Journal articles: 'Systems for Machine Learning'

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Molino, Piero, and Christopher Ré. "Declarative machine learning systems." Communications of the ACM 65, no. 1 (January 2022): 42–49. http://dx.doi.org/10.1145/3475167.

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Molino, Piero, and Christopher Ré. "Declarative Machine Learning Systems." Queue 19, no. 3 (June 30, 2021): 46–76. http://dx.doi.org/10.1145/3475965.3479315.

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The people training and using ML models now are typically experienced developers with years of study working within large organizations, but the next wave of ML systems should allow a substantially larger number of people, potentially without any coding skills, to perform the same tasks. These new ML systems will not require users to fully understand all the details of how models are trained and used for obtaining predictions, but will provide them a more abstract interface that is less demanding and more familiar. Declarative interfaces are well-suited for this goal, by hiding complexity and favoring separation of interest, and ultimately leading to increased productivity.

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Schneier, Bruce. "Attacking Machine Learning Systems." Computer 53, no. 5 (May 2020): 78–80. http://dx.doi.org/10.1109/mc.2020.2980761.

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Litz, Heiner, and Milad Hashemi. "Machine Learning for Systems." IEEE Micro 40, no. 5 (September 1, 2020): 6–7. http://dx.doi.org/10.1109/mm.2020.3016551.

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Sidorov, Denis, Fang Liu, and Yonghui Sun. "Machine Learning for Energy Systems." Energies 13, no. 18 (September 10, 2020): 4708. http://dx.doi.org/10.3390/en13184708.

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The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking and improvement of energy systems due to the progress in heterogeneous data acquisition, data fusion, numerical methods, machine learning, and high-performance computing. The editor of this special issue has made an attempt to publish a book containing original contributions addressing theory and various applications of machine learning in energy systems’ operation, monitoring, and design. The response to our call had 27 submissions from 11 countries (Brazil, Canada, China, Denmark, Germany, Russia, Saudi Arabia, South Korea, Taiwan, UK, and USA), of which 12 were accepted and 15 were rejected. This issue contains 11 technical articles, one review, and one editorial. It covers a broad range of topics including reliability of power systems analysis, power quality issues in railway electrification systems, test systems of transformer oil, industrial control problems in metallurgy, power control for wind turbine fatigue balancing, advanced methods for forecasting of PV output power as well as wind speed and power, control of the AC/DC hybrid power systems with renewables and storage systems, electric-gas energy systems’ risk assessment, battery’s degradation status prediction, insulators fault forecasting, and autonomous energy coordination using blockchain-based negotiation model. In addition, review of the blockchain technology for information security of the energy internet is given. We believe that this special issue will be of interest not only to academics and researchers, but also to all the engineers who are seriously concerned about the unsolved problems in contemporary power engineering, multi-energy microgrids modeling.

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Et. al., Mathew Chacko,. "Cyber-Physical Quality Systems in Manufacturing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 2006–18. http://dx.doi.org/10.17762/turcomat.v12i2.1805.

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Digital Twin-based Cyber-Physical Quality System (DT-CPQS) concept involves automated quality checking, simulation, and prediction of manufacturing operations to improve production efficiency and flexibility as part of Industrie4.0 initiatives. DT-CPQS will provide the basis for the manufacturing process to march towards an autonomous quality platform for zero defect manufacturing in the future. Analysing sensor data from the CNC machine and vision monitoring system it was concluded that there was enough signal data to detect quality issues in a part being machined in advance using statistical/mathematical models (Smart PLS) and using machine learning algorithms. This allows the operator to take corrective actions before the resultant part ends in a quality failure and reduces the inspection time. The proposed approach forms the basis in expanding this concept to a large machine shop wherein by monitoring various parameters of the machines and state variables of the tools we can detect quality issues and develop an automated quality system using machine learning techniques.

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Kelly, Terence. "Steampunk Machine Learning." Queue 19, no. 6 (December 31, 2021): 5–17. http://dx.doi.org/10.1145/3511543.

