Multi Class Research Articles

User Task Priority Based Resource Allocation with Multi Class Task Scheduling Strategy and Load Balancing in Cloud Environment

User Task Priority Based Resource Allocation with Multi Class Task Scheduling Strategy and Load Balancing in Cloud Environment

Classification of Shoulder Implant Manufacturer Using Pre-Trained DenseNet201 Combined With Capsule Network.

This study aims to accelerate revision surgery and treatment using X-ray imaging and deep learning to identify shoulder implant manufacturers in advance. A feature engineering approach based on principal component analysis and a k-means algorithm was used to cluster shoulder implant data. In addition, a pre-trained DenseNet201 combined with a capsule network (DenseNet201-Caps) shoulder implant classification model was proposed. DenseNet201-Caps was the most effective classification model on the clustered dataset with an accuracy of 94.25% and an F1 score of 96.30%. Notably, clustering the dataset in advance improved the accuracy and the Caps implementations successfully enhanced the performance of all convolutional neural network models. The analysed results indicate that DenseNet201-Caps struggled to distinguish between the Cofield and Depuy manufacturers. Hence, a multistage classification approach was developed with an improved accuracy of 96.55% achieved. The DenseNet201-Caps method enables the accurate identification of shoulder implant manufacturers.

ECMTrans-net: Multi-Class Segmentation Network Based on Tumor Tissue in Endometrial Cancer Pathology Images

Endometrial cancer has the second highest incidence of malignant tumors in the female reproductive system, and accurate and efficient endometrial cancer pathology image analysis is one of the important research components of computer-aided diagnosis. However, endometrial cancer pathologic images have the challenges of smaller solid tumors, lesion areas varying in morphology, and difficulty distinguishing solid and non-solid tumors, which would impact the accuracy of subsequent pathological analyses. Therefore, an Endometrial Cancer Multi-class Transformer Network (ECMTrans-net) is proposed to improve the segmentation accuracy of endometrial cancer pathology images. On the one hand, an ECM-Attention is proposed, which can sequentially infer attention maps along three separate dimensions: channel, local spatial, and global spatial, and multiply the attention maps and the input feature map for adaptive feature refinement, solving the problems of the small size of solid tumors and similar characteristics of solid tumors to non-solid tumors and further improving the accuracy of segmentation of solid tumors. On the other hand, an ECM-Transformer is proposed, which can fuse multi-class feature information and dynamically adjust the receptive field, solving the issue of complex tumor features. Experiments on the solid tumor endometrial cancer pathological (ST-ECP) dataset show that the ECMTrans-net performs superior to state-of-the-art image segmentation methods, and the average values of Accuracy, MIoU, Precision, and Dice were 0.952, 0.927, 0.931 and 0.901, respectively.

Tensor multi-view clustering method for natural image segmentation

Image segmentation methods based on clustering have become a advancement in the field of computer vision, and the performance of these methods mainly depends on the performance of the clustering algorithm. However, these methods have some shortcomings. First, a single feature cannot accurately obtain the complete information of the superpixels. Second, the commonly used Euclidean distance cannot extract the nonlinear manifold structure between superpixels. Third, the high-dimensional information between superpixels is not considered. Therefore, this article proposes a Tensor Multi-view Clustering Method for Natural Image Segmentation (TMCNIS), in which firstly the multiclass features of the superpixels are extracted to obtain complete information. Secondly, the Jensen–Shannon divergence is used to delineate the relationship between superpixels to obtain a more complete nonlinear structure. Thirdly, an adaptively weighted tensor Schatten-p norm is proposed to better approximate the target rank of the tensor. It also automatically shrinks the singular values appropriately to construct a finer tensor, thus fully capturing the spatial structure in the tensor. Experimental results on four clustering datasets and two image segmentation datasets demonstrate the superiority of TMCNIS compared to existing methods.

Innovative Animal Identification Model usingCNN–Based Multi–Classification

Innovative Animal Identification Model usingCNN–Based Multi–Classification

WIMOAD: Weighted Integration of Multi-Omics data for Alzheimer's Disease (AD) Diagnosis.

