Hierarchical Feature Extraction Research Articles

Two-Stream Modality-Based Deep Learning Approach for Enhanced Two-Person Human Interaction Recognition in Videos.

Human interaction recognition (HIR) between two people in videos is a critical field in computer vision and pattern recognition, aimed at identifying and understanding human interaction and actions for applications such as healthcare, surveillance, and human-computer interaction. Despite its significance, video-based HIR faces challenges in achieving satisfactory performance due to the complexity of human actions, variations in motion, different viewpoints, and environmental factors. In the study, we proposed a two-stream deep learning-based HIR system to address these challenges and improve the accuracy and reliability of HIR systems. In the process, two streams extract hierarchical features based on the skeleton and RGB information, respectively. In the first stream, we utilised YOLOv8-Pose for human pose extraction, then extracted features with three stacked LSM modules and enhanced them with a dense layer that is considered the final feature of the first stream. In the second stream, we utilised SAM on the input videos, and after filtering the Segment Anything Model (SAM) feature, we employed integrated LSTM and GRU to extract the long-range dependency feature and then enhanced them with a dense layer that was considered the final feature for the second stream module. Here, SAM was utilised for segmented mesh generation, and ImageNet was used for feature extraction from images or meshes, focusing on extracting relevant features from sequential image data. Moreover, we newly created a custom filter function to enhance computational efficiency and eliminate irrelevant keypoints and mesh components from the dataset. We concatenated the two stream features and produced the final feature that fed into the classification module. The extensive experiment with the two benchmark datasets of the proposed model achieved 96.56% and 96.16% accuracy, respectively. The high-performance accuracy of the proposed model proved its superiority.

CoSEF-DBP: Convolution scope expanding fusion network for identifying DNA-binding proteins through bilingual representations

Precisely recognizing DNA-binding proteins (DBPs) from sequences is crucial for a profound comprehension of the mechanisms governing protein-DNA interactions in various cellular processes. However, traditional in-silico methods for DBP identification encounter several challenges, such as time-consuming evolutionary modeling based on multiple sequence alignments, and intricate feature engineering associated with machine or deep learning approaches. In this paper, we introduce a novel end-to-end predictor for identifying DNA-binding proteins without intricate feature engineering, which innovatively enriches the semantics of amino acid sequences through the fusion of bilingual representations derived from distinct language models. We further design a convolution scope expanding (CoSE) module to widen the receptive fields of convolution kernels, thereby forming protein-level CoSE representation sequences. These representations are subsequently integrated via BiLSTM in conjunction with a simplified capsule network, enhancing the hierarchical feature extraction capability. Extensive experiments confirm that our model surpasses existing baselines across diverse benchmark datasets, notably achieving at least a 5.1% improvement in MCC value on the UniSwiss dataset.

MPCTrans: Multi-Perspective Cue-Aware Joint Relationship Representation for 3D Hand Pose Estimation via Swin Transformer.

The objective of 3D hand pose estimation (HPE) based on depth images is to accurately locate and predict keypoints of the hand. However, this task remains challenging because of the variations in hand appearance from different viewpoints and severe occlusions. To effectively address these challenges, this study introduces a novel approach, called the multi-perspective cue-aware joint relationship representation for 3D HPE via the Swin Transformer (MPCTrans, for short). This approach is designed to learn multi-perspective cues and essential information from hand depth images. To achieve this goal, three novel modules are proposed to utilize features from multiple virtual views of the hand, namely, the adaptive virtual multi-viewpoint (AVM), hierarchy feature estimation (HFE), and virtual viewpoint evaluation (VVE) modules. The AVM module adaptively adjusts the angles of the virtual viewpoint and learns the ideal virtual viewpoint to generate informative multiple virtual views. The HFE module estimates hand keypoints through hierarchical feature extraction. The VVE module evaluates virtual viewpoints by using chained high-level functions from the HFE module. Transformer is used as a backbone to extract the long-range semantic joint relationships in hand depth images. Extensive experiments demonstrate that the MPCTrans model achieves state-of-the-art performance on four challenging benchmark datasets.

