Node Weight Research Articles

DyGAT-FTNet: A Dynamic Graph Attention Network for Multi-Sensor Fault Diagnosis and Time–Frequency Data Fusion

Fault diagnosis in modern industrial and information systems is critical for ensuring equipment reliability and operational safety, but traditional methods have difficulty in effectively capturing spatiotemporal dependencies and fault-sensitive features in multi-sensor data, especially rarely considering dynamic features between multi-sensor data. To address these challenges, this study proposes DyGAT-FTNet, a novel graph neural network model tailored to multi-sensor fault detection. The model dynamically constructs association graphs through a learnable dynamic graph construction mechanism, enabling automatic adjacency matrix generation based on time–frequency features derived from the short-time Fourier transform (STFT). Additionally, the dynamic graph attention network (DyGAT) enhances the extraction of spatiotemporal dependencies by dynamically assigning node weights. The time–frequency graph pooling layer further aggregates time–frequency information and optimizes feature representation.Experimental evaluations on two benchmark multi-sensor fault detection datasets, the XJTUSuprgear dataset and SEU dataset, show that DyGAT-FTNet significantly outperformed existing methods in classification accuracy, with accuracies of 1.0000 and 0.9995, respectively, highlighting its potential for practical applications.

IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network.

Interleukin-6 (IL-6) is a potent glycoprotein that plays a crucial role in regulating innate and adaptive immunity, as well as metabolism. The expression and release of IL-6 are closely correlated with the severity of various diseases. IL-6-inducing peptides are critical for the development of immunotherapy and diagnostic biomarkers for some diseases. Most existing methods for predicting IL-6-induced peptides use traditional machine learning methods, whose feature selection is based on prior knowledge. In addition, none of these methods take into account the three-dimensional (3D) structure of peptides, which is essential for their functional properties. In this study, we propose a novel IL-6-inducing peptide prediction method called DGIL-6, which integrates 3D structural information with graph neural networks. DGIL-6 represents a peptide sequence as a graph, where each amino acid is treated as a node, and the adjacency matrix, representing the relationships between nodes, is derived from the predicted residue contact graph of the peptide sequence. In addition to commonly used amino acid representations, such as one-hot encoding and position encoding, the pre-trained model ESM-1b is employed to extract amino acid features as node features. In order to simultaneously consider node weights and information updates, a dual-channel method combining Graph Attention Network (GAT) and Graph Convolutional Network (GCN) is adopted. Finally, the extracted features from both channels are merged for the classification of IL-6-inducing peptides. A series of experiments including cross-validation, independent testing, ablation studies, and visualizations demonstrate the effectiveness of the DGIL-6 method.

Secure Vehicle-to-Vehicle Communication using Routing Protocol based on Trust Authentication Secure Sugeno Fuzzy Inference System Scheme

Introduction: Vehicular Ad-hoc Network (VANET) is wireless communication between Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising technology that effectively manages traffic and ensures road safety. However, communication in an open-access environment presents real challenges to security and privacy issues, which may affect large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and communication are the most challenging areas in previous methods. Vehicular communications face challenges due to vehicle interference and severe delays. Method: To overcome the drawbacks, this work proposed a new method based on the Artificial Neural Network Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators. Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating the node weights for data transferring; reduced the attacks accuracy of network malicious attacks. Result: In the improved cluster-based VANET routing protocol, each node obtains an address using a new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission. Conclusion: Simulation results show that in the proposed ANN-based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance is 0.55sec, throughput performance is 94%, bandwidth is 55bits/sec, Network security is 92%, and the transmission ratio is 89%, attack detection is 90%.

