Classification Accuracy Of Method Research Articles

HybridGT: An Integration of Graph Transformer and LSTM for Effective Hyperspectral Band Selection

ABSTRACT Hyperspectral imagery has a high-dimensional curse due to numerous spectral bands. Band selection (BS) is crucial for efficiently reducing dimensionality, retaining only essential bands containing valuable information. However, deep learning-based techniques have gained more attention through trained networks for band selection. Recently, graph-based learning has been extensively used in hyperspectral imagery, revealing intrinsic data relationships. This article presents a novel hybrid approach for hyperspectral band selection, addressing the curse of dimensionality in hyperspectral imagery (HSI). Integrating Long Short Term Memory (LSTM) and Graph Transformer (GT), the method employs Bi-dimensional Empirical Mode Decomposition (BEMD) for spatial data enhancement. Using transfer learning, we explore a ResNet-50 deep network to identify optimal intrinsic mode functions (IMFs). The final band subset will be obtained by concatenating the features extracted from the graph transformer and LSTM networks from selected IMFs and residual IMF, respectively. The proposed HybridGT-BS technique surpasses state-of-the-art methods in classification accuracy across three well-known HSI datasets – IP-Indian Pines, SA-Salinas, and PU-PaviaU. With the support of experimental results, the proposed technique significantly outperforms the classification accuracy with the best bands of the HSIs.

Geographical classification of population: Analysis of amino acid in fingermark residues using UHPLC-QQQ-MS/MS combined with machine learning

ObjectiveTo determine the living regions of individuals based on amino acids in fingermark residues and to establish a rapid and accurate regional classification method using machine learning. Methods: A total of 71 fingermark donors from six different provinces in various regions of China were selected. The content of 18 amino acids in their fingermarks was detected using UHPLC-QQQ-MS/MS. Classification models were established using various machine learning algorithms, and the cross-validation accuracy of 72 combinations, including feature engineering, classification algorithms, and optimization algorithms, was compared. Results: UHPLC-QQQ-MS/MS successfully quantified 16 amino acids. Significant differences in the relative content of amino acids were found between the fingermarks from the eastern and western regions of China, as well as among neighboring provinces. The combination of SFS+SVM+BO was identified as the optimal classification model, achieving an accuracy of 90.14%. Conclusion: The study found regional differences in the relative content of amino acids in fingermarks and established a regional classification method combining UHPLC-QQQ-MS/MS and machine learning. The method developed in this study can be applied to incomplete or distorted fingermarks, and the experimental results can be directly used in police investigations. This research uncovers the multidimensional information carried by fingerprint substances, demonstrating innovation and application value. It not only saves and shortens investigation time and provides investigative leads, but also enables previously unusable physical evidence to play a role again, enhancing the profiling of suspects.

Classification of Variable Star Light Curves with Convolutional Neural Network

The classification of variable stars is essential for understanding stellar evolution and dynamics. With the growing volume of light curve data from extensive surveys, there is a need for automated and accurate classification methods. Traditional methods often rely on manual feature extraction and selection, which can be time-consuming and less adaptable to large datasets. In this work, we present an approach using a convolutional neural network (CNN) to classify variable stars using only raw light curve data and their known periods, without the need for manual feature extraction or hand-selected data preprocessing. Our method utilizes phase-folding to organize the light curves and directly learns the variability patterns crucial for classification. Trained and tested on the Optical Gravitational Lensing Experiment (OGLE) dataset, our model demonstrates an average accuracy of 88% and an F1 score of 0.89 across five well-known classes of variable stars. We also compared our classification model with the Random Forest (RF) classifier and showed that our model gives better results across all of the classification metrics. By leveraging CNN, our approach does not need manual feature extraction and can handle diverse light curve shapes and sampling cadences. This automated, data-driven method offers a powerful tool for classifying variable stars, enabling efficient processing of large datasets from current and future sky surveys.

