Merging Features Research Articles

The Yearning to Be Everything to Everyone: Examining the Homogenization of Social Media Platforms in the Context of Convergence

Social media platforms are continually adding new features and enhancing existing ones to attract users and extend their screen time. These platforms constantly innovate to capture users' attention and gain an edge in the competitive landscape of social media. This study focuses on identifying the similarities between features of popular social media platforms and examines whether these similarities lead to homogenization. Specifically, presenting the chronological development of these features over time is crucial for understanding the historical development and evolution of social media. The framework of this research is based on the concept of convergence. The convergence concept is used to assess whether there are commonalities in the tools, interfaces, and interaction modes offered by the platforms to their users. In this context, the study investigates how the features provided by social media platforms become similar over time and how user experiences become homogenized. The 'walkthrough method' was employed to analyze the interfaces of the selected platforms. This method involves a step-by-step examination of user experiences on the platforms to understand how features are presented and how they are utilized by users. Data were collected through notes and recordings made by researchers while using the platforms. The results indicate a significant similarity and convergence in the features offered by popular social media platforms. For instance, features such as story sharing, live streaming, filter usage, and the explore tab have become widespread across different platforms. Platforms that consider users' habits and preferences offer familiar and easily accessible experiences through these similar features. Consequently, it can be said that social media platforms create a form of homogenization in user experiences by providing similar features over time. This phenomenon offers important insights into the evolution and future development of social media. Additionally, the study suggests that the concept of convergence not only encompasses the merging of features from different technologies but also indicates a tendency for similar platforms to converge internally.

Hierarchical Degradation-aware Network for Full-Reference Image Quality Assessment

Full-Reference Image Quality Assessment (FR-IQA) algorithms excel in evaluating perceptual distortions by comparing reference and distorted images. However, as the severity and quantity of distortions in datasets increase, existing FR-IQA methods struggle to capture complex nonlinear perceptual features. This limitation results in reduced adaptability and inaccurate assessments for images with more severe or multiple distortions. Recognizing the importance of understanding image degradation mechanisms, we propose a novel hierarchical degradation-aware network (HDaN) method. First, by exploring the degradation mechanisms from the reference image to the distorted image, our degradation network matches distortions that align more closely with the human visual system (HVS). Next, we design a convertor to project the matched features into multiple spaces, creating multidimensional feature representations that more comprehensively capture the complexity of image distortions rather than being confined to a single feature space. Then, we calculate a similarity matrix between the distorted and mapped features, selecting the most similar (top-k) features for merging. Finally, a regression network maps the merged features to quality scores, providing the final quality prediction. The experimental results demonstrate that our proposed HDaN method outperforms traditional deep learning-based FR-IQA methods. Specifically, the HDaN shows higher PLCC and SROCC metrics on benchmark datasets, significantly improving over existing methods. Moreover, the method exhibits better adaptability to images with varying degrees and types of distortions, thereby greatly enhancing the overall performance of IQA.

An Improved Deep Learning Framework for Multimodal Medical Data Analysis

Lung disease is one of the leading causes of death worldwide. This emphasizes the need for early diagnosis in order to provide appropriate treatment and save lives. Physicians typically require information about patients’ clinical symptoms, various laboratory and pathology tests, along with chest X-rays to confirm the diagnosis of lung disease. In this study, we present a transformer-based multimodal deep learning approach that incorporates imaging and clinical data for effective lung disease diagnosis on a new multimodal medical dataset. The proposed method employs a cross-attention transformer module to merge features from the heterogeneous modalities. Then unified fused features are used for disease classification. The experiments were performed and evaluated on several classification metrics to illustrate the performance of the proposed approach. The study’s results revealed that the proposed method achieved an accuracy of 95% in terms of accurate classification of tuberculosis and outperformed other traditional fusion methods on multimodal tuberculosis data used in this study.