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Fitting models to data is all the rage nowadays but has long been an essential skill of engineers. Veterans know that real-world systems foil textbook techniques by interleaving routine operating conditions with bouts of overload and failure; to be practical, a method must model the former without distortion by the latter. Surprisingly effective aid comes from an unlikely quarter: a simple and intuitive model-fitting approach that predates the Babbage Engine. The foundation of industrial-strength decision support and anomaly detection for production datacenters, this approach yields accurate yet intelligible models without hand-holding or fuss. It is easy to practice with modern analytics software and is widely applicable to computing systems and beyond.

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Ambore, Anil Kumar, T. Sri Sai Charan, U. Rohit Reddy, T. Samara Simha Reddy, and Tarun G. "Flood Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 363–67. http://dx.doi.org/10.22214/ijraset.2023.51528.

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Abstract: The most frequent natural disaster in the world, flooding affects hundreds of millions of people and kills between6,000 and 18,000 people annually, with 20% of those deaths occurring in India. Several people lack access to reliable early warning systems, despite the fact that those systems already exists demonstrated may avoid an large portion of economy anddeath loss. Improved performance and cost-effective solutions are offered by this prediction system's development. In order toforecast the occurrence of floods brought on by rainfall, a prediction model is created in this article. Based on the rainfall range for certain places, the model forecasts if "flood mayhappen or not". information about rainfall in Indian districts.

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Anggi Rachmawati and Yossaepurrohman. "Analysis of Machine Learning Systems for Cyber Physical Systems." International Transactions on Education Technology (ITEE) 1, no. 1 (November 24, 2022): 1–9. http://dx.doi.org/10.34306/itee.v1i1.170.

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This study summarizes major literature reviews on machine learning systems for network analysis and intrusion detection. Furthermore, it provides a brief lesson description of each machine learning approach. Because data is so important in machine learning methods, this study The primary tools for assessing network traffic and spotting anomalies are machine learning approaches, and the study focuses on the datasets utilized in these techniques. This research examine the multiple advantages (reasonable use) that machine learning has made possible, particularly for security and cyber-physical systems, including enhanced intrusion detection techniques and judgment accuracy. Additionally, this study discusses the difficulties of utilizing machine learning for cybersecurity and offers suggestions for further study.

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Anggi Rachmawati and Yossaepurrohman. "Analysis of Machine Learning Systems for Cyber Physical Systems." International Transactions on Education Technology (ITEE) 1, no. 1 (November 24, 2022): 1–9. http://dx.doi.org/10.33050/itee.v1i1.170.

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This study summarizes major literature reviews on machine learning systems for network analysis and intrusion detection. Furthermore, it provides a brief lesson description of each machine learning approach. Because data is so important in machine learning methods, this study The primary tools for assessing network traffic and spotting anomalies are machine learning approaches, and the study focuses on the datasets utilized in these techniques. This research examine the multiple advantages (reasonable use) that machine learning has made possible, particularly for security and cyber-physical systems, including enhanced intrusion detection techniques and judgment accuracy. Additionally, this study discusses the difficulties of utilizing machine learning for cybersecurity and offers suggestions for further study.

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Jagannath, Salunkhe Madhav, Rajendra B. Mohite, Mukesh Kumar Gupta, and Onkar S. Lamba. "Implementation of Machine Learning and Deep Learning for Securing the Devices in IOT Systems." Indian Journal Of Science And Technology 16, no. 9 (May 3, 2023): 640–47. http://dx.doi.org/10.17485/ijst/v16i9.99.

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CHALUP, STEPHAN K. "INCREMENTAL LEARNING IN BIOLOGICAL AND MACHINE LEARNING SYSTEMS." International Journal of Neural Systems 12, no. 06 (December 2002): 447–65. http://dx.doi.org/10.1142/s0129065702001308.

