Classification Results Research Articles

Design of Time Series Fuzzy Segmentation Algorithm for Communication Data Classification

In order to solve the problem that the imbalance of communication data sets leads to a significant increase in classification difficulty, a classification algorithm for fuzzy segmentation of time series is proposed. The principal component analysis method is used to obtain the eigenvector with the largest eigenvalue. The time series of data is established according to the interval number theory. The segmentation target of the communication data time series is characterized by the Langmuir distance measurement function between data and categories. The classification result of fuzzy segmentation is obtained based on the judgment relationship between the difference of fuzzy classification matrix and the closing condition. The experimental results show that the number of data in the three classification situations of the algorithm in this paper is always at the corresponding ideal level, with high accuracy and low error and failure.

Application of business intelligence based on big data in E-commerce data evaluation

With the vigorous development of e-commerce, more and more goods are sold online. The electronic platform not only brings convenience to people's lives but also gives more people the opportunity of employment and entrepreneurship, which contributes to the promotion of economic value and the creation of wealth. With the gradual maturity of network technology represented by Big Data, it has also led to the further development of e-commerce. In the past, e-commerce mostly used business intelligence systems for data analysis. However, as the data becomes more and more complex, the innovation ability and data analysis ability of traditional business intelligence systems are relatively conservative, so it is of great significance to update and strengthen business intelligence to analyze its role in e-commerce data. Therefore, this paper proposed the application of business intelligence based on Big Data in e-commerce data analysis. By combining Big Data and a business intelligence system and taking the e-commerce data of a certain brand of beverage as the research object, the Days-Times-Money (DTM) model was established, and the data mining technology was used to classify the brand consumers. The data results showed that among the three consumption attributes, the highest consumption density of consumption days, consumption times, and consumption amount was 68.63 %, 67.99 %, and 69.72 % respectively. These were not more than 70 %, which indicated that the consumption of this brand of beverages had a high space for growth. According to the consumption density, the users of this brand of beverage were divided into four consumer groups. According to the classification results, it provided feasible marketing reference opinions for the brand beverage and could provide direction for brand value-added by mining the hidden data of the brand beverage. This paper hoped to use the brand beverage as a case to provide reference and reality for the development of e-commerce enterprises and provide a reference for the application and promotion of business intelligence based on Big Data in e-commerce data analysis.

Performance improvement of active sonar target classification using feature fusion

In this study, we propose a neural network architecture considering feature fusion to improve active sonar target classification, where training data is scarce. We use various hand-crafted features with different perspectives for the active sonar one- and two-dimention raw data to enhance target classification performance. We use two kinds of features, in 1D feature and 2D features, because different perspectives on the same sample complement effectively by exchanging. For this, we contrived attention based complementary learning module to extract comprehensive features using the two kinds of features. To verify the generalization performance of this model, two real-ocean datasets were used and the classification results are compared with existing DL models.

Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions.

Falls represent a significant risk factor, necessitating accurate classification methods. This study aims to identify the optimal placement of wearable sensors-specifically accelerometers, gyroscopes, and magnetometers-for effective fall-direction classification. Although previous research identified optimal sensor locations for distinguishing falls from non-falls, limited attention has been given to the classification of fall direction across different body regions. This study assesses inertial measurement unit (IMU) sensors placed at 12 distinct body locations to determine the most effective positions for capturing fall-related data. The research was conducted in three phases: first, comparing classifiers across all sensor locations to identify the most effective; second, evaluating performance differences between sensors placed on the left and right sides of the body; and third, exploring the efficacy of combining sensors from the upper and lower body regions. Statistical analyses of the results for the most effective classifier model demonstrate that the support vector machine (SVM) is more effective than other classifiers across all sensor locations, with statistically significant differences in performance. At the same time, the comparison between the left and right sensor locations shows no significant performance differences within the same anatomical areas. Regarding optimal sensor placement, the findings indicate that sensors positioned on the pelvis and upper legs in the lower body, as well as on the shoulder and head in the upper body, were the most effective results for accurate fall-direction classification. The study concludes that the optimal sensor configuration for fall-direction classification involves strategically combining sensors placed on the pelvis, upper legs, and lower legs.

