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Recognition and quality mapping of traditional herbal drugs:way forward towards artificial intelligence

Traditional Medicine Research 2025年第1期10卷 12-26页

作者： Sanyam Sharma Subh Naman Ashish Baldi Faculty of Pharmacy Pacific Academy of Higher Education and Research UniversityUdaipur 313024India Pharma Innovation Lab Department of Pharmaceutical Sciences and TechnologyMaharaja Ranjit Singh Punjab Technical UniversityBathinda 151001India Faculty of Pharmaceutical Sciences The ICFAI UniversityBaddi 174103India.

The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health ***,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal *** article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal *** focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning *** article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based *** also provides an overview of global herbal image databases and the models employed for herbal plant/drug *** will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural *** article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal *** article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.

关键词： artificial intelligence Ayurveda machine learning models herbal drugs image pre-processing medicinal plants

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An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction

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Frontiers of Computer Science 2017年第4期11卷 717-727页

作者： Sudipta ROY Debnath BHATTACHARYYA Samir Kumar BANDYOPADHYAY Tai-Hoon KIM Department of Computer Science and Engineering Institute of Computer Technology (UVPCE) Ganpat University Ahmadabad 380060 India Information Technology Department College of Engineering Bharati Vidyapeeth deemed University Pune 411043 India Department of Computer Science and Engineering University of Calcutta Technology Campus Kolkata 700098 India Department of Convergence Security Sungshin Women's University Seoul 136-742 Korea

This paper propose a computerized method of magnetic resonance imaging （MR/） of brain binarization for the uses of preprocessing of features extraction and brain ab- normality identification. One of the main problems of MR/ binarization is that many pixels of brain part cannot be cor- rectly binarized due to extensive black background or large variation in contrast between background and foreground of MR/. We have proposed a binarization that uses mean, vari- ance, standard deviation and entropy to determine a thresh- old value followed by a non-gamut enhancement which can overcome the binarization problem of brain component. The proposed binarization technique is extensively tested with a variety of MR/and generates good binarization with im- proved accuracy and reduced error. A comparison is carried out among the obtained outcome with this innovative method with respect to other well-known methods.

关键词： image binarization thresholding image pre-processing segmentation performance analysis accuracy es-timation MRI of brain entropy

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X-ray image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

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Computer Systems Science & Engineering 2022年第11期43卷 671-682页

作者： Shabana Habib Saleh Alyahya Aizaz Ahmed Muhammad Islam Sheroz Khan Ishrat Khan Muhammad Kamil Department of Information Technology College of ComputerQassim UniversityBuraidah52571Saudi Arabia Department of Electrical Engineering College of Engineering&Information TechnologyOnaizah CollegesOnaizah51911Saudi Arabia Digital image processing Lab Islamia CollegePeshawar25120KPKPakistan Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electrical Engineering National University of Computer and Emerging SciencesPeshawarPakistan

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic *** the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected *** COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain *** paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral *** approach includes preprocessing,feature extraction,and ***,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature ***,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is *** results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic *** is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.

关键词： image pre-processing detection classification x-ray images filter

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Automatic Visual Leakage Detection and Localization from Pipelines in Chemical Process Plants Using Machine Vision Techniques

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Engineering 2021年第6期7卷 758-776页

作者： Mina Fahimipirehgalin Emanuel Trunzer Matthias Odenweller Birgit Vogel-Heuser Institute of Automation and Information Systems Technical University of MunichGarching 85748Germany Evonik Technology and Infrastructure GmbH Hanau 63450Germany

Liquid leakage from pipelines is a critical issue in large-scale process *** in pipelines affects the normal operation of the plant and increases maintenance ***,it causes unsafe and hazardous situations for ***,the detection and localization of leakages is a crucial task for maintenance and condition ***,the use of infrared(IR)cameras was found to be a promising approach for leakage detection in large-scale *** cameras can capture leaking liquid if it has a higher(or lower)temperature than its *** this paper,a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process *** the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid,it is applicable for any type of liquid leakage(i.e.,water,oil,etc.).In this method,subsequent frames are subtracted and divided into ***,principle component analysis is performed in each block to extract features from the *** subtracted frames within the blocks are individually transferred to feature vectors,which are used as a basis for classifying the *** k-nearest neighbor algorithm is used to classify the blocks as normal(without leakage)or anomalous(with leakage).Finally,the positions of the leakages are determined in each anomalous *** order to evaluate the approach,two datasets with two different formats,consisting of video footage of a laboratory demonstrator plant captured by an IR camera,are *** results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR *** proposed method has high accuracy and a reasonable detection time for leakage *** possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.

关键词： Leakage detection and localization image analysis image pre-processing Principle component analysis k-nearest neighbor classification

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基于视频的英文字符识别系统设计

基于视频的英文字符识别系统设计

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2006年全国理论计算机科学学术年会

作者：庞云福贲可荣张秀山海军工程大学计算机工程系海军工程大学计算机工程系海军工程大学计算机工程系

＜正＞1 引言在阅读英语文献的时候,难免会碰到生词,甚至偏难怪的非常用词汇。目前的解决办法主要是依靠手工查词典或利用电子词典,使用起来效率不高。本文提出一种利用CMOS视频获取英文单词图像,

关键词： Optical character recognition image pre-processing Fature extraction RBF neural network

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