Hierarchical Learning Research Articles

The ImmunoSkills Guide: Competencies for undergraduate immunology curricula

Immune literacy garnered significant attention in recent years due to the threat posed by emerging infectious diseases. The pace of immunological discoveries and their relevance to society are substantial yet coordinated educational efforts have been rare. This motivated us to create a task force of educators to reflect on pedagogical approaches to teaching immunology and to draft, develop, and evaluate key competencies for undergraduate immunology education. The research questions addressed include: 1) Which competencies are considered important by educators? 2) Are the illustrative skills clear, accurate and well aligned with the core competencies listed in the Vision and Change report?; 3) What are the concerns of immunology educators about competencies and skills? We collected data on the draft competencies using surveys, focus groups, and interviews. The iterative revision phase followed the community review phase before finalizing the framework. Here, we report a hierarchical learning framework, with six core competencies, twenty illustrative skills, and companion immunology-specific example learning outcomes. Predominant themes from interviews and focus groups, which informed revisions of this framework are shared. With the growing need for immunology education across the sciences, the ImmunoSkills Guide and accompanying discussion can be used as a resource for educators, administrators and policymakers.

NEURO DEEP FUZZY GENETIC ALGORITHM APPROACH FOR CLASSIFICATION AND DETECTION OF BRAIN TUMOR FROM LARGE DATASETS

Brain tumors represent one of the most critical and life-threatening diseases. Early detection and accurate classification are essential for effective treatment planning and survival rate improvement. With the exponential growth of medical data, particularly in imaging and diagnostic datasets, traditional algorithms face limitations in handling large-scale, high-dimensional data efficiently. Conventional machine learning and diagnostic methods often struggle with classification accuracy, computational complexity, and overfitting in large, noisy datasets. Addressing these issues is crucial for the development of robust diagnostic tools capable of handling real-world clinical data. This paper presents a novel hybrid approach combining Neural Networks, Deep Learning, Fuzzy Logic, and Genetic Algorithms, termed Neuro Deep Fuzzy Genetic Algorithm (NDFGA), for the classification and detection of brain tumors from large datasets. Neural Networks and Deep Learning architectures are leveraged for feature extraction and hierarchical learning. Fuzzy Logic improves interpretability and manages uncertainty in medical data, while Genetic Algorithms optimize feature selection and model parameters. This hybrid method is designed to maximize classification accuracy while minimizing false positives and computational overhead. The proposed approach was tested on a large brain tumor dataset comprising over 10,000 MRI scans. The NDFGA approach demonstrated superior performance compared to standalone methods. It achieved a classification accuracy of 97.8%, a sensitivity of 96.5%, and a specificity of 98.1%. The model also showed improved robustness in handling large datasets, reducing false positives by 12% and computational time by 15% compared to traditional methods. This hybrid model presents a scalable and efficient solution for brain tumor detection, especially in clinical environments.

Efficient semi-supervised hierarchical training for segmenting choroidal vessels and other structures on OCT images of multiple sclerosis patients

Optical coherence tomography (OCT) is a non-invasive imaging technique used to diagnose ocular and systemic diseases. Recently, several clinical studies have linked changes in different ocular layers to the development of multiple sclerosis (MS), so accurate segmentation of these structures has become an essential task. Unfortunately, segmenting the entire set of structures involved is a very difficult task, due to their large number and variability. These two factors hinder the labelling of images and therefore severely restrict the ability to achieve a large dataset with all structures manually annotated, limiting the use of a standard supervised approach. In this paper, we propose a semi-supervised learning methodology to robustly segment ocular structures in OCT images using a limited number of partially labeled images. Our methodology maximizes the information we can extract from labeled images through hierarchical learning, where multiple decoders are used to extract segmented structures progressively. We use a reconstruction loss function to provide structural coherence to the segmentation and a teacher-student strategy to effectively leverage the information present in the set of unlabeled images. In addition to the segmentation of labelled structures, this hierarchical approach allows segmenting structures that are not labelled in the dataset such as the choroidal vessels. To validate the proposed methodology, we have carried out extensive experimentation using two datasets with different characteristics. These experiments have demonstrated a great potential of this methodology to train networks efficiently with partially labeled images, which allows to accurately extract the main biomarkers linked to the development of MS.

