Journal articles: 'Multitask regression'

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Bernard, Elsa, Yunlong Jiao, Erwan Scornet, Veronique Stoven, Thomas Walter, and Jean-Philippe Vert. "Kernel Multitask Regression for Toxicogenetics." Molecular Informatics 36, no. 10 (September 26, 2017): 1700053. http://dx.doi.org/10.1002/minf.201700053.

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Xin Gu, Fu-Lai Chung, Hisao Ishibuchi, and Shitong Wang. "Multitask Coupled Logistic Regression and its Fast Implementation for Large Multitask Datasets." IEEE Transactions on Cybernetics 45, no. 9 (September 2015): 1953–66. http://dx.doi.org/10.1109/tcyb.2014.2362771.

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Tam, Clara M., Dong Zhang, Bo Chen, Terry Peters, and Shuo Li. "Holistic multitask regression network for multiapplication shape regression segmentation." Medical Image Analysis 65 (October 2020): 101783. http://dx.doi.org/10.1016/j.media.2020.101783.

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Xu, Yong-Li, Di-Rong Chen, and Han-Xiong Li. "Least Square Regularized Regression for Multitask Learning." Abstract and Applied Analysis 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/715275.

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The study of multitask learning algorithms is one of very important issues. This paper proposes a least-square regularized regression algorithm for multi-task learning with hypothesis space being the union of a sequence of Hilbert spaces. The algorithm consists of two steps of selecting the optimal Hilbert space and searching for the optimal function. We assume that the distributions of different tasks are related to a set of transformations under which any Hilbert space in the hypothesis space is norm invariant. We prove that under the above assumption the optimal prediction function of every task is in the same Hilbert space. Based on this result, a pivotal error decomposition is founded, which can use samples of related tasks to bound excess error of the target task. We obtain an upper bound for the sample error of related tasks, and based on this bound, potential faster learning rates are obtained compared to single-task learning algorithms.

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Fan, Jianqing, Lingzhou Xue, and Hui Zou. "Multitask Quantile Regression Under the Transnormal Model." Journal of the American Statistical Association 111, no. 516 (October 1, 2016): 1726–35. http://dx.doi.org/10.1080/01621459.2015.1113973.

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Goncalves, Andre, Priyadip Ray, Braden Soper, David Widemann, Mari Nygård, Jan F. Nygård, and Ana Paula Sales. "Bayesian multitask learning regression for heterogeneous patient cohorts." Journal of Biomedical Informatics: X 4 (December 2019): 100059. http://dx.doi.org/10.1016/j.yjbinx.2019.100059.

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Zhang, Linjuan, Jiaqi Shi, Lili Wang, and Changqing Xu. "Electricity, Heat, and Gas Load Forecasting Based on Deep Multitask Learning in Industrial-Park Integrated Energy System." Entropy 22, no. 12 (November 30, 2020): 1355. http://dx.doi.org/10.3390/e22121355.

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Different energy systems are closely connected with each other in industrial-park integrated energy system (IES). The energy demand forecasting has important impact on IES dispatching and planning. This paper proposes an approach of short-term energy forecasting for electricity, heat, and gas by employing deep multitask learning whose structure is constructed by deep belief network (DBN) and multitask regression layer. The DBN can extract abstract and effective characteristics in an unsupervised fashion, and the multitask regression layer above the DBN is used for supervised prediction. Then, subject to condition of practical demand and model integrity, the whole energy forecasting model is introduced, including preprocessing, normalization, input properties, training stage, and evaluating indicator. Finally, the validity of the algorithm and the accuracy of the energy forecasts for an industrial-park IES system are verified through the simulations using actual operating data from load system. The positive results turn out that the deep multitask learning has great prospects for load forecast.

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Schwab, David, Puneet Singla, and Sean O’Rourke. "Angles-Only Initial Orbit Determination via Multivariate Gaussian Process Regression." Electronics 11, no. 4 (February 15, 2022): 588. http://dx.doi.org/10.3390/electronics11040588.

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Vital for Space Situational Awareness, Initial Orbit Determination (IOD) may be used to initialize object tracking and associate observations with a tracked satellite. Classical IOD algorithms provide only a point solution and are sensitive to noisy measurements and to certain target-observer geometry. This work examines the ability of a Multivariate GPR (MV-GPR) to accurately perform IOD and quantify the associated uncertainty. Given perfect test inputs, MV-GPR performs comparably to a simpler multitask learning GPR algorithm and the classical Gauss–Gibbs IOD in terms of prediction accuracy. It significantly outperforms the multitask learning GPR algorithm in uncertainty quantification due to the direct handling of output dimension correlations. A moment-matching algorithm provides an analytic solution to the input noise problem under certain assumptions. The algorithm is adapted to the MV-GPR formulation and shown to be an effective tool to accurately quantify the added input uncertainty. This work shows that the MV-GPR can provide a viable solution with quantified uncertainty which is robust to observation noise and traditionally challenging orbit-observer geometries.

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Zhang, Heng-Chang, Qing Wu, Fei-Yan Li, and Hong Li. "Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast." Axioms 11, no. 6 (June 15, 2022): 292. http://dx.doi.org/10.3390/axioms11060292.

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Various factors make stock market forecasting difficult and arduous. Single-task learning models fail to achieve good results because they ignore the correlation between multiple related tasks. Multitask learning methods can capture the cross-correlation among subtasks and achieve a satisfactory learning effect by training all tasks simultaneously. With this motivation, we assume that the related tasks are close enough to share a common model whereas having their own independent models. Based on this hypothesis, we propose a multitask learning least squares support vector regression (MTL-LS-SVR) algorithm, and an extension, EMTL-LS-SVR. Theoretical analysis shows that these models can be converted to linear systems. A Krylov-Cholesky algorithm is introduced to determine the optimal solutions of the models. We tested the proposed models by applying them to forecasts of the Chinese stock market index trend and the stock prices of five stated-owned banks. The experimental results demonstrate their validity.

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Ruiz, Carlos, Carlos M. Alaíz, and José R. Dorronsoro. "Multitask Support Vector Regression for Solar and Wind Energy Prediction." Energies 13, no. 23 (November 30, 2020): 6308. http://dx.doi.org/10.3390/en13236308.