Design a new scheme for image security using a deep learning technique of hierarchical parameters

Abstract With the continued exponential growth of digital images, concerns about the security and confidentiality of visual data have increased. In this session, a new developed approach was presented for image security and confidentiality by taking advantage of deep learning (DL) technology and producing data hierarchies. Due to the development taking place in the field of images and the large circulation of them through modern applications, it has become necessary to maintain their security. DL technology was used to encrypt and decrypt images, and based on hierarchical variables to complicate the encryption process. Convolutional neural networks are used in automatic learning to extract hierarchical features from an image, and to ensure adaptability, the model is trained on a variety of images. In order to encrypt the image, multi-layered hierarchical processes are used, and there are layers added during the work for complexity and to thwart attacks. Manipulating the layers of the neural network in a hierarchical manner to benefit from the outputs of the layers in feedback reflects the importance of the contributions here. Likewise, scattering the columns and rows of the image in a descending or ascending manner increases the efficiency of the contribution in this study. The use of hierarchical parameters facilitates encryption and decryption for authorized users. The evaluation of the research was conducted using established picture metrics and compared to pre-existing encryption techniques. The experimental findings substantiated the efficacy of the proposed approach in upholding image security, with the inclusion of hierarchical information further bolstering its ability to thwart attacks. Consequently, it emerges as a very promising strategy for ensuring image security. The proposed method is a significant advancement in creating an image security strategy using DL and a hierarchical variable creation process. The study provides a good and adaptable solution to evolving image security challenges in the digital age.

Distributed Denial of Service Attack Detection and Prevention Using Pipit Fox-Attentional Deep Learning in Software-Defined Networking

The Software-Defined Network (SDN) remains the innovative model that assists in satisfying the new application requirements of future networks. However, destructive attacks, particularly Distributed Denial of Service (DDoS) attacks, are aimed primarily at the SDN control panel, presenting a significant risk to network security. The traditional approaches for DDoS attack detection exhibit several limitations including interpretability challenges, overfitting problems, and false predictions. To mitigate these drawbacks, the research proposed a Pipit Flying Fox Optimized Deep Neural Network (PPF-DNN) and Intelligent Pipit Forage Optimized-Attentional Convolutional Neural Network (IPFO-ACNN) model. The channel attention is employed to enable the network to focus selectively on relevant information, improving the model’s sensitivity to subtle anomalies associated with DDoS attacks. By dynamically adjusting the attention weights across channels, the mechanism enhances the capability of the model to discriminate the normal network traffic and malicious patterns, which results in improved precision and reduced false positives. This approach harnesses the power of CNN to automatically extract hierarchical features from network data, enhancing the efficacy of the ACNN model in attack detection. IPFO and PPF algorithms integrate the unique strengths of bio-inspired algorithms, creating a synergistic approach for efficient model parameter tuning. In the case of Training Percentage (TP) 80, the accuracy, sensitivity, and specificity of the IPFO-ACNN model are evaluated as 98.87%, 99.24% and 97.91%, respectively. Similarly, the PPF-DNN model’s performance is assessed as 98.49%, 92.50% and 98.55%, which presents a promising avenue for bolstering network security infrastructure, ensuring more robust DDoS detection and mitigation capabilities in an increasingly complex cyber threat landscape.

MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features.

