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A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics

artificial INTELLIGENCE IN MEDICINE 2005年第3期33卷 251-260页

作者： Bhatikar, SR DeGroff, C Mahajan, RL Univ Colorado Dept Mech Engn Boulder CO 80309 USA Childrens Hosp Dept Pediat Cardiol Denver CO 80218 USA

Objective: This research work was aimed at developing a reliable screening device for diagnosis of heart murmurs in pediatrics. This is a significant problem in pediatric cardiology because of the high rate of incidence of heart murmurs in this population (reportedly 77-95%), of which only a small fraction arises from congenital heart disease. The screening devices currently available (e.g. chest X-ray, electrocardiogram, etc.) suffer from poor sensitivity and specificity in detecting congenital heart disease. Thus, patients with heart murmurs today are frequently assessed by consultation as well with advanced imaging techniques. The most prominent among these is echocardiography. However, echocardiography is expensive and is usually only available in healthcare centers in major cities. Thus, for patients being evaluated with a heart murmur, developing a more accurate screening device is vital to efforts in reducing health care costs. Methods and material: The data set was collected from incoming pediatrics at the cardiology clinic of The Children's Hospital (Denver, Colorado), on whom echocardiography had been performed to identify congenital heart disease. Recordings of approximately 10-15 s duration were made at 44,100 Hz and the average record Length was approximately 60,000 points. The best three cycles with respect to signal quality sounds were extracted from the original recording. The resulting data comprised 241 examples, of which 88 were examples of innocent murmurs and 153 were examples of pathological murmurs. The selected phonocardiograms were subject to the digital signal processing (DSP) technique of fast Fourier transform (FFT) to extract the energy spectrum in frequency domain. The spectral range was 0300 Hz at a resolution of 1 Hz. The processed signals were used to develop statistical classifiers and a classifier based on our in-house artificial neural network (ANN) software. For the tatter, we also tried enhancements to the basic ANN scheme. Thes

关键词： pediatric cardiac auscultation artificial neural network classifier phonocardiography

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Dynamic robustness evaluation for automated model selection in operation

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INFORMATION AND SOFTWARE TECHNOLOGY 2025年 178卷

作者： Zhang, Jin Li, Jingyue Yang, Zhirong Southwest Jiaotong Univ Sch Integrated Circuit Sci & Engn Chengdu 611756 Peoples R China Norwegian Univ Sci & Technol Dept Comp Sci Trondheim Norway Tech Univ Denmark Dept Civil & Mech Engn Sect Engn Design & Prod Dev Kongens Lyngby Denmark Zhejiang Univ Jinhua Inst Jinhua 321002 Zhejiang Peoples R China

Context: The increasing use of artificial neural network (ANN) classifiers in systems, especially safety-critical systems (SCSs), requires ensuring their robustness against out-of-distribution (OOD) shifts in operation, which are changes in the underlying data distribution from the data training the classifier. However, measuring the robustness of classifiers in operation with only unlabeled data is challenging. Additionally, machine learning engineers may need to compare different models or versions of the same model and switch to an optimal version based on their robustness. Objective: This paper explores the problem of dynamic robustness evaluation for automated model selection. We aim to find efficient and effective metrics for evaluating and comparing the robustness of multiple ANN classifiers using unlabeled operational data. Methods: To quantitatively measure the differences between the model outputs and assess robustness under OOD shifts using unlabeled data, we choose distance-based metrics. An empirical comparison of five such metrics, suitable for higher-dimensional data like images, is performed. The selected metrics include Wasserstein distance (WD), maximum mean discrepancy (MMD), Hellinger distance (HL), Kolmogorov-Smirnov statistic (KS), and Kullback-Leibler divergence (KL), known for their efficacy in quantifying distribution differences. We evaluate these metrics on 20 state-of-the-art models (ten CIFAR10-based models, five CIFAR100-based models, and five ImageNet-based models) from a widely used robustness benchmark ( RobustBench ) using data perturbed with various types and magnitudes of corruptions to mimic real-world OOD shifts. Results: Our findings reveal that the WD metric outperforms others when ranking multiple ANN models for CIFAR10- and CIFAR100-based models, while the KS metric demonstrates superior performance for ImageNet-based models. MMD can be used as a reliable second option for both datasets. Conclusion: This study highlights the