Enhanced Malware Detection Using Grey Wolf Optimization and Deep Belief Neural Networks

Standard identification methods are flattering and less effective as attacks from malware get increasingly sophisticated. Considering current malware outbreaks employ tactics such as polymorphism, obfuscation and encryption, to avert identification, growing complicated approaches must be developed. This paper deals with a mixed model utilizing Deep Belief Neural Network (DBNN) for classifying and Grey Wolf Optimization (GWO) for choosing features. Whereas DBNN encodes complicated patterns by hierarchical learning, GWO optimizes the choosing of the more essential features, lowering the cost of computing and dataset complexity. Investigations reveal that the suggested GWO-DBNN model beats existing machine learning procedures in terms of detection accuracy, recall, precision, and false positive rate (FPR). These mixed tactics offer dependable and scalable solutions to the challenges faced by modern malware threats.

Enhanced Malware Detection Using Grey Wolf Optimization and Deep Belief Neural Networks

Standard identification methods are flattering and less effective as attacks from malware get increasingly sophisticated. Considering current malware outbreaks employ tactics such as polymorphism, obfuscation and encryption, to avert identification, growing complicated approaches must be developed. This paper deals with a mixed model utilizing Deep Belief Neural Network (DBNN) for classifying and Grey Wolf Optimization (GWO) for choosing features. Whereas DBNN encodes complicated patterns by hierarchical learning, GWO optimizes the choosing of the more essential features, lowering the cost of computing and dataset complexity. Investigations reveal that the suggested GWO-DBNN model beats existing machine learning procedures in terms of detection accuracy, recall, precision, and false positive rate (FPR). These mixed tactics offer dependable and scalable solutions to the challenges faced by modern malware threats.

Enhanced “learning to learn” through a hierarchical dual-learning system: the case of action video game players

In contrast to conventional cognitive training paradigms, where learning effects are specific to trained parameters, playing action video games has been shown to produce broad enhancements in many cognitive functions. These remarkable generalizations challenge the conventional theory of generalization that learned knowledge can be immediately applied to novel situations (i.e., immediate generalization). Instead, a new “learning to learn” theory has recently been proposed, suggesting that these broad generalizations are attained because action video game players (AVGPs) can quickly acquire the statistical regularities of novel tasks in order to increase the learning rate and ultimately achieve better performance. Although enhanced learning rate has been found for several tasks, it remains unclear whether AVGPs efficiently learn task statistics and use learned task knowledge to guide learning. To address this question, we tested 34 AVGPs and 36 non-video game players (NVGPs) on a cue-response associative learning task. Importantly, unlike conventional cognitive tasks with fixed task statistics, in this task, cue-response associations either remain stable or change rapidly (i.e., are volatile) in different blocks. To complete the task, participants should not only learn the lower-level cue-response associations through explicit feedback but also actively estimate the high-level task statistics (i.e., volatility) to dynamically guide lower-level learning. Such a dual learning system is modelled using a hierarchical Bayesian learning framework, and we found that AVGPs indeed quickly extract the volatility information and use the estimated higher volatility to accelerate learning of the cue-response associations. These results provide strong evidence for the “learning to learn” theory of generalization in AVGPs. Taken together, our work highlights enhanced hierarchical learning of both task statistics and cognitive abilities as a mechanism underlying the broad enhancements associated with action video game play.