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Given the impact of renewable sources in the overall energy production, accurate predictions are becoming essential, with machine learning becoming a very important tool in this context. In many situations, the prediction problem can be divided into several tasks, more or less related between them but each with its own particularities. Multitask learning (MTL) aims to exploit this structure, training several models at the same time to improve on the results achievable either by a common model or by task-specific models. In this paper, we show how an MTL approach based on support vector regression can be applied to the prediction of photovoltaic and wind energy, problems where tasks can be defined according to different criteria. As shown experimentally with three different datasets, the MTL approach clearly outperforms the results of the common and specific models for photovoltaic energy, and are at the very least quite competitive for wind energy.

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Majumdar, Subhabrata, and Snigdhansu Chatterjee. "Non-convex penalized multitask regression using data depth-based penalties." Stat 7, no. 1 (2018): e174. http://dx.doi.org/10.1002/sta4.174.

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Li, Yi, and A. Adam Ding. "Double‐structured sparse multitask regression with application of statistical downscaling." Environmetrics 30, no. 4 (October 22, 2018): e2534. http://dx.doi.org/10.1002/env.2534.

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Shi, Meng, Yu Zheng, Youzhen Wu, and Quansheng Ren. "Multitask Attention-Based Neural Network for Intraoperative Hypotension Prediction." Bioengineering 10, no. 9 (August 31, 2023): 1026. http://dx.doi.org/10.3390/bioengineering10091026.

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Timely detection and response to Intraoperative Hypotension (IOH) during surgery is crucial to avoid severe postoperative complications. Although several methods have been proposed to predict IOH using machine learning, their performance still has space for improvement. In this paper, we propose a ResNet-BiLSTM model based on multitask training and attention mechanism for IOH prediction. We trained and tested our proposed model using bio-signal waveforms obtained from patient monitoring of non-cardiac surgery. We selected three models (WaveNet, CNN, and TCN) that process time-series data for comparison. The experimental results demonstrate that our proposed model has optimal MSE (43.83) and accuracy (0.9224) compared to other models, including WaveNet (51.52, 0.9087), CNN (318.52, 0.5861), and TCN (62.31, 0.9045), which suggests that our proposed model has better regression and classification performance. We conducted ablation experiments on the multitask and attention mechanisms, and the experimental results demonstrated that the multitask and attention mechanisms improved MSE and accuracy. The results demonstrate the effectiveness and superiority of our proposed model in predicting IOH.

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Rosli, Mohd Shafie, Nor Shela Saleh, Baharuddin Aris, Maizah Hura Ahmad, and Shaharuddin Md. Salleh. "Ubiquitous Hub for Digital Natives." International Journal of Emerging Technologies in Learning (iJET) 11, no. 02 (February 23, 2016): 29. http://dx.doi.org/10.3991/ijet.v11i02.4993.

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This study was conducted to construct a model on ubiquitous hub for digital natives. Respondents were 250 digital native generation students, from a higher learning institution in Malaysia. The result of the regression, structural equation model and path analysis revealed that multitask as well as gratification and reward nurture digital natives to learn in ubiquitous computing environment. Digital natives characteristics of reliant on graphic for communication, and attitude toward technology are rejected from the model based on the statistical evidence. Test of the relationship between multitask toward gratification and reward via structural equation model shows that both influence each other. Conclusion on the set-up of ubiquitous hub for digital natives based on the model derived are discussed.

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Huang, Xiaoying, Yun Tian, Shifeng Zhao, Tao Liu, Wei Wang, and Qingjun Wang. "Direct full quantification of the left ventricle via multitask regression and classification." Applied Intelligence 51, no. 8 (January 15, 2021): 5745–58. http://dx.doi.org/10.1007/s10489-020-02130-3.

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Zhao, Sicheng, Hongxun Yao, Yue Gao, Rongrong Ji, and Guiguang Ding. "Continuous Probability Distribution Prediction of Image Emotions via Multitask Shared Sparse Regression." IEEE Transactions on Multimedia 19, no. 3 (March 2017): 632–45. http://dx.doi.org/10.1109/tmm.2016.2617741.

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Zhang, K., J. W. Gray, and B. Parvin. "Sparse multitask regression for identifying common mechanism of response to therapeutic targets." Bioinformatics 26, no. 12 (June 6, 2010): i97—i105. http://dx.doi.org/10.1093/bioinformatics/btq181.

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Chen, Kai, Feng Huang, and Heming Zhang. "Fan Rotation Speed Real-Time Optimizations of Continuous Annealing Line with Mechanism-Guided Multitask Classification and Regression Model." Journal of Physics: Conference Series 2575, no. 1 (August 1, 2023): 012010. http://dx.doi.org/10.1088/1742-6596/2575/1/012010.

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Abstract Fast cooling with gas jets in the rapid cooling section plays significant role in the process of steel strip galvanization. The large number of the cooling fans to generate the gas jets and the frequent change of the strip dimension, the strip velocity and the inlet strip temperature contribute to the great complexity of the fan rotation speed regulations. The experience-dominated regulation method does not work well in real production. This paper proposes a multitask classification and regression (MTCR) model to optimize the fan rotation speed in real time. A heat transfer model is firstly built in the form of partial differential equations (PDEs) and is used to construct the features of the MTCR model. The overall heat transfer coefficient is calculated and analysed. More than 575, 000 data records from the real production line are used to construct the features, and train and evaluate the MTCR model. In addition, the predictions of the MTCR model are compared with that of the multitask regression (MTR) model and the single task regression (STR) model. All the models perform well for the rotation speed predictions of the switched-on fans, while the MTCR model performs better in the whole test dataset.

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Forouzannezhad, Parisa, Dominic Maes, Daniel S. Hippe, Phawis Thammasorn, Reza Iranzad, Jie Han, Chunyan Duan, et al. "Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer." Cancers 14, no. 5 (February 26, 2022): 1228. http://dx.doi.org/10.3390/cancers14051228.

