Few-shot Learning Approach Research Articles

Multi-granularity self-attention mechanisms for few-shot Learning

Few-shot learning aims to classify novel data categories with limited labeled samples. Although metric-based meta-learning has shown better generalization ability as a few-shot classification method, it still faces challenges in handling data noise and maintaining inter-sample distance stability. To address these issues, our study proposes an innovative few-shot learning approach to enhance image features' global and local semantic representation. Initially, our method employs a multiscale residual module to facilitate extracting multi-granularity features within images. Subsequently, it optimizes the fusion of local and global features using the self- attention mechanism inherent in the Transformer module. Additionally, a weighted metric module is integrated to improve the model's resilience against noise interference. Empirical evaluations on CIFAR-FS and MiniImageNet few-shot datasets using 5-way 1-shot and 5-way 5-shot scenarios demonstrate the effectiveness of our approach in capturing multi-level and multi-granularity image representations. Compared to other methods, our method improves accuracy by 2.63% and 1.27% for 5-shot scenes on these two datasets. The experimental results validate the efficacy of our model in significantly enhancing few-shot image classification performance.

Few-shot learning based histopathological image classification of colorectal cancer

Background Colorectal cancer is a prevalent and deadly disease worldwide, posing significant diagnostic challenges. Traditional histopathologic images classification is often inefficient and subjective. Although some histopathologists use computer-aided diagnosis (CAD) to improve efficiency, these methods depend heavily on extensive data and specific annotations, limiting their development. To address these challenges, this paper proposes a method based on few-shot learning. Methods This study introduces a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant categories. The model comprises modules for feature extraction, dimensionality reduction, and classification, trained using a combination of contrast loss and cross-entropy loss. In this paper, we detail the setup of hyperparameters:n-way, k-shot, β and the creation of support, query, and test datasets. Results Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per category. We documented the model’s loss, accuracy, and the confusion matrix of the results. Additionally, we employed the t-SNE algorithm to analyze and assess the model’s classification performance. Conclusion The proposed model demonstrates significant advantages in accuracy and minimal data dependency, performing robustly across all tested n-way, k-shot scenarios. It consistently achieved over 93% accuracy on comprehensive test datasets, including 1916 samples, confirming its high classification accuracy and strong generalization capability. Our research advances the use of few-shot learning in medical diagnostics, and also lays the groundwork for extending it to deal with rare, difficult-to-diagnose cases.

Exploring Large Language Models for Detecting Online Vaccine Reactions.

Social media offers a rich source of real-time health data, including potential vaccine reactions. However, extracting meaningful insights is challenging due to the noisy nature of social media content. This paper explores using large language models (LLMs) and prompt engineering to detect personal mentions of vaccine reactions. Different prompting strategies were evaluated on two LLM models (GPT-3.5 and GPT-4) using Reddit data focused on shingles (zoster) vaccines. Zero-shot and few-shot learning approaches with both standard and chain-of-thought prompts were compared. The findings demonstrate that GPT-based models with carefully crafted chain-of-thought prompts could identify the relevant social media posts. Few-shot learning helped GPT4 models to identify more of the marginal cases, although less precisely. The use of LLMs for classification with lightweight supervised pretrained language models (PLMs) found that PLMs outperform LLMs. However, a potential benefit in using LLMs to help identify records for training PLMs was revealed, especially to eliminate false negatives, and LLMs could be used as classifiers when insufficient data exists to train a PLM.

Programming Chatbots Using Natural Language: Generating Cervical Spine MRI Impressions.

The utility of machine learning, specifically large language models (LLMs), in the medical field has gained considerable attention. However, there is a scarcity of studies that focus on the application of LLMs in generating custom subspecialty radiology impressions. The primary objective of this study is to evaluate and compare the performance of multiple LLMs in generating specialized, accurate, and clinically useful radiology impressions for degenerative cervical spine MRI reports. The study employed a comparative analysis of multiple LLMs, including OpenAI's ChatGPT-3.5 and GPT-4 (OpenAI, San Francisco, CA), Antrhopic's Claude 2 (Anthropic PBC, San Francisco, CA), Google's Bard (Google Inc., Mountain View, CA), and Meta's Llama 2 (Meta Platforms, Inc., Menlo Park, CA). This was performed during January-February 2024. These models were evaluated using a few-shot learning approach on a dataset consisting of 10 examples from 50 synthetically generated MRI reports. Performance metrics evaluated were diagnostic accuracy, stylistic accuracy, and redundancy. While Claude 2 maintained consistent high performance across 40 cases, GPT-4 required midway re-training to improve its declining scores. Both Claude 2 and GPT-4 demonstrated the ability to generate structured impressions, but Claude 2's specialized summarization capabilities provided an edge in maintaining accuracy without continuous feedback. The other LLMs' performance was subpar. The findings of this study suggest that LLMs can be a valuable tool in automating the generation of radiology impressions. Claude 2, in particular, exhibited promising results, indicating its potential for clinical implementation. However, the study also points to the necessity for further research, especially in optimizing model performance and evaluating real-world applicability.

