Task Accuracy Research Articles

A Comparative Analysis of Machine Learning Algorithms for Big Data Applications in Predictive Analytics

As the volume and complexity of data continue to grow, predictive analytics has emerged as a vital tool for extracting actionable insights from big data, driving decision-making across various domains such as healthcare, finance, and e-commerce. However, selecting an appropriate machine learning algorithm for predictive analytics applications is challenging due to differences in algorithmic performance, computational requirements, and scalability, especially in the context of big data. This paper provides a comprehensive comparative analysis of popular machine learning algorithms utilized in predictive analytics, specifically focusing on their effectiveness and feasibility in big data environments. The study categorizes algorithms based on learning types—supervised, unsupervised, and reinforcement learning—and evaluates their performance across multiple dimensions: prediction accuracy, computational efficiency, scalability, and suitability for real-time analytics. Through a detailed analysis of algorithms, including linear regression, decision trees, support vector machines, neural networks, and clustering techniques, we assess each method’s strengths and limitations in handling large datasets. Additionally, the study introduces a series of metrics, such as accuracy, F1-score, and training time, as benchmarks for assessing the algorithms’ predictive capabilities and computational viability. A hypothetical case study demonstrates the application of these algorithms on a sample big data set, providing insights into their real-world performance across different predictive analytics scenarios. Visual data representations, including comparative tables and performance graphs, offer a clearer perspective on the trade-offs among algorithm choices. The findings highlight that while certain algorithms like random forests and neural networks achieve higher accuracy in prediction tasks, they may also require substantial computational resources, posing limitations for real-time processing in big data applications. This paper concludes with recommendations for selecting machine learning algorithms based on specific predictive analytics objectives, data characteristics, and processing requirements. Furthermore, it discusses the challenges associated with implementing these algorithms in big data contexts and explores potential advancements, such as the integration of deep learning and the use of distributed computing, as promising directions for enhancing predictive analytics performance in future applications.

Design and Implementation of Virtualization Cloud Computing System Intelligent Terminal Application Layer

Cloud task scheduling has become a trend, and the shortcomings of traditional scheduling algorithms can be optimized through mathematical models of other cloud task scheduling scenarios. In order to improve the virtualization data processing effect of intelligent terminal application layer, this paper proposes an improved krill swarm optimization algorithm based on adaptive weight. The optimization of cluster load balancing and task average response time ratio are used to improve the convergence and accuracy of task scheduling algorithm. Moreover, this paper uses CloudSim simulation tool to conduct experiments to verify the effectiveness of the proposed model. In addition, this paper proposes an application-based virtualization method, which virtualizes the application programs inside the host machine into the virtualization software inside the virtual machine, so that the virtual machine can access it. Finally, this paper verifies the reliability of the proposed method with experiments, thus providing a theoretical reference for the subsequent design of intelligent terminal application layer virtualization cloud computing system. Compared with the traditional way of using physical hardware, using virtual machine hardware is more flexible, efficient and safe, which brings great convenience to the development and deployment of applications.

Graph-driven multi-vessel long-term trajectories prediction for route planning under complex waters

Current collision avoidance algorithms used in autonomous navigation vessels are hindered by limited long-term visibility of dynamic obstacles, resulting in overly cautious and frequent avoidance maneuvers that can actually increase the risk of collisions in complex waterways. Therefore, this paper proposed a novel graph-driven multi-vessel long-term trajectory prediction method (termed GMLTP), which extends the length of real-time and accurate prediction of other vessel trajectories. GMLTP can enhance collision avoidance algorithms by enabling more anticipatory trajectory prediction, ultimately supporting real-time route planning in creating safer, more efficient, and optimized routes. Specifically, GMLTP consists of a multi-graph spatial convolution (MGSC) layer and a probability sparse (ProbSparse) self-attention Transformer. MGSC is a finer spatial interactions extractor, which supports the modeling of spatial dependency evolution on longer sequences and thus improves the perception of long-term trends, while ProbSparse self-attention Transformer is used to reduce the computation of learning long-term global time dependencies and thus extending the predictable length. Extensive experimental results indicate that GMLTP exhibits better accuracy and generalization in prediction tasks beyond 48-timesteps (each time step is 10 s), which can provide better assistance to current real-time route planning tasks.

Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep-learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed-models analysis. With AI assistance, we found a significant increase in accuracy (p < 0.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44 s (32%) reduction in slide review time (p < 0.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10-point scale (p < 0.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process.

Estrogen predicts multimodal emotion recognition accuracy across the menstrual cycle.

Researchers have proposed that variation in sex hormones across the menstrual cycle modulate the ability to recognize emotions in others. Existing research suggests that accuracy is higher during the follicular phase and ovulation compared to the luteal phase, but findings are inconsistent. Using a repeated measures design with a sample of healthy naturally cycling women (N = 63), we investigated whether emotion recognition accuracy varied between the follicular and luteal phases, and whether accuracy related to levels of estrogen (estradiol) and progesterone. Two tasks assessed recognition of a range of positive and negative emotions via brief video recordings presented in visual, auditory, and multimodal blocks, and non-linguistic vocalizations (e.g., laughter, sobs, and sighs). Multilevel models did not show differences in emotion recognition between cycle phases. However, coefficients for estrogen were significant for both emotion recognition tasks. Higher within-person levels of estrogen predicted lower accuracy, whereas higher between-person estrogen levels predicted greater accuracy. This suggests that in general having higher estrogen levels increases accuracy, but that higher-than-usual estrogen at a given time decreases it. Within-person estrogen further interacted with cycle phase for both tasks and showed a quadratic relationship with accuracy for the multimodal task. In particular, women with higher levels of estrogen were more accurate in the follicular phase and middle of the menstrual cycle. We propose that the differing role of within- and between-person hormone levels could explain some of the inconsistency in previous findings.

Biomedical relation extraction method based on ensemble learning and attention mechanism

BackgroundRelation extraction (RE) plays a crucial role in biomedical research as it is essential for uncovering complex semantic relationships between entities in textual data. Given the significance of RE in biomedical informatics and the increasing volume of literature, there is an urgent need for advanced computational models capable of accurately and efficiently extracting these relationships on a large scale.ResultsThis paper proposes a novel approach, SARE, combining ensemble learning Stacking and attention mechanisms to enhance the performance of biomedical relation extraction. By leveraging multiple pre-trained models, SARE demonstrates improved adaptability and robustness across diverse domains. The attention mechanisms enable the model to capture and utilize key information in the text more accurately. SARE achieved performance improvements of 4.8, 8.7, and 0.8 percentage points on the PPI, DDI, and ChemProt datasets, respectively, compared to the original BERT variant and the domain-specific PubMedBERT model.ConclusionsSARE offers a promising solution for improving the accuracy and efficiency of relation extraction tasks in biomedical research, facilitating advancements in biomedical informatics. The results suggest that combining ensemble learning with attention mechanisms is effective for extracting complex relationships from biomedical texts. Our code and data are publicly available at: https://github.com/GS233/Biomedical.

Graph Attention Networks with Local and Global Attention Mechanisms for Learning Single-Shot Omics Data Representations

Aiming at the high-dimensional " size " problem in biological omics data where the number of genes is much larger than the number of samples pn, a graph attention network GATOr with local and global attention mechanisms is proposed. The model first calculates the correlation between features using the Pearson correlation coefficient on omics data and constructs a single-sample network of omics data. Then, a graph attention network combining local and global attention mechanisms is proposed to learn graph-based omics feature representation from the single-sample network, thereby converting the high-dimensional characteristics of omics data into low-dimensional representation. Experimental results show that GATOr has achieved better performance in classification task accuracy and other indicators than other traditional classification algorithms.

