High Classification Performance Research Articles

Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals

Background: Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. In this context, we aimed to investigate the cortical activities of psychotic criminal subjects by deploying an explainable feature engineering (XFE) model using an EEG psychotic criminal dataset. Methods: In this study, a new EEG psychotic criminal dataset was curated, containing EEG signals from psychotic criminal and control groups. To extract meaningful findings from this dataset, we presented a new channel-based feature extraction function named Zipper Pattern (ZPat). The proposed ZPat extracts features by analyzing the relationships between channels. In the feature selection phase of the proposed XFE model, an iterative neighborhood component analysis (INCA) feature selector was used to choose the most distinctive features. In the classification phase, we employed an ensemble and iterative distance-based classifier to achieve high classification performance. Therefore, a t-algorithm-based k-nearest neighbors (tkNN) classifier was used to obtain classification results. The Directed Lobish (DLob) symbolic language was used to derive interpretable results from the identities of the selected feature vectors in the final phase of the proposed ZPat-based XFE model. Results: To obtain the classification results from the ZPat-based XFE model, leave-one-record-out (LORO) and 10-fold cross-validation (CV) methods were used. The proposed ZPat-based model achieved over 95% classification accuracy on the curated EEG psychotic criminal dataset. Moreover, a cortical connectome diagram related to psychotic criminal detection was created using a DLob-based explainable artificial intelligence (XAI) method. Conclusions: In this regard, the proposed ZPat-based XFE model achieved both high classification performance and interpretability. Thus, the model contributes to feature engineering, psychiatry, neuroscience, and forensic sciences. Moreover, the presented ZPat-based XFE model is one of the pioneering XAI models for investigating psychotic criminal/criminal individuals.

Computerized classification method for significant coronary artery stenosis on whole-heart coronary MRA using 3D convolutional neural networks with attention mechanisms.

This study aims to develop a computerized classification method for significant coronary artery stenosis on whole-heart coronary magnetic resonance angiography (WHCMRA) images using a 3D convolutional neural network (3D-CNN) with attention mechanisms. The dataset included 951 segments from WHCMRA images of 75 patients who underwent both WHCMRA and invasive coronary angiography (ICA). Forty-two segments with significant stenosis (luminal diameter reduction 75%) on ICA were annotated on WHCMRA images by an experienced radiologist, whereas 909 segments without it were annotated at representative sites. Volumes of interest (VOIs) of 21 × 21 × 21 voxels centered on annotated points were extracted. The network comprises two feature extractors, two attention mechanisms (for the coronary artery and annotated points), and a classifier. The feature extractors first extracted the feature maps from the VOI. The two attention mechanisms weighted the feature maps of the coronary artery and those the neighborhood of the annotated point, respectively. The classifier finally classified the VOIs into those with and without significant coronary artery stenosis. Using fivefold cross-validation, the classification accuracy, sensitivity, specificity, and AUROC (area under the receiver operating characteristic curve) were 0.875, 0.905, 0.873, and 0.944, respectively. The proposed method showed high classification performance for significant coronary artery stenosis and appears to have a substantial impact on the interpretation of WHCMRA images.

Feature Selection in Cancer Classification: Utilizing Explainable Artificial Intelligence to Uncover Influential Genes in Machine Learning Models

This study investigates the use of machine learning (ML) models combined with explainable artificial intelligence (XAI) techniques to identify the most influential genes in the classification of five recurrent cancer types in women: breast cancer (BRCA), lung adenocarcinoma (LUAD), thyroid cancer (THCA), ovarian cancer (OV), and colon adenocarcinoma (COAD). Gene expression data from RNA-seq, extracted from The Cancer Genome Atlas (TCGA), were used to train ML models, including decision trees (DTs), random forest (RF), and XGBoost (XGB), which achieved accuracies of 98.69%, 99.82%, and 99.37%, respectively. However, the challenges in this analysis included the high dimensionality of the dataset and the lack of transparency in the ML models. To mitigate these challenges, the SHAP (Shapley Additive Explanations) method was applied to generate a list of features, aiming to understand which characteristics influenced the models’ decision-making processes and, consequently, the prediction results for the five tumor types. The SHAP analysis identified 119, 80, and 10 genes for the RF, XGB, and DT models, respectively, totaling 209 genes, resulting in 172 unique genes. The new list, representing 0.8% of the original input features, is coherent and fully explainable, increasing confidence in the applied models. Additionally, the results suggest that the SHAP method can be effectively used as a feature selector in gene expression data. This approach not only enhances model transparency but also maintains high classification performance, highlighting its potential in identifying biologically relevant features that may serve as biomarkers for cancer diagnostics and treatment planning.

Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models

BackgroundThe proportion of residual leukemic blasts after chemotherapy assessed by multiparameter flow cytometry, is an important prognostic factor for the risk of relapse and overall survival in acute myeloid leukemia (AML). This measurable residual disease (MRD) is used in clinical trials to stratify patients for more or less intensive consolidation therapy. However, an objective and reproducible analysis method to assess MRD status from flow cytometry data is lacking, yet is highly anticipated for broader implementation of MRD testing.MethodsWe propose a computational pipeline based on Gaussian mixture modeling that allows a fully automated assessment of MRD status while remaining completely interpretable for clinical diagnostic experts. Our pipeline requires limited training data, which makes it easily transferable to other medical centers and cytometry platforms.ResultsWe identify all healthy and leukemic immature myeloid cells in with high concordance (Spearman’s Rho = 0.974) and classification performance (median F-score = 0.861) compared to manual analysis. Using control samples (n = 18), we calculate a computational MRD percentage with high concordance to expert gating (Spearman’s rho = 0.823) and predict MRD status in a cohort of 35 AML follow-up measurements with high accuracy (97%).ConclusionsWe demonstrate that our pipeline provides a powerful tool for fast (~3 s) and objective automated MRD assessment in AML.

Internet of vehicles intrusion detection method based on CFS-COA feature selection and spatio-temporal feature extraction

Abstract With the rapid spread of the Internet of Vehicles (IoV) technology, vehicle network security is facing increasingly severe challenges. Intrusion detection technology has become a crucial tool for ensuring the information security of IoV. Since the traffic data of the IoV is large and has spatio-temporal characteristics, most previous studies are based on a single deep learning method to extract temporal or spatial features, which does not fully extract features of IoV data. To address the above issues, a spatio-temporal feature extraction model with feature selection is proposed. First, to solve the problem of long detection time with huge data traffic, a new feature selection method is proposed to screen the optimal feature subset by combining the correlation-based feature selection method with the crayfish optimization algorithm (CFS-COA). Second, the selected optimal features are used in a spatio-temporal feature extraction model that combines a Temporal Convolutional Network and a Bidirectional Gated Recurrent Unit (TCN-BiGRU) for classification. Finally, the performance of the model is evaluated using two types of datasets: the NSL-KDD and UNSW-NB15 datasets for external communications, and the Car-Hacking dataset for in-vehicle networks. The experimental results indicate that the proposed model demonstrates high classification performance and lightweight characteristics, achieving 100% accuracy on the Car-Hacking dataset.

HiCervix: An Extensive Hierarchical Dataset and Benchmark for Cervical Cytology Classification.

Cervical cytology is a critical screening strategy for early detection of pre-cancerous and cancerous cervical lesions. The challenge lies in accurately classifying various cervical cytology cell types. Existing automated cervical cytology methods are primarily trained on databases covering a narrow range of coarse-grained cell types, which fail to provide a comprehensive and detailed performance analysis that accurately represents real-world cytopathology conditions. To overcome these limitations, we introduce HiCervix, the most extensive, multi-center cervical cytology dataset currently available to the public. HiCervix includes 40,229 cervical cells from 4,496 whole slide images, categorized into 29 annotated classes. These classes are organized within a three-level hierarchical tree to capture fine-grained subtype information. To exploit the semantic correlation inherent in this hierarchical tree, we propose HierSwin, a hierarchical vision transformer-based classification network. HierSwin serves as a benchmark for detailed feature learning in both coarse-level and fine-level cervical cancer classification tasks. In our comprehensive experiments, HierSwin demonstrated remarkable performance, achieving 92.08% accuracy for coarse-level classification and 82.93% accuracy averaged across all three levels. When compared to board-certified cytopathologists, HierSwin achieved high classification performance (0.8293 versus 0.7359 averaged accuracy), highlighting its potential for clinical applications. This newly released HiCervix dataset, along with our benchmark HierSwin method, is poised to make a substantial impact on the advancement of deep learning algorithms for rapid cervical cancer screening and greatly improve cancer prevention and patient outcomes in real-world clinical settings.

