Pattern Recognition Model Research Articles

Effects of Urban Illumination on Insect Populations: AI-powered Insights into Environmental Challenges and Solutions

Artificial urban lighting seriously disrupts insect populations, creating a cascade effect on ecosystems and biodiversity. This research explores the many ecological challenges presented by urban lighting in terms of how it influences the behavior, physiology, and population dynamics of insects. Urban lighting changes behavioral patterns such as foraging, mating, and navigation, thereby affecting nocturnal activity and vulnerability to predators. This light disrupts insect circadian rhythms physiologically, impairs hormonal regulation, and affects their reproductive success. That jeopardizes their survival and fitness as a population while having adverse population dynamics in abundance and diversity decline that ripple down the food web, ultimately destabilizing ecosystems. Using AI approaches driven by data, the study seeks to understand, predict, and mitigate these effects. Advanced pattern recognition and risk modeling become facilitated with the aid of AI tools in adaptive lighting systems with the potential for reduced harm to the environment. The potential in creating sustainable solutions to urban lighting with regard to ecological integrity alongside human needs, this work thus sets forth a new dimension for such issues as calls for inter-disciplinary cooperation by highlighting the pressing nature of technological innovation into ecological understanding. Through the presentation of a comparative analysis of current mitigation strategies, we are able to identify gaps and suggest future directions for urban ecological management. These include biodiversity-sensitive lighting designs, integration of AI-based monitoring systems, and policy frameworks that prioritize ecosystem health alongside urban development.

A novel technique for rapid determination of pressure injury stages using intelligent machine vision

A developed intelligent machine vision system combined with deep-learning algorithms was attempted to determine pressure injury (PI) stages rapidly. A total of 500 images were selected according to the color and texture characteristics of probable PI sites closely related to fie PI stages based on the guidance of PI experts. Each target box of the PI site was labeled by the same researcher for label consistency. Characteristic values of pressure injuries were extracted from segmented images for further model construction. In developing the rapid determination models, five you just look once (YOLO) pattern recognition models (i.e., YOLO8n, YOLO8s, YOLO8m, YOLO8l, and YOLO8x) were constructed, and they were optimized among 100 epochs. Compared with other models, the YOLO8l model showed the best result, with the precision values among pressure injury stage I to V (i.e., PI_I, PI_II, PI_III, PI_IV, and PI_V) of 0.98, 0.97, 0.95, 0.95, and 0.94, respectively. The overall results suggest that this intelligent machine vision system is useful for PI stage determination and perhaps other disease diagnoses closely related to color and texture characteristics.

Enhancing the Recognition of Collinear Building Patterns by Shape Cognition Based on Graph Neural Networks

ABSTRACT Building patterns are important components of urban structures and functions, and their accurate recognition is the foundation of urban spatial analysis, cartographic generalization, and other tasks. Current building pattern recognition methods are often based on a shape index that can only characterize shape features from one aspect, resulting in significant errors. In this study, a building pattern recognition method based on a graph neural network is proposed to enhance shape cognition and focus on recognizing collinear patterns. First, a building shape classification model that integrates global shape and graph node structure features was constructed to quantitatively study shape cognition. Subsequently, a collinear pattern recognition (CPR) model was established based on a dual building graph. The shape cognition results were integrated into the model to enhance its recognition ability. The results show that the shape classification model can be used to effectively distinguish different shape categories and support building pattern recognition tasks. Based on the CPR model, false recognitions can be avoided, and recognition results similar to those of visual cognition can be obtained. Compared with the comparative methods, both models have significant advantages in terms of statistical results and implementation.

Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

Neural Network Approaches for Early Breast Cancer Detection

Breast cancer research remains essential due to its high mortality rates and the critical need for improved diagnostic accuracy. This study investigates the application of neural network techniques to enhance early breast cancer detection, utilizing Artificial Neural Networks (ANN) implemented in MATLAB. By analyzing datasets from the UCI Machine Learning Repository, specifically the Coimbra and Wisconsin Breast Cancer datasets, this research demonstrates the accuracy and efficiency of neural networks in classifying malignant and benign cases. Methodologically, the study involves data preprocessing, ANN pattern recognition modeling, and testing across multiple metrics including confusion matrices and ROC plots to validate the model's predictive performance. The findings underscore the potential of neural networks as a diagnostic support tool, facilitating faster and more accurate cancer detection, thereby contributing to improved patient outcomes and supporting global health initiatives for early diagnosis.