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Medical imaging provides quantitative and spatial information to evaluate treatment response in the management of patients with non-small cell lung cancer (NSCLC). High throughput extraction of radiomic features on these images can potentially phenotype tumors non-invasively and support risk stratification based on survival outcome prediction. The prognostic value of radiomics from different imaging modalities and time points prior to and during chemoradiation therapy of NSCLC, relative to conventional imaging biomarker or delta radiomics models, remains uncharacterized. We investigated the utility of multitask learning of multi-time point radiomic features, as opposed to single-task learning, for improving survival outcome prediction relative to conventional clinical imaging feature model benchmarks. Survival outcomes were prospectively collected for 45 patients with unresectable NSCLC enrolled on the FLARE-RT phase II trial of risk-adaptive chemoradiation and optional consolidation PD-L1 checkpoint blockade (NCT02773238). FDG-PET, CT, and perfusion SPECT imaging pretreatment and week 3 mid-treatment was performed and 110 IBSI-compliant pyradiomics shape-/intensity-/texture-based features from the metabolic tumor volume were extracted. Outcome modeling consisted of a fused Laplacian sparse group LASSO with component-wise gradient boosting survival regression in a multitask learning framework. Testing performance under stratified 10-fold cross-validation was evaluated for multitask learning radiomics of different imaging modalities and time points. Multitask learning models were benchmarked against conventional clinical imaging and delta radiomics models and evaluated with the concordance index (c-index) and index of prediction accuracy (IPA). FDG-PET radiomics had higher prognostic value for overall survival in test folds (c-index 0.71 [0.67, 0.75]) than CT radiomics (c-index 0.64 [0.60, 0.71]) or perfusion SPECT radiomics (c-index 0.60 [0.57, 0.63]). Multitask learning of pre-/mid-treatment FDG-PET radiomics (c-index 0.71 [0.67, 0.75]) outperformed benchmark clinical imaging (c-index 0.65 [0.59, 0.71]) and FDG-PET delta radiomics (c-index 0.52 [0.48, 0.58]) models. Similarly, the IPA for multitask learning FDG-PET radiomics (30%) was higher than clinical imaging (26%) and delta radiomics (15%) models. Radiomics models performed consistently under different voxel resampling conditions. Multitask learning radiomics for outcome modeling provides a clinical decision support platform that leverages longitudinal imaging information. This framework can reveal the relative importance of different imaging modalities and time points when designing risk-adaptive cancer treatment strategies.

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Wistuba-Hamprecht, Jacqueline, Bernhard Reuter, Rolf Fendel, Stephen L. Hoffman, Joseph J. Campo, Philip L. Felgner, Peter G. Kremsner, Benjamin Mordmüller, and Nico Pfeifer. "Machine learning prediction of malaria vaccine efficacy based on antibody profiles." PLOS Computational Biology 20, no. 6 (June 7, 2024): e1012131. http://dx.doi.org/10.1371/journal.pcbi.1012131.

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Immunization through repeated direct venous inoculation of Plasmodium falciparum (Pf) sporozoites (PfSPZ) under chloroquine chemoprophylaxis, using the PfSPZ Chemoprophylaxis Vaccine (PfSPZ-CVac), induces high-level protection against controlled human malaria infection (CHMI). Humoral and cellular immunity contribute to vaccine efficacy but only limited information about the implicated Pf-specific antigens is available. Here, we examined Pf-specific antibody profiles, measured by protein arrays representing the full Pf proteome, of 40 placebo- and PfSPZ-immunized malaria-naïve volunteers from an earlier published PfSPZ-CVac dose-escalation trial. For this purpose, we both utilized and adapted supervised machine learning methods to identify predictive antibody profiles at two different time points: after immunization and before CHMI. We developed an adapted multitask support vector machine (SVM) approach and compared it to standard methods, i.e. single-task SVM, regularized logistic regression and random forests. Our results show, that the multitask SVM approach improved the classification performance to discriminate the protection status based on the underlying antibody-profiles while combining time- and dose-dependent data in the prediction model. Additionally, we developed the new fEature diStance exPlainabilitY (ESPY) method to quantify the impact of single antigens on the non-linear multitask SVM model and make it more interpretable. In conclusion, our multitask SVM model outperforms the studied standard approaches in regard of classification performance. Moreover, with our new explanation method ESPY, we were able to interpret the impact of Pf-specific antigen antibody responses that predict sterile protective immunity against CHMI after immunization. The identified Pf-specific antigens may contribute to a better understanding of immunity against human malaria and may foster vaccine development.

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Li, Jiafeng, Lixia Cao, and Guoliang Zhang. "Research on automatic matching of online mathematics courses and design of teaching activities based on multiobjective optimization algorithm." PeerJ Computer Science 9 (August 21, 2023): e1501. http://dx.doi.org/10.7717/peerj-cs.1501.

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The teaching of the optimization algorithm is a new kind of swarm intelligence optimization technique, which is superior in optimizing many simple functions. Still, it is not evident in processing some complex problems (group and teaching classification). Achieving automatic matching and knowledge transfer in online courses is imperative in mathematics education. This study proposes a design scheme MTCBO-LR (multiobjective capability optimizer-logistic regression), based on multitask optimization, which enables precise knowledge transfer and data interaction among many educators. It incorporates the standard TLBO algorithm to optimize, provides a variety of learning tactics for students at different stages of mathematics instruction, and is capable of adaptively adjusting these strategies in response to actual teaching needs. Experimental results on various datasets reveal that the proposed method enhances searchability and group diversity in various optimization extremes and outperforms similar methods in resolving to multitask teaching problems.

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He, Dan, David Kuhn, and Laxmi Parida. "Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction." Bioinformatics 32, no. 12 (June 15, 2016): i37—i43. http://dx.doi.org/10.1093/bioinformatics/btw249.

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Xu, Beilei, Wencheng Wu, Lei Lin, Rachel Melnyk, and Ahmed Ghazi. "Task Evoked Pupillary Response for Surgical Task Difficulty Prediction via Multitask Learning." Electronic Imaging 2021, no. 3 (June 18, 2021): 109–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.3.mobmu-109.

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In operating rooms, excessive cognitive stress can impede the performance of a surgeon, while low engagement can lead to unavoidable mistakes due to complacency. As a consequence, there is a strong desire in the surgical community to be able to monitor and quantify the cognitive stress of a surgeon while performing surgical procedures. Quantitative cognitive-load-based feedback can also provide valuable insights during surgical training to optimize training efficiency and effectiveness. Various physiological measures have been evaluated for quantifying cognitive stress for different mental challenges. In this paper, we present a study using the cognitive stress measured by the task evoked pupillary response extracted from the time series eye-tracking measurements to predict task difficulties in a virtual reality based robotic surgery training environment. In particular, we proposed a differential-task-difficulty scale, utilized a comprehensive feature extraction approach, and implemented a multitask learning framework and compared the regression accuracy between the conventional single-task-based and three multitask approaches across subjects.

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Lewin, Collin, Erin Kara, Dan Wilkins, Guglielmo Mastroserio, Javier A. García, Rachel C. Zhang, William N. Alston, et al. "X-Ray Reverberation Mapping of Ark 564 Using Gaussian Process Regression." Astrophysical Journal 939, no. 2 (November 1, 2022): 109. http://dx.doi.org/10.3847/1538-4357/ac978f.

