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The weld crack leakage due to stress concentration and external load is a significant safety risk in pressure pipelines. Microstructural variations and dynamic propagation lead to unpredictable changes in leakage rate over time and conditions. To address the above problems, a novel framework called OILS-TCN for weld crack pattern recognition and leakage rate prediction is proposed. Firstly, the adaptive threshold optimization algorithm is introduced into the self-organizing incremental neural network to update and increase the crack leakage pattern. Secondly, the depth first search algorithm is combined with the radial basis function neural network to perform online increment labelling of the leakage state. Then, according to the attenuation characteristics of acoustic emission signals, a portable input-attention module is designed to add different weights to the input sequence. Finally, the accurate prediction of leakage rate under different conditions is realized based on the temporal convolutional network. Compared with other advanced methods, the proposed method has obvious advantages in the adaptability and accuracy of pipeline weld crack leakage rate prediction. In addition, the validity and necessity of each part of the framework proposed are discussed based on ablation experiments. The proposed method can predict the leakage rate in real time without modifying the hyperparameters of the model, and can provide a powerful guide for the online monitoring of the leakage AE technology of pressure pipeline in complex systems. [ABSTRACT FROM AUTHOR]

The degradation of the pretightening state of bolts is a multistage process. Utilising a single model to monitor the pretightening state in the full degradation stage and continuously updating the monitoring model is important. Therefore, a quantitative monitoring method for the pretightening state of bolts based on nonlinear Lamb waves and incremental learning is proposed. In the proposed method, phase reversal technology is first adopted to enhance the sensitivity for bolt loosening, and then a relative nonlinear coefficient based on phase reversal (RCP) is constructed. The disadvantage that linear indicators are insensitive to early loosening is overcome and the critical points of the multidegradation process are identified by this indicator, the tight contact stage (TCS) and the significant loosening stage. After the TCS is determined, a quantitative monitoring model, which fuses seven nonlinear damage indexes, is established based on incremental canonical correlation forests (ICCF). This algorithm achieves incremental learning by continuously increasing the number of decision trees. To verify the effectiveness of the method, an experimental study is carried out on four bolts. The monitoring effects of different indicators show that the method has higher accuracy and retains the ability to dynamically update data. [ABSTRACT FROM AUTHOR]

Traditional object detection algorithms operate within a closed set, where the training data may not cover all real-world objects. Therefore, the issue of open-world object detection has attracted significant attention. Open-world object detection faces two major challenges: "neglecting unknown objects" and "misclassifying unknown objects as known ones." In our study, we address these challenges by utilizing the Region Proposal Network (RPN) outputs to identify potential unknown objects with high object scores that do not overlap with ground truth annotations. We introduce the reselection mechanism, which separates unknown objects from the background. Subsequently, we employ the simulated annealing algorithm to disentangle features of unknown and known classes, guiding the detector's learning process. Our method has improved on multiple evaluation metrics such as U-mAP, U-recall, and UDP, greatly alleviating the challenges faced by open world object detection. [ABSTRACT FROM AUTHOR]

Collaborative robots (cobots) are increasingly integrated into Industry 4.0 dynamic manufacturing environments that require frequent system reconfiguration due to changes in cobot paths and payloads. This necessitates fast methods for identifying payload inertial parameters to compensate the cobot controller and ensure precise and safe operation. Our prior work used Incremental Ensemble Model (IEM) to identify payload parameters, eliminating the need for an excitation path and thus removing the separate identification step. However, this approach suffers from catastrophic forgetting. This paper introduces a novel incremental ensemble learning method that addresses the problem of catastrophic forgetting by adding a new weak learner to the ensemble model for each new training bag. Moreover, it proposes a new classification model that assists the ensemble model in identifying which weak learner provides the most accurate estimation for new input data. The proposed method incrementally updates the identification model while the cobot navigates any task path, maintaining accuracy on old weak learner even after updating with new data. Validation performed on the Franka Emika cobot showcases the model's superior accuracy and adaptability, effectively eliminating the problem of catastrophic forgetting. [ABSTRACT FROM AUTHOR]

Gesture recognition techniques based on surface electromyography (sEMG) signals face instability problems caused by electrode displacement and the time-varying characteristics of the signals in cross-time applications. This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. The results show that, after multiple increments, the framework achieves an average recognition rate of 96.5% from eight subjects, which is significantly better than that of cross-day analysis. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to select representative samples to update the replayed dataset, achieving a 93.7% recognition rate with fewer samples, which is better than the other three conventional sample selection methods. In addition, a comparison of full dataset training with incremental learning training demonstrates that the framework improves the recognition rate by nearly 1%, exhibits better recognition performance, significantly shortens the training time, reduces the cost of model updating and iteration, and is more suitable for practical applications. This study also investigates the use of the incremental learning of action classes, achieving an average recognition rate of 88.6%, which facilitates the supplementation of action types according to the demand, and further improves the application value of the action pattern recognition technology based on sEMG signals. [ABSTRACT FROM AUTHOR]

The identification of agricultural lands is crucial for sustainable development in rural areas. In this paper, an augmented model that utilizes multimodal satellite-based data samples for identifying agricultural lands via incremental learning. The Whale Optimization Algorithm (WOA) is used for augmenting collected images and data samples to enhance the accuracy of the model under real-time conditions. To identify agricultural lands, Deep Convolutional Neural Networks (CNNs) are trained on the augmented data samples. Additionally, the incorporation of Q-Learning for continuous optimization of the model to enhance its efficiency and effectiveness in identifying agricultural lands. The proposed model offers many edges over existing methods. Firstly, the use of multimodal satellite-based data samples allows for a comprehensive and accurate analysis of agricultural lands. Secondly, the incorporation of the Whale Optimization Algorithm enables the augmentation of collected data samples, leading to improved accuracy and reliability of the model. Thirdly, Deep CNNs allows the extraction of complex features from the data, leading to more accurate identification of agricultural lands. Finally, the use of Q-Learning ensures that the model is continuously optimized to improve its efficiency and effectiveness. The need for this work arises from the limitations of existing methods in accurately identifying agricultural lands. Traditional methods based on manual surveys and visual interpretation are time-consuming, expensive, and prone to errors. Moreover, existing automated methods often lack the ability to analyse multimodal satellite-based data samples and fail to provide accurate results. Based on these observations, the proposed augmented model offers a promising solution for identifying agricultural lands for sustainable development. The use of multimodal satellite-based data samples, WOA, Deep CNNs, and Q-Learning allows for an accurate and efficient analysis of agricultural lands, which can aid in sustainable development planning and decision-making operations. [ABSTRACT FROM AUTHOR]