Object-oriented U-GCN for open-pit mining extraction from high spatial resolution remote-sensing images of complex scenes

ABSTRACT Precise extraction of open-pit mines is crucial for resource management and ecological and environmental dynamic monitoring. Current methods for extracting open-pit mines encounter challenges such as low accuracy and difficulty detecting complex scenes of open-pit mining. To address these issues, this paper proposes an object-oriented intelligent extraction method for complex mining scenes using Gaofen-2(GF-2) high-resolution remote-sensing images, which expresses the pixel-level features at the object level and utilizes the unique feature propagation and aggregation capabilities of the graph structure for the extraction of the mines. First, an object-oriented feature expression strategy is introduced to express multi-level pixel-level features as object-level features by constructing objects with appropriate multi-resolution segmentation parameters. This approach effectively reduces the impact of isolated pixel noise and outliers on classification results. Second, this paper proposes a U-GCN-based open-pit mining extraction method that combines the powerful multi-level feature extraction capabilities of U-GCN to propagate and aggregate information in a graph structure, effectively modelling spatial relationships between different objects. This method achieves high-precision extraction of open-pit mining areas. In experiments conducted on two study areas of varying scales, the F1 scores for open-pit mine extraction reached 93.32% and 83.06%. Comparative experiments demonstrate that the proposed object-oriented U-GCN method performs superiorly in terms of accuracy, stability and robustness across mining scenes with different levels of complexity. The proposed open-pit mine extraction method offers new insights and methodologies for current extraction practices.

Sentiment Analysis Using Improved CT-BERT_CONVLayer Fusion Model for COVID-19 Vaccine Recommendation

COVID-19 has significantly impacted individuals, communities, and countries worldwide. These effects include health impacts, economics impacts, social impacts, educational, political and environmental impacts. The COVID-19 vaccine development was crucial for disease control and monitoring, yet the threat still looms large. Vaccine recommender systems can help the health practitioners in combating COVID-19 by providing the information and guidance on the benefits and risks of COVID-19 vaccines to individuals based on their preferences and medical history. In this paper, we have proposed sentiment analysis based recommender system for COVID-19 vaccines. We used Twitter data of 10,000 tweets about COVID-19 vaccines and applied pre-processing steps. We propose an ensemble of random forest with CT-BERT_CONVLayerFusion model, a novel algorithm, for classifying the tweets into seven different categories of sentiments. We also performed aspect-based review categorization which works on the queries given by a user. We compared the results of sentiment classification with the state-of-the-art with metrics including accuracy, recall, precision, and F1-score, and found out that our proposed approach outperformed all other state-of-the-art model by achieving maximum accuracy, recall, precision and F1-measure. Hence, such advanced methods can help somehow to fight COVID-19 as well as reducing the vaccine hesitancy by suggesting proper vaccines to patients based on the their specific concerns and questions.

Cnn-assisted multi-hop graph attention network for hyperspectral image classification

ABSTRACT Recently, the convolutional neural network (CNN) has gained widespread adoption in the hyperspectral image (HSI) classification owing to its remarkable feature extraction capability. However, the fixed acceptance domain of CNN restricts it to Euclidean image data only, making it difficult to capture complex information in hyperspectral data. To overcome this problem, much attention has been paid to the graph attention network (GAT), which can effectively model graph structure and capture complex dependencies between nodes. However, GAT usually acts on superpixel nodes, which may lead to the loss of pixel-level information. To better integrate the advantages of both, we propose a CNN-assisted multi-hop graph attention network (CMGAT) for HSI classification. Specifically, a parallel dual-branch architecture is first constructed to simultaneously capture spectral-spatial features from hyperspectral data at the superpixel and pixel levels using GAT and CNN, respectively. On this basis, the multi-hop and multi-scale mechanisms are further employed to construct a multi-hop GAT module and a multi-scale CNN module to capture diverse feature information. Secondly, an attention module is cascaded before the multi-scale CNN module to improve classification performance. Eventually, the output information from the two branches is weighted and fused to produce the classification result. We performed experiments on four benchmark HSI datasets, including Indian Pines (IP), University of Pavia (UP), Salinas Valley (SV) and WHU-Hi-LongKou (LK). The results demonstrate that the proposed method outperforms several deep learning methods, achieving overall accuracies of 95.67%, 99.04%, 99.55% and 99.51%, respectively, even with fewer training samples.