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Abstract Ark 564 is an extreme high-Eddington narrow-line Seyfert 1 galaxy, known for being one of the brightest, most rapidly variable soft X-ray active galactic nuclei (AGN), and for having one of the lowest temperature coronae. Here, we present a 410 ks NuSTAR observation and two 115 ks XMM-Newton observations of this unique source, which reveal a very strong, relativistically broadened iron line. We compute the Fourier-resolved time lags by first using Gaussian processes to interpolate the NuSTAR gaps, implementing the first employment of multitask learning for application in AGN timing. By simultaneously fitting the time lags and the flux spectra with the relativistic reverberation model reltrans, we constrain the mass at 2.3 − 1.3 + 2.6 × 10 6 M ⊙ , although additional components are required to describe the prominent soft excess in this source. These results motivate future combinations of machine learning, Fourier-resolved timing, and the development of reverberation models.

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Ning, Shuluo, and Hyunsoo Yoon. "A New Model for Building Energy Modeling and Management Using Predictive Analytics: Partitioned Hierarchical Multitask Regression (PHMR)." Indoor Air 2024 (March 11, 2024): 1–11. http://dx.doi.org/10.1155/2024/5595459.

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Buildings are major consumers of energy, accounting for a significant proportion of total energy use worldwide. This substantial energy consumption not only leads to increased operational costs but also contributes to environmental concerns such as greenhouse gas emissions. In the United States, building energy consumption accounts for about 40% of total energy use, highlighting the importance of efficient energy management. Therefore, accurate prediction of energy usage in buildings is crucial. However, accurate prediction of building energy consumption remains a challenge due to the intricate interaction of indoor and outdoor variables. This study introduces the Partitioned Hierarchical Multitask Regression (PHMR), an innovative model integrating recursive partition regression (RPR) with multitask learning (hierML). PHMR adeptly predicts building energy consumption by integrating both indoor factors, such as building design and operational variables, and outdoor environmental influences. Rigorous simulation studies illustrate PHMR’s efficacy. It outperforms traditional single-predictor regression models, achieving a 32.88% to 41.80% higher prediction accuracy, especially in scenarios with limited training data. This highlights PHMR’s robustness and adaptability. The practical application of PHMR in managing a modular house’s Heating, Ventilation, and Air Conditioning (HVAC) system in Spain resulted in a 37% improvement in prediction accuracy. This significant efficiency gain is evidenced by a high Pearson correlation coefficient (0.8) between PHMR’s predictions and actual energy consumption. PHMR not only offers precise predictions for energy consumption but also facilitates operational cost reductions, thereby enhancing sustainability in building energy management. Its application in a real-world setting demonstrates the model’s potential as a valuable tool for architects, engineers, and facility managers in designing and maintaining energy-efficient buildings. The model’s integration of comprehensive data analysis with domain-specific knowledge positions it as a crucial asset in advancing sustainable energy practices in the building sector.

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Su, Zhibin, Shige Lin, Luyue Zhang, Yiming Feng, and Wei Jiang. "Multitask Learning-Based Affective Prediction for Videos of Films and TV Scenes." Applied Sciences 14, no. 11 (May 22, 2024): 4391. http://dx.doi.org/10.3390/app14114391.

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Film and TV video scenes contain rich art and design elements such as light and shadow, color, composition, and complex affects. To recognize the fine-grained affects of the art carrier, this paper proposes a multitask affective value prediction model based on an attention mechanism. After comparing the characteristics of different models, a multitask prediction framework based on the improved progressive layered extraction (PLE) architecture (multi-headed attention and factor correlation-based PLE), incorporating a multi-headed self-attention mechanism and correlation analysis of affective factors, is constructed. Both the dynamic and static features of a video are chosen as fusion input, while the regression of fine-grained affects and classification of whether a character exists in a video are designed as different training tasks. Considering the correlation between different affects, we propose a loss function based on association constraints, which effectively solves the problem of training balance within tasks. Experimental results on a self-built video dataset show that the algorithm can give full play to the complementary advantages of different features and improve the accuracy of prediction, which is more suitable for fine-grained affect mining of film and TV scenes.

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Zhang, Heng-Chang, Qing Wu, and Fei-Yan Li. "Application of online multitask learning based on least squares support vector regression in the financial market." Applied Soft Computing 121 (May 2022): 108754. http://dx.doi.org/10.1016/j.asoc.2022.108754.

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Lin, Zhaozhou, Qiao Zhang, Shengyun Dai, and Xiaoyan Gao. "Discovering Temporal Patterns in Longitudinal Nontargeted Metabolomics Data via Group and Nuclear Norm Regularized Multivariate Regression." Metabolites 10, no. 1 (January 13, 2020): 33. http://dx.doi.org/10.3390/metabo10010033.

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Temporal associations in longitudinal nontargeted metabolomics data are generally ignored by common pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). To discover temporal patterns in longitudinal metabolomics, a multitask learning (MTL) method employing structural regularization was proposed. The group regularization term of the proposed MTL method enables the selection of a small number of tentative biomarkers while maintaining high prediction accuracy. Meanwhile, the nuclear norm imposed into the regression coefficient accounts for the interrelationship of the metabolomics data obtained on consecutive time points. The effectiveness of the proposed method was demonstrated by comparison study performed on a metabolomics dataset and a simulating dataset. The results showed that a compact set of tentative biomarkers charactering the whole antipyretic process of Qingkailing injection were selected with the proposed method. In addition, the nuclear norm introduced in the new method could help the group norm to improve the method’s recovery ability.

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Hong, Danfeng, Naoto Yokoya, Jocelyn Chanussot, Jian Xu, and Xiao Xiang Zhu. "Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction." ISPRS Journal of Photogrammetry and Remote Sensing 158 (December 2019): 35–49. http://dx.doi.org/10.1016/j.isprsjprs.2019.09.008.

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Daniels, John, Pau Herrero, and Pantelis Georgiou. "A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems." Sensors 22, no. 2 (January 8, 2022): 466. http://dx.doi.org/10.3390/s22020466.

