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Lumbar Disease classification Using an Involutional Neural Based VGG Nets (INVGG)

IEEE ACCESS 2024年 12卷 27518-27529页

作者： Abuhayi, Biniyam Mulugeta Bezabh, Yohannes Agegnehu Ayalew, Aleka Melese Univ Gondar Dept Informat Technol Gondar 196 Ethiopia

Degenerative diseases of the lumbar spine, such as spondylolisthesis, disc degeneration, and lumbar spinal stenosis, are major contributors to global disability. Accurate classification of lumbar diseases is crucial for effective medical diagnosis. This paper introduces an innovative methodology for lumbar disease classification, addressing the limitations of traditional convolutional neural networks (CNNs). We propose a novel approach, InVGG, which combines involutional neural networks with the VGG architecture. Unlike traditional CNNs, InVGG utilizes involution kernels that are location-specific and channel-agnostic, enhancing its adaptability to varied visual patterns in medical images. Our study focuses on a four-class lumbar disease classification problem using sagittal T2 MRI images. The evaluation of InVGG is compared with traditional CNNs (VGG model) and machine learning algorithms, demonstrating superior performance in terms of accuracy, precision, recall, and AUC ROC values. InVGG achieves an impressive 96% accuracy on the testing set and 99% on the training set, showcasing its potential for accurate spinal lumbar disease classification. The reduced parameter count of InVGG compared to CNNs (VGG) makes it more resource-efficient, especially in scenarios with limited computational resources and datasets. The promising results position InVGG as a valuable tool for precise lumbar disease classification, with implications for improving patient care in resource-constrained scenarios.

关键词： Diseases Magnetic resonance imaging Computational modeling Solid modeling Medical diagnostic imaging Convolutional neural networks Neural networks classification algorithms Machine learning Spinal cord injury Lumbar disease involution neural network convolution neural network classification machine learning

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Attach-SwiNet: Multimodal Attachment Style classification Model Based on Non-Verbal Signals

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IEEE ACCESS 2024年 12卷 79151-79165页

作者： Maghfira, Tusty Nadia Basaruddin, T. Alfa Krisnadhi, Adila Redatin Retno Pudjiati, Sri Univ Indonesia Fac Comp Sci Depok 16424 Indonesia Univ Indonesia Fac Psychol Depok 16424 Indonesia

Attachment systems, which signify emotional bonds with significant others, play a crucial role in influencing self-development and social interactions. Research on adult attachment in psychology has predominantly utilized questionnaires and interviews, focusing mainly on romantic relationships and parent-child interactions during childhood. Although machine learning approaches in adult attachment research have begun to assess non-verbal behaviors objectively, the connection between these behaviors and attachment styles has not yet been fully explored. This paper presents Attach-SwiNet, a new multimodal model for classifying attachment styles in close relationships among young adults. Our model combines representations of emotions from non-verbal behaviors with subjective responses to categorize attachment styles. It utilizes pre-trained Swin Transformers to analyze emotional cues in facial expression videos and pre-trained ResNet50 to examine speech responses. By integrating the most effective emotion representations from both datasets with rating data from the Experiences in Close Relationships - Relationship Structures (ECR-RS), our model significantly enhances the accuracy of classifying attachment styles. Experimental results show that our approach improves performance over traditional unimodal behavioral data and subjective questionnaire responses by 1.13%.

关键词： Feature extraction Videos Hidden Markov models Interviews Analytical models Transformers Heart rate variability Emotion recognition Speech analysis classification algorithms Attachment emotion facial expression speech machine learning multimodal

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LoHi-WELD: A Novel Industrial Dataset for Weld Defect Detection and classification, a Deep Learning Study, and Future Perspectives

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IEEE ACCESS 2024年 12卷 77442-77453页

作者： Block, Sylvio Biasuz da Silva, Ricardo Dutra Lazzaretti, Andre Eugnio Minetto, Rodrigo Fed Univ Technol Parana UTFPR Dept Comp BR-3165 Curitiba Parana Brazil Fed Univ Technol UTFPR Dept Elect Engn BR-3165 Curitiba Parana Brazil

