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1
Huanhuan Chen, Peter Tino, Ali Rodan, and Xin Yao. "Learning in the Model Space for Cognitive Fault Diagnosis." IEEE Transactions on Neural Networks and Learning Systems 25, no. 1 (2014): 124–36. http://dx.doi.org/10.1109/tnnls.2013.2256797.
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Song, Mengmeng, Zicheng Xiong, Jianhua Zhong, Shungen Xiao, and Jihua Ren. "Fault Diagnosis of Planetary Gearbox Based on Dynamic Simulation and Partial Transfer Learning." Biomimetics 8, no. 4 (2023): 361. http://dx.doi.org/10.3390/biomimetics8040361.
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To address the problem of insufficient real-world data on planetary gearboxes, which makes it difficult to diagnose faults using deep learning methods, it is possible to obtain sufficient simulation fault data through dynamic simulation models and then reduce the difference between simulation data and real data using transfer learning methods, thereby applying diagnostic knowledge from simulation data to real planetary gearboxes. However, the label space of real data may be a subset of the label space of simulation data. In this case, existing transfer learning methods are susceptible to interference from outlier label spaces in simulation data, resulting in mismatching. To address this issue, this paper introduces multiple domain classifiers and a weighted learning scheme on the basis of existing domain adversarial transfer learning methods to evaluate the transferability of simulation data and adaptively measure their contribution to label predictor and domain classifiers, filter the interference of unrelated categories of simulation data, and achieve accurate matching of real data. Finally, partial transfer experiments are conducted to verify the effectiveness of the proposed method, and the experimental results show that the diagnostic accuracy of this method is higher than existing transfer learning methods.
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Wang, Guang Bin, Xue Jun Li, Zhi Cheng He, and Y. Q. Kong. "Fault Diagnosis Method Based on Supervised Manifold Learning and SVM." Advanced Materials Research 216 (March 2011): 223–27. http://dx.doi.org/10.4028/www.scientific.net/amr.216.223.
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In order to better identify the fault of bearing,one new fualt diagnosis method based on supervised Linear local tangent space alignment (SLLTSA) and support vector machine (SVM) is proposed..In this methd, the supervised learning is embedded into the linear local tangent space alignment algorithm,making full use of experience category information for fault feature extraction, and then using linear transformation matrix to fast process the new monitoring data, finally distinguishing fault status of the incremental data by nonlinear SVM algorithm. The experiment result for roller bearing fault diagnosis shows that SLLTSA-SVM method has better diagnosis effect than related unsupervised methods.
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Hu, Xiujian, Yicheng Xie, Hui Zhao, Guanglei Sheng, Khin Wee Lai, and Yuanpeng Zhang. "Electroencephalography (EEG) based epilepsy diagnosis via multiple feature space fusion using shared hidden space-driven multi-view learning." PeerJ Computer Science 10 (March 7, 2024): e1874. http://dx.doi.org/10.7717/peerj-cs.1874.
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Epilepsy is a chronic, non-communicable disease caused by paroxysmal abnormal synchronized electrical activity of brain neurons, and is one of the most common neurological diseases worldwide. Electroencephalography (EEG) is currently a crucial tool for epilepsy diagnosis. With the development of artificial intelligence, multi-view learning-based EEG analysis has become an important method for automatic epilepsy recognition because EEG contains difficult types of features such as time-frequency features, frequency-domain features and time-domain features. However, current multi-view learning still faces some challenges, such as the difference between samples of the same class from different views is greater than the difference between samples of different classes from the same view. In view of this, in this study, we propose a shared hidden space-driven multi-view learning algorithm. The algorithm uses kernel density estimation to construct a shared hidden space and combines the shared hidden space with the original space to obtain an expanded space for multi-view learning. By constructing the expanded space and utilizing the information of both the shared hidden space and the original space for learning, the relevant information of samples within and across views can thereby be fully utilized. Experimental results on a dataset of epilepsy provided by the University of Bonn show that the proposed algorithm has promising performance, with an average classification accuracy value of 0.9787, which achieves at least 4% improvement compared to single-view methods.
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Zhu, He, Ren Togo, Takahiro Ogawa, and Miki Haseyama. "Diversity Learning Based on Multi-Latent Space for Medical Image Visual Question Generation." Sensors 23, no. 3 (2023): 1057. http://dx.doi.org/10.3390/s23031057.
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Auxiliary clinical diagnosis has been researched to solve unevenly and insufficiently distributed clinical resources. However, auxiliary diagnosis is still dominated by human physicians, and how to make intelligent systems more involved in the diagnosis process is gradually becoming a concern. An interactive automated clinical diagnosis with a question-answering system and a question generation system can capture a patient’s conditions from multiple perspectives with less physician involvement by asking different questions to drive and guide the diagnosis. This clinical diagnosis process requires diverse information to evaluate a patient from different perspectives to obtain an accurate diagnosis. Recently proposed medical question generation systems have not considered diversity. Thus, we propose a diversity learning-based visual question generation model using a multi-latent space to generate informative question sets from medical images. The proposed method generates various questions by embedding visual and language information in different latent spaces, whose diversity is trained by our newly proposed loss. We have also added control over the categories of generated questions, making the generated questions directional. Furthermore, we use a new metric named similarity to accurately evaluate the proposed model’s performance. The experimental results on the Slake and VQA-RAD datasets demonstrate that the proposed method can generate questions with diverse information. Our model works with an answering model for interactive automated clinical diagnosis and generates datasets to replace the process of annotation that incurs huge labor costs.
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Liao, Hui, Pengfei Xie, Sier Deng, and Hengdi Wang. "Intelligent Early Fault Diagnosis of Space Flywheel Rotor System." Sensors 23, no. 19 (2023): 8198. http://dx.doi.org/10.3390/s23198198.
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Three frequently encountered problems—a variety of fault types, data with insufficient labels, and missing fault types—are the common challenges in the early fault diagnosis of space flywheel rotor systems. Focusing on the above issues, this paper proposes an intelligent early fault diagnosis method based on the multi-channel convolutional neural network with hierarchical branch and similarity clustering (HB-SC-MCCNN). First, a similarity clustering (SC) method is integrated into the parameter-shared dual MCCNN architecture to set up as the basic structural block. The hierarchical branch model and additional loss are then added to SC-MCCNN to form a hierarchical branch network, which simplifies the problem of fault multi-classification into binary classification with multi-steps. Based on the self-learning characteristics of the proposed model, the unlabeled data and the missing fault types in the training set are re-labeled to realize the re-training of the network. The results of the experiments for comparing the abilities between the proposed method and several advanced deep learning models confirm that on the established early fault dataset of the space flywheel rotor system, the proposed method successfully achieves the hierarchical diagnosis and presents stronger competitiveness in the case of insufficient labeled data and missing fault types at the same time.
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Gao, Fei, and Jiangang Lv. "Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/7939607.