关键词： artificial neural network classifier Automated model selection Robustness Dynamic evaluation Distance-based metrics

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Subsampling-Based Blind Image Forgery Detection Using Support Vector Machine and artificial neural network classifiers

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第2期43卷 555-568页

作者： Birajdar, Gajanan K. Mankar, Vijay H. Priyadarshini Inst Engn & Technol Dept Elect & Commun Engn Nagpur 440019 Maharashtra India Ramrao Adik Inst Technol Dept Elect Engn Navi Mumbai 400706 Maharashtra India Govt Polytech Dept Elect & Telecommun Ahmednagar 414001 Maharashtra India

In order to create convincing doctored images, forged images are exposed to some linear transformations (like rotation and resizing) which involve a resampling step. In copy-paste image forgery, the pasted portion is rescaled in order to hide traces of malicious tampering. In this paper, an algorithm is proposed to detect the global resizing operation of the doctored image blindly based on features extracted using subsampling. Fisher criterion is employed in order to choose the relevant features and reduce the dimensionality of the statistical features. Support vector machine (SVM) and multi-layer feedforward artificial neural network (ANN) are used for classification. Experimental results using , and grayscale images demonstrate that the proposed method has good rescaling detection performance even when dealing with distortions like JPEG compression. The results indicate that SVM performs better compared to ANN classifier.

关键词： Image forgery detection Image forensic Rescaling detection artificial neural network classifier Subsampling

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Prediction of induction motor faults using machine learning

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Heliyon 2025年第1期11卷 e41493页

作者： Abdulkareem, Ademola Anyim, Tochukwu Popoola, Olawale Abubakar, John Ayoade, Agbetuyi Electrical and Information Engineering Department Covenant University P.M.B 1023 Ogun State Ota 112212 Nigeria Electrical Engineering Department Centre for Energy and Electric Power Faculty of Engineering and the Built Environment Tshwane University of Technology Nigeria Department of Computer Science and Engineering University of Bologna BO Italy

Unplanned downtime in industrial sectors presents significant challenges, impacting both production efficiency and profitability. To tackle this issue, companies are actively working towards optimizing their operations and reducing disruptions that hinder their ability to meet customer demands and financial goals. Predictive maintenance, utilizing advanced technologies like data analytics, machine learning, and IoT devices, offers real-time equipment data monitoring and analysis. This research study centers on the development of a versatile machine-learning model for predicting faults in induction motors within industrial environments. Implementing such a model can enable proactive maintenance, ultimately leading to decreased downtime in industrial operations. The study involved the acquisition of a dataset comprising healthy and faulty conditions of four 3-phase induction motors, along with relevant features for fault prediction. Multiple machine learning algorithms were trained using this dataset, exhibiting promising performance. The Random Forest (RF) model achieved the highest accuracy at 0.91, closely followed by the artificial neural network (ANN) and k-nearest Neighbors (k-NN) models, both achieving an accuracy of 0.9. Meanwhile, the Decision Tree (DT) model showed the lowest accuracy at 0.89. Further model evaluation was carried out using a confusion matrix, which provided a detailed breakdown of the models' performance for each class, revealing the number of correctly and incorrectly classified induction motor conditions. The results from the confusion matrix indicate that the models effectively classified the various states and conditions of the induction motors. To enhance model performance in future work, potential avenues include refining the ANN and RF models, exploring transfer learning or ensemble methods, and incorporating diverse datasets to improve generalization. © 2024

关键词： artificial neural network classifier Decision tree classifier Induction motors k-NN classifier Predictive maintenance Random Forest classifier

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Novel colour image encoding system combined with ANN for discharges pattern recognition on polluted insulator model

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2020年第6期14卷 718-725页

作者： Ferrah, Imene Chaou, Ahmed Khaled Maadjoudj, Djamal Teguar, Madjid Ecole Natl Polytech Lab Rech Electrotech 10 Ave Hassen Badi BP 182 Algiers 16200 Algeria