The automated inspection of weld beads is of great importance for many industrial processes. Failures may cause a loss of mechanical resistance of the weld bead and compromise the manufactured part. Several methods have been proposed in the literature to address this problem, and recently, methods based on deep learning have gained prominence in terms of performance and applicability. However, such methods require vast and reliable datasets for different real defects, which have yet to be available in recent literature. Hence, this paper presents LoHi-WELD, an original and public database to address the problem of weld defect detection and classification of four common types of defects - pores, deposits, discontinuities, and stains - with 3,022 real weld bead images manually annotated for visual inspection, composed by low and high-resolution images, acquired from a Metal Active Gas robotic welding industrial process. We also explore variations of a baseline deep architecture for the proposed dataset based on a YOLOv7 network and discuss several case analyses. We show that a lightweight architecture, ideal for industrial edge devices, can achieve up to 0.69 of mean average precision (mAP) considering a fine-grained defect classification and 0.77 mAP for a coarse classification. Open challenges are also presented, promoting future research and enabling robust solutions for industrial scenarios. The proposed dataset, architecture, and trained models are publicly available on https://***/SylvioBlock/LoHi-Weld.

关键词： Welding Testing Inspection Visualization Deep learning Metals X-rays classification algorithms Weld defect detection and classification deep learning gas metal arc welding (GMAW) metal active gas welding (MAG) weld bead industrial public dataset

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Streamlining Fault classification of Dissolved Gases in Transformer Using Data Synthesis and Dimension Reduction

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2024年第5期31卷 2451-2460页

作者： Kumar, Dhruba Dutta, Saurabh Illias, Hazlee Azil Natl Inst Technol Patna Dept Elect Engn Patna 800005 Bihar India Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia

This article addresses the challenge of limited data availability of dissolved gas analysis (DGA) in fault diagnosis of power transformers. While various classifiers have been employed for fault classification in the prior research, their accuracy depends heavily on balanced and synthetic training data. Although resampling techniques have been implemented to address that, their statistical validity remains unproven. Synthetic data generation is particularly crucial for imbalanced and scarce DGA fault samples. However, there is limited discussion on generating synthetic data for imbalanced datasets, which can negatively impact classifier training and machine learning (ML) models. This article introduces a novel approach for generating synthetic data to overcome the limited availability of fault data in power transformer diagnosis. The effectiveness of the generated data is ensured through ${z}$ -score normalization of the original gas concentration values. In addition, dimension reduction from 5-D to 2-D data is performed to simplify the data generation process. The proposed approach utilizes t-distributed stochastic neighbor embedding (t-SNE) as a superior dimension reduction technique, verified through reconstruction error and explained variance (Ev) metrics. The performance of the proposed generative adversarial network (GAN)-based approach is compared with four standard algorithms using multiple evaluation metrics. The results demonstrate that the GAN outperforms the other algorithms in generating synthetic data that closely resembles actual data. The proposed method achieves an accuracy of up to 98.10%, surpassing existing methods. This confirms the proposed GAN-based approach, in conjunction with t-SNE, effectively processes actual data for power transformer fault diagnosis, thus overcoming the limitations of limited data availability.

关键词： Dimensionality reduction Synthetic data Data visualization classification algorithms Training Measurement Monte Carlo methods classification data synthesis dimension reduction dissolved gas analysis (DGA) Duval pentagon Duval triangle

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Deep Learning-Based In-Band Interference Detection and classification

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IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 2024年第6期66卷 1958-1966页

作者： Andersson, Andreas Eliardsson, Patrik Axell, Erik Hagglund, Kristoffer Wiklundh, Kia Swedish Def Res Agcy FOI Dept Robust Radiocommun S-58330 Linkoping Sweden

Artificial intelligence has recently entered into the electromagnetic compatibility (EMC) area for design and analysis of EMC. Signal detection and classification are powerful tools for interference management to protect and ensure the availability of wireless communications. In this work, we study detection and classification of different types of interference signals, that interfere with a communication signal within the communication bandwidth. We propose two classification algorithms based on deep convolutional neural networks: a joint model based on one single neural network that distinguishes between all different types of interference, and a composite model based on multiple neural networks that each detects a distinct type of interference. The proposed algorithms are evaluated by Monte Carlo simulations. The composite model is shown to perform well in terms of high probability of correct classification and low probability of false classification. The joint model, however, tends to favor the pulsed interference signal and therefore yields too much false classifications.