A modality separation approach for facial sketch synthesis

AbstractThe technology for face‐to‐sketch synthesis transforms optical face images into a sketch‐style format. However, traditional style losses are insufficient to discern the modal differences between optical and sketch domain images, leading to unclear images. At the same time, generated images lack clarity due to traditional approaches' disregard for high‐frequency texture. To address these issues, a modality separation approach for facial sketch synthesis is proposed. First, a modality separation structure is proposed, using a quicksort algorithm to merge features of optical and sketch images as target modality (positive samples), ensuring the generated images' feature distribution matches real sketches. By controlling the Euclidean distance between generated images (anchors) and both target and filtered modality (positive and negative samples), irrelevant information is effectively filtered out. Next, an edge‐promoting module feeds processed blurry sketch images into the discriminator to enhance robustness. Lastly, a detail optimization module uses Laplacian filtering to extract high‐frequency texture from optical face images for local enhancement. Experimental validation on CUHK, AR, and XM2VTS datasets shows that this method outperforms mainstream sketch face synthesis methods in terms of Fréchet inception distance and learned perceptual image patch similarity, producing more realistic and natural images with richer texture details.

3D printing single‐stroke path planning, local reinforcement and performance testing of MBB beams and cantilever beams

Abstract3D printing of continuous fiber reinforced thermoplastic composites (CFRTPCs) has been applied for the molding of composite complex structures due to the advantages of flexibility and one‐step forming. In this study, feature points simplification, merging and rearrangement were accomplished for Messerschmitt‐Bölkow‐Blohm (MBB) beam and cantilever beam to generate single‐stroke print paths for the specimens. For the significant stress areas in the finite element analysis results of these structures, a local reinforcement method using orthogonal layups was proposed, which was achieved by raising and lowering the print head and adjusting the fiber volume fraction during the printing. After experimental validation and failure modes analysis, the path planning significantly improved the peak load and structural stiffness of the specimens, and the local reinforcement further enhanced the equivalent specific strength and specific stiffness. This study provides a single‐stroke 3D printing strategy for complex structure molding in conjunction with parameter adjustments.Highlights A single‐stroke path planning was developed for MBB beams and cantilever beams, including simplification, merging and rearrangement methods for feature points. A local reinforcement method was proposed, which was achieved by raising and lowering the print head and adjusting the fiber volume fraction during the printing process. The effect of different print paths on specimen properties was investigated.

Non-traumatic brachial plexopathy identification from routine MRIs: Retrospective studies with deep learning networks

PurposeThis study aims to seek an optimized deep learning model for differentiating non-traumatic brachial plexopathy from routine MRI scans. Materials and methodsThis retrospective study collected patients through the electronic medical records (EMR) or pathological reports at Mayo Clinic and underwent BP MRI from January 2002 to December 2022. Using sagittal T1, fluid-sensitive and post-gadolinium images, a radiology panel selected BP’s region of interest (ROI) to form 3 dimensional volumes for this study. We designed six deep learning schemes to conduct BP abnormality differentiation across three MRI sequences. Utilizing five prestigious deep learning networks as the backbone, we trained and validated these models by nested five-fold cross-validation schemes. Furthermore, we defined a ’method score’ derived from the radar charts as a quantitative indicator as the guidance of the preference of the best model. ResultsThis study selected 196 patients from initial 267 candidates. A total of 256 BP MRI series were compiled from them, comprising 123 normal and 133 abnormal series. The abnormal series included 4 sub-categories, et al. breast cancer (22.5 %), lymphoma (27.1 %), inflammatory conditions (33.1 %) and others (17.2 %). The best-performing model was produced by feature merging mode with triple MRI joint strategy (AUC, 92.2 %; accuracy, 89.5 %) exceeding the multiple channel merging mode (AUC, 89.6 %; accuracy, 89.0 %), solo channel volume mode (AUC, 89.2 %; accuracy, 86.7 %) and the remaining. Evaluated by method score (maximum 2.37), the feature merging mode with backbone of VGG16 yielded the highest score of 1.75 under the triple MRI joint strategy. ConclusionDeployment of deep learning models across sagittal T1, fluid-sensitive and post-gadolinium MRI sequences demonstrated great potential for brachial plexopathy diagnosis. Our findings indicate that utilizing feature merging mode and multiple MRI joint strategy may offer satisfied deep learning model for BP abnormalities than solo-sequence analysis.

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.

Image Inpainting via Correlated Multi-Resolution Feature Projection.

