by 2nd year students of the Andijan Machine-Building Institute in the direction of "Ground transport systems and their operation." Results: The research results are presented in the form of some data by means of mathematical statistical processing. The Pearson, Kolmagorov and Romanovsky criteria were also used to check the accuracy of the study results. Conclusion: it is stated that a software package aimed at creating a technology for remote and adaptive testing without the participation of the human factor will allow processing the results of experiments in educational and research centers and achieving them in practice.

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Haque, Md Enamul, and Talal M. Alkharobi. "Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms." International Journal of Machine Learning and Computing 5, no. 1 (February 2015): 17–23. http://dx.doi.org/10.7763/ijmlc.2015.v5.476.

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Golden, Richard M. "Adaptive Learning Algorithm Convergence in Passive and Reactive Environments." Neural Computation 30, no. 10 (October 2018): 2805–32. http://dx.doi.org/10.1162/neco_a_01117.

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Although the number of artificial neural network and machine learning architectures is growing at an exponential pace, more attention needs to be paid to theoretical guarantees of asymptotic convergence for novel, nonlinear, high-dimensional adaptive learning algorithms. When properly understood, such guarantees can guide the algorithm development and evaluation process and provide theoretical validation for a particular algorithm design. For many decades, the machine learning community has widely recognized the importance of stochastic approximation theory as a powerful tool for identifying explicit convergence conditions for adaptive learning machines. However, the verification of such conditions is challenging for multidisciplinary researchers not working in the area of stochastic approximation theory. For this reason, this letter presents a new stochastic approximation theorem for both passive and reactive learning environments with assumptions that are easily verifiable. The theorem is widely applicable to the analysis and design of important machine learning algorithms including deep learning algorithms with multiple strict local minimizers, Monte Carlo expectation-maximization algorithms, contrastive divergence learning in Markov fields, and policy gradient reinforcement learning.

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Ghana, Satyajit, Shikhar Singh, Aryan Jalali, Vivek Badani, and Sahana P. Shankar. "Adaptive Visual Learning Using Augmented Reality and Machine Learning Techniques." Journal of Computational and Theoretical Nanoscience 17, no. 11 (November 1, 2020): 4952–56. http://dx.doi.org/10.1166/jctn.2020.8982.

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The current curriculum forces students to understand topics by visualizing axonometric structures in their cognitive minds depending upon the conceptual texts and information. This methodology is inconsistent as the idea of visualization through conceptual knowledge is dependent on the level of reasoning and IQ (Intelligence Quotient) a student possesses. It is usually common for a student to misinterpret an information due to lack of reasoning and imaginative skills. Our educational model aims to diminish this intellectual barrier by incorporating Augmented Reality (AR) and Machine Learning (ML) techniques together and create an Adaptive Visual Learning experience for students. A mobile interface with OCR (Optical Character Recognition) and TTS (Text-To-Speech) feature is given to make this whole process simple and easy to use for any student. In this paper, two ML techniques Logistic Regression and Neural Network are applied in order to enhance and modify the existing educational system by removing the intellectual barrier involved due to neurodiversity. A comparative study is performed between the two ML algorithms, where in Logistic Regression performed better than the Neural Network. This form of adaptive visual learning aims to boost student performance in academia.

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Rodríguez-Gracia, Diego, José A. Piedra-Fernández, Luis Iribarne, Javier Criado, Rosa Ayala, Joaquín Alonso-Montesinos, and Capobianco-Uriarte Maria de las Mercedes. "Microservices and Machine Learning Algorithms for Adaptive Green Buildings." Sustainability 11, no. 16 (August 9, 2019): 4320. http://dx.doi.org/10.3390/su11164320.

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In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings.

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Mannaru, Pujitha, Balakumar Balasingam, Krishna Pattipati, Ciara Sibley, and Joseph Coyne. "Cognitive Context Detection for Adaptive Automation." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 223–27. http://dx.doi.org/10.1177/1541931213601050.