关键词： Bit error rate classification algorithms convolutional neural networks (CNNs) deep learning multiple signal classification phase shift keying (PSK) radio frequency interference Bit error rate classification algorithms convolutional neural networks (CNNs) deep learning multiple signal classification phase shift keying (PSK) radio frequency interference

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Enhancement of Stress classification Using Web Camera-Based Imaging Photoplethysmography With a Frame Alignment Method

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IEEE ACCESS 2024年 12卷 122313-122327页

作者： Hendryani, Atika Rizkinia, Mia Gunawan, Dadang Univ Indonesia Dept Elect Engn Depok 16425 West Java Indonesia

Stress is a mental health problem that is hazardous if not recognized early. A promising approach for noninvasive stress detection involves leveraging camera technology;however, there are notable challenges involved in this method, particularly regarding signal accuracy and quality, which are primarily caused by motion artifacts. This study aims to improve stress classification accuracy by using web camera-based imaging photoplethysmography signals. We introduce a frame alignment method that can significantly correct noise to mitigate motion artifacts. We use heart rate (HR) and HR variability metrics to monitor stress and classify the measurements into stress and no-stress conditions. The sample in this study comprises students who were stressed using the arithmetic task method. Our findings showed that the mean HR and low-frequency component increased under stressful conditions while the high-frequency component decreased. The primary contribution of this study involves refining the accuracy and reducing the time required for stress classification time. Notably, the proposed approach markedly improves the accuracy, with substantial noise correction in the resultant signal. We evaluate various classification methods, including logistic regression, support vector machine, na & iuml;ve Bayes, and random forest. Our results demonstrate that logistic regression achieved the highest accuracy of 98.5%, with a receiver operating curve value of 98%, an F1 score of 98.4%, and a computation time of 4.7 s. Overall, the proposed methodology holds considerable promise for camera-based noninvasive human stress assessment.

关键词： Human factors Heart rate variability Accuracy Cameras Stress measurement Feature extraction Biomedical imaging Logistics classification algorithms heart rate (HR) HR estimation feature extraction logistic

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Belief Renyi Divergence of Divergence and its Application in Time Series classification

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 3670-3681页

作者： Zhang, Lang Xiao, Fuyuan Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China

Time series data contains the amount of information to reflect the development process and state of a subject. Especially, the complexity is a valuable factor to illustrate the feature of the time series. However, it is still an open issue to measure the complexity of sophisticated time series due to its uncertainty. In this study, based on the belief Renyi divergence, a novel time series complexity measurement algorithm, called belief Renyi divergence of divergence (BReDOD), is proposed. Specifically, the BReDOD algorithm takes the boundaries of time series value into account. What is more, according to the Dempster-Shafer (D-S) evidence theory, the time series is converted to the basic probability assignments (BPAs) and it measures the divergence of a divergence sequence. Then, the secondary divergence of the time series is figured out to represent the complexity of the time series. In addition, the BReDOD algorithm is applied to sets of cardiac inter-beat interval time series, which shows the superiority of the proposed method over classical machine learning methods and recent well-known works.

关键词： Complexity theory Time series analysis Time measurement Evidence theory Feature extraction Measurement uncertainty classification algorithms D-S evidence theory uncertainty belief Renyi divergence belief divergence of divergence complexity time series analysis classification

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A Multi-Section Hierarchical Deep Neural Network Model for Time Series classification: Applied to Wearable Sensor-Based Human Activity Recognition

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IEEE ACCESS 2024年 12卷 137851-137869页

作者： Ghorrati, Zahra Esmaeili, Ahmad Matson, Eric T. Purdue Univ Dept Comp & Informat Technol W Lafayette IN 47907 USA Wichita State Univ Coll Engn Sch Comp Wichita KS 67260 USA

Time Series classification, is one of the very challenging areas in pattern recognition problems. As the volume of time series data increases, a multitude of TSC algorithms have been developed. Notably, only a limited number of these approaches have ventured into the realm of Deep Neural Networks to tackle this task. This paper presents a new hierarchical deep neural network architecture with a multi-section learning mechanism for the classification of multi-output time series datasets. The hierarchical structure of the proposed approach uses separate segments to detect output classes of different granular levels. The presented model does not suffer from heavy computations during training found in commonly used in other deep learning models such as convolutional neural network. Additionally, our suggested method handles data dimension reduction automatically in the hidden layers of different network sections and does not require separate pre-processing units for this purpose. To demonstrate the effectiveness of the proposed model, we have utilized it in the wearable sensor-based Human Activity Recognition problem and evaluated its performance on several benchmark datasets in TSC and HAR, such as PAMAP2 and Opportunity, with several different configurations. The empirical results on the benchmark datasets show that our proposed model outperforms several other deep learning-based solutions based on measures such as F-1-Score, accuracy, and ROC curve.

