Статті в журналах: "TRANSFER LEARNING APPROACH"

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Durgut, Rafet, Mehmet Emin Aydin, and Abdur Rakib. "Transfer Learning for Operator Selection: A Reinforcement Learning Approach." Algorithms 15, no. 1 (January 17, 2022): 24. http://dx.doi.org/10.3390/a15010024.

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In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the field of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. However, existing research fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time.

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Zhao, Peng, Guoqin Wu, Sheng Yao, and HuiTing Liu. "A Transductive Transfer Learning Approach Based on Manifold Learning." Computing in Science & Engineering 22, no. 1 (January 1, 2020): 77–87. http://dx.doi.org/10.1109/mcse.2018.2882699.

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Mishra, Bishwas, and Abhishek Samanta. "Quantum Transfer Learning Approach for Deepfake Detection." Sparklinglight Transactions on Artificial Intelligence and Quantum Computing 02, no. 01 (2022): 17–27. http://dx.doi.org/10.55011/staiqc.2022.2103.

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Deepfake image manipulation has achieved great attention in the previous year’s owing to brings solemn challenges from the public self-confidence. Forgery detection in face imaging has made considerable developments in detecting manipulated images. However, there is still a need for an efficient deepfake detection approach in complex background environments. This paper applies the state-of-the-art quantum transfer learning approach for classifying deepfake images from original face images. The proposed model comprises classical pre-trained ResNet-18 and quantum neural network layers that provide efficient features extraction to learn the different patterns of the deepfake face images. The proposed model is validated on a real-world deepfake dataset created using commercial software. An accuracy of 96.1 % was obtained.

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Huang, Shuai, Jing Li, Kewei Chen, Teresa Wu, Jieping Ye, Xia Wu, and Li Yao. "A transfer learning approach for network modeling." IIE Transactions 44, no. 11 (January 2, 2012): 915–31. http://dx.doi.org/10.1080/0740817x.2011.649390.

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Raza, Noman, Asma Naseer, Maria Tamoor, and Kashif Zafar. "Alzheimer Disease Classification through Transfer Learning Approach." Diagnostics 13, no. 4 (February 20, 2023): 801. http://dx.doi.org/10.3390/diagnostics13040801.

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Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, we discuss the segmentation and classification of the Magnetic resonance imaging (MRI) of Alzheimer’s disease, through the concept of transfer learning and customizing of the convolutional neural network (CNN) by specifically using images that are segmented by the Gray Matter (GM) of the brain. Instead of training and computing the proposed model accuracy from the start, we used a pre-trained deep learning model as our base model, and, after that, transfer learning was applied. The accuracy of the proposed model was tested over a different number of epochs, 10, 25, and 50. The overall accuracy of the proposed model was 97.84%.

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Cao, Bin, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung, and Qiang Yang. "Adaptive Transfer Learning." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 3, 2010): 407–12. http://dx.doi.org/10.1609/aaai.v24i1.7682.

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Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the target task be related, even though many tasks are not related in reality. However, when two tasks are unrelated, the knowledge extracted from a source task may not help, and even hurt, the performance of a target task. Thus, how to avoid negative transfer and then ensure a "safe transfer" of knowledge is crucial in transfer learning. In this paper, we propose an Adaptive Transfer learning algorithm based on Gaussian Processes (AT-GP), which can be used to adapt the transfer learning schemes by automatically estimating the similarity between a source and a target task. The main contribution of our work is that we propose a new semi-parametric transfer kernel for transfer learning from a Bayesian perspective, and propose to learn the model with respect to the target task, rather than all tasks as in multi-task learning. We can formulate the transfer learning problem as a unified Gaussian Process (GP) model. The adaptive transfer ability of our approach is verified on both synthetic and real-world datasets.

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Rani, Rajneesh, and Harpreet Singh. "Fingerprint Presentation Attack Detection Using Transfer Learning Approach." International Journal of Intelligent Information Technologies 17, no. 1 (January 2021): 53–67. http://dx.doi.org/10.4018/ijiit.2021010104.

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In this busy world, biometric authentication methods are serving as fast authentication means. But with growing dependencies on these systems, attackers have tried to exploit these systems through various attacks; thus, there is a strong need to protect authentication systems. Many software and hardware methods have been proposed in the past to make existing authentication systems more robust. Liveness detection/presentation attack detection is one such method that provides protection against malicious agents by detecting fake samples of biometric traits. This paper has worked on fingerprint liveness detection/presentation attack detection using transfer learning for which the authors have used a pre-trained NASNetMobile model. The experiments are performed on publicly available liveness datasets LivDet 2011 and LivDet 2013 and have obtained good results as compared to state of art techniques in terms of ACE(average classification error).