DMCCT: Dual-Branch Multi-Granularity Convolutional Cross-Substitution Transformer for Hyperspectral Image Classification

In the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress. However, convolutional neural network models encounter challenges in hyperspectral image classification due to limitations in their receptive fields. Conversely, the global modeling capability of Transformers has garnered attention in hyperspectral image classification. Nevertheless, the high computational cost and inadequate local feature extraction hinder its widespread application. In this study, we propose a novel fusion model of convolutional neural networks and Transformers to enhance performance in hyperspectral image classification, namely the dual-branch multi-granularity convolutional cross-substitution Transformer (DMCCT). The proposed model adopts a dual-branch structure to separately extract spatial and spectral features, thereby mitigating mutual interference and information loss between spectral and spatial data during feature extraction. Moreover, a multi-granularity embedding module is introduced to facilitate multi-scale and multi-level local feature extraction for spatial and spectral information. In particular, the improved convolutional cross-substitution Transformer module effectively integrates convolution and Transformer, reducing the complexity of attention operations and enhancing the accuracy of hyperspectral image classification tasks. Subsequently, the proposed method is evaluated against existing approaches using three classical datasets, namely Pavia University, Kennedy Space Center, and Indian Pines. Experimental results demonstrate the efficacy of the proposed method, achieving significant classification results on these datasets with overall classification accuracies of 98.57%, 97.96%, and 96.59%, respectively. These results establish the superiority of the proposed method in the context of hyperspectral image classification under similar experimental conditions.

Development of novel computational models based on artificial intelligence technique to predict liquids mixtures separation via vacuum membrane distillation

The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models’ effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R2 value of 0.9969 and a standard deviation of 0.0001, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks. It demonstrates that using SVM, optimized with DE improves accuracy and consistency for this specific predictive task in membrane separation context.

Uncertainty quantification driven machine learning for improving model accuracy in imbalanced regression tasks

Several factors are known to determine the quality of machine learning models, one of which is the dataset quality. One problem related to the quality of a dataset is the imbalance issue. An imbalanced dataset contains significantly more data points for certain values of the output variable which increases the overfitting risk and negatively affects the prediction accuracy. In this article, we propose using epistemic uncertainty quantification (UQ) of machine learning models to identify rare samples in imbalanced regression problems for balancing the dataset. The developed algorithm, uncertainty quantification-driven imbalanced regression (UQDIR), is guided by UQ to restructure the training set with an adequate weight function using existent samples, eliminating the need for new data collection. After identifying rare samples with UQ, the algorithm selects a sample from the training set, assigns a resampling weight using the new weight function, and finally resamples the selected sample according to its assigned weight. We test UQDIR on several benchmark datasets and different machine learning algorithms, then compare its performance with similar imbalanced regression methods. A metamaterial design problem application is also provided for demonstrating the effectiveness of the algorithm in real-world scenarios. We show that improving the quality of UQ metrics results in improved model accuracy.

Efficient Biomedical Waste Management: Optimizing Segmentation and Classification with EnU-Net-DNN-BMWC

&lt;p style="margin-bottom:8px;text-align:justify;"&gt;&lt;span style="font-family:&amp;quot;Times New Roman&amp;quot;,serif;font-size:12.0pt;line-height:200%;tab-stops:0in 21.3pt;"&gt;In India, biomedical waste (BMW) management is governed by strict regulations to mitigate health and environmental risks. It includes materials such as sharps, infectious wastes, pharmaceuticals, and non-hazardous items contaminated with potentially infectious substances. Proper management and disposal of BMW are critical to prevent environmental contamination and health risks.&amp;nbsp; This article introduces an Enhanced U-Network (EnU-Net) integrated with Deep Neural Network BMW Classification (EnU-Net-DNN-BMWC) to enhance accuracy in this critical task. Leveraging the U-Net framework with an Encoder-Decoder Network (EDN) and pixel-wise classification layer initially optimizes image segmentation. Bayesian functions mitigate segmentation uncertainties, while Content-Sensitive Sampling (CSS) refines pixel sampling to prioritize data-sparse regions. Data collected from G. Kuppuswamy Naidu Memorial Hospital and Kovai Medical Center and Hospital (KMCH) in Coimbatore over six months, categorizing sharps, infectious materials, pharmaceuticals, and non-hazardous waste, informs waste management strategies. Experimental validation using 100 biomedical waste images demonstrates EnU-Net-DNN-BMWC achieving good accuracy, surpassing standalone DNN-BMWC using Matlab 2022. The comparative analysis across different metrics such as accuracy, precision, F-measure, recall, error rate, and RMSE between DNN-BMWC and the proposed EnU-Net-DNN-BMWC framework were evaluated in this study, highlighting EnU-Net-DNN-BMWC's superior performance. Finally, this study underscores EnU-Net-DNN-BMWC’s efficacy in enhancing biomedical waste classification, crucial for sustainable waste management practices and regulatory compliance in healthcare settings.&lt;/span&gt;&lt;/p&gt;