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An important research challenge in Human Machine Systems (HMS) is to create machines that are able to better understand human behavior so that the overall efficiency of the HMS can be enhanced through increased productivity and reduced safety risk. The research question posed in this paper is the following: Can an understanding of physiological behavior of humans be combined with statistical machine learning theory to develop predictive models that are able to accurately predict the cognitive difficulty experienced by humans? In this paper, we answer this question in the affirmative by demonstrating the use of two physiological measurements, pupil dilation and eye-gaze patterns, as indices of cognitive workload. Specifically, we demonstrate the possibility of cognitive context detection through machine learning and classification using eye-tracking data from NRL’s Supervisory Control Operations User Testbed (SCOUTTM), a flexible simulation environment that represents the tasks that a future UAS operator would engage in, while controlling multiple UAS.

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Csaji, B. C., and L. Monostori. "Adaptive Stochastic Resource Control: A Machine Learning Approach." Journal of Artificial Intelligence Research 32 (June 25, 2008): 453–86. http://dx.doi.org/10.1613/jair.2548.

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The paper investigates stochastic resource allocation problems with scarce, reusable resources and non-preemtive, time-dependent, interconnected tasks. This approach is a natural generalization of several standard resource management problems, such as scheduling and transportation problems. First, reactive solutions are considered and defined as control policies of suitably reformulated Markov decision processes (MDPs). We argue that this reformulation has several favorable properties, such as it has finite state and action spaces, it is aperiodic, hence all policies are proper and the space of control policies can be safely restricted. Next, approximate dynamic programming (ADP) methods, such as fitted Q-learning, are suggested for computing an efficient control policy. In order to compactly maintain the cost-to-go function, two representations are studied: hash tables and support vector regression (SVR), particularly, nu-SVRs. Several additional improvements, such as the application of limited-lookahead rollout algorithms in the initial phases, action space decomposition, task clustering and distributed sampling are investigated, too. Finally, experimental results on both benchmark and industry-related data are presented.

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Donné, Simon, Jonas De Vylder, Bart Goossens, and Wilfried Philips. "MATE: Machine Learning for Adaptive Calibration Template Detection." Sensors 16, no. 11 (November 4, 2016): 1858. http://dx.doi.org/10.3390/s16111858.

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Park, Jingyeong, Daisuke Kodaira, Kofi Afrifa Agyeman, Taeyoung Jyung, and Sekyung Han. "Adaptive Power Flow Prediction Based on Machine Learning." Energies 14, no. 13 (June 25, 2021): 3842. http://dx.doi.org/10.3390/en14133842.

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Power flow analysis is an inevitable methodology in the planning and operation of the power grid. It has been performed for the transmission system, however, along with the penetration of the distributed energy resources, the target has been expanded to the distribution system as well. However, it is not easy to apply the conventional method to the distribution system since the essential information for the power flow analysis, say the impedance and the topology, are not available for the distribution system. To this end, this paper proposes an alternative method based on practically available parameters at the terminal nodes without the precedent information. Since the available information is different between high-voltage and low-voltage systems, we develop two various machine learning schemes. Specifically, the high-voltage model incorporates the slack node voltage, which can be practically obtained at the substation, and yields a time-invariant model. On the other hand, the low voltage model utilizes the deviation of voltages at each node for the power changes, subsequently resulting in a time-varying model. The performance of the suggested models is also verified using numerical simulations. The results are analyzed and compared with another power flow scheme for the distribution system that the authors suggested beforehand.

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Kershaw, Joseph, Rui Yu, YuMing Zhang, and Peng Wang. "Hybrid machine learning-enabled adaptive welding speed control." Journal of Manufacturing Processes 71 (November 2021): 374–83. http://dx.doi.org/10.1016/j.jmapro.2021.09.023.