Channel Minimised Depth-Wise CNN with Node Weighted Tree-LSTM Model to Detect Nested Query based SQL Injection Attacks

Channel Minimised Depth-Wise CNN with Node Weighted Tree-LSTM Model to Detect Nested Query based SQL Injection Attacks

A Study on Clinical Characteristics of Locally Advanced HNSCC Patients

Background: Head and Neck Squamous Cell Carcinoma (HNSCC) is a heterogeneous group of malignancies arising from the mucosal linings of the oral cavity, pharynx, and larynx. Locally advanced HNSCC (LA-HNSCC), encompassing stages III and IV, involves large tumors and regional lymph node metastases without distant spread, contributing to high morbidity and mortality. Understanding the clinical features of LA-HNSCC is crucial for improving treatment strategies and patient outcomes. Objective: To evaluate the clinical characteristics of patients with LA-HNSCC and their impact on treatment outcomes, specifically comparing the TPLF and LFP regimens. Methods: An experimental study was conducted at Khwaja Yunus Ali Medical College & Hospital from January to December 2015, involving 60 patients with LA-HNSCC. Patients were treated with neoadjuvant chemotherapy (TPLF or LFP regimens) followed by concurrent chemoradiation. Data was collected through patient interviews, clinical evaluations, and laboratory tests. The primary outcome measures were tumor regression, clinical response, and acute toxicities. Statistical analysis was performed using SPSS, with significance set at p < 0.05. Results: The majority of patients were male (80% in Arm A and 77% in Arm B) and over 50 years old. Smoking prevalence was high (83.3% in Arm A and 80% in Arm B). The most common primary tumor sites were the oral cavity (Arm A: 30%, Arm B: 33.3%) and oropharynx (Arm A: 23.3%, Arm B: 26.7%). Neck node swelling, pain, dysphagia, and weight loss were the most frequent clinical complaints. No statistically significant differences were found between the two treatment arms in terms of age, smoking, occupation, ECOG performance status, or histological grading. Conclusion: Both TPLF and LFP regimens provided similar outcomes in terms of clinical characteristics and treatment response in patients with LA-HNSCC. These results suggest that the choice between TPLF and LFP regimens may not significantly influence treatment outcomes. Further research is needed to explore additional factors affecting treatment efficacy and refine therapeutic strategies for LA-HNSCC.

Range-wide post- and pre-breeding migratory networks of a declining neotropical–nearctic migratory bird, the blackpoll warbler

Identifying the drivers of population declines in migratory species requires an understanding of how individuals are distributed between periods of the annual cycle. We built post- (fall) and pre-breeding (spring) migratory networks for the blackpoll warbler (Setophaga striata), a Neotropical-Nearctic songbird, using tracking data from 47 light-level geolocators deployed at 11 sites across its breeding range. During pre-breeding migration, two stopover nodes (regions) on the U.S. eastern seaboard received high scores in our network metrics (betweenness centrality and time-adjusted node weight), likely acting as key refuelling areas for most of the global blackpoll warbler population before their multi-day flights over the Atlantic Ocean. During post-breeding migration, highly ranked stopover nodes in the southeastern U.S. acted as a geographical bottleneck before birds dispersed to their boreal breeding destinations. Nodes located in northern Colombia and Venezuela were also ranked highly during both migrations and were likely used to prepare for (pre-breeding) and recover from (post-breeding) Atlantic flights. Blackpoll warblers showed a crosswise migration pattern, whereby individuals from western breeding populations tended to spend the nonbreeding season in the eastern part of the nonbreeding range and vice-versa. Despite this, the strength of migratory connectivity between the breeding and nonbreeding grounds ranged from moderate to low, largely because many individuals used more than one node during the ‘stationary’ nonbreeding period. Our results suggest that the number of breeding populations affected by a threat in the blackpoll warbler’s range will strongly depend on where and when this threat occurs. Consequently, our migratory network should be key to inform future conservation planning and population monitoring efforts.

Stall prediction model based on deep learning network in axial flow compressor

To predict stall and surge in advance that make the aero-engine compressor operate safely, a stall prediction model based on deep learning theory is established in the current study. The Long Short-Term Memory (LSTM) originating from the recurrent neural network is used, and a set of measured dynamic pressure datasets including the stall process is used to learn what determines the weight of neural network nodes. Subsequently, the structure and function hyperparameters in the model are deeply optimized, and a set of measured pressure data is used to verify the prediction effects of the model. On this basis of the above good predictive capability, stall in low- and high-speed compressor are predicted by using the established model. When a period of non-stall pressure data is used as input in the model, the model can quickly complete the prediction of subsequent time series data through the self-learning and prediction mechanism. Comparison with the real-time measured pressure data demonstrates that the starting point of the predicted stall is basically the same as that of the measured stall, and the stall can be predicted more than 1 s in advance so that the occurrence of stall can be avoided. The model of stall prediction in the current study can make up for the uncertainty of threshold selection of the existing stall warning methods based on measured data signal processing. It has a great application potential to predict the stall occurrence of aero-engine compressor in advance and avoid the accidents.