Intelligent sniffer: A chip-based portable e-nose for accurate and fast essence evaluation

Electronic nose (e-nose) is capable of identifying gases of different odors and concentrations. However, accurate odor evaluation of complicated volatile compounds (e.g., from essence) at human perception level remains challenging, especially in a fast and cheap way. This study presents a portable e-nose designed to evaluate different essence samples and their mixtures that outperforms human olfaction. Given the complexity and similarity of ingredients among different essence types, six sensors were integrated on a miniaturized chip after the sensor array optimization from 18 gas-sensing materials with varying doping, film thicknesses, operated at different temperatures. A pattern recognition model based on Long-short term memory network and convolutional neural network was developed to automatically extract sensors’ dynamic response features to determine essence type and concentration. Our e-nose exhibited high sensitivity and repeatability to analyze low-concentration essence volatiles better than human olfactory tests. The classification accuracy for four similar textile essence and their mixtures could reach up to 96.9 %. And the R2 and RMSE for relative concentration achieved 0.98 and 0.068 %, respectively. Furthermore, only the first 60 s signals are needed for the sample analysis which significantly reduced the sample testing time compared to the traditional base-peak-base test method (over 200 s). This research provides a favorable opportunity for essence fragrance prediction in various applications such as food, textile, cosmetics, etc.

Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning

Across the globe, landslides cause significant loss of life, injuries, and widespread damage to homes and infrastructure. Therefore, assessing and analyzing landslide hazards is crucial to human, environmental, cultural, economic, and social sustainability. This study utilizes ArcGIS 10.8 and Python 3.9 to create landslide databases for Niigata Prefecture (NIG), Iwate and Miyagi Prefectures (IWT-MYG), and Hokkaido (HKD), drawing on data obtained from the National Research Institute for Earth Science and Disaster Resilience, Japan. A distinguishing feature of this study is the application of a Convolutional Neural Network (CNN), which significantly outperforms traditional machine learning models in image-based pattern recognition by extracting contextual information from surrounding areas, a distinct advantage in image and pattern recognition tasks. Unlike conventional methods that often require manual feature selection and engineering, CNNs automate feature extraction, enabling a more nuanced understanding of complex patterns. By experimenting with CNN input window sizes ranging from 3 × 3 to 27 × 27 pixels and employing diverse sampling techniques, we demonstrate that larger windows enhance the model’s predictive accuracy by capturing a wider range of environmental interactions critical for effective landslide modeling. CNN models with 19 × 19 pixel windows typically yield the best overall performance, with CNN-19 achieving an AUC of 0.950, 0.982 and 0.969 for NIG, HKD, and IWT-MYG, respectively. Furthermore, we improve prediction reliability using oversampling and a random window-moving method. For instance, in the NIG region, the AUC of the oversampling CNN-19 is 0.983, while the downsampling AUC is 0.950). These techniques, less commonly applied in traditional machine learning approaches to landslide detection, help address the issue of data imbalance often seen in landslide datasets, where instances of landslides are far outnumbered by non-landslide occurrences. While challenges remain in enhancing the model’s generalization, this research makes significant progress in developing more robust and adaptable tools for landslide prediction, which are vital for ensuring environmental and societal resilience.

Pseudo-Labeling and Time-Series Data Analysis Model for Device Status Diagnostics in Smart Agriculture

This study proposes an automated data-labeling model that combines a pseudo-labeling algorithm with waveform segmentation based on Long Short-Term Memory (LSTM) to effectively label time-series data in smart agriculture. This model aims to address the inefficiency of manual labeling for large-scale data generated by agricultural systems, enhancing the performance and scalability of predictive models. Our proposed method leverages key features of time-series data to automatically generate labels for new data, thereby improving model accuracy and streamlining data processing. By automating the labeling process, we reduce dependence on manual labeling, which is often labor-intensive and prone to errors in large datasets. This approach enables the efficient preparation of labeled data for applications such as anomaly detection, pattern recognition, and predictive modeling in smart agriculture. Experimental results demonstrate that the automated labeling model achieves 89% accuracy in agricultural environments and reduces data processing time by 30%. Future research will focus on extending the model’s applicability to diverse agricultural settings, enhancing generalization performance, and improving real-time processing capabilities, thereby advancing intelligent and sustainable smart agriculture systems.