This paper explores the comparative analysis of federated learning (FL) and centralized learning (CL) models in the context of multi-class traffic flow classification for network applications, a timely study in the context of increasing privacy preservation concerns. Unlike existing literature that often omits detailed class-wise performance evaluation, and consistent data handling and feature selection approaches, our study rectifies these gaps by implementing a feed-forward neural network and assessing FL performance under both independent and identically distributed (IID) and non-independent and identically distributed (non-IID) conditions, with a particular focus on incremental training. In our cross-silo experimental setup involving five clients per round, FL models exhibit notable adaptability. Under IID conditions, the accuracy of the FL model peaked at 96.65%, demonstrating its robustness. Moreover, despite the challenges presented by non-IID environments, our FL models demonstrated significant resilience, adapting incrementally over rounds to optimize performance; in most scenarios, our FL models performed comparably to the idealistic CL model regarding multiple well-established metrics. Through a comprehensive traffic flow classification use case, this work (i) contributes to a better understanding of the capabilities and limitations of FL, offering valuable insights for the real-world deployment of FL, and (ii) provides a novel, large, carefully curated traffic flow dataset for the research community. [ABSTRACT FROM AUTHOR]

Functional linear regression is one of the main modeling tools for working with functional data. Since functional data are usually stream data essentially and there are some noises in functional data. Many numerical research studies of machine learning indicate that the noise samples not only increase the amount of storage space, but also affect the performance of algorithm. Therefore, in this paper we consider a new learning strategy by introducing incremental learning, Markov sampling for functional linear regression and propose a novel functional incremental linear square regression algorithm based on Markov sampling (FILSR-MS). To have a better understanding of the proposed FILSR-MS, we not only estimate the generalization bound of the proposed algorithm and establish the fast learning rate, but also present some useful discussions. The performance of the proposed algorithm is validated by the numerical experiments for benchmark repository. [ABSTRACT FROM AUTHOR]

Incremental learning is one of the effective methods of learning from the accumulated training samples and the large-scale dataset. The main advantages of incremental learning consist of making full use of historical information, reducing the training scale greatly and saving space and time consumption. Despite extensive research on incremental support vector machine (SVM) learning algorithms, most of them are based on independent and identically distributed samples (i.i.d.). Not only that, there has been no theoretical analysis of incremental SVM learning algorithms. In this paper, we mainly study the generalization bounds of this incremental SVM learning algorithm whose samples are based on uniformly geometric Markov chains, and exponentially strongly mixing sequence. As a special case, we also obtain the generalization bounds of i.i.d. samples. [ABSTRACT FROM AUTHOR]

In this paper, we improve the broad learning system (BLS) by speeding up the incremental learning for added inputs. We propose an efficient implementation for a step that is in the pseudoinverse computation of a partitioned matrix, to reduce the computational complexity. The proposed efficient implementation has two different forms for the cases of q > k and q ≤ k , respectively, where q and k denote the number of additional training samples and the total number of nodes, respectively. The proposed implementation for q > k utilizes the inverse of a sum of matrices to compute only a k × k matrix inverse, instead of a q × q matrix inverse in the original implementation, and the corresponding speedup for the matrix inversion operation in the number of floating-point operations is 1 2 (q / k) 3 . Moreover, it also speeds up two relevant matrix multiplication operations in the original implementation. On the other hand, the proposed implementation for q ≤ k speeds up one matrix multiplication operation in the original implementation. The numerical simulations show that both the proposed and original implementations always achieve the same testing accuracy. On the Modified National Institute of Standards and Technology dataset, the speedups of the proposed efficient BLS implementation over the original BLS implementation in total training time are 1.35–1.57 when q > k and 1.09–1.13 when q < k , while on the NYU object recognition benchmark dataset, the speedups are 1.10–1.35 when q > k and 1.07–1.09 when q < k. [ABSTRACT FROM AUTHOR]

Few-Shot Class Incremental Learning (FSCIL) is a trending topic in deep learning, addressing the need for models to incrementally learn novel classes, particularly in real-world scenarios where continuously emerging classes come with limited labeled samples. However, the majority of FSCIL research has been dedicated to image classification and object recognition tasks, with limited attention given to video action classification. In this paper, we present a new Cluster Compression and Generative Separation (CCGS) method for Incremental Few-Shot Video Action Recognition (iFSVAR), which introduces contrastive learning to boost the degree of class separation in the base session. Simultaneously, it creates numerous fine-grained classes with diverse semantics, effectively filling the unallocated representation space. Experimental results on UCF101, Kinetics, and Something-Something-V2 demonstrate the effectiveness of the framework. [ABSTRACT FROM AUTHOR]

Just-in-time software defect prediction technology enables just-in-time defect prediction at the granularity of code changes, which is crucial for improving software code quality and ensuring software reliability. Traditional static software defect prediction models suffer from 'knowledge forgetting' when processing just-in-time software data streams, leading to poor model generalization performance. To address this, this paper proposed an incremental model method based on knowledge replay for just-in-time software defect prediction. Firstly, it used the knowledge replay mechanism stores model parameters and random samples to facilitate the learning of old knowledge. Secondly, this paper used a distributed training framework to perform incremental learning on just-in-time software data streams on local devices, achieving real-time model updates through restructuring. Lastly, this paper employed the knowledge distillation technique to construct a global incremental prediction model. Experiments show that this model performs better in terms of comprehensive performance compared to common modeling algorithms while ensuring training efficiency. [ABSTRACT FROM AUTHOR]

This study introduces a two-step reinforcement learning (RL) strategy tailored for “The Lord of the Rings: The Card Game”, a complex multistage strategy card game. The research diverges from conventional RL methods by adopting a phased learning approach, beginning with a foundational learning step in a simplified version of the game and subsequently progressing to the complete, intricate game environment. This methodology notably enhances the AI agent’s adaptability and performance in the face of the unpredictable and challenging nature of the game. The paper also explores a multi-phase system where distinct RL agents are employed for various decision-making phases of the game. This approach has demonstrated remarkable improvement, with the RL agents achieving a winrate of 78.5% at the highest difficulty level. [ABSTRACT FROM AUTHOR]