A multitask interpretable model with graph attention mechanism for activity prediction of low-data PIM inhibitors.

The aberrant expression of proviral integration site for Moloney murine leukemia virus (PIM) kinases is closely related to various tumors and chemotherapy resistance, making them attractive targets for cancer therapy. However, due to the extremely high homology among the three PIM isoforms (PIM1, PIM2, PIM3) and the limited availability of existing bioactivity data, screening and designing selective PIM inhibitors remain a daunting challenge. To address this issue, this study constructed a multitask regression model that can simultaneously predict the half-maximal inhibitory concentration (IC50 values). The model utilizes an attention mechanism to capture effects within local atomic groups and the interactions between different groups of atoms. Through weight sharing, the model enhances the accuracy of predicting PIM3 inhibitors by leveraging the rich and highly correlated data from PIM1 and PIM2 isoforms. Additionally, visualizing the weights of nodes (atoms in the molecule) in the model helps us to intuitively understand the relationship between molecular features and prediction outcomes, thereby enhancing the interpretability of the model. In summary, this work provides new insights and methods for performing activity prediction tasks for multiple similar targets in low-data scenarios.

A Hybrid Recognition Framework for Highly Interacting Machining Features Based on Primitive Decomposition, Learning and Reconstruction

For the highly interacting machining features, Layered Projection Decomposition Method presents inferior recognition efficiency and accuracy, due to its high-cost 3D projection and failures in determining projection faces for internal occluded faces. To address these issues, we propose a potential hybrid recognition framework. We first introduce a straightforward adjacent projection wire (APW) over UV wires, automatically restoring the full projection wires from highly interacting features. Building on APWs, an efficient hybrid boundary representation and its corresponding unambiguous primitive definitions are proposed by combining with graph-based boundary representations. Subsequently, we design an efficient primitive decomposition method by introducing primitive boundary matching to decide the initial projection faces, and introducing iterative projection boundary expansion to complete the full primitives from occluded faces. Moreover, we establish an efficient Graph Neural Network to learn the distinguishable distributions over the decomposed primitives. Specifically, an Adjacency Attention Unit is proposed to automatically perceive the influence weight of adjacent nodes, leading to more discriminative self-adaptive shape embedding for efficient primitive recognition. Finally, we summarize convenient reconstruction rules to correct the wrong predictions of feature faces with indistinguishable adjacent relationships. To evaluate the effectiveness of the proposed recognition framework, CAD models of complex aircraft structural parts are collected to present a challenging machining feature dataset. Extensive numerical experiments demonstrate that the proposed hybrid recognition framework enables significant improvements over the state-of-the-art machining feature recognition techniques.

Caloric Vestibular Stimulation Alleviates Cold Water Swimming Stress Induced Changes in BDNF and Haematological Parameters

Introduction: Stress is inevitable in daily life. Stress in long term has been implicated in the development of physical and neuronal disorders. Various studies have described the levels and role of BDNF in the brain following stress. Studies representing the changes in serum BDNF and wet weight of organs in stress are limited and controversial. Aim: The aim of the present study is to find the effect of stress on serum BDNF concentration, wet weight of selected organs, haematological parameters and to evaluate the effectiveness of caloric vestibular stimulation on stress-induced changes in these parameters. Methods and material: Male Wistar rats were exposed to cold water swimming stress for 14 days for a duration of 30 minutes per day. Serum corticosterone and serum BDNF were measured using the ELISA method. Weight of liver, spleen, thymus and lymph nodes were measured and their relation to body weight was calculated. Total RBC count, total WBC count, platelet count and haemoglobin concentration were analysed. Results: A statistically significant increase in serum corticosterone and BDNF was observed following stress. Liver weight, thymus weight and lymph nodes weight did not show any significant changes but spleen weight has reduced in the normal recovery group following stress. Total RBC count, total WBC count, platelet count and haemoglobin levels were significantly higher in the stress group. Caloric vestibular stimulation was effective in managing stress-induced changes in serum corticosterone, BDNF and spleen weight. Conclusion: Caloric vestibular stimulation is effective in restoring the stress-induced changes.