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Single-Stage Extreme Learning Machine (SS-ELM) is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM) has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.
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Jinhua, Wang, Ma Xuehua, and Cao Jie. "Fault diagnosis method of Bayesian network based on association rules." Transactions of the Institute of Measurement and Control 47, no. 9 (2025): 1906–14. https://doi.org/10.1177/01423312241269710.
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When the number of samples is large, the scale of the Bayesian network (BN) structure search space increases exponentially with the number of nodes, resulting in a sharp increase in the difficulty of learning the BN structure. Aiming at this problem, this paper proposes a fault diagnosis model construction method combining association rules and a BN network. The Euclidean distance under the Symbolic Aggregation Approximation (SAX) algorithm is utilized to compute and average the distance between the standard and faulty samples and filter the candidate nodes by the average value, which in turn reduces the search sample space. The method of combining Association Rules algorithm with traditional BN structure learning results is used to solve the problem of wrong edges in structure learning. Finally, the maximum likelihood estimation method is used for parameter learning to complete the construction of the diagnostic network. The experimental results show that the running time of the Bayesian Network based on the Association Rules (AR-BN) model proposed in this paper is short and that the Hamming distance with the original structure is small, so this model can effectively reduce the search space and solve the problem of wrong edges, and it also has a good performance in fault diagnosis.
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Badr, Eman. "Images in Space and Time." ACM Computing Surveys 54, no. 6 (2021): 1–38. http://dx.doi.org/10.1145/3453657.
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Medical imaging diagnosis is mostly subjective, as it depends on medical experts. Hence, the service provided is limited by expert opinion variations and image complexity as well. However, with the increasing advancements in deep learning field, techniques are developed to help in the diagnosis and risk assessment processes. In this article, we survey different types of images in healthcare. A review of the concept and research methodology of Radiomics will highlight the potentials of integrated diagnostics. Convolutional neural networks can play an important role in next generations of automated imaging biomarker extraction and big data analytics systems. Examples are provided of what is already feasible today and also describe additional technological components required for successful clinical implementation.
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Emam Atteia, Ghada. "Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis." Computer Systems Science and Engineering 45, no. 1 (2023): 361–76. http://dx.doi.org/10.32604/csse.2023.029597.
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Yu, Mengqin, Yi Shan Lee, and Junghui Chen. "Fault diagnosis-based SDG transfer for zero-sample fault symptom." International Journal of Advances in Intelligent Informatics 9, no. 3 (2023): 551. http://dx.doi.org/10.26555/ijain.v9i3.1434.
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The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a new unseen fault class appears in the test set, but there is no training sample of this fault in the training set. Therefore, studying the unseen cause-effect problem of fault symptoms is extremely challenging. As various faults often occur in a chemical plant, it is necessary to perform fault causal-effect diagnosis to find the root cause of the fault. However, only some fault causal-effect data are always available to construct a reliable causal-effect diagnosis model. Another worst thing is that measurement noise often contaminates the collected data. The above problems are very common in industrial operations. However, past-developed data-driven approaches rarely include causal-effect relationships between variables, particularly in the zero-shot of causal-effect relationships. This would cause incorrect inference of seen faults and make it impossible to predict unseen faults. This study effectively combines zero-shot learning, conditional variational autoencoders (CVAE), and the signed directed graph (SDG) to solve the above problems. Specifically, the learning approach that determines the cause-effect of all the faults using SDG with physics knowledge to obtain the fault description. SDG is used to determine the attributes of the seen and unseen faults. Instead of the seen fault label space, attributes can easily create an unseen fault space from a seen fault space. After having the corresponding attribute spaces of the failure cause, some failure causes are learned in advance by a CVAE model from the available fault data. The advantage of the CVAE is that process variables are mapped into the latent space for dimension reduction and measurement noise deduction; the latent data can more accurately represent the actual behavior of the process. Then, with the extended space spanned by unseen attributes, the migration capabilities can predict the unseen causes of failure and infer the causes of the unseen failures. Finally, the feasibility of the proposed method is verified by the data collected from chemical reaction processes.
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Zhang, Liping. "Learning Factors Knowledge Tracing Model Based on Dynamic Cognitive Diagnosis." Mathematical Problems in Engineering 2021 (October 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/8777160.
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This paper mainly studies the influence of dynamic cognitive diagnosis on personalized learning. Considering the influence of knowledge correlation factors and human brain memory factors on learning activities, a knowledge tracing model integrating learning factors is proposed. Firstly, based on the exercise-knowledge association information, the model maps learners and exercises to the knowledge space with clear meaning. Then, the evolution process of learners’ knowledge learning is quantitatively modeled in the knowledge space by integrating the classical learning curve and forgetting curve theory of pedagogy. On the other hand, considering the influence of topic semantics in the learning process, a knowledge tracing model integrating topic semantics is proposed in this paper. Firstly, the model designs a dynamic enhanced memory network to store the common information of knowledge and describes the learners’ dynamic mastery of knowledge. Secondly, the depth representation method of exercise resources is proposed to mine the text personality information and integrate it into the process of learners’ knowledge change modeling. Through a large number of experiments on exercise records, it is verified that the proposed model has accurate prediction performance and knowledge tracing interpretability.
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Costa, Luís, Miguel F. Gago, Darya Yelshyna, et al. "Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer’s Disease." Computational Intelligence and Neuroscience 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/3891253.
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The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer’s disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support Vector Machines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.
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Fernandes, Kelwin, Davide Chicco, Jaime S. Cardoso, and Jessica Fernandes. "Supervised deep learning embeddings for the prediction of cervical cancer diagnosis." PeerJ Computer Science 4 (May 14, 2018): e154. http://dx.doi.org/10.7717/peerj-cs.154.
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Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.
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Wang, Guang Bin, Xian Qiong Zhao, and Yu Hui He. "Fault Diagnosis Method Based on Supervised Incremental Local Tangent Space Alignment and SVM." Applied Mechanics and Materials 34-35 (October 2010): 1233–37. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1233.
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To enhance the effect of fault diagnosis, a new fualt diagnosis method based on supervised incremental local tangent space alignment (SILTSA) and support vector machine (SVM) is proposed. The supervised learning approach is embedded into the incremental local tangent space alignment algorithm, to realize fault feature extraction and new data processing for equipment fault signal, and then correctly classify the faults by non-linear support vector machines. The experiment result for roller bearing fault diagnosis shows that SILTSA-SVM method has better diagnosis effect to related methods
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Sreekumar, K. T., C. Santhosh Kumar, and K. I. Ramachandran. "Deep Discriminative Feature Learning and Feature Space Transformation for Scalable Machine Fault Diagnosis." IEEE Access 12 (2024): 107944–58. http://dx.doi.org/10.1109/access.2024.3438099.
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Chen, Huanhuan, Peter Tiňo, and Xin Yao. "Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space." Computers & Chemical Engineering 67 (August 2014): 33–42. http://dx.doi.org/10.1016/j.compchemeng.2014.03.015.