With the advancement in image editing applications, image inpainting is gaining more attention due to its ability to recover corrupted images efficiently. Also, the existing methods for image inpainting either use two-stage coarse-to-fine architectures or single-stage architectures with a deeper network. On the other hand, shallow network architectures lack the quality of results and the methods with remarkable inpainting quality have high complexity in terms of number of parameters or average run time. Despite the improvement in the inpainting quality, these methods still lack the correlated local and global information. In this work, we propose a single-stage multi-resolution generator architecture for image inpainting with moderate complexity and superior outcomes. Here, a multi-kernel non-local (MKNL) attention block is proposed to merge the feature maps from all the resolutions. Further, a feature projection block is proposed to project features of MKNL to respective decoder for effective reconstruction of image. Also, a valid feature fusion block is proposed to merge encoder skip connection features at valid region and respective decoder features at hole region. This ensures that there will not be any redundant feature merging while reconstruction of image. Effectiveness of the proposed architecture is verified on CelebA-HQ Liu, et al. 2015, Karras et al. 2017, and Places2 Zhou et al. 2018 datasets corrupted with publicly available NVIDIA mask dataset Liu et al. 2018. The detailed ablation study, extensive result analysis, and application of object removal prove the robustness of the proposed method over existing state-of-the-art methods for image inpainting.

Multi-view human pose and shape estimation via mesh-aligned voxel interpolation

Although multi-view human pose and shape regression methods have information from other views for complementing and correcting, existing ones still have its own drawback of not fully taking advantage of multi-view setup. Thus they are far from efficiently aligning and merging features in different views. In order to tackle these problems, we propose a multi-view framework where features from all views are well aligned and merged through multi-view voxel aggregation with inverse projection. Our framework highlights three major characteristics. Firstly, we use a multi-view volumetric aggregation module for better prediction by exploiting various information in different-scale feature maps. Secondly, in our framework, instead of using all voxels, a mesh-aligned voxel selection module is proposed to make effective prediction by eliminating redundant background voxels. Lastly, the framework further improves the performance of human body parametric modeling by adopting a dual-branch strategy, where one branch for parametric human model prediction and the other for 3D keypoints prediction. Their mutual influence is critical to the improvement for both tasks. Additionally, we find the scarcity of datasets also hinders the development of multi-view methods, so we propose a approach for creating occlusion datasets specifically for multi-view occlusion case. Experimental results verify the effectiveness of the proposed framework on two benchmarks, Human3.6M and MPI-INF-3DHP.

Retina Blood Vessels Segmentation and Classification with the Multi-featured Approach.

Segmenting retinal blood vessels poses a significant challenge due to the irregularities inherent in small vessels. The complexity arises from the intricate task of effectively merging features at multiple levels, coupled with potential spatial information loss during successive down-sampling steps. This particularly affects the identification of small and faintly contrasting vessels. To address these challenges, we present a model tailored for automated arterial and venous (A/V) classification, complementing blood vessel segmentation. This paper presents an advanced methodology for segmenting and classifying retinal vessels using a series of sophisticated pre-processing and feature extraction techniques. The ensemble filter approach, incorporating Bilateral and Laplacian edge detectors, enhances image contrast and preserves edges. The proposed algorithm further refines the image by generating an orientation map. During the vessel extraction step, a complete convolution network processes the input image to create a detailed vessel map, enhanced by attention operations that improve modeling perception and resilience. The encoder extracts semantic features, while the Attention Module refines blood vessel depiction, resulting in highly accurate segmentation outcomes. The model was verified using the STARE dataset, which includes 400 images; the DRIVE dataset with 40 images; the HRF dataset with 45 images; and the INSPIRE-AVR dataset containing 40 images. The proposed model demonstrated superior performance across all datasets, achieving an accuracy of 97.5% on the DRIVE dataset, 99.25% on the STARE dataset, 98.33% on the INSPIREAVR dataset, and 98.67% on the HRF dataset. These results highlight the method's effectiveness in accurately segmenting and classifying retinal vessels.

