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Journal articles on the topic 'Self-supervised learning (artificial intelligence)'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

Neghawi, Elie, and Yan Liu. "Enhancing Self-Supervised Learning through Explainable Artificial Intelligence Mechanisms: A Computational Analysis." Big Data and Cognitive Computing 8, no. 6 (2024): 58. http://dx.doi.org/10.3390/bdcc8060058.

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Self-supervised learning continues to drive advancements in machine learning. However, the absence of unified computational processes for benchmarking and evaluation remains a challenge. This study conducts a comprehensive analysis of state-of-the-art self-supervised learning algorithms, emphasizing their underlying mechanisms and computational intricacies. Building upon this analysis, we introduce a unified model-agnostic computation (UMAC) process, tailored to complement modern self-supervised learning algorithms. UMAC serves as a model-agnostic and global explainable artificial intelligence

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Manal, Al-otaibi. "Training and Artificial Intelligence." International Journal of Social Science and Humanities Research 12, no. 3 (2024): 166–76. https://doi.org/10.5281/zenodo.13268751.

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<strong>Abstract:</strong> Artificial intelligence is a branch of science that is growing at a fast rate to make computers behave like humans. Training and AI is one of the tech advancements that cover a wide range of areas like neural networks, natural language processing, gaming, robotics, and automation. Today, neural networks, computing, and automation are some of the hottest areas in AI that are successfully implemented in areas like natural language processing and voice recognition. This research paper explores the intersection of training and artificial intelligence (AI) and its implica

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CHAN, JASON, IRENA KOPRINSKA, and JOSIAH POON. "SEMI-SUPERVISED CLASSIFICATION USING BRIDGING." International Journal on Artificial Intelligence Tools 17, no. 03 (2008): 415–31. http://dx.doi.org/10.1142/s0218213008003972.

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Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging. In this paper, we propose the use of bridging in a semi-supervised setting. We introduce a new bridging algorithm that can be used as a base classifier in most semi-supervised approaches. We empirically show that the classification performance of

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Yuya, KOBAYASHI, Masahiro SUZUKI, and Yutaka MATSUO. "Scene Interpretation Method using Transformer and Self-supervised Learning." Transactions of the Japanese Society for Artificial Intelligence 37, no. 2 (2022): I—L75_1–17. http://dx.doi.org/10.1527/tjsai.37-2_i-l75.

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Hrycej, Tomas. "Supporting supervised learning by self-organization." Neurocomputing 4, no. 1-2 (1992): 17–30. http://dx.doi.org/10.1016/0925-2312(92)90040-v.

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6

Wang, Fei, and Changshui Zhang. "Robust self-tuning semi-supervised learning." Neurocomputing 70, no. 16-18 (2007): 2931–39. http://dx.doi.org/10.1016/j.neucom.2006.11.004.

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Biscione, Valerio, and Jeffrey S. Bowers. "Learning online visual invariances for novel objects via supervised and self-supervised training." Neural Networks 150 (June 2022): 222–36. http://dx.doi.org/10.1016/j.neunet.2022.02.017.

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8

Wei, Kaibin, Haifeng Li, Qing Liu, and Xiongjian Zhang. "Self-Supervised, Multi-View, Semantics-Aware Anchor Clustering." Electronics 13, no. 23 (2024): 4782. https://doi.org/10.3390/electronics13234782.

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Data-driven artificial intelligence systems effectively enhance accuracy and robustness by utilizing multi-view learning to aggregate consistent and complementary information from multi-source data. As one of the most important branches of multi-view learning, multi-view anchor clustering greatly reduces the time complexity via learning similarity graphs between anchors and data instead of data-to-data similarities, which has gained widespread attention in data-driven artificial intelligence domains. However, two issues still exist in current methods: (1) They commonly utilize orthogonal regul

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Ma, Jun, Yakun Wen, and Liming Yang. "Lagrangian supervised and semi-supervised extreme learning machine." Applied Intelligence 49, no. 2 (2018): 303–18. http://dx.doi.org/10.1007/s10489-018-1273-4.

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Vo Khuong Linh and Nguyen Hoa Nhat Quang. "Malware detection in PE files using deep learning with self-supervised learning techniques." Tạp chí Khoa học Lạc Hồng 1, no. 20 (2025): 6–12. https://doi.org/10.61591/jslhu.20.608.