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Zhu, Ying, Yameng Li, Yuan Cui, et al. "A Knowledge-Enhanced Hierarchical Reinforcement Learning-Based Dialogue System for Automatic Disease Diagnosis." Electronics 12, no. 24 (2023): 4896. http://dx.doi.org/10.3390/electronics12244896.
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Deep Reinforcement Learning is a key technology for the diagnosis-oriented medical dialogue system, determining the type of disease according to the patient’s utterances. The existing dialogue models for disease diagnosis cannot achieve good performance due to the large number of symptoms and diseases. In this paper, we propose a knowledge-enhanced hierarchical reinforcement learning model for strategy learning in the medical dialogue system for disease diagnosis. Our hierarchical strategy alleviates the problem of a large action space in reinforcement learning. In addition, the knowledge enhancement module integrates a learnable disease–symptom relationship matrix and medical knowledge graph into the hierarchical strategy for higher diagnosis success rate. Our proposed model has been proved to be effective on a medical dialogue dataset for automatic disease diagnosis.
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Yang, Zheng, Fei Chen, Binbin Xu, Boquan Ma, Zege Qu, and Xin Zhou. "Metric Learning-Guided Semi-Supervised Path-Interaction Fault Diagnosis Method for Extremely Limited Labeled Samples under Variable Working Conditions." Sensors 23, no. 15 (2023): 6951. http://dx.doi.org/10.3390/s23156951.
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The lack of labeled data and variable working conditions brings challenges to the application of intelligent fault diagnosis. Given this, extracting labeled information and learning distribution-invariant representation provides a feasible and promising way. Enlightened by metric learning and semi-supervised architecture, a triplet-guided path-interaction ladder network (Tri-CLAN) is proposed based on the aspects of algorithm structure and feature space. An encoder–decoder structure with path interaction is built to utilize the unlabeled data with fewer parameters, and the network structure is simplified by CNN and an element additive combination activation function. Metric learning is introduced to the feature space of the established algorithm structure, which enables the mining of hard samples from extremely limited labeled data and the learning of working condition-independent representations. The generalization and applicability of Tri-CLAN are proved by experiments, and the contribution of the algorithm structure and the metric learning in the feature space are discussed.
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Smith-Miller, Cheryl A., Diane C. Berry, and Cass T. Miller. "The Space Between: Transformative Learning and Type 2 Diabetes Self-Management." Hispanic Health Care International 18, no. 2 (2019): 85–97. http://dx.doi.org/10.1177/1540415319888435.
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Introduction: Immigrant populations experience higher type 2 diabetes mellitus (T2DM) prevalence rates and worse health outcomes secondary to T2DM than native-born populations. But as the largest immigrant population in the United States, the experience of T2DM diagnosis and self-management among Spanish-speaking, limited English-language proficient Latinx immigrants remains largely unexamined. This study used semistructured interviews to explore these phenomena among a cohort of 30 recent Latinx immigrants. Method: All aspects of data collection were conducted in Spanish. Quantitative and qualitative data were collected. Data analysis included descriptive statistical procedures. Qualitative data analysis was conducted using a grounded theory approach. Results: Patterns in the data analysis of 30 interviews identified accepting T2DM as a common transitional process that required significant changes in individuals’ self-perspective and ways of being. Accepting T2DM was identified by the participants as a precursor to treatment initiation. And while for most participants this transition period was brief, for some it took months to years. Distinct transitional stages were identified, categorized, and considered within the context of several theoretical orientations and were observed to align with those in transformative learning. Conclusion: Understanding differing responses and processing of a T2DM diagnosis could be leveraged to better support patients’ acceptance and transition into treatment.
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Zhuang, Gewei, Zhen Gu, He Qing, Jingyue Zhang, Honghong Zhang, and Lei Zhou. "Research on abnormal diagnosis model of electric power measurement based on small sample learning." Journal of Physics: Conference Series 2781, no. 1 (2024): 012008. http://dx.doi.org/10.1088/1742-6596/2781/1/012008.
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Abstract For a long time, abnormal metering of electricity meters has caused huge economic losses to power grid companies. Abnormal diagnosis of power metering is an important means to ensure the normal operation of electricity meters and power automation operation and maintenance systems and is a hot topic of research for power workers. This article proposes a known measurement anomaly diagnosis model based on small sample learning to address the problem of insufficient labeled samples in power measurement anomaly diagnosis. The embedded network maps samples from the original sample space to the embedded space adjusts the embedded network structure, and improves the loss function. The experimental results show that the improved classification network has a higher recognition accuracy for known anomalies than the original network and other small sample learning models.
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Li, DZ, X. Zheng, QW Xie, and QB Jin. "A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 232, no. 6 (2017): 635–49. http://dx.doi.org/10.1177/0954408917733130.
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A novel fault diagnosis approach based on a combination of discrete wavelet transform, phase space reconstruction, singular value decomposition, and improved extreme learning machine is presented in rolling bearing fault identification and classification. The proposed method provides proper solutions for improving the accuracy of faults classification. To achieve this goal, initial signals are divided into sub-band wavelet coefficients using discrete wavelet transform. Then, each of sub-band is mapped into three-dimensional space using the phase space reconstruction method to completely describe characteristics in the high dimension. Thereafter, singular values are calculated by singular value decomposition method, which demonstrate crucial variances in original vibration signal. Lastly, an improved extreme learning machine is adopted as a classifier for fault classification. The proposed method is applied to the rolling bearing fault diagnosis with non-linear and non-stationary characteristics. Based on outputs of the improved extreme learning machine, the working condition and fault location could be determined accurately and quickly. Achieved results, compared with other schemes, show that the proposed scheme in this article can be regarded as an effective and reliable method for rolling bearing fault diagnosis.
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Wang, Guang Bin, Y. I. Liu, and X. Q. Zhao. "Fault Diagnosis of Rolling Bearings Based on LLE_KFDA." Materials Science Forum 626-627 (August 2009): 529–34. http://dx.doi.org/10.4028/www.scientific.net/msf.626-627.529.
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Locally linear embedding (LLE) algorithm is an unsupervised technique recently proposed for nonlinear dimension reduction. In this paper,LLE manifold learning algorithm is introduced into the field of equipment fault diagnosis firstly, a method of the fault diagnosis based on LLE_KFDA is proposed. By LLE algorithm, original sample data is directly mapped to its’ intrinsical dimension space,which data still keep primary nonlinear form. then via kernel fisher discriminant analysis(KFDA), the characteristics data in intrinsical dimension space are mapped into knernel high-dimensional linear space,and then different fault data are discriminated based on a criterion of between-class and insid-class deviatione ratio maximum. LLE_KFDA algorithm is based on original data, avoided from fall of pattern recognition ability which caused by inappropriate or blind choice of the feature parameters in the traditional fault diagnosis method.The experiment to fault diagnosis of rolling bearing shows this method can effectively identify the equipment fault pattern, diagnostic result is good.