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Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. In order to move towards fully automated closed-loop glucose control, we propose an algorithm based on a deep learning framework that performs multitask quantile regression, for both meal detection and carbohydrate estimation. Our proposed method is evaluated in silico on 10 adult subjects from the UVa/Padova simulator with a Bio-inspired Artificial Pancreas (BiAP) control algorithm over a 2 month period. Three different configurations of the AP are evaluated -BiAP without meal announcement (BiAP-NMA), BiAP with meal announcement (BiAP-MA), and BiAP with meal detection (BiAP-MD). We present results showing an improvement of BiAP-MD over BiAP-NMA, demonstrating 144.5 ± 6.8 mg/dL mean blood glucose level (−4.4 mg/dL, p< 0.01) and 77.8 ± 6.3% mean time between 70 and 180 mg/dL (+3.9%, p< 0.001). This improvement in control is realised without a significant increase in mean in hypoglycaemia (+0.1%, p= 0.4). In terms of detection of meals and snacks, the proposed method on average achieves 93% precision and 76% recall with a detection delay time of 38 ± 15 min (92% precision, 92% recall, and 37 min detection time for meals only). Furthermore, BiAP-MD handles hypoglycaemia better than BiAP-MA based on CVGA assessment with fewer control errors (10% vs. 20%). This study suggests that multitask quantile regression can improve the capability of AP systems for postprandial glucose control without increasing hypoglycaemia.

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Przybyła, Piotr, Austin J. Brockmeier, and Sophia Ananiadou. "Quantifying risk factors in medical reports with a context-aware linear model." Journal of the American Medical Informatics Association 26, no. 6 (March 6, 2019): 537–46. http://dx.doi.org/10.1093/jamia/ocz004.

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Abstract Objective We seek to quantify the mortality risk associated with mentions of medical concepts in textual electronic health records (EHRs). Recognizing mentions of named entities of relevant types (eg, conditions, symptoms, laboratory tests or behaviors) in text is a well-researched task. However, determining the level of risk associated with them is partly dependent on the textual context in which they appear, which may describe severity, temporal aspects, quantity, etc. Methods To take into account that a given word appearing in the context of different risk factors (medical concepts) can make different contributions toward risk level, we propose a multitask approach, called context-aware linear modeling, which can be applied using appropriately regularized linear regression. To improve the performance for risk factors unseen in training data (eg, rare diseases), we take into account their distributional similarity to other concepts. Results The evaluation is based on a corpus of 531 reports from EHRs with 99 376 risk factors rated manually by experts. While context-aware linear modeling significantly outperforms single-task models, taking into account concept similarity further improves performance, reaching the level of human annotators’ agreements. Conclusion Our results show that automatic quantification of risk factors in EHRs can achieve performance comparable to human assessment, and taking into account the multitask structure of the problem and the ability to handle rare concepts is crucial for its accuracy.

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Lucena, André, Joana Guedes, Mário Vaz, Luiz Silva, Denisse Bustos, and Erivaldo Souza. "Modeling Energy Expenditure Estimation in Occupational Context by Actigraphy: A Multi Regression Mixed-Effects Model." International Journal of Environmental Research and Public Health 18, no. 19 (October 3, 2021): 10419. http://dx.doi.org/10.3390/ijerph181910419.

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The accurate prediction of energy requirements for healthy individuals has many useful applications. The occupational perspective has also been proven to be of great utility for improving workers’ ergonomics, safety, and health. This work proposes a statistical regression model based on actigraphy and personal characteristics to estimate energy expenditure and cross-validate the results with reference standardized methods. The model was developed by hierarchical mixed-effects regression modeling based on the multitask protocol data. Measurements combined actigraphy, indirect calorimetry, and other personal and lifestyle information from healthy individuals (n = 50) within the age of 29.8 ± 5 years old. Results showed a significant influence of the variables related to movements, heart rate and anthropometric variables of body composition for energy expenditure estimation. Overall, the proposed model showed good agreement with energy expenditure measured by indirect calorimetry and evidenced a better performance than the methods presented in the international guidelines for metabolic rate assessment proving to be a reliable alternative to normative guidelines. Furthermore, a statistically significant relationship was found between daily activity and energy expenditure, which raised the possibility of further studies including other variables, namely those related to the subject’s lifestyle.

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Wang, Shaofeng, Shuang Liang, Qiao Chang, Li Zhang, Beiwen Gong, Yuxing Bai, Feifei Zuo, Yajie Wang, Xianju Xie, and Yu Gu. "STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning." Diagnostics 14, no. 5 (February 26, 2024): 497. http://dx.doi.org/10.3390/diagnostics14050497.

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Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the automatic annotation of the target region, a modified convolutional neural network-based detection subnetwork (DSN) was used for tooth recognition and boundary regression, and an effective region segmentation subnetwork (RSSN) was used for region segmentation. The features extracted using RSSN and DSN were fused to optimize the quality of boundary regression, which provided impressive results for multiple evaluation metrics. Specifically, the proposed framework achieved a top F1 score of 0.9849, a top Dice metric score of 0.9629, and an mAP (IOU = 0.5) score of 0.9810. This framework holds great promise for enhancing the clinical efficiency of dentists in tooth segmentation and numbering tasks.

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Alarfaj, Abeer Abdulaziz, and Hanan Ahmed Hosni Mahmoud. "Feature Fusion Deep Learning Model for Defects Prediction in Crystal Structures." Crystals 12, no. 9 (September 19, 2022): 1324. http://dx.doi.org/10.3390/cryst12091324.

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Detection of defective crystal structures can help in refute such defective structures to decrease industrial defects. In our research, we are concerned with Silicon nitride crystals. There are four types of crystal structure classes, namely no-defect structures, pristine crystal structures, defective random displacement crystal structures, and defective 25% vacancies crystal structures. This paper proposes a deep learning model to detect the four types of crystal structures with high accuracy and precision. The proposed model consists of both classification and regression models with a new loss function definition. After training both models, the features extracted are fused and utilized as an input to a perceptron classifier to identify the four types of crystal structures. A novel dense neural network (DNN) is proposed with a multitasking tactic. The developed multitask tactic is validated using a dataset of 16,000 crystal structures, with 30% highly defective crystals. Crystal structure images are captured under cobalt blue light. The multitask DNN model achieves an accuracy and precision of 97% and 96% respectively. Also, the average area under the curve (AUC) is 0.96 on average, which outperforms existing detection methods for crystal structures. The experiments depict the computational time comparison of a single training epoch of our model versus state-of-the-art models. the training computational time is performed using crystal structures diffraction image database of twelve image batches. It can be realized that the prediction computational time of our multitasking model is the least time of 21 s.

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Kumaresan, M., M. Senthil Kumar, and Nehal Muthukumar. "Analysis of mobility based COVID-19 epidemic model using Federated Multitask Learning." Mathematical Biosciences and Engineering 19, no. 10 (2022): 9983–10005. http://dx.doi.org/10.3934/mbe.2022466.