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Aswathi, T., T. R. Swapna, and S. Padmavathi. "Transfer Learning approach for grading of Diabetic Retinopathy." Journal of Physics: Conference Series 1767, no. 1 (February 1, 2021): 012033. http://dx.doi.org/10.1088/1742-6596/1767/1/012033.

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Oh, YongKyung, Namu Kim, and Sungil Kim. "Transfer Learning based Approach for Mixture Gas Classification." Journal of the Korean Institute of Industrial Engineers 47, no. 2 (April 30, 2021): 144–59. http://dx.doi.org/10.7232/jkiie.2021.47.2.144.

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Cvetkovic, Stevica, Nemanja Savic, and Ivan Ciric. "Deep Transfer Learning Approach for Robust Hand Detection." Intelligent Automation & Soft Computing 36, no. 1 (2023): 967–79. http://dx.doi.org/10.32604/iasc.2023.032526.

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Abdulazeem, Yousry, Hossam Magdy Balaha, Waleed M. Bahgat, and Mahmoud Badawy. "Human Action Recognition Based on Transfer Learning Approach." IEEE Access 9 (2021): 82058–69. http://dx.doi.org/10.1109/access.2021.3086668.

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Hien, Ngo Le Huy, Luu Van Huy, and Nguyen Van Hieu. "Artwork style transfer model using deep learning approach." Cybernetics and Physics, Volume 10, 2021, Number 3 (October 30, 2021): 127–37. http://dx.doi.org/10.35470/2226-4116-2021-10-3-127-137.

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Art in general and fine arts, in particular, play a significant role in human life, entertaining and dispelling stress and motivating their creativeness in specific ways. Many well-known artists have left a rich treasure of paintings for humanity, preserving their exquisite talent and creativity through unique artistic styles. In recent years, a technique called ’style transfer’ allows computers to apply famous artistic styles into the style of a picture or photograph while retaining the shape of the image, creating superior visual experiences. The basic model of that process, named ’Neural Style Transfer,’ has been introduced promisingly by Leon A. Gatys; however, it contains several limitations on output quality and implementation time, making it challenging to apply in practice. Based on that basic model, an image transform network was proposed in this paper to generate higher-quality artwork and higher abilities to perform on a larger image amount. The proposed model significantly shortened the execution time and can be implemented in a real-time application, providing promising results and performance. The outcomes are auspicious and can be used as a referenced model in color grading or semantic image segmentation, and future research focuses on improving its applications.

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Stegmann, Patrick G., Benjamin Johnson, Isaac Moradi, Bryan Karpowicz, and Will McCarty. "A deep learning approach to fast radiative transfer." Journal of Quantitative Spectroscopy and Radiative Transfer 280 (April 2022): 108088. http://dx.doi.org/10.1016/j.jqsrt.2022.108088.

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Mathew, Alwyn, Jimson Mathew, Mahesh Govind, and Asif Mooppan. "An Improved Transfer learning Approach for Intrusion Detection." Procedia Computer Science 115 (2017): 251–57. http://dx.doi.org/10.1016/j.procs.2017.09.132.

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Wang, Danshi, Yilan Xu, Jianqiang Li, Min Zhang, Jin Li, Jun Qin, Cheng Ju, Zhiguo Zhang, and Xue Chen. "Comprehensive Eye Diagram Analysis: A Transfer Learning Approach." IEEE Photonics Journal 11, no. 6 (December 2019): 1–19. http://dx.doi.org/10.1109/jphot.2019.2947705.

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Shi, Jihao, Weikang Xie, Junjie Li, Xinqi Zhang, Xinyan Huang, Asif Sohail Usmani, Faisal Khan, and Guoming Chen. "Real-time plume tracking using transfer learning approach." Computers & Chemical Engineering 172 (April 2023): 108172. http://dx.doi.org/10.1016/j.compchemeng.2023.108172.

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Shirahatti, Abhinandan, Vijay Rajpurohit, and Sanjeev Sannakki. "Fine grained irony classification through transfer learning approach." Computer Science and Information Technologies 4, no. 1 (March 1, 2023): 43–49. http://dx.doi.org/10.11591/csit.v4i1.p43-49.