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Kavitha, M., G. Lavanya, J. Janani, and Balaji J. "ENHANCED SVM CLASSIFIER FOR BREAST CANCER DIAGNOSIS." International Journal of Engineering Technologies and Management Research 5, no. 3 (2020): 67–74. http://dx.doi.org/10.29121/ijetmr.v5.i3.2018.178.
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Breast cancer is the leading disease to cause death especially in women. In this paper, a frame work based algorithm for the classification of cancerous/non-cancerous data is developed using application of supervised machine learning. In feature selection, we derive basis set in the kernel space and then we extend the margin based feature selection algorithm. We are trying to explore several feature selection, extraction techniques and combine the optimal feature subsets with various learning classification methods such as KNN, PNN and Support Vector Machine (SVM) classifiers. The best classification performance for breast cancer diagnosis is attained equal to 99.17% between radius and compact features using SVM classifier. And also derive the features of a breast image in the WBCD dataset.
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Dr.M.Kavitha, G.Lavanya, J.Janani, and Balaji.J. "ENHANCED SVM CLASSIFIER FOR BREAST CANCER DIAGNOSIS." International Journal of Engineering Technologies and Management Research 5, no. 3 (2018): 67–74. https://doi.org/10.5281/zenodo.1207413.
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<strong><em>Breast cancer is the leading disease to cause death especially in women. In this paper, a frame work based algorithm for the classification of cancerous/non-cancerous data is developed using application of supervised machine learning. In feature selection, we derive basis set in the kernel space and then we extend the margin based feature selection algorithm. We are trying to explore several feature selection, extraction techniques and combine the optimal feature subsets with various learning classification methods such as KNN, PNN and Support Vector Machine (SVM) classifiers. The best classification performance for breast cancer diagnosis is attained equal to 99.17% between radius and compact features using SVM classifier. And also derive the features of a breast image in the WBCD dataset</em>.</strong>
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Liu, Yan, and Kaiyu Fan. "Roller Bearing Fault Diagnosis Using Deep Transfer Learning and Adaptive Weighting." Journal of Physics: Conference Series 2467, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2467/1/012011.
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Abstract A fault diagnosis approach for roller bearings utilizing deep transfer learning and adaptive weighting is suggested to address the issue that extra fault state samples in the target domain data of roller bearings impair the fault diagnostic accuracy. CNN-LSTM is a network model proposed by Lecun et al., which has good performance in image processing and image processing. It can effectively apply predictive local perception of time series and weight sharing of CNN, which can greatly reduce the number of networks and improve the efficiency of model learning. The method first establishes a feature extraction module, and uses a deep convolutional neural network to map bearing samples to a high-dimensional feature space. Secondly, uses the transfer learning concept to design a weighted domain discriminator, and adaptively weights the samples; and finally, through the confrontation in the feature space, the bearing samples are classified. Training to increase the domain similarity of the healthy state samples shared by the target domain and the source domain. Then measuring the similarity between these samples based on the sample weight size, and setting a threshold to label the additional fault state samples of the target domains as unknown. The suggested technique is validated using gearbox bearing data, roller bearing data from Case Western Reserve University, and locomotive wheel bearing data. The diagnostic accuracy of the samples is less than 80%, suggesting that the suggested approach can successfully overcome the effects of extra fault state samples and diagnose roller bearing faults.
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Manikandan, Sankarakutti Palanichamy, Sandeep Reddy Narani, Sakthivel Karthikeyan, and Nagarajan Mohankumar. "Deep learning for skin melanoma classification using dermoscopic images in different color spaces." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 319. http://dx.doi.org/10.11591/ijece.v15i1.pp319-327.
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Skin cancer begins in the skin cells. The damage to the skin cells can cause genetic mutations that lead to uncontrolled growth and the formation of tumors. It is estimated that millions of people are diagnosed with skin cancer of different kinds each year. The earlier a skin cancer is diagnosed, the better the patient's prognosis and the lower their chance of complications. In this work, an efficient deep learning classification (EDLCS) to classify dermoscopic images is developed. The importance of color in the diagnosis of skin melanoma has caused color analysis to attract considerable attention from researchers of image-based skin melanoma analysis. Three different color spaces such as red-green-blue (RGB), hue-saturation-lightness (HIS) and LAB are investigated in this study. The obtained dermoscopic images are in RGB color space. The RGB dermoscopic images are first converted into HSV and LAB spaces to investigate the HSV and LAB color spaces for melanoma classification. Then, the color space converted image is fed to the proposed EDLCS to evaluate their performances. Results show that the proposed EDLCS provides 99.58% accuracy while using the LAB color model to classify preprocessed images while other models provide 99.17%.
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Nikhita, Mishra, Chaturvedi Ipshitta, and Mehta Janhvi. "Semiconductor Bearing Fault Recognition." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 1 (2021): 21–26. https://doi.org/10.35940/ijeat.F3090.1011121.
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<strong>Abstract: </strong>Semiconductor manufacturing is consid-ered to be one of the most technologically complicated manufacturing processes. Bearing, being a critical part of the rotating machinery used in the process, plays an essential role as it supports the mechanical rotating body and decreases the friction coefficient. However, extensive use makes this element a target of health degradation, which indirectly causes machine failure. A defective bearing causes approximately 50% of failures in electrical machines. Hence, there arises a dire need for effective fault detection and diagnosis methods to recog-nise fault patterns and help take preventive measures. This paper carries out a comprehensive comparative study of the pre-existing machine learning and deep learning techniques used for diagnosing bearing faults and further devises a novel framework for bearing fault diagnosis based on the results. Unlike the conventional Fault Detection Classifiers (FDC) that operate in the original data space, this algorithm explores the scope for feature extraction and transferability empowered by the deep learning models used.
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Nikhita, Mishra, Chaturvedi Ipshitta, and Mehta Janhvi. "Semiconductor Bearing Fault Recognition." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 1 (2021): 21–26. https://doi.org/10.35940/ijeat.F3090.10110121.
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Semiconductor manufacturing is consid-ered to be one of the most technologically complicated manufacturing processes. Bearing, being a critical part of the rotating machinery used in the process, plays an essential role as it supports the mechanical rotating body and decreases the friction coefficient. However, extensive use makes this element a target of health degradation, which indirectly causes machine failure. A defective bearing causes approximately 50% of failures in electrical machines. Hence, there arises a dire need for effective fault detection and diagnosis methods to recog-nise fault patterns and help take preventive measures. This paper carries out a comprehensive comparative study of the pre-existing machine learning and deep learning techniques used for diagnosing bearing faults and further devises a novel framework for bearing fault diagnosis based on the results. Unlike the conventional Fault Detection Classifiers (FDC) that operate in the original data space, this algorithm explores the scope for feature extraction and transferability empowered by the deep learning models used.