As the most common subtype of dementia, Alzheimer's disease (AD) is characterized by a progressive decline in cognitive functions, especially in memory, thinking, and reasoning ability. Early diagnosis and interventions enable the implementation of measures to reduce or slow further regression of the disease, preventing individuals from severe brain function decline. The current framework of AD diagnosis depends on A/T/(N) biomarkers detection from cerebrospinal fluid or brain imaging data, which is invasive and expensive during the data acquisition process. Moreover, the pathophysiological changes of AD accumulate in amino acids, metabolism, neuroinflammation, etc., resulting in heterogeneity in newly registered patients. Recently, next generation sequencing (NGS) technologies have found to be a non-invasive, efficient and less-costly alternative on AD screening. However, most of existing studies rely on single omics only. To address these concerns, we introduce WIMOAD, a weighted integration of multi-omics data for AD diagnosis. WIMOAD synergistically leverages specialized classifiers for patients' paired gene expression and methylation data for multi-stage classification. The resulting scores were then stacked with MLP-based meta-models for performance improvement. The prediction results of two distinct meta-models were integrated with optimized weights for the final decision-making of the model, providing higher performance than using single omics only. Remarkably, WIMOAD achieves significantly higher performance than using single omics alone in the classification tasks. The model's overall performance also outperformed most existing approaches, highlighting its ability to effectively discern intricate patterns in multi-omics data and their correlations with clinical diagnosis results. In addition, WIMOAD also stands out as a biologically interpretable model by leveraging the SHapley Additive exPlanations (SHAP) to elucidate the contributions of each gene from each omics to the model output. We believe WIMOAD is a very promising tool for accurate AD diagnosis and effective biomarker discovery across different progression stages, which eventually will have consequential impacts on early treatment intervention and personalized therapy design on AD.

Large-scale IoT attack detection scheme based on LightGBM and feature selection using an improved salp swarm algorithm

Due to the swift advancement of the Internet of Things (IoT), there has been a significant surge in the quantity of interconnected IoT devices that send and exchange vital data across the network. Nevertheless, the frequency of attacks on the Internet of Things is steadily rising, posing a persistent risk to the security and privacy of IoT data. Therefore, it is crucial to develop a highly efficient method for detecting cyber threats on the Internet of Things. Nevertheless, several current network attack detection schemes encounter issues such as insufficient detection accuracy, the curse of dimensionality due to excessively high data dimensions, and the sluggish efficiency of complex models. Employing metaheuristic algorithms for feature selection in network data represents an effective strategy among the myriad of solutions. This study introduces a more comprehensive metaheuristic algorithm called GQBWSSA, which is an enhanced version of the Salp Swarm Algorithm with several strategy improvements. Utilizing this algorithm, a threshold voting-based feature selection framework is designed to obtain an optimized set of features. This procedure efficiently decreases the number of dimensions in the data, hence preventing the negative effects of having a high number of dimensions and effectively extracting the most significant and crucial information. Subsequently, the extracted feature data is combined with the LightGBM algorithm to form a lightweight and efficient ensemble learning scheme for IoT attack detection. The proposed enhanced metaheuristic algorithm has superior performance in feature selection compared to the recent metaheuristic algorithms, as evidenced by the experimental evaluation conducted using the NSLKDD and CICIoT2023 datasets. Compared to current popular ensemble learning solutions, the proposed overall solution exhibits excellent performance on multiple key indicators, including accuracy, precision, as well as training and detection time. Especially on the large-scale dataset CICIoT2023, the proposed scheme achieves an accuracy rate of 99.70% in binary classification and 99.41% in multi classification.

Early Glaucoma Detection using LSTM-CNN integrated with Multi Class SVM

Early Glaucoma Detection using LSTM-CNN integrated with Multi Class SVM

Learning by counterexamples in remote sensing image classification: a case study for road extraction

ABSTRACT The classification of multi- and hyper-spectral imagery is essential for several remote sensing applications. However, several challenges still hinder the accuracy and efficiency of learning algorithms. In diverse remote sensing applications, the high spectral heterogeneity between and within classes is often an issue that adds uncertainties to the classification. Most classifiers do not consider the influence of non-target classes and variations in the spectral similarity level between classes during classification. This paper presents a novel learning strategy for remote sensing image classification, which exploits non-target classes to enhance the generation of classification masks and the performance of supervised methods for specific classes of interest. It produces multiple classification instances, based on the incorporation of counterexample information into the training data set, in order to refine the separation of classes of interest in the available feature space. The experimental results demonstrate the potential of the proposed counterexample assisted learning for road extraction, using high spatial-resolution imagery data.

Design of a model for multistage classification of diabetic retinopathy and glaucoma

This study addresses the escalating prevalence of diabetic retinopathy (DR) and glaucoma, major global causes of vision impairment. We propose an innovative iterative Q-learning model that integrates with fuzzy C-means clustering to improve diagnostic accuracy and classification speed. Traditional diagnostic frameworks often struggle with accuracy and delay in disease stage classification, particularly in discerning complex features like exudates and veins. Our model overcomes these challenges by combining fuzzy C means with Q learning, enhancing precision in identifying key retinal components. The core of our approach is a custom-designed 45-layer 2D convolutional neural network (CNN) optimized for nuanced detection of DR and glaucoma stages. Compared to previous approaches, the performance on the IDRID and SMDG-19 datasets and associated samples shows a 10.9% rise in precision, an 8.5% improvement in overall accuracy, an 8.3% enhancement in recall, a 10.4% larger area under the curve (AUC), a 5.9% boost in specificity, and a 2.9% decrease in latency. This methodology has the potential to bring about significant changes in the field of DR and glaucoma diagnosis, leading to prompt medical interventions and possibly decreasing vision loss. The use of sophisticated machine learning techniques in medical imaging establishes a model for future investigations in ophthalmology and other clinical situations.