The Convergence of Artificial Intelligence and Human Marketing: A Framework for Enhanced Customer Insights and Personalization

This article examines the transformative integration of artificial intelligence and human expertise in modern marketing analytics, focusing on customer insight generation and personalization strategies. We present a comprehensive framework for human-AI collaboration in marketing operations through a mixed-methods approach combining quantitative analysis of customer data from 127 retail organizations and qualitative interviews with 34 marketing professionals. The findings reveal that organizations implementing AI-enhanced customer analytics in conjunction with human-driven creative strategy achieved a 47% improvement in customer engagement metrics and a 31% increase in conversion rates compared to traditional approaches. The article demonstrates that while AI excels at real-time pattern recognition and predictive modeling of customer behavior, human marketers provide crucial emotional intelligence and contextual interpretation that significantly enhance the effectiveness of personalization efforts. The article introduces the Dual Intelligence Marketing Framework (DIMF), which outlines optimal integration points between AI capabilities and human expertise throughout the customer journey. The framework addresses critical challenges in implementation, including technical infrastructure requirements, talent adaptation, and ethical considerations. This article contributes to both theoretical understanding and practical application of AI in marketing, offering actionable insights for organizations seeking to leverage the complementary strengths of artificial and human intelligence in their marketing operations.

Bridging Classical Methodologies in Salmonella Investigation with Modern Technologies: A Comprehensive Review.

Advancements in genomics and machine learning have significantly enhanced the study of Salmonella epidemiology. Whole-genome sequencing has revolutionized bacterial genomics, allowing for detailed analysis of genetic variation and aiding in outbreak investigations and source tracking. Short-read sequencing technologies, such as those provided by Illumina, have been instrumental in generating draft genomes that facilitate serotyping and the detection of antimicrobial resistance. Long-read sequencing technologies, including those from Pacific Biosciences and Oxford Nanopore Technologies, offer the potential for more complete genome assemblies and better insights into genetic diversity. In addition to these sequencing approaches, machine learning techniques like decision trees and random forests provide powerful tools for pattern recognition and predictive modeling. Importantly, the study of bacteriophages, which interact with Salmonella, offers additional layers of understanding. Phages can impact Salmonella population dynamics and evolution, and their integration into Salmonella genomics research holds promise for novel insights into pathogen control and epidemiology. This review revisits the history of Salmonella and its pathogenesis and highlights the integration of these modern methodologies in advancing our understanding of Salmonella.