The outbreak of emerging infectious diseases poses significant challenges to global public health. Accurate early forecasting is crucial for effective resource allocation and emergency response planning. This study aims to develop a comprehensive predictive model for emerging infectious diseases, integrating the blending framework, transfer learning, incremental learning, and the biological feature Rt to increase prediction accuracy and practicality. By transferring features from a COVID-19 dataset to a monkeypox dataset and introducing dynamically updated incremental learning techniques, the model's predictive capability in data-scarce scenarios was significantly improved. The research findings demonstrate that the blending framework performs exceptionally well in short-term (7-day) predictions. Furthermore, the combination of transfer learning and incremental learning techniques significantly enhanced the adaptability and precision, with a 91.41% improvement in the RMSE and an 89.13% improvement in the MAE. In particular, the inclusion of the Rt feature enabled the model to more accurately reflect the dynamics of disease spread, further improving the RMSE by 1.91% and the MAE by 2.17%. This study underscores the significant application potential of multimodel fusion and real-time data updates in infectious disease prediction, offering new theoretical perspectives and technical support. This research not only enriches the theoretical foundation of infectious disease prediction models but also provides reliable technical support for public health emergency responses. Future research should continue to explore integrating data from multiple sources and enhancing model generalization capabilities to further enhance the practicality and reliability of predictive tools. [ABSTRACT FROM AUTHOR]

Natural-language dialog is key for an intuitive human-robot interaction. It can be used not only to express humans' intents but also to communicate instructions for improvement if a robot does not understand a command correctly. It is of great importance to let robots learn from such interaction experiences in an incremental way to allow them to improve their behaviors or avoid mistakes in the future. In this paper, we propose a system to achieve such incremental learning of complex high-level behavior from natural interaction and demonstrate its implementation on a humanoid robot. Our system deploys large language models (LLMs) for high-level orchestration of the robot's behavior based on the idea of enabling the LLM to generate Python statements in an interactive console to invoke both robot perception and action. Human instructions, environment observations, and execution results are fed back to the LLM, thus informing the generation of the next statement. Since an LLM can misunderstand (potentially ambiguous) user instructions, we introduce incremental learning from the interaction, which enables the system to learn from its mistakes. For that purpose, the LLM can call another LLM responsible for code-level improvements in the current interaction based on human feedback. Subsequently, we store the improved interaction in the robot's memory so that it can later be retrieved on semantically similar requests. We integrate the system in the robot cognitive architecture of the humanoid robot ARMAR-6 and evaluate our methods both quantitatively (in simulation) and qualitatively (in simulation and real-world) by demonstrating generalized incrementally learned knowledge. [ABSTRACT FROM AUTHOR]

Global Positioning System (GPS)/Inertial Navigation System (INS) integration is a widely used technique for navigation and positioning applications. It combines the advantages of GPS and INS to provide accurate and reliable information. However, the GPS/INS integration suffers from performance degradation during a GPS outage, which occurs when natural or artificial factors block the GPS signal. The novelty of this paper is improving GPS/INS integration performance during GPS outages using Incremental Regularized LSTM (IncRLSTM) learning. Incremental learning is a learning paradigm that can be learned from streaming data online and updating the model parameters without forgetting the previous ones. Also, regularization is a technique that prevents overfitting and improves the generalization of the network by adding some constraints or penalties to the model. IncRLSTM learning models the GPS signal as a multi-objective regression and corrects the INS output with the Faded Memory Kalman filter. The results on real-world datasets significantly show the reduction of positioning errors by an average of 72 % during GPS outages and the improvement of the accuracy and robustness of GPS/INS integration by an average of 58 % compared with existing methods. Moreover, IncRLSTM presents, on average, a 10 % improvement compared to the existing methods. [ABSTRACT FROM AUTHOR]

Modern natural language processing (NLP) state-of-the-art (SoTA) deep learning (DL) models have hundreds of millions of parameters, making them extremely complex. Large datasets are required for training these models, and while pretraining has reduced this requirement, human-labelled datasets are still necessary for fine-tuning. Few-shot learning (FSL) techniques, such as meta-learning, try to train models from smaller datasets to mitigate this cost. However, the tasks used to evaluate these meta-learners frequently diverge from the problems in the real world that they are meant to resolve. This work aims to apply meta-learning to a problem that is more pertinent to the real world: class incremental learning (IL). In this scenario, after completing its training, the model learns to classify newly introduced classes. One unique quality of meta-learners is that they can generalise from a small sample size to classes that have never been seen before, which makes them especially useful for class incremental learning (IL). The method describes how to emulate class IL using proxy new classes. This method allows a meta-learner to complete the task without the need for retraining. To generate predictions, the transformer-based aggregation function in a meta-learner that modifies data from examples across all classes has been proposed. The principal contributions of the model include concurrently considering the entire support and query sets, and prioritising attention to crucial samples, such as the question, to increase the significance of its impact during inference. The outcomes demonstrate that the model surpasses prevailing benchmarks in the industry. Notably, most meta-learners demonstrate significant generalisation in the context of class IL even without specific training for this task. This paper establishes a high-performing baseline for subsequent transformer-based aggregation techniques, thereby emphasising the practical significance of meta-learners in class IL. [ABSTRACT FROM AUTHOR]

A modified fuzzy min-max classifier is presented that differs from the original in the way that the hyperbox expansion operation is performed. The classifier has been tested on the solution of cybersecurity problems, such as detecting spam, phishing sites and attacks on network connections. The results of experiments results showed an improvement in the accuracy relative to the original fuzzy min-max classifier. Comparisons with six alternative incremental learning classifiers showed competitive results on the false acceptance rate, the false reject rate, and the F1-score values. [ABSTRACT FROM AUTHOR]