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<abstract><p>Aggregating a massive amount of disease-related data from heterogeneous devices, a distributed learning framework called Federated Learning(FL) is employed. But, FL suffers in distributing the global model, due to the heterogeneity of local data distributions. To overcome this issue, personalized models can be learned by using Federated multitask learning(FMTL). Due to the heterogeneous data from distributed environment, we propose a personalized model learned by federated multitask learning (FMTL) to predict the updated infection rate of COVID-19 in the USA using a mobility-based SEIR model. Furthermore, using a mobility-based SEIR model with an additional constraint we can analyze the availability of beds. We have used the real-time mobility data sets in various states of the USA during the years 2020 and 2021. We have chosen five states for the study and we observe that there exists a correlation among the number of COVID-19 infected cases even though the rate of spread in each case is different. We have considered each US state as a node in the federated learning environment and a linear regression model is built at each node. Our experimental results show that the root-mean-square percentage error for the actual and prediction of COVID-19 cases is low for Colorado state and high for Minnesota state. Using a mobility-based SEIR simulation model, we conclude that it will take at least 400 days to reach extinction when there is no proper vaccination or social distance.</p></abstract>

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Zhao, Chengqian, Dengwang Li, Cheng Feng, and Shuo Li. "OF-UMRN: Uncertainty-guided multitask regression network aided by optical flow for fully automated comprehensive analysis of carotid artery." Medical Image Analysis 70 (May 2021): 101982. http://dx.doi.org/10.1016/j.media.2021.101982.

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Liu, Xiaoli, Peng Cao, Jinzhu Yang, and Dazhe Zhao. "Linearized and Kernelized Sparse Multitask Learning for Predicting Cognitive Outcomes in Alzheimer’s Disease." Computational and Mathematical Methods in Medicine 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/7429782.

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Alzheimer’s disease (AD) has been not only the substantial financial burden to the health care system but also the emotional burden to patients and their families. Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer’s disease. Recently, the multitask learning (MTL) methods with sparsity-inducing norm (e.g., l2,1-norm) have been widely studied to select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, these previous works formulate the prediction tasks as a linear regression problem. The major limitation is that they assumed a linear relationship between the MRI features and the cognitive outcomes. Some multikernel-based MTL methods have been proposed and shown better generalization ability due to the nonlinear advantage. We quantify the power of existing linear and nonlinear MTL methods by evaluating their performance on cognitive score prediction of Alzheimer’s disease. Moreover, we extend the traditional l2,1-norm to a more general lql1-norm (q≥1). Experiments on the Alzheimer’s Disease Neuroimaging Initiative database showed that the nonlinear l2,1lq-MKMTL method not only achieved better prediction performance than the state-of-the-art competitive methods but also effectively fused the multimodality data.

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Lafond, Daniel, Benoît Roberge-Vallières, François Vachon, and Sébastien Tremblay. "Judgment Analysis in a Dynamic Multitask Environment: Capturing Nonlinear Policies Using Decision Trees." Journal of Cognitive Engineering and Decision Making 11, no. 2 (August 9, 2016): 122–35. http://dx.doi.org/10.1177/1555343416661889.

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Policy capturing is a judgment analysis method that typically uses linear statistical modeling to estimate expert judgments. A variant to this technique is to capture decision policies using data-mining algorithms designed to handle nonlinear decision rules, missing attributes, and noisy data. In the current study, we tested the effectiveness of a decision-tree induction algorithm and an instance-based classification method for policy capturing in comparison to the standard linear approach. Decision trees are relevant in naturalistic decision-making contexts since they can be used to represent “fast-and-frugal” judgment heuristics, which are well suited to describe human cognition under time pressure. We examined human classification behavior using a simulated naval air defense task in order to empirically compare the C4.5 decision-tree algorithm, the k-nearest neighbors algorithm, and linear regression on their ability to capture individual decision policies. Results show that C4.5 outperformed the other methods in terms of goodness of fit and cross-validation accuracy. Decision-tree models of individuals’ judgment policies actually classified contacts more accurately than their human counterparts, resulting in a threefold reduction in error rates. We conclude that a decision-tree induction algorithm can yield useful models for training and decision support applications, and we discuss the application of judgmental bootstrapping in real time in dynamic environments.

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Zhang, Kun, Pengcheng Lin, Jing Pan, Peixia Xu, Xuechen Qiu, Danny Crookes, Liang Hua, and Lin Wang. "End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images." Computational Intelligence and Neuroscience 2023 (March 16, 2023): 1–18. http://dx.doi.org/10.1155/2023/3018320.

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Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energy X-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.

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Wang, Hua, Feiping Nie, Heng Huang, Sungeun Kim, Kwangsik Nho, Shannon L. Risacher, Andrew J. Saykin, and Li Shen. "Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort." Bioinformatics 28, no. 2 (December 6, 2011): 229–37. http://dx.doi.org/10.1093/bioinformatics/btr649.

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Mokhtaridoost, Milad, Philipp G. Maass, and Mehmet Gönen. "Identifying Tissue- and Cohort-Specific RNA Regulatory Modules in Cancer Cells Using Multitask Learning." Cancers 14, no. 19 (October 9, 2022): 4939. http://dx.doi.org/10.3390/cancers14194939.

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MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA–mRNA regulatory modules in cohorts of primary tumor tissues are fundamental for understanding the biology of tumor heterogeneity and precise diagnosis and treatment. We established a multitask learning sparse regularized factor regression (MSRFR) method to determine key tissue- and cohort-specific miRNA–mRNA regulatory modules from expression profiles of tumors. MSRFR simultaneously models the sparse relationship between miRNAs and mRNAs and extracts tissue- and cohort-specific miRNA–mRNA regulatory modules separately. We tested the model’s ability to determine cohort-specific regulatory modules of multiple cancer cohorts from the same tissue and their underlying tissue-specific regulatory modules by extracting similarities between cancer cohorts (i.e., blood, kidney, and lung). We also detected tissue-specific and cohort-specific signatures in the corresponding regulatory modules by comparing our findings from various other tissues. We show that MSRFR effectively determines cancer-related miRNAs in cohort-specific regulatory modules, distinguishes tissue- and cohort-specific regulatory modules from each other, and extracts tissue-specific information from different cohorts of disease-related tissue. Our findings indicate that the MSRFR model can support current efforts in precision medicine to define tumor-specific miRNA–mRNA signatures.

