Feature Extraction Step Research Articles

PreAlgPro: Prediction of allergenic proteins with pre-trained protein language model and efficient neutral network

Allergy is a prevalent phenomenon, involving allergens such as nuts and milk. Avoiding exposure to allergens is the most effective preventive measure against allergic reactions. However, current homology-based methods for identifying allergenic proteins encounter challenges when dealing with non-homologous data. Traditional machine learning approaches rely on manually extracted features, which lack important protein functional characteristics, including evolutionary information. Consequently, there is still considerable room for improvement in existing methods. In this study, we present PreAlgPro, a method for identifying allergenic proteins based on pre-trained protein language models and deep learning techniques. Specifically, we employed the ProtT5 model to extract protein embedding features, replacing the manual feature extraction step. Furthermore, we devised an Attention-CNN neural network architecture to identify potential features that contribute to the classification of allergenic proteins. The performance of our model was evaluated on four independent test sets, and the experimental results demonstrate that PreAlgPro surpasses existing state-of-the-art methods. Additionally, we collected allergenic protein samples to validate the robustness of the model and conducted an analysis of model interpretability.

Multi-Robot Collaborative Mapping with Integrated Point-Line Features for Visual SLAM.

Simultaneous Localization and Mapping (SLAM) enables mobile robots to autonomously perform localization and mapping tasks in unknown environments. Despite significant progress achieved by visual SLAM systems in ideal conditions, relying solely on a single robot and point features for mapping in large-scale indoor environments with weak-texture structures can affect mapping efficiency and accuracy. Therefore, this paper proposes a multi-robot collaborative mapping method based on point-line fusion to address this issue. This method is designed for indoor environments with weak-texture structures for localization and mapping. The feature-extraction algorithm, which combines point and line features, supplements the existing environment point feature-extraction method by introducing a line feature-extraction step. This integration ensures the accuracy of visual odometry estimation in scenes with pronounced weak-texture structure features. For relatively large indoor scenes, a scene-recognition-based map-fusion method is proposed in this paper to enhance mapping efficiency. This method relies on visual bag of words to determine overlapping areas in the scene, while also proposing a keyframe-extraction method based on photogrammetry to improve the algorithm's robustness. By combining the Perspective-3-Point (P3P) algorithm and Bundle Adjustment (BA) algorithm, the relative pose-transformation relationships of multi-robots in overlapping scenes are resolved, and map fusion is performed based on these relative pose relationships. We evaluated our algorithm on public datasets and a mobile robot platform. The experimental results demonstrate that the proposed algorithm exhibits higher robustness and mapping accuracy. It shows significant effectiveness in handling mapping in scenarios with weak texture and structure, as well as in small-scale map fusion.

A Machine Learning Prediction Model for Myelitis and Multiple Sclerosis Based on Fourier Transform Features from MRI Images

Myelitis is a neurodegenerative disease positioned in the spinal cord, with multiple sclerosis (MS) being a common subtype. Radiological indicators enable the diagnosis of these diseases. This study proposes a classification framework to detect myelitis, MS, and healthy control (HC) groups using magnetic resonance imaging (MRI) images. The feature extraction step involves applying the fast Fourier transform (FFT) to MRI images. FFT is important because it converts spatial data into the frequency domain, making it easier to identify patterns and abnormalities that indicate these diseases. Then, statistical features (mean, minimum, maximum, standard deviation, skewness, kurtosis, and total energy) are extracted from this frequency information. These features are then used to train support vector machine (SVM), k-nearest neighbor (KNN), and decision tree algorithms. In multi-class classification (myelitis vs. MS vs. HC), the proposed method achieves a classification accuracy of 99.31% with SVM, with average precision, recall, and F1-score values of 99.27%, 99.21%, and 99.24%, respectively, indicating effective classification across all classes. In the binary class classification (HC vs. MS, MS vs. myelitis, HC vs. myelitis), the SVM achieves an outstanding classification accuracy of 99.36%, 99.71%, and 100% respectively. This study highlights the efficiency of FFT-based feature extraction in forming detection patterns for classifying HC, MS, and myelitis classes.