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Nowadays irony appears to be pervasive in all social media discussion forums and chats, offering further obstacles to sentiment analysis efforts. The aim of the present research work is to detect irony and its types in English tweets We employed a new system for irony detection in English tweets, and we propose a distilled bidirectional encoder representations from transformers (DistilBERT)light transformer model based on the bidirectional encoder representations from transformers (BERT) architecture, this is further strengthened by the use and design of bidirectional long-short term memory (Bi-LSTM) network this configuration minimizes data preprocessing tasks proposed model tests on a SemEval-2018 task 3, 3,834 samples were provided. Experiment results show the proposed system has achieved a precision of 81% for not irony class and 66% for irony class, recall of 77% for not irony and 72% for irony, and F1 score of 79% for not irony and 69% for irony class since researchers have come up with a binary classification model, in this study we have extended our work for multiclass classification of irony. It is significant and will serve as a foundation for future research on different types of irony in tweets.

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Trinh, Van Hai, Johann Guilleminot, Camille Perrot, and Viet Dung Vu. "Learning acoustic responses from experiments: A multiscale-informed transfer learning approach." Journal of the Acoustical Society of America 151, no. 4 (April 2022): 2587–601. http://dx.doi.org/10.1121/10.0010187.

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A methodology to learn acoustical responses based on limited experimental datasets is presented. From a methodological standpoint, the approach involves a multiscale-informed encoder used to cast the learning task in a finite-dimensional setting. A neural network model mapping parameters of interest to the latent variables is then constructed and calibrated using transfer learning and knowledge gained from the multiscale surrogate. The relevance of the approach is assessed by considering the prediction of the sound absorption coefficient for randomly-packed rigid spherical beads of equal diameter. A two-microphone method is used in this context to measure the absorption coefficient on a set of configurations with various monodisperse particle diameters and sample thicknesses, and a hybrid numerical approach relying on the Johnson-Champoux-Allard-Pride-Lafarge model is deployed as the multiscale-based predictor. It is shown that the strategy allows for the relationship between the micro-/structural parameters and the experimental acoustic response to be well approximated, even if a small physical dataset (comprised of ten samples) is used for training. The methodology, therefore, enables the identification and validation of acoustical models under constraints related to data limitation and parametric dependence. It also paves the way for an efficient exploration of the parameter space for acoustical materials design.

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Han, Kaixu, Jinxin He, Yongzhi Wang, Yue Xiong, and Chi Zhang. "An Image Classification Approach based on Deep Learning and Transfer Learning." IOP Conference Series: Materials Science and Engineering 768 (March 31, 2020): 072055. http://dx.doi.org/10.1088/1757-899x/768/7/072055.

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Leimbach, Michael. "Learning transfer model: a research‐driven approach to enhancing learning effectiveness." Industrial and Commercial Training 42, no. 2 (March 16, 2010): 81–86. http://dx.doi.org/10.1108/00197851011026063.

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Renta-Davids, Ana-Inés, José-Miguel Jiménez-González, Manel Fandos-Garrido, and Ángel-Pío González-Soto. "Transfer of learning." European Journal of Training and Development 38, no. 8 (August 27, 2014): 728–44. http://dx.doi.org/10.1108/ejtd-03-2014-0026.

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Purpose – This paper aims to analyse transfer of learning to workplace regarding to job-related training courses. Training courses analysed in this study are offered under the professional training for employment framework in Spain. Design/methodology/approach – During the training courses, trainees completed a self-reported survey of reasons for participation (time 1 data collection, N = 447). Two months after training, a second survey was sent to the trainees by email (time 2 data collection, N = 158). Factor analysis, correlations and multiple hierarchical regressions were performed. Findings – The results of this study demonstrate the importance of training relevance and training effectiveness in transfer of training. Results indicated that relevance, the extent training courses were related to participant’s workplace activities and professional development, positively influences transfer of training. Effectiveness, training features which facilitated participants to acquire knowledge and skills, also has a significantly positive influence in transfer of training. Motivation to participate and learning-conducive workplace features also have a positive influence in transfer of training. Originality/value – This study contributes to the understanding of transfer of learning in work-related training programmes by analysing the factors that influence transfer of learning back to the workplace. The study has practical implication for training designers and education providers to enhance work-related training in the context of the Professional Training for Employment Subsystem in Spain.

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Davis, Jesse, and Pedro Domingos. "Deep Transfer: A Markov Logic Approach." AI Magazine 32, no. 1 (March 16, 2011): 51. http://dx.doi.org/10.1609/aimag.v32i1.2330.

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This article argues that currently the largest gap between human and machine learning is learning algorithms' inability to perform deep transfer, that is, generalize from one domain to another domain containing different objects, classes, properties and relations. We argue that second-order Markov logic is ideally suited for this purpose, and propose an approach based on it. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Our approach has successfully transferred learned knowledge among molecular biology, Web and social network domains.

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Zhu, Fei, Quan Liu, Hui Wang, Xiaoke Zhou, and Yuchen Fu. "Unregistered Biological Words Recognition by Q-Learning with Transfer Learning." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/173290.