Efficient forest fire detection based on an improved YOLO model

A forest fire is a natural disaster characterized by rapid spread, difficulty in extinguishing, and widespread destruction, which requires an efficient response. Existing detection methods fail to balance global and local fire features, resulting in the false detection of small or hidden fires. In this paper, we propose a novel detection technique based on an improved YOLO v5 model to enhance the visual representation of forest fires and retain more information about global interactions. We add a plug-and-play global attention mechanism to improve the efficiency of neck and backbone feature extraction of the YOLO v5 model. Then, a re-parameterized convolutional module is designed, and a decoupled detection head is used to accelerate the convergence speed. Finally, a weighted bi-directional feature pyramid network (BiFPN) is introduced to merge feature information for local information processing. In the evaluation, we use the complete intersection over union (CIoU) loss function to optimize the multi-task loss for different kinds of forest fires. Experiments show that the precision, recall, and mean average precision are increased by 4.2%, 3.8%, and 4.6%, respectively, compared with the classic YOLO v5 model. In particular, the mAP@0.5:0.95 is 2.2% higher than the other detection methods, while meeting the requirements of real-time detection.

CFNet: Cross-scale fusion network for medical image segmentation

Learning multi-scale feature representations is essential for medical image segmentation. Most existing frameworks are based on U-shape architecture in which the high-resolution representation is recovered progressively by connecting different levels of the decoder with the low-resolution representation from the encoder. However, intrinsic defects in complementary feature fusion inhibit the U-shape from aggregating efficient global and discriminative features along object boundaries. While Transformer can help model the global features, their computation complexity limits the application in real-time medical scenarios. To address these issues, we propose a Cross-scale Fusion Network (CFNet), combining a cross-scale attention module and pyramidal module to fuse multi-stage/global context information. Specifically, we first utilize large kernel convolution to design the basic building block capable of extracting global and local information. Then, we propose a Bidirectional Atrous Spatial Pyramid Pooling (BiASPP), which employs atrous convolution in the bidirectional paths to capture various shapes and sizes of brain tumors. Furthermore, we introduce a cross-stage attention mechanism to reduce redundant information when merging features from two stages with different semantics. Extensive evaluation was performed on five medical image segmentation datasets: a 3D volumetric dataset, namely Brats benchmarks. CFNet-L achieves 85.74% and 90.98% dice score for Enhanced Tumor and Whole Tumor on Brats2018, respectively. Furthermore, our largest model CFNet-L outperformed other methods on 2D medical image. It achieved 71.95%, 82.79%, and 80.79% SE for STARE, DRIVE, and CHASEDB1, respectively. The code will be available at https://github.com/aminabenabid/CFNet

WNet: A dual‐encoded multi‐human parsing network

AbstractIn recent years, multi‐human parsing has become a focal point in research, yet prevailing methods often rely on intermediate stages and lacking pixel‐level analysis. Moreover, their high computational demands limit real‐world efficiency. To address these challenges and enable real‐time performance, low‐latency end‐to‐end network is proposed. This approach leverages vision transformer and convolutional neural network in a dual‐encoded network, featuring a lightweight Transformer‐based vision encoder) and a convolution encoder based on Darknet. This combination adeptly captures long‐range dependencies and spatial relationships. Incorporating a fuse block enables the seamless merging of features from the encoders. Residual connections in the decoder design amplify information flow. Experimental validation on crowd instance‐level human parsing and look into person datasets showcases the WNet's effectiveness, achieving high‐speed multi‐human parsing at 26.7 frames per second. Ablation studies further underscore WNet's capabilities, emphasizing its efficiency and accuracy in complex multi‐human parsing tasks.

Hydraulic system fault diagnosis decoupling method based on 2D time-series modeling and self-attention fusion

Hydraulic systems play a pivotal and extensive role in mechanics and energy. However, the performance of intelligent fault diagnosis models for multiple components is often hindered by the complexity, variability, strong hermeticity, intricate structures, and fault concealment in real-world conditions. This study proposes a new approach for hydraulic fault diagnosis that leverages 2D temporal modeling and attention mechanisms for decoupling compound faults and extracting features from multisample rate sensor data. Initially, to address the issue of oversampling in some high-frequency sensors within the dataset, variable frequency data sampling is employed during the data preprocessing stage to resample redundant data. Subsequently, two-dimensional convolution simultaneously captures both the instantaneous and long-term features of the sensor signals for the coupling signals of hydraulic system sensors. Lastly, to address the challenge of feature fusion with multisample rate sensor data, where direct merging of features through maximum or average pooling might dilute crucial information, a feature fusion and decoupling method based on a probabilistic sparse self-attention mechanism is designed, avoiding the issue of long-tail distribution in multisample rate sensor data. Experimental validation showed that the proposed model can effectively utilize samples to achieve accurate fault decoupling and classification for different components, achieving a diagnostic accuracy exceeding 97% and demonstrating robust performance in hydraulic system fault diagnosis under noise conditions.