Time Series Classification of Badminton Pose using LSTM with Landmark Tracking

Traditional methods of analyzing badminton matches, such as video movement analysis, are time-consuming, prone to errors, and rely heavily on manual annotation. This creates challenges in accurately and efficiently classifying badminton actions and player poses. This paper aims to develop an accurate time series classification method for badminton poses using landmark tracking. The proposed method integrates Long Short-Term Memory (LSTM) networks with landmark tracking to classify badminton poses in a time series, addressing the limitations of traditional video analysis techniques. The dataset consists of 30 respondents performing three distinct activities—lob, smash, and serve—under two conditions: good and bad execution. The approach combines LSTM networks with landmark tracking data, utilizing intra-class variation from a multi-view dataset to enhance pose classification accuracy. The LSTM model achieved high accuracy in classifying badminton poses, successfully detecting serves, lobs, and smashes in real-time with over 90% accuracy. Additionally, the system improved match analysis, achieving 85% accuracy in detection and classification, demonstrating the effectiveness of combining landmark tracking with machine learning for sports analysis. This study underscores the importance of pose estimation in badminton analysis, particularly through landmark tracking, which significantly improves the accuracy of classifying player poses and contributes to the advancement of automated sports analysis.

Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.

In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemannian space has been frequently utilized to extract spatial features from electroencephalogram (EEG) signals. However, the intrinsic high dimensionality of SPD matrices introduces too much computational burden to hinder the real-time applications of such BCI, especially in handling dynamic tasks, like incremental learning. Directly reducing the dimensionality of SPD matrices with conventional dimensionality reduction (DR) methods will alter the fundamental properties of SPD matrices. Moreover, current DR methods for incremental learning always necessitate retaining old data to update their representations under new mapping. To this end, a bidirectional two-dimensional principal component analysis for SPD manifold (B2DPCA-SPD) is proposed to reduce the dimensionality of SPD matrices, in such way that the reduced matrices remain on SPD manifold. Afterwards, the B2DPCA-SPD is extended to adapt to incremental learning tasks without saving old data. The incremental B2DPCA-SPD can be seamlessly integrated with the matrix-formed growing neural gas network (MF-GNG) to achieve an incremental EEG classification, where the new low-dimensional representations of the prototypes in old classifiers can be easily recalculated with the updated projection matrix. Extensive experiments are conducted on two public datasets to perform the EEG classification. The results demonstrate that our method significantly reduces computation time by 38.53% and 35.96%, and outperforms conventional methods in classification accuracy by 4.21% to 19.59%.

Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy

Topography is one of the dominant factors in regional soil formation and development. Soil distribution has a certain pattern from high to low in space, and this pattern has a high degree of consistency with slope position. Most of the current research on soil mapping uses landscape types generated by existing methods directly as environmental covariates, and there are few landscape classification methods specifically oriented toward soil surveys. There is rarely any research on landform classification using relative slope position (RSP) and elevation. Therefore, we designed a landform classification method based on RSP and elevation, Terrainforms (TF), and combined the landform type with land use type to construct soil–landscape units for soil type and attribute spatial prediction. In this study, two commonly used landform classification methods, Geomorphons and Landforms, were also used to compare with this design method. It was found that the constructed soil–landscape units had a high consistency with the soil spatial distribution. The landform types based on RSP and elevation obtained the second-highest prediction accuracy in both soil type and soil organic carbon (SOC), and the constructed soil–landscape types obtained the highest prediction accuracy. The results show that the landform classification method based on RSP and elevation is not easily limited by the analysis scale, and is an efficient and accurate landform classification method. The TF landform type and its constructed soil–landscape types can be used as an important environmental variable in soil prediction and sampling, which can provide some guidance and reference for landform classification and digital soil mapping.