AIoT-enabled defect detection with minimal data: A few-shot learning approach combining prototypical and relational networks for smart manufacturing

Defect detection is crucial in manufacturing processes but traditional AI-based algorithms require large datasets for accurate results. For new or customized products, the number of images with detected defects is limited. Therefore, we developed a few-shot learning approach integrating a prototypical and relation network (PRN), algorithms with meta-learning, and the Artificial Internet of Things (AIoT). For rapid defect detection with IoT sensors, such minimal data are used for a smart manufacturing ecosystem., making it ideal for dynamic production environments. We tested the AIOT-enhanced PRN on two datasets using the following data augmentation methods: random rotation and horizontal translation (RH), random rotation and vertical translation (RV), and horizontal and vertical translation (HV). The developed PRN efficiently learned from minimal data to reduce the occurrence of overfitting issues in the MVTec 3D-AD dataset which are caused by a limited number of defect sample images. When testing the AIOT-enhanced PRN with the NEU-DET dataset, accuracies in 5-way 5-shot settings using RV, RH, 15° rotation, and HV were 100 %. Under Gaussian noise, the AIOT-enhanced PRN showed an accuracy of 100 % in 5-way 5-shot and 5-way 1-shot scenarios using HV. For salt-and-pepper noise, the accuracy of the AIOT-enhanced PRN ranged from 98.49 to 99.04 %. The developed AIOT-enhanced PRN improved defect detection accuracy and real-time monitoring capability with minimal data. The developed AIOT-enhanced PRN can be used for efficient and flexible product quality control in Industry 4.0.

A mutual reconstruction network model for few-shot classification of histological images: addressing interclass similarity and intraclass diversity.

The automated classification of histological images is crucial for the diagnosis of cancer. The limited availability of well-annotated datasets, especially for rare cancers, poses a significant challenge for deep learning methods due to the small number of relevant images. This has led to the development of few-shot learning approaches, which bear considerable clinical importance, as they are designed to overcome the challenges of data scarcity in deep learning for histological image classification. Traditional methods often ignore the challenges of intraclass diversity and interclass similarities in histological images. To address this, we propose a novel mutual reconstruction network model, aimed at meeting these challenges and improving the few-shot classification performance of histological images. The key to our approach is the extraction of subtle and discriminative features. We introduce a feature enhancement module (FEM) and a mutual reconstruction module to increase differences between classes while reducing variance within classes. First, we extract features of support and query images using a feature extractor. These features are then processed by the FEM, which uses a self-attention mechanism for self-reconstruction of features, enhancing the learning of detailed features. These enhanced features are then input into the mutual reconstruction module. This module uses enhanced support features to reconstruct enhanced query features and vice versa. The classification of query samples is based on weighted calculations of the distances between query features and reconstructed query features and between support features and reconstructed support features. We extensively evaluated our model using a specially created few-shot histological image dataset. The results showed that in a 5-way 10-shot setup, our model achieved an impressive accuracy of 92.09%. This is a 23.59% improvement in accuracy compared to the model-agnostic meta-learning (MAML) method, which does not focus on fine-grained attributes. In the more challenging, 5-way 1-shot setting, our model also performed well, demonstrating a 18.52% improvement over the ProtoNet, which does not address this challenge. Additional ablation studies indicated the effectiveness and complementary nature of each module and confirmed our method's ability to parse small differences between classes and large variations within classes in histological images. These findings strongly support the superiority of our proposed method in the few-shot classification of histological images. The mutual reconstruction network provides outstanding performance in the few-shot classification of histological images, successfully overcoming the challenges of similarities between classes and diversity within classes. This marks a significant advancement in the automated classification of histological images.

Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions.

This paper presents a systematic literature review, providing a comprehensive taxonomy of Data Augmentation (DA), Transfer Learning (TL), and Self-Supervised Learning (SSL) techniques within the context of Few-Shot Learning (FSL) for EEG signal classification. EEG signals have shown significant potential in various paradigms, including Motor Imagery, Emotion Recognition, Visual Evoked Potentials, Steady-State Visually Evoked Potentials, Rapid Serial Visual Presentation, Event-Related Potentials, and Mental Workload. However, challenges such as limited labeled data, noise, and inter/intra-subject variability have impeded the effectiveness of traditional machine learning (ML) and deep learning (DL) models. This review methodically explores how FSL approaches, incorporating DA, TL, and SSL, can address these challenges and enhance classification performance in specific EEG paradigms. It also delves into the open research challenges related to these techniques in EEG signal classification. Specifically, the review examines the identification of DA strategies tailored to various EEG paradigms, the creation of TL architectures for efficient knowledge transfer, and the formulation of SSL methods for unsupervised representation learning from EEG data. Addressing these challenges is crucial for enhancing the efficacy and robustness of FSL-based EEG signal classification. By presenting a structured taxonomy of FSL techniques and discussing the associated research challenges, this systematic review offers valuable insights for future investigations in EEG signal classification. The findings aim to guide and inspire researchers, promoting advancements in applying FSL methodologies for improved EEG signal analysis and classification in real-world settings.

Mask-Guided Vision Transformer for Few-Shot Learning.

Learning with little data is challenging but often inevitable in various application scenarios where the labeled data are limited and costly. Recently, few-shot learning (FSL) gained increasing attention because of its generalizability of prior knowledge to new tasks that contain only a few samples. However, for data-intensive models such as vision transformer (ViT), current fine-tuning-based FSL approaches are inefficient in knowledge generalization and, thus, degenerate the downstream task performances. In this article, we propose a novel mask-guided ViT (MG-ViT) to achieve an effective and efficient FSL on the ViT model. The key idea is to apply a mask on image patches to screen out the task-irrelevant ones and to guide the ViT focusing on task-relevant and discriminative patches during FSL. Particularly, MG-ViT only introduces an additional mask operation and a residual connection, enabling the inheritance of parameters from pretrained ViT without any other cost. To optimally select representative few-shot samples, we also include an active learning-based sample selection method to further improve the generalizability of MG-ViT-based FSL. We evaluate the proposed MG-ViT on classification, object detection, and segmentation tasks using gradient-weighted class activation mapping (Grad-CAM) to generate masks. The experimental results show that the MG-ViT model significantly improves the performance and efficiency compared with general fine-tuning-based ViT and ResNet models, providing novel insights and a concrete approach toward generalizing data-intensive and large-scale deep learning models for FSL.

Review of few-shot learning application in CSI human sensing

Wi-Fi sensing has garnered increasing interest for its significant advantages, primarily leveraging Wi-Fi signal fluctuations induced by human activities and advanced neural network algorithms. However, its application faces challenges due to limited generalizability, necessitating frequent data recollection and neural network retraining for adaptation to new environments. To address these limitations, some researchers introduced few-shot learning into Wi-Fi sensing applications because it offers a promising solution with its ability to achieve remarkable performance in novel scenarios using minimal training samples. Despite its potential, a comprehensive review of its applications within this domain remains absent. This study endeavors to fill this gap by exploring prominent Wi-Fi sensing applications that incorporate few-shot learning, aiming to delineate their key features. We categorize few-shot learning approaches into three distinct methodologies: transfer learning, metric learning, and meta-learning, based on their neural network training strategies. Through this classification, we examine representative systems from an application perspective and elucidate the principles of few-shot learning implementation. These systems are evaluated in terms of learning methodology, data modality, and recognition accuracy. Finally, this paper highlights the challenges and future directions for few-shot learning in Channel State Information (CSI) based human sensing, providing a valuable resource for researchers in the field of Wi-Fi human sensing leveraging few-shot learning.