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Xu, Gonglin, Mei Zhang, Wanli Chen, and Zhihui Wang. "Transformer Fault Diagnosis Utilizing Feature Extraction and Ensemble Learning Model." Information 15, no. 9 (2024): 561. http://dx.doi.org/10.3390/info15090561.
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This paper proposes a novel method for diagnosing faults in oil-immersed transformers, leveraging feature extraction and an ensemble learning algorithm to enhance diagnostic accuracy. Initially, Dissolved Gas Analysis (DGA) data from transformers undergo a cleaning process to ensure data quality and reliability. Subsequently, an interactive ratio method is employed to augment features and project DGA data into a high-dimensional space. To refine the feature set, a combined Filter and Wrapper algorithm is utilized, effectively eliminating irrelevant and redundant features. The final step involves optimizing the Light Gradient Boosting Machine (LightGBM) model using IAOS algorithm for transformer fault classification; this model is an ensemble learning model. Experimental results demonstrate that the proposed feature extraction method enhances LightGBM model’s accuracy to 86.84%, representing a 6.58% improvement over the baseline model. Furthermore, optimization with IAOS algorithm increases the diagnostic accuracy of LightGBM model to 93.42%, an additional gain of 6.58%.
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Jang, Gye-Bong, and Sung-Bae Cho. "Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions." Sensors 21, no. 4 (2021): 1417. http://dx.doi.org/10.3390/s21041417.
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In recent years, various deep learning models have been developed for the fault diagnosis of rotating machines. However, in practical applications related to fault diagnosis, it is difficult to immediately implement a trained model because the distribution of source data and target domain data have different distributions. Additionally, collecting failure data for various operating conditions is time consuming and expensive. In this paper, we introduce a new transformation method for the latent space between domains using the source domain and normal data of the target domain that can be easily collected. Inspired by semantic transformations in an embedded space in the field of word embedding, discrepancies between the distribution of the source and target domains are minimized by transforming the latent representation space in which fault attributes are preserved. To match the feature area and distribution, spatial attention is applied to learn the latent feature spaces, and the 1D CNN LSTM architecture is implemented to maximize the intra-class classification. The proposed model was validated for two types of rotating machines such as a dataset of rolling bearings as CWRU and a gearbox dataset of heavy machinery. Experimental results show the proposed method has higher cross-domain diagnostic accuracy than others, therefore showing reliable generalization performance in rotating machines operating under various conditions.
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Zhang, Di, Yuan Wei, Baoqiang Wang, and Shulin Liu. "Scale adaptive subdomain matching network for bearing fault diagnosis." Measurement Science and Technology 33, no. 2 (2021): 025006. http://dx.doi.org/10.1088/1361-6501/ac3627.
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Abstract The wide application of transfer learning technology can effectively solve the problem of the difference between data collection and actual application equipment of traditional intelligent fault diagnosis methods in the practical application process. However, the difference in subdomain space and the serious imbalance of data samples in the process of simultaneous transfer restricts the deep transfer learning technology to the engineering application of high-precision diagnosis. In order to solve the problem of subdomain matching with different subspaces and unbalanced data samples, in this paper we study the subdomain adaptive method and propose a scale adaptive subdomain matching (SASM) method. The SASM method divides the global feature space according to the sample labels, and features with the same label will be divided into the same sub-feature space. Using the edge distribution of the sample and the category weight of the label, the SASM method can effectively optimize the feature distribution of the same subdomain and the weight distribution of different subdomains. Based on the establishment of a clearer internal structure of features, the field adaptation effect is improved, and the matching ability is enhanced when the sample is unevenly distributed. At the same time, the SASM network (SASMN) method for unsupervised bearing fault diagnosis is constructed and validated by experiments. The results indicate that SASMN can effectively optimize the subdomain adaptive effect, and the diagnostic accuracy of the target domain data set is significantly higher than the other three currently popular domain adaptive fault diagnosis methods.
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Naji, Ayat, Sahar A. Abboud, Basim A. Jumaa, and Mohammed N. Abdullah. "Gait Classification Using Machine Learning for Foot Disseises Diagnosis." Technium: Romanian Journal of Applied Sciences and Technology 4, no. 4 (2022): 37–49. http://dx.doi.org/10.47577/technium.v4i4.6528.
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Person recognition systems based on biometrics have recently attracted a lot of attention in the scientific community. It’s an ever-evolving technology that aspires to do biometric recognition automatically, rapidly, precisely, and consistently. In recent decades, gait recognition has emerged as a type of biometric identification that focuses on recognizing individuals using personal measures and correlations, such as trunk and limb size, as well as space-time information linked to intrinsic patterns in individuals’ motions. Lower-limb surgery is one of the leading causes of loss of autonomy in patients. An improved rehabilitation process is a vital aspect for care facilities since it improves both the patient’s quality of life and the associated costs of the post-surgery procedure. Proper progress monitoring is critical to the success of a rehabilitation program. In this paper, we employed machine learning methods as classifiers to classify foot diseases and then monitor the progress in the patient case. Five classifiers were utilized to train and test the EMG dataset in the lower limb. These classifiers are K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Stochastic Gradient Descent (SGD). The experimental results show high accuracy reaching 99% in both KNN and RF classifiers and 97% in the DT classifier. The fundamental benefit of the suggested procedures is their high estimation accuracy, which leads to better therapeutic results.
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Wang, Xiang, and Yuan Zheng. "Machinery Fault Diagnosis Based on Supervised Locally Linear Embedding." Applied Mechanics and Materials 536-537 (April 2014): 49–52. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.49.
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Fault diagnosis is essentially a kind of pattern recognition. In this paper propose a novel machinery fault diagnosis method based on supervised locally linear embedding is proposed first. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The ball bearing fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.
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Xu, Chuannuo, Jiming Li, and Xuezhen Cheng. "Comprehensive Learning Particle Swarm Optimized Fuzzy Petri Net for Motor-Bearing Fault Diagnosis." Machines 10, no. 11 (2022): 1022. http://dx.doi.org/10.3390/machines10111022.
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Petri net is a widely used fault-diagnosis algorithm. However, it presents poor fault-diagnosis effectiveness and accuracy caused by the parameter setting and adjustment, depending entirely on expert experience in a system with a single input signal type. To address this problem, a comprehensive learning particle swarm optimized fuzzy Petri net (CLPSO-FPN) algorithm is proposed for motor-bearing fault diagnosis. CLPSO is employed to obtain an adaptive system parameter set to reduce the fault-diagnosis error caused by human subjective factors. Moreover, a new proposed concept of the transition influence factor replaces the traditional transition confidence to improve the nonlinear expression ability of traditional Petri nets, which suppresses the space explosion problem of the fault-diagnosis model. Finally, experiments are implemented on a dataset of motor bearings. Compared with traditional faults diagnosis methods, the proposed method realized better performance in the fault location and prediction functions of motor bearings, which is beneficial for troubleshooting and motor maintenance.