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Xie, Qian, Ning Jin, and Shanshan Lu. "Lightweight Football Motion Recognition and Intensity Analysis Using Low-Cost Wearable Sensors." Applied Bionics and Biomechanics 2023 (July 12, 2023): 1–10. http://dx.doi.org/10.1155/2023/2354728.

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In recent years, machine learning has been utilized in health informatics and sports science. There is a great demand and development potential for combining the Internet of Things (IoT) and artificial intelligence (AI) to be applied to football sports. The conventional teaching and training methods of football sports have limited collection and mining of real raw data using wearable devices, and lack human motion capture and gesture recognition based on sports science theories. In this study, a low-cost AI + IoT system framework is designed to recognize football motion and analyze motion intensity. To reduce the communication delay and the computational resource consumption caused by data operations, a multitask learning model is designed to achieve motion recognition and intensity estimation. The model can perform classification and regression tasks in parallel and output the results simultaneously. A feature extraction scheme is designed in the initial data processing, and feature data augmentation is performed to solve the small sample data problem. To evaluate the performance of the designed football motion recognition algorithm, this paper proposes a data extraction experimental scheme to complete the data collection of different motions. Model validation is performed using three publicly available datasets, and the features learning strategies are analyzed. Finally, experiments are conducted on the collected football motion datasets and the experimental results show that the designed multitask model can perform two tasks simultaneously and can achieve high computational efficiency. The multitasking single-layer long short-term memory (LSTM) network with 32 neural units can achieve the accuracy of 0.8372, F1 score of 0.8172, mean average precision (mAP) of 0.7627, and mean absolute error (MAE) of 0.6117, while the multitasking single-layer LSTM network with 64 neural units can achieve the accuracy of 0.8407, F1 score of 0.8132, mAP of 0.7728, and MAE of 0.5966.

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Moon, Taewon, Woo-Joo Choi, Se-Hun Jang, Da-Seul Choi, and Myung-Min Oh. "Growth Analysis of Plant Factory-Grown Lettuce by Deep Neural Networks Based on Automated Feature Extraction." Horticulturae 8, no. 12 (November 29, 2022): 1124. http://dx.doi.org/10.3390/horticulturae8121124.

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The mechanisms of lettuce growth in plant factories under artificial light (PFALs) are well known, whereby the crop is generally used as a model in horticultural science. Deep learning has also been tested several times using PFALs. Despite their numerous advantages, the performance of deep learning models is commonly evaluated based only on their accuracy. Therefore, the objective of this study was to train deep neural networks and analyze the deeper abstraction of the trained models. In total, 443 images of three lettuce cultivars were used for model training, and several deep learning algorithms were compared using multivariate linear regression. Except for linear regression, all models showed adequate accuracies for the given task, and the convolutional neural network (ConvNet) model showed the highest accuracy. Based on color mapping and the distribution of the two-dimensional t-distributed stochastic neighbor embedding (t-SNE) results, ConvNet effectively perceived the differences among the lettuce cultivars under analysis. The extension of the target domain knowledge with complex models and sufficient data, similar to ConvNet with multitask learning, is possible. Therefore, deep learning algorithms should be investigated from the perspective of feature extraction.

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Bae, Chul-Young, Bo-Seon Kim, Sun-Ha Jee, Jong-Hoon Lee, and Ngoc-Dung Nguyen. "A Study on Survival Analysis Methods Using Neural Network to Prevent Cancers." Cancers 15, no. 19 (September 27, 2023): 4757. http://dx.doi.org/10.3390/cancers15194757.

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Background: Cancer is one of the main global health threats. Early personalized prediction of cancer incidence is crucial for the population at risk. This study introduces a novel cancer prediction model based on modern recurrent survival deep learning algorithms. Methods: The study includes 160,407 participants from the blood-based cohort of the Korea Cancer Prevention Research-II Biobank, which has been ongoing since 2004. Data linkages were designed to ensure anonymity, and data collection was carried out through nationwide medical examinations. Predictive performance on ten cancer sites, evaluated using the concordance index (c-index), was compared among nDeep and its multitask variation, Cox proportional hazard (PH) regression, DeepSurv, and DeepHit. Results: Our models consistently achieved a c-index of over 0.8 for all ten cancers, with a peak of 0.8922 for lung cancer. They outperformed Cox PH regression and other survival deep neural networks. Conclusion: This study presents a survival deep learning model that demonstrates the highest predictive performance on censored health dataset, to the best of our knowledge. In the future, we plan to investigate the causal relationship between explanatory variables and cancer to reduce cancer incidence and mortality.

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Xu, Hao, Panpan Zhu, Xiaobo Luo, Tianshou Xie, and Liqiang Zhang. "Extracting Buildings from Remote Sensing Images Using a Multitask Encoder-Decoder Network with Boundary Refinement." Remote Sensing 14, no. 3 (January 25, 2022): 564. http://dx.doi.org/10.3390/rs14030564.

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Extracting buildings from high-resolution remote sensing images is essential for many geospatial applications, such as building change detection, urban planning, and disaster emergency assessment. Due to the diversity of geometric shapes and the blurring of boundaries among buildings, it is still a challenging task to accurately generate building footprints from the complex scenes of remote sensing images. The rapid development of convolutional neural networks is presenting both new opportunities and challenges with respect to the extraction of buildings from high-resolution remote sensing images. To capture multilevel contextual information, most deep learning methods extract buildings by integrating multilevel features. However, the differential responses between such multilevel features are often ignored, leading to blurred contours in the extraction results. In this study, we propose an end-to-end multitask building extraction method to address these issues; this approach utilizes the rich contextual features of remote sensing images to assist with building segmentation while ensuring that the shape of the extraction results is preserved. By combining boundary classification and boundary distance regression, clear contour and distance transformation maps are generated to further improve the accuracy of building extraction. Subsequently, multiple refinement modules are used to refine each part of the network to minimize the loss of image feature information. Experimental comparisons conducted on the SpaceNet and Massachusetts building datasets show that the proposed method outperforms other deep learning methods in terms of building extraction results.

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Du, Lei, Fang Liu, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew J. Saykin, and Li Shen. "Identifying diagnosis-specific genotype–phenotype associations via joint multitask sparse canonical correlation analysis and classification." Bioinformatics 36, Supplement_1 (July 1, 2020): i371—i379. http://dx.doi.org/10.1093/bioinformatics/btaa434.

