Classification Error Rate Research Articles

Phase-Angle-Encoded Snake Optimization Algorithm for K-Means Clustering

The rapid development of metaheuristic algorithms proves their advantages in optimization. Data clustering, as an optimization problem, faces challenges for high accuracy. The K-means algorithm is traditaaional but has low clustering accuracy. In this paper, the phase-angle-encoded snake optimization algorithm (θ-SO), based on mapping strategy, is proposed for data clustering. The disadvantages of traditional snake optimization include slow convergence speed and poor optimization accuracy. The improved θ-SO uses phase angles for boundary setting and enables efficient adjustments in the phase angle vector to accelerate convergence, while employing a Gaussian distribution strategy to enhance optimization accuracy. The optimization performance of θ-SO is evaluated by CEC2013 datasets and compared with other metaheuristic algorithms. Additionally, its clustering optimization capabilities are tested on Iris, Wine, Seeds, and CMC datasets, using the classification error rate and sum of intra-cluster distances. Experimental results show θ-SO surpasses other algorithms on over 2/3 of CEC2013 test functions, hitting a 90% high-performance mark across all clustering optimization tasks. The method proposed in this paper effectively addresses the issues of data clustering difficulty and low clustering accuracy.

NRN1 genetic variability and methylation changes as biomarkers for cognitive remediation therapy response in schizophrenia

Cognitive remediation therapy (CRT) demonstrates potential in enhancing cognitive function in schizophrenia (SZ), though the identification of molecular biomarkers remains challenging. The Neuritin-1 gene (NRN1) emerges as a promising candidate gene due to its association with SZ, cognitive performance and response to neurotherapeutic treatments. We aimed to investigate whether NRN1 genetic variability and methylation changes following CRT are related to cognitive improvements.Twenty-five SZ patients were randomly assigned to CRT or treatment-as-usual (TAU) groups, with cognitive function and NRN1 methylation assessed pre- and post-intervention using the MATRICS Consensus Cognitive Battery and EpiTYPER. Besides, eleven NRN1 polymorphisms were genotyped. Methylation changes (Δm = post - pre) were analyzed via sparse Partial Least Square Discriminant Analysis (sPLS-DA) to identify latent components (LCs) distinguishing CRT from TAU. To further explore methylation patterns of these LCs, CpG units were grouped into two subsets, yielding Δm means for those with increased and decreased methylation. Cognitive changes (Δcog = post - pre) were used to identify CRT improvers (CRTI, Δcog ≥1), and the association between methylation changes and cognitive improvements post-therapy was also tested.We identified two LCs that differentiated CRT from TAU with a classification error rate of 0.28. The main component, LC1, included 25 CpG units. The subsets of CpG units with increased and decreased post-therapy methylation differed significantly between the two treatment arms, suggesting that differences were not merely data-driven but reflected meaningful biological variation. Additionally, CpG units linked to therapy were also associated with cognitive improvement, with LC1 and the subset of CpG units showing increased methylation post-therapy distinguishing CRT-I from the rest of the patients across multiple cognitive domains. Furthermore, the effect of LC1 on speed processing improvement after CRT was enhanced by considering the NRN1-rs9405890 polymorphism. Notably, these CpG units, particularly those with increased methylation after CRT, overlapped with key gene regulatory elements.Our model, integrating genetics and epigenetics, boosts the understanding of CRT response variability and highlights this multi-level approach as a promising strategy for identifying potential NRN1-related biomarkers of CRT effects, though further studies with larger samples are needed.