Sarcopenia screening based on the assessment of gait with inertial measurement units: a systematic review

BackgroundGait variables assessed by inertial measurement units (IMUs) show promise as screening tools for aging-related diseases like sarcopenia. The main aims of this systematic review were to analyze and synthesize the scientific evidence for screening sarcopenia based on gait variables assessed by IMUs, and also to review articles that investigated which gait variables assessed by IMUs were related to sarcopenia.MethodsSix electronic databases (PubMed, SportDiscus, Web of Science, Cochrane Library, Scopus and IEEE Xplore) were searched for journal articles related to gait, IMUs and sarcopenia. The search was conducted until December 5, 2023. Titles, abstracts and full-length texts for studies were screened to be included.ResultsA total of seven articles were finally included in this review. Despite some methodological variability among the included studies, IMUs demonstrated potential as effective tools for detecting sarcopenia when coupled with artificial intelligence (AI) models, which outperformed traditional statistical methods in classification accuracy. The findings suggest that gait variables related to the stance phase such as stance duration, double support time, and variations between feet, are key indicators of sarcopenia.ConclusionsIMUs could be useful tools for sarcopenia screening based on gait analysis, specifically when artificial intelligence is used to process the recorded data. However, more development and research in this field is needed to provide an effective screening tool for doctors and health systems.

Beyond traditional methods: Innovative integration of LISS IV and Sentinel 2A imagery for unparalleled insight into Himalayan ibex habitat suitability.

The utilization of satellite images in conservation research is becoming more prevalent due to advancements in remote sensing technologies. To achieve accurate classification of wildlife habitats, it is important to consider the different capabilities of spectral and spatial resolution. Our study aimed to develop a method for accurately classifying habitat types of the Himalayan ibex (Capra sibirica) using satellite data. We used LISS IV and Sentinel 2A data to address both spectral and spatial issues. Furthermore, we integrated the LISS IV data with the Sentinel 2A data, considering their individual geometric information. The Random Forest approach outperformed other algorithms in supervised classification techniques. The integrated image had the highest level of accuracy, with an overall accuracy of 86.17% and a Kappa coefficient of 0.84. Furthermore, to delineate the suitable habitat for the Himalayan ibex, we employed ensemble modelling techniques that incorporated Land Cover Land Use data from LISS IV, Sentinel 2A, and Integrated image, separately. Additionally, we incorporated other predictors including topographical features, soil and water radiometric indices. The integrated image demonstrated superior accuracy in predicting the suitable habitat for the species. The identification of suitable habitats was found to be contingent upon the consideration of two key factors: the Soil Adjusted Vegetation Index and elevation. The study findings are important for advancing conservation measures. Using accurate classification methods helps identify important landscape components. This study offers a novel and important approach to conservation planning by accurately categorising Land Cover Land Use and identifying critical habitats for the species.

Comparing ultrasound-derived fat fraction and MRI-PDFF for quantifying hepatic steatosis: a real-world prospective study.

To compare the agreement between ultrasound-derived fat fraction (UDFF) with magnetic resonance proton density fat fraction (MRI-PDFF) for quantification of hepatic steatosis and verify its reliability and diagnostic performance by comparing with MRI-PDFF as the reference standard. This prospective study included a primary analysis of 191 patients who underwent MRI-PDFF and UDFF from February 2023 to February 2024. MRI-PDFF were derived from three liver segment measurements with calculation of an overall median PDFF. UDFF was performed by two different sonographers for each of the six measurements, and the median was taken. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to assess agreement. Receiver operating characteristics (ROC) curves were used to evaluate the diagnostic performance of UDFF in detecting different degrees of hepatic steatosis. A total of 176 participants were enrolled in the final cohort of this study (median age, 36.0 years; 82 men, 94 women). The median MRI-PDFF value was 11.3% (interquartile range (IQR) 7.5-18.9); 84.7% patients had a median MRI-PDFF value ≥ 6.4%. The median UDFF measured by different sonographers were 9.5% (IQR: 5.0-18.0) and 9.0% (IQR: 5.0-18.0), respectively. The interobserver agreement of UDFF measurement was excellent agreement (ICC = 0.951 [95% CI: 0.934-0.964], p < 0.001). UDFF was positively strongly correlated with MRI-PDFF with ICC of 0.899 (95% CI: 0.852-0.930). The Bland-Altman analysis showed high agreement between UDFF and MRI-PDFF measurements, with a mean bias of 1.7% (95% LOA, -8.7 to 12.2%). The optimal UDFF cutoff values were 5.5%, 15.5% and 17.5% for detecting MRI-PDFF at historic thresholds of 6.4%, 17.4%, and 22.1%, with AUC of 0.851, 0.952, and 0.948, respectively. The sensitivity was 79.2%, 87.5%, 88.9%, and specificity was 81.5%, 90.6%, 90.0%, respectively. UDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF. The UDFF cutoff values of 5.5%, 15.5%, and 17.5% provide high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively. Question Is ultrasound-derived fat fraction (UDFF) reliable for the quantitative detection of hepatic steatosis compared to MRI proton density fat fraction (MRI-PDFF)? Findings UDFF cutoff values of 5.5%, 15.5%, and 17.5% provided high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively. Clinical relevance UDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF and high reproducibility of liver fat content by different sonographers.