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Abstract Motivation Brain imaging genetics studies the complex associations between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). The neurodegenerative disorders usually exhibit the diversity and heterogeneity, originating from which different diagnostic groups might carry distinct imaging QTs, SNPs and their interactions. Sparse canonical correlation analysis (SCCA) is widely used to identify bi-multivariate genotype–phenotype associations. However, most existing SCCA methods are unsupervised, leading to an inability to identify diagnosis-specific genotype–phenotype associations. Results In this article, we propose a new joint multitask learning method, named MT–SCCALR, which absorbs the merits of both SCCA and logistic regression. MT–SCCALR learns genotype–phenotype associations of multiple tasks jointly, with each task focusing on identifying one diagnosis-specific genotype–phenotype pattern. Meanwhile, MT–SCCALR cannot only select relevant SNPs and imaging QTs for each diagnostic group alone, but also allows the selection of those shared by multiple diagnostic groups. We derive an efficient optimization algorithm whose convergence to a local optimum is guaranteed. Compared with two state-of-the-art methods, MT–SCCALR yields better or similar canonical correlation coefficients and classification performances. In addition, it owns much better discriminative canonical weight patterns of great interest than competitors. This demonstrates the power and capability of MTSCCAR in identifying diagnostically heterogeneous genotype–phenotype patterns, which would be helpful to understand the pathophysiology of brain disorders. Availability and implementation The software is publicly available at https://github.com/dulei323/MTSCCALR. Supplementary information Supplementary data are available at Bioinformatics online.

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Shin, Changho, Sunghwan Joo, Jaeryun Yim, Hyoseop Lee, Taesup Moon, and Wonjong Rhee. "Subtask Gated Networks for Non-Intrusive Load Monitoring." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1150–57. http://dx.doi.org/10.1609/aaai.v33i01.33011150.

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Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household’s aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous household appliances can be avoided, and only one device needs to be installed. The problem has been well-known since Hart’s seminal paper in 1992, and recently significant performance improvements have been achieved by adopting deep networks. In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements. Specifically, we propose a subtask gated network that combines the main regression network with an on/off classification subtask network. Unlike typical multitask learning algorithms where multiple tasks simply share the network parameters to take advantage of the relevance among tasks, the subtask gated network multiply the main network’s regression output with the subtask’s classification probability. When standby-power is additionally learned, the proposed solution surpasses the state-of-the-art performance for most of the benchmark cases. The subtask gated network can be very effective for any problem that inherently has on/off states.

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Gui, Renzhou, Tongjie Chen, and Han Nie. "Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning." Computational Intelligence and Neuroscience 2020 (August 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/7691294.

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In the research work of the brain-computer interface and the function of human brain work, the state classification of multitask state fMRI data is a problem. The fMRI signal of the human brain is a nonstationary signal with many noise effects and interference. Based on the commonly used nonstationary signal analysis method, Hilbert–Huang transform (HHT), we propose an improved circle-EMD algorithm to suppress the end effect. The algorithm can extract different intrinsic mode functions (IMFs), decompose the fMRI data to filter out low frequency and other redundant noise signals, and more accurately reflect the true characteristics of the original signal. For the filtered fMRI signal, we use three existing different machine learning methods: logistic regression (LR), support vector machine (SVM), and deep neural network (DNN) to achieve effective classification of different task states. The experiment compares the results of these machine learning methods and confirms that the deep neural network has the highest accuracy for task-state fMRI data classification and the effectiveness of the improved circle-EMD algorithm.

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Zhan, Lili. "Classification Algorithm for Heterogeneous Network Data Streams Based on Big Data Active Learning." Journal of Applied Mathematics 2022 (October 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/2996725.

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Data classification is one of the main tasks in the current data mining field, and the existing network data triage algorithms have problems such as too small a proportion of labeled samples, a large amount of noise, and redundant data, which lead to low classification accuracy of data stream implementation. Network embedding can effectively improve these problems, but the network embedding itself has problems such as capturing relational honor and ambiguity. This study proposes a SNN-RODE based LapRLS heterogeneous network data classification algorithm to achieve deep embedding of structure and semantics among nodes by constructing a multitask SNN and selecting dead song datasets to perform mining tasks to train the neural network. Then a semisupervised learning classifier based on Laplace regular least squares regression model is designed to use the relative support difference function as the decision method and optimize the function. The simulation experimental results show that the SNN-RODE-LapRLS algorithm improves the performance by 14%-51% over the mainstream classification algorithms, and the consumption time meets the demand of real-time classification.

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Wei, Xiaochen, Xiaolei Lv, and Kaiyu Zhang. "Road Extraction in SAR Images Using Ordinal Regression and Road-Topology Loss." Remote Sensing 13, no. 11 (May 25, 2021): 2080. http://dx.doi.org/10.3390/rs13112080.

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The road extraction task is mainly composed of two subtasks, namely, road detection and road centerline extraction. As the road detection task and road centerline extraction task are strongly correlated, in this paper, we introduce a multitask learning framework to detect roads and extract road centerlines simultaneously. For the road centerline extraction problem, existing works rely either on regression-based methods, or classification-based methods. The regression-based methods suffer from slow convergence and unsatisfactory local solutions. The classification-based methods ignore the fact that the closer the pixel is to the centerline, the higher our tolerance for its misclassification. To overcome these problems, we first convert the road centerline extraction problem into the problem of discrete normalized distance label prediction, which can be resolved by training an ordinal regressor. For the road extraction task, most of the previous studies apply pixel-wise loss function, for example, Cross-Entropy loss, which is not sufficient, as the road has special topology characteristics such as connectivity. Therefore, we propose a road-topology loss function to improve the connectivity and completeness of the extracted road. The road-topology loss function has two key characteristics: (i) The road-topology loss function combines road detection prediction and road centerline extraction prediction to promote the two subtasks to each other by using the correlation between the two subtasks; (ii) The road-topology loss can emphatically penalize gaps that often appear in road detection results and spurious segments that easily appear in centerline extraction results. In this paper, we select the AdamW optimizer to minimize the road-topology loss. Since there is no public dataset, we build a road extraction dataset to evaluate our method. State-of-the-art semantic segmentation networks (LinkNet34, DLinkNet34, DeeplabV3plus) are used as baseline methods to compare with two kinds of method. The first kind of method modifies the baseline method by adding the road centerline extraction task branch based on ordinal regression. The second kind of method uses the road topology loss and has the same network architecture as the first kind of method. For the road detection task, the two kinds of methods improve the baseline methods by up to 3.51% and 11.98% in IoU metric on our test dataset, respectively. For the road centerline extraction task, the two kinds of methods improve the baseline methods by up to 8.22% and 10.9% in the Quality metric on our test dataset.

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Journal articles on the topic 'Multitask regression'

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