Classification of Simulated Fake Bandwidth Data Using LSTM

Hardly will someone acknowledge that the bandwidth we use every day is as authentic as most ISPs advertise, even those offering dedicated services. There are usually shortcomings, especially on upload and download bandwidth speeds. This paper presents the classification of simulated fake bandwidth data using the Long Short-Term Memory model, which though seldom found, is a very effective approach in network analysis. There were 1400 bandwidth data points collected from the MikroTik RB 1100 AHx device in a month, then further processed with normalization, and divided to have 80% training and 20% testing. The LSTM model applied had an accuracy rate of 98.93%, proving that it is capable of classifying either genuine or fake bandwidth instances accordingly. Of 1,400 test data points, the model managed to classify 723 as fake bandwidth and another 677 as genuine, resulting in a classification error rate of only 1.07%. The results clearly prove that LSTM has huge potential for real-time bandwidth manipulation detection, key to enhancing trust and efficiency in network management. In this respect, this research shows that bandwidth analysis combined with LSTM can be an original solution for network monitoring

Block structure-based covariance tensor decomposition for group identification in matrix variables

In research fields such as financial market analysis and social network research, understanding variable grouping relationships is fundamental to effective data analysis. This study describes the concept of the covariance tensor and emphasizes its significant role in analyzing matrix variable groupings through block structures. We propose a novel tensor decomposition-based method to exploit these structures for group identification. In addition, we explore the asymptotic properties of our estimators, focusing on the precision of the estimation of the number of groups and the asymptotic convergence of classification error rates to zero. We validate the effectiveness of the method through extensive numerical simulations across diverse data volumes and complexities, affirming its capability in variable grouping.

Single-objective and multi-objective mixed-variable grey wolf optimizer for joint feature selection and classifier parameter tuning

Feature selection plays an essential role in data preprocessing, which can extract valuable information from extensive data, thereby enhancing the performance of machine learning classification. However, existing feature selection methods primarily focus on selecting feature subsets without considering the impact of classifier parameters on the optimal subset. Different from these works, this paper considers jointly optimizing the feature subset and classifier parameters to minimize the number of features and achieve a low classification error rate. Since feature selection is an optimization problem with binary solution space while classifier parameters involve both continuous and discrete variables, our formulated problem becomes a complex multi-objective mixed-variable problem. To address this challenge, we consider a single-objective optimization method and a multi-objective optimization approach. Specifically, in the single-objective optimization method, we adopt the linear weight method to convert our multiple objectives into a fitness function and then propose a mixed-variable grey wolf optimizer (MGWO) to optimize the function. The proposed MGWO introduces Chaos-Faure initialization, Log convergence factor adjustment, and optimal solution adaptive update operators to enhance its adaptability and balance the global and local search of the algorithm. Subsequently, an improved multi-objective grey wolf optimizer (IMOGWO) is introduced to directly address the problem. The proposed IMOGWO introduces improved initialization, local search, and binary variable mutation operators to balance its exploration and exploitation abilities, making it more suitable for our mixed-variable problem. Extensive simulation results show that our MGWO and IMOGWO outperform recent and classic baselines. Moreover, we also find that jointly optimizing classifier parameters can significantly improve classification accuracy.

A Multiform Optimization Framework for Multiobjective Feature Selection in Classification

Feature selection in machine learning as a key data processing technique has two conflicting goals: minimizing the classification error rate and minimizing the number of features selected. However, most of the existing multi-objective feature selection methods face the problems of easily falling into local optima and slow convergence by virtue of their problem characteristics, such as partially conflicting objectives and highly discontinuous Pareto fronts. To solve these problems, this article proposes a multiform optimization framework to solve a multi-objective feature selection task together with several auxiliary single-objective feature selection tasks in a multitask environment. The proposed framework uses the problem-solving experience of single-objective tasks to assist the multi-objective feature selection task in exploring more promising regions and accelerating the convergence speed. Specifically, a knowledge transfer strategy based on the search experience of different tasks is developed to accomplish multiform optimization. In addition, a diversity enhancement mechanism is presented to improve search ability in promising decision space areas by considering historical information about the population. In most cases, the experiment results on 27 datasets demonstrate that the proposed technique can uncover more diversified feature subsets on the Pareto front in less time than existing state-of-the-art methods.