The increasing prevalence of skin diseases necessitates accurate and efficient diagnostic tools. This research introduces a novel skin disease classification model leveraging advanced deep learning techniques. The proposed architecture combines the MobileNet-V2 backbone, Squeeze-and-Excitation (SE) blocks, Atrous Spatial Pyramid Pooling (ASPP), and a Channel Attention Mechanism. The model was trained on four diverse datasets such as PH2 dataset, Skin Cancer MNIST: HAM10000 dataset, DermNet. dataset, and Skin Cancer ISIC dataset. Data preprocessing techniques, including image resizing, and normalization, played a crucial role in optimizing model performance. In this paper, the MobileNet-V2 backbone is implemented to extract hierarchical features from the preprocessed dermoscopic images. The multi-scale contextual information is fused by the ASPP model for generating a feature map. The attention mechanisms contributed significantly, enhancing the extraction ability of inter-channel relationships and multi-scale contextual information for enhancing the discriminative power of the features. Finally, the output feature map is converted into probability distribution through the softmax function. The proposed model outperformed several baseline models, including traditional machine learning approaches, emphasizing its superiority in skin disease classification with 98.6% overall accuracy. Its competitive performance with state-of-the-art methods positions it as a valuable tool for assisting dermatologists in early classification. The study also identified limitations and suggested avenues for future research, emphasizing the model's potential for practical implementation in the field of dermatology.

Analyzing convolutional neural networks and linear regression models for wind speed forecasting in sustainable energy provision

Wind energy's significance lies in its contribution to electricity production. Accurate wind speed prediction is crucial for precisely forecasting electricity generation, enhancing its overall importance. For the wind speed prediction two methods are developed in this paper that are the Linear Regression Method and Convolutional Neural Networks (CNNs). Linear regression makes a linear relation between input and output which are used to predict continuous outcomes. The second proposed leverage hierarchical feature extraction through convolutional layers, enabling them to excel at tasks like pattern recognition by capturing spatial patterns and hierarchies of information. Hyperparameter tuning is applied on both the models to minimize the errors. Then both the model’s performances are compared under different error matrices including MAE, RMSE, MSE and MAPE. The observations indicate that both the Linear Regression Model and CNN models are capable of forecasting wind speed However, the Linear Regression Model after Hyperparameter tuning performs better in terms of the calculated errors.

Accurate crowd counting for intelligent video surveillance systems

The paper presents a novel deep learning approach for crowd counting in intelligent video surveillance systems, addressing the growing need for accurate monitoring of public spaces in urban environments. The demand for precise crowd estimation arises from challenges related to security, public safety, and efficiency in urban areas, particularly during large public events. Existing crowd counting techniques, including feature-based object detection and regression-based methods, face limitations in high-density environments due to occlusions, lighting variations, and diverse human figures. To overcome these challenges, the authors propose a new deep encoder-decoder architecture based on VGG16, which incorporates hierarchical feature extraction with spatial and channel attention mechanisms. This architecture enhances the model’s ability to manage variations in crowd density, leveraging adaptive pooling and dilated convolutions to extract meaningful features from dense crowds. The model’s decoder is further refined to handle sparse and crowded scenes through separate density maps, improving its adaptability and accuracy. Evaluations of the proposed model on benchmark datasets, including Shanghai Tech and UCF CC 50, demonstrate superior performance over state-of-the-art methods, with significant improvements in mean absolute error and mean squared error metrics. The paper emphasizes the importance of addressing environmental variability and scale differences in crowded environments and shows that the proposed model is effective in both sparse and dense crowd conditions. This research contributes to the advancement of intelligent video surveillance systems by providing a more accurate and efficient method for crowd counting, with potential applications in public safety, transportation management, and urban planning.

A learning-based influence maximization framework for complex networks via K-core hierarchies and reinforcement learning

Influence maximization (IM) is a critical aspect of complex network analysis, as it holds considerable commercial value across various domains such as recommendation systems and viral marketing. Its objective is to identify a set of seed nodes that can initiate the most extensive cascade of influence within the network. However, IM encounters significant challenges due to its NP-hard nature of accurately identifying the optimal seed nodes and estimating the spread of influence. Although recent learning-based IM methods have shown promising results, there are still challenges related to scalability, efficiency, and avoiding influence overlap that need to be addressed. This paper proposes CoreQ, a novel reinforcement learning-based framework that leverages K-core hierarchies to address IM challenges and make advised decisions to learn the optimal policy to select seed nodes. CoreQ significantly enhances IM by reducing computational costs and effectively addressing the influence overlap problem, making it a scalable and efficient solution. CoreQ first utilizes K-core decomposition to analyze the network structure and extract hierarchical features that guide the process of seed node identification. Then, it employs a novel maximum likelihood-based approach to identify a sufficient number of candidate seed nodes from K-core hierarchies. Finally, it leverages Q-learning’s decision-making capabilities to optimize a policy for learning the most effective seed selection strategy. The experimental results demonstrate that CoreQ not only outperforms state-of-the-art IM methods in terms of influence spread under the weighted independent cascade model but also surpasses other competing learning-based IM methods in terms of time efficiency.