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Unregistered biological words recognition is the process of identification of terms that is out of vocabulary. Although many approaches have been developed, the performance approaches are not satisfactory. As the identification process can be viewed as a Markov process, we put forward a Q-learning with transfer learning algorithm to detect unregistered biological words from texts. With the Q-learning, the recognizer can attain the optimal solution of identification during the interaction with the texts and contexts. During the processing, a transfer learning approach is utilized to fully take advantage of the knowledge gained in a source task to speed up learning in a different but related target task. A mapping, required by many transfer learning, which relates features from the source task to the target task, is carried on automatically under the reinforcement learning framework. We examined the performance of three approaches with GENIA corpus and JNLPBA04 data. The proposed approach improved performance in both experiments. The precision, recall rate, andFscore results of our approach surpassed those of conventional unregistered word recognizer as well as those of Q-learning approach without transfer learning.

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Mat Said, Noor Azura, Siti Mariam Bujang, Nor Aishah Buang, Harlina Harlizah Siraj @ Ramli, and Mohd Nasri Awang Besar. "CONCEPTUALIZING CRITICAL THINKING LEARNING TRANSFER MODEL: A QUALITATIVE APPROACH." Malaysian Journal of Learning and Instruction 18, Number 1 (January 31, 2021): 111–30. http://dx.doi.org/10.32890/mjli2021.18.1.5.

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Purpose – Although there is a growing interest in Critical Thinking Learning Transfer (CTLT), previous studies have presented less detailed information regarding the transfer. Besides, a few pieces of literature have been focusing on medical contexts. In Malaysia, there are small number of reviews regarding the concept compared to other countries. This issue raises the question: How do the medical undergraduates in Malaysia transfer their critical thinking learning? Thus, the authors sought to explore CTLT process among medical undergraduates in Malaysia. Then, the authors synthesized the CTLT model which presented the types of CTLT. Methodology – This study adopted a qualitative case study approach. Eight medical undergraduates in Universiti Kebangsaan Malaysia were selected using two sampling strategies under the purposive sampling. Data obtained using in-depth interviews. Data were analysed using thematic analysis. Findings – The findings showed three types of CTLT, namely near transfer, far transfer, and integrated transfer. Each types of the transfer were specified into components. In summary, the medical undergraduates’ conceptions on the CTLT process led to the development of a model. The model presented the types of CTLT that provide a better understanding about the extension of occurrence of CTLT among the medical undergraduates. Significance – The CTLT model presented extra value to the description of the CTLT process. This model led to a better understanding of the extension of critical thinking learning transfer occurrence among students especially in the context of early clinical year medical programme. Besides, the model may influence the future development of critical thinking pedagogies. Keywords: Conceptualization, critical thinking, learning transfer, extension of occurrence, medical undergraduates, qualitative case study.

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Ali, W., and S. Kolyubin. "EMG-Based Grasping Force Estimation for Robot Skill Transfer Learning." Nelineinaya Dinamika 18, no. 5 (2022): 0. http://dx.doi.org/10.20537/nd221221.

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In this study, we discuss a new machine learning architecture, the multilayer preceptron-random forest regressors pipeline (MLP-RF model), which stacks two ML regressors of different kinds to estimate the generated gripping forces from recorded surface electromyographic activity signals (EMG) during a gripping task. We evaluate our proposed approach on a publicly available dataset, putEMG-Force, which represents a sEMG-Force data profile. The sEMG signals were then filtered and preprocessed to get the features-target data frame that will be used to train the proposed ML model. The proposed ML model is a pipeline of stacking 2 different natural ML models; a random forest regressor model (RF regressor) and a multiple layer perceptron artificial neural network (MLP regressor). The models were stacked together, and the outputs were penalized by a Ridge regressor to get the best estimation of both models. The model was evaluated by different metrics; mean squared error and coefficient of determination, or $r^{2}$ score, to improve the model prediction performance. We tuned the most significant hyperparameters of each of the MLP-RF model components using a random search algorithm followed by a grid search algorithm. Finally, we evaluated our MLP-RF model performance on the data by training a recurrent neural network consisting of 2 LSTM layers, 2 dropouts, and one dense layer on the same data (as it is the common approach for problems with sequential datasets) and comparing the prediction results with our proposed model. The results show that the MLP-RF outperforms the RNN model.

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Eshratifar, Amir Erfan, Mohammad Saeed Abrishami, David Eigen, and Massoud Pedram. "A Meta-Learning Approach for Custom Model Training." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9937–38. http://dx.doi.org/10.1609/aaai.v33i01.33019937.