Enhanced classification of left ventricular hypertrophy in cardiac patients using extended Siamese CNN

Objective. Left ventricular hypertrophy (LVH) is the thickening of the left ventricle wall of the heart. The objective of this study is to develop a novel approach for the accurate assessment of LVH) severity, addressing the limitations of traditional manual grading systems. Approach. We propose the Multi-purpose Siamese Weighted Euclidean Distance Model (MSWED), which utilizes convolutional Siamese neural networks and zero-shot/few-shot learning techniques. Unlike traditional methods, our model introduces a cutoff distance-based approach for zero-shot learning, enhancing accuracy. We also incorporate a weighted Euclidean distance targeting informative regions within echocardiograms. Main results. We collected comprehensive datasets labeled by experienced echocardiographers, including Normal heart and various levels of LVH severity. Our model outperforms existing techniques, demonstrating significant precision enhancement, with improvements of up to 13% for zero-shot and few-shot learning approaches. Significance. Accurate assessment of LVH severity is crucial for clinical prognosis and treatment decisions. Our proposed MSWED model offers a more reliable and efficient solution compared to traditional grading systems, reducing subjectivity and errors while providing enhanced precision in severity classification.

HyperMAML: Few-shot adaptation of deep models with hypernetworks

Few-Shot learning aims to train models which can adapt to previously unseen tasks based on small amounts of data. One of the leading Few-Shot learning approaches is Model-Agnostic-Meta-Learning (MAML), which learns the general weights of the meta-model that are later adapted to downstream tasks. However, MAML’s main limitation lies in that the update procedure is realized by gradient-based optimization, which cannot always modify weights to the essential level in one or even a few iterations. Moreover, using many gradient steps results in time-consuming optimization and inference procedures. In this paper, we propose HyperMAML, a novel generalization of MAML, where the update procedure is also a part of the model. Namely, we replace gradient descent with a trainable Hypernetwork which updates the weights. Consequently, the model can generate significant updates whose range is not limited to a fixed number of gradient steps. Experiments show that HyperMAML outperforms MAML in most cases and performs comparably to state-of-the-art techniques in standard Few-Shot learning benchmarks.

Dual-Channel Prototype Network for Few-Shot Pathology Image Classification.

In the field of pathology, the scarcity of certain diseases and the difficulty of annotating images hinder the development of large, high-quality datasets, which in turn affects the advancement of deep learning-assisted diagnostics. Few-shot learning has demonstrated unique advantages in modeling tasks with limited data, yet explorations of this method in the field of pathology remain in the early stages. To address this issue, we present a dual-channel prototype network (DCPN), a novel few-shot learning approach for efficiently classifying pathology images with limited data. The DCPN leverages self-supervised learning to extend the pyramid vision transformer (PVT) to few-shot classification tasks and combines it with a convolutional neural network to construct a dual-channel network for extracting multi-scale, high-precision pathological features, thereby substantially enhancing the generalizability of prototype representations. Additionally, we design a soft voting classifier based on multi-scale features to further augment the discriminative power of the model in complex pathology image classification tasks. We constructed three few-shot classification tasks with varying degrees of domain shift using three publicly available pathological datasets-CRCTP, NCTCRC, and LC25000-to emulate real-world clinical scenarios. The results demonstrated that the DCPN outperformed the prototypical network across all metrics, achieving the highest accuracies in same-domain tasks-70.86% for 1-shot, 82.57% for 5-shot, and 85.2% for 10-shot setups-corresponding to improvements of 5.51%, 5.72%, and 6.81%, respectively, over the prototypical network. Notably, in the same-domain 10-shot setting, the accuracy of the DCPN (85.2%) surpassed that of the PVT-based supervised learning model (85.15%), confirming its potential to diagnose rare diseases within few-shot learning frameworks.

Few-shot biomedical relation extraction using data augmentation and domain information

Relation extraction (RE) plays a pivotal role in biomedical information extraction. However, traditional approaches are often limited by high data annotation costs and extensive time investments. To address this challenge, this study proposes an innovative few-shot learning approach for biomedical RE tasks that utilizes data augmentation and domain information. Specifically, this method enhances the diversity of training data through a synthesized data augmentation strategy. At the same time, it combines domain information with prompt learning techniques. By incorporating domain information, the model’s generalization capability for rare data instances is improved. Furthermore, the prompt learning approach, by integrating prompt templates into the model input, guides the pre-trained language model to more accurately adapt to the RE task. Our model demonstrated exceptional performance in three different biomedical RE tasks, particularly on the I2B2-2010 RE dataset. In the 1-shot, 10-shot, and 50-shot settings, the F1 scores increased by 14.38%, 9.50%, and 6.05%, respectively.