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.

Specialized gray matter segmentation via a generative adversarial network: application on brain white matter hyperintensities classification.

White matter hyperintensities (WMH) observed in T2 fluid-attenuated inversion recovery (FLAIR) images have emerged as potential markers of neurodegenerative diseases like Multiple Sclerosis (MS). Lacking comprehensive automated WMH classification systems in current research, there is a need to develop accurate detection and classification methods for WMH that will benefit the diagnosis and monitoring of brain diseases. Juxtacortical WMH (JCWMH) is a less explored subtype of WMH, primarily due to the hard definition of the cortex in FLAIR images, which is escalated by the presence of lesions to obtain appropriate gray matter (GM) masks. In this study, we present a method to perform a specialized GM segmentation developed for the classification of WMH, especially JCWMH. Using T1 and FLAIR images, we propose a pipeline to integrate masks of white matter, cerebrospinal fluid, ventricles, and WMH to create a unique mask to refine the primary GM map. Subsequently, we utilize this pipeline to generate paired data for training a conditional generative adversarial network (cGAN) to substitute the pipeline and reduce the inputs to only FLAIR images. The classification of WMH is then based on the distances between WMH and ventricular and GM masks. Due to the lack of multi-class labeled WMH datasets and the need for extensive data for training deep learning models, we attempted to collect a large local dataset and manually segment and label some data for WMH and ventricles. In JCWMH classification, the proposed method exhibited a Dice similarity coefficient, precision, and sensitivity of 0.76, 0.69, and 0.84, respectively. With values of 0.66, 0.55, and 0.81, the proposed method clearly outperformed the approach commonly used in the literature, which uses extracted GM masks from registered T1 images on FLAIR. After training, the method proves its efficiency by providing results in less than one second. In contrast, the usual approach would require at least two minutes for registration and segmentation alone. The proposed method is automated and fast and requires no initialization as it works exclusively with FLAIR images. Such innovative methods will undoubtedly facilitate accurate and meaningful analysis of WMH in clinical practice by reducing complexity and increasing efficiency.

An Evaluation Methodology News Headline Classification using Comprehensive Pattern Learning

Text classification plays a pivotal role in numerous applications, ranging from spam detection to sentiment analysis. With the exponential growth of textual data stemming from social media interactions and news articles, the demand for accurate text classification methods has surged. This research delves into the evaluation of a novel weighting function designed to adjust the representation of texts during multilabel classification [1]. This innovative approach combines two distinct strategies: problem transformation and model adaptation. By incorporating this weighting function, the classification process undergoes a significant enhancement, ultimately improving the accuracy and efficiency of multilabel classification tasks [1]. To assess the efficacy of the proposed methodology, rigorous testing was conducted across ten referential textual datasets. Comparative analysis was performed against alternative techniques, with a keen focus on three key performance measures. The results obtained from these evaluations provide valuable insights into the effectiveness and robustness of the weighting function, shedding light on its potential to revolutionize text classification paradigms.

Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding.