Inspiration from Visual Ecology for Advancing Multifunctional Robotic Vision Systems: Bio-inspired Electronic Eyes and Neuromorphic Image Sensors.

In robotics, particularly for autonomous navigation and human-robot collaboration, the significance of unconventional imaging techniques and efficient data processing capabilities is paramount. The unstructured environments encountered by robots, coupled with complex missions assigned to them, present numerous challenges necessitating diverse visual functionalities, and consequently, the development of multifunctional robotic vision systems has become indispensable. Meanwhile, rich diversity inherent in animal vision systems, honed over evolutionary epochs to meet their survival demands across varied habitats, serves as a profound source of inspirations. Here, recent advancements in multifunctional robotic vision systems drawing inspiration from natural ocular structures and their visual perception mechanisms are delineated. First, unique imaging functionalities of natural eyes across terrestrial, aerial, and aquatic habitats and visual signal processing mechanism of humans are explored. Then, designs and functionalities of bio-inspired electronic eyes are explored, engineered to mimic key components and underlying optical principles of natural eyes. Furthermore, neuromorphic image sensors are discussed, emulating functional properties of synapses, neurons, and retinas and thereby enhancing accuracy and efficiency of robotic vision tasks. Next, integration examples of electronic eyes with mobile robotic/biological systems are introduced. Finally, a forward-looking outlook on the development of bio-inspired electronic eyes and neuromorphic image sensors is provided.

How can geostatistics help us understand deep learning? An exploratory study in SAR-based aircraft detection

Deep Neural Networks (DNNs) have garnered significant attention across various research domains due to their impressive performance, particularly Convolutional Neural Networks (CNNs), known for their exceptional accuracy in image processing tasks. However, the opaque nature of DNNs has raised concerns about their trustworthiness, as users often cannot understand how the model arrives at its predictions or decisions. This lack of transparency is particularly problematic in critical fields such as healthcare, finance, and law, where the stakes are high. Consequently, there has been a surge in the development of explanation methods for DNNs. Typically, the effectiveness of these methods is assessed subjectively via human observation on the heatmaps or attribution maps generated by eXplanation AI (XAI) methods. In this paper, a novel GeoStatistics Explainable Artificial Intelligence (GSEAI) framework is proposed, which integrates spatial pattern analysis from Geostatistics with XAI algorithms to assess and compare XAI understandability. Global and local Moran’s I indices, commonly used to assess the spatial autocorrelation of geographic data, assist in comprehending the spatial distribution patterns of attribution maps produced by the XAI method, through measuring the levels of aggregation or dispersion. Interpreting and analyzing attribution maps by Moran’s I scattergram and LISA clustering maps provide an accurate global objective quantitative assessment of the spatial distribution of feature attribution and achieves a more understandable local interpretation. In this paper, we conduct experiments on aircraft detection in SAR images based on the widely used YOLOv5 network, and evaluate four mainstream XAI methods quantitatively and qualitatively. By using GSEAI to perform explanation analysis of the given DNN, we could gain more insights about the behavior of the network, to enhance the trustworthiness of DNN applications. To the best of our knowledge, this is the first time XAI has been integrated with geostatistical algorithms in SAR domain knowledge, which expands the analytical approaches of XAI and also promotes the development of XAI within SAR image analytics.