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In recent years, there has been a surge in new malware created by hackers globally, posing challenges for traditional detection methods. This paper explores using advanced artificial intelligence, specifically Deep Learning with Self-supervised learning, to identify malware in executable files. Our study focuses on comparing the effectiveness of popular deep learning techniques like CNN models and fine-tuned CNN models, against Autoencoder models. The key contribution of this paper lies in comparing the results of these different approaches to malware detection.

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Che, Feihu, Guohua Yang, Dawei Zhang, Jianhua Tao, and Tong Liu. "Self-supervised graph representation learning via bootstrapping." Neurocomputing 456 (October 2021): 88–96. http://dx.doi.org/10.1016/j.neucom.2021.03.123.

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12

Saravana Kumar, N. M. "IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN IMPARTING EDUCATION AND EVALUATING STUDENT PERFORMANCE." Journal of Artificial Intelligence and Capsule Networks 01, no. 01 (2019): 1–9. http://dx.doi.org/10.36548/jaicn.2019.1.001.

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Simulation of human intelligence process is made possible with the help of artificial intelligence. The learning, reasoning and self-correction properties are made possible in computer systems. Along with AI, other technologies are combined effectively in order to create remarkable applications. We apply the changing role of AI and its techniques in new educational paradigms to create a personalised teaching-learning environment. Features like recognition, pattern matching, decision making, reasoning, problem solving and so on are applied along with knowledge based system and supervised machin

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13

Gu, Nannan, Pengying Fan, Mingyu Fan, and Di Wang. "Structure regularized self-paced learning for robust semi-supervised pattern classification." Neural Computing and Applications 31, no. 10 (2018): 6559–74. http://dx.doi.org/10.1007/s00521-018-3478-1.

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14

Deng, Ruoyu, and Yang Zhao. "Ethical Evaluation and Optimization of Artificial Intelligence Algorithms Based on Self Supervised Learning." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3594–602. http://dx.doi.org/10.12694/scpe.v25i5.3183.

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Active learning solves the problem of requiring a large amount of manpower and resources due to the large size of training samples. The core problem is how to select valuable samples to reduce annotation costs. Using neural networks as classifiers, most methods choose samples with large amounts of information without considering the issue of information redundancy between the selected samples. Through the study of redundancy issues, the author proposes a sample selection optimization method to reduce information redundancy. Using uncertainty methods to select samples with high information cont

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15

Wei, Chen, Yiping Tang, Chuang Niu Chuang Niu, Haihong Hu, Yue Wang, and Jimin Liang. "Self-Supervised Representation Learning for Evolutionary Neural Architecture Search." IEEE Computational Intelligence Magazine 16, no. 3 (2021): 33–49. http://dx.doi.org/10.1109/mci.2021.3084415.

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16

Parab, Tejaswini Subhash. "Study of Supervised Machine Learning and Artificial Intelligence." International Journal of Renewable Energy Exchange 11, no. 10 (2023): 131–37. http://dx.doi.org/10.58443/ijrex.11.10.2023.131-137.

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17

Serey, Joel, Luis Quezada, Miguel Alfaro, et al. "Artificial Intelligence Methodologies for Data Management." Symmetry 13, no. 11 (2021): 2040. http://dx.doi.org/10.3390/sym13112040.

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This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the main methodological contributions of artificial intelligence through machine learning. The methodology used to study the documents was content analysis; the basic terminology of the study corresponds to machine learning, artificial intelligence, and big data between the years 2017 and 2021. For this s

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18

Xi, Liang, Zichao Yun, Han Liu, Ruidong Wang, Xunhua Huang, and Haoyi Fan. "Semi-supervised Time Series Classification Model with Self-supervised Learning." Engineering Applications of Artificial Intelligence 116 (November 2022): 105331. http://dx.doi.org/10.1016/j.engappai.2022.105331.

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19

Kozhuharov, Mihail. "Artificial Intelligence: Basic Concepts." Педагогически форум 11, no. 4 (2023): 3–24. http://dx.doi.org/10.15547/pf.2023.023.