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Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks. In few-class, few-shot target task settings (i.e. when there are only a few classes and training examples available in the target task), meta-learning approaches that optimize for future task learning have outperformed the typical transfer approach of initializing model weights from a pretrained starting point. But as we experimentally show, metalearning algorithms that work well in the few-class setting do not generalize well in many-shot and many-class cases. In this paper, we propose a joint training approach that combines both transfer-learning and meta-learning. Benefiting from the advantages of each, our method obtains improved generalization performance on unseen target tasks in both few- and many-class and few- and many-shot scenarios.

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Braylan, Alexander, and Risto Miikkulainen. "Object-Model Transfer in the General Video Game Domain." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 12, no. 1 (June 25, 2021): 136–42. http://dx.doi.org/10.1609/aiide.v12i1.12870.

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A transfer learning approach is presented to address the challenge of training video game agents with limited data. The approach decomposes games into objects, learns object models, and transfers models from known games to unfamiliar games to guide learning. Experiments show that the approach improves prediction accuracy over a comparable control, leading to more efficient exploration. Training of game agents is thus accelerated by transferring object models from previously learned games.

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Kiranov, Dmitry Maratovich, Maxim Alexeevitch Ryndin, and Ilya Sergeevich Kozlov. "Active learning and transfer learning for document segmentation." Proceedings of the Institute for System Programming of the RAS 33, no. 6 (2021): 205–16. http://dx.doi.org/10.15514/ispras-2021-33(6)-14.

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In this paper, we investigate the effectiveness of classical approaches of active learning in the problem of segmentation of document images in order to reduce the training sample. A modified approach to the selection of images for marking and subsequent training is presented. The results obtained through active learning are compared to transfer learning using fully labeled data. It also investigates how the subject area of the training set, on which the model is initialized for transfer learning, affects the subsequent additional training of the model.

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Shapiee, Muhammad Nur Aiman, Muhammad Ar Rahim Ibrahim, Muhammad Amirul Abdullah, Rabiu Muazu Musa, Noor Azuan Abu Osman, Anwar P.P Abdul Majeed, and Mohd Azraai Mohd Razman. "The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach." MEKATRONIKA 2, no. 2 (October 27, 2020): 1–12. http://dx.doi.org/10.15282/mekatronika.v2i2.6683.

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The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision.

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Ye, Zhuyifan, Yilong Yang, Xiaoshan Li, Dongsheng Cao, and Defang Ouyang. "An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction." Molecular Pharmaceutics 16, no. 2 (December 20, 2018): 533–41. http://dx.doi.org/10.1021/acs.molpharmaceut.8b00816.

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Dyatlova, Olga V., Igor A. Elman, and Roman I. Krivonogov. "Transfer of learning: approaches to definition and applicability in adaptive learning." Yaroslavl Pedagogical Bulletin 5, no. 122 (2021): 185–94. http://dx.doi.org/10.20323/1813-145x-2021-5-122-185-194.

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The central problem of our work is to study the transfer of learning: how the previously acquired experience in solving problems affects the acquisition of experience in solving new problems. In this article, we have carried out a literary review of the main approaches to the definition of the «transfer of learning» concept in the psychological and pedagogical literature. Each approach is described from the point of view of the paradigm and methodology that underlies and reflects the idea of the nature of the learning process. For each approach, an assessment of the possibilities and limitations of its use in adaptive learning is given. Examples are given of how in the researches on adaptive learning, using digital technologies, certain tasks and measurements related to learning and learning transfer are implemented. The main conclusion from the literature review is that the transfer cannot claim to be an explanatory construct, this concept describes a phenomenon and the task of investigating the influence of previously acquired experience on a new one. The investigation of the transfer of learning is reduced to the investigation of learning in the situation of transfer. We propose the own model of learning transfer and requirements for possible measurable digital traces are put forward. The novelty of the model is that the transfer of learning is observed within the framework of the systemic approach and the paradigm of enactivism, in terms of individual experience organization, without classification of types of tasks or thinking and reasoning processes from the perspective of an external observer-expert as it happens in the most studies. Based on the proposed model, we put forward the methodological requirements in the framework of adaptive learning and the problem of learning transfer for what digital traces to measure and from what methodological view to investigate them.

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Barlas, Georgios, and Efstathios Stamatatos. "A transfer learning approach to cross-domain authorship attribution." Evolving Systems 12, no. 3 (April 9, 2021): 625–43. http://dx.doi.org/10.1007/s12530-021-09377-2.

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Kakde, Aditya, Durgansh Sharma, and Nitin Arora. "Optimal Classification of COVID-19: A Transfer Learning Approach." International Journal of Computer Applications 176, no. 20 (May 15, 2020): 25–31. http://dx.doi.org/10.5120/ijca2020920165.