Using multivariate partial least squares on fNIRS data to examine load-dependent brain-behaviour relationships in aging.

Researchers implementing non-invasive neuroimaging have reported distinct load-dependent brain activity patterns in older adults compared with younger adults. Although findings are mixed, these age-related patterns are often associated with compensatory mechanisms of cognitive decline even in the absence of direct comparisons between brain activity and cognitive performance. This study investigated the effects of cognitive load on brain-behavior relationships in younger and older adults using a data-driven, multivariate partial least squares (PLS) analysis of functional near-infrared spectroscopy (fNIRS) data. We measured bilateral prefrontal brain activity in 31 older and 27 younger adults while they performed single and dual 2-back tasks. Behavioral PLS analysis was used to determine relationships between performance metrics (reaction time and error rate) and brain oxygenation (HbO) and deoxygenation (HbR) patterns across groups and task loads. Results revealed significant age-group differences in brain-behavior relationships. In younger adults, increased brain activity (i.e., increased HbO and decreased HbR) was associated with faster reaction times and better accuracy in the single task, indicating sufficient neural capacity. Conversely, older adults showed a negative correlation between HbR and error rates in the single task; however, in the dual task, they demonstrated a positive relationship between HbO and performance, indicative of compensatory mechanisms under the higher cognitive load. Overall, older adults' showed relationships with either HbR or HbO, but not both, indicating that the robustness of the relationship between brain activity and behavior varies across task load conditions. Our PLS approach revealed distinct load-dependent brain activity between age groups, providing further insight into neurocognitive aging patterns, such as compensatory mechanisms, by emphasizing the variability and complexity of brain-behavior relationships. Our findings also highlight the importance of considering task complexity and cognitive demands in interpreting age-related brain activity patterns.

An Exploratory Study of the Impact of Task Selection Strategies on Worker Performance in Crowdsourcing Microtasks

In microtask crowdsourcing systems like Amazon Mechanical Turk (AMT) and Appen Figure-Eight, workers often employ task selection strategies, completing sequences of tasks to maximize earnings. While previous literature has explored the effects of sequential tasks with varying complexities of the same type, there is a lack of knowledge on the consequences when multiple types of tasks with similar levels of difficulty are performed. This study examines the impact of sequences of three frequently employed task types, namely image classification, text classification, and surveys, on workers' engagement, accuracy, and perceived workloads. In addition, we analyze the influence of workers' personality traits on their strategies for selecting tasks. Our study, which involved 558 participants using AMT, found that engaging in sequences of distinct task types had a detrimental effect on classification task engagement and accuracy. It also increases the perceived task load and the worker's frustration. Nevertheless, the precise order of tasks does not significantly impact these results. Moreover, we showed a slight association between personality traits and the workers' selection strategy for the tasks. The results offered valuable knowledge for designing an efficient and inclusive crowdsourcing platform.

Cura-LLaMA: Evaluating open-source large language models question answering capability on medical domain

This paper presents the development and evaluation of Cura-LLaMA, an open-source large language model (LLM) tailored for the medical domain. Based on the TinyLlama model, Cura-LLaMA was fine-tuned using the PubMedQA dataset to enhance its ability to address complex medical queries. The models performance was compared with the original TinyLlama, focusing on its accuracy and relevance in medical question-answering tasks. Despite improvements, the study highlights challenges in using keyword detection methods for evaluation and the limitations of omitting non-essential columns during fine-tuning. The findings underscore the potential of fine-tuning open-source models for specialized applications, particularly in resource-limited settings, while pointing to the need for more sophisticated evaluation metrics and comprehensive datasets to further enhance accuracy and

Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do not ensure adequate classification accuracy or may even produce inadequate results. This paper presents an enhanced input-doubling method for classification tasks in the case of limited data analysis, achieved via expanding the number of independent attributes in the augmented dataset with probabilities of belonging to each class of the task. The authors have developed an algorithmic implementation of the improved method using two Naïve Bayes classifiers. The method was modeled on a small dataset for cardiovascular risk assessment. The authors explored two options for the combined use of Naïve Bayes classifiers at both stages of the method. It was found that using different methods at both stages potentially enhances the accuracy of the classification task. The results of the improved method were compared with a range of existing methods used for solving the task. It was demonstrated that the improved input-doubling method achieved the highest classification accuracy based on various performance indicators.