Component-based modeling of cascading failure propagation in directed dual-weight software networks

Software vulnerabilities often lead to cascading failures, resulting in service unavailability and potential breaches of user data. However, existing models for cascading failure propagation typically focus solely on the static design’s calling relationships, disregarding dynamic runtime propagation paths. Moreover, current network topology models primarily consider function calling frequency while overlooking critical factors like internal failure probability and component failure tolerance rates. Yet, these factors significantly influence the actual propagation of software cascading failures. In this study, we address these limitations by incorporating internal failure probabilities and calling frequencies as node and edge weights, respectively. This forms the basis of our component-based directed dual-weight software network cascading failure propagation model. This model encompasses the evaluation of cascading failure propagation through intra-component and inter-component propagation probabilities, alongside the constraint of component failure tolerance rates. Through extensive experiments conducted on six real-world software applications, our model has demonstrated its effectiveness in predicting software cascading failure propagation processes. This method deepens our understanding of software failures and structures, equipping software testers with the knowledge to make well-informed judgments regarding software quality concerns.

In Vivo Biocompatibility of Synechococcus sp. PCC 7002-Integrated Scaffolds for Skin Regeneration.

Cyanobacteria, commonly known as blue-green algae, are prevalent in freshwater systems and have gained interest for their potential in medical applications, particularly in skin regeneration. Among these, Synechococcus sp. strain PCC 7002 stands out because of its rapid proliferation and capacity to be genetically modified to produce growth factors. This study investigates the safety of Synechococcus sp. PCC 7002 when used in scaffolds for skin regeneration, focusing on systemic inflammatory responses in a murine model. We evaluated the following three groups: scaffolds colonized with genetically engineered bacteria producing hyaluronic acid, scaffolds with wild-type bacteria, and control scaffolds without bacteria. After seven days, we assessed systemic inflammation by measuring changes in cytokine profiles and lymphatic organ sizes. The results showed no significant differences in spleen, thymus, and lymph node weights, indicating a lack of overt systemic toxicity. Blood cytokine analysis revealed elevated levels of IL-6 and IL-1β in scaffolds with bacteria, suggesting a systemic inflammatory response, while TNF-α levels remained unaffected. Proteome profiling identified distinct cytokine patterns associated with bacterial colonization, including elevated inflammatory proteins and products, indicative of acute inflammation. Conversely, control scaffolds exhibited protein profiles suggestive of a rejection response, characterized by increased levels of cytokines involved in T and B cell activation. Our findings suggest that Synechococcus sp. PCC 7002 does not appear to cause significant systemic toxicity, supporting its potential use in biomedical applications. Further research is necessary to explore the long-term effects and clinical implications of these responses.

A simulation data-driven semi-supervised framework based on MK-KNN graph and ESSGAT for bearing fault diagnosis

Current supervised intelligent fault diagnosis relies on abundant labeled data. However, collecting and labeling data are typically both expensive and time-consuming. Fault diagnosis with unlabeled data remains a significant challenge. To address this issue, a simulation data-driven semi-supervised framework based on multi-kernel K-nearest neighbor (MK-KNN) and edge self-supervised graph attention network (ESSGAT) is proposed. The novel MK-KNN establishes the neighborhood relationships between simulation data and real data. The developed multi-kernel function mitigates the risks of overfitting and underfitting, thereby enhancing the robustness of the simulation-real graphs. The designed ESSGAT employs two forms of self-supervised attention to predict the presence of edges, increasing the weights of crucial neighboring nodes in the MK-KNN graph. The performance of the proposed method is evaluated using a public bearing dataset and a self-constructed dataset of high-speed train axle box bearings. The results show that the proposed method achieves better diagnostic performance compared with other state-of-the-art graph construction methods and graph convolutional networks.