Automatic Identification of Sunken Oil in Homogeneous Information Perturbed Environment through Fusion Image Enhancement with Convolutional Neural Network

With the development of the marine oil industry, leakage accidents are one of the most serious problems threatening maritime and national security. The spilt crude oil can float and sink in the water column, posing a serious long-term threat to the marine environment. High-frequency sonar detection is currently the most efficient method for identifying sunken oil. However, due to the complicated environment of the deep seabed and the interference of the sunken oil signals with homogeneous information, sonar detection data are usually difficult to interpret, resulting in low efficiency and a high failure rate. Previous works have focused on features designed by experts according to the detection environments and the identification of sunken oil targets regardless of the feature extraction step. To automatically identify sunken oil targets without a prior knowledge of the complex seabed conditions during the image acquisition process for sonar detection, a systematic framework is contrived for identifying sunken oil targets that combines image enhancement with a convolutional neural network (CNN) classifier for the final decision on sunken oil targets examined in this work. Case studies are conducted using datasets obtained from a sunken oil release experiment in an outdoor water basin. The experimental results show that (i) the method can effectively distinguish between the sunken oil target, the background, and the interference target; (ii) it achieved an identification accuracy of 83.33%, outperforming feature-based recognition systems, including SVM; and (iii) it provides important information about sunken oil such as the location of the leak, which is useful for decision-making in emergency response to oil spills at sea. This line of research offers a more robust and, above all, more objective option for the difficult task of automatically identifying sunken oils under complex seabed conditions.

Improving deep learning based bluespotted ribbontail ray (Taeniura Lymma) recognition

This paper presents the novel task of bluespotted ribbontail (BR) ray (Taeniura lymma) recognition using deep learning based computer vision methods to enable the identification of specific individuals of this species. Mapping the specific individuals in relation to location and time will allow marine researchers to understand their movement patterns, habitat choice, life span, size of the population and more – data which could allow monitoring and establishing a tailor-made conservation plan for this species.Our work is pioneer on this recognition problem. We give a detailed description of the three basic steps of detection, feature extraction and recognition in this vision problem and perform experiments to explore the system configuration and what improves the performance. A feature extraction enhancement as well as a crucial effect of a split into different main poses are demonstrated. Though the precision results achieved in this paper are still moderate and should be further improved, they are nevertheless promising and reasonable for practical use if the six best matches are chosen. For this scenario, almost 85% precision for upper-pose model, and almost 80% precision for left- and right-pose models, are achieved demonstrating the feasibility of the pipeline suggested as well as opportunities for improvement.

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.

Evaluation of fish meal freshness using a metal-oxide semiconductor electronic nose combined with the long short-term memory feature extraction method.

To improve the classification and regression performance of the total volatile basic nitrogen (TVB-N) and acid value (AV) of different freshness fish meal samples detected by a metal-oxide semiconductor electronic nose (MOS e-nose), 402 original features, 62 manually extracted features, manually extracted and selected features by the RFRFE method, and the features extracted by the long short-term memory (LSTM) network were used as inputs to identify the freshness. The classification performance of the freshness grades and the estimation performance of the TVB-N and AV values of fish meal with different freshness were compared. According to the sensor response curve, preprocessing and feature extraction steps were first applied to the original data. Then, five classification algorithms and four regression algorithms were used for modeling. The results showed that a total of 30 features were extracted using the LSTM network, and the number of extracted features was significantly reduced. In the classification, the highest accuracy rate of 95.4% was obtained using the support vector machine method. In the regression, the least squares support vector regression method obtained the best root mean square error (RMSE). The coefficient of determination (R2), RMSE, and relative standard deviation (RSD) between the predicted value of TVBN and the actual value were 0.963, 11.01, and 7.9%, respectively. The R2, RMSE, and RSD between the predicted value of AV and the actual value were 0.972, 0.170, and 6.05%, respectively. The LSTM feature extraction method provided a new method and reference for feature extraction using an E-nose to identify other animal-derived material samples.

Motor imagery electroencephalography channel selection based on deep learning: A shallow convolutional neural network

Electroencephalography (EEG) motor imagery (MI) signals have recently attracted much attention because of their potential to communicate with the surrounding environment in a specific way without the need for muscular and physical movement. Despite these advantages, these signals are difficult to detect due to their low signal to noise (SNR) rate and non-stationary and dynamic nature. Convolutional neural networks (CNN) can extract appropriate spatial and temporal features without the need for separate feature extraction and classification steps. Clean input data for CNN significantly improves its performance, but sometimes, common interference occurs between multi-channel signals. This challenge, along with the noisy EEG signals, degrades the performance of CNN networks. This paper presents a channel selection method based on convolutional neural networks to obviate these challenges. The proposed shallow convolutional neural network (SCNN) is designed with temporal convolution and pointwise convolution (using 1×1 convolution) to select the best channel with minimal computational load. Following the channel selection step, the multi-layer fusion CNN model was used to classify the signals. Choosing suitable and relevant EEG channels for motor imagery tasks makes it possible to create appropriate inputs for the classification model so that the model can be improved in the end. For High Gamma Dataset and Brian-Computer Interface (BCI) Competition IV-2a, the following accuracies were obtained: 81.15% and 72.01%, which are higher than when the other channel selections such as Mutual Information, Sequential Feature Forward Selection (SFFS) and wrapper methods are used.