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Incremental learning concepts are reviewed in machine learning and neurobiology. They are identified in evolution, neurodevelopment and learning. A timeline of qualitative axon, neuron and synapse development summarizes the review on neurodevelopment. A discussion of experimental results on data incremental learning with recurrent artificial neural networks reveals that incremental learning often seems to be more efficient or powerful than standard learning but can produce unexpected side effects. A characterization of incremental learning is proposed which takes the elaborated biological and machine learning concepts into account.

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Cauvin, Bertrand, and Pierre Benning. "Machine Learning." International Journal of 3-D Information Modeling 6, no. 3 (July 2017): 1–16. http://dx.doi.org/10.4018/ij3dim.2017070101.

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A Bridge Data Dictionary contains an exhaustive list of terms used in the field of bridges. These terms are classified in systems in order to avoid any lacks, to identify all the expected object attributes, and to allow machines to understand the associated concepts. The main objectives of a Bridge Data Dictionary are many: ensure the sustainability of information over time; facilitate information exchange between the actors of the same project; ensure interoperability between the software packages. Other objectives have been reached during the process: to test a working methodology to be applied by other infrastructure domains (Roads, Rails, Tunnels, etc.); to check the current functions and capabilities of a buildingSMART Data Dictionary platform; and to define a common term list, in order to facilitate standardization and IFC-Bridge classes' development.

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Kim, Insu, Beopsoo Kim, and Denis Sidorov. "Machine Learning for Energy Systems Optimization." Energies 15, no. 11 (June 3, 2022): 4116. http://dx.doi.org/10.3390/en15114116.

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This editorial overviews the contents of the Special Issue “Machine Learning for Energy Systems 2021” and review the trends in machine learning (ML) techniques for energy system (ES) optimization [...]

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Fazelpour, Sina, and Maria De-Arteaga. "Diversity in sociotechnical machine learning systems." Big Data & Society 9, no. 1 (January 2022): 205395172210820. http://dx.doi.org/10.1177/20539517221082027.

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There has been a surge of recent interest in sociocultural diversity in machine learning research. Currently, however, there is a gap between discussions of measures and benefits of diversity in machine learning, on the one hand, and the broader research on the underlying concepts of diversity and the precise mechanisms of its functional benefits, on the other. This gap is problematic because diversity is not a monolithic concept. Rather, different concepts of diversity are based on distinct rationales that should inform how we measure diversity in a given context. Similarly, the lack of specificity about the precise mechanisms underpinning diversity’s potential benefits can result in uninformative generalities, invalid experimental designs, and illicit interpretations of findings. In this work, we draw on research in philosophy, psychology, and social and organizational sciences to make three contributions: First, we introduce a taxonomy of different diversity concepts from philosophy of science, and explicate the distinct epistemic and political rationales underlying these concepts. Second, we provide an overview of mechanisms by which diversity can benefit group performance. Third, we situate these taxonomies of concepts and mechanisms in the lifecycle of sociotechnical machine learning systems and make a case for their usefulness in fair and accountable machine learning. We do so by illustrating how they clarify the discourse around diversity in the context of machine learning systems, promote the formulation of more precise research questions about diversity’s impact, and provide conceptual tools to further advance research and practice.

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Jindal, Alekh, and Matteo Interlandi. "Machine learning for cloud data systems." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 3202–5. http://dx.doi.org/10.14778/3476311.3476408.

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The goal of this tutorial is to educate the audience about the state of the art in ML for cloud data systems, both in research and in practice. The tutorial is divided in two parts: the progress, and the path forward. Part I covers the recent successes in deploying machine learning solutions for cloud data systems. We will discuss the practical considerations taken into account and the progress made at various levels. The goal is to compare and contrast the promise of ML for systems with the ground actually covered in industry. Finally, Part II discusses practical issues of machine learning in the enterprise covering the generation of explanations, model debugging, model deployment, model management, constraints on eyes-on data usage and anonymization, and a discussion of the technical debt that can accrue through machine learning and models in the enterprise.