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Pridhiviraj, Paidipeddi, Tomar Dheerendra S, and P. Muralidhar. "Adaptive Post-silicon Server Validation using Machine Learning." International Journal of Applied Information Systems 9, no. 1 (June 6, 2015): 24–32. http://dx.doi.org/10.5120/ijais15-451358.

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Lin, Hengwei, Kai Sun, Zheng-Hua Tan, Chengxi Liu, Josep M. Guerrero, and Juan C. Vasquez. "Adaptive protection combined with machine learning for microgrids." IET Generation, Transmission & Distribution 13, no. 6 (March 26, 2019): 770–79. http://dx.doi.org/10.1049/iet-gtd.2018.6230.

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Yang, Ping, Yue Xiao, Ming Xiao, Yong Liang Guan, Shaoqian Li, and Wei Xiang. "Adaptive Spatial Modulation MIMO Based on Machine Learning." IEEE Journal on Selected Areas in Communications 37, no. 9 (September 2019): 2117–31. http://dx.doi.org/10.1109/jsac.2019.2929404.

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Anagnostopoulos, Theodoros, Christos Anagnostopoulos, and Stathes Hadjiefthymiades. "An Adaptive Machine Learning Algorithm for Location Prediction." International Journal of Wireless Information Networks 18, no. 2 (April 23, 2011): 88–99. http://dx.doi.org/10.1007/s10776-011-0142-4.

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Rajput, Savita, Apurva Ware, Karan Umredkar, and Prof Jaya Jeshwani. "Digital Watermarking Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2081–85. http://dx.doi.org/10.22214/ijraset.2022.40991.

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Abstract: Digital watermarking is a technique used for the information of the images that provides security for the confidentiality. The repetitions of the multimedia objects (i.e. audio, video, text, etc.) have been protected by some of the developed digital watermarking techniques. Digital Watermarking is the process of concealing messages in digital contents in order to verify the rightful owner of the copyright protection. In this paper we have proposed a method that would assist its users to embed a watermark to the cover image based on an adaptive approach in a much robust way while maintaining the quality of the cover image. The implementation of this algorithm is based upon cascading the features of DWT and PCA using Bhattacharyya distance and Kurtosis. PCA decompose and compress the watermark, which results in better PSNR and NCC values for the tested images. The proposed algorithm uses Bhattacharyya distance and Kurtosis to detect the scaling and embedding factors making it adaptive to the input image rather than providing constant value. Also, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the performance. Keywords: Digital watermarking, DWT-PCA, PSNR, Image denoising, convolutional neural network, DnCNN, residual learning, batch normalization.

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Rajput, Savita, Apurva Ware, Karan Umredkar, and Prof Jaya Jeshwani. "Digital Watermarking Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2081–85. http://dx.doi.org/10.22214/ijraset.2022.40991.

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Abstract: Digital watermarking is a technique used for the information of the images that provides security for the confidentiality. The repetitions of the multimedia objects (i.e. audio, video, text, etc.) have been protected by some of the developed digital watermarking techniques. Digital Watermarking is the process of concealing messages in digital contents in order to verify the rightful owner of the copyright protection. In this paper we have proposed a method that would assist its users to embed a watermark to the cover image based on an adaptive approach in a much robust way while maintaining the quality of the cover image. The implementation of this algorithm is based upon cascading the features of DWT and PCA using Bhattacharyya distance and Kurtosis. PCA decompose and compress the watermark, which results in better PSNR and NCC values for the tested images. The proposed algorithm uses Bhattacharyya distance and Kurtosis to detect the scaling and embedding factors making it adaptive to the input image rather than providing constant value. Also, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the performance. Keywords: Digital watermarking, DWT-PCA, PSNR, Image denoising, convolutional neural network, DnCNN, residual learning, batch normalization.