Local-global feature fusion network for hyperspectral image classification

ABSTRACT Hyperspectral images (HSIs) are rich in spatial and spectral information, making them crucial for accurate classification. While existing convolutional neural network (CNN) methods have shown promise in HSI classification, they primarily focus on local features and struggle with establishing long-range dependencies. To address these limitations, we propose a Local-Global Feature Fusion Network (LGFFN). LGFFN effectively learns joint local-global information from HSIs. Firstly, we employ a group-general convolution parallel module (GGCPM) to extract both local and global spectral features. Next, we utilize parallel branches: a CNN branch for capturing fine-grained spatial information and an improved transformer encoder (ITE) branch for capturing long-distance spatial-spectral features and exploiting semantic information. Finally, we elegantly integrate the diverse features obtained from these branches to obtain classification results. We validate our method on four datasets and compare it with six state-of-the-art hyperspectral classification methods. Our experimental results demonstrate that LGFFN achieves high classification accuracy, outperforming existing methods.

Multimodal brain tumor segmentation and classification from MRI scans based on optimized DeepLabV3+ and interpreted networks information fusion empowered with explainable AI

Explainable artificial intelligence (XAI) aims to offer machine learning (ML) methods that enable people to comprehend, properly trust, and create more explainable models. In medical imaging, XAI has been adopted to interpret deep learning black box models to demonstrate the trustworthiness of machine decisions and predictions. In this work, we proposed a deep learning and explainable AI-based framework for segmenting and classifying brain tumors. The proposed framework consists of two parts. The first part, encoder-decoder-based DeepLabv3+ architecture, is implemented with Bayesian Optimization (BO) based hyperparameter initialization. The different scales are performed, and features are extracted through the Atrous Spatial Pyramid Pooling (ASPP) technique. The extracted features are passed to the output layer for tumor segmentation. In the second part of the proposed framework, two customized models have been proposed named Inverted Residual Bottleneck 96 layers (IRB-96) and Inverted Residual Bottleneck Self-Attention (IRB-Self). Both models are trained on the selected brain tumor datasets and extracted features from the global average pooling and self-attention layers. Features are fused using a serial approach, and classification is performed. The BO-based hyperparameters optimization of the neural network classifiers is performed and the classification results have been optimized. An XAI method named LIME is implemented to check the interpretability of the proposed models. The experimental process of the proposed framework was performed on the Figshare dataset, and an average segmentation accuracy of 92.68 % and classification accuracy of 95.42 % were obtained, respectively. Compared with state-of-the-art techniques, the proposed framework shows improved accuracy.

Analysis of Morphologic Classification System for Acute Promyelocytic Leukemia and Its Correlation with Laboratory Tests and FLT3-ITD Mutation

To establish a morphologic classification system for characterizing blast cells in patients with acute promyelocytic leukemia (APL) and analyze the correlation of different APL morphologic characteristics with conventional tests and genetic variants. Based on the morphological characteristics of APL blast cells, a classification system of 14 categories was established to characterize the inter- and intra-individual cellular morphological heterogeneity of patients. The classification system was used for the morphological analysis of 40 APL patients, and the classification results were statistically analyzed with the patients' conventional test indexes and gene variant characteristics to analyze the correlation of different APL blast cell morphological features with conventional test indexes and gene variants. In the FLT3-ITD mutation-positive group, there were significantly fewer cells with regular nuclear shape, hyper granularity, and missing Auer rods (category 1) than in the FLT3 mutation-negative group (P < 0.05). The activated partial thromboplastin (APTT) was significantly longer in the group with regular nucleus compared to the group with irregular nucleus (P < 0.05). In the hypo-granular group, the APTT was also significantly longer compared to the hyper-granular group (P < 0.01), and the proportion of myeloid blast cells was relatively lower (P < 0.05). The peripheral blood white blood cell counts, D-dimer, lactate dehydrogenase and proportion of bone marrow blast cells were significantly higher in the Auer rods (-) group than Auer rods increasing group (all P < 0.05). The newly established morphologic classification system in this study can objectively characterize different types of APL blast cells, which helps to better assess the intra- and inter-individual heterogeneity of APL blast cells, and further use in accurately analyzing the correlation of morphological phenotypes with biological properties of APL.