INTELLIGENT VIDEO ANALYSIS TECHNOLOGY FOR AUTOMATIC FIRE CONTROL TARGET RECOGNITION BASED ON MACHINE LEARNING

Context. Target recognition is a priority in military affairs. This task is complicated by the fact that it is necessary to recognize moving objects, different terrain and landscape create obstacles for recognition. Combat actions can take place at different times of the day, accordingly, it is necessary to take into account the perspective of lighting and general lighting. It is necessary to detect the object in the video by segmenting the video frames, recognize and classify. Objective of the study is to develop a technology for the analysis of the development of a technology for recognizing targets in real time as a component of the fire control system, due to the use of artificial intelligence, YOLO and machine learning. Method. The article develops a video stream analysis technology for automatic target recognition of the fire control system based on machine learning. The paper proposes the development of a target recognition module as a component of the fire control system within the framework of the proposed information technology using artificial intelligence. The YOLOv8 pattern recognition model family was used to develop the target recognition module. The methods used during the study of the formed dataset. – Bounding Box: Noise – Up to 15% of pixels (limiting frame: adding salt and pepper noise to the image – up to 15% of pixels). – Bounding Box: Blur – Up to 2.5px (bounding box: adding Gaussian blur to the image – up to 2.5 pixels). – Cutout – 3 boxes with 10% size each (cut out a part of the image – 3 boxes of 10% size each). – Brightness Between –25% and +25% (changing the brightness of the image to increase the resistance of the model to changes in lighting and camera settings – from –25% to +25%). – Rotation – Between –15 and +15 (rotation of the image object – clockwise or counterclockwise by degrees from –15 to +15). – Flip – Horizontal (flip the image object horizontally). Results. The data is collected from open sources, in particular, from videos posted in open sources on the YouTube platform. The main task of data preprocessing is the classification of three classes of objects on video or in real time – APC, BMP and TANK. The dataset is formed using the Roboflow platform based on the labeling tools and subsequently the augmentation tools. The dataset consists of 1193 unique images – approximately equally for each class. The training was conducted using Google Colab resources. It took 100 epochs to train the model. Conclusions. Analysis is performed according to mAP50 (average precision as 0.85), mAP50-95 (0.6), precision (0.89) and recall (0.75). Big losses are due to the fact that the background was not taken into account during the research – training the module on the basis of confirmed data (images) of the background without technology

An overview of machine learning applications in elastic power systems

Abstract. With the development of economy and the access of renewable energy, the power system is facing new challenges. Machine learning provides a new way for the intelligent management of power system through big data analysis, pattern recognition and predictive modeling. This paper reviews the application of machine learning technology in elastic power system, including load forecasting, transient stability assessment, intelligent scheduling and fault diagnosis. Machine learning will better improve the stability, reliability and economic benefit of the system. Future research will focus on algorithm optimization, model adaptability improvement and integration with traditional power system knowledge to promote the development of power system to a higher level of intelligence and automation.

Transformer assisted by DSC and BiLSTM for bearing fault pattern recognition under strong noise interference

ABSTRACT Deep learning has greatly promoted intelligent fault diagnosis for bearings and has led to the development of many intelligent fault recognition methods based on deep neural networks. However, bearing fault signals in industrial applications are susceptible to noise interference, and intelligent recognition methods for bearing faults under strong noise interference are lacking. An intelligent fault pattern recognition model termed IFPR-MDL, which is based on a Transformer assisted by depthwise separable convolutional (DSC) and bidirectional long short-term memory (BiLSTM) networks, is designed to identify bearing faults under strong noise conditions. In this model, multiscale features are first extracted by a multiscale feature extraction module designed on the basis of DSC; then, contextual information is further extracted by a BiLSTM, and information from different scales is integrated by Transformer encoders. The DSC network facilitates model parameter reduction, the BiLSTM network has expertise in mining temporal features, and the Transformer network excels at capturing long-range feature correlations. This model fully utilises the advantages of these models and complements them to improve the anti-interference ability of bearing fault pattern recognition. Tests on several public datasets show that the proposed model has higher recognition accuracy, stronger generalisation ability, and noise resistance in strong noise situations.

Standardized maps – an emerging approach to leverage quantitative information in knee imaging

ObjectiveProperty maps, which capture spatial variations across the entire joint, are emerging as a powerful means for extracting and analyzing quantitative information from knee 3D imaging datasets, particularly from CT and MRI data. This perspective paper aims to discuss the processing pipelines used so far, as well as the results they have enabled with respect to osteoarthritis. DesignThe key methodological steps for obtaining property maps, including segmentation, property calculation, and standardization are presented and analysis methods are discussed. Representative studies are also examined to illustrate the state-of-the-art in this field. ResultsThree main processing pipelines have been used, with the segmentation, property calculation, and standardization steps occurring in different orders. Many methods have been successfully considered for ordering these steps, without any looking generally preferable to the others. Thanks to recent advances in segmentation and standardization techniques, routine processing of property maps appears conceivable in the near future. Maps have been analyzed for multiple purposes, including group comparisons, pattern recognition, and cross-property modelling. Mostly maps of cartilage thickness and composition, as well as maps of bone shape and mineral density have been reported. They revealed distinct patterns associated with osteoarthritis severity, achieved high diagnostic accuracy, and identified relationships among tissue properties. ConclusionsProperty maps represent a promising approach for leveraging the extensive information in imaging data. They are particularly interesting for standardizing complex spatial variations in tissue properties, enabling global analysis and modelling. Once challenging to obtain and interpret, current mapping methods are being improved to the point that property maps may well be in routine use in the near future.