Predicting the remaining useful life of rails based on improved deep spiking residual neural network

the remaining useful life (RUL) of rails is important to ensure safe and reliable operation of railway transportation lines. However, the severity of deterioration of rails from different types of damage varies. Multichannel vibrational data collected by sensors can be utilized to identify the relevant complex correlations and temporal relationships; however, existing methods struggle to predict rail damage accurately. Thus, this paper proposes a RUL prediction method for rails based on an improved deep spiking residual neural network. First, multichannel data collected by sensors are used directly as input to the predictive network without requiring a separate feature extraction step. Additionally, an encoding module is designed to adaptively convert the temporal sequences of data into spiking signals to reduce the information loss during the encoding process. Second, a spiking residual convolutional neural network (CNN) is incorporated into the proposed method to extract optimal features. The separable, spiking CNN can model the relationships among multichannel data accurately, and an attention mechanism is implemented to recalibrate the spiking feature maps such that the predictive network can distinguish between items of information effectively. Third, the spiking residual connections are altered to mitigate network degradation caused by the incompatibility between the conventional residual connections and the spiking neural network. Finally, the obtained spiking features are input to a fully connected layer to predict the rail RUL. Experimental results demonstrated that the proposed method can predict the rail RUL accurately, and validation results obtained on a rolling bearing dataset demonstrated the high generalizability of the proposed method.

Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction

Machine learning-based software defect prediction (SDP) approaches have been commonly proposed to help to deliver high-quality software. Unfortunately, all the previous research conducted without effective feature reduction suffers from high-dimensional data, leading to unsatisfactory prediction performance measures. Moreover, without proper feature reduction, the interpretability and generalization ability of machine learning models in SDP may be compromised, hindering their practical utility in diverse software development environments. In this paper, an SDP approach using deep Q-learning network (DQN)-based feature extraction is proposed to eliminate irrelevant, redundant, and noisy features and improve the classification performance. In the data preprocessing phase, the undersampling method of BalanceCascade is applied to divide the original datasets. As the first step of feature extraction, the weight ranking of all the metric elements is calculated according to the expected cross-entropy. Then, the relation matrix is constructed by applying random matrix theory. After that, the reward principle is defined for computing the Q value of Q-learning based on weight ranking, relation matrix, and the number of errors, according to which a convolutional neural network model is trained on datasets until the sequences of metric pairs are generated for all datasets acting as the revised feature set. Various experiments have been conducted on 11 NASA and 11 PROMISE repository datasets. Sensitive analysis experiments show that binary classification algorithms based on SDP approaches using the DQN-based feature extraction outperform those without using it. We also conducted experiments to compare our approach with four state-of-the-art approaches on common datasets, which show that our approach is superior to these methods in precision, F-measure, area under receiver operating characteristics curve, and Matthews correlation coefficient values.

Training Datasets for Epilepsy Analysis: Preprocessing and Feature Extraction from Electroencephalography Time Series

We describe 20 datasets derived through signal filtering and feature extraction steps applied to the raw time series EEG data of 20 epileptic patients, as well as the methods we used to derive them. Background: Epilepsy is a complex neurological disorder which has seizures as its hallmark. Electroencephalography plays a crucial role in epilepsy assessment, offering insights into the brain’s electrical activity and advancing our understanding of seizures. The availability of tagged training sets covering all seizure phases—inter-ictal, pre-ictal, ictal, and post-ictal—is crucial for data-driven epilepsy analyses. Methods: Using the sliding window technique with a two-second window length and a one-second time slip, we extract multiple features from the preprocessed EEG time series of 20 patients from the Freiburg Seizure Prediction Database. In addition, we assign a class label to each instance to specify its corresponding seizure phase. All these operations are made through a software application we developed, which is named Training Builder. Results: The 20 tagged training datasets each contain 1080 univariate and bivariate features, and are openly and publicly available. Conclusions: The datasets support the training of data-driven models for seizure detection, prediction, and clustering, based on features engineering.