Unsupervised Modelling of E-Customers’ Profiles: Multiple Correspondence Analysis with Hierarchical Clustering of Principal Components and Machine Learning Classifiers

The rapid growth of e-commerce has transformed customer behaviors, demanding deeper insights into how demographic factors shape online user preferences. This study performed a threefold analysis to understand the impact of these changes. Firstly, this study investigated how demographic factors (e.g., age, gender, education) influence e-customer preferences in Serbia. From a sample of n = 906 respondents, conditional dependencies between demographics and user preferences were tested. From a hypothetical framework of 24 tested hypotheses, this study successfully rejected 8/24 (with p < 0.05), suggesting a high association between demographics with purchase frequency and reasons for quitting the purchase. However, although the reported test statistics suggested an association, understanding how interactions between categories shape e-customer profiles was still required. Therefore, the second part of this study considers an MCA-HCPC (Multiple Correspondence Analysis with Hierarchical Clustering on Principal Components) to identify user profiles. The analysis revealed three main clusters: (1) young, female, unemployed e-customers driven mainly by customer reviews; (2) retirees and older adults with infrequent purchases, hesitant to buy without experiencing the product in person; and (3) employed, highly educated, male, middle-aged adults who prioritize fast and accurate delivery over price. In the third stage, the clusters are used as labels for Machine Learning (ML) classification tasks. Particularly, Gradient Boosting Machine (GBM), Decision Tree (DT), k-Nearest Neighbors (kNN), Gaussian Naïve Bayes (GNB), Random Forest (RF), and Support Vector Machine (SVM) were used. The results suggested that GBM, RF, and SVM had high classification performance in identifying user profiles. Lastly, after performing Permutation Feature Importance (PFI), the findings suggested that age, work status, education, and income are the main determinants of shaping e-customer profiles and developing marketing strategies.

Development of extraction features for Detecting Adolescent Personality with Machine Learning Algorithms

This study aims to develop a Natural Language Processing (NLP)-based feature extraction algorithm optimized for personality type classification in adolescents. The algorithm used is TF-IDF + N-Gram Z, which combines Term Frequency-Inverse Document Frequency (TF-IDF) with the N-Gram Z technique to improve the feature representation of the analyzed text. TF-IDF functions to measure the importance of words in a document, while N-Gram Z enriches the context by considering the order of words that appear sequentially. The dataset in this study consists of 3,200 sentences generated by adolescent respondents through a survey designed to explore aspects of their personality. After the feature extraction process is complete, three variants of the Naïve Bayes method are applied for classification, namely Multinomial Naïve Bayes, Bernoulli Naïve Bayes, and Complement Naïve Bayes. Each variant has distinctive characteristics in handling certain data types, such as binomial and multinomial data. The results of the study show that the combined TF-IDF + N-Gram Z algorithm can produce highly representative features, as evidenced by high classification performance. The Multinomial Naïve Bayes and Complement Naïve Bayes variants each achieved 98% accuracy. These findings provide significant contributions to the development of NLP-based personality classification methods for Detecting Adolescent Personality. The combination of the TF-IDF + N-Gram Z algorithm with various Naïve Bayes variants produces an exceedingly high level of accuracy and can be applied in practice in the fields of psychology and adolescent education.

Cardioish: Lead-Based Feature Extraction for ECG Signals.