A MobileFaceNet-Based Face Anti-Spoofing Algorithm for Low-Quality Images

The Face Anti-Spoofing (FAS) methods plays a very important role in ensuring the security of face recognition systems. The existing FAS methods perform well in short-distance scenarios, e.g., phone unlocking, face payment, etc. However, it is still challenging to improve the generalization of FAS in long-distance scenarios (e.g., surveillance) due to the varying image quality. In order to address the lack of low-quality images in real scenarios, we build a Low-Quality Face Anti-Spoofing Dataset (LQFA-D) by using Hikvision’s surveillance cameras. In order to deploy the model on an edge device with limited computation, we propose a lightweight FAS network based on MobileFaceNet, in which the Coordinate Attention (CA) attention model is introduced to capture the important spatial information. Then, we propose a multi-scale FAS framework for low-quality images to explore multi-scale features, which includes three multi-scale models. The experimental results of the LQFA-D show that the Average Classification Error Rate (ACER) and detection time of the proposed method are 1.39% and 45 ms per image for the low-quality images, respectively. It demonstrates the effectiveness of the proposed method in this paper.

Structural prior-driven feature extraction with gradient-momentum combined optimization for convolutional neural network image classification

Recent image classification efforts have achieved certain success by incorporating prior information such as labels and logical rules to learn discriminative features. However, these methods overlook the variability of features, resulting in feature inconsistency and fluctuations in model parameter updates, which further contribute to decreased image classification accuracy and model instability. To address this issue, this paper proposes a novel method combining structural prior-driven feature extraction with gradient-momentum (SPGM), from the perspectives of consistent feature learning and precise parameter updates, to enhance the accuracy and stability of image classification. Specifically, SPGM leverages a structural prior-driven feature extraction (SPFE) approach to calculate gradients of multi-level features and original images to construct structural information, which is then transformed into prior knowledge to drive the network to learn features consistent with the original images. Additionally, an optimization strategy integrating gradients and momentum (GMO) is introduced, dynamically adjusting the direction and step size of parameter updates based on the angle and norm of the sum of gradients and momentum, enabling precise model parameter updates. Extensive experiments on CIFAR10 and CIFAR100 datasets demonstrate that the SPGM method significantly reduces the top-1 error rate in image classification, enhances the classification performance, and outperforms state-of-the-art methods.

Pedagogical sentiment analysis based on the BERT-CNN-BiGRU-attention model in the context of intercultural communication barriers.

Amid the wave of globalization, the phenomenon of cultural amalgamation has surged in frequency, bringing to the fore the heightened prominence of challenges inherent in cross-cultural communication. To address these challenges, contemporary research has shifted its focus to human-computer dialogue. Especially in the educational paradigm of human-computer dialogue, analysing emotion recognition in user dialogues is particularly important. Accurately identify and understand users' emotional tendencies and the efficiency and experience of human-computer interaction and play. This study aims to improve the capability of language emotion recognition in human-computer dialogue. It proposes a hybrid model (BCBA) based on bidirectional encoder representations from transformers (BERT), convolutional neural networks (CNN), bidirectional gated recurrent units (BiGRU), and the attention mechanism. This model leverages the BERT model to extract semantic and syntactic features from the text. Simultaneously, it integrates CNN and BiGRU networks to delve deeper into textual features, enhancing the model's proficiency in nuanced sentiment recognition. Furthermore, by introducing the attention mechanism, the model can assign different weights to words based on their emotional tendencies. This enables it to prioritize words with discernible emotional inclinations for more precise sentiment analysis. The BCBA model has achieved remarkable results in emotion recognition and classification tasks through experimental validation on two datasets. The model has significantly improved both accuracy and F1 scores, with an average accuracy of 0.84 and an average F1 score of 0.8. The confusion matrix analysis reveals a minimal classification error rate for this model. Additionally, as the number of iterations increases, the model's recall rate stabilizes at approximately 0.7. This accomplishment demonstrates the model's robust capabilities in semantic understanding and sentiment analysis and showcases its advantages in handling emotional characteristics in language expressions within a cross-cultural context. The BCBA model proposed in this study provides effective technical support for emotion recognition in human-computer dialogue, which is of great significance for building more intelligent and user-friendly human-computer interaction systems. In the future, we will continue to optimize the model's structure, improve its capability in handling complex emotions and cross-lingual emotion recognition, and explore applying the model to more practical scenarios to further promote the development and application of human-computer dialogue technology.