ADCMDES: Design of an augmented cross-domain collaborative recommendation model using novel distance metric and ensemble stratification

Collaborative recommendation involves leveraging information from one entity to suggest attributes of other entities through pattern analysis. These models are invaluable for understanding data behaviour’s impact on related entities. Researchers propose various pattern recognition and correlation models, employing distance metrics like Jaccard, Cosine, etc., to estimate correlations between user queries and recommendation datasets. However, these models become less efficient as dataset size increases due to exponential correlation estimation delays. To enhance scalability while retaining recommendation quality, a new approach is introduced: ADCMDES. This novel augmented cross-domain collaborative model employs a hybrid distance metric and ensemble stratification for dataset pruning. It operates semi-supervisedly, requiring information about the entities being collaborated upon. This data is used to cluster similar collaborative entities, producing a condensed cluster with greater relevancy to user queries. Utilizing word2vec, records are transformed into features for an ensemble classification engine. The resultant model categorizes user input, directing it to the most relevant cluster. Entries within this cluster are ranked using a hybrid metric amalgamating 18 distance measures, enhancing correlation between input queries and recommendations. In testing, ADCMDES exhibited 15% better accuracy, 8% better precision, 9% better recall, and 3% lower RMSE compared to standard models across datasets. While some delay is introduced due to ensemble classification and augmented feature pooling, this doesn't considerably affect long-term recommendation performance and can be mitigated through parallel processing and redundancy reduction techniques.

Artificial Intelligence in Dry Eye Disease: A Narrative Review.

Dry eye disease (DED) is a multifactorial condition affecting millions worldwide, characterized by discomfort, visual disturbance, and potential damage to the ocular surface. The complexity of its diagnosis and management, driven by the diversity of symptoms and underlying causes, presents significant challenges to clinicians. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering potential solutions to these challenges through its data analysis, pattern recognition, and predictive modeling capabilities. This narrative review explores the role of AI in diagnosing, treating, and managing dry eye disease. AI-driven tools such as machine learning algorithms, imaging technologies, and diagnostic platforms are examined for their ability to enhance diagnostic accuracy, personalize treatment approaches, and optimize patient outcomes. Furthermore, the review addresses the limitations of AI technologies in ophthalmology, including the need for robust clinical validation, data privacy concerns, and the ethical considerations of integrating AI into clinical practice. The findings suggest that while AI holds promise for improving the care of patients with DED, ongoing research and development are crucial to realizing its full potential.

Characterization of volatile profiles and markers prediction of eleven popular edible boletes using SDE-GC–MS and FT-NIR combined with chemometric analysis

Wild edible boletes mushrooms are regarded as a delicacy in many countries and regions due to their rich nutritional contents and strong aromatic compounds. This study aimed to identify 445 samples of 11 boletes species collected from Yunnan and Sichuan provinces through molecular analysis. Using simultaneous distillation–extraction (SDE) combined with gas chromatography-mass spectrometry (GC–MS), 97 volatile compounds were identified. Chemometric methods were then applied to analyze the heterogeneity of these volatile compounds among the different species. The results showed that, 22 and 21 volatile compounds were selected using variable importance in projection (VIP > 1) and relative odor activity values (ROAV > 0.1), respectively. Partial least squares discrimnatint analysis (PLS-DA) was then employed to develop pattern recognition models for 11 species, which demonstrated strong identification performance. Furthermore, correlation heat maps, volcano plots, and Fisher linear discriminant analysis identified five volatile organic compounds, including methyl (9E)-9-octadecenoate, 2, 6-dimethylpyrazine, 1-decen-3-one, furfural, and methional as markers for distinguishing 11 boletes species. Ultimately, the rapid content prediction models of partial least squares regression (PLSR) were established by combining Fourier Transform Near-Infrared Spectroscopy (FT-NIR) with the concentrations of these five marker compounds. These findings provide a methodological strategy for the effective species identification of wild edible mushrooms and the rapid prediction of their characteristic aroma compounds.