Background: Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals. The primary objective of this research is to propose a new feature engineering model that achieves both high classification accuracy and explainable results using ECG signals. To this end, a symbolic language, named Cardioish, has been introduced. Methods: In this research, two publicly available datasets were used: (i) a mental disorder classification dataset and (ii) a myocardial infarction (MI) dataset. These datasets contain ECG beats and include 4 and 11 classes, respectively. To obtain explainable results from these ECG signal datasets, a new explainable feature engineering (XFE) model has been proposed. The Cardioish-based XFE model consists of four main phases: (i) lead transformation and transition table feature extraction, (ii) iterative neighborhood component analysis (INCA) for feature selection, (iii) classification, and (iv) explainable results generation using the recommended Cardioish. In the feature extraction phase, the lead transformer converts ECG signals into lead indexes. To extract features from the transformed signals, a transition table-based feature extractor is applied, resulting in 144 features (12 × 12) from each ECG signal. In the feature selection phase, INCA is used to select the most informative features from the 144 generated, which are then classified using the k-nearest neighbors (kNN) classifier. The final phase is the explainable artificial intelligence (XAI) phase. In this phase, Cardioish symbols are created, forming a Cardioish sentence. By analyzing the extracted sentence, XAI results are obtained. Additionally, these results can be integrated into connectome theory for applications in cardiology. Results: The presented Cardioish-based XFE model achieved over 99% classification accuracy on both datasets. Moreover, the XAI results related to these disorders have been presented in this research. Conclusions: The recommended Cardioish-based XFE model achieved high classification performance for both datasets and provided explainable results. In this regard, our proposal paves a new way for ECG classification and interpretation.

Batik Image Representation using Multi Texton Co-occurrence Histogram

This paper introduces a novel approach to batik image representation using the texton-based and statistical Multi Texton Co-occurrence Histogram (MTCH). The MTCH framework is leveraged as a robust batik image descriptor, capable of encapsulating a comprehensive range of visual features, including the intricate interplay of color, texture, shape, and statistical attributes. The research extensively evaluates the effectiveness of MTCH through its application on two well-established public batik datasets, namely Batik 300 and Batik Nitik 960. These datasets serve as benchmarks for assessing the performance of MTCH in both classification and image retrieval tasks. In the classification domain, four distinct scenarios were explored, employing various classifiers: the K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB). Each classifier was rigorously tested to determine its efficacy in correctly identifying batik patterns based on the MTCH descriptors. On the other hand, the image retrieval tasks were conducted using several distance metrics, including the Euclidean distance, City Block, Bray Curtis, and Canberra, to gauge the retrieval accuracy and the robustness of the MTCH framework in matching similar batik images. The empirical results derived from this study underscore the superior performance of the MTCH descriptor across all tested scenarios. The evaluation metrics, including accuracy, precision, and recall, indicate that MTCH not only achieves high classification performance but also excels in retrieving images with high similarity to the query. These findings suggest that MTCH is a highly effective tool for batik image analysis, offering significant potential for applications in cultural heritage preservation, textile pattern recognition, and automated batik classification systems.

Minimum and Maximum Pattern-Based Self-Organized Feature Engineering: Fibromyalgia Detection Using Electrocardiogram Signals.

The primary objective of this research is to propose a new, simple, and effective feature extraction function and to investigate its classification ability using electrocardiogram (ECG) signals. In this research, we present a new and simple feature extraction function named the minimum and maximum pattern (MinMaxPat). In the proposed MinMaxPat, the signal is divided into overlapping blocks with a length of 16, and the indexes of the minimum and maximum values are identified. Then, using the computed indices, a feature map is calculated in base 16, and the histogram of the generated map is extracted to obtain the feature vector. The length of the generated feature vector is 256. To evaluate the classification ability of this feature extraction function, we present a new feature engineering model with three main phases: (i) feature extraction using MinMaxPat, (ii) cumulative weight-based iterative neighborhood component analysis (CWINCA)-based feature selection, and (iii) classification using a t-algorithm-based k-nearest neighbors (tkNN) classifier. To obtain results, we applied the proposed MinMaxPat-based feature engineering model to a publicly available ECG fibromyalgia dataset. Using this dataset, three cases were analyzed, and the proposed MinMaxPat-based model achieved over 80% classification accuracy with both leave-one-record-out (LORO) cross-validation (CV) and 10-fold CV. These results clearly demonstrate that this simple model achieved high classification performance. Therefore, this model is surprisingly effective for ECG signal classification.