Event detection is a crucial task in information extraction. Existing research primarily focuses on machine automatic detection tasks, which often perform poorly in certain practical applications. To address this, an interactive event-detection mode of "machine recommendation-human review–machine incremental learning" was proposed. In this mode, we study a few-shot continual class-incremental learning scenario, where the challenge is to learn new-class events with limited samples while preserving memory of old class events. To tackle these challenges, we propose a class-incremental learning method for interactive event detection via Interaction, Contrast and Distillation (ICD). We design a replay strategy based on representative and confusable samples to retain the most valuable samples under limited conditions; we introduce semantic-boundary-smoothness contrastive learning for effective learning of new-class events with few samples; and we employ hierarchical distillation to mitigate catastrophic forgetting. These methods complement each other and show strong performance. Experimental results demonstrate that, in the 5-shot 5-round class incremental-learning settings on two Chinese event-detection datasets ACE and DuEE, our method achieves final recall rates of 71.48% and 90.39%, respectively, improving by 6.86% and 3.90% over the best baseline methods. [ABSTRACT FROM AUTHOR]

The incremental recommendation involves updating existing models by extracting information from interaction data at current time-step, with the aim of maintaining model accuracy while addressing limitations including parameter dependencies and inefficient training. However, real-time user interaction data is often afflicted by substantial noise and invalid samples, presenting the following key challenges for incremental model updating: (1) how to effectively extract valuable new knowledge from interaction data at the current time-step to ensure model accuracy and timeliness, and (2) how to safeguard against the catastrophic forgetting of long-term stable preference information, thus preserving the model's sensitivity during cold-starts. In response to these challenges, we propose the Incremental Recommendation with Stable Latent Side-information Updating (SIIFR). This model employs a side-information augmenter to extract valuable latent side-information from user interaction behavior at time-step T, thereby sidestepping the interference caused by noisy interaction data and acquiring stable user preference. Moreover, the model utilizes rough interaction data at time-step T + 1 , in conjunction with existing side-information enhancements to achieve incremental updates of latent preferences, thereby ensuring the model's efficacy during cold-start. Furthermore, SIIFR leverages the change rate in user latent side-information to mitigate catastrophic forgetting that results in the loss of long-term stable preference information. The effectiveness of the proposed model is validated and compared against existing models using four popular incremental datasets. The model code can be achieved at: https://github.com/LNNU-computer-research-526/FR-sii. [ABSTRACT FROM AUTHOR]

Modulation recognition is an important technology used to correctly identify the modulation modes of wireless signals and is widely used in cooperative and confrontational scenarios. Traditional modulation-recognition algorithms require the assistance of expert experiences, which constrains their applications. With the rapid development of artificial intelligence in recent years, deep learning (DL) is widely advocated for intelligent modulation recognition. Typically, DL-based modulation-recognition algorithms implicitly assume a relatively static scenario in which the signal samples of all the modulation modes can be completely collected in advance. In practical situations, the radio environment is quite dynamic and the signal samples with new modulation modes may appear sequentially, in which the current DL-based modulation-recognition algorithms may require unacceptable time and computing resource consumption to re-train the optimal model from scratch. In this study, we leveraged incremental learning (IL) and designed a novel IL-based modulation-recognition algorithm that consists of an initial stage and multiple incremental stages. The main novelty of the proposed algorithm lies in the new loss function design in each incremental stage, which combines the distillation loss of recognizing old modulation modes and the cross-entropy loss of recognizing new modulation modes. With the proposed algorithm, the knowledge of the signal samples with new modulation modes can be efficiently learned in the current stage without forgetting the knowledge learned in the previous stages. The simulation results demonstrate that the proposed algorithm could achieve a recognition accuracy close to the upper bound with a much lower computing time and it outperformed the existing IL-based benchmarks. [ABSTRACT FROM AUTHOR]

Building systems for predicting human socio-emotional states has promising applications; however, if trained on biased data, such systems could inadvertently yield biased decisions. Bias mitigation remains an open problem, which tackles the correction of a model's disparate performance over different groups defined by particular sensitive attributes (e.g., gender, age, and race). In this work, we design a novel fairness loss function named Multi-Group Parity (MGP) to provide a generalised approach for bias mitigation in personality computing. In contrast to existing works in the literature, MGP is generalised as it features four ‘multiple’ properties (4Mul): multiple tasks, multiple modalities, multiple sensitive attributes, and multi-valued attributes. Moreover, we explore how to incrementally mitigate the biases when more sensitive attributes are taken into consideration sequentially. Towards this problem, we introduce a novel algorithm that utilises an incremental learning framework to mitigate bias against one attribute data at a time without compromising past fairness. Extensive experiments on two large-scale multi-modal personality recognition datasets validate the effectiveness of our approach in achieving superior bias mitigation under the proposed four properties and incremental debiasing settings. [ABSTRACT FROM AUTHOR]

In recent years, semi-supervised learning (SSL) methods based on pseudo-labeling algorithms have been widely applied and achieved significant success. However, most existing deep semi-supervised learning methods suffer from the problem of distribution shift between the source and target domains, as well as the issue of "cognitive bias" in pseudo labeling algorithms, where the model's errors are difficult to rectify as they accumulate through the pseudo-labeling process. This paper introduces the concept of causal invariance and proposes an incremental repeated labeling strategy with a high confidence threshold to enhance the utilization of unlabeled samples. It effectively solves the issue of distribution discrepancy between the source and target domains in the field of semi-supervised learning, as well as the problem of pseudo label "cognitive bias", thus improving the accuracy of the model. Extensive experiments on CIFAR-10, CIFAR-100, SVHN, STL-10, PACS and VLCS demonstrate that semi-supervised incremental models based on causal invariance have a significant improvement in domain generalization ability compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract Human pose estimation has a wide range of applications. Existing methods perform well in conventional domains, but there are certain defects when they are applied to sports activities. The first is lack of estimation of the extremity posture, making it impossible to comprehensively evaluate the movement posture; the second is insufficient occlusion handling. Therefore, we propose a human pose compensation network based on incremental learning, which obtains shared weights to extract detailed features under the premise of limited extremity training data. We propose a higher-order feature compensator (HOF-compensator) to embed the attributes of the extremity into the torso and limbs topology structure, building a complete higher-order feature. In addition, to improve the occlusion handling performance, we propose an occlusion feature enhancement attention mechanism (OFE-attention) that can identify occluded keypoints and enhance attention to occlusion areas. We design comparative experiments on three public datasets and a self-built sports dataset, achieving the highest mean accuracy among all comparative methods. In addition, we design a series of ablation analysis and visualization displays to verify that our method performs best in sports pose estimation.