Detection and Classification of Banana Leaf Diseases: Systematic Literature Review

Bananas, a staple fruit globally, are essential for sustenance, employment, and income. However, diseases like Sigatoka, Bacterial Wilt, Bunchy Top, and Fusarium Wilt pose a threat to their cultivation, affecting both small-scale and large-scale production. This survey investigates methods for the early identification and classification of these banana leaf diseases using deep learning and machine learning techniques. A systematic review of 15 studies revealed that the majority of research concentrates on binary classification, which distinguishes healthy from diseased leaves. Common preprocessing steps include image resizing, color space conversion, and background removal to improve model accuracy. We utilize techniques such as ensemble approaches, support vector machines (SVM), random forests, K-means clustering, and convolutional neural networks (CNNs), with CNNs demonstrating superior performance, achieving accuracy rates ranging from 85% to 98.97%. CNNs excel in hierarchical feature extraction but require significant computational power. Traditional machine learning methods offer simplicity and resistance to overfitting but need careful parameter tuning. Advanced deep learning architectures, such as DenseNet and Inception V3, achieve high accuracy but with greater computational demands. Lightweight models like SqueezeNet balance performance and size, but ensemble methods, while improving generalization, add complexity. The choice of method depends on dataset characteristics, available computational resources, and desired trade-offs between performance and complexity. This study provides an overview of current research in banana leaf disease classification, discussing the strengths and limitations of various approaches and suggesting directions for future research to improve detection accuracy and robustness.

Beyond signatures and thresholds: Intelligent DDoS detection using Convolutional Neural Networks

Distributed Denial of Service (DDoS) attacks have evolved into sophisticated threats against digital infrastructure. This research investigates Convolutional Neural Networks (CNNs) for DDoS detection using the CIC-IDS2017 dataset. A CNN model was developed, leveraging convolutional and pooling layers for hierarchical feature extraction. Preprocessing ensured dataset consistency and generalization. The model achieved 99.978% test accuracy, demonstrating proficiency in recognizing DDoS patterns. Comparatively, a Deep Neural Network (DNN) benchmark obtained 99.9512% accuracy, indicating CNNs superiority. However, CNN training time was longer. While results highlight deep learning's potential against DDoS attacks, optimizations for real-time deployment warrant exploration. This empirical evaluation and comparative analysis enriches the discourse on utilizing machine learning for robust cybersecurity.

Machine learning based battery pack health prediction using real-world data

The complex operational conditions in real-world electric vehicles (EVs) contribute to the complexity of managing and maintaining battery packs. Adding to these challenges is the intricate task of modeling the inconsistent coupling among individual cells within these packs. This study addresses the ongoing challenges in modeling lithium-ion battery (LIB) cells within packs and estimating their state of health (SOH) for practical applications. This research proposed a PCA-CNN-Transformer method to model and predict the SOH model of real-world EV. Three main contributions are presented: a novel approach to defining an attenuation SOH model based on delivered energy, a methodology utilizing Principal Component Analysis (PCA) for cell modeling, and an SOH estimation model employing CNN-Transformer architecture. To address both pack and cell-level modeling, a hierarchical feature extraction approach is proposed. The health features extracted from both levels are assessed using grey relational analysis, showing a strong correlation with LIB SOH, exceeding 0.70. The proposed cell modeling method significantly reduces data size by 96%, enhancing computational efficiency. Furthermore, the integration of 1D-CNN in the SOH estimation model overcomes the limitations of the attention mechanism, achieving a MAE with 0.0406 and r-square of 0.9327, improved the original transformer network performance by 10.95%. This study also examines and discusses the performance of the informer and transformer models, elaborating why the informer model underperformed in this dataset.