Multi-fault bearing diagnosis under time-varying conditions using Empirical Wavelet Transform, Gaussian mixture model, and Random Forest classifier

Bearing faults can cause heavy disruptions in machinery operation, which is why their reliable diagnosis is crucial. While current research into bearing fault analysis focuses on analyzing vibration data under constant working conditions, it is important to consider the challenges that arise when machinery runs at variable speeds, which is usually the case. This article proposes a multistage classifier for diagnosing bearings under time-variable conditions. We validate our method using vibration signals from five bearing health states, including a combined fault case. Our approach involves decomposing the signals using Empirical Wavelet Transform and computing temporal and frequency domain attributes. We use the Expectation-Maximization Gaussian mixture model for optimization concerns to identify relevant parameters and train the Random Forest classifier with the selected features. Our method, evaluated using the Polygon Area Metric, has demonstrated high effectiveness in diagnosing bearings under time-variable conditions. Our approach offers a promising solution that efficiently addresses speed variability and combined fault recognition issues.

Malignant thyroid lump multi classification by TIRADS using DBA with transfer learning

Thyroid diseases have developed into significant illnesses in recent decades. These diseases affect the thyroid glands and are caused by elevated thyroid hormone levels or infections in the thyroid organs. It is challenging to resolve thyroid diagnosis using conventional parametric and nonparametric statistical techniques since it can be viewed as a classification problem. However, there are certain barriers in the manner of obtaining both efficacy and accuracy in thyroid nodule diagnosis. Deep learning (DL) and machine learning (ML) models have emerged as useful instruments for the diagnosis of sickness in the modern era. For the purpose of diagnosing and classifying thyroid diseases, this research introduces a novel deep belief network (DBF) with transfer learning, known as DBNTL. In this study, the pre-processed image was first pre-processed using a conventional multiresolution bilateral technique, and then it was subjected to a novel segmentation technique called fusion pooling integrated U-net segmentation. The DBN with transfer learning model is used to classify and grade malignant thyroid nodules in compliance with thyroid imaging-reporting-and-data-system (TIRADS) guidelines. In this model, the model's weights are obtained by transfer learning. A major metric for evaluating the efficacy of biological image processing applications, good sensitivity and specificity (97.28 and 97.22, respectively) were obtained for the recommended modes.

Research on transformer fault diagnosis method based on ACGAN and CGWO-LSSVM

This paper proposes a transformer fault diagnosis method based on ACGAN and CGWO-LSSVM to address the problem of misjudgment and low diagnostic accuracy caused by the small number and uneven distribution of some fault samples in transformer fault diagnosis. Firstly, generate adversarial networks through auxiliary classification conditions, The ACGAN method expands a small and imbalanced number of samples to obtain balanced and expanded data; Secondly, the non coding ratio method is used to construct the characteristics of dissolved gases in oil, and kernel principal component analysis is used, KPCA method for feature fusion; Finally, using the improved cubic gray wolf optimization algorithm, CGWO for least square support vector machines, optimize the parameters of the LSSVM model and construct a transformer fault diagnosis model. The results show that the proposed method has a low false alarm rate and a diagnostic accuracy of 97.66%, compared to IGOA-LSSVM the IChOA-LSSVM and PSO-LSSVM methods improved accuracy by 0.12, 1.76, and 2.58%, respectively. This method has been proven to solve the problems of misjudgment and low diagnostic accuracy caused by small sample sizes and uneven distribution. It is suitable for multi classification fault diagnosis of transformer imbalanced datasets and is superior to other methods.

A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction

Epilepsy is one of the most well-known neurological disorders globally, leading to individuals experiencing sudden seizures and significantly impacting their quality of life. Hence, there is an urgent necessity for an efficient method to detect and predict seizures in order to mitigate the risks faced by epilepsy patients. In this paper, a new method for seizure detection and prediction is proposed, which is based on multi-class feature fusion and the convolutional neural network-gated recurrent unit-attention mechanism (CNN-GRU-AM) model. Initially, the Electroencephalography (EEG) signal undergoes wavelet decomposition through the Discrete Wavelet Transform (DWT), resulting in six subbands. Subsequently, time–frequency domain and nonlinear features are extracted from each subband. Finally, the CNN-GRU-AM further extracts features and performs classification. The CHB-MIT dataset is used to validate the proposed approach. The results of tenfold cross validation show that our method achieved a sensitivity of 99.24% and 95.47%, specificity of 99.51% and 94.93%, accuracy of 99.35% and 95.16%, and an AUC of 99.34% and 95.15% in seizure detection and prediction tasks, respectively. The results show that the method proposed in this paper can effectively achieve high-precision detection and prediction of seizures, so as to remind patients and doctors to take timely protective measures.