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Jeon, Jin Yong, Na Young Kim, Sang Heon Kim, Hong Jin Kim, and Gwun Il Park. "Pneumonia diagnosis algorithm based on room impulse responses using cough sounds." Journal of the Acoustical Society of America 152, no. 4 (2022): A49—A50. http://dx.doi.org/10.1121/10.0015503.
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For data augmentation of the pneumonia diagnosis algorithm by deep learning, an image conversion method through a convolutional method utilizing spatial impulse and sound quality factors of cough sounds is proposed. First, reverberant spaces with different volumes were implemented, and spatial impulse responses were generated for each space through computer simulation of spatial models according to sound source and receiver points. Sound quality analysis was performed to improve accuracy, and 2-D sound quality data of time series was converted into 3-D image data using the Gramian Angular Field (GAF) method for combination between heterogeneous data. As a result, 97.5% accuracy was obtained for the configured dataset. The result of this study is expected to be used to develop diagnostic algorithms for various respiratory diseases including pneumonia in the future.
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Rumman, Mahfujul Islam, Naoaki Ono, Kenoki Ohuchida, MD Altaf-Ul-Amin, Ming Huang, and Shigehiko Kanaya. "Information maximization-based clustering of histopathology images using deep learning." PLOS Digital Health 2, no. 12 (2023): e0000391. http://dx.doi.org/10.1371/journal.pdig.0000391.
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Pancreatic cancer is one of the most adverse diseases and it is very difficult to treat because the cancer cells formed in the pancreas intertwine themselves with nearby blood vessels and connective tissue. Hence, the surgical procedure of treatment becomes complicated and it does not always lead to a cure. Histopathological diagnosis is the usual approach for cancer diagnosis. However, the pancreas remains so deep inside the body that experts sometimes struggle to detect cancer in it. Computer-aided diagnosis can come to the aid of pathologists in this scenario. It assists experts by supporting their diagnostic decisions. In this research, we carried out a deep learning-based approach to analyze histopathology images. We collected whole-slide images of KPC mice to implement this work. The pancreatic abnormalities observed in KPC mice develop similar histological features to human beings. We created random patches from whole-slide images. Then, a convolutional autoencoder framework was used to embed these patches into an integrated latent space. We applied ‘information maximization’, a deep learning clustering technique to cluster the identical patches in an unsupervised manner since our dataset does not have annotation. Moreover, Uniform manifold approximation and projection, a nonlinear dimension reduction technique was utilized to visualize the embedded patches in a 2-dimensional space. Finally, we calculated a few internal cluster validation metrics to determine the optimal cluster set. Our work concentrated on patch-based anomaly detection in the whole slide histopathology images of KPC mice.
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Hu, Wei, Yi Bing Deng, Hong Qi Feng, Qing E. Wu, Bin Tang, and Jian Hua Zou. "A Framework Design of Automatic Fault Diagnosis System." Applied Mechanics and Materials 330 (June 2013): 635–38. http://dx.doi.org/10.4028/www.scientific.net/amm.330.635.
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To resolve a lasting suitable cabin environment for the astronauts, this paper proposes an effective framework design for automatic fault diagnosis system. This framework can implement a real-time online diagnosis and decision support for fault, and carry out an early diagnosis for weak fault. Finally, this paper achieves an online automatic fault diagnosis system by using neural networks self-learning characteristics and expert knowledge. In two-men-two-days simulated manned space flight test, the software of diagnosis system framework worked well, which has been assessed and verified comprehensively.
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Dong, Zhiang, Jingyuan Chen, and Fei Wu. "Knowledge Is Power: Harnessing Large Language Models for Enhanced Cognitive Diagnosis." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 164–72. https://doi.org/10.1609/aaai.v39i1.31992.
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Cognitive Diagnosis Models (CDMs) are designed to assess students' cognitive states by analyzing their performance across a series of exercises. However, existing CDMs often struggle with diagnosing infrequent students and exercises due to a lack of rich prior knowledge. With the advancement in large language models (LLMs), which possess extensive domain knowledge, their integration into cognitive diagnosis presents a promising opportunity. Despite this potential, integrating LLMs with CDMs poses significant challenges. LLMs are not well-suited for capturing the fine-grained collaborative interactions between students and exercises, and the disparity between the semantic space of LLMs and the behavioral space of CDMs hinders effective integration. To address these issues, we propose a novel Knowledge-enhanced Cognitive Diagnosis (KCD) framework, which is a model-agnostic framework utilizing LLMs to enhance CDMs and compatible with various CDM architectures. The KCD framework operates in two stages: LLM Diagnosis and Cognitive Level Alignment. In the LLM Diagnosis stage, both students and exercises are diagnosed to achieve comprehensive and detailed modeling. In the Cognitive Level Alignment stage, we bridge the gap between the CDMs' behavioral space and the LLMs' semantic space using contrastive learning and mask-reconstruction approaches. Experiments on several real-world datasets demonstrate the effectiveness of our proposed framework.
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Gou, Xiaohong, and Xuenong He. "Deep Learning-Based Detection and Diagnosis of Subarachnoid Hemorrhage." Journal of Healthcare Engineering 2021 (November 22, 2021): 1–10. http://dx.doi.org/10.1155/2021/9639419.
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Subarachnoid hemorrhage (SAH) is one of the critical and severe neurological diseases with high morbidity and mortality. Head computed tomography (CT) is among the preferred methods for the diagnosis of SAH, which is confirmed by CT showing high-density shadow in the subarachnoid space. Analysis of these images through a deep learning-based subarachnoid hemorrhage will reduce the approximate rate of misdiagnosis in general and missed diagnosis by clinicians in particular. Deep learning-based detection of subarachnoid hemorrhage mainly includes two tasks, i.e., subarachnoid hemorrhage classification and subarachnoid hemorrhage region segmentation. However, it is difficult to effectively judge reliability of the model and classify bleeding which is based on limited predictive probability of convolutional neural network output. Moreover, deep learning-based bleeding area segmentation requires a large amount of training data to be marked in advance and the large number of network parameters makes the model training unable to reach the optimal. To resolve these problems associated with existing models, Bayesian deep learning and neural network-based hybrid model is presented in this paper to estimate uncertainty and efficiently classify subarachnoid hemorrhage. Uncertainty estimation of the proposed model helps in judging whether the model’s prediction is reliable or not. Additionally, it is used to guide clinicians to find the neglected subarachnoid hemorrhage area. In addition, a teacher-student mechanism deep learning model was designed to introduce observational uncertainty estimation for semisupervised learning of subarachnoid hemorrhage. Observation uncertainty estimation detects the uncertain bleeding areas in CT images and then selects areas with high reliability. Finally, it uses these unlabeled data for model training purposes as well.
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Bian, Congchao, Can Hu, and Ning Cao. "Exploiting K-Space in Magnetic Resonance Imaging Diagnosis: Dual-Path Attention Fusion for K-Space Global and Image Local Features." Bioengineering 11, no. 10 (2024): 958. http://dx.doi.org/10.3390/bioengineering11100958.