Data-efficient multimodal human action recognition for proactive human–robot collaborative assembly: A cross-domain few-shot learning approach

With the recent vision of Industry 5.0, the cognitive capability of robots plays a crucial role in advancing proactive human–robot collaborative assembly. As a basis of the mutual empathy, the understanding of a human operator’s intention has been primarily studied through the technique of human action recognition. Existing deep learning-based methods demonstrate remarkable efficacy in handling information-rich data such as physiological measurements and videos, where the latter category represents a more natural perception input. However, deploying these methods in new unseen assembly scenarios requires first collecting abundant case-specific data. This leads to significant manual effort and poor flexibility. To deal with the issue, this paper proposes a novel cross-domain few-shot learning method for data-efficient multimodal human action recognition. A hierarchical data fusion mechanism is designed to jointly leverage the skeletons, RGB images and depth maps with complementary information. Then a temporal CrossTransformer is developed to enable the action recognition with very limited amount of data. Lightweight domain adapters are integrated to further improve the generalization with fast finetuning. Extensive experiments on a real car engine assembly case show the superior performance of proposed method over state-of-the-art regarding both accuracy and finetuning efficiency. Real-time demonstrations and ablation study further indicate the potential of early recognition, which is beneficial for the robot procedures generation in practical applications. In summary, this paper contributes to the rarely explored realm of data-efficient human action recognition for proactive human–robot collaboration.

Which images to label for few-shot medical image analysis?

The success of deep learning methodologies hinges upon the availability of meticulously labeled extensive datasets. However, when dealing with medical images, the annotation process for such abundant training data often necessitates the involvement of experienced radiologists, thereby consuming their limited time resources. In order to alleviate this burden, few-shot learning approaches have been developed, which manage to achieve competitive performance levels with only several labeled images. Nevertheless, a crucial yet previously overlooked problem in few-shot learning is about the selection of template images for annotation before learning, which affects the final performance. In this study, we propose a novel TEmplate Choosing Policy (TECP) that aims to identify and select “the most worthy” images for annotation, particularly within the context of multiple few-shot medical tasks, including landmark detection, anatomy detection, and anatomy segmentation. TECP is composed of four integral components: (1) Self-supervised training, which entails training a pre-existing deep model to extract salient features from radiological images; (2) Alternative proposals for localizing informative regions within the images; and (3) Representative Score Estimation, which involves the evaluation and identification of the most representative samples or templates. (4) Ranking, which rank all candidates and select one with highest representative score. The efficacy of the TECP approach is demonstrated through a series of comprehensive experiments conducted on multiple public datasets. Across all three medical tasks, the utilization of TECP yields noticeable improvements in model performance.

Multimodal Sentiment Analysis for Movie Scenes Based on a Few-Shot Learning Approach

Multimodal sentiment analysis involves discerning the emotions of speakers through diverse features such as sound, text, and images. However, current research in this field heavily relies on extensive supervised datasets, demanding substantial labor and computational resources. This study introduces a meta-learning-based approach for multimodal sentiment analysis, aiming to delve into emotional information within movie scenes. Leveraging meta-learning techniques, this approach seeks to accurately capture emotions in movie scenes using a limited number of annotated samples, achieving model generalization under constrained labeled data. Specifically, we introduce an optimization-based meta-learning approach for the multimodal sentiment analysis tasks in text and vision, enhancing the model’s ability to generalize to new tasks with limited annotations. Additionally, an innovative strategy based on Meta-Prompting is proposed to handle multimodal data. The merits of the proposed method are validated across different datasets.