Gradings on the algebra of triangular matrices as a Lie algebra: Revisited

We investigate the group gradings on the algebras of upper triangular matrices over an arbitrary field, viewed as Lie algebras. Classification results were obtained in 2017 by the same authors when the base field has characteristic different from 2. In this paper we provide streamlined proofs of these results. Moreover we present a complete classification of isomorphism classes of the group gradings on these algebras over an arbitrary field. Recall that two graded Lie algebras L1 and L2 are practically-isomorphic if there exists an (ungraded) algebra isomorphism L1→L2 that induces a graded-algebra isomorphism L1/z(L1)→L2/z(L2). We provide a classification of the practically-isomorphism classes of the group gradings on the Lie algebra of upper triangular matrices. The latter classification is a better alternative way to consider these gradings up to being essentially the same object. Finally, we investigate in details the case where the characteristic of the base field is 2, a topic that was neglected in previous works.

VarClaMM: A reference meta-model to understand DNA variant classification

Determining the significance of a DNA variant in patients’ health status – a complex process known as variant classification – is highly critical for precision medicine applications. However, there is still debate on how to combine and weigh diverse available evidence to achieve proper and consistent conclusions. Indeed, currently, there are more than 200 different variant classification guidelines available to the scientific community, aiming to establish a framework for standardizing the classification process. Yet, these guidelines are qualitative and vague by nature, hindering their practical application and potential automation. Consequently, more precise definitions are needed.In this work, we discuss our efforts to create VarClaMM, a UML meta-model that aims to provide a clear specification of the key concepts involved in variant classification, serving as a common framework for the process. Through this accurate characterization of the domain, we were able to find contradictions or inconsistencies that might have an effect on the classification results. VarClaMM’s conceptualization efforts will lay the ground for the operationalization of variant classification, enabling any potential automation to be based on precise definitions.

An Internet of Things Task Scheduling Framework Based on Agile Virtual Network on Demand Service Model

In order to improve the efficiency of cloud computing resource utilization and avoid the problem of computing resource allocation and scheduling lagging behind load changes, the author proposes a cloud computing resource on-demand allocation and elastic scheduling method based on network load prediction. Firstly, the author takes the network load data of Wikimedia as the research object and proposes an adaptive two-stage multi network model load prediction method based on LSTM, i.e. ATSMNN-LSTM load prediction method). This method can classify the network load data into climbing and descending types based on the trend and characteristics of the input network load data, And adaptively schedule the input network load data to the LSTM load prediction model that matches its type for prediction based on the classification results. The author proposes a maximum cloud service revenue computing resource quantity search algorithm based on network load prediction (i.e. MaxCSPRNWP algorithm), which aims to improve cloud service revenue as the optimization objective. Under the premise of ensuring task service quality and system stability, the algorithm allocates cloud computing resources on demand and flexibly schedules them in advance based on the predicted network load results. The experimental results show that the ATSMNNLSTM load prediction method proposed by the author can obtain more accurate network load prediction results compared to other load prediction methods, and the MaxCSPR-NWP algorithm, which is based on network load prediction and is capable of effectively converting the network load prediction results into the required number of cloud servers, is the maximum cloud service revenue computing resource quantity search algorithm proposed by the author, not only does it achieve the early allocation and scheduling of cloud computing resources, thereby avoiding the impact of lagging behind in computing resource allocation and scheduling due to load changes on the quality of cloud computing task services and resource utilization efficiency, at the same time, it has also achieved on-demand allocation and flexible scheduling of cloud computing resources with the goal of improving cloud service revenue.