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Spagnolo, Nicolò, Alessandro Lumino, Emanuele Polino, Adil S. Rab, Nathan Wiebe, and Fabio Sciarrino. "Machine Learning for Quantum Metrology." Proceedings 12, no. 1 (August 23, 2019): 28. http://dx.doi.org/10.3390/proceedings2019012028.

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Phase estimation represents a significant example to test the application of quantum theory for enhanced measurements of unknown physical parameters. Several recipes have been developed, allowing to define strategies to reach the ultimate bounds in the asymptotic limit of a large number of trials. However, in certain applications it is crucial to reach such bound when only a small number of probes is employed. Here, we discuss an asymptotically optimal, machine learning based, adaptive single-photon phase estimation protocol that allows us to reach the standard quantum limit when a very limited number of photons is employed.

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Lee, Juhee, and Bongsoon Kang. "Improving Performance of Machine Learning-based Algorithms with Adaptive Learning Rate." Journal of Korean Institute of Information Technology 18, no. 10 (October 31, 2020): 9–14. http://dx.doi.org/10.14801/jkiit.2020.18.10.9.

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ADNAN, Muhammad, Asad HABIB, Jawad ASHRAF, and Shafaq MUSSADIQ. "Cloud-supported machine learning system for context-aware adaptive M-learning." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 27, no. 4 (July 26, 2019): 2798–816. http://dx.doi.org/10.3906/elk-1811-196.

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Settles, Burr, Geoffrey T. LaFlair, and Masato Hagiwara. "Machine Learning–Driven Language Assessment." Transactions of the Association for Computational Linguistics 8 (July 2020): 247–63. http://dx.doi.org/10.1162/tacl_a_00310.

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We describe a method for rapidly creating language proficiency assessments, and provide experimental evidence that such tests can be valid, reliable, and secure. Our approach is the first to use machine learning and natural language processing to induce proficiency scales based on a given standard, and then use linguistic models to estimate item difficulty directly for computer-adaptive testing. This alleviates the need for expensive pilot testing with human subjects. We used these methods to develop an online proficiency exam called the Duolingo English Test, and demonstrate that its scores align significantly with other high-stakes English assessments. Furthermore, our approach produces test scores that are highly reliable, while generating item banks large enough to satisfy security requirements.

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Nguyen, Tran-Hieu, and Anh-Tuan Vu. "Evaluating structural safety of trusses using Machine Learning." Frattura ed Integrità Strutturale 15, no. 58 (September 25, 2021): 308–18. http://dx.doi.org/10.3221/igf-esis.58.23.

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In this paper, a machine learning-based framework is developed to quickly evaluate the structural safety of trusses. Three numerical examples of a 10-bar truss, a 25-bar truss, and a 47-bar truss are used to illustrate the proposed framework. Firstly, several truss cases with different cross-sectional areas are generated by employing the Latin Hypercube Sampling method. Stresses inside truss members as well as displacements of nodes are determined through finite element analyses and obtained values are compared with design constraints. According to the constraint verification, the safety state is assigned as safe or unsafe. Members’ sectional areas and the safety state are stored as the inputs and outputs of the training dataset, respectively. Three popular machine learning classifiers including Support Vector Machine, Deep Neural Network, and Adaptive Boosting are used for evaluating the safety of structures. The comparison is conducted based on two metrics: the accuracy and the area under the ROC curve. For the two first examples, three classifiers get more than 90% of accuracy. For the 47-bar truss, the accuracies of the Support Vector Machine model and the Deep Neural Network model are lower than 70% but the Adaptive Boosting model still retains the high accuracy of approximately 98%. In terms of the area under the ROC curve, the comparative results are similar. Overall, the Adaptive Boosting model outperforms the remaining models. In addition, an investigation is carried out to show the influence of the parameters on the performance of the Adaptive Boosting model.

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Pertseva, Margarita, Beichen Gao, Daniel Neumeier, Alexander Yermanos, and Sai T. Reddy. "Applications of Machine and Deep Learning in Adaptive Immunity." Annual Review of Chemical and Biomolecular Engineering 12, no. 1 (June 7, 2021): 39–62. http://dx.doi.org/10.1146/annurev-chembioeng-101420-125021.