Dual-modal non-local context guided multi-stage fusion for indoor RGB-D semantic segmentation

Complementarily fusing RGB and depth images while effectively suppressing task-irrelevant noise is crucial for achieving accurate indoor RGB-D semantic segmentation. In this paper, we propose a novel deep model that leverages dual-modal non-local context to guide the aggregation of complementary features and the suppression of noise at multiple stages. Specifically, we introduce a dual-modal non-local context encoding (DNCE) module to learn global representations for each modality at each stage, which are then utilized to facilitate cross-modal complementary clue aggregation (CCA). Subsequently, the enhanced features from both modalities are merged together. Additionally, we propose a semantic guided feature rectification (SGFR) module to exploit rich semantic clues in the top-level merged features for suppressing noise in the lower-stage merged features. Both the DNCE-CCA and the SGFR modules provide dual-modal global views that are essential for effective RGB-D fusion. Experimental results on two public indoor datasets, NYU Depth V2 and SUN-RGBD, demonstrate that our proposed method outperforms state-of-the-art models of similar complexity.

EFE-CNA Net: An Approach for Effective Image Deblurring Using an Edge-Sensitive Focusing Encoder

Deep learning-based image deblurring techniques have made great advancements, improving both processing speed and deblurring efficacy. However, existing methods still face challenges when dealing with complex blur types and the semantic understanding of images. The segment anything model (SAM), a versatile deep learning model that accurately and efficiently segments objects in images, facilitates various tasks in computer vision. This article leverages SAM’s proficiency in capturing object edges and enhancing image content comprehension to improve image deblurring. We introduce the edge-sensitive focusing encoder (EFE) module, which utilizes masks generated by the SAM framework and re-weights the masked portion following SAM segmentation by detecting its features and high-frequency information. The EFE module uses the masks to locate the position of the blur in an image while identifying the intensity of the blur, allowing the model to focus more accurately on specific features. Masks with greater high-frequency information are assigned higher weights, prompting the network to prioritize them during processing. Based on the EFE module, we develop a deblurring network called the edge-sensitive focusing encoder-based convolution–normalization and attention network (EFE-CNA Net), which utilizes the EFE module to enhance the deblurring process, employs an image-mask decoder to merge features from both the image and the mask from the EFE module, and incorporates the CNA Net as its base network. This design enables the model to focus on distinct features at various locations, enhancing its learning process through the guidance provided by the EFE module and the blurred images. Testing results on the RealBlur and REDS datasets demonstrate the effectiveness of the EFE-CNA Net, achieving peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics of 28.77, 0.902 (RealBlur-J), 36.40, 0.956 (RealBlur-R), 31.45, and 0.919 (REDS).

Research on Product Advertising Design Combining Feature Extraction Technology and Web3D Technology

This work, built on the Unity3D development platform, presents a way for merging feature extraction technology and Web3D technology into advertising design to effectively address the issues of poor efficiency and distortion in the field. Using the candidate text layout generating technique of visual salience, we first build the vector function set based on the three main colors, then we produce the visual communication partition model of advertising design. Next, the number of feature parameters of the shape advertising design is obtained via the establishment of coding coefficient constraint features and the use of an upgraded neural network technique to extract local feature parameter information about the product. Finally, the product design model is brought to life using Web3D technology to boost advertising design's productivity and accuracy. The experiments show that this method not only results in a high rate of correct product identification but also offers a fresh viewpoint on the visual communication of product advertising design by merging the two disciplines.

