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Bilateral photoplethysmography for peripheral arterial disease screening in haemodialysis patients using astable multivibrator and machine learning classifier

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2019年第9期13卷 1277-1286页

作者： Wu, Jian-Xing Lin, Chia-Hung Kan, Chung-Dann Chen, Wei-Ling Natl Chin Yi Univ Technol Dept Elect Engn Taichung 41170 Taiwan Natl Chin Yi Univ Technol Artificial Intelligence Applicat Res Ctr Taichung 41170 Taiwan Natl Cheng Kung Univ Natl Cheng Kung Univ Hosp Coll Med Dept Surg Tainan 70101 Taiwan Kaohsiung Vet Gen Hosp KSVGH Originals & Enterprises Kaohsiung 81362 Taiwan Kaohsiung Vet Gen Hosp Dept Engn & Maintenance Kaohsiung 81362 Taiwan

Peripheral arterial disease (PAD) is highly prevalent in haemodialysis (HD) patients with type 2 diabetes. Atherosclerosis may occur in both lower and upper peripheral arteries, causing progressive dialysis access stenosis in HD patients. To assess the risk of PAD, non-invasive bilateral photoplethysmography (PPG) can be used to obtain continuous variations in blood flow volume in in vivo examinations. The authors propose an astable multivibrator to model the peripheral circulation system and to produce PPG oscillation with time constants, duty ratio (rising time), and amplitude ratio of systolic and diastolic pressures. Then, the bilateral differences in the time constant and duty ratio are used to separate the normal condition from PAD. The machine learning decision-making process utilises a screening method to automatically detect subjects with and without the risk of PAD. The radial-based function is employed to parameterise the similarity and dissimilarity levels using probability values. Colour relation analysis incorporates the probability values into the perceptual colour relationships for PAD screening. The experimental results indicate that in comparison with bilateral timing parameters, degree of stenosis, and resistive index, the proposed screening method is efficient in preventing complications of PAD and is easily implemented in an embedded system.

关键词： learning (artificial intelligence) diseases medical signal processing photoplethysmography haemodynamics blood vessels probability peripheral arterial disease screening haemodialysis patients astable multivibrator machine learning classifier type 2 diabetes upper peripheral arteries progressive dialysis access stenosis HD patients noninvasive bilateral photoplethysmography continuous variations blood flow volume peripheral circulation system PPG oscillation time constants duty ratio rising time amplitude ratio systolic pressures diastolic pressures bilateral differences screening method probability values PAD screening bilateral timing parameters in vivo examinations atherosclerosis lower peripheral arteries machine learning decision-making process radial-based function colour relation analysis perceptual colour relationships resistive index embedded system

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Plant leaf species identification using LBHPG feature extraction and machine learning classifier technique

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SOFT COMPUTING 2024年第6期28卷 5609-5623页

作者： Jadhav, Sachin B. Patil, Sanjay B. Jawaharlal Nehru Univ JNU Sch Comp & Syst Sci SC&SS New Delhi India Shri Chhatrapati Shivajiraje Coll Engn Pune Maharashtra India

This paper presents the identification and classification of Indian agricultural crop species using a novel combined local binary histogram pattern of gradient (LBHPG) image feature extraction technique. Initially, a partition of the leaf image background is done through the newly developed fast adaptive fuzzy C-mean clustering (FAFCM) technique. After that, leaf objects within the image are identified using the LBHPG method. For the classification, KNN, PNN, and SVM shallow machine learning classifiers are used for crop species identification. The performance evaluation is done using LBP and HOG individually along with the new proposed LBHPG technique for classification using KNN, PNN, and SVM classifiers. The performance evaluation is based on six metrics parameters of the confusion matrix, viz., accuracy, sensitivity, specificity, precision, recall, and F-measure. The experimental results show that the proposed novel LBHP feature extraction technique with PNN classifier gives the highest accuracy of 94.58%.

关键词： Plant leaf identification DIP-digital image processing FAFCM-fast adaptive fuzzy C-mean clustering technique Feature extraction LBHPG-local binary histogram pattern of gradient machine learning classifier

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A single nucleotide polymorphism panel for individual identification and ancestry assignment in Caucasians and four East and Southeast Asian populations using a machine learning classifier

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FORENSIC SCIENCE MEDICINE AND PATHOLOGY 2019年第1期15卷 67-74页