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Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide–MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.

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Zhang, Tong, Wei Ye, Baosong Yang, Long Zhang, Xingzhang Ren, Dayiheng Liu, Jinan Sun, Shikun Zhang, Haibo Zhang, and Wen Zhao. "Frequency-Aware Contrastive Learning for Neural Machine Translation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11712–20. http://dx.doi.org/10.1609/aaai.v36i10.21426.

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Low-frequency word prediction remains a challenge in modern neural machine translation (NMT) systems. Recent adaptive training methods promote the output of infrequent words by emphasizing their weights in the overall training objectives. Despite the improved recall of low-frequency words, their prediction precision is unexpectedly hindered by the adaptive objectives. Inspired by the observation that low-frequency words form a more compact embedding space, we tackle this challenge from a representation learning perspective. Specifically, we propose a frequency-aware token-level contrastive learning method, in which the hidden state of each decoding step is pushed away from the counterparts of other target words, in a soft contrastive way based on the corresponding word frequencies. We conduct experiments on widely used NIST Chinese-English and WMT14 English-German translation tasks. Empirical results show that our proposed methods can not only significantly improve the translation quality but also enhance lexical diversity and optimize word representation space. Further investigation reveals that, comparing with related adaptive training strategies, the superiority of our method on low-frequency word prediction lies in the robustness of token-level recall across different frequencies without sacrificing precision.

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Golz, Martin, Sebastian Thomas, and Adolf Schenka. "EEG-Based Classification of the Driver Alertness State." Current Directions in Biomedical Engineering 6, no. 3 (September 1, 2020): 353–56. http://dx.doi.org/10.1515/cdbme-2020-3091.

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AbstractGMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning with an adaptive metric. While training, the prototype vectors as well as the weight matrix of the metric are adapted simultaneously. The method is presented in more detail and compared with other machine learning methods employing a fixed metric. It was investigated how accurately the methods can assign the 6-channel EEG of 25 young drivers, who drove overnight in the simulation lab, to the two classes of mild and severe drowsiness. Results of cross-validation show that GMLVQ is at 81.7 ± 1.3 % mean classification accuracy. It is not as accurate as support-vector machines (SVM) and gradient boosting machines (GBM) and cannot exploit the potential of learning adaptive metrics in the case of EEG data. However, information is provided on the relevance of each signal feature from the weighting matrix.

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Manuaba, IBG, Muhammad Abdillah, Ramon Zamora, and Herlambang Setiadi. "Adaptive Power System Stabilizer Using Kernel Extreme Learning Machine." International Journal of Intelligent Engineering and Systems 14, no. 3 (June 30, 2021): 468–80. http://dx.doi.org/10.22266/ijies2021.0630.39.

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Mogyorosi, Ferenc, Alija Pasic, Richard Cziva, Peter Revisnyei, Zsolt Kenesi, and Janos Tapolcai. "Adaptive Protection of Scientific Backbone Networks Using Machine Learning." IEEE Transactions on Network and Service Management 18, no. 1 (March 2021): 1064–76. http://dx.doi.org/10.1109/tnsm.2021.3050964.

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Frye, M., D. Gyulai, J. Bergmann, and R. H. Schmitt. "ADAPTIVE SCHEDULING THROUGH MACHINE LEARNING-BASED PROCESS PARAMETER PREDICTION." MM Science Journal 2019, no. 04 (November 13, 2019): 3060–66. http://dx.doi.org/10.17973/mmsj.2019_11_2019051.

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Damdoo, Rina. "Adaptive Hand Gesture Recognition System Using Machine Learning Approach." Bioscience Biotechnology Research Communications 13, no. 14 (December 25, 2020): 106–10. http://dx.doi.org/10.21786/bbrc/13.14/26.