A Novel YOLOv10-DECA Model for Real-Time Detection of Concrete Cracks

The You Only Look Once (YOLO) series algorithms have been widely adopted in concrete crack detection, with attention mechanisms frequently being incorporated to enhance recognition accuracy and efficiency. However, existing research is confronted by two primary challenges: the suboptimal performance of attention mechanism modules and the lack of explanation regarding how these mechanisms influence the model’s decision-making process to improve accuracy. To address these issues, a novel Dynamic Efficient Channel Attention (DECA) module is proposed in this study, which is designed to enhance the performance of the YOLOv10 model in concrete crack detection, and the effectiveness of this module is visually demonstrated through the application of interpretable analysis algorithms. In this paper, a concrete dataset with a complex background is used. Experimental results indicate that the DECA module significantly improves the model’s accuracy in crack localization and the detection of discontinuous cracks, outperforming the existing Efficient Channel Attention (ECA). When compared to the similarly sized YOLOv10n model, the proposed YOLOv10-DECA model demonstrates improvements of 4.40%, 3.06%, 4.48%, and 5.56% in precision, recall, mAP50, and mAP50-95 metrics, respectively. Moreover, even when compared with the larger YOLOv10s model, these performance indicators are increased by 2.00%, 0.04%, 2.27%, and 1.12%, respectively. In terms of speed evaluation, owing to the lightweight design of the DECA module, the YOLOv10-DECA model achieves an inference speed of 78 frames per second, which is 2.5 times faster than YOLOv10s, thereby fully meeting the requirements for real-time detection. These results demonstrate that an optimized balance between accuracy and speed in concrete crack detection tasks has been achieved by the YOLOv10-DECA model. Consequently, this study provides valuable insights for future research and applications in this field.

Adaptive spatial down-sampling method based on object occupancy distribution for video coding for machines

As the performance of machine vision continues to improve, it is being used in various industrial fields to analyze and generate massive amounts of video data. Although the demand for and consumption of video data by machines has increased significantly, video coding for machines needs to be improved. It is therefore necessary to consider a new codec that differs from conventional codecs based on the human visual system (HVS). Spatial down-sampling plays a critical role in video coding for machines because it reduces the volume of the video data to be processed while maintaining the shape of the data’s features that are important for the machine to reference when processing the video. An effective method of determining the intensity of spatial down-sampling as an efficient coding tool for machines is still in the early stages. Here, we propose a method of determining an optimal scale factor for spatial down-sampling by collecting and analyzing information on the number of objects and the ratio of the area occupied by the object within a picture. We compare the data reduction ratio to the machine accuracy error ratio (DRAER) to evaluate the performance of the proposed method. By applying the proposed method, the DRAER was found to be a maximum of 21.40 dB and a minimum of 11.94 dB. This shows that video coding gain for the machines could be achieved through the proposed method while maintaining the accuracy of machine vision tasks.

Optimized biomedical entity relation extraction method with data augmentation and classification using GPT-4 and Gemini.

Despite numerous research efforts by teams participating in the BioCreative VIII Track 01 employing various techniques to achieve the high accuracy of biomedical relation tasks, the overall performance in this area still has substantial room for improvement. Large language models bring a new opportunity to improve the performance of existing techniques in natural language processing tasks. This paper presents our improved method for relation extraction, which involves integrating two renowned large language models: Gemini and GPT-4. Our new approach utilizes GPT-4 to generate augmented data for training, followed by an ensemble learning technique to combine the outputs of diverse models to create a more precise prediction. We then employ a method using Gemini responses as input to fine-tune the BioNLP-PubMed-Bert classification model, which leads to improved performance as measured by precision, recall, and F1 scores on the same test dataset used in the challenge evaluation. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-viii/track-1/.