This study introduces a novel methodology for electrical discharges recognition elaborating an algorithm based on the RGB colour image model and artificial neural network (ANN) classifier. The developed RGB-ANN algorithm aims to detect and monitor the propagation of electrical discharges until flashover, through analysis of six colours appearing in the discharges images, extracted from the flashover videos recorded on a plan glass insulator model under uniform pollution. First, more than 300 colours images are collected and divided into sets to form a large database. Using an RGB encoding system, each pixel is represented by (R, G, B) coordinates and each image is encoded by 3D matrix. For the discharge image, the coordinates of each pixel are compared to all database ones. The colour of the database having the same coordinates of the discharge image pixel is attributed to this latter. Based on the ratio of the pixels number of a given colour to the total pixels number of the discharge image, six indicators are quantified and grouped to form a feature vector. This latter is used as input of the ANN, in order to classify the evolution of discharges into five classes. As the main result, >98% of images have been well classified.

关键词： flashover insulator contamination pattern recognition neural nets image colour analysis image coding image classification matrix algebra polluted insulator model RGB colour image model artificial neural network classifier plan glass insulator model RGB encoding system discharge image pixel colour image encoding system discharge pattern recognition RGB-ANN algorithm electrical discharge recognition ANN classifier electrical discharge propagation flashover 3D matrix image classification

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artificial neural network and phasor data-based islanding detection in smart grid

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第21期12卷 5843-5850页

作者： Kumar, Dhruba Bhowmik, Partha Sarathee Natl Inst Technol Durgapur Dept Elect Engn MG Ave Durgapur India

Islanding is an unusual condition in a power system where the generating station continues to supply the local load after one or multiple transmission line outage. This study develops a new islanding detection technique using the artificial neural network (ANN) classifier, which is provided with synchronised phasor measurements from a nine-bus Western Electricity Coordinating Council power system. An excessive number of data frames are generated in the phasor data concentrator. Before sending these data to the classifier, multiplier-based method (MBM) and Andrews plot-based method (APBM) are applied for dimension reduction and feature extraction. Comparisons are prepared with other dimension reduction algorithms. The accuracy of the classifier has been increased by increasing the number of hidden layers, the best accuracy is observed at a certain level for APBM. Non-detection zone (NDZ) for APBM is also evaluated. It is observed that the classification accuracy, and the detection time change when the neural network is retrained. All the results are compared and analysed statistically. This method can perform faster compared to other existing algorithms with an excellent accuracy and smaller NDZ.

关键词： feature extraction power system interconnection distributed power generation power grids smart power grids power distribution faults power system measurement power engineering computing phase measurement neural nets phasor measurement phasor data-based islanding detection smart grid unusual condition generating station local load multiple transmission line outage islanding detection technique artificial neural network classifier synchronised phasor measurements nine-bus Western Electricity Coordinating Council power system excessive number data frames phasor data concentrator APBM dimension reduction algorithms nondetection zone detection time change

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Mammographic masses classification: novel and simple signal analysis method

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ELECTRONICS LETTERS 2011年第1期47卷 14-U28页

作者： Fraschini, M. Univ Cagliari Dept Cardiovasc & Neurol Sci I-09124 Cagliari Italy

Breast cancer represents the leading cause of fatality among cancers for women and there is still no known way of preventing this pathology. Computer-aided analysis systems could be very helpful to improve both the sensitivity and the specificity. Presented is a computer-aided diagnosis system for malignant/benign masses classification of regions of interest in mammograms based on wavelet transform decomposition and an artificial neural network classifier is shown. The main novelty of the system is to consider only small 1D signals crossing the region of interest, allowing one to drastically reduce the amount of data to be processed. An experimental analysis performed on a set of images from the DDSM database has shown the effectiveness of the proposed method.