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Pescador, Fernando, and Saraju P. Mohanty. "Machine Learning for Smart Electronic Systems." IEEE Transactions on Consumer Electronics 67, no. 4 (November 2021): 224–25. http://dx.doi.org/10.1109/tce.2021.3134505.

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Roy, Sayan, and Debanjan Rana. "Machine Learning in Nonlinear Dynamical Systems." Resonance 26, no. 7 (July 2021): 953–70. http://dx.doi.org/10.1007/s12045-021-1194-0.

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Thangalakshmi, Dr S., and Dr K. Sivasami. "Machine Learning Methods for Marine Systems." IOP Conference Series: Materials Science and Engineering 1177, no. 1 (August 1, 2021): 012002. http://dx.doi.org/10.1088/1757-899x/1177/1/012002.

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Siddiqui, Farhana. "Recommendation Systems using Machine Learning Approach." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 2643–46. http://dx.doi.org/10.22214/ijraset.2020.5441.

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N, Sai Tanishq. "Machine Learning for Database Management Systems." International Journal of Engineering and Computer Science 9, no. 08 (August 19, 2020): 25132–47. http://dx.doi.org/10.18535/ijecs/v9i08.4520.

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Machine Learning (ML) is transforming the world with research breakthroughs that are leading to the progress of every field. We are living in an era of data explosion. This further improves the output as data that can be fed to the models is more than it has ever been. Therefore, prediction algorithms are now capable of solving many of the complex problems that we face by leveraging the power of data. The models are capable of correlating a dataset and its features with an accuracy that humans fail to achieve. Bearing this in mind, this research takes an in-depth look into the of the problem- solving potential of ML in the area of Database Management Systems (DBMS). Although ML hallmarks significant scientific milestones, the field is still in its infancy. The limitations of ML models are also studied in this paper.

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Boehm, Matthias, Arun Kumar, and Jun Yang. "Data Management in Machine Learning Systems." Synthesis Lectures on Data Management 14, no. 1 (February 25, 2019): 1–173. http://dx.doi.org/10.2200/s00895ed1v01y201901dtm057.

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Colyer, Adrian. "Putting Machine Learning into Production Systems." Queue 17, no. 4 (August 2019): 17–18. http://dx.doi.org/10.1145/3358955.3365847.

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Benton, William C. "Machine Learning Systems and Intelligent Applications." IEEE Software 37, no. 4 (July 2020): 43–49. http://dx.doi.org/10.1109/ms.2020.2985224.

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Riedl, Johannes, Daniel Puckmayr, and Dominik Brunner. "Tractor Assistance Systems Using Machine Learning." ATZheavy duty worldwide 13, no. 2 (June 2020): 50–55. http://dx.doi.org/10.1007/s41321-020-0079-6.

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Grünberger, Thomas. "Machine Learning for Process Monitoring Systems." PhotonicsViews 17, no. 3 (May 28, 2020): 56–59. http://dx.doi.org/10.1002/phvs.202000026.

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Ha, Nam, Kai Xu, Guanghui Ren, Arnan Mitchell, and Jian Zhen Ou. "Machine Learning‐Enabled Smart Sensor Systems." Advanced Intelligent Systems 2, no. 9 (July 14, 2020): 2000063. http://dx.doi.org/10.1002/aisy.202000063.

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Kuwajima, Hiroshi, Hirotoshi Yasuoka, and Toshihiro Nakae. "Engineering problems in machine learning systems." Machine Learning 109, no. 5 (April 23, 2020): 1103–26. http://dx.doi.org/10.1007/s10994-020-05872-w.

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Isbell, Charles, Michael L. Littman, and Peter Norvig. "Software Engineering of Machine Learning Systems." Communications of the ACM 66, no. 2 (January 20, 2023): 35–37. http://dx.doi.org/10.1145/3539783.