Research on electroencephalogram-based motor imagery (MI-EEG) can identify the limbs of subjects that generate motor imagination by decoding EEG signals, which is an important issue in the field of brain-computer interface (BCI). Existing deep-learning-based classification methods have not been able to entirely employ the topological information among brain regions, and thus, the classification performance needs further improving. In this paper, we propose a multi-view graph convolutional attention network (MGCANet) with residual learning structure for multi-class MI decoding. Specifically, we design a multi-view graph convolution spatial feature extraction method based on the topological relationship of brain regions to achieve more comprehensive information aggregation. During the modeling, we build an adaptive weight fusion (Awf) module to adaptively merge feature from different brain views to improve classification accuracy. In addition, the self-attention mechanism is introduced for feature selection to expand the receptive field of EEG signals to global dependence and enhance the expression of important features. The proposed model is experimentally evaluated on two public MI datasets and achieved a mean accuracy of 78.26% (BCIC IV 2a dataset) and 73.68% (OpenBMI dataset), which significantly outperforms representative comparative methods in classification accuracy. Comprehensive experiment results verify the effectiveness of our proposed method, which can provide novel perspectives for MI decoding.

DSEception: a noval neural networks architecture for enhancing pneumonia and tuberculosis diagnosis.

Pneumonia and tuberculosis are prevalent pulmonary diseases globally, each demanding specific care measures. However, distinguishing between these two conditions imposes challenges due to the high skill requirements for doctors, the impact of imaging positions and respiratory intensity of patients, and the associated high healthcare costs, emphasizing the imperative need for intelligent and efficient diagnostic methods. This study aims to develop a highly accurate automatic diagnosis and classification method for various lung diseases (Normal, Pneumonia, and Tuberculosis). We propose a hybrid model, which is based on the InceptionV3 architecture, enhanced by introducing Deepwise Separable Convolution after the Inception modules and incorporating the Squeeze-and-Excitation mechanism. This architecture successfully enables the model to extract richer features without significantly increasing the parameter count and computational workload, thereby markedly improving the performance in predicting and classifying lung diseases. To objectively assess the proposed model, external testing and five-fold cross-validation were conducted. Additionally, widely used baseline models in the scholarly community were constructed for comparison. In the external testing phase, the our model achieved an average accuracy (ACC) of 90.48% and an F1-score (F1) of 91.44%, which is an approximate 4% improvement over the best-performing baseline model, ResNet. In the five-fold cross-validation, our model's average ACC and F1 reached 88.27% ± 2.76% and 89.29% ± 2.69%, respectively, demonstrating exceptional predictive performance and stability. The results indicate that our model holds promise for deployment in clinical settings to assist in the diagnosis of lung diseases, potentially reducing misdiagnosis rates and patient losses. Utilizing deep learning for automatic assistance in the diagnosis of pneumonia and tuberculosis holds clinical significance by enhancing diagnostic accuracy, reducing healthcare costs, enabling rapid screening and large-scale detection, and facilitating personalized treatment approaches, thereby contributing to widespread accessibility and improved healthcare services in the future.

Enzyme-Mediated Duplex Exponential Amplification: A New Platform for Rapid Screening of &amp;lt;i&amp;gt;Hylurgus ligniperda&amp;lt;/i&amp;gt;

As the world&amp;apos;s second largest timber importer, wood demand in China has been growing extremely rapidly, leading to an increase of 163% from 2009 to 2018. The plant quarantine pest &amp;lt;i&amp;gt;H. ligniperda Fabricius, 1787&amp;lt;/i&amp;gt; is an invasive species frequently intercepted at ports. &amp;lt;i&amp;gt;H. ligniperda&amp;lt;/i&amp;gt; causes damage mainly to pine and spruce. To improve the efficiency of on-site inspection and the efficacy of early detection, tight quarantine in ports, time-effective identification, and a national surveillance program for high-risk invasive bark beetles are in urgent need. In this study, a simple, fast and accurate classification method for &amp;lt;i&amp;gt;H. ligniperda &amp;lt;/i&amp;gt;is established based on the enzyme-mediated duplex exponential amplification (EmDEA) technique. Partial region from &amp;lt;i&amp;gt;inhibitor of apoptosis 2&amp;lt;/i&amp;gt; (&amp;lt;I&amp;gt;IAP2)&amp;lt;/I&amp;gt; gene was selected as the target and 6 primer/probe combinations were designed. Through selection, the combination of 3-HY-F3, 3-HY-R2 and 3-HY-RNA5 was chosen as the final primer-probe set, as it showed the lowest Ct with highest final fluorescence signal. Method validation and specificity test using 6 other beetle species living on coniferous wood showed that this method is result reliable and specific. Through parameter analysis with positive plasmid, the detection limit was calculated to be 13.6 copies/μL (9×10&amp;lt;sup&amp;gt;-7&amp;lt;/sup&amp;gt; ng DNA/ reaction), much higher than conventional molecular methods such as PCR. The whole process including isothermal amplification, data analysis, and result output can be finished in 30 min, which is highly time-effective. Besides, the operation is simple and little training is needed for non-professionals. The application prospects of this rapid screening system include customs screening in ports, wild survey in non-lab situations and early warning system development. The new analysis platform EmDEA, can also be implemented in rapid detection and identification of other forestry pests.