NCNet: Deformable medical image registration network based on neighborhood cross-attention combined with multi-resolution constraints

Objective. Existing registration networks based on cross-attention design usually divide the image pairs to be registered into patches for input. The division and merging operations of a series of patches are difficult to maintain the topology of the deformation field and reduce the interpretability of the network. Therefore, our goal is to develop a new network architecture based on a cross-attention mechanism combined with a multi-resolution strategy to improve the accuracy and interpretability of medical image registration. Approach. We propose a new deformable image registration network NCNet based on neighborhood cross-attention combined with multi-resolution strategy. The network structure mainly consists of a multi-resolution feature encoder, a multi-head neighborhood cross-attention module and a registration decoder. The hierarchical feature extraction capability of our encoder is improved by introducing large kernel parallel convolution blocks; the cross-attention module based on neighborhood calculation is used to reduce the impact on the topology of the deformation field and double normalization is used to reduce its computational complexity. Main result. We performed atlas-based registration and inter-subject registration tasks on the public 3D brain magnetic resonance imaging datasets LPBA40 and IXI respectively. Compared with the popular VoxelMorph method, our method improves the average DSC value by 7.9% and 3.6% on LPBA40 and IXI. Compared with the popular TransMorph method, our method improves the average DSC value by 4.9% and 1.3% on LPBA40 and IXI. Significance. We proved the advantages of the neighborhood attention calculation method compared to the window attention calculation method based on partitioning patches, and analyzed the impact of the pyramid feature encoder and double normalization on network performance. This has made a valuable contribution to promoting the further development of medical image registration methods.

EC number prediction of protein sequences based on combination of hierarchical and global features.

The identification of enzyme functions plays a crucial role in understanding the mechanisms of biological activities and advancing the development of life sciences. However, existing enzyme EC number prediction methods did not fully utilize protein sequence information and still had shortcomings in identification accuracy. To address this issue, we proposed an EC number prediction network using hierarchical features and global features (ECPN-HFGF). This method first utilized residual networks to extract generic features from protein sequences, and then employed hierarchical feature extraction modules and global feature extraction modules to further extract hierarchical and global features of protein sequences. Subsequently, the prediction results of both feature types were combined, and a multitask learning framework was utilized to achieve accurate prediction of enzyme EC numbers. Experimental results indicated that the ECPN-HFGF method performed best in the task of predicting EC numbers for protein sequences, achieving macro F1 and micro F1 scores of 95.5% and 99.0%, respectively. The ECPN-HFGF method effectively combined hierarchical and global features of protein sequences, allowing for rapid and accurate EC number prediction. Compared to current commonly used methods, this method offers significantly higher prediction accuracy, providing an efficient approach for the advancement of enzymology research and enzyme engineering applications.

NHi-SEGA: n-Hierarchy SEmantic Guided Attention for few-shot learning

Humans excel at learning and recognizing objects, swiftly adapting to new concepts with just a few samples. However, current studies in computer vision on few-shot learning have not yet achieved human performance in integrating prior knowledge during the learning process. Humans utilize a hierarchical structure of object categories based on past experiences to facilitate learning and classification. Therefore, we propose a method named n-Hierarchy SEmantic Guided Attention (nHi-SEGA) that acquires abstract superclasses. This allows the model to associate with and pay attention to different levels of objects utilizing semantics and visual features embedded in the class hierarchy (e.g., house finch-bird-animal, goldfish-fish-animal, rose-flower-plant), resembling human cognition. We constructed an nHi-Tree using WordNet and Glove tools and devised two methods to extract hierarchical semantic features, which were then fused with visual features to improve sample feature prototypes.