关键词： Human activity recognition Feature extraction Time series analysis Hidden Markov models Computational modeling Principal component analysis Convolutional neural networks Deep learning classification algorithms Wearable sensors time series classification hierarchical classification human activity recognition wearable sensors

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Timeseries Fault classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning

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IEEE ACCESS 2024年 12卷 93426-93449页

作者： Nawaz, Rab Albalawi, Hani A. Bukhari, Syed Basit Ali Mehmood, Khawaja Khalid Sajid, Muhammad Mirpur Univ Sci & Technol Dept Elect Engn Mirpur 10250 Jammu & Kashmir Pakistan Univ Tabuk Fac Engn Dept Elect Engn Tabuk 47913 Saudi Arabia Univ Tabuk Renewable Energy & Energy Efficiency Ctr REEEC Tabuk 71491 Saudi Arabia Univ Azad Jammu & Kashmir Dept EE Muzaffarabad 13100 Jammu & Kashmir Pakistan Eindhoven Univ Technol Dept Elect Engn EE NL-5612 AZ Eindhoven Netherlands

This paper presents a supervised machine learning approach using eight popular classifiers for fault classification in power transmission lines. The classification of faults, indicated by the behavior of the electrical signals associated with them, plays a pivotal role in maintaining the reliability, stability, and security of electrical grids. However, most of the previous studies on fault analysis in power systems relied on proprietary data and lack of standardized benchmarks, hindering the comparison of algorithms and making performance more erratic. Moreover, the nonavailability of labeled data for all types of faults is the most problematic. This paper proposes to perform fault classification on a data set created from a real-time Simulink model to standardize performance and advance research in this area. A new strategy for non-intensive feature extraction is applied using relatively simpler techniques, eliminating computationally expensive techniques such as wavelets. Feature selection through dimensionality reduction techniques is used to improve model performance and more efficient use of computational resources. The performance of the learning algorithms (e.g. Decision Tree, Random Forest, etc.) has been analyzed with various preprocessing techniques (e.g. data scaling, transformation, etc.) and tuning of parameters, focusing on the accuracy and computational time ( $T_{c}$ ), for performance generalization and efficiency. Performing specific operations on data in sequence of steps provided flexibility and adaptability in processing the data, making it easy to train, evaluate, and validate the learning algorithms. The results demonstrated that the proposed scheme can be effectively used for fault classification with high accuracy and significant reductions in $T_{c}$ under various operating conditions. The study also determined the best estimator for each classifier when building and training the classifier models, offering a variety of options. Logistic R

关键词： Circuit faults Feature extraction classification algorithms Support vector machines Discrete wavelet transforms Power transmission lines Training Fault diagnosis Performance evaluation fault classification feature extraction feature selection performance standardization dimensionality reductions

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One-Step Joint Learning of Self-Supervised Spectral Clustering With Anchor Graph and Fuzzy Clustering for Land Cover classification

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 11178-11193页

作者： Wu, Chengmao Zhang, Jiale Xian Univ Posts & Telecommun Xian 710121 Peoples R China

Spectral clustering, as an algorithm based on graph theory and spectral theory, has shown excellent performance in classification tasks of hyperspectral images in recent years. Although better results have been achieved, some challenges still exist. The inclusion of a priori information can increase the performance of spectral clustering algorithms;however, in practice, it is often unable to meet the demand for a priori information;at the same time, the two-stage structure of spectral clustering leads to bias and information loss during its clustering process. To address the above problems, we propose a one-step joint learning of self-supervised spectral clustering with anchor graph and fuzzy clustering. In this algorithm, we first use the BKHK method to generate anchor points that vary with the size of the dataset to enhance the quality of the similarity graph. After that, we use the idea of fuzzy clustering to optimize the membership and simultaneously learn the local and global structures of the data. Moreover, clustering information is integrated with pairwise constraint information in a unified update framework, which achieves mutual learning of clustering results and constraints under soft partitioning. After comparing with the existing algorithms, the proposed algorithm has better performance in classifying numerical datasets, remote sensing images, and hyperspectral datasets.

关键词： Clustering algorithms Hyperspectral imaging Partitioning algorithms classification algorithms Unsupervised learning Training Optimization Anchor graph fuzzy clustering hyperspectral image self-supervised spectral clustering

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