作者： Hwa, Hsiao-Lin Wu, Ming-Yih Lin, Chih-Peng Hsieh, Wei Hsin Yin, Hsiang-I Lee, Tsui-Ting Lee, James Chun-I Natl Taiwan Univ Dept & Grad Inst Forens Med Coll Med 1Sect 1Jen Ai Rd Taipei 100 Taiwan Natl Taiwan Univ Hosp Dept Obstet & Gynecol 7 Chung Shan S Rd Taipei 100 Taiwan Natl Taiwan Univ Hosp Dept Med Genet 7 Chung Shan S Rd Taipei 100 Taiwan Yourgene Biosci 376-5 Fuxing Rd New Taipei 238 Taiwan

Single nucleotide polymorphism (SNP) profiling is an effective means of individual identification and ancestry inferences in forensic genetics. This study established a SNP panel for the simultaneous individual identification and ancestry assignment of Caucasian and four East and Southeast Asian populations. We analyzed 220 SNPs (125 autosomal, 17 X-chromosomal, 30 Y-chromosomal, and 48 mitochondrial SNPs) of the DNA samples from 563 unrelated individuals of five populations (89 Caucasian, 234 Taiwanese Han, 90 Filipino, 79 Indonesian and 71 Vietnamese) and 18 degraded DNA samples. Informativeness for assignment (In) was used to select ancestry informative SNPs (AISNPs). A machine learning classifier, support vector machine (SVM), was used for ancestry assignment. Of the 220 SNPs, 62 were individual identification SNPs (IISNPs) (51 autosomal and 11 X-chromosomal SNPs) and 191 were AISNPs (100 autosomal, 13 X-chromosomal, 30 Y-chromosomal, and 48 mitochondrial SNPs). The 51 autosomal IISNPs offered cumulative random match probabilities (cRMPs) ranging from 1.56x10(-21) to 3.16x10(-22) among these five populations. Using AISNPs with the SVM, the overall accuracy rate of ancestry inference achieved in the testing dataset between Caucasian, Taiwanese Han, and Filipino populations was 88.9%, whereas it was 70.0% between Caucasians and each of the four East and Southeast Asian populations. For the 18 degraded DNA samples with incomplete profiling, the accuracy rate of ancestry assignment was 94.4%. We have developed a 220-SNP panel for simultaneous individual identification and ethnic origin differentiation between Caucasian and the four East and Southeast Asian populations. This SNP panel may assist with DNA analysis of forensic casework.

关键词： Ancestry assignment Array Individual identification machine learning classifier Single nucleotide polymorphism Support vector machine

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XSSclassifier: An Efficient XSS Attack Detection Approach Based on machine learning classifier on SNSs

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2017年第4期13卷 1014-1028页

作者： Rathore, Shailendra Sharma, Pradip Kumar Park, Jong Hyuk Seoul Natl Univ Sci & Technol SeoulTech Dept Comp Sci & Engn Seoul South Korea

Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as cross-site scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learning-based approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.

关键词： Cross-Site Scripting Attack Detection Dataset JavaScript machine learning classifier Social Networking Services

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Damage detection of in-service steel railway bridges using a fine k-nearest neighbor machine learning classifier

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STRUCTURES 2022年 45卷 1920-1935页

作者： Ghiasi, Alireza Ng, Ching-Tai Sheikh, Abdul Hamid Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5000 Australia Australian Rail Track Corp ARTC 11 Sir Donald Bradman Dr Keswick Terminal SA 5035 Australia

Minor areas of surface corrosion in steel railway bridges can grow progressively and lead to localized section losses and structural failure over time. This paper proposes a novel combined damage detection approach for the classification of various extents and degrees of cross section losses due to damages like corrosion using a k -Nearest Neighbor (kNN) machine learning classifier. A Finite Element (FE) model of an in-service railway bridge is developed and validated using vibration data from field testing and these combined FE-field data are trained and tested to classify various corrosion cases following the Australian Standard AS7636. The results show that the proposed technique is practical and highly accurate in classifying damages in steel railway bridges, even if a minor level of steel corrosion is intended to be classified. Furthermore, a comparison between the accuracies of kNN classifier and the Radial Basis Function (RBF) Gaussian kernel Support Vector machine (SVM) is presented.

关键词： Steel railway bridge Continuous wavelet transform Modal identification machine learning classifier k -Nearest neighbor Support vector machine

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An Approach for Detection of Botnet Based on machine learning classifier

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SN Computer Science 2024年第3期5卷 300页

作者： Tikekar, Priyanka C. Sherekar, Swati S. Kumar, Jatinder Department Computer Science Sant Gadge Baba Amravati University Maharashtra Amravati 444602 India Department of Computer Applications National Institute of Technology Kurukshetra Haryana Kurukshetra 136119 India