Early Assessment of Diabetic Retinopathy by Detecting Microaneurysm with Deep Neural Network

Objectives: This study aims to detect early signs of diabetic retinopathy, also known as microaneurysm (MA). Early detection of MA can help in diagnosing diabetic retinopathy effectively. Methods: To achieve this objective a method is proposed which is based on a deep learning model that incorporates transfer learning. The dataset used in this study is E-Ophtha which consists of 381 high-quality images. The proposed model consists of three steps which are preprocessing, feature extraction and classification. The method uses CLAHE to enhance the details of the fundus image. The feature extraction step and the classification are done using a deep learning algorithm. The proposed method is compared by using parameters such as Recall, Precision, and accuracy. Findings: The accuracy achieved by the model is about 98.82%-100%, a recall/sensitivity of 100%, and precision is also 100% which is close to the state of the art. This implies that deep learning methods are a good fit for identifying microaneurysms and ultimately detecting diabetic retinopathy. Novelty: Early detection and diagnosis of diabetic retinopathy can be achieved with this approach, and appropriate medication can impede the disease's development. Keywords: Diabetic Retinopathy, PDR, NPDR, Image Processing, Deep Learning

The development of a model for the threat detection system with the use of machine learning and neural network methods

This study examines the development of a model for the threat detection system with the use of machine learning and neural network methods. The fast development of Internet technologies has led to the appearance of many digital systems and platforms. However, despite the impressive technological progress, another side also emerged in the spread of a massive number of different cyber threats. Although various ways have been created to detect and prevent them, the threats are also developing and becoming more complex each year. Therefore, new system defense and data protection methods using machine and deep learning approaches have been proposed recently. The methods based on these approaches have proved to be especially effective in the wave of new Artificial Intelligence applications. In this paper, a threat detection system has been designed to disclose different kinds of threats while maintaining the security, confidentiality, and availability of the computer system. The development of machine learning models for detecting DDoS and man-in-the-middle attacks, Structured Query Language (SQL) injections, phishing, and malware was examined. The data scaling, feature selection, feature extraction, and classification steps were also thoroughly described. Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and Deep Neural Network algorithms were utilized for training the cyber threat detection models. The experimental results evaluated all the models using accuracy, precision, recall, and F1-score metrics. The best models achieved scores in the range of 0.90 to 1.00.

A Semantic-Spatial Aware Data Conflation Approach for Place Knowledge Graphs

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places.

Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach

Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac anomalies seeks early detection of arrhythmias using automated classification techniques. In the past, numerous automated arrhythmia detection techniques have been developed that are based on electrocardiogram (ECG) signal analysis. Focusing on the prospective research in this field, this article reports a comprehensive review of existing techniques that are obtained using search engines such as IEEE explore, Google scholar and science direct. Based on the review, the existing techniques are broadly categorized into two types: machine-learning and deep-learning-based techniques. In this study, it is noticed that the performance of the machine-learning-based arrhythmia detection techniques depend on pre-processing of ECG signal, R-peaks detection, features extraction and classification tools while the deep-learning-based techniques do not require the features extraction step. Generally, the existing techniques utilize Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database to evaluate the classification performance. The classification performance of automated techniques also depends on ECG data used for training and testing of the classifier. It is expected that the performance should be evaluated using a variety of ECG signals including the cases of inter-patient and intra-patient paradigm. The existing techniques also require to deal with the class-imbalance problem. In addition to this, a specific partition-ratio between training and testing datasets should be maintained for fair comparison of performance of different techniques.

A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms

Background and objectiveHypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement. MethodsIn this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately. ResultsThe SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP. ConclusionThe findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features.

Multimodal Emotion Recognition Using Bi-LG-GCN for MELD Dataset

Emotion recognition using multimodal data is a widely adopted approach due to its potential to enhance human interactions and various applications. By leveraging multimodal data for emotion recognition, the quality of human interactions can be significantly improved. We present the Multimodal Emotion Lines Dataset (MELD) and a novel method for multimodal emotion recognition using a bi-lateral gradient graph neural network (Bi-LG-GNN) and feature extraction and pre-processing. The multimodal dataset uses fine-grained emotion labeling for textual, audio, and visual modalities. This work aims to identify affective computing states successfully concealed in the textual and audio data for emotion recognition and sentiment analysis. We use pre-processing techniques to improve the quality and consistency of the data to increase the dataset’s usefulness. The process also includes noise removal, normalization, and linguistic processing to deal with linguistic variances and background noise in the discourse. The Kernel Principal Component Analysis (K-PCA) is employed for feature extraction, aiming to derive valuable attributes from each modality and encode labels for array values. We propose a Bi-LG-GCN-based architecture explicitly tailored for multimodal emotion recognition, effectively fusing data from various modalities. The Bi-LG-GCN system takes each modality's feature-extracted and pre-processed representation as input to the generator network, generating realistic synthetic data samples that capture multimodal relationships. These generated synthetic data samples, reflecting multimodal relationships, serve as inputs to the discriminator network, which has been trained to distinguish genuine from synthetic data. With this approach, the model can learn discriminative features for emotion recognition and make accurate predictions regarding subsequent emotional states. Our method was evaluated on the MELD dataset, yielding notable results in terms of accuracy (80%), F1-score (81%), precision (81%), and recall (81%) when using the MELD dataset. The pre-processing and feature extraction steps enhance input representation quality and discrimination. Our Bi-LG-GCN-based approach, featuring multimodal data synthesis, outperforms contemporary techniques, thus demonstrating its practical utility.