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

This study constructs a model for the rapid identification of the origins of edible sunflower (Helianthus) using Kernel Extreme Learning Machine (KELM) with multi-source information fusion technology. Near-infrared spectroscopy (NIRS) and nuclear magnetic resonance spectroscopy (NMRS) were utilized to analyze 180 sunflower samples from the Xinjiang, Heilongjiang, and Inner Mongolia regions. Initially, the identification models for the origin of sunflowers using NIR and NMR data were compared between two algorithms: the Extreme Learning Machine (ELM) and KELM, combined with various spectral preprocessing methods. The experiment found that the NIR spectral model preprocessed with standard normal variate (SNV) using the KELM algorithm was the most accurate, achieving accuracies of 98.7% in the training set and 97.2% in the test set. The spin-echo NMR spectral model preprocessed with non-local means (NLMs) using the KELM algorithm was the second best, with accuracies of 98.4% in the training set and 96.4% in the test set. To further improve the accuracy of the identification models, innovative sunflower origin identification models were developed based on data layer fusion and feature layer fusion using NIRS and NMRS. In the data layer fusion model, the KELM algorithm model was optimal, achieving a test set accuracy and F1 score of 98.2% and 98.18%, respectively, an improvement of 1.0% over the best single data source model. In the feature layer fusion model, four types of feature-layer information-fusion identification models were established using two feature extraction algorithms, Competitive Adaptive Reweighted Sampling (CARS) and Variable Importance Projection (VIP), combined with joint feature and simple merging feature strategies. The CARS-KELM algorithm combined with the joint feature method was found to be the best, achieving 100% accuracy in both the training and test sets, an improvement of 2.8% over the best single data source model. Identifying the origin of edible sunflower using NIRS and NMRS is demonstrated as feasible by the results. The best single-spectrum sunflower origin identification model was achieved using the KELM algorithm with SNV preprocessing. The feature layer fusion method combining NIRS and NMRS data is suitable for handling the task of sunflower origin identification. This method significantly improves the recognition accuracy of the model compared to a single model, achieving fast and accurate origin identification of edible sunflowers. The research results provide a new method for rapid identification of sunflower origin.

Developing Standards for Educational Datasets by School Level: A Framework for Sustainable K-12 Education

As artificial intelligence (AI) and data science education gain importance in K-12 curricula, there is a growing need for well-designed sustainable educational datasets tailored to different school levels. Sustainable datasets should be reusable, adaptable, and accessible to support long-term AI and data science education goals. However, research on the systematic categorization of difficulty levels in educational datasets is limited. This study aims to address this gap by developing a framework for sustainable educational dataset standards based on learners’ developmental stages and data preprocessing requirements. The proposed framework consists of five levels: Level 1 (grades 1–4), where data preprocessing is unnecessary; Level 2 (grades 5–6), involving basic data cleaning; Level 3 (grades 7–9), requiring attribute manipulation; Level 4 (grades 10–12), involving feature merging and advanced preprocessing; and Level 5 (teachers/adults), requiring the entire data science process. An expert validity survey was conducted with 22 elementary and secondary school teachers holding advanced degrees in AI education. The results showed high validity for Levels 1–4 but relatively lower validity for Level 5, suggesting the need for separate training and resources for teachers. Based on the CVR results and expert feedback, the standards for Educational Datasets were revised, particularly for Stage 5, which targets teachers and adult learners. The findings highlight the importance of expert validation, step-by-step experiences, and an interdisciplinary approach in developing educational datasets. This study contributes to the theoretical understanding of educational datasets and provides practical implications for teachers, students, educational institutions, and policymakers in implementing effective and sustainable AI and data science education in K-12 settings, ultimately fostering a more sustainable future.

High-accuracy models for iris recognition with merging features

Due to advancements in computer science and technology, impersonation has become more common. Today, biometrics technology is widely used in various aspects of people's lives. Iris recognition, known for its high accuracy and speed, is a significant and challenging field of study. As a result, iris recognition technology and biometric systems are utilized for security in numerous applications, including human-computer interaction and surveillance systems. It is crucial to develop advanced models to combat impersonation crimes. This study proposes sophisticated artificial intelligence models with high accuracy and speed to eliminate these crimes. The models use linear discriminant analysis (LDA) for feature extraction and mutual information (MI), along with analysis of variance (ANOVA) for feature selection. Two iris classification systems were developed: one using LDA as an input for the OneR machine learning algorithm and another innovative hybrid model based on a One Dimensional Convolutional Neural Network (HM-1DCNN). The MMU database was employed, achieving a performance measure of 94.387% accuracy for the OneR model. Additionally, the HM-1DCNN model achieved 99.9% accuracy by integrating LDA with MI and ANOVA. Comparisons with previous studies show that the HM-1DCNN model performs exceptionally well, with at least 1.69% higher accuracy and lower processing time.