Botnet detection systems are becoming more important as cybercriminals continue to develop new Bot tools and applications. A botnet is a collection of several compromised systems that are connected to the central controller called a botmaster. These compromised devices are carried out various malicious activities, such as DDoS attacks, phishing, Email Spam, identity theft, stealing personal credentials of the user, etc. due to the dynamic change in the botnet size, it is difficult to detect the botnet. As long as botmasters are coming up with new ways to attack, sophisticated solutions for botnet detection are very essential. To illustrate how to use these tools, this paper will discuss several tools and processes involved in developing a Botnet detection system. Different libraries like Scikit Learn, Pandas, Theano, Matplotlib, Pickel, and NumPy are used. Additionally, the processes for utilising these tools are illustrated in this paper. The features are extracted like packet size, packet byes, source address, destination address, length, and corresponding protocols. Feature extraction requires a significant amount of domain expertise and manual work from professionals in current machine learning-based botnet detection systems. Botnets are divided based on their protocol, such as Internet relay chat, DNS, and P2P which are used by the C&C Server. In this paper, we proposed a model to detect Botnet using three machine learning algorithms, i.e., K-Nearest Neighbor (KNN), Decision Tree (DT), and Naive Bayes (NB) for the experiments on a dataset among these three classifiers NB performs the best and has an accuracy of 90.62%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Botnet C& C server Detection technique machine learning classifier

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Building a machine learning classifier for Malware Detection 2

Building a Machine Learning Classifier for Malware Detection

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Proceedings of the 2014 Second Workshop on Anti-malware Testing Research

作者： Markel, Zane Bilzor, Michael US Naval Acad Dept Comp Sci Annapolis MD 21402 USA

ISBN: (纸本)9781479960705

Current signature-based antivirus software is ineffective against many modern malicious software threats. machine learning methods can be used to create more effective antimalware software, capable of detecting even zero-day attacks. Some studies have investigated the plausibility of applying machine learning to malware detection, primarily using features from n-grams of an executables file's byte code. We propose an approach that primarily learns from metadata, mostly contained in the headers of executable files, specifically the Windows Portable Executable 32-bit (PE32) file format. Our experiments indicate that executable file metadata is highly discriminative between malware and benign software. We also employ various machine learning methods, finding that Decision Tree classifiers outperform Logistic Regression and Naive Bayes in this setting. We analyze various features of the PE32 header and identify those most suitable for machine learning classifiers. Finally, we evaluate changes in classifier performance when the malware prevalence (fraction of malware versus benign software) is varied.

关键词： decision trees invasive software learning (artificial intelligence) pattern classification regression analysis Windows Portable Executable file format antimalware software decision tree classifiers logistic regression machine learning classifier malicious software threat malware detection malware prevalence meta data naive Bayes signature-based antivirus software zero-day attacks Databases Decision trees Feature extraction Logistics Malware Software Training Spyware Decision Trees Metadata Invasive software computer software Pattern recognition Logistic Regression Logistics Feature extraction Artificial Intelligence machine learning Database classifiers regression analysis Functional training

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Diagnosis of Community-Acquired pneumonia in children using photoplethysmography and machine learning-based classifier

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期87卷

作者： Kanwal, Kehkashan Khalid, Syed Ghufran Asif, Muhammad Zafar, Farhana Qurashi, Aisha Ghazal Ziauddin Univ Fac Engn Sci Technol & Management Dept Elect Engn Karachi Pakistan Nottingham Trent Univ Sch Sci & Technol Dept Engn Nottingham England Sir Syed Univ Engn & Technol Fac Comp & Appl Sci Karachi Pakistan Ziauddin Univ Dept Paediat Karachi Pakistan Jubilee Healthcare Ctr Coventry England

This paper presents a novel approach for diagnosing Community-Acquired Pneumonia (CAP) in children using single-channel photoplethysmography (PPG) using machine learning Traditional diagnostic methods (x-rays systems and blood tests) for pneumonia suffer from limitations e.g., unavailability in remote rural areas, time consumption, financial burden, and reliance on invasive procedures. This novel approach uses the PPG recording alone to generate accurate and rapid diagnoses of CAP in children that may facilitate healthcare practitioners in low-resource clinical settings in future. A cross-sectional study was carried out to collect the PPG recordings of 67 paediatric participants (31 CAP and 36 healthy). Five different machine learning classifiers namely Fine Decision tree, Linear Discriminant Analysis, Weighted K Nearest Neighbour, Wide Neural Network, and Ensemble of Bagged Trees using eight PPG signal features were employed. Using weighted KNN we predicted 9 out of 10 test subjects correctly. These results demonstrate the potential of the system to improve clinical decision-making and patient outcomes since despite the thriving advancements in healthcare paediatric pneumonia remains a major health concern.