Intelligent inversion of mesoscale eddy temperature anomaly profiles based on multi-source remote sensing data

Inversion of the underwater temperature anomaly of mesoscale eddies from sea surface remote sensing data plays an important role in understanding the three-dimensional structure of eddies. In this article, a neural network structure, named the eddy temperature anomaly inversion network (EddyTAINet), is proposed and established to invert the vertical temperature anomalies of eddies using 20-year (2002–2022) Argo profiles and multi-source remote sensing data in the northwestern Pacific Ocean. Vertical temperature anomaly profiles with 45 depth levels ranging from 10 to 1000 m are derived as the outputs of the inversion networks. The input feature sequences are extracted from the mesoscale eddy product based on sea level anomaly (SLA) data, and sea surface temperature (SST) data are also introduced into the proposed network structure to improve its accuracy. For the problem of inconsistent sizes of the 2D matrix SST data corresponding to mesoscale eddies, the spatial pyramid pooling (SPP) network module is introduced, which allows different sizes of SST data to be simply convolved and then subjected to hierarchical feature extraction to obtain fixed-size feature vectors. Validation and comparison of the network structure are performed and show the improved performance of EddyTAINet. The average root mean square error (RMSE) and mean absolute error (MAE) are 0.63 °C/0.66 °C and 0.48 °C/0.51 °C for cyclonic and anticyclonic eddies, respectively, which are more than 20% lower than the results of the network using the SLA-based data.

Research On Cultivating the Employment Concept of College Students with The Great Spirit of Building the Party - Based on A Data-Driven Approach

One of the most essential ways to see the worth of college students is via employment, which is also the main focus of student development programs for the Spirit of Building the Party. Some real-world issues with the conventional approach to college career counselling stem from the disconnection between students' education and the real world. For example, there is often a misalignment between students' social needs and their comprehensive skill sets and between students' job-seeking needs and their actual employment situations. There has to be extensive research into how to better assist college graduates in finding work that suits their skills and experience so that society can fulfil the practical demands of its growth in this area for the Spirit of Building the Party. Problems in the workplace have garnered a lot of attention because they threaten society's steady progress and the quality of life for its citizens. The nation now needs to tackle the issue of challenging employment for the Spirit of Building the Party. Institutions of higher learning have responded to societal and economic demands by emphasizing the development of students' social abilities and increasing their access to the job market. To provide a solid groundwork for creating employment assistance programs, this research suggests an analytical strategy for college students' jobs based on a data-driven approach for the Spirit of Building the Party. The proposed model is used to plan the supply and structure of skills from a talent supply standpoint. In contrast, the regression model forecasts university students' employment requirements for the Spirit of Building the Party. The data mining technique used in this work combines enhanced fuzzy hierarchical clustering and feature extraction based on semantic similarity correlation. The testing findings showed that the method performed better in categorizing data and could easily handle and analyze big-text datasets

Learning feature relationships in CNN model via relational embedding convolution layer

Establishing the relationships among hierarchical visual attributes of objects in the visual world is crucial for human cognition. The classic convolution neural network (CNN) can successfully extract hierarchical features but ignore the relationships among features, resulting in shortcomings compared to humans in areas like interpretability and domain generalization. Recently, algorithms have introduced feature relationships by external prior knowledge and special auxiliary modules, which have been proven to bring multiple improvements in many computer vision tasks. However, prior knowledge is often difficult to obtain, and auxiliary modules bring additional consumption of computing and storage resources, which limits the flexibility and practicality of the algorithm. In this paper, we aim to drive the CNN model to learn the relationships among hierarchical deep features without prior knowledge and consumption increasing, while enhancing the fundamental performance of some aspects. Firstly, the task of learning the relationships among hierarchical features in CNN is defined and three key problems related to this task are pointed out, including the quantitative metric of connection intensity, the threshold of useless connections, and the updating strategy of relation graph. Secondly, Relational Embedding Convolution (RE-Conv) layer is proposed for the representation of feature relationships in convolution layer, followed by a scheme called use & disuse strategy which aims to address the three problems of feature relation learning. Finally, the improvements brought by the proposed feature relation learning scheme have been demonstrated through numerous experiments, including interpretability, domain generalization, noise robustness, and inference efficiency. In particular, the proposed scheme outperforms many state-of-the-art methods in the domain generalization community and can be seamlessly integrated with existing methods for further improvement. Meanwhile, it maintains comparable precision to the original CNN model while reducing floating point operations (FLOPs) by approximately 50%.