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Keates, S., P. Varker, and F. Spowart. "Human-machine design considerations in advanced machine-learning systems." IBM Journal of Research and Development 55, no. 5 (September 2011): 4:1–4:10. http://dx.doi.org/10.1147/jrd.2011.2163274.

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Reddy, V. Sandeep Kumar, Saravanan T., N. T. Velusudha, and T. Sunder Selwyn. "Smart Grid Management System Based on Machine Learning Algorithms for Efficient Energy Distribution." E3S Web of Conferences 387 (2023): 02005. http://dx.doi.org/10.1051/e3sconf/202338702005.

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This abstract describes the smart grid management system is an emerging technology that utilizes machine learning algorithms for efficient energy distribution. The paper presents an overview of the architecture, benefits, and challenges of smart grid management systems. The paper also discusses various machine learning algorithms used in smart grid management systems such as neural networks, decision trees, and Support Vector Machines (SVM). The advantages of using machine learning algorithms in smart grid management systems include increased energy efficiency, reduced energy wastage, improved reliability, and reduced costs. The challenges in implementing machine learning algorithms in smart grid management systems include data security, privacy, and scalability. The paper concludes by discussing future research directions in smart grid management systems based on machine learning algorithms.

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Butlin, Patrick. "Machine Learning, Functions and Goals." Croatian journal of philosophy 22, no. 66 (December 27, 2022): 351–70. http://dx.doi.org/10.52685/cjp.22.66.5.

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Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects of outputs on subsequent inputs to achieve good performance over episodes of interaction with their environments. Supervised learning systems, in contrast, merely learn to produce better outputs in response to individual inputs.

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Cussens, J. "Machine learning." Computing & Control Engineering Journal 7, no. 4 (August 1, 1996): 164–68. http://dx.doi.org/10.1049/cce:19960402.

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Mubarakova,, S. R., S. T. Amanzholova,, and R. K. Uskenbayeva,. "USING MACHINE LEARNING METHODS IN CYBERSECURITY." Eurasian Journal of Mathematical and Computer Applications 10, no. 1 (March 2022): 69–78. http://dx.doi.org/10.32523/2306-6172-2022-10-1-69-78.

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Abstract Cybersecurity is an ever-changing field, with advances in technology that open up new opportunities for cyberattacks. In addition, even though serious secu- rity breaches are often reported, small organizations still have to worry about security breaches as they can often be the target of viruses and phishing. This is why it is so important to ensure the privacy of your user profile in cyberspace. The past few years have seen a rise in machine learning algorithms that address major cybersecu- rity issues such as intrusion detection systems (IDS), detection of new modifications of known malware, malware, and spam detection, and malware analysis. In this arti- cle, algorithms have been analyzed using data mining collected from various libraries, and analytics with additional emerging data-driven models to provide more effective security solutions. In addition, an analysis was carried out of companies that are en- gaged in cyber attacks using machine learning. According to the research results, it was revealed that the concept of cybersecurity data science allows you to make the computing process more efficient and intelligent compared to traditional processes in the field of cybersecurity. As a result, according to the results of the study, it was revealed that machine learning, namely unsupervised learning, is an effective method of dealing with risks in cybersecurity and cyberattacks.

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Pandey, Mrs Arjoo. "Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 864–69. http://dx.doi.org/10.22214/ijraset.2023.55224.