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Yang, Yuting, and Gang Mei. "Deep Transfer Learning Approach for Identifying Slope Surface Cracks." Applied Sciences 11, no. 23 (November 25, 2021): 11193. http://dx.doi.org/10.3390/app112311193.

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Geohazards such as landslides, which are often accompanied by surface cracks, have caused great harm to public safety and property. If these surface cracks could be identified in time, this would be of great significance for the monitoring and early warning of geohazards. Currently, the most common method for crack identification is manual detection, which has low efficiency and accuracy. In this paper, a deep transfer learning approach is proposed to effectively and efficiently identify slope surface cracks for the sake of fast monitoring and early warning of geohazards, such as landslides. The essential idea is to employ transfer learning by training (a) a large sample dataset of concrete cracks and (b) a small sample dataset of soil and rock masses’ cracks. In the proposed approach, (1) pretrained crack identification models are constructed based on a large sample dataset of concrete cracks; (2) refined crack identification models are further constructed based on a small sample dataset of soil and rock masses’ cracks. The proposed approach could be applied to conduct UAV surveys on high and steep slopes to provide monitoring and early warning of landslides to ensure the safety of people and property.

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S. Alrumiah, Sarah, and Amal A. Al-Shargabi. "A Transfer Learning-Based Approach to Detect Cerebral Microbleeds." Computers, Materials & Continua 71, no. 1 (2022): 1903–23. http://dx.doi.org/10.32604/cmc.2022.021930.

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Dhanapriya, B. "Diverse Approach on Image Categorization Using Transfer Learning Methods." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1209–15. http://dx.doi.org/10.22214/ijraset.2022.40033.

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Abstract: It is an ingrained ability of humans to recognize and classify an image within a millisecond. This is because since our childhood, the human brain is accustomed to seeing a variety of images from the same category. However image classification in computers is a challenging process. To train computers to recognize and categorize images to a specific category, thousands of images of the same category must be sent, by which the computers can figure out and store the pattern from all the images of that specific category. When an image of the same category is sent again, it will easily recognize the image belongs to a specific class based on the patterns that are stored for that class. The objective of this paper is to explore the different transfer learning techniques that can be used for image classification task with high accuracy. Keywords: VGG19, ResNet, Densenet, Inceptionv3, Xception

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Ma, Yuxin, Jiayi Xu, Xiangyang Wu, Fei Wang, and Wei Chen. "A visual analytical approach for transfer learning in classification." Information Sciences 390 (June 2017): 54–69. http://dx.doi.org/10.1016/j.ins.2016.03.021.

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HATTAB, Abdessalam, and Ali BEHLOUL. "A Robust Iris Recognition Approach Based on Transfer Learning." International Journal of Computing and Digital Systems 13, no. 1 (April 16, 2023): 1065–80. http://dx.doi.org/10.12785/ijcds/130186.

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Yoon, Jeonghyeon, Jisoo Oh, and Seungku Kim. "Transfer Learning Approach for Indoor Localization with Small Datasets." Remote Sensing 15, no. 8 (April 17, 2023): 2122. http://dx.doi.org/10.3390/rs15082122.

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Indoor pedestrian localization has been the subject of a great deal of recent research. Various studies have employed pedestrian dead reckoning, which determines pedestrian positions by transforming data collected through sensors into pedestrian gait information. Although several studies have recently applied deep learning to moving object distance estimations using naturally collected everyday life data, this data collection approach requires a long time, resulting in a lack of data for specific labels or a significant data imbalance problem for specific labels. In this study, to compensate for the problems of the existing PDR, a method based on transfer learning and data augmentation is proposed for estimating moving object distances for pedestrians. Consistent high-performance moving object distance estimation is achieved using only a small training dataset, and the problem of the concentration of training data only on labels within a certain range is solved using window warping and scaling methods. The training dataset consists of the three-axes values of the accelerometer sensor and the pedestrian’s movement speed calculated based on GPS coordinates. All data and GPS coordinates are collected through the smartphone. A performance evaluation of the proposed moving pedestrian distance estimation system shows a high distance error performance of 3.59 m with only approximately 17% training data compared to other moving object distance estimation techniques.

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40

Jain, Ayushi. "A Transfer Learning-based Approach for Multimodal Emotion Recognition." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (December 15, 2020): 2001–9. http://dx.doi.org/10.17762/turcomat.v11i3.13597.