DRADTiP: Drug repurposing for aging disease through drug-target interaction prediction

MotivationThe greatest risk factor for many non-communicable diseases is aging. Studies on model organisms have demonstrated that genetic and chemical perturbation alterations can lengthen longevity and overall health. However, finding longevity-enhancing medications and their related targets is difficult. MethodIn this work, we designed a novel drug repurposing model by identifying the interaction between aging-related genes or targets and drugs similar to aging disease. Each disease is associated with certain specific genetic factors for the occurrence of that disease. The factors include gene expression, pathway, miRNA, and degree of genes in the protein-protein interaction network. In this paper, we aim to find the drugs that prolong the life span of humans with their aging-related targets using the above-mentioned factors. In addition, the contribution or importance of each factor may vary among drugs and targets. Therefore, we designed a novel multi-layer random walk-based network representation learning model including node and edge weight to learn the features of drugs and targets respectively. ResultThe performance of the proposed model is demonstrated using k-fold cross-validation (k = 5). This model achieved better performance with scores of 0.93 and 0.91 for precision and recall respectively. The drugs identified by the system are evaluated to be potential candidates for aging since the degree of interaction between the potential drugs and their gene sets are high. In addition, the genes that are interacting with drugs produce the same biological functions. Hence the life span of the human will be increased or prolonged.

Weighted graph convolutional network with feature mask for low back pain prediction

Low back pain (LBP) is one of the most common physical disabilities worldwide, imposing a heavy medical economic burden. To help medical experts make disease predictions at an early stage, this paper proposes an LBP abnormality diagnosis model based on a weighted graph convolutional network with feature mask. Unlike popular neural network architectures, we represent the biomechanical characteristics of the spine as a weighted feature graph. The potential relationship between patients is mined through edge connections, and the importance of features for different patients is dynamically captured through the internal feature weighting of nodes. Finally, the importance score of each feature is calculated according to its identification and independence to generate the feature importance mask. It realizes feature sparsification and reinforces important features at the same time, so as to improve the accuracy and stability of the model. The proposed model is verified on a public dataset available in the Kaggle repository, and the classification accuracy achieves 90.2%, which is 2.14% higher than the current best result. Furthermore, when compared for stability against several commonly used machine learning algorithms, the standard deviation of accuracy over ten experiments is only about 1/3 of that of other algorithms.

Dynamic Hybrid Models With Active Sampling and Adaptive Selection of Double-Domain Features for the Tuning of Microwave Cavity Filters.

Microwave cavity filters are essential electromechanical coupling devices in communication systems. Structural-parameter tuning by experienced operators improves the filter performance but is demanding and time-consuming. The automatic tuning method has received extensive research attentions using data-driven modeling approaches. However, two main issues affect the accuracy and efficiency of the model construction: 1) features of tuning processes, as model inputs, have limited adaptability and extraction accuracy to different resonant states and 2) models require plentiful training data and the training process is time-consuming. Thus, dynamic hybrid models are developed in this study with self-selected inputs, self-organized samples, and a self-learning structure. First, spatial features are extracted to flexibly depict the tuning characteristic, and double-domain (spatial or circuital) features are selected adaptively to accommodate distinct resonance states. Second, a trustworthiness-curiosity-driven active sampling method is exploited to attain fewer and better-training data. Third, an improved glsms broad learning system acrlong BLS is developed using new modules of incremental node calculation and weight pruning, characterized by more lightweight and flexible structures. The proposed method is effective and flexible demonstrated by simulations and experiments, and the tuning task of microwave cavity filters is fulfilled in a more accurate and efficient manner.

POST-NATAL DEVELOPMENT OF PRESCAPULAR LYMPH NODE IN ONE-HUMPED CAMEL (Camelus dromedarius): A MORPHOMTRIC STUDY

The study aimed at forming a base line data on prescapular lymph node of one- humped camel (Camelus dromedarius) at post-natal stage of development and determination of the role of the lymph node in disease resistance of the animal species. A morphometric study was conducted on the prescapular lymph nodes of thirty camels (15 each from male and female) collected from Sokoto metropolitan abattoir over a period of three weeks at different post-natal ages. The approximate age of the camels was estimated using rostral dentition. The morphologic observations revealed that the lymph nodes were relatively large elongated, pinkish, palpable structures situated in the caudal and ventral part of the neck, cranial to the shoulder joint and between the superficial pectoral and the sternocephalic muscles. There is a progressive increase in length, width, weight and volume of the lymph nodes with advancement in age in both sexes. These biometrical variables are relatively higher in male than in female.