关键词： Photoplethysmography Pneumonia detection machine learning classifier Decision Tree Discriminant analysis

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COMPARATIVE ANALYSIS OF THE CLASSIFICATION PERFORMANCE OF machine learning classifierS AND DEEP NEURAL NETWORK classifier FOR PREDICTION OF PARKINSON DISEASE 15

COMPARATIVE ANALYSIS OF THE CLASSIFICATION PERFORMANCE OF MA...

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15th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Ul Haq, Amin Li, Jianping Memon, Muhammad Hammad Khan, Jalaluddin Din, Salah Ud Ahad, Ijaz Sun, Ruinan Lai, Zhilong Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781728115351

The accurate diagnosis of Parkinson disease specifically in its initial stages is extremely complex and time consuming. Thus the accurate and efficient diagnosis of Parkinson disease has been a significant challenge for medical experts and researchers. In order to tackle the accurate diagnosis of Parkinson disease issue we proposed machine learning and deep neural networks based non-invasive prediction system for accurately and on time diagnosis of Parkinson disease. In the development of the system machine learning predictive models such as support vector machine, logistic regression and deep neural network were used for people with Parkinson disease and healthy people classification. The data set was splits into 70% for training purpose and 30% for testing. Furthermore, performance evaluation metrics such as classification accuracy, sensitivity, specificity and Matthews's correlation coefficient were utilized for model performance evaluation. The Parkinson disease dataset of 23 attributes and 195 instances available on UCI machine learning repository was used for testing of the proposed system. Through our experimental results analysis shows that the proposed system classified the Parkinson disease and healthy people effectively. We also investigated that deep neural performance of classification was excellent as compared to traditional machines learning classifiers. These finding suggest that the proposed diagnosis system could be used to accurately predict Parkinson disease.

关键词： Parkinson disease Classification machine learning classifier Deep neural network Diagnosis

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Predicting vulnerable coronary arteries: A combined radiomics-biomechanics approach

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2025年 260卷 108552-108552页

作者： Corti, Anna Stefanati, Marco Leccardi, Matteo De Filippo, Ovidio Depaoli, Alessandro Cerveri, Pietro Migliavacca, Francesco Corino, Valentina D. A. Matas, Jose F. Rodriguez Mainardi, Luca Dubini, Gabriele Politecn Milan Dept Elect Informat & Bioengn Via Ponzio 34-5 I-20133 Milan Italy Politecn Milan Dept Chem Mat & Chem Engn Giulio Natta Lab Biol Struct Mech LaBS Milan Italy Univ Turin Citta Salute & Sci Torino Hosp Dept Med Sci Div Cardiol Turin Italy Univ Turin Citta Salute & Sci Torino Hosp Dept Surg Sci Radiol Unit Turin Italy Univ Pavia Dept Ind & Informat Engn Pavia Italy Ctr Cardiol Monzino IRCCS Cardiotech Lab Milan Italy

Background and Objective: Nowadays, vulnerable coronary plaque detection from coronary computed tomography angiography (CCTA) is suboptimal, although being crucial for preventing major adverse cardiac events. Moreover, despite the suggestion of various vulnerability biomarkers, encompassing image and biomechanical factors, accurate patient stratification remains elusive, and a comprehensive approach integrating multiple markers is lacking. To this aim, this study introduces an innovative approach for assessing vulnerable coronary arteries and patients by integrating radiomics and biomechanical markers through machine learning methods. Methods: The study included 40 patients (7 high-risk and 33 low-risk) who underwent both CCTA and coronary optical coherence tomography (OCT). The dataset comprised 49 arteries (with 167 plaques), 7 of which (with 28 plaques) identified as vulnerable by OCT. Following image preprocessing and segmentation, CCTA-based radiomic features were extracted and a finite element analysis was performed to compute the biomechanical features. A novel machine learning pipeline was implemented to stratify coronary arteries and patients. For each stratification task, three independent predictive models were developed: a radiomic, a biomechanical and a combined radiomic-biomechanical model. Both k-nearest neighbors (KNN) and decision tree (DT) classifiers were considered. Results: The best radiomic model (KNN) detected all 7 vulnerable arteries and patients and was associated with a balanced accuracy of 0.86 (sensitivity=1, specificity=0.71) for the artery model and of 0.83 (sensitivity=1, specificity=0.67) for the patient model. The best biomechanical model (DT) detected 6 over 7 vulnerable arteries and patients and remarkably increased the specificity, resulting in a balanced accuracy of 0.89 (sensitivity=0.86, specificity=0.93) for the artery model and of 0.88 (sensitivity=0.86, specificity=0.91) for the patient model. Notably, the combined approach

关键词： Coronary computed tomography angiography Atherosclerotic plaque Major adverse cardiac events Radiomic features Finite element analysis machine learning classifier

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