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This study presents basic concepts embedded in the scientific field of artificial intelligence with an emphasis on key aspects and methods of application. Concepts such as machine learning with its varieties such as supervised, unsupervised machine learning and reinforcement learning are explored, emphasizing the importance of machine learning, which allows systems to adapt and improve without explicit programming. The specifics of deep machine learning are discussed, with an emphasis on their capacity to process complex data and extract patterns. The article also examines some of the areas th

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20

Takama, Yasufumi. "Web Intelligence and Artificial Intelligence." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 1 (2017): 25–30. http://dx.doi.org/10.20965/jaciii.2017.p0025.

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This paper briefly summarizes the progress of artificial intelligence (AI) and web intelligence (WI) in the last two decades. The reason why we mention AI and WI together is because those have strong relationship with each other. This paper first summarizes the history of AI, and then gives brief description of supervised learning, which I think has played a major role in AI in the last two decades. As most history of WI is in the target decades, this paper first briefly describes major WI topics, and then gives more detailed description about information recommendation, which I think one of m

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21

Ledziński, Łukasz, and Grzegorz Grześk. "Artificial Intelligence Technologies in Cardiology." Journal of Cardiovascular Development and Disease 10, no. 5 (2023): 202. http://dx.doi.org/10.3390/jcdd10050202.

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As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science.

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22

Dushkin, R. V. "Semantic Supervised Training for General Artificial Cognitive Agents." Siberian Journal of Philosophy 19, no. 2 (2021): 51–64. http://dx.doi.org/10.25205/2541-7517-2021-19-2-51-64.

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The article describes the author's approach to the construction of general-level artificial cognitive agents based on the so-called "semantic supervised learning", within which, in accordance with the hybrid paradigm of artificial intelligence, both machine learning methods and methods of the symbolic ap proach and knowledge-based systems are used ("good old-fashioned artificial intelligence"). А descrip tion of current proЬlems with understanding of the general meaning and context of situations in which narrow AI agents are found is presented. The definition of semantic supervised learning

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23

Yamauchi, K., M. Oota, and N. Ishii. "A self-supervised learning system for pattern recognition by sensory integration." Neural Networks 12, no. 10 (1999): 1347–58. http://dx.doi.org/10.1016/s0893-6080(99)00064-7.

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24

Florence, Peter, Lucas Manuelli, and Russ Tedrake. "Self-Supervised Correspondence in Visuomotor Policy Learning." IEEE Robotics and Automation Letters 5, no. 2 (2020): 492–99. http://dx.doi.org/10.1109/lra.2019.2956365.

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25

Pal, S. K., A. Pathak, and C. Basu. "Dynamic guard zone for self-supervised learning." Pattern Recognition Letters 7, no. 3 (1988): 135–44. http://dx.doi.org/10.1016/0167-8655(88)90056-6.

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26

Miranda, Enrique, and Jordi Suñé. "Memristors for Neuromorphic Circuits and Artificial Intelligence Applications." Materials 13, no. 4 (2020): 938. http://dx.doi.org/10.3390/ma13040938.

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Artificial Intelligence has found many applications in the last decade due to increased computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses in the so-called Deep Neural Networks (DNNs). Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. As far as the training is concerned, we can distinguish between supervised and unsupervised learning. The former requires labelled data and is based

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27

Soni, Kuldeep, Nidhi Pateria, and Gulafsha Anjum. "Artificial Intelligence and Machine Learning in Sport Medicines." International Journal of Innovative Research in Computer and Communication Engineering 12, Special Is (2024): 69–73. http://dx.doi.org/10.15680/ijircce.2024.1203511.

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Orthopedic sports medicine is starting to feel the impact of machine learning (ML), which is transforming healthcare procedures. Orthopedic sports medicine professionals can now analyze enormous volumes of patient data to obtain insights that were previously unreachable through traditional approaches by utilizing machine learning algorithms .Large datasets can be tested more easily with machine learning to find complex saga between input and output variables. These correlations may be more complicated than what can be achieved with conventional statistical techniques, allowing for precise outp

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28

Weinlichová, Jana, and Jiří Fejfar. "Usage of self-organizing neural networks in evaluation of consumer behaviour." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, no. 6 (2010): 625–32. http://dx.doi.org/10.11118/actaun201058060625.