An approach to detect and predict epileptic seizures with high accuracy using convolutional neural networks and single-lead-ECG signal

One of the epileptic patients’ challenges is to detect the time of seizures and the possibility of predicting. This research aims to provide an algorithm based on deep learning to detect and predict the time of seizure from one to two minutes before its occurrence. The proposed Convolutional Neural Network (CNN) can detect and predict the occurrence of focal epilepsy seizures through single-lead-ECG signal processing instead of using EEG signals. The structure of the proposed CNN for seizure detection and prediction is the same. Considering the requirements of a wearable system, after a few light pre-processing steps, the ECG signal can be used as input to the neural network without any manual feature extraction step. The desired neural network learns purposeful features according to the labelled ECG signals and then performs the classification of these signals. Training of 39-layer CNN for seizure detection and prediction has been done separately. The proposed method can detect seizures with an accuracy of 98.84% and predict them with an accuracy of 94.29%. With this approach, the ECG signal can be a promising indicator for the construction of portable systems for monitoring the status of epileptic patients.

Braiding drive data processing through rote learning

It is difficult for traditional data processing methods to make full use of the potential of braiding driven data, unable to quickly collect and preprocess data, and difficult to ensure the accuracy of data. Rote learning (RL) is part of the research field of artificial intelligence, which aims to enable computers to learn autonomously, just like humans. This allows understanding of relationships and patterns between data and helps computers process information quickly. In order to solve the problems of poor data integrity, slow data processing efficiency and poor information sharing in traditional data processing, and further optimize the braiding driven data processing technology, this paper combined RL with braiding driven data. Through the method of mechanical learning, the potential of weaving driving data is fully exerted, so that it can better cope with nonlinear relations and high-dimensional features. It used the effective method provided by the RL to process the braiding drive data, collect the data, and preprocessed the collected data to ensure the correctness of the data. It extracted the features of the data, which was convenient to classify the data according to its attributes. At the same time, this paper verified it by the steps of feature extraction, model training and data analysis. In order to test whether braiding drive data processing by RL can effectively solve the problems existing in traditional drive data technology, this paper tested the performance of compiled drive data processing, and the analysis results were as follows. The data integrity rate of braiding drive data was as low as 81%, which was much higher than that of traditional drive data processing. The recognition ability of data acquisition and matching was much higher than that of traditional drive data processing. Compared with the traditional drive data processing, the information sharing has been greatly improved. In terms of data processing efficiency, it is also much higher than the traditional drive data processing. It can be seen that the method of braiding drive data processing through RL effectively improves the accuracy of data processing. It strengthens the identification ability of data collection and matching, improves the sharing of information, enables users to obtain data and analyze it faster, and also improves the processing efficiency of data.

Model-Agnostic Binary Patch Grouping for Bone Marrow Whole Slide Image Representation

Histopathology is the reference standard for pathology diagnosis, and has evolved with the digitization of glass slides, i.e. whole slide images (WSIs). Trained histopathologists can help to diagnose disease by examining WSIs visually, but this process is time-consuming and prone to variability. To address these issues, AI models are being developed to create slide-level representations of WSIs, summarizing the entire slide as a single vector. This enables various computational pathology applications, including inter-slide search, multi-modal training, and slide-level classification. Achieving expressive and robust slide-level representations hinges on patch feature extraction and aggregation steps. We propose integrating an additional Binary Patch Grouping (BPG) step, a plugin that can be integrated into various slide-level representation pipelines to enhance the quality of slide-level representation in bone marrow histopathology. BPG excludes patches with less clinical relevance through minimal interaction with the pathologist: a one-time human intervention for the entire process. We further investigated domain-general versus domain-specific feature extraction models based on convolution and attention and examined two different feature aggregation methods, with and without BPG, showing BPG’s generalizability. We show that BPG boosts the performance of WSI retrieval (mAP@10) by 4% and improves WSI classification (weighted-F1) by 5% relative to not using BPG. Additionally, we found that the pipeline with BPG, domain-general large models and parameterized pooling produced the best-quality slide-level representations.