Hierarchical Learning-Enhanced Chaotic Crayfish Optimization Algorithm: Improving Extreme Learning Machine Diagnostics in Breast Cancer

Extreme learning machines (ELMs), single hidden-layer feedforward neural networks, are renowned for their speed and efficiency in classification and regression tasks. However, their generalization ability is often undermined by the random generation of hidden layer weights and biases. To address this issue, this paper introduces a Hierarchical Learning-based Chaotic Crayfish Optimization Algorithm (HLCCOA) aimed at enhancing the generalization ability of ELMs. Initially, to resolve the problems of slow search speed and premature convergence typical of traditional crayfish optimization algorithms (COAs), the HLCCOA utilizes chaotic sequences for population position initialization. The ergodicity of chaos is leveraged to boost population diversity, laying the groundwork for effective global search efforts. Additionally, a hierarchical learning mechanism encourages under-performing individuals to engage in extensive cross-layer learning for enhanced global exploration, while top performers directly learn from elite individuals at the highest layer to improve their local exploitation abilities. Rigorous testing with CEC2019 and CEC2022 suites shows the HLCCOA’s superiority over both the original COA and nine renowned heuristic algorithms. Ultimately, the HLCCOA-optimized extreme learning machine model, the HLCCOA-ELM, exhibits superior performance over reported benchmark models in terms of accuracy, sensitivity, and specificity for UCI breast cancer diagnosis, underscoring the HLCCOA’s practicality and robustness, as well as the HLCCOA-ELM’s commendable generalization performance.

Emotion recognition using hierarchical spatial–temporal learning transformer from regional to global brain

Emotion recognition is an essential but challenging task in human–computer interaction systems due to the distinctive spatial structures and dynamic temporal dependencies associated with each emotion. However, current approaches fail to accurately capture the intricate effects of electroencephalogram (EEG) signals across different brain regions on emotion recognition. Therefore, this paper designs a transformer-based method, denoted by R2G-STLT, which relies on a spatial–temporal transformer encoder with regional to global hierarchical learning that learns the representative spatiotemporal features from the electrode level to the brain-region level. The regional spatial–temporal transformer (RST-Trans) encoder is designed to obtain spatial information and context dependence at the electrode level aiming to learn the regional spatiotemporal features. Then, the global spatial–temporal transformer (GST-Trans) encoder is utilized to extract reliable global spatiotemporal features, reflecting the impact of various brain regions on emotion recognition tasks. Moreover, the multi-head attention mechanism is placed into the GST-Trans encoder to empower it to capture the long-range spatial–temporal information among the brain regions. Finally, subject-independent experiments are conducted on each frequency band of the DEAP, SEED, and SEED-IV datasets to assess the performance of the proposed model. Results indicate that the R2G-STLT model surpasses several state-of-the-art approaches.

An adaptable fuzzy reinforcement learning method for non-stationary environments

How do we know when a reinforcement learning policy needs to adapt? In non-stationary environments, agents must adapt and learn in environments that change dynamically. We propose a finite-horizon model-free solution using a hierarchical learning structure with fuzzy systems. The higher-level learning policy advises the lower-level policy when to start and stop learning based on the temporal differences calculated within the lower-level. Major differences in the temporal difference of each action produced by an agent may indicate environment change. This structure is tested with multi-agent differential games in both the cooperative and competitive aspect. Results show that this method is quick to notice and adapt the policy within relatively few learning episodes.

Hierarchical learning multi-objective firefly algorithm for high-dimensional feature selection

Feature selection is a crucial data preprocessing technique extensively employed in machine learning and image processing. However, feature selection encounters significant challenges when addressing high-dimensional data due to the huge and discrete decision space. This paper proposes a hierarchical learning multi-objective firefly algorithm (HMOFA) for solving the feature selection task in high-dimensional data. The main contributions are as follows: 1) Features are clustered based on the evaluation of multiple metrics, which are used to initialize the population and improve the quality of the initial population; 2) A hierarchy-guided learning model is proposed, where individuals move toward superior solutions while moving away from inferior solutions, avoiding the oscillation phenomenon that occurs under the full attraction model, and reducing the likelihood of the population being trapped in a local optimum; 3) Use duplicate solution modification mechanism to reduce the number of duplicate individuals in the population. The proposed method is compared with 8 competitive feature selection methods using 15 datasets, and the results demonstrate that HMOFA can achieve higher classification accuracy while selecting fewer features.