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Abstract: Machine learning refers to the study and development of machine learning algorithms and techniques at a conceptual level, focusing on theoretical foundations, algorithmic design, and mathematical analysis rather than specific implementation details or application domains. It aimsto provide a deeper understanding of the fundamental principles and limitations of machine learning, enabling researchers to develop novel algorithms and advance the field. In abstract machine learning, the emphasis is on formalizing and analyzing learning tasks, developing mathematical models for learning processes, and studying the properties and behavior of various learning algorithms. This involves investigating topics such as learning theory, statistical learning, optimization, computational complexity, and generalization. The goalis to develop theoretical frameworks and mathematical tools that help explain why certain algorithms work and how they can be improved. Abstract machine learning also explores fundamental questions related to the theoretical underpinnings of machine learning, such as the trade-offs between bias and variance, the existence of optimal learning algorithms, the sample complexity of learning tasks, and the limits of what can be learned from data. It provides a theoretical foundation for understanding the capabilities and limitations of machine learning algorithms, guiding the development of new algorithms and techniques. Moreover, abstract machine learning serves as a bridge between theory and practice, facilitating the transfer of theoretical insights into practical applications. Theoretical advances in abstract machine learning can inspire new algorithmic approaches and inform the design of real-world machine learning systems. Conversely, practical challenges and observations from realworld applications can motivate and guide theoretical investigations in abstract machine learning. Overall, abstract machine learning plays a crucial role in advancing the field of machine learning by providing rigorous theoretical frameworks, mathematical models, and algorithmic principles that deepen our understanding of learning processes and guide the development of more effectiveand efficient machine learning algorithms.

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Jackson, A. H. "Machine learning." Expert Systems 5, no. 2 (May 1988): 132–50. http://dx.doi.org/10.1111/j.1468-0394.1988.tb00341.x.

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Popkov, Yu S. "Randomized Machine Learning Procedures." Automation and Remote Control 80, no. 9 (September 2019): 1653–70. http://dx.doi.org/10.1134/s0005117919090078.

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Rasheed, Fareeha, and Abdul Wahid. "Learning style detection in E-learning systems using machine learning techniques." Expert Systems with Applications 174 (July 2021): 114774. http://dx.doi.org/10.1016/j.eswa.2021.114774.

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RUZICKA, Marek, and Martin CHOVANEC. "THE APPLICATION OF THE MACHINE LEARNING PRINCIPLES IN THE SPORTS BETTING SYSTEMS." Acta Electrotechnica et Informatica 19, no. 3 (December 4, 2019): 16–20. http://dx.doi.org/10.15546/aeei-2019-0018.

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Prorok, Máté. "Applications of artificial intelligence systems." Deliberationes 15, Különszám (2022): 76–88. http://dx.doi.org/10.54230/delib.2022.k.sz.76.

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Nowadays artificial intelligence is a rapidly developing technology that encompasses the development of intelligent algorithms and machines capable of learning. Therefore, it is relevant and timely to examine the topic. These artificial intelligence algorithms and machines have the ability to perform tasks that traditionally relied on human intelligence in the past. This study provides an in-depth exploration of artificial intelligence systems and their key components. It examines various aspects of artificial intelligence systems, including natural language processing, machine learning, detection and pattern recognition, and knowledge representation and other form of artificial intelligence systems. Natural language processing enables machines to understand and generate human language, while machine learning empowers systems to learn from data and improve their performance over time. Detection and pattern recognition allow artificial intelligence systems to interpret and understand complex sensory inputs, while knowledge representation enables the storage and utilization of information. Furthermore, other form of artificial intelligence systems will be also discussed. This study sheds light on the fundamental elements of artificial intelligence systems, paving the way for their practical applications and advancements.

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Zelensky, A. A., T. K. Abdullin, M. M. Zhdanova, V. V. Voronin, and A. A. Gribkov. "Challenge of the performance management of trust control systems with deep learning." Advanced Engineering Research 22, no. 1 (March 30, 2022): 57–66. http://dx.doi.org/10.23947/2687-1653-2022-22-1-57-66.