Deep Learning and Face Recognition: Face Recognition Approach Based on the DS-CDCN Algorithm

To enhance the performance and reliability of the face recognition algorithm that is based on deep learning technology, this study utilizes a density-based noise-applied spatial clustering algorithm to cluster a large-scale face image dataset, resulting in a self-constructed dataset. A deep separable center differential convolutional network algorithm is utilized for face recognition. The impact of convolutional parameters on the algorithm’s performance is verified through experiments with ablated convolutional parameters. The study found that the density-based noise-applied spatial clustering algorithm resulted in time savings of 43.66% and 51.22% compared to the K-means clustering algorithm and the hierarchical clustering algorithm, respectively, when analyzing 8000 images. Additionally, the depth-separable center difference convolutional network algorithm had a lower average classification error rate compared to the other two algorithms, with reductions of 2.49% and 17.01%, respectively. The depth-separable center difference convolutional network technique is an advanced method for identifying the faces of people of different races, according to the experimental investigation. It can provide efficient and accurate services for the face recognition needs of various races.

Three-way decision based on three-way preference measures and three-level dominance relations in interval-valued systems

Interval-valued systems facilitate data decision, and their preference measures and dominance relations become pivotal for three-way decision (3WD). However, the recent D-type preference measure and partial-overall dominance relation between intervals respectively exhibit the weak representation and incomplete hierarchy, so corresponding results need further development. Aiming at interval-valued systems, an S-type measure is proposed to constitute three-way preference measures, and three-level dominance relations are established, so criss-cross knowledge granulations motivate systematic 3WD models and applications. First, the S-type preference measure is proposed from the sigmoid function, it exhibits good learning semantics and mathematical properties, and its supplementation and improvement induce three-way preference measures. Then, by three-level constructions, three-way preference measures are applied for sorting and classification, and the S-type measure exhibits decision effectiveness and recognition superiority. Furthermore, hierarchical three-way preference measures induce three-level dominance relations, and vertical-horizontal condition granulations generate multiple 3WD models on preference decision classes. Finally, all 3WD strategies from criss-cross dominance relations are comprehensively compared and selected via classification error rates; by data experiments, the S-type measure and its hierarchical relations become effective and optimal for 3WD, and the corresponding 3WD approaches outperform the existing methods from the D-type measure and partial-overall relation.

Enhancing Sales Strategies In Prime Market Retail Business Using Tuned Gradient Boosting

In the retail sector, comprehending customer behavior and employing effective customer segmentation is pivotal for refining marketing strategies and augmenting profits. This study delves into predictive modeling for customer segmentation at Prime Market, a prominent retail entity. The research initially yields a classification error rate of 25.10% by employing Gradient Boosting for customer classification. However, through meticulous parameter tuning, this rate dramatically improves to 8.6%, achieving an impressive accuracy of 91.4%. This refined model furnishes invaluable insights into Prime Market's customer segments, enabling the customization of marketing tactics and strategic business approaches. Armed with these insights, Prime Market can make data-driven decisions to enhance customer segmentation accuracy, better comprehend customer preferences, and pinpoint potential avenues for revenue growth. Leveraging advanced data analytics and predictive modeling empowers Prime Market to maintain a competitive edge and deliver its clientele a personalized, gratifying shopping experience.