Semantic Code Clone Detection Based on Community Detection

Semantic code clone detection is to find code snippets that are structurally or syntactically different, but semantically identical. It plays an important role in software reuse, code compression. Many existing studies have achieved good performance in non-semantic clone, but semantic clone is still a challenging task. Recently, several works have used tree or graph, such as Abstract Syntax Tree (AST), Control Flow Graph (CFG) or Program Dependency Graph (PDG) to extract semantic information from source codes. In order to reduce the complexity of tree and graph, some studies transform them into node sequences. However, this transformation will lose some semantic information. To address this issue, we propose a novel high-performance method that utilizes community detection to extract features of AST while preserving its semantic information. First, based on the AST of source code, we exploit community detection to split AST into different subtrees to extract the underlying semantics information of different code blocks, and use centrality analysis to quantify the semantic information as the weight of AST nodes. Then, the AST is converted into a sequence of tokens with weights, and a Siamese neural network model is used to detect the similarity of token sequences for semantic code clone detection. Finally, to evaluate our approach, we conduct experiments on two standard benchmark datasets, Google Code Jam (GCJ) and BigCloneBench (BCB). Experimental results show that our model outperforms the eight publicly available state-of-the-art methods in detecting code clones. It is five times faster than the tree-based method (ASTNN) in terms of time complexity.

A Recurrent Stochastic Configuration Network for Dynamic System Modeling and Its Application

In order to model nonlinear dynamic systems quickly and accurately, a recurrent stochastic configuration network (RSCN) with universal approximation ability is proposed in this paper. The method consists of network parameter learning and network structure determination. The network parameter learning strategy consists of input weights and the biases of hidden nodes generated randomly through a supervision mechanism, while the output weights of hidden nodes are determined by solving the least-squares problem. The network structure is determined incrementally by the training error of a single-layer recurrent neural network. The effectiveness of RSCN is tested and evaluated by using three nonlinear dynamic functions and historical data from a municipal solid waste incineration (MSWI) process. The experimental results show that the proposed method can improve modeling accuracy and reduce training time, which is valuable for applications in the field of industrial process modeling.

Network Topology Reconfiguration-Based Blind Equalization over Sensor Network.

Distributed in-network processing has garnered much attention due to its capability to estimate the unknown parameter of interest from noisy measurements based on a set of cooperating sensor nodes. In previous studies, distributed in-network processing mainly focused on short-distance communication systems, wherein sensor nodes collect certain parameters of interest within their maximum communication distance. In addition, the estimation of certain parameter vectors of interest from noisy measurements, relying heavily on training signals, is achieved with a non-blind distributed estimation algorithm. However, in some applications, acquiring knowledge of training signals beforehand is difficult. Therefore, it is necessary to perform distributed estimation algorithms for receivers without training signals, a concept known as blind distributed estimation. In this paper, the generalized Sato algorithm is used to design the blind equalizer for the signal estimation. In addition, we consider extending the short-distance communication system to a long-distance communication system for an unmanned aerial vehicle (UAV) cooperating with sensor nodes in the wireless sensor network (WSN). In this scenario, the data signal is transmitted from a UAV to the WSN and is received by sensor nodes. However, the performance of the blind equalizer is susceptible to the transmission channel in long-distance communication systems. Here, we present a network topology reconfiguration approach to address the issue of distributed blind equalization. The objective of the proposed method is to discard the influence of ill-channels on the other sensor nodes by detecting ill-channels and redesigning the sensor node weights. Through computer simulation experiments, we evaluated the performance of the blind equalizer using the average mean square error (MSE) and average symbol error rate (SER). In the results of the computer simulation experiments, the blind equalizer using the proposed method outperformed the conventional methods in terms of prediction accuracy and convergence speed.