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Yao, Zhewei, Amir Gholami, Sheng Shen, Mustafa Mustafa, Kurt Keutzer, and Michael Mahoney. "ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10665–73. http://dx.doi.org/10.1609/aaai.v35i12.17275.

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Incorporating second-order curvature information into machine learning optimization algorithms can be subtle, and doing so naïvely can lead to high per-iteration costs associated with forming the Hessian and performing the associated linear system solve. To address this, we introduce ADAHESSIAN, a new stochastic optimization algorithm. ADAHESSIAN directly incorporates approximate curvature information from the loss function, and it includes several novel performance-improving features, including: (i) a fast Hutchinson based method to approximate the curvature matrix with low computational overhead; (ii) a spatial averaging to reduce the variance of the second derivative; and (iii) a root-mean-square exponential moving average to smooth out variations of the second-derivative across different iterations. We perform extensive tests on NLP, CV, and recommendation system tasks, and ADAHESSIAN achieves state-of-the-art results. In particular, we find that ADAHESSIAN: (i) outperforms AdamW for transformers by0.13/0.33 BLEU score on IWSLT14/WMT14, 2.7/1.0 PPLon PTB/Wikitext-103; (ii) outperforms AdamW for Squeeze-Bert by 0.41 points on GLUE; (iii) achieves 1.45%/5.55%higher accuracy on ResNet32/ResNet18 on Cifar10/ImageNetas compared to Adam; and (iv) achieves 0.032% better score than Adagrad for DLRM on the Criteo Ad Kaggle dataset. The cost per iteration of ADAHESSIANis comparable to first-order methods, and ADAHESSIAN exhibits improved robustness towards variations in hyperparameter values. The code for ADAHESSIAN is open-sourced and publicly-available [1].

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Liu, Qidi, Benjamin Gily, and Mable P. Fok. "Adaptive Photonic Microwave Instantaneous Frequency Estimation Using Machine Learning." IEEE Photonics Technology Letters 33, no. 24 (December 15, 2021): 1511–14. http://dx.doi.org/10.1109/lpt.2021.3128867.

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Hu, Rongyao, Leyuan Zhang, and Jian Wei. "Adaptive Laplacian Support Vector Machine for Semi-supervised Learning." Computer Journal 64, no. 7 (April 30, 2021): 1005–15. http://dx.doi.org/10.1093/comjnl/bxab024.

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Abstract Laplacian support vector machine (LapSVM) is an extremely popular classification method and relies on a small number of labels and a Laplacian regularization to complete the training of the support vector machine (SVM). However, the training of SVM model and Laplacian matrix construction are usually two independent process. Therefore, In this paper, we propose a new adaptive LapSVM method to realize semi-supervised learning with a primal solution. Specifically, the hinge loss of unlabelled data is considered to maximize the distance between unlabelled samples from different classes and the process of dealing with labelled data are similar to other LapSVM methods. Besides, the proposed method embeds the Laplacian matrix acquisition into the SVM training process to improve the effectiveness of Laplacian matrix and the accuracy of new SVM model. Moreover, a novel optimization algorithm considering primal solver is proposed to our adaptive LapSVM model. Experimental results showed that our method outperformed all comparison methods in terms of different evaluation metrics on both real datasets and synthetic datasets.

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Wu, Zi-Nan, Xiao-Lei Han, An He, Yan-Fei Cai, and Jing Ji. "Machine learning-based adaptive degradation model for RC beams." Engineering Structures 253 (February 2022): 113817. http://dx.doi.org/10.1016/j.engstruct.2021.113817.

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Ruiz, P. M., J. A. Botia, and A. Gomez-Skarmeta. "Providing QoS Through Machine-Learning-Driven Adaptive Multimedia Applications." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 3 (June 2004): 1398–411. http://dx.doi.org/10.1109/tsmcb.2004.825912.

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