Detecting and mitigating security anomalies in Software-Defined Networking (SDN) using Gradient-Boosted Trees and Floodlight Controller characteristics

Cutting-edge and innovative software solutions are provided to address network security, network virtualization, and other network-related challenges in highly congested SDN-powered networks. However, these networks are susceptible to the same security issues as traditional networks. For instance, SDNs are significantly vulnerable to distributed denial of service (DDoS) attacks. Previous studies have suggested various anomaly detection techniques based on machine learning, statistical analysis, or entropy measurement to combat DDoS attacks and other security threats in SDN networks. However, these techniques face challenges such as collecting sufficient and relevant flow data, extracting and selecting the most informative features, and choosing the best model for identifying and preventing anomalies. This paper introduces a new and advanced multi-stage modular approach for anomaly detection and mitigation in SDN networks. The approach consists of four modules: data collection, feature selection, anomaly classification, and anomaly response. The approach utilizes the NetFlow standard to gather data and generate a dataset, employs the Information Gain Ratio (IGR) to select the most valuable features, uses gradient-boosted trees (GBT), and leverages Representational State Transfer Application Programming Interfaces (REST API) and Static Entry Pusher within the floodlight controller to construct an exceptionally efficient structure for detecting and mitigating anomalies in SDN design. We conducted experiments on a synthetic dataset containing 15 types of anomalies, such as DDoS attacks, port scans, worms, etc. We compared our model with four existing techniques: SVM, KNN, DT, and RF. Experimental results demonstrate that our model outperforms the existing techniques in terms of enhancing Accuracy (AC) and Detection Rate (DR) while simultaneously reducing Classification Error (CE) and False Alarm Rate (FAR) to 98.80 %, 97.44 %, 1.2 %, and 0.38 %, respectively.

Optimization Method Based on Machine Learning for College Students’ Psychological Control Source Propensity Classification

Abstract College students tend to have more locus of control, which is greatly affected by college students, resulting in higher classification error rate and longer classification time. An optimized method for classifying the tendency of college students’ locus of control tendency based on machine learning is proposed in this article. Collect the data of college students’ locus of control tendency, build an emotional dictionary based on it, and extract the emotional words and text features from it. According to the feature extraction results, the support vector machine is used to build a base classifier to obtain the preliminary classification results. The deep belief network is used to optimize the preliminary classification results of college students’ locus of control tendency, and the final optimization results of college students’ locus of control tendency classification are obtained. The experimental results show that the error rate of college students’ locus of control tendency classification is −1∼1 %, the average recall rate is 96.2 %, and the average classification time is 0.7 s.

Intelligent UAV fire monitoring method based on improved YOLOv5 algorithm

Aiming at the existing UAV fire detection system with low small target detection accuracy, a high leakage rate, and a slow rate, an improved YOLOv5 UAV flame detection algorithm is proposed. First, the anchor box clustering is optimized using the K-mean++algorithm to reduce the classification error rate. Second, the original backbone network is enhanced with the CBAM attention mechanism, which scans the whole globe to obtain the target area with a high weighting proportion and needs to be focused on. Replace the PANet network with the BiFPN network in the neck and introduce jump connections when performing feature fusion, which can better retain the semantic information of high-level and low-level features. Finally, the α-IoU loss function is added to achieve the regression accuracy of different levels of the bounding box by modulating α, which improves the detection accuracy of small datasets and the robustness to noise. According to the experimental results, using a randomly segmented dataset, the modified YOLOv5 algorithm obtains a mAP value of 80.2%, which is 6.7% higher than the original YOLOv5 method, while maintaining an FPS of 64 frames per second. The method helps to improve the accuracy of UAVs for fire monitoring, and the performance is better than the existing flame detection algorithms, which meet the requirements of practical applications.

A deep learning approach for Named Entity Recognition in Urdu language.

Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies.