Abstract The outbreak of emerging infectious diseases poses significant challenges to global public health. Accurate early forecasting is crucial for effective resource allocation and emergency response planning. This study aims to develop a comprehensive predictive model for emerging infectious diseases, integrating the blending framework, transfer learning, incremental learning, and the biological feature Rt to increase prediction accuracy and practicality. By transferring features from a COVID-19 dataset to a monkeypox dataset and introducing dynamically updated incremental learning techniques, the model's predictive capability in data-scarce scenarios was significantly improved. The research findings demonstrate that the blending framework performs exceptionally well in short-term (7-day) predictions. Furthermore, the combination of transfer learning and incremental learning techniques significantly enhanced the adaptability and precision, with a 91.41% improvement in the RMSE and an 89.13% improvement in the MAE. In particular, the inclusion of the Rt feature enabled the model to more accurately reflect the dynamics of disease spread, further improving the RMSE by 1.91% and the MAE by 2.17%. This study underscores the significant application potential of multimodel fusion and real-time data updates in infectious disease prediction, offering new theoretical perspectives and technical support. This research not only enriches the theoretical foundation of infectious disease prediction models but also provides reliable technical support for public health emergency responses. Future research should continue to explore integrating data from multiple sources and enhancing model generalization capabilities to further enhance the practicality and reliability of predictive tools.

Global Positioning System (GPS)/Inertial Navigation System (INS) integration is a widely used technique for navigation and positioning applications. It combines the advantages of GPS and INS to provide accurate and reliable information. However, the GPS/INS integration suffers from performance degradation during a GPS outage, which occurs when natural or artificial factors block the GPS signal. The novelty of this paper is improving GPS/INS integration performance during GPS outages using Incremental Regularized LSTM (IncRLSTM) learning. Incremental learning is a learning paradigm that can be learned from streaming data online and updating the model parameters without forgetting the previous ones. Also, regularization is a technique that prevents overfitting and improves the generalization of the network by adding some constraints or penalties to the model. IncRLSTM learning models the GPS signal as a multi-objective regression and corrects the INS output with the Faded Memory Kalman filter. The results on real-world datasets significantly show the reduction of positioning errors by an average of 72 % during GPS outages and the improvement of the accuracy and robustness of GPS/INS integration by an average of 58 % compared with existing methods. Moreover, IncRLSTM presents, on average, a 10 % improvement compared to the existing methods.

Abstract Modern natural language processing (NLP) state-of-the-art (SoTA) deep learning (DL) models have hundreds of millions of parameters, making them extremely complex. Large datasets are required for training these models, and while pretraining has reduced this requirement, human-labelled datasets are still necessary for fine-tuning. Few-shot learning (FSL) techniques, such as meta-learning, try to train models from smaller datasets to mitigate this cost. However, the tasks used to evaluate these meta-learners frequently diverge from the problems in the real world that they are meant to resolve. This work aims to apply meta-learning to a problem that is more pertinent to the real world: class incremental learning (IL). In this scenario, after completing its training, the model learns to classify newly introduced classes. One unique quality of meta-learners is that they can generalise from a small sample size to classes that have never been seen before, which makes them especially useful for class incremental learning (IL). The method describes how to emulate class IL using proxy new classes. This method allows a meta-learner to complete the task without the need for retraining. To generate predictions, the transformer-based aggregation function in a meta-learner that modifies data from examples across all classes has been proposed. The principal contributions of the model include concurrently considering the entire support and query sets, and prioritising attention to crucial samples, such as the question, to increase the significance of its impact during inference. The outcomes demonstrate that the model surpasses prevailing benchmarks in the industry. Notably, most meta-learners demonstrate significant generalisation in the context of class IL even without specific training for this task. This paper establishes a high-performing baseline for subsequent transformer-based aggregation techniques, thereby emphasising the practical significance of meta-learners in class IL.

Federated Learning has received much attention due to its data privacy benefits, but most existing approaches assume that client classes are fixed. Clients may remove old classes and add new ones, leading to catastrophic forgetting of the model. Existing methods have limitations, such as requiring additional client storage and distillation methods becoming less effective as new classes increase. For this reason, this paper proposes the Data-Model Anti-Forgetting (DMAF) framework. Specifically, in the proposed framework, an auxiliary client and group aggregation method to mitigate catastrophic forgetting at the data level has been proposed, which does not require clients to allocate additional storage space to store synthetic data and can balance class distributions. A multi-teacher integrated knowledge distillation method was adopted to retain old class knowledge by distilling multiple teacher models and design task fusion for further tuning of the global model. Finally, this paper conducts extensive experiments on public datasets to validate the effectiveness of DMAF. [ABSTRACT FROM AUTHOR]

Human pose estimation has a wide range of applications. Existing methods perform well in conventional domains, but there are certain defects when they are applied to sports activities. The first is lack of estimation of the extremity posture, making it impossible to comprehensively evaluate the movement posture; the second is insufficient occlusion handling. Therefore, we propose a human pose compensation network based on incremental learning, which obtains shared weights to extract detailed features under the premise of limited extremity training data. We propose a higher-order feature compensator (HOF-compensator) to embed the attributes of the extremity into the torso and limbs topology structure, building a complete higher-order feature. In addition, to improve the occlusion handling performance, we propose an occlusion feature enhancement attention mechanism (OFE-attention) that can identify occluded keypoints and enhance attention to occlusion areas. We design comparative experiments on three public datasets and a self-built sports dataset, achieving the highest mean accuracy among all comparative methods. In addition, we design a series of ablation analysis and visualization displays to verify that our method performs best in sports pose estimation. [ABSTRACT FROM AUTHOR]

In this paper, a method is introduced to control the dark knowledge values also known as soft targets, with the purpose of improving the training by knowledge distillation for multi-class classification tasks. Knowledge distillation effectively transfers knowledge from a larger model to a smaller model to achieve efficient, fast, and generalizable performance while retaining much of the original accuracy. The majority of deep neural models used for classification tasks append a SoftMax layer to generate output probabilities and it is usual to take the highest score and consider it the inference of the model, while the rest of the probability values are generally ignored. The focus is on those probabilities as carriers of dark knowledge and our aim is to quantify the relevance of dark knowledge, not heuristically as provided in the literature so far, but with an inductive proof on the SoftMax operational limits. These limits are further pushed by using an incremental decision tree with information gain split. The user can set a desired precision and an accuracy level to obtain a maximal temperature setting for a continual classification process. Moreover, by fitting both the hard targets and the soft targets, one obtains an optimal knowledge distillation effect that mitigates better catastrophic forgetting. The strengths of our method come from the possibility of controlling the amount of distillation transferred non-heuristically and the agnostic application of this model-independent study.