A Method for the Pattern Recognition of Acoustic Emission Signals Using Blind Source Separation and a CNN for Online Corrosion Monitoring in Pipelines with Interference from Flow-Induced Noise.

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert-Huang transform (HHT) was applied to each source signal to obtain a joint time-frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.

Analysis of Wilms' tumor protein 1 specific TCR repertoire in AML patients uncovers higher diversity in patients in remission than in relapsed.

The Wilms' tumor protein 1 (WT1) is a well-known and prioritized tumor-associated antigen expressed in numerous solid and blood tumors. Its abundance and immunogenicity have led to the development of different WT1-specific immune therapies. The driving player in these therapies, the WT1-specific T-cell receptor (TCR) repertoire, has received much less attention. Importantly, T cells with high affinity against the WT1 self-antigen are normally eliminated after negative selection in the thymus and are thus rare in peripheral blood. Here, we developed computational models for the robust and fast identification of WT1-specific TCRs from TCR repertoire data. To this end, WT137-45 (WT1-37) and WT1126-134 (WT1-126)-specific T cells were isolated from WT1 peptide-stimulated blood of healthy individuals. The TCR repertoire from these WT1-specific T cells was sequenced and used to train a pattern recognition model for the identification of WT1-specific TCR patterns for the WT1-37 or WT1-126 epitopes. The resulting computational models were applied on an independent published dataset from acute myeloid leukemia (AML) patients, treated with hematopoietic stem cell transplantation, to track WT1-specific TCRs in silico. Several WT1-specific TCRs were found in AML patients. Subsequent clustering analysis of all repertoires indicated the presence of more diverse TCR patterns within the WT1-specific TCR repertoires of AML patients in complete remission in contrast to relapsing patients. We demonstrate the possibility of tracking WT1-37 and WT1-126-specific TCRs directly from TCR repertoire data using computational methods, eliminating the need for additional blood samples and experiments for the two studied WT1 epitopes.

Glioma Identification Based on Digital Multimodal Spectra Integrated With Deep Learning Feature Fusion Using a Miniature Raman Spectrometer.

The miniature fiber Raman spectroscopy detection technology can reflect the properties of biomolecules through spectral characteristics and has the advantages of noninvasiveness, real-time, safety, label-free operation, and potential for early cancer diagnosis. This technology holds promise for developing portable, low-cost, intraoperative tumor detection instruments. Glioma is one of the most common malignant tumors of the central nervous system with rapid growth and a short disease course. However, the considerable heterogeneity of the glioma sample leads to substantial intraclass variance in collected spectra, coupled with the miniature Raman spectrometer's low signal-to-noise ratio. These factors diminish the accuracy of the brain glioma recognition model. To address this issue, a glioma identification method based on digital multimodal spectra integrated with deep learning features fusion (DMS-DLFF) using the miniature Raman spectrometer is proposed. Different from existing multimodal tumor detection methods employing multiple spectral instruments, DMS-DLFF enhances tumor identification accuracy without increasing hardware costs. The method mathematically decomposes the original spectra to Raman and fluorescence spectra, so as to augment the biospectral information. Then, the deep learning method is used to extract the feature information of the two kinds of spectra, respectively, and the digital multimodal spectral fusion is realized at the feature level. Moreover, a two-layer pattern recognition model is constructed based on the ensemble strategy, amalgamating the strengths of diverse classifiers. Meanwhile, the bagging strategy is introduced to improve support vector machine algorithms, one of the basic classifiers. Compared with traditional methodologies, DMS-DLFF operates at both the feature level and decision level, employing high-information-density feature vectors to train ensemble classification models for increasing overall recognition accuracy. This study collected 260 Raman spectra of glioma and 151 Raman spectra of normal brain tissue. The accuracy, sensitivity, and specificity were 91.9%, 96.7%, and 80.8%, respectively. The proposed method outperforms traditional algorithms in brain glioma detection, which helps doctors formulate precise surgical plans and thereby improve patient prognosis.