Lightweight advanced deep-learning models for stress detection on social media

Nowadays, stress reveals itself as a ubiquitous presence, manifesting in novel forms in our modern daily life. Indeed, digital platforms and social media collect various impressions, reactions, and feelings that could provide valuable real-time sentiment data. Nevertheless, understanding stress and mental states among people is difficult because it relies on self-reporting and detecting related expressions, statements, and articulations. In this paper, we consider extracting nuanced insights and stress expressions from Reddit and Twitter posts using lightweight advanced deep-learning methods and Bidirectional Encoder Representations from Transformers (BERT) embeddings. Our findings highlight the potency of transformer BERT models, whether utilized as embedding feature extractors or as text sentiment classifiers. Moreover, the proposed lightweight deep architectural models promoted the field of stress detection in social media, achieving high classification performance. Practically, the BERT Electra model reached 85.67% accuracy on the small Reddit dataset, while our Convolutional Neural Network (CNN) model obtained 97.62% on the large Twitter dataset. Our contributions are not only restricted to the scientific understanding of stress but also extend to the well-being of individuals and global mental health.

VaxLLM: Leveraging Fine-tuned Large Language Model for automated annotation of Brucella Vaccines.

Vaccines play a vital role in enhancing immune defense and preventing the hosts against a wide range of diseases. However, research relating to vaccine annotation remains a labor-intensive task due to the ever-increasing volume of scientific literature. This study explores the application of Large Language Models (LLMs) to automate the classification and annotation of scientific literature on vaccines as exemplified on Brucella vaccines. We developed an automatic pipeline to automatically perform the classification and annotation of Brucella vaccine-related articles, using abstract and title. The pipeline includes VaxLLM (Vaccine Large Language Model), which is a fine-tuned Llama 3 model. VaxLLM systematically classifies articles by identifying the presence of vaccine formulations and extracts the key information about vaccines, including vaccine antigen, vaccine formulation, vaccine platform, host species used as animal models, and experiments used to investigate the vaccine. The model demonstrated high performance in classification (Precision: 0.90, Recall: 1.0, F1-Score: 0.95) and annotation accuracy (97.9%), significantly outperforming a corresponding non-fine-tuned Llama 3 model. The outputs from VaxLLM are presented in a structured format to facilitate the integration into databases such as the VIOLIN vaccine knowledgebase. To further enhance the accuracy and depth of the Brucella vaccine data annotations, the pipeline also incorporates PubTator, enabling cross comparison with VaxLLM annotations and supporting downstream analyses like gene enrichment. VaxLLM rapidly and accurately extracted detailed itemized vaccine information from publications, significantly outperforming traditional annotation methods in both speed and precision. VaxLLM also shows great potential in automating knowledge extraction in the domain of vaccine research. All data is available at https://github.com/xingxianli/VaxLLM , and the model was also uploaded to HuggingFace ( https://huggingface.co/Xingxian123/VaxLLM ).

Artificial intelligence-aided electrochemical sensors for capturing and analyzing fingerprint profiles of medicinal materials

This study explores the application of artificial intelligence-aided electrochemical sensors for authenticating medicinal materials, focusing on sika deer antler cap powder. Utilizing differential pulse voltammetry and graphene-modified screen-printed electrodes, we developed a novel method to capture unique electrochemical fingerprints of authentic, counterfeit, and adulterated samples. Three machine learning models—Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM)—were evaluated using both full voltammogram and principal component analysis (PCA) reduced features. The SVM model with PCA-reduced features emerged as the optimal approach, achieving a classification accuracy of 97.9 % while reducing training time by 65.6 % (from 3.2 s to 1.1 s) and prediction time by 71.4 % (from 0.07 s to 0.02 s per sample) compared to using full voltammogram features. This reduction in computational complexity was achieved by decreasing the input dimensionality from 601 to 5 features through PCA, while maintaining high classification performance across all sample categories. This model demonstrated high sensitivity (>97 %) and specificity (>98 %) across all sample categories, with a notably low limit of detection for adulteration at 2.8 %. Characteristic peaks, such as the pantocrin peak at 0.25 V for authentic samples, provided a robust basis for differentiation. The method's effectiveness in detecting subtle adulterations was evidenced by its ability to identify samples with as low as 5 % adulteration. Furthermore, the approach showed excellent generalization, maintaining 97.0 % accuracy on an independent validation set. These findings highlight the potential of this technique for rapid, accurate, and cost-effective authentication of medicinal materials, addressing the growing challenge of counterfeit products in the pharmaceutical industry.