A Privacy-Preserving AIoT Framework for Fall Detection and Classification Using Hierarchical Learning With Multilevel Feature Fusion

A Privacy-Preserving AIoT Framework for Fall Detection and Classification Using Hierarchical Learning With Multilevel Feature Fusion

Multifidelity graph neural networks for efficient and accurate mesh‐based partial differential equations surrogate modeling

AbstractAccurately predicting the dynamics of complex systems governed by partial differential equations (PDEs) is crucial in various applications. Traditional numerical methods such as finite element methods (FEMs) offer precision but are resource‐intensive, particularly at high mesh resolutions. Machine learning–based surrogate models, including graph neural networks (GNNs), present viable alternatives by reducing computation times. However, their accuracy is significantly contingent on the availability of substantial high‐fidelity training data. This paper presents innovative multifidelity GNN (MFGNN) frameworks that efficiently combine low‐fidelity and high‐fidelity data to train more accurate surrogate models for mesh‐based PDE simulations, while reducing training computational cost. The proposed methods capitalize on the strengths of GNNs to manage complex geometries across different fidelity levels. Incorporating a hierarchical learning strategy and curriculum learning techniques, the proposed models significantly reduce computational demands and improve the robustness and generalizability of the results. Extensive validations across various simulation tasks show that the MFGNN frameworks surpass traditional single‐fidelity GNN models. The proposed approaches, hence, provide a scalable and practical solution for conducting detailed computational analyses where traditional high‐fidelity simulations are time‐consuming.

Data-driven hierarchical learning approach for multi-point servo control of Pan–Tilt–Zoom cameras

Pan–Tilt–Zoom (PTZ) cameras, with their significant features of free rotation and zoom, are widely used in areas such as border security, ecological conservation, emergency management, and the military. PTZ cameras can achieve automatic monitoring of a selected area through multiple servo operations, known as multi-point servo control. However, due to the deficiencies in the servo control Software Development Kit (SDK), hardware wear, and interference from complex external environments, the multi-point servo control process generates significant errors. This paper proposes a precise multi-point servo control framework based on Deep Reinforcement Learning (DRL) to address this issue. The complexity of real-world environments necessitates a reward function design that fully considers various factors, for which we propose the directional gravity reward function. Due to the instability during the training process, prolonged trial-and-error interactions between the agent and the equipment can cause irreversible damage to the devices. This framework employs a phased training approach, where the agent learns sequentially from offline, off-policy, and on-policy data, reducing direct interaction with the equipment while enhancing the agent’s overall performance. Additionally, real-time and accurate device status can be obtained by performing feature matching on images from adjacent time frames, which is crucial for the system’s operation. Evaluation results indicate that our proposed precise multi-point servo control framework significantly outperforms other methods in 4-point servo control tasks in both virtual and real-world scenarios. Additionally, the operational process fully considers safety issues.

Graph Feature Refinement and Fusion in Transformer for Structural Damage Detection.

Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field. However, little progress has been made in studying the relationship between the global and local information of structural response data. In this paper, we have presented an innovative Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network for structural damage detection. The proposed CGsformer network introduces an innovative approach for hierarchical learning from global to local information to extract acceleration response signal features for structural damage representation. The key advantage of this network is the integration of a graph convolutional network in the learning process, which enables the construction of a graph structure for global features. By incorporating node learning, the graph convolutional network filters out noise in the global features, thereby facilitating the extraction to more effective local features. In the verification based on the experimental data of four-story steel frame model experiment data and IASC-ASCE benchmark structure simulated data, the CGsformer network achieved damage identification accuracies of 92.44% and 96.71%, respectively. It surpassed the existing traditional damage detection methods based on deep learning. Notably, the model demonstrates good robustness under noisy conditions.