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Introduction. The significance of machine learning under the conditions of digital transformation of industry, and methods of implementing deep learning to provide the performance of trust management systems are considered. The necessity of using convolutional artificial neural networks for deep machine learning is determined. Various technologies and architectures for the implementation of artificial neural networks are briefly considered; a comparative analysis of their performance is carried out. The work objective is to study the need to develop new approaches to the architecture of computing machines for solving problems of deep machine learning in the trust management system implementation.Materials and Methods. In the context of digital transformation, the use of artificial intelligence reaches a new level. The technical implementation of artificial neural systems with deep machine learning is based on the use of one of three basic technologies: high performance computing (HPC) with parallel data processing, neuromorphic computing (NC), and quantum computing (QC).Results. Implementation models for deep machine learning, basic technologies and architecture of computing machines, as well as requirements for trust assurance in control systems using deep machine learning are analyzed. The problem of shortage of computation power for solving such problems is identified. None of the currently existing technologies can solve the full range of learning and impedance problems. The current level of technology does not provide information security and reliability of neural networks. The practical implementation of trust management systems with deep machine learning based on existing technologies for a significant part of the tasks does not provide a sufficient level of performance.Discussion and Conclusions. The study made it possible to identify the challenge of the computation power shortage for solving problems of deep machine learning. Through the analysis of the requirements for trust management systems, the external challenges of their implementation on the basis of existing technologies, and the need to develop new approaches to the computer architecture are determined.

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Parnes, Dror. "Performance Measurements for Machine-Learning Trading Systems." Journal of Trading 10, no. 4 (September 30, 2015): 5–16. http://dx.doi.org/10.3905/jot.2015.10.4.005.

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Thakur, Archana, and Ramesh Thakur. "Machine Learning Algorithms for Intelligent Mobile Systems." International Journal of Computer Sciences and Engineering 6, no. 6 (June 30, 2018): 1257–61. http://dx.doi.org/10.26438/ijcse/v6i6.12571261.

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Hu, Hanpeng, Dan Wang, and Chuan Wu. "Distributed Machine Learning through Heterogeneous Edge Systems." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7179–86. http://dx.doi.org/10.1609/aaai.v34i05.6207.

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Many emerging AI applications request distributed machine learning (ML) among edge systems (e.g., IoT devices and PCs at the edge of the Internet), where data cannot be uploaded to a central venue for model training, due to their large volumes and/or security/privacy concerns. Edge devices are intrinsically heterogeneous in computing capacity, posing significant challenges to parameter synchronization for parallel training with the parameter server (PS) architecture. This paper proposes ADSP, a parameter synchronization model for distributed machine learning (ML) with heterogeneous edge systems. Eliminating the significant waiting time occurring with existing parameter synchronization models, the core idea of ADSP is to let faster edge devices continue training, while committing their model updates at strategically decided intervals. We design algorithms that decide time points for each worker to commit its model update, and ensure not only global model convergence but also faster convergence. Our testbed implementation and experiments show that ADSP outperforms existing parameter synchronization models significantly in terms of ML model convergence time, scalability and adaptability to large heterogeneity.

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BURDO, G. B., and A. N. BOLOTOV. "MACHINE LEARNING MECHANISM IN AUTOMATED DESIGN SYSTEMS." Bulletin of the Tver State Technical University Series «Engineering», no. 4 (2021): 66–75. http://dx.doi.org/10.46573/2658-5030-2021-4-66-75.

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Dang, Quang-Vinh. "Using Machine Learning for Intrusion Detection Systems." Computing and Informatics 41, no. 1 (2022): 12–33. http://dx.doi.org/10.31577/cai_2022_1_12.

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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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48

Kim, Jaehun. "Increasing trust in complex machine learning systems." ACM SIGIR Forum 55, no. 1 (June 2021): 1–3. http://dx.doi.org/10.1145/3476415.3476435.