Gamma–lindley regression cure model for corporate credit default prediction

Predicting the firm’s default over time is marked persistently with certain flaws for firms seeking to avoid their exposure to credit risk and losses induced by defaults. From this perspective, a Gamma–lindley cure model, relying on a Gamma–lindley distribution and the survival analysis framework, was introduced to model the default probability and time of corporate credits referring to their inference ability once the firm is likely to default. In addition, it extends the standard mixture cure model allowing more flexibility for modeling lifetime data through relaxing the independence assumption of the probability and the time of default. Furthermore, it handles the missing defaulting labels as a latent variable and identifies three classes of default probability distribution, namely short, medium, and long-term through its potentially accurate classification method. A relevant parameter-learning procedure was described resting on the Maximum Likelihood approach. The results of an empirical analysis on a firms dataset reveal that the advanced Gamma–lindley cure model has a rather effective performance in terms of default time prediction compared to Exponential, Gamma, Weibull, and Cox proportional hazards regression modeling specifications, which yield a meaningful solution for timely credit risk management and cluster firms defaulter in three classes, enabling a deeper analysis of firm’s defaults situation. The numerical experiments were conducted with Python language.

Partial label learning via weighted centroid clustering disambiguation

Partial Label Learning (PLL) is a weakly supervised learning problem that induces a multi-class classifier from data with candidate labels, among which only one is the ground-truth label. The crucial challenge in PLL is to disambiguate the false-positive labels from the candidate labels. However, most existing PLL methods fail to simultaneously consider both the instance-level similarity and the class-level information during label disambiguation. In this paper, we propose a novel two-stage method based on weighted centroid clustering, which efficiently utilizes both the local similarity among instances and the per-class global information. In the first stage, we initialize the center of each class using label propagation based on the instance-level similarity, and obtain the disambiguated instances via weighted centroid clustering derived from the per-class global information. In the second stage, the disambiguated instances are used to train a multi-class classifier. Extensive experiments on both controlled UCI datasets and real-world datasets show the superiority of the proposed method in classification accuracy.

Optimizing robotic arm control using deep Q-learning and artificial neural networks through demonstration-based methodologies: A case study of dynamic and static conditions

This paper uses robot programming techniques, such as Deep Q Network, Artificial Neural Network, and Artificial Deep Q Network, to address challenges related to controlling robotic arms through demonstration learning. Static and dynamic states of the subjects were the subjects of experiments. Each method's classification accuracy process success values and experimental condition combination were evaluated. The DQN method demonstrated favourable classification accuracy outcomes, achieving an Accuracy value of 0.64 for the fixed dice and 0.52 for the moving dice. The Response value was 0.51 for the fixed dice and 0.41 for the moving dice, indicating a moderate level. The ANN method demonstrated lower accuracy, with Accuracy values of 0.59 and 0.56 and Response values of 0.61 and 0.58, respectively. The ADQN method demonstrated superior outcomes, with Accuracy values of 0.66 and 0.59 and Response values of 0.67 and 0.61. During the initial learning iterations, ADQN demonstrated the highest success rate at 33.67 %, whereas DQN and ANN achieved 28.39 % and 20.13 % success rates, respectively. As the number of iterations increased, all methods demonstrated improvement in their results. ADQN maintained a high success rate of 97.59 %, while DQN and ANN attained 82.16 % and 88.66 %, respectively. As the number of iterations increases, the results of all methods improve, but the success rate of the Artificial Deep Q Network remains high. As the number of iterations increases, both Deep Q Network and Artificial Neural Network demonstrate the potential to achieve good results. Overall, the findings support the efficacy of robot programming techniques that incorporate demonstration learning. The Artificial Deep Q Network is the most successful and fast-converging method suitable for various robot control tasks. These findings provide a foundation for future research and large-scale, comprehensive learning applications for complex rot control.