Updating Correlation-Enhanced Feature Learning for Multi-Label Classification

In the domain of multi-label classification, label correlations play a crucial role in enhancing prediction precision. However, traditional methods heavily depend on ground-truth label sets, which can be incompletely tagged due to the diverse backgrounds of annotators and the significant cost associated with procuring extensive labeled datasets. To address these challenges, this paper introduces a novel multi-label classification method called updating Correlation-enhanced Feature Learning (uCeFL), which extracts label correlations directly from the data instances, circumventing the dependency on potentially incomplete label sets. uCeFL initially computes a revised label matrix by multiplying the incomplete label matrix with the label correlations extracted from the data matrix. This revised matrix is then utilized to enrich the original data features, enabling a neural network to learn correlation-enhanced representations that capture intricate relationships between data features, labels, and their interactions. Notably, label correlations are not static; they are dynamically updated during the neural network’s training process. Extensive experiments carried out on various datasets emphasize the effectiveness of the proposed approach. By leveraging label correlations within data instances, along with the hierarchical learning capabilities of neural networks, it offers a significant improvement in multi-label classification, even in scenarios with incomplete labels.

Chronic pain – A maladaptive compensation to unbalanced hierarchical predictive processing

The ability to perceive pain presents an interesting evolutionary advantage to adapt to an ever-changing environment. However, in the case of chronic pain (CP), pain perception hinders the capacity of the system to adapt to changing sensory environments. Similar to other chronic perceptual disorders, CP is also proposed to be a maladaptive compensation to aberrant sensory predictive processing. The local-global oddball paradigm relies on learning hierarchical rules and processing environmental irregularities at a local and global level. Prediction errors (PE) between actual and predicted input typically trigger an update of the forward model to limit the probability of encountering future PEs. It has been hypothesised that CP hinders forward model updating, reflected in increased local deviance and decreased global deviance. In the present study, we used the local-global paradigm to examine how CP influences hierarchical learning relative to healthy controls. As hypothesised, we observed that deviance in the stimulus characteristics evoked heightened local deviance and decreased global deviance of the stimulus-driven PE. This is also accompanied by respective changes in theta phase locking that is correlated with the subjective pain perception. Changes in the global deviant in the stimulus-driven-PE could also be explained by dampened attention-related responses. Changing the context of the auditory stimulus did not however show a difference in the context-driven PE. These findings suggest that CP is accompanied by maladaptive forward model updating where the constant presence of pain perception disrupts local deviance in non-nociceptive domains. Furthermore, we hypothesise that the auditory-processing based biomarker identified here could be a marker of domain-general dysfunction that could be confirmed by future research.

Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning

Accurate estimation of state-of-charge (SOC) in batteries is of paramount importance for effective and safe battery system management. Sodium-ion batteries' distinctive features on open-circuit voltage and their near-linear relationships with SOC provide a fresh perspective on SOC estimation compared to lithium-ion counterparts. Therefore, this study proposes a low-complexity and wide-adaptability data-driven model for SOC estimation of sodium-ion batteries. Enhanced pulse test across a wide spectrum of conditions is conducted to construct the training set and a hierarchical learning strategy is designed to train the model parameters. To validate this model, tests and evaluations using data of three driving cycles are conducted. The proposed model yields root mean square errors of 0.89 % and 0.63 % for 3.2 Ah and 10 Ah sodium-ion batteries from different manufacturers, respectively. Furthermore, we demonstrate the strong robustness of the model by applying Gaussian noises to the sampled data. Compared to existing data-driven methods for battery SOC estimation, our proposed model achieves remarkably promising performance with significantly lower parameter counts and FLOPs, which is quite attractive in implementation. This work underscores the significant potential of leveraging the distinctive characteristics of sodium-ion batteries to achieve practical and reliable state estimation.