In response to the issue that traditional lung nodule detection models cannot dynamically optimize and update with the increase of new data, a new lung nodule detection model—task incremental meta-learning model (TIMLM) is proposed. This model comprises of two loops: the inner loop imposes incremental learning regularization update constraints, while the outer loop employs a meta-update strategy to sample old and new knowledge and learn a set of generalized parameters that adapt to old and new data. Under the condition that the main structure of the model is not changed as much as possible, it preserves the old knowledge that was learned previously. Experimental verification on the publicly available lung dataset showed that, compared with traditional deep network models and mainstream incremental models, TIMLM has greatly improved in terms of accuracy, sensitivity, and other indicators, demonstrating good continuous learning and anti-forgetting capabilities.

Stock price prediction has always been a focal topic in financial research and quantitative investment. Currently, most deep learning models for stock price prediction are based on batch learning settings, which require prior knowledge of the training dataset. These models are not scalable for real-time data stream prediction, and their performance decreases when the data distribution dynamically changes. To address the issue of poor prediction accuracy for non-stationary stock price data in existing research, this paper proposes an online stock price prediction model (Increformer) based on incremental learning and continuous attention mechanism. By leveraging continuous self-attention mechanism to capture the temporal dependencies among feature variables and employing continuous normalization mechanism to handle non-stationary data, the model enhances prediction accuracy through the incremental training strategy based on elastic weighting consolidation to acquire new knowledge from the data stream. Five public datasets are selected from the stock index and individual stock price sequences in the stock market for experiments. Experimental results demonstrate that Increformer effectively extracts temporal information and feature dimension correlation from the data, thus improving the prediction performance of stock prices. Additionally, ablation experiments are conducted to evaluate the effects and necessity of the continuous normalization mechanism, continuous attention mechanism, and incremental training strategy, thereby verifying the accuracy and generalizability of the proposed model. Increformer can effectively capture the trends and fluctuations of stock price series.

Defect detection constitutes one of the most crucial processes in industrial production. With a continuous increase in the number of defect categories and samples, the defect detection model underpinned by deep learning finds it challenging to expand to new categories, and the accuracy and real-time performance of product defect detection are also confronted with severe challenges. This paper addresses the problem of insufficient detection accuracy of existing lightweight models on resource-constrained edge devices by presenting a new lightweight YoloV5 model, which integrates four modules, SCDown, GhostConv, RepNCSPELAN4, and ScalSeq. Here, this paper abbreviates it as SGRS-YoloV5n. Through the incorporation of these modules, the model notably enhances feature extraction and computational efficiency while reducing the model size and computational load, making it more conducive for deployment on edge devices. Furthermore, a cloud-edge collaborative defect detection system is constructed to improve detection accuracy and efficiency through initial detection by edge devices, followed by additional inspection by cloud servers. An incremental learning mechanism is also introduced, enabling the model to adapt promptly to new defect categories and update its parameters accordingly. Experimental results reveal that the SGRS-YoloV5n model exhibits superior detection accuracy and real-time performance, validating its value and stability for deployment in resource-constrained environments. This system presents a novel solution for achieving efficient and accurate real-time defect detection. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the era of big data, there exist complex structure between different classes labels. Hierarchical structure, among others, has become a representative one, which is mathematically depicted as a tree-like structure or directed acyclic graph. Most studies in the literature focus on static feature selection in hierarchical information system. In this study, in order to solve the incremental feature selection problem of hierarchical classification in a dynamic environment, we develop two incremental algorithms for this purpose (IHFSGR-1 and IHFSGR-2 for short). As a preliminary step, we propose a new uncertainty measure to quantify the amount of information contained in the hierarchical classification system, and based on this, we develop a non-incremental hierarchical feature selection algorithm. Next, we investigate the updating mechanism of this uncertainty measure upon the arrival of samples, and propose two strategies for adding and deleting features, leading to the development of two incremental algorithms. Finally, we conduct some comparative experiments with several non-incremental algorithms. The experimental results suggest that compared with several non-incremental algorithms, our incremental algorithms can achieve better performance in terms of the classification accuracy and two hierarchical evaluation metrics, and can significantly accelerate the fuzzy rough set-based hierarchical feature selection. [ABSTRACT FROM AUTHOR]

Partial order formal structure analysis (POFSA) is an emerging theory in the field of concept cognitive learning (CCL). Attribute partial order structure diagram (APOSD) is the visual expression of the knowledge structure in POFSA. It has the advantages of explicit expression of the hierarchies of attributes and concise visual expression of the knowledge structure. This paper mainly focuses on the incremental CCL of APOSD in dynamic data circumstances. Firstly, the concept of location information coding of nodes in APOSD is proposed to express the position of nodes in the entire diagram as well as the relationships between nodes, which is an important tool throughout this paper. Secondly, by analyzing the relationship between new objects and objects in the original diagram, dynamic learning strategy for APOSD is proposed. Thirdly, in order to balance the efficiency and accuracy of dynamic learning, a dynamic-static alternating self-learning method for APOSD is proposed, which is an improved incremental learning strategy. Finally, comparative experiments illustrate that compared with non-incremental learning method of APOSD and concept lattice, the two proposed incremental learning methods of APOSD can effectively achieve dynamic self-updating of the knowledge base when processing dynamic data, and provide another perspective for discovering knowledge from the same data. Besides, the effectiveness of the improved incremental learning strategy is verified as well. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Incremental learning is a learning paradigm in which a model is updated continuously as new data becomes available, and its main challenge is to adapt to non-stationary environments without the time-consuming re-training process. Many efforts have been made on incremental supervised learning. However, providing sufficient labeled data remains a major problem. Recently, domain adaptation methods have gained attention. These methods aim to leverage the knowledge from an auxiliary source domain to boost the performance of the model in the target domain by reducing the domain discrepancy between them. Regarding these issues, in the present paper, a proposed model aims to incrementally learn a new domain characterized by drifts due to a non-stationary environment. It utilizes an unsupervised, fuzzy-based domain adaptation to classify data streams faced with concept drift while accounting for a label-agnostic incremental setting in the target domain. Incremental learning updates occur whenever the entropy-based metric indicates uncertainty, ensuring informative samples are integrated. Also, outdated samples are forgotten during the training stage using the dynamic sample weighting strategy. Through experimentation on forty-five tasks, the superiority of the proposed model in handling dynamic adaptation on non-stationary domains is demonstrated, showcasing improvements in accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