A Genetic Programming Approach With Building Block Evolving and Reusing to Image Classification

Genetic programming (GP) has shown promising results in image classification in the last decade. However, most existing GP-based image classification methods often have a complex tree/program structure and a large search space, which may lead to poor performance. To address this, this paper develops a two-stage-based GP approach to automatically evolving solutions/ensembles for image classification. In the new approach, the process of constructing an image classification solution is divided into two stages, i.e., evolving small building blocks for feature extraction and evolving ensembles of classifiers by reusing these blocks. Accordingly, at each stage, a simple tree structure can be designed to facilitate the search. In the first stage, a simple block representation and a new search mechanism including a population updating strategy are developed to evolve diverse and effective blocks. In the second stage, a small set of diverse blocks are selected and transformed into primitives, which produces a new tree representation to evolve ensembles of classifiers for image classification. The new designs allow the proposed approach to construct sufficiently but not over complex solutions for difficult tasks by using/searching small trees. The new approach outperforms most GP-based and non-GP-based comparison methods on five image datasets including CIFAR10, Fashion_MNIST and SVHN. Deep analysis is conducted to provide more insights into the proposed approach.

An Evidential Multi-Target Domain Adaptation Method Based on Weighted Fusion for Cross-Domain Pattern Classification.

For cross-domain pattern classification, the supervised information (i.e., labeled patterns) in the source domain is often employed to help classify the unlabeled target domain patterns. In practice, multiple target domains are usually available. The unlabeled patterns (in different target domains) which have high-confidence predictions, can also provide some pseudo-supervised information for the downstream classification task. The performance in each target domain would be further improved if the pseudo-supervised information in different target domains can be effectively used. To this end, we propose an evidential multi-target domain adaptation (EMDA) method to take full advantage of the useful information in the single-source and multiple target domains. In EMDA, we first align distributions of the source and target domains by reducing maximum mean discrepancy (MMD) and covariance difference across domains. After that, we use the classifier learned by the labeled source domain data to classify query patterns in the target domains. The query patterns with high-confidence predictions are then selected to train a new classifier for yielding an extra piece of soft classification results of query patterns. The two pieces of soft classification results are then combined by evidence theory. In practice, their reliabilities/weights are usually diverse, and an equal treatment of them often yields the unreliable combination result. Thus, we propose to use the distribution discrepancy across domains to estimate their weighting factors, and discount them before fusing. The evidential combination of the two pieces of discounted soft classification results is employed to make the final class decision. The effectiveness of EMDA was verified by comparing with many advanced domain adaptation methods on several cross-domain pattern classification benchmark datasets.

Trustworthiness evaluation of workers in critical facilities using electroencephalography-based acquaintance test

This study emphasized the critical importance of prioritizing workers' trustworthiness in safeguarding critical facilities from the potential harm caused by insiders' betrayal. By noting the limitations of existing physical protection systems in critical facilities, this study showed that effectively characterizing and detecting malicious insider activity can be possible by observing immediate brain physiological reactions to specific stimuli. Based on the finding, a novel approach using electroencephalography (EEG)-based acquaintance tests was introduced to objectively assess ’a level of suspicion between colleagues by measuring their brain responses when he/she is exposed to familiar and unfamiliar stimuli. If a person claims falsely about one's acquaintance, the model assumes he/she has malicious or suspicious intent by violating the reporting obligation. The experiment was designed with a relatively short time of monitoring of less than 2 min and with a simple EEG headband device called MUSE. The experiment is to analyze whether the model could provide reliable prediction of disguised acquaintance avoiding complex preparation process. Averaged N170 peak analysis indicated that MUSE provided adequate signal characteristics to classify acquaintance. Also, a machine learning-based subject-wise classification model showed adequate capability to differentiate the EEG signals of acquaintances from unknowns. The final prediction combined multiple single-trial classification results, correctly detected the participant's acquaintance about 94.1 % of the cases, with similar performance when strangers were presented. The results indicated the possibility of using biosignal to enhance security culture and mitigate insider threats in critical facilities, by providing an indication of behavior that disregards security policies and procedures.

Hyperspectral Image Denoising: From Model-Driven, Data-Driven, to Model-Data-Driven.

Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications. In this technical review, we first give the noise analysis in different noisy HSIs and conclude crucial points for programming HSI denoising algorithms. Then, a general HSI restoration model is formulated for optimization. Later, we comprehensively review existing HSI denoising methods, from model-driven strategy (nonlocal mean, total variation, sparse representation, low-rank matrix approximation, and low-rank tensor factorization), data-driven strategy [2-D convolutional neural network (CNN), 3-D CNN, hybrid, and unsupervised networks], to model-data-driven strategy. The advantages and disadvantages of each strategy for HSI denoising are summarized and contrasted. Behind this, we present an evaluation of the HSI denoising methods for various noisy HSIs in simulated and real experiments. The classification results of denoised HSIs and execution efficiency are depicted through these HSI denoising methods. Finally, prospects of future HSI denoising methods are listed in this technical review to guide the ongoing road for HSI denoising. The HSI denoising dataset could be found at https://qzhang95.github.io.

An evolutionary algorithm‐based classification method for high‐dimensional imbalanced mixed data with missing information

AbstractThe data scale keeps growing by leaps and the majority of it is high‐dimensional imbalanced data, which is hard to classify. Data missing often happens in software which further aggravates the difficulty of classifying the data. In order to resolve high‐dimensional imbalanced mixed‐variables missing data classification problem, a novel method based on particle swarm optimization is developed. It has three original components including multiple feature selection, mixed attribute imputation, and quantum oversampling. Multiple feature selection uses a two‐stage strategy to obtain stable relevant features. Mixed attribute imputation separates features into continuous and discrete features and fills missing values with different models. Quantum oversampling chooses instances to balance data based on the quantum operator. Furthermore, particle swarm optimization is employed to optimize the parameters of the components to obtain preferable classification results. Six representative classification datasets, six typical algorithms, and four measures are taken to conduct exhaust experiments, and results indicate that the proposed method is superior to the comparison algorithms.

A knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes

Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes. The number of structural planes and rock blocks were reduced in previous studies. This impairs the ability to characterize complex rock slopes accurately and inhibits the identification of key blocks. In this paper, a knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes is proposed. Our basic idea is to integrate key block theory into data-driven models based on finely characterizing structural features to identify key blocks in complex rock slopes accurately. The proposed novel paradigm consists of (1) representing rock slopes as graph-structured data based on complex systems theory, (2) identifying key nodes in the graph-structured data using graph deep learning, and (3) mapping the key nodes of graph-structured data to corresponding key blocks in the rock slope. Verification experiments and real-case applications are conducted by the proposed method. The verification results demonstrate excellent model performance, strong generalization capability, and effective classification results. Moreover, the real case application is conducted on the northern slope of the Yanqianshan Iron Mine. The results show that the proposed method can accurately identify key blocks in complex rock slopes, which can provide a decision-making basis and rational recommendations for effective support and instability prevention of rock slopes, thereby ensuring the stability of rock engineering and the safety of life and property.

Classification-based consensus model considering quantum interference in linguistic distribution environment

Multi-attribute group classification (MAGC) is widely used in dealing with a variety of practical problems. However, cognitive biases, resulting from factors such as irrational behaviors, epistemic uncertainty, and interference phenomena, may critically affect the consensus reaching process (CRP) of MAGC. To eliminate such influences, this study proposes an ordinal classification consensus model in a linguistic environment based on quantum probability theory (QPT), with the aim of classifying the alternatives into several predefined ordinal classifications accepted by a majority of experts. First, the linguistic distribution assessments (LDAs), which can depict the quantitative distribution and qualitative vagueness, are used to express the uncertain preference of experts. Second, the LDAs best-worst method (BWM) and LDAs maximizing deviation model (MDM) are developed to identify the subjective and objective weight of attributes, and then these two types of attributes weight are integrated by quantum-like Bayesian networks (QLBN). Third, a maximum consensus model (MCM), maximizing the consensus level of ordinal classification results of experts, is provided to determine the parameters of QLBN. When the consensus level is lower than the predetermined threshold, a minimum adjustment distance model (MADM) is designed to guide the preference-modification of experts to derive the consensus classification of alternatives. Finally, a risk assessment case study is conducted to verify the feasibility and effectiveness of the proposed method. Sensitivity analysis and comparison analysis are performed to verify the feasibility and effectiveness of the proposed method