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Magnetic resonance imaging (MRI) diagnosis, enhanced by deep learning methods, plays a crucial role in medical image processing, facilitating precise clinical diagnosis and optimal treatment planning. Current methodologies predominantly focus on feature extraction from the image domain, which often results in the loss of global features during down-sampling processes. However, the unique global representational capacity of MRI K-space is often overlooked. In this paper, we present a novel MRI K-space-based global feature extraction and dual-path attention fusion network. Our proposed method extracts global features from MRI K-space data and fuses them with local features from the image domain using a dual-path attention mechanism, thereby achieving accurate MRI segmentation for diagnosis. Specifically, our method consists of four main components: an image-domain feature extraction module, a K-space domain feature extraction module, a dual-path attention feature fusion module, and a decoder. We conducted ablation studies and comprehensive comparisons on the Brain Tumor Segmentation (BraTS) MRI dataset to validate the effectiveness of each module. The results demonstrate that our method exhibits superior performance in segmentation diagnostics, outperforming state-of-the-art methods with improvements of up to 63.82% in the HD95 distance evaluation metric. Furthermore, we performed generalization testing and complexity analysis on the Automated Cardiac Diagnosis Challenge (ACDC) MRI cardiac segmentation dataset. The findings indicate robust performance across different datasets, highlighting strong generalizability and favorable algorithmic complexity. Collectively, these results suggest that our proposed method holds significant potential for practical clinical applications.
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Khammad, Vasilii, Jose Javier Otero, Yolanda Cabello Izquierdo, et al. "Application of machine learning algorithms for the diagnosis of primary brain tumors." Journal of Clinical Oncology 38, no. 15_suppl (2020): 2533. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.2533.
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2533 Background: Primary lesions of the CNS refer to a heterogeneous group of benign or malignant tumors arising in different parts of the brain and spinal cord. According to the 2016 CNS WHO classification, the accurate diagnosis of primary brain tumors requires a layered approach of histologic, anatomic and molecular features to generate an integrated diagnosis with clinical and prognostic significance. However, in the US and worldwide, scarce resources are available to perform all the required tests routinely, so methods that improve pre-test probabilities and decrease false positive results have significant clinical and financial impact. Aims: 1) validate new diagnostic workflows with implementation of modern machine learning/artificial intelligence approaches; 2) design a reliable and interactive computational platform for primary CNS tumor diagnosis. Methods: To achieve these goals we have developed a population model in Rstudio, “La Tabla”, based on the articles from open resources of MEDLINE database and the latest version of WHO classification of CNS tumors. The data of “La Tabla” is comprised of more than 100,000 adult and pediatric cases, including rare brain tumor diagnoses, such as Gangliocytoma, Diffuse Midline Glioma and etc. Results: Boruta package and weights function in R have been used to distinguish the most important features for diagnosis prediction. To visualize correlation between these features (age, ki67 level, tumor location, presence of myxoid areas, calcifications, necrosis and etc.) and all diagnoses in two-dimensional space, we used a t-SNE algorithm. Models trained with decision tree algorithms (randomForest, XGBoost and C5.0) showed high overall accuracy in predicting diagnoses of “La Tabla” (95%, 94% and 92%) and 300 patients at OSUCCC-James (93%, 74% and 87%) in the absence of IHC and molecular data. Neural networks provided by keras and nnet packages predicted diagnoses using just clinical and histological findings with 94% and 88% accuracy on “La Tabla” and James patient databases respectively. Currently, we are building “Shiny” applications with R to deliver easily operated platform for pathologists and physicians. Conclusions: In conclusion, we managed to generate models that are able to diagnose primary brain lesions using basic clinical data (age, gender, tumor location), ki67 levels and distinct features of histological architecture. Most of the models distinguish brain tumors and associated molecular status with high accuracy and will serve as a reliable tool for second opinion in clinical neuro-oncology.
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Son, Dong-Min, Yeong-Ah Yoon, Hyuk-Ju Kwon, and Sung-Hak Lee. "Combined Deep Learning Techniques for Mandibular Fracture Diagnosis Assistance." Life 12, no. 11 (2022): 1711. http://dx.doi.org/10.3390/life12111711.
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Mandibular fractures are the most common fractures in dentistry. Since diagnosing a mandibular fracture is difficult when only panoramic radiographic images are used, most doctors use cone beam computed tomography (CBCT) to identify the patient’s fracture location. In this study, considering the diagnosis of mandibular fractures using the combined deep learning technique, YOLO and U-Net were used as auxiliary diagnostic methods to detect the location of mandibular fractures based on panoramic images without CBCT. In a previous study, mandibular fracture diagnosis was performed using YOLO learning; in the detection performance result of the YOLOv4-based mandibular fracture diagnosis module, the precision score was approximately 97%, indicating that there was almost no misdiagnosis. In particular, fractures in the symphysis, body, angle, and ramus tend to be distributed in the middle of the mandible. Owing to the irregular fracture types and overlapping location information, the recall score was approximately 79%, which increased the detection of undiagnosed fractures. In many cases, fractures that are clearly visible to the human eye cannot be grasped. To overcome these shortcomings, the number of undiagnosed fractures can be reduced using a combination of the U-Net and YOLOv4 learning modules. U-Net is advantageous for the segmentation of fractures spread over a wide area because it performs semantic segmentation. Consequently, the undiagnosed case in the middle of the mandible, where YOLO was weak, was somewhat supplemented by the U-Net module. The precision score of the combined module was 95%, similar to that of the previous method, and the recall score improved to 87%, as the number of undiagnosed cases was reduced. Through this study, the performance of a deep learning method that can be used for the diagnosis of the mandibular bone has been improved, and it is anticipated that as an auxiliary diagnostic inspection device, it will assist dentists in making diagnoses.
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Whardana, Adithya Kusuma, and Parma Hadi Rantelinggi. "DETECTION DIABETIC RETINOPATHY WITH SUPERVISED LEARNING." JEECS (Journal of Electrical Engineering and Computer Sciences) 8, no. 2 (2023): 157–62. http://dx.doi.org/10.54732/jeecs.v8i2.7.
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Diabetic retinopathy is a common complication that occurs in people with diabetes mellitus. Diabetic retinopathy damage is characterized in the blood vessel system in the layer at the back of the eye, especially in tissues that respond to light. This research aims to detect diabetic retinopathy early by using SVM and Random forest. SVM is a classification technique that divides the input space into two classes. Random Forest is a supervised learning algorithm that utilizes a collection of decision trees trained using the bagging method. This research uses datasets from diaretdb1 and messidor to evaluate the performance of both methods. The diaretdb1 dataset consists of 178 data points with the diagnosis of Proliferative Diabetic Retinopathy and Non-Diabetic Retinopathy. In addition, the messidor dataset consists of 105 data points with the diagnosis of Diabetic Retinopathy and Non-Diabetic Retinopathy. Experimental results on the diaretdb1 dataset showed that SVM achieved 88% accuracy, while Random Forest achieved 91% accuracy. Similarly, on the messidor dataset, SVM achieved 80% accuracy, while Random Forest achieved 85% accuracy.