Identifying Black Cow Actions Using Kalman Filter Velocity and Multi-Stage Classification

Identifying Black Cow Actions Using Kalman Filter Velocity and Multi-Stage Classification

Human Motion Recognition Using Temporal Foot-Lift Features Extracted from a Small Number of Skeleton Data Frames and Multi Classifiers

Human Motion Recognition Using Temporal Foot-Lift Features Extracted from a Small Number of Skeleton Data Frames and Multi Classifiers

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis. This challenge has spurred research into computer-aided techniques aimed at diagnosing the plethora of generated images swiftly and accurately. The novelty of the proposed methodology lies in the development of a system tailored for the diagnosis of gastrointestinal diseases. The proposed work used an object detection method called Yolov5 for identifying abnormal region of interest and Deep LabV3+ for segmentation of abnormal regions in GERD. Further, the features are extracted from the segmented image and given as an input to the seven different machine learning classifiers and custom deep neural network model for multi-stage classification of GERD. The DeepLabV3+ attains an excellent segmentation accuracy of 95.2% and an F1 score of 93.3%. The custom dense neural network obtained a classification accuracy of 90.5%. Among the seven different machine learning classifiers, support vector machine (SVM) outperformed with classification accuracy of 87% compared to all other class outperformed combination of object detection, deep learning-based segmentation and machine learning classification enables the timely identification and surveillance of problems associated with GERD for healthcare providers.

Teaching Optimization Algorithm and Simulation Analysis based on Self-learning Mechanism and Multi-class Interaction

Teaching optimization algorithm is an intelligent optimization algorithm applied in the education. At the same time, it can solve complex optimization problems in other fields such as traffic flow optimization and logistics optimization. In response to the weak development ability, a teaching optimization algorithm based on self-learning mechanism is proposed by referring to general reverse learning methods. Meanwhile, a multi class interactive teaching optimization algorithm is proposed by combining clustering and partitioning methods based on Euclidean distance. By combining the two algorithms, a personalized and collaborative learning teaching environment is provided. When the function dimension is 30, the average function evaluations for the teaching optimization algorithm based on self-learning mechanism on unimodal function f1 is only 3859. On the multimodal function f2, the average function evaluations for this algorithm are only 4735, which is 2057 and 1367 less than the other two algorithms, respectively. Meanwhile, the success rates of this algorithm are all 100%. In addition, on the unconstrained function f6, the multi class interactive teaching optimization algorithm tends to converge when the function evaluations are 0.1×104. Traditional teaching optimization algorithms tend to converge only at 1.0×104. The two improved algorithms proposed in the study have better solution accuracy and stability, providing a reliable method reference for solving modern complex engineering problems.

Quantum support vector machine for multi classification

Classical machine learning algorithms seem to be totally incapable of processing tremendous data, while quantum machine learning algorithms could deal with big data unhurriedly and provide exponential acceleration over classical counterparts. In this paper, we propose two quantum support vector machine algorithms for multi classification. One is the quantum version of the directed acyclic graph support vector machine. The other one is to use the Grover search algorithm before measurement, which amplifies the amplitude of the phase storing of the classification result. For k classification, the former provides quadratic reduction in computational complexity when classifying. The latter accelerates the training speed significantly and more importantly, the classification result can be read out with a probability of at least 50% using only one measurement. We conduct numerical simulations on two algorithms, and their classification success rates are 96% and 88.7%, respectively.

Neural networks application based on language features in the classification of complex English textbooks granularity

The surge in modern information has led to a significant increase in text complexity. To meet the needs of various fields for effective information extraction, research on text complexity grading urgently is urgently needed. The study uses the Flesh-Kincaid Grade Level (FKGL) model to extract language features, selects English textbooks as training corpus, and introduces the Graph Convolutional Network of Attention Mechanism (GCN_ATT) model of attention mechanism to construct a text complexity grading model. The research results indicated that in the 10-fold crossover experiment, GCN_ATT’s accuracy, recall, and F1 all reached over 88%. Compared to multi class logistic regression models, GCN_ATT’s various performance indicators were approximately 2% to 3% higher. Meanwhile, GCN_ ATT’s F1 standard deviation decreased by 0.7% and 1.78% compared to the other two models. In addition, GCN_ATT’s fluctuation range of recall and accuracy was less than 20%, a decrease of 12% and 18% compared to the ordered multi classification regression model. Explanation based on GCN_ATT’s text complexity grading has higher accuracy and more stable performance, providing an effective method reference for current text complexity grading problems.