Classifying attack traffic in IoT environments via few-shot learning

The Internet of Things (IoT) is a key enabler for critical systems, but IoT devices are increasingly targeted by cyberattacks due to their diffusion and hardware and software limitations. This calls for designing and evaluating new effective approaches for protecting IoT systems at the network level. While recent proposals based on machine- and deep-learning provide effective solutions to the problem of attack-traffic classification, their adoption is severely challenged by the amount of labeled traffic they require to train the classification models. In fact, this results in the need for collecting and labeling large amounts of malicious traffic, which may be hindered by the nature of the malware possibly generating little and hard-to-capture network activity. To tackle this challenge, we adopt few-shot learning approaches for attack-traffic classification, with the objective to improve detection performance for attack classes with few labeled samples. We leverage advanced deep-learning architectures to perform feature extraction and provide an extensive empirical study—using recent and publicly available datasets—comparing the performance of an ample variety of solutions based on different learning paradigms, and exploring a number of design choices in depth (impact of embedding function, number of classes of attacks, or number of attack samples). In comparison to non-few-shot baselines, we achieve a relative improvement in the F1-score ranging from 8% to 27%.

Few-shot classification of ultrasound breast cancer images using meta-learning algorithms

Medical datasets often have a skewed class distribution and a lack of high-quality annotated images. However, deep learning methods require a large amount of labeled data for classification. In this study, we present a few-shot learning approach for the classification of ultrasound breast cancer images using meta-learning methods. We used prototypical networks and model agnostic meta-learning (MAML) algorithms as meta-learning methods. The breast ultrasound images (BUSI) dataset, which has three classes and is difficult to use in meta-learning, was used for meta-testing in a cross-domain approach along with other datasets for meta-training. Our proposed approach yielded an accuracy range of 0.882–0.889, achieved by implementing the ResNet50 backbone with ProtoNet in a 10-shot setting. These results represent a significant improvement ranging from 6.27 to 7.10% over the baseline accuracy of 0.831. The results showed that ProtoNet outperformed the MAML method for all k-shot settings. In addition, the use of ResNet models as the backbone network for feature extraction was found to be more successful than the use of a four-layer convolutional model. Our proposed method is the first attempt to apply meta-learning for few-shot classification in the BUSI dataset while providing higher accuracy compared to deep learning methods for medical images with small-scale datasets and few classes. The methodology used in this study can be adapted to other datasets with similar problems.

Enhancing IoT Security: A Few-Shot Learning Approach for Intrusion Detection

Recently, the number of Internet of Things (IoT)-connected devices has increased daily. Consequently, cybersecurity challenges have increased due to the natural diversity of the IoT, limited hardware resources, and limited security capabilities. Intrusion detection systems (IDSs) play a substantial role in securing IoT networks. Several researchers have focused on machine learning (ML) and deep learning (DL) to develop intrusion detection techniques. Although ML is good for classification, other methods perform better in feature transformation. However, at the level of accuracy, both learning techniques have their own certain compromises. Although IDSs based on ML and DL methods can achieve a high detection rate, the performance depends on the training dataset size. Incidentally, collecting a large amount of data is one of the main drawbacks that limits performance when training datasets are lacking, and such methods can fail to detect novel attacks. Few-shot learning (FSL) is an emerging approach that is employed in different domains because of its proven ability to learn from a few training samples. Although numerous studies have addressed the issues of IDSs and improved IDS performance, the literature on FSL-based IDSs is scarce. Therefore, an investigation is required to explore the performance of FSL in IoT IDSs. This work proposes an IoT intrusion detection model based on a convolutional neural network as a feature extractor and a prototypical network as an FSL classifier. The empirical results were analyzed and compared with those of recent intrusion detection approaches. The accuracy results reached 99.44%, which shows a promising direction for involving FSL in IoT IDSs.

Production Capacity Prediction and Optimization in the Glycerin Purification Process: A Simulation-Assisted Few-Shot Learning Approach

Chemical process control relies on a tightly controlled, narrow range of margins for critical variables, ensuring process stability and safeguarding equipment from potential accidents. The availability of historical process data is limited to a specific setpoint of operation. This challenge raises issues for process monitoring in predicting and adjusting to deviations outside of the range of operational parameters. Therefore, this paper proposes simulation-assisted deep transfer learning for predicting and optimizing the final purity and production capacity of the glycerin purification process. The proposed network is trained by the simulation domain to generate a base feature extractor, which is then fine-tuned using few-shot learning techniques on the target learner to extend the working domain of the model beyond historical practice. The result shows that the proposed model improved prediction performance by 24.22% in predicting water content and 79.72% in glycerin prediction over the conventional deep learning model. Additionally, the implementation of the proposed model identified production and product quality improvements for enhancing the glycerin purification process.