As a widely used data preprocessing method, feature selection with rough sets aims to delete redundant conditional features. However, most of the traditional feature selection methods target to the static data set environment, and the importance of features is used as the base for feature selection. These methods only consider the importance of features themselves and do not consider the impact of features on classification. In order to overcome such shortcomings, we first use the information of knowledge granules to calculate the similarity of samples in the same cluster and samples in different clusters; Secondly, from the perspective of clustering, we stick to the principle that the samples in the same cluster are as close as possible, and the samples in different clusters are as far away as possible, then a feature selection model of knowledge granularity (in short SKG) based on the clustering background is designed; Thirdly, in order to make the SKG model adapt to the reduction of dynamic data sets, we discuss the incremental learning mechanism of sample and feature changes, and two incremental models SKGOA and SKGAA are designed to deal with the dynamic feature reduction when some samples and features are added into the decision system. Finally, some numerical experiments are conducted to assess the performance of the proposed algorithms, and the results shown that our approaches are of a prominent advantage in terms of computational time and classification accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a method is introduced to control the dark knowledge values also known as soft targets, with the purpose of improving the training by knowledge distillation for multi-class classification tasks. Knowledge distillation effectively transfers knowledge from a larger model to a smaller model to achieve efficient, fast, and generalizable performance while retaining much of the original accuracy. The majority of deep neural models used for classification tasks append a SoftMax layer to generate output probabilities and it is usual to take the highest score and consider it the inference of the model, while the rest of the probability values are generally ignored. The focus is on those probabilities as carriers of dark knowledge and our aim is to quantify the relevance of dark knowledge, not heuristically as provided in the literature so far, but with an inductive proof on the SoftMax operational limits. These limits are further pushed by using an incremental decision tree with information gain split. The user can set a desired precision and an accuracy level to obtain a maximal temperature setting for a continual classification process. Moreover, by fitting both the hard targets and the soft targets, one obtains an optimal knowledge distillation effect that mitigates better catastrophic forgetting. The strengths of our method come from the possibility of controlling the amount of distillation transferred non-heuristically and the agnostic application of this model-independent study. [ABSTRACT FROM AUTHOR]

The energy consumption of air-jet looms mainly comes from air compressors. Predicting the air consumption of air-jet looms for the upcoming period is significant for the variable frequency adjustment of air compressors, thereby aiding in energy saving and reducing fabric costs. This paper proposes an innovative method that combines Sliding Time Windows (STW), feature analysis, and incremental learning to improve the accuracy of short-term air consumption prediction. First, the STW method is employed during the data collection phase to enhance data reliability. Through feature analysis, significant factors affecting the air consumption of air-jet looms, beyond traditional research, are explored and incorporated into the prediction model. The experimental results indicate that the introduction of new features improved the model's R2 from 0.905 to 0.950 and reduced the MSE from 32.369 to 16.239. The STW method applied to the same random forest model increased the R2 from 0.906 to 0.950 and decreased the MSE from 32.244 to 16.239. The decision tree method, compared to the linear regression model, improved the R2 from 0.928 to 0.950 and reduced the MSE from 23.541 to 16.239, demonstrating significant predictive performance enhancement. After establishing the optimal model, incremental learning is used to continuously improve the reliability and accuracy of short-term predictions. Experiments show that the incremental learning method, compared to static models, offers better resilience and reliability when new data are collected. The proposed method significantly improves the accuracy of air consumption prediction for air-jet looms, providing strong support for the variable frequency adjustment of air compressors, and contributes to the goals of energy saving and cost reduction. The research results indicate that this method not only enhances prediction accuracy but also provides new insights and methods for future energy-saving research. [ABSTRACT FROM AUTHOR]

Domain Generation Algorithms (DGAs) are algorithms present in most malware used by botnets and advanced persistent threats. These algorithms dynamically generate domain names to maintain and obfuscate communication between the infected device and the attacker's command and control server. Since DGAs are used by many threats, it is extremely important to classify a given DGA according to the threat it is related to. In addition, as new threats emerge daily, classifier models tend to become obsolete over time. Deep neural networks tend to lose their classification ability when retrained with a dataset that is significantly different from the initial one, a phenomenon known as catastrophic forgetting. This work presents a computational scheme composed of a deep learning model based on CNN and natural language processing and an incremental learning technique for class increment through transfer learning to classify 60 DGA families and include a new family to the classifier model, training the model incrementally using some examples from known families, avoiding catastrophic forgetting and maintaining metric levels. The proposed methodology achieved an average precision of 86.75%, an average recall of 83.06%, and an average F1 score of 83.78% with the full dataset, and suffered minimal losses when applying the class increment. [ABSTRACT FROM AUTHOR]

For over a century, the neuro- and pathophysiological, behavioral, and cognitive correlates of consciousness have been an active field of theoretical considerations and empirical research in a wide range of modern disciplines. Conscious cognitive processing of information cannot be observed directly, but might be inferred from step-like discontinuities in learning performance or sudden insight-based improvements in problem solving behavior. It is assumed that a sudden step of knowledge associated with insight requires a creative reorganization of mental representations of task- or problemrelevant information and the restructuration of the task, respectively problem to overcome an cognitive dead-end or impasse. Discontinuities in learning performance or problem solving after an insight event can be used as timetags to capture the time window in which conscious cognitive information processing must have taken place. According to the platform theory of conscious cognitive information processing, the reorganization and restructuration processes, require the maintenance of task- or problem-relevant information in working memory for the operation of executive functions on these mental representations. Electrophysiological evidence suggests that the reorganization and restructuration processes in working memory, that precede insight-based problem solutions are accompanied by an increase in the power of gamma oscillations in cortical areas including the prefrontal cortex. Empirical evidence and theoretical assumptions argue for an involvement of gap junction channels and connexin hemichannels in cortical gamma-oscillations and working memory processes. Discontinuities in learning or problem solving performance might be used as time-tags to investigate the implication of gap junction channels and hemichannels in conscious cognitive processing. [ABSTRACT FROM AUTHOR]