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The topic of multimodal emotion recognition is one that is expanding at a rapid rate. The goal of this field is to identify and comprehend human emotions through the use of many modalities, such as speech, facial expressions, and physiological data. Transfer learning strategies have been found to be successful in overcoming the issues of processing and integrating material from a variety of modalities, as demonstrated by the findings of a number of studies. For testing multimodal emotion detection models, it is helpful to make use of publicly accessible datasets like IEMOCAP, EmoReact, and AffectNet. They provide useful resources. Data variability, data quality, modality integration, limited labelled data, privacy and ethical issues, and interpretability are only few of the hurdles that must be overcome in order to construct accurate and effective models. In order to address these challenges, a multidisciplinary approach must be taken, and research must continue to be conducted in this area. The goal of this research is to develop more robust and accurate models for multimodal emotion recognition that can be applied across a variety of contexts and populations.

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Binfeng, Yu, and Ji Haibo. "Near-infrared calibration transfer via support vector machine and transfer learning." Analytical Methods 7, no. 6 (2015): 2714–25. http://dx.doi.org/10.1039/c4ay02462a.

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Nguyen Duc, Tho, Chanh Minh Tran, Nguyen Gia Bach, Phan Xuan Tan, and Eiji Kamioka. "Repetition-Based Approach for Task Adaptation in Imitation Learning." Sensors 22, no. 18 (September 14, 2022): 6959. http://dx.doi.org/10.3390/s22186959.

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Transfer learning is an effective approach for adapting an autonomous agent to a new target task by transferring knowledge learned from the previously learned source task. The major problem with traditional transfer learning is that it only focuses on optimizing learning performance on the target task. Thus, the performance on the target task may be improved in exchange for the deterioration of the source task’s performance, resulting in an agent that is not able to revisit the earlier task. Therefore, transfer learning methods are still far from being comparable with the learning capability of humans, as humans can perform well on both source and new target tasks. In order to address this limitation, a task adaptation method for imitation learning is proposed in this paper. Being inspired by the idea of repetition learning in neuroscience, the proposed adaptation method enables the agent to repeatedly review the learned knowledge of the source task, while learning the new knowledge of the target task. This ensures that the learning performance on the target task is high, while the deterioration of the learning performance on the source task is small. A comprehensive evaluation over several simulated tasks with varying difficulty levels shows that the proposed method can provide high and consistent performance on both source and target tasks, outperforming existing transfer learning methods.

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Mia, Md Jueal, Syeda Khadizatul Maria, Shahrun Siddique Taki, and Al Amin Biswas. "Cucumber disease recognition using machine learning and transfer learning." Bulletin of Electrical Engineering and Informatics 10, no. 6 (December 1, 2021): 3432–43. http://dx.doi.org/10.11591/eei.v10i6.3096.

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Cucumber is grown, as a cash crop besides it is one of the main and popular vegetables in Bangladesh. As Bangladesh's economy is largely dependent on the agricultural sector, cucumber farming could make economic and productivity growth more sustainable. But many diseases diminish the situation of cucumber. Early detection of disease can help to stop disease from spreading to other healthy plants and also accurate identifying the disease will help to reduce crop losses through specific treatments. In this paper, we have presented two approaches namely traditional machine learning (ML) and CNN-based transfer learning. Then we have compared the performance of the applied techniques to find out the most appropriate techniques for recognizing cucumber diseases. In our ML approach, the system involves five steps. After collecting the image, pre-processing is done by resizing, filtering, and contrast-enhancing. Then we have compared various ML algorithms using k-means based image segmentation after extracted 10 relevant features. Random forest gives the best accuracy with 89.93% in the traditional ML approach. We also studied and applied CNN-based transfer learning to investigate the further improvement of recognition performance. Lastly, a comparison among various transfer learning models such as InceptionV3, MobileNetV2, and VGG16 has been performed. Between these two approaches, MobileNetV2 achieves the highest accuracy with 93.23%.

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Bokade, Aarti, and Ankit Shah. "Breast Cancer Diagnosis in Mammography Images Using Deep Convolutional Neural Network-Based Transfer and Scratch Learning Approach." Indian Journal Of Science And Technology 16, no. 18 (May 9, 2023): 1385–94. http://dx.doi.org/10.17485/ijst/v16i18.39.

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45

Teo, Christopher T. H., Milad Abdollahzadeh, and Ngai-Man Cheung. "Fair Generative Models via Transfer Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 2429–37. http://dx.doi.org/10.1609/aaai.v37i2.25339.