Novel feature selection method for accurate breast cancer classification using Correlation coefficient and Modified GWO Algorithm

Breast cancer is perceived as the most common cause of mortality among women globally. Early detection of this disease is critical to reduce significantly the possibility of death. Machine learning techniques have been proved to be efficient and very successful for an accurate breast cancer diagnosis. In this paper, an efficient hybrid Feature Selection (FS) method named a Correlation technique-Modified Grey Wolf Optimizer (CMGWO) was proposed for accurate breast cancer classification based on dimensionality reduction. The suggested technique is based on two stages: the feature selection step and the classification step. Feature selection is the process of picking the most significant characteristics from a dataset. This stage is crucial in machine learning. Firstly, we focus on the filter method by using a Correlation technique for dimensionality reduction. This technique is intended to eliminate and reduce the number of features by selecting one feature from the other correlated features. Secondly, we use the Modified Grey Wolf Optimization algorithm (MGWO) to locate and determine the most significant features from uncorrelated features. After that, we use multiple classifiers to classify breast cancer disease based on the selected features. The Wisconsin Diagnostic Breast Cancer (WDBC) database was used to prove the performance of our proposed work. The experimental results show that the combination of the correlation method and MGWO for feature selection increases the accuracy rate of classification with a minimum number of features. The performances of different machine learning algorithms were evaluated, including Random Forest classifier (RF), Support Vector Machine (SVM) Classifier, and Naïve Bayes (NB) Classifier for the classification step. The suggested technique proves to be the best approach and reliable one among all studied approaches since it increases classification accuracy to 99.12\% obtained by CMGWO using Random Forest classifier and demonstrates its significance in detecting breast cancer.

A self-training method based on fast binary bare-bones particle swarm optimization for semi-supervised classification

The generalization performance of self-training methods for semi-supervised classification depends on the selection of pseudo-labeled samples. While multiple variations of self-training methods are developed, strategies for selecting pseudo-labeled samples still suffer from the following challenging issues: a) heavily relying on specific assumptions about geometric and class relationships between different instances; b) parameter dependency; c) failure to effectively eliminate mislabeled pseudo-labeled noisy samples. These issues lead to low robustness and performance deterioration. To select pseudo-labeled samples more effectively and overcome the above-mentioned challenging issues, a self-training method based on fast binary bare-bones particle swarm optimization (ST-F3BPSO) is proposed. The core ideas of ST-F3BPSO are that a) a binary bare-bones particle swarm optimization (F3BPSO) with a new search space reduction strategy, a new particle update mechanism, and a new fitness function is first proposed; b) an F3BPSO-based sample sub-space optimization (SSO–F3BPSO) is second proposed to help ST-F3BPSO select pseudo-labeled samples and eliminate mislabeled pseudo-labeled noisy samples in the iterative self-training process without parameters and assumptions about geometric and class relationships of data. Intensive experiments on benchmark data sets have proven that a) ST-F3BPSO outperforms 7 state-of-the-art non-evolutionary algorithms-based self-training methods and 2 state-of-the-art evolutionary algorithms-based self-training methods in classification accuracy and marco F-measure of 2 popular base classifiers with various ratios of the initial labeled data or the class noise; b) ST-F3BPSO outperforms 2 state-of-the-art evolutionary algorithms-based self-training methods in computational efficiency.