The key point of current research on few shot object detection based on meta learning is how to make better use of the information of support branch to help query branch to recognize novel objects more effectively. However, many current methods fuse the features from support branch and query branch in the depth direction, ignoring the spatial position relationship between features. Therefore, this study proposes non local support attention network. This method not only adds support information into the proposals features, but also fuses the support information with the features that fed into region proposal network. The spatial position relationship between features is considered at the same time. It also adds the information of negative supports to the detection module to help the model distinguish the objects from different categories. This method obtains good performance on base classes and novel classes of COCO2017 dataset, particularly under the case of incremental learning. Compared with method before improvement, 3.3/3.8/4.7 mAP is increased in AP/AP50/AP75 of novel classes. 2.7/0.5/3.3 mAP is increased in AP/AP50/AP75 of the base classes, and outperformed the performance of SOTA (Sort-Of-The Art) model DAnA (Dual-Awareness Attention) under the same setting. [ABSTRACT FROM AUTHOR]

While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget catastrophically whenever they face a changing training data distribution. Rehearsal-based Continual Learning (CL) approaches have been established as a versatile and reliable solution to overcome this limitation; however, sudden input disruptions and memory constraints are known to alter the consistency of their predictions. We study this phenomenon by investigating the geometric characteristics of the learner's latent space and find that replayed data points of different classes increasingly mix up, interfering with classification. Hence, we propose a geometric regularizer that enforces weak requirements on the Laplacian spectrum of the latent space, promoting a partitioning behavior. Our proposal, called Continual Spectral Regularizer for Incremental Learning (CaSpeR-IL), can be easily combined with any rehearsal-based CL approach and improves the performance of SOTA methods on standard benchmarks. • We study the geometry of a model's latent space in a Continual Learning setting. • We propose Continual Spectral Regularizer, a geometrically motivated regularizer. • We combine CaSpeR with SOTA rehearsal-based CL approaches in standard benchmarks. • We compare our proposal with recent contrastive-based CL approaches. • We reveal that CaSpeR achieves increased accuracy and reduced forgetting. [ABSTRACT FROM AUTHOR]

In this contribution, the model which is dedicated to reducing the number of false positive instances is proposed. This is a self-tuning model using aggregation functions and time-series data periods. As a case study, the proposed model is tested in the context of phishing link detection. In the proposed model, well-known aggregation functions are applied to combine the confidence values of multiple Classification models for email phishing. The division of the dataset into multiple segments and subsets facilitates the implementation of incremental learning strategies. This approach enables the iterative enhancement of model performance through the training of new data while leveraging previously acquired knowledge. In our research, two datasets are considered, namely the existing PhiUSIIL phishing URL dataset as well as the dataset provided by the FreshMail company are applied. The proposed algorithm achieves a small number of expected false positives. This reduces the costs associated with manual analysis of such cases by domain experts (in the case of incorrect prediction as phishing mail). [ABSTRACT FROM AUTHOR]

The perception system plays a crucial role by integrating LiDAR and various sensors to perform localization and object detection, which ensures the security of intelligent driving. However, existing research indicates that LiDAR is vulnerable to sensor attacks, which lead to inappropriate driving strategies and need effective attack recognition methods. Previous LiDAR attack recognition methods rely on fixed anomaly thresholds obtained from depth map data distributions in specific scenarios as static anomaly boundaries, which lead to reduced accuracy, increased false alarm rates, and a lack of performance stability. To address these problems, we propose an adaptive LiDAR attack recognition framework capable of adjusting to different driving scenarios. This framework initially models the perception system by integrating the vehicle dynamics model and object tracking algorithms to extract data features, subsequently employing Gaussian Processes for the probabilistic modeling of these features. Finally, the framework employs sparsification computing techniques and a sliding window strategy to continuously update the Gaussian Process model with window data, which achieves incremental learning that generates uncertainty estimates as dynamic anomaly boundaries to recognize attacks. The performance of the proposed framework has been evaluated extensively using the real-world KITTI dataset covering four driving scenarios. Compared to previous methods, our framework achieves a 100% accuracy rate and a 0% false positive rate in the localization system, and an average increase of 3.43% in detection accuracy in the detection system across the four scenarios, which demonstrates superior adaptive capabilities. [ABSTRACT FROM AUTHOR]

Last decade demonstrate the massive growth in organizational data which keeps on increasing multi‐fold as millions of records get updated every second. Handling such vast and continuous data is challenging which further opens up many research areas. The continuously flowing data from various sources and in real‐time is termed as streaming data. While deriving valuable statistics from data streams, the variation that occurs in data distribution is called concept drift. These drifts play a significant role in a variety of disciplines, including data mining, machine learning, ubiquitous knowledge discovery, quantitative decision theory, and so forth. As a result, a substantial amount of research is carried out for studying methodologies and approaches for dealing with drifts. However, the available material is scattered and lacks guidelines for selecting an effective technique for a particular application. The primary novel objective of this survey is to present an understanding of concept drift challenges and allied studies. Further, it assists researchers from diverse domains to accommodate detection and adaptation algorithms for concept drifts in their applications. Overall, this study aims to contribute to deeper insights into the classification of various types of drifts and methods for detection and adaptation along with their key features and limitations. Furthermore, this study also highlights performance metrics used to evaluate the concept drift detection methods for streaming data. This paper presents the future research scope by highlighting gaps in the existing literature for the development of techniques to handle concept drifts. This article is categorized under:Algorithmic Development > Ensemble MethodsApplication Areas > Data Mining Software ToolsFundamental Concepts of Data and Knowledge > Big Data Mining [ABSTRACT FROM AUTHOR]

The article provides a comprehensive overview of incremental learning in the context of big data processing. The basic concepts, modern approaches, and key aspects of incremental learning are considered. The advantages of this approach for processing large amounts of data are analyzed, including the efficient use of computing resources, the ability to process streaming data in real time, and adaptability to changes in data. The main limitations and challenges, such as the problem of "catastrophic forgetting", the difficulty of balancing new and old knowledge, dependence on the order of data arrival, and potential loss of accuracy, are investigated. An analysis of specific problems is presented, including the handling of conceptual drift, unbalanced classes, and missing features. Applications of incremental learning in various fields, including data analytics, robotics, autonomous driving, and activity recognition, are discussed. We suggest directions for future research to address the identified problems and improve the effectiveness of incremental learning in the context of big data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)