Short-term wind power forecasting based on multi-scale receptive field-mixer and conditional mixture copula

Integrating renewable wind power (WP) into the grid exacerbates variability and challenges reliability. Establishing an effective forecasting system is crucial for risk avoidance, but achieving effective, interpretable, and accurate predictions remains an obstacle due to the high volatility and uncertainty of WP. This study proposes an innovative and trustworthy wind power forecasting system combined with point and interval predictions. Specifically, the adaptive multi-scale convolutional receptive field (AMSCF) combined with gated recurrent unit (GRU) for WP point prediction is proposed to capture the spatiotemporal dependencies of WP generation. This AMSCF method uses different size receptive fields, fuses them using channel dimension concatenation, operates pooling layer and adaptive fusion through field attention, which extracts hierarchical features for WP forecasting. Then conditional mixture Copula (Con-mCopula) function integrated with multiobjective optimizer is established for WP interval prediction, which reduces the WP interval prediction accuracy error as some information neglected. The wind power cluster dataset from Germany was selected for experiments. Based on the results, for both datasets, the designed prediction system can achieve better point and interval prediction performance resulting in a reduction of mean absolute error (MAE) by approximately 40 % compared to other existing components in the market. This study deconstructs the “Black Box” in deep network for energy forecasting through interpretability analysis, which accelerates the development of AI technology in WP forecasting system and provides decision-makers with trustworthy WP forecasting assistance.

Early detection of autism spectrum disorder via deep-learning application of fMRI and machine learning for ASD children identifications

This study unveils an advanced convolutional-neural-network (CNN) algorithm that was meticulously engineered to examine resting-state functional magnetic resonance imaging (fMRI) for early ASD detection in pediatric cohorts. The CNN architecture amalgamates convolutional, pooling, batch-normalization, dropout, and fully connected layers, optimized for high-dimensional data interpretation. Rigorous preprocessing yielded 22,176 two-dimensional echo planar samples from 126 subjects (56 ASD, 70 controls) who were sourced from the Autism Brain Imaging Data Exchange (ABIDE I) repository. The model, trained on 17,740 samples across 50 epochs, demonstrated unparalleled diagnostic metrics – accuracy of 99.39%, recall of 98.80%, precision of 99.85%, and an F1 score of 99.32% – and thereby eclipsed extant computational methodologies. Feature map analyses substantiated the model’s hierarchical feature extraction capabilities. This research elucidates a deep learning framework for computer-assisted ASD screening via fMRI, with transformative implications for early diagnosis and intervention. And, this study addresses the critical need for early detection and intervention in autism spectrum disorder (ASD) using machine learning. Specific therapies are needed for ASD, a neurodevelopmental disease that affects social interaction and communication. To find trends in ASD, our research uses a variety of early childhood screening tests as training sets for machine learning algorithms. The methodology that has been suggested utilizes methods of machine learning to compute the ASD spectrum, considering its many expressions. By using multidisciplinary methods and sophisticated screening instruments, we want to create an accurate system for early ASD detection. Algorithmic transparency, data protection, and ethical considerations are essential. This study seeks to build precise instruments for early ASD detection by promoting collaboration between specialists in neurodevelopment, psychology, and machine learning. A robust instrument that enhances the knowledge of medical practitioners is machine learning. Results show how innovation may transform early interventions and help people on the autistic spectrum achieve enhanced results.