关键词： DDSM database artificial neural network classifier benign masses classification breast cancer cancer computer aided analysis computer-aided analysis computer-aided diagnosis malignant masses classification mammographic masses classification mammography medical computing neural nets patient diagnosis signal analysis small 1D signals wavelet transform decomposition wavelet transforms

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An Efficient Quality Inspection of Food Products Using neural network Classification

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JOURNAL OF INTELLIGENT SYSTEMS 2020年第1期29卷 1425-1440页

作者： Ali, Syed Sumera Ershad Dildar, Sayyad Ajij Dr Babasaheb Ambedkar Marathwada Univ Chhatrapati Shahu Maharaj Shikshan Sanstha Chh Shahu Coll Engn Dept Elect & Telecommun Engn Aurangabad Maharashtra India MIT Coll Engn Dept Elect & Telecommun Engn Aurangabad Maharashtra India

Currently, there is a necessity for the expansion of precise, rapid, and intentional quality assurance with respect to the character of food and horticultural food items, because it is difficult to maintain and organize food products in an elevated quality and secure manner for the increasing population. In this article, we propose a procedure to resolve difficulties and to categorize food as either a broken or quality product. Therefore, the proposed process encompasses four segments, such as preprocessing, segmentation of broken division, feature extraction, and classification. At the first stage, the preprocessing method is used to remove all unnecessary noises. After that, modified region expansion-related segmentation is undertaken to segment the broken division of the food product. Then, feature extraction is used to remove the distinctive attributes of each food product to categorize their evaluation. Finally, the neural network classification procedure is used to examine the food quality. The proposed method is executed in the operational platform of MATLAB, and the consequences are examined by using obtainable methods.

关键词： Histogram equalization modified region growing segmentation color histogram features gray level co-variance matrix features artificial neural network classifier back-propagation algorithm

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Prediction of HIV drug resistance from genotype with encoded three-dimensional protein structure

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BMC GENOMICS 2014年第Sup5期15卷 1-13页

作者： Yu, Xiaxia Weber, Irene T. Harrison, Robert W. Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Georgia State Univ Petit Sci Ctr Dept Biol Atlanta GA 30303 USA

Background: Drug resistance has become a severe challenge for treatment of HIV infections. Mutations accumulate in the HIV genome and make certain drugs ineffective. Prediction of resistance from genotype data is a valuable guide in choice of drugs for effective therapy. Results: In order to improve the computational prediction of resistance from genotype data we have developed a unified encoding of the protein sequence and three-dimensional protein structure of the drug target for classification and regression analysis. The method was tested on genotype-resistance data for mutants of HIV protease and reverse transcriptase. Our graph based sequence-structure approach gives high accuracy with a new sparse dictionary classification method, as well as support vector machine and artificial neural networks classifiers. Cross-validated regression analysis with the sparse dictionary gave excellent correlation between predicted and observed resistance. Conclusion: The approach of encoding the protein structure and sequence as a 210-dimensional vector, based on Delaunay triangulation, has promise as an accurate method for predicting resistance from sequence for drugs inhibiting HIV protease and reverse transcriptase.

关键词： Support Vector Machine Delaunay Triangulation Standard Support Vector Machine Structural Encode artificial neural network classifier

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A novel binary feature descriptor to discriminate normal and abnormal chest CT images using dissimilarity measures

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PATTERN ANALYSIS AND APPLICATIONS 2019年第4期22卷 1517-1526页

作者： Yelampalli, Praveen Kumar Reddy Nayak, Jagadish Gaidhane, Vilas Haridas Dubai Int Acad City Birla Inst Technol & Sci Pilani Dept Elect & Elect Engn Dubai CampusPOB 345055 Dubai U Arab Emirates

In this paper, a new feature descriptor local diagonal Laplacian pattern (LDLP) is proposed to separate the normal and emphysematous lesions in computed tomographic (CT) images containing pulmonary emphysema. LDLP employs a diagonal element approach in which the relationship of the centre pixel with the diagonal elements is obtained using the second-order derivatives (Laplacian). This results in a low-dimensional feature vector with richer information about the local structure. In the proposed framework, the feature histograms of chest CT image slices of EMPHYSEMA database are first determined. Further, the distance between the features of normal and emphysematous tissues is measured and analysed using ANOVA statistical method. Furthermore, a four-class classification is performed using an artificial neural network classifier. The classification performance of the proposed LDLP approach is compared with the prominent methods like local binary pattern, local tetra pattern, and local diagonal extrema pattern. The observational results show that the LDLP outperforms the existing binary feature descriptors in the segregation and classification of healthy and abnormal chest CT images.

关键词： Binary patterns Computed tomography Pulmonary emphysema Distance measures Statistical analysis Classification artificial neural network classifier

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