Comparative Analysis of Diabetic Prediction Using Machine Learning Algorithms

Diabetes mellitus (DM) is a severe worldwide health problem, and its prevalence is quickly growing. It is a spectrum of metabolic illnesses definite by continually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of difficulties, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be mainly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to detect the strong ML algorithm to forecast it. For this numerous ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to this work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the research was implemented using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This research discussed performance matrices and error rates of classifiers for both datasets. The outcomes showed that for the PID database (PIDD), SVM works improved with an accuracy of 74% whereas for Germany RF and KNN work improved with 98.7% accuracy. This study can helps healthcare facilities and researchers in understanding the value and application of ML algorithms in predicting diabetes at an initial stage

Multiple strategies based Grey Wolf Optimizer for feature selection in performance evaluation of open-ended funds

The methods for selecting the features in evaluating fund performance rely heavily on traditional statistics, which can potentially lead to excessive data dimensions in a multi-dimensional context. Grey Wolf Optimizer (GWO), a swarm intelligence optimization algorithm with its simple structure and few parameters, is widely used in feature selection. However, the algorithm suffers from local optimality and the imbalance in exploration and exploitation. This paper proposes a Multi-Strategy Grey Wolf Optimizer (MSGWO) to address the limitations, and identify the relevant features for evaluating fund performance. Random Opposition-based Learning is applied to enhance population quality during the initialization phase. Moreover, the convergence factor is nonlinearized to coordinate the global exploration and local exploitation capabilities. Finally, a two-stage hybrid mutation operator is applied to modify the updating mechanism, so as to increase population diversity and balance the exploration and exploitation abilities of GWO. The proposed algorithm is compared against 6 related algorithms and verified by the Wilcoxon signed-rank test on 12 quarterly datasets (2020-2022) of Chinese open-ended funds. The results inform that MSGWO reduces the feature size as well as the classification error rate.

An Improved k-Nearest Neighbor Algorithm for Recognition and Classification of Thyroid Nodules.

To complete the task of automatic recognition and classification of thyroid nodules and solve the problem of high classification error rates when the samples are imbalanced. An improved k-nearest neighbor (KNN) algorithm is proposed and a method for automatic thyroid nodule classification based on the improved KNN algorithm is established. In the improved KNN algorithm, we consider not only the number of class labels for various classes of data in KNNs, but also the corresponding weights. And we use the Minkowski distance measure instead of the Euclidean distance measure. A total of 508 ultrasound images of thyroid nodules, including 415 benign nodules and 93 malignant nodules, were used in the paper. Experimental results show the improved KNN has 0.872549 accuracy, 0.867347 precision, 1 recall, and 0.928962 F1-score. At the same time, we also considered the influence of different distance weights, the value of k, different distance measures on the classification results. A comparison result shows that our method has a better performance than the traditional KNN and other classical machine learning methods.

Application of deep learning and feature selection technique on external root resorption identification on CBCT images.

Artificial intelligence has been proven to improve the identification of various maxillofacial lesions. The aim of the current study is two-fold: to assess the performance of four deep learning models (DLM) in external root resorption (ERR) identification and to assess the effect of combining feature selection technique (FST) with DLM on their ability in ERR identification. External root resorption was simulated on 88 extracted premolar teeth using tungsten bur in different depths (0.5mm, 1mm, and 2mm). All teeth were scanned using a Cone beam CT (Carestream Dental, Atlanta, GA). Afterward, a training (70%), validation (10%), and test (20%) dataset were established. The performance of four DLMs including Random Forest (RF) + Visual Geometry Group 16 (VGG), RF + EfficienNetB4 (EFNET), Support Vector Machine (SVM) + VGG, and SVM + EFNET) and four hybrid models (DLM + FST: (i) FS + RF + VGG, (ii) FS + RF + EFNET, (iii) FS + SVM + VGG and (iv) FS + SVM + EFNET) was compared. Five performance parameters were assessed: classification accuracy, F1-score, precision, specificity, and error rate. FST algorithms (Boruta and Recursive Feature Selection) were combined with the DLMs to assess their performance. RF + VGG exhibited the highest performance in identifying ERR, followed by the other tested models. Similarly, FST combined with RF + VGG outperformed other models with classification accuracy, F1-score, precision, and specificity of 81.9%, weighted accuracy of 83%, and area under the curve (AUC) of 96%. Kruskal Wallis test revealed a significant difference (p = 0.008) in the prediction accuracy among the eight DLMs. In general, all DLMs have similar performance on ERR identification. However, the performance can be improved by combining FST with DLMs.