Evaluating the deep learning software tools for large-scale enterprises using a novel TODIFFA-MCDM framework

Deep learning (DL) is one of the most promising technological developments emerging in the fourth industrial revolution era for businesses to improve processes, increase efficiency, and reduce errors. Accordingly, hierarchical learning software selection is one of the most critical decision-making problems in integrating neural network applications into business models. However, selecting appropriate reinforcement learning software for integrating deep learning applications into enterprises’ business models takes much work for decision-makers. There are several reasons for this: first, practitioners’ limited knowledge and experience of DL makes it difficult for decision-makers to adapt this technology into their enterprises’ business model and significantly increases complex uncertainties. Secondly, according to the authors’ knowledge, no study in the literature addresses deep structured learning solutions with the help of MCDM approaches. Consequently, making inferences concerning criteria that should be considered in an evaluation process is impossible by considering the studies in the relevant literature. Considering these gaps, this study presents a novel decision-making approach developed by the authors. It involves the combination of two new decision-making approaches, MAXC (MAXimum of Criterion) and TODIFFA (the total differential of alternative), which were developed to solve current decision-making problems. When the most important advantages of this model are considered, it associates objective and subjective approaches and eliminates some critical limitations of these methodologies. Besides, it has an easily followable algorithm without the need for advanced mathematical knowledge for practitioners and provides highly stable and reliable results in solving complex decision-making problems. Another novelty of the study is that the criteria are determined with a long-term negotiation process that is part of comprehensive fieldwork with specialists. When the conclusions obtained using this model are briefly reviewed, the C2 “Data Availability and Quality” criterion is the most influential in selecting deep learning software. The C7 “Time Constraints” criterion follows the most influential factor. Remarkably, prior research has overlooked the correlation between the performance of Deep Learning (DL) platforms and the quality and accessibility of data. The findings of this study underscore the necessity for DL platform developers to devise solutions to enable DL platforms to operate effectively, notwithstanding the availability of clean, high-quality, and adequate data. Finally, the robustness check carried out to test the validity of the proposed model confirms the accuracy and robustness of the results obtained by implementing the suggested model.

Hierarchical learning of gastric cancer molecular subtypes by integrating multi‐modal DNA‐level omics data and clinical stratification

Hierarchical learning of gastric cancer molecular subtypes by integrating multi‐modal DNA‐level omics data and clinical stratification

Ambient floor vibration sensing advances the accessibility of functional gait assessments for children with muscular dystrophies

Muscular dystrophies (MD) are a group of genetic neuromuscular disorders that cause progressive weakness and loss of muscles over time, influencing 1 in 3500–5000 children worldwide. New and exciting treatment options have led to a critical need for a clinical post-marketing surveillance tool to confirm the efficacy and safety of these treatments after individuals receive them in a commercial setting. For MDs, functional gait assessment is a common approach to evaluate the efficacy of the treatments because muscle weakness is reflected in individuals’ walking patterns. However, there is little incentive for the family to continue to travel for such assessments due to the lack of access to specialty centers. While various existing sensing devices, such as cameras, force plates, and wearables can assess gait at home, they are limited by privacy concerns, area of coverage, and discomfort in carrying devices, which is not practical for long-term, continuous monitoring in daily settings. In this study, we introduce a novel functional gait assessment system using ambient floor vibrations, which is non-invasive and scalable, requiring only low-cost and sparsely deployed geophone sensors attached to the floor surface, suitable for in-home usage. Our system captures floor vibrations generated by footsteps from patients while they walk around and analyzes such vibrations to extract essential gait health information. To enhance interpretability and reliability under various sensing scenarios, we translate the signal patterns of floor vibration to pathological gait patterns related to MD, and develop a hierarchical learning algorithm that aggregates insights from individual footsteps to estimate a person’s overall gait performance. When evaluated through real-world experiments with 36 subjects (including 15 patients with MD), our floor vibration sensing system achieves a 94.8% accuracy in predicting functional gait stages for patients with MD. Our approach enables accurate, accessible, and scalable functional gait assessment, bringing MD progressive tracking into real life.