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This work addresses fair generative models. Dataset biases have been a major cause of unfairness in deep generative models. Previous work had proposed to augment large, biased datasets with small, unbiased reference datasets. Under this setup, a weakly-supervised approach has been proposed, which achieves state-of-the-art quality and fairness in generated samples. In our work, based on this setup, we propose a simple yet effective approach. Specifically, first, we propose fairTL, a transfer learning approach to learn fair generative models. Under fairTL, we pre-train the generative model with the available large, biased datasets and subsequently adapt the model using the small, unbiased reference dataset. We find that our fairTL can learn expressive sample generation during pre-training, thanks to the large (biased) dataset. This knowledge is then transferred to the target model during adaptation, which also learns to capture the underlying fair distribution of the small reference dataset. Second, we propose fairTL++, where we introduce two additional innovations to improve upon fairTL: (i) multiple feedback and (ii) Linear-Probing followed by Fine-Tuning (LP-FT). Taking one step further, we consider an alternative, challenging setup when only a pre-trained (potentially biased) model is available but the dataset that was used to pre-train the model is inaccessible. We demonstrate that our proposed fairTL and fairTL++ remain very effective under this setup. We note that previous work requires access to the large, biased datasets and is incapable of handling this more challenging setup. Extensive experiments show that fairTL and fairTL++ achieve state-of-the-art in both quality and fairness of generated samples. The code and additional resources can be found at bearwithchris.github.io/fairTL/.

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Luo, Aoning, Yuanjia Feng, Chunyan Zhu, Yipei Wang, and Xiaoqin Wu. "Transfer Learning for Modeling Plasmonic Nanowire Waveguides." Nanomaterials 12, no. 20 (October 16, 2022): 3624. http://dx.doi.org/10.3390/nano12203624.

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Retrieving waveguiding properties of plasmonic metal nanowires (MNWs) through numerical simulations is time- and computational-resource-consuming, especially for those with abrupt geometric features and broken symmetries. Deep learning provides an alternative approach but is challenging to use due to inadequate generalization performance and the requirement of large sets of training data. Here, we overcome these constraints by proposing a transfer learning approach for modeling MNWs under the guidance of physics. We show that the basic knowledge of plasmon modes can first be learned from free-standing circular MNWs with computationally inexpensive data, and then reused to significantly improve performance in predicting waveguiding properties of MNWs with various complex configurations, enabling much smaller errors (~23–61% reduction), less trainable parameters (~42% reduction), and smaller sets of training data (~50–80% reduction) than direct learning. Compared to numerical simulations, our model reduces the computational time by five orders of magnitude. Compared to other non-deep learning methods, such as the circular-area-equivalence approach and the diagonal-circle approximation, our approach enables not only much higher accuracies, but also more comprehensive characterizations, offering an effective and efficient framework to investigate MNWs that may greatly facilitate the design of polaritonic components and devices.

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Ćwiklinski, Bartosz, Agata Giełczyk, and Michał Choraś. "Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers." Entropy 23, no. 1 (January 10, 2021): 90. http://dx.doi.org/10.3390/e23010090.

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Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.

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Ćwiklinski, Bartosz, Agata Giełczyk, and Michał Choraś. "Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers." Entropy 23, no. 1 (January 10, 2021): 90. http://dx.doi.org/10.3390/e23010090.

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Анотація:

Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.

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49

Insani, Istyadi, Jumadil Saputra, and Hardi Warsono. "The Factors that Influence Job Transfer and Its Impact on Organizational Performance: Mini-Review Approach." Journal of Madani Society 1, no. 1 (April 30, 2022): 34–58. http://dx.doi.org/10.56225/jmsc.v1i1.127.

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Job transfer is a way for an individual to obtain more experience and exposure. Because fewer individuals inhabit each successive layer as they ascend the organizational hierarchy, it is frequently more available than a promotion. Various strategies for implementing activities continue to be developed by Human Resources (HR) practitioners and researchers who regulate the organizational structure, human capital, employee performance, employee satisfaction, and task execution. Many studies have been conducted to identify the factors that influence employee transfer positions. This study aims to provide an overview of other factors that can encourage the success of job transfer. This study explicitly analyzes the content and context of the relationship between job transfer and organization, resources, learning and development, rewards, and employment relationships. This study uses a qualitative method by reviewing 27 journals from previous researchers. This study showed an association between variables such as organization, resources, learning and development, remuneration, and employee relations in transferring positions directly or indirectly through motivation as an intermediate variable. Furthermore, this study found that organization, resources, learning and development, remuneration and employment relations, and remuneration and employment relations directly or indirectly affect the transfer of positions. Thus, we expected to provide a new theory for predicting employee performance improvement through job transfers for improving organizational performance.

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Cuzzocrea, Alfredo, Carson K. Leung, Deyu Deng, Jiaxing Jason Mai, Fan Jiang, and Edoardo Fadda. "A Combined Deep-Learning and Transfer-Learning Approach for Supporting Social Influence Prediction." Procedia Computer Science 177 (2020): 170–77. http://dx.doi.org/10.1016/j.procs.2020.10.025.

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