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Cheng, Qifeng, Tiantian Wang, Sicong Guo, et al. "The Logistic Regression from the Viewpoint of the Factor Space Theory." International Journal of Computers Communications & Control 12, no. 4 (2017): 492. http://dx.doi.org/10.15837/ijccc.2017.4.2918.
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Logistic regression plays an important role in machine learning. People excitingly use it in conceptual matching yet with some details to be understood further. This paper aims to present a reasonable statement on logistic regression based on fuzzy sets and the factor space theory. An example about breast cancer diagnosis is displayed to show how the factor space theory can be incorporated into the understanding and use of logistic regression.
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Ma, Mingyu Derek, Xiaoxuan Wang, Yijia Xiao, et al. "Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24786–94. https://doi.org/10.1609/aaai.v39i23.34660.
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Clinical diagnosis prediction models, when provided with a patient's medical history, aim to detect potential diseases early, facilitating timely intervention and improving prognostic outcomes. However, the inherent scarcity of patient data and large disease candidate space often pose challenges in developing satisfactory models for this intricate task. The exploration of leveraging Large Language Models (LLMs) for encapsulating clinical decision processes has been limited. We introduce MERA, a clinical diagnosis prediction model that bridges pertaining natural language knowledge with medical practice. We apply hierarchical contrastive learning on a disease candidate ranking list to alleviate the large decision space issue. With concept memorization through fine-tuning, we bridge the natural language clinical knowledge with medical codes. Experimental results on MIMIC-III and IV datasets show that MERA achieves the state-of-the-art diagnosis prediction performance and dramatically elevates the diagnosis prediction capabilities of generative LMs.
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Khan, Asif, Hyunho Hwang, and Heung Soo Kim. "Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines." Mathematics 9, no. 18 (2021): 2336. http://dx.doi.org/10.3390/math9182336.
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As failures in rotating machines can have serious implications, the timely detection and diagnosis of faults in these machines is imperative for their smooth and safe operation. Although deep learning offers the advantage of autonomously learning the fault characteristics from the data, the data scarcity from different health states often limits its applicability to only binary classification (healthy or faulty). This work proposes synthetic data augmentation through virtual sensors for the deep learning-based fault diagnosis of a rotating machine with 42 different classes. The original and augmented data were processed in a transfer learning framework and through a deep learning model from scratch. The two-dimensional visualization of the feature space from the original and augmented data showed that the latter’s data clusters are more distinct than the former’s. The proposed data augmentation showed a 6–15% improvement in training accuracy, a 44–49% improvement in validation accuracy, an 86–98% decline in training loss, and a 91–98% decline in validation loss. The improved generalization through data augmentation was verified by a 39–58% improvement in the test accuracy.
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Xu, Qinghong, Hong Jiang, Xiangfeng Zhang, Jun Li, and Lan Chen. "Multiscale Convolutional Neural Network Based on Channel Space Attention for Gearbox Compound Fault Diagnosis." Sensors 23, no. 8 (2023): 3827. http://dx.doi.org/10.3390/s23083827.
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Gearboxes are one of the most widely used speed and power transfer elements in rotating machinery. Highly accurate compound fault diagnosis of gearboxes is of great significance for the safe and reliable operation of rotating machinery systems. However, traditional compound fault diagnosis methods treat compound faults as an independent fault mode in the diagnosis process and cannot decouple them into multiple single faults. To address this problem, this paper proposes a gearbox compound fault diagnosis method. First, a multiscale convolutional neural network (MSCNN) is used as a feature learning model, which can effectively mine the compound fault information from vibration signals. Then, an improved hybrid attention module, named the channel–space attention module (CSAM), is proposed. It is embedded into the MSCNN to assign weights to multiscale features for enhancing the feature differentiation processing ability of the MSCNN. The new neural network is named CSAM-MSCNN. Finally, a multilabel classifier is used to output single or multiple labels for recognizing single or compound faults. The effectiveness of the method was verified with two gearbox datasets. The results show that the method possesses higher accuracy and stability than other models for gearbox compound fault diagnosis.
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Tripura Sundari, Yeluripati Bala, and K. Usha Mahalakshmi. "Enhancing Brain Tumor Diagnosis: A 3D Auto-Encoding Approach for Accurate Classification." International Journal of Scientific Methods in Engineering and Management 01, no. 09 (2023): 38–46. http://dx.doi.org/10.58599/ijsmem.2023.1905.
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The brain’s capacity to control and coordinate the body’s other organs makes it an integral part of the nervous system. Brain tumours, which form when abnormal cells in the brain grow uncontrolled, may be deadly if not diagnosed and treated promptly. The use of image processing technology is essential in the quest to identify malignancies in medical imaging. Because of this, the final depiction will be more complete. Only a tiny percentage of 3D form instances will be viable for feature learning because of its complex spatial structure. These issues have inspired the development of some potential solutions, such as automatic encoders for learning properties from 2D images and the translation of 3D shapes into 2D space. With the help of camera images and state-space structures, the suggested 3D-based Spatial Auto Encoder method may automatically learn a representation of the state. Autoencoders can be taught to use the prototypes they generate to rebuild a picture, with the resulting learned coefficients being put to use in 3D form matching and retrieval. It’s possible that the learned coefficients may serve this function. The auto-encoder’s impressive results in image retrieval have been attributed, at least in part, to the ease with which it can learn new features from existing ones.
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Jiang, Raymond, Yulia Kumar, and Dov Kruger. "Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning." Applied Sciences 15, no. 6 (2025): 3004. https://doi.org/10.3390/app15063004.
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In recent years, advances in diagnosing and classifying diseases using machine learning (ML) have grown exponentially. However, due to the many privacy regulations regarding personal data, pooling together data from multiple sources and storing them in a single (centralized) location for traditional ML model training are often infeasible. Federated learning (FL), a collaborative learning paradigm, can sidestep this major pitfall by creating a global ML model that is trained by aggregating model weights from individual models that are separately trained on their own data silos, therefore avoiding most data privacy concerns. This study addresses the centralized data issue with FL by applying a novel DataWeightedFed architectural approach for effective fundus disease diagnosis from ophthalmic images. It includes a novel method for aggregating model weights by comparing the size of each model’s data and taking a dynamically weighted average of all the model’s weights. Experimental results showed a small average 1.85% loss in accuracy when training using FL compared to centralized ML model systems, a nearly 92% improvement over the conventional 55% accuracy loss. The obtained results demonstrate that this study’s FL architecture can maximize both privacy preservation and accuracy for ML in fundus disease diagnosis and provide a secure, collaborative ML model training solution within the eye healthcare space.