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This article deals with evaluation of consumer data by Artificial Intelligence methods. In methodical part there are described learning algorithms for Kohonen maps on the principle of supervised learning, unsupervised learning and semi-supervised learning. The principles of supervised learning and unsupervised learning are compared. On base of binding conditions of these principles there is pointed out an advantage of semi-supervised learning. Three algorithms are described for the semi-supervised learning: label propagation, self-training and co-training. Especially usage of co-training in Ko

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29

Okadome, Yuya, Kenshiro Ata, Hiroshi Ishiguro, and Yutaka Nakamura. "Self-supervised Learning Method for Behavior Prediction during Dialogue Based on Temporal Consistency." Transactions of the Japanese Society for Artificial Intelligence 37, no. 6 (2022): B—M43_1–13. http://dx.doi.org/10.1527/tjsai.37-6_b-m43.

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30

Li, Li, Kaiyi Zhao, Sicong Li, Ruizhi Sun, and Saihua Cai. "Extreme Learning Machine for Supervised Classification with Self-paced Learning." Neural Processing Letters 52, no. 3 (2020): 1723–44. http://dx.doi.org/10.1007/s11063-020-10286-9.

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31

Hashimoto, Daniel A., Elan Witkowski, Lei Gao, Ozanan Meireles, and Guy Rosman. "Artificial Intelligence in Anesthesiology." Anesthesiology 132, no. 2 (2020): 379–94. http://dx.doi.org/10.1097/aln.0000000000002960.

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Abstract Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (includi

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32

Benavides-Prado, Diana, Yun Sing Koh, and Patricia Riddle. "Towards Knowledgeable Supervised Lifelong Learning Systems." Journal of Artificial Intelligence Research 68 (May 8, 2020): 159–224. http://dx.doi.org/10.1613/jair.1.11432.

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Learning a sequence of tasks is a long-standing challenge in machine learning. This setting applies to learning systems that observe examples of a range of tasks at different points in time. A learning system should become more knowledgeable as more related tasks are learned. Although the problem of learning sequentially was acknowledged for the first time decades ago, the research in this area has been rather limited. Research in transfer learning, multitask learning, metalearning and deep learning has studied some challenges of these kinds of systems. Recent research in lifelong machine lear

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33

Okori, Washington, and Joseph Obua. "SUPERVISED LEARNING ALGORITHMS FOR FAMINE PREDICTION." Applied Artificial Intelligence 25, no. 9 (2011): 822–35. http://dx.doi.org/10.1080/08839514.2011.611930.

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34

Poulos, Jason, and Rafael Valle. "Missing Data Imputation for Supervised Learning." Applied Artificial Intelligence 32, no. 2 (2018): 186–96. http://dx.doi.org/10.1080/08839514.2018.1448143.

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35

Lu, Keyu, Chengyi Zeng, and Yonghu Zeng. "Self-supervised learning of monocular depth using quantized networks." Neurocomputing 488 (June 2022): 634–46. http://dx.doi.org/10.1016/j.neucom.2021.11.071.

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36

Hou, Wenjie, Zheyun Qin, Xiaoming Xi, Xiankai Lu, and Yilong Yin. "Learning disentangled representation for self-supervised video object segmentation." Neurocomputing 481 (April 2022): 270–80. http://dx.doi.org/10.1016/j.neucom.2022.01.066.

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37

Chen, Long, Wen Tang, Tao Ruan Wan, and Nigel W. John. "Self-supervised monocular image depth learning and confidence estimation." Neurocomputing 381 (March 2020): 272–81. http://dx.doi.org/10.1016/j.neucom.2019.11.038.

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38

Mody, Rohit, Debabrata Dash, and Deepanshu Mody. "Artificial intelligence in coronary physiology: where do we stand?" Journal of Transcatheter Interventions 30 (October 28, 2022): 1–9. http://dx.doi.org/10.31160/jotci202230a20220009.

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The use of invasive coronary physiology to select individuals for coronary revascularization has been established in current guidelines for the management of stable coronary artery disease. Compared to angiography alone, coronary physiology has been proven to improve clinical outcomes and cost-effectiveness in the revascularization process. Randomized controlled trials, however, have questioned the efficacy of ischemia testing in selecting individuals for revascularization. After an angiographically successful percutaneous coronary intervention, 20 to 40% of patients experienced persistent or

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39

Aryal, Gopi. "Artificial intelligence in surgical pathology." Journal of Pathology of Nepal 9, no. 1 (2019): I. http://dx.doi.org/10.3126/jpn.v9i1.23444.