Systematic construction of composite radiation therapy dataset using automated data pipeline for prognosis prediction

BackgroundExisting research on medical data has primarily focused on single time-points or single-modality data. This study aims to collect all data generated during radiotherapy comprehensively to improve the treatment and prognosis of patients with malignant tumors. MethodsThe data collected from each medical institution were transmitted to the lead organization, where they underwent a file integrity check and were processed using a data pipeline. The key metadata of the collected data were compiled into a database, which were examined by data analysts to identify outliers based on theoretical and institution-specific characteristics. Appropriate filters were applied and the filtered data were subsequently reviewed by artificial intelligence (AI)-based models and researchers for radiotherapy organ slides. Finally, they were annotated by specialists. ResultsThe final dataset included 30,136 three-dimensional cone-beam computed tomography scans and 5,019 tabular data entries collected from 5,019 patients. It comprised 2,043,162 Digital Imaging and Communications in Medicine-format files with a total file size of 832 GB. Quality verification of the data using AI models revealed high classification performance for most organs, with relatively poor performance for the rectum. Overall, the macro AUROC value was 0.947. ConclusionsThis study implemented an automated data pipeline and AI-based verification to enhance the quality of collected radiotherapy data. The constructed dataset can be utilized for various types of future research and is expected to contribute to the improvement of radiotherapy efficiency.

Can Large Language Models Assess Serendipity in Recommender Systems?

Serendipity-oriented recommender systems aim to counteract the overspecialization of user preferences. However, evaluating a user’s serendipitous response to a recommended item can be challenging owing to its emotional nature. In this study, we address this issue by leveraging the rich knowledge of large language models (LLMs) that can perform various tasks. First, it explores the alignment between the serendipitous evaluations made by LLMs and those made by humans. In this study, a binary classification task was assigned to the LLMs to predict whether a user would find the recommended item serendipitously. The predictive performances of three LLMs were measured on a benchmark dataset in which humans assigned the ground truth to serendipitous items. The experimental findings revealed that LLM-based assessment methods do not have a very high agreement rate with human assessments. However, they performed as well as or better than the baseline methods. Further validation results indicate that the number of user rating histories provided to LLM prompts should be carefully chosen to avoid both insufficient and excessive inputs and that interpreting the output of LLMs showing high classification performance is difficult.

Joint Classification of Hyperspectral and LiDAR Data via Multiprobability Decision Fusion Method

With the development of sensor technology, the sources of remotely sensed image data for the same region are becoming increasingly diverse. Unlike single-source remote sensing image data, multisource remote sensing image data can provide complementary information for the same feature, promoting its recognition. The effective utilization of remote sensing image data from various sources can enhance the extraction of image features and improve the accuracy of feature recognition. Hyperspectral remote sensing (HSI) data and light detection and ranging (LiDAR) data can provide complementary information from different perspectives and are frequently combined in feature identification tasks. However, the process of joint use suffers from data redundancy, low classification accuracy and high time complexity. To address the aforementioned issues and improve feature recognition in classification tasks, this paper introduces a multiprobability decision fusion (PRDRMF) method for the combined classification of HSI and LiDAR data. First, the original HSI data and LiDAR data are downscaled via the principal component–relative total variation (PRTV) method to remove redundant information. In the multifeature extraction module, the local texture features and spatial features of the image are extracted to consider the local texture and spatial structure of the image data. This is achieved by utilizing the local binary pattern (LBP) and extended multiattribute profile (EMAP) for the two types of data after dimensionality reduction. The four extracted features are subsequently input into the corresponding kernel–extreme learning machine (KELM), which has a simple structure and good classification performance, to obtain four classification probability matrices (CPMs). Finally, the four CPMs are fused via a multiprobability decision fusion method to obtain the optimal classification results. Comparison experiments on four classical HSI and LiDAR datasets demonstrate that the method proposed in this paper achieves high classification performance while reducing the overall time complexity of the method.