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Machine learning (ML) has become a core technology for many real-world applications. Modern ML models are applied to unprecedentedly complex and difficult challenges, including very large and subjective problems. For instance, applications towards multimedia understanding have been advanced substantially. Here, it is already prevalent that cultural/artistic objects such as music and videos are analyzed and served to users according to their preference, enabled through ML techniques. One of the most recent breakthroughs in ML is Deep Learning (DL), which has been immensely adopted to tackle such complex problems. DL allows for higher learning capacity, making end-to-end learning possible, which reduces the need for substantial engineering effort, while achieving high effectiveness. At the same time, this also makes DL models more complex than conventional ML models. Reports in several domains indicate that such more complex ML models may have potentially critical hidden problems: various biases embedded in the training data can emerge in the prediction, extremely sensitive models can make unaccountable mistakes. Furthermore, the black-box nature of the DL models hinders the interpretation of the mechanisms behind them. Such unexpected drawbacks result in a significant impact on the trustworthiness of the systems in which the ML models are equipped as the core apparatus. In this thesis, a series of studies investigates aspects of trustworthiness for complex ML applications, namely the reliability and explainability. Specifically, we focus on music as the primary domain of interest, considering its complexity and subjectivity. Due to this nature of music, ML models for music are necessarily complex for achieving meaningful effectiveness. As such, the reliability and explainability of music ML models are crucial in the field. The first main chapter of the thesis investigates the transferability of the neural network in the Music Information Retrieval (MIR) context. Transfer learning, where the pre-trained ML models are used as off-the-shelf modules for the task at hand, has become one of the major ML practices. It is helpful since a substantial amount of the information is already encoded in the pre-trained models, which allows the model to achieve high effectiveness even when the amount of the dataset for the current task is scarce. However, this may not always be true if the "source" task which pre-trained the model shares little commonality with the "target" task at hand. An experiment including multiple "source" tasks and "target" tasks was conducted to examine the conditions which have a positive effect on the transferability. The result of the experiment suggests that the number of source tasks is a major factor of transferability. Simultaneously, it is less evident that there is a single source task that is universally effective on multiple target tasks. Overall, we conclude that considering multiple pre-trained models or pre-training a model employing heterogeneous source tasks can increase the chance for successful transfer learning. The second major work investigates the robustness of the DL models in the transfer learning context. The hypothesis is that the DL models can be susceptible to imperceptible noise on the input. This may drastically shift the analysis of similarity among inputs, which is undesirable for tasks such as information retrieval. Several DL models pre-trained in MIR tasks are examined for a set of plausible perturbations in a real-world setup. Based on a proposed sensitivity measure, the experimental results indicate that all the DL models were substantially vulnerable to perturbations, compared to a traditional feature encoder. They also suggest that the experimental framework can be used to test the pre-trained DL models for measuring robustness. In the final main chapter, the explainability of black-box ML models is discussed. In particular, the chapter focuses on the evaluation of the explanation derived from model-agnostic explanation methods. With black-box ML models having become common practice, model-agnostic explanation methods have been developed to explain a prediction. However, the evaluation of such explanations is still an open problem. The work introduces an evaluation framework that measures the quality of the explanations employing fidelity and complexity. Fidelity refers to the explained mechanism's coherence to the black-box model, while complexity is the length of the explanation. Throughout the thesis, we gave special attention to the experimental design, such that robust conclusions can be reached. Furthermore, we focused on delivering machine learning framework and evaluation frameworks. This is crucial, as we intend that the experimental design and results will be reusable in general ML practice. As it implies, we also aim our findings to be applicable beyond the music applications such as computer vision or natural language processing. Trustworthiness in ML is not a domain-specific problem. Thus, it is vital for both researchers and practitioners from diverse problem spaces to increase awareness of complex ML systems' trustworthiness. We believe the research reported in this thesis provides meaningful stepping stones towards the trustworthiness of ML.

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Slugocki, Michael, Allison Sekuler, and Patrick Bennett. "Evaluating Shape Representations using Machine Learning Systems." Journal of Vision 18, no. 10 (September 1, 2018): 1314. http://dx.doi.org/10.1167/18.10.1314.

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Xu, Xin, Haibo He, Dongbin Zhao, Shiliang Sun, Lucian Busoniu, and Simon X. Yang. "Machine Learning with Applications to Autonomous Systems." Mathematical Problems in Engineering 2015 (2015): 1–2. http://dx.doi.org/10.1155/2015/385028.

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Journal articles on the topic 'Systems for Machine Learning'

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