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Artificial intelligence (AI) is machine intelligence that mimics human cognitive function. It denotes the intelligence presented by some artificial entities including computers and robots. In supervised learning, a machine is trained with data that contain pairs of inputs and outputs. In unsupervised learning, machines are given data inputs that are not explicitly programmed.1 Machine learning refines a model that predicts outputs using sample inputs (features) and a feedback loop. It relies heavily on extracting or selecting salient features, which is a combination of art and science (“featur

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40

Xu, Ke, Guoqiang Zhong, Zhaoyang Deng, Kang Zhang, and Kaizhu Huang. "Self-supervised generative learning for sequential data prediction." Applied Intelligence, April 20, 2023. http://dx.doi.org/10.1007/s10489-023-04578-5.

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41

"Self-Supervised Learning: Paving the Path for the Future of AI." Galore International Journal of Applied Sciences and Humanities 7, no. 2 (2024): 70–75. http://dx.doi.org/10.52403/gijash.20230211.

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Self-supervised learning (SSL) represents a significant shift in the field of artificial intelligence (AI), addressing the challenge of leveraging vast amounts of unlabeled data. Unlike traditional supervised learning that relies heavily on labeled datasets, SSL enables models to generate their own labels from the data itself through pretext tasks. This paradigm has shown remarkable success in various domains such as natural language processing, computer vision, and healthcare. By learning useful representations without the need for extensive labeled data, SSL not only reduces the cost and eff

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Li, Simou, Yuxing Mao, Jian Li, et al. "FedUTN: federated self-supervised learning with updating target network." Applied Intelligence, August 26, 2022. http://dx.doi.org/10.1007/s10489-022-04070-6.

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Kim, Sangwon, Jimi Lee, and Byoung Chul Ko. "SSL-MOT: self-supervised learning based multi-object tracking." Applied Intelligence, April 22, 2022. http://dx.doi.org/10.1007/s10489-022-03473-9.

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Wang, Zhipeng, Chunping Hou, Guanghui Yue, and Qingyuan Yang. "Dynamic-boosting attention for self-supervised video representation learning." Applied Intelligence, July 1, 2021. http://dx.doi.org/10.1007/s10489-021-02440-0.

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"Artificial Intelligence Methodologies for Supervised Learning." International Journal of Advanced Research in Big Data Management System 3, no. 1 (2019). http://dx.doi.org/10.21742/ijarbms.2019.3.1.03.

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Hafez, Muhammad Burhan, and Stefan Wermter. "Continual Robot Learning Using Self-Supervised Task Inference." IEEE Transactions on Cognitive and Developmental Systems, 2023, 1. http://dx.doi.org/10.1109/tcds.2023.3315513.

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Wang, Jing, Jun Wu, Caiyan Jia, and Zhifei Zhang. "Self-supervised variational autoencoder towards recommendation by nested contrastive learning." Applied Intelligence, February 14, 2023. http://dx.doi.org/10.1007/s10489-023-04488-6.

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Li, Jinlong, Zequn Jie, Xu Wang, Yu Zhou, Lin Ma, and Jianmin Jiang. "Weakly supervised semantic segmentation via self-supervised destruction learning." Neurocomputing, September 2023, 126821. http://dx.doi.org/10.1016/j.neucom.2023.126821.

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Liu, Jiabin, Biao Li, Minglong Lei, and Yong Shi. "Self-supervised knowledge distillation for complementary label learning." Neural Networks, August 2022. http://dx.doi.org/10.1016/j.neunet.2022.08.014.

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Sun, Jennifer J. "Toward collaborative artificial intelligence development for animal well-being." Journal of the American Veterinary Medical Association, January 10, 2025, 1–8. https://doi.org/10.2460/javma.24.10.0650.

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Abstract This review focuses on opportunities and challenges of future AI developments in veterinary medicine, from the perspective of computer science researchers in developing AI systems for animal behavior analysis. We examine the paradigms of supervised learning, self-supervised learning, and foundation models, highlighting their applications and limitations in automating animal behavior analysis. These emerging technologies present future challenges in data, modeling, and evaluation in veterinary medicine. To address this, we advocate for a collaborative approach that integrates the exper

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