Fine construction of gene coexpression network analysis using GTOM and RECODE detected a critical module of neuroblastoma stages 4 and 4S

BackgroundStage 4 neuroblastoma (NBL), a solid tumor of childhood, has a poor prognosis. Despite intensive molecular genetic studies, no targetable gene abnormalities have been identified. Stage 4S NBL has a characteristic of spontaneous regression, and elucidation of the mechanistic differences between stages 4 and 4S may improve treatment. Conventional NBL studies have mainly focused on the detection of abnormalities in individual genes and have rarely examined abnormalities in gene networks. While the gene coexpression network is expected to contribute to the detection of network abnormalities, the fragility of the network due to data noise and the extraction of arbitrary topological structures for the high-dimensional network are issues.ResultsThe present paper concerns the classification method of stages 4 and 4S NBL patients using highly accurate gene coexpression network analysis based on RNA-sequencing data of transcription factors (TFs). In particular, after applying a noise reduction method RECODE, generalized topological overlapping measure (GTOM), which weighs the connections of nodes in the network structure, succeeded in extracting a cluster of TFs that showed high classification performance for stages 4 and 4S. In addition, we investigated how these clusters correspond to clinical information and to TFs which control the normal adrenal tissue and NBL characters.ConclusionsA clustering method is presented for finding intermediate-scale clusters of TFs that give considerable separation performance for distinguishing between stages 4 and 4S. It is suggested that this method is useful as a way to extract factors that contribute to the separation of groups from multiple pieces of information such as gene expression levels.

An Automated Diagnosis of Parkinson's Disease from MRI Scans Based on Enhanced Residual Dense Network with Attention Mechanism.

The increasing prevalence of neurodegenerative diseases has recently heightened interest in research on early diagnosis of these diseases. Parkinson's disease (PD), among the most prominent of these conditions, is a neurological disorder causing the loss of nerve cells and significantly affecting movement control. Detection of PD in early stages is of critical importance to prevent the progression of the disease and improve treatment processes. The aim of the current study is to develop a deep learning model that can perform accurate classification for early diagnosis of PD from MRI images. In this study, a densely connected feature fusion network with residual learning is designed to diagnose PD patients. The designed network consists of a serial dense block with skip connections and efficient attention mechanisms. In this architecture, squeeze-excitation (SE) blocks with ResNeXt (SE-ResNeXt block) modules are utilized to extract distinctive and high-level features. In the experiments, a publicly available T2-weighted MRI dataset is used, and an offline augmentation process is applied to limited data to increase the generalization ability and classification performance. The proposed method is evaluated and compared with current state-of-the-art deep learning methods. The obtained results show that the proposed model gives higher classification performance with an overall accuracy of 94.44%, precision of 91.67%, sensitivity of 91.67%, specificity of 95.83%, F1-score of 91.67%, and Matthew's correlation coefficient of 87.50% for the PD and healthy control subjects.

Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning

Quality control at every stage of production in the textile industry is essential for maintaining competitiveness in the global market. Manual fabric defect inspections are often characterized by low precision and high time costs, in contrast to intelligent anomaly detection systems implemented in the early stages of fabric production. To achieve successful automated fabric defect identification, significant challenges must be addressed, including accurate detection, classification, and decision-making processes. Traditionally, fabric defect classification has relied on inefficient and labor-intensive human visual inspection, particularly as the variety of fabric defects continues to increase. Despite the global chip crisis and its adverse effects on supply chains, electronic hardware costs for quality control systems have become more affordable. This presents a notable advantage, as vision systems can now be easily developed with the use of high-resolution, advanced cameras. In this study, we propose a discrete curvature algorithm, integrated with the Gabor transform, which demonstrates significant success in near real-time defect classification. The primary contribution of this work is the development of a modified curvature algorithm that achieves high classification performance without the need for training. This method is particularly efficient due to its low data storage requirements and minimal processing time, making it ideal for real-time applications. Furthermore, we implemented and evaluated several other methods from the literature, including Gabor and Convolutional Neural Networks (CNNs), within a unified coding framework. Each defect type was analyzed individually, with results indicating that the proposed algorithm exhibits comparable success and robust performance relative to deep learning-based approaches.