AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

Classification and Nomenclature of Plant Metallothionein-like Proteins Based on Their Cysteine Arrangement Patterns

随着植物基因组研究的进展 ,在基因文库和蛋白文库登录的植物类金属硫蛋白基因已超过 5 0个 ,接近金属硫蛋白总数的 1/ 3,而且有不断上升的趋势。鉴于目前植物类金属硫蛋白命名与分类随意性太大 ,很有必要建立一个统一合理的命名与分类法。对植物类金属硫蛋白一级结构进行详细分析后 ,发现该蛋白两端富含半胱氨酸的区域内半胱氨酸的排列方式颇具规律性 ,进而提出了以半胱氨酸排列方式为基础的分类及命名法 ,并阐述了采用这种方法的理由及其可行性。

Improved Algorithm of Pattern Classification and Recognition Applied in a Coal Dust Sensor

To resolve the conflicting requirements of measurement precision and real-time performance speed,an im-proved algorithm for pattern classification and recognition was developed. The angular distribution of diffracted light varies with particle size. These patterns could be classified into groups with an innovative classification based upon ref-erence dust samples. After such classification patterns could be recognized easily and rapidly by minimizing the vari-ance between the reference pattern and dust sample eigenvectors. Simulation showed that the maximum recognition speed improves 20 fold. This enables the use of a single-chip,real-time inversion algorithm. An increased number of reference patterns reduced the errors in total and respiring coal dust measurements. Experiments in coal mine testify that the accuracy of sensor achieves 95%. Results indicate the improved algorithm enhances the precision and real-time ca-pability of the coal dust sensor effectively.

Progressive transductive learning pattern classification via single sphere

In many machine learning problems, a large amount of data is available but only a few of them can be labeled easily. This provides a research branch to effectively combine unlabeled and labeled data to infer the labels of unlabeled ones, that is, to develop transductive learning. In this article, based on Pattern classification via single sphere （SSPC）, which seeks a hypersphere to separate data with the maximum separation ratio, a progressive transductive pattern classification method via single sphere （PTSSPC） is proposed to construct the classifier using both the labeled and unlabeled data. PTSSPC utilize the additional information of the unlabeled samples and obtain better classification performance than SSPC when insufficient labeled data information is available. Experiment results show the algorithm can yields better performance.

Hierarchical pattern recognition of landform elements considering scale adaptation

Landform elements with varying morphologies and spatial arrangements are recognized as feature indicator of landform classification and play a critical role in geomorphological studies.Differential geometry method has been extensively applied in prior landform element research,while its efficacy in differentiating similar morphological characteristics remains inadequate to date.To reduce reliance on geomorphometric variables and increase awareness of landform patterns,geomorphons method was generated in previous study corresponding to specific landform reclassification map based on lookup table.Besides,to address the problem of feature similarity,hierarchical classification was proposed and effectively utilized for terrain recognition through the analytical strategy of fuzzy gradient features.Thus,combining the advantages of these two aspects,a hierarchical framework was proposed in this study for landform element pattern recognition considering the morphology and hierarchy factors.First,the local triplet patterns derived from geomorphons were enhanced by setting the flatness threshold,and subsequently adopted for the primary landform element recognition.Then,as geomorphic units with the same morphology possess different spatial analytical scales,the unidentified landform elements under the principle of scale adaptation were determined by calculating the spatial correlation and entropy information.To ensure the effectiveness of this proposed method,the sampling points were randomly selected from NASADEM data and then validated against a real 3D terrain model.Quantitative results of landform element pattern recognition demonstrate that our approach can reach above 77%average accuracy.Additionally,it delineates local details more effectively than geomorphons in visual assessment,resulting in a 7%accuracy improvement in overall scale.

Classification of 3D Film Patterns with Deep Learning

Researches on pattern recognition have been tremendously performed in various fields because of its wide use in both machines and human beings. Previously, traditional methods used to study pattern recognition problems were not strong enough to recognize patterns accurately as compared to optimization algorithms. In this study, we employ both traditional based methods to detect the edges of each pattern in an image and apply convolutional neural networks to classify the right and wrong pattern of the cropped part of an image from the raw image. The results indicate that edge detection methods were not able to detect clearly the patterns due to low quality of the raw image while CNN was able to classify the patterns at an accuracy of 84% within 1.5 s for 10 epochs.

A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK

Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.

Gender Classification from Fingerprint Using Hybrid CNN-SVM

Gender classification is used in numerous applications such as biometrics,criminology,surveillance,HCI,and business profiling.Although biometric factors like gait,face,hand shape,and iris have been used to classify people into genders,the majority of research has focused on facial traits due to their more recognizable qualities.This research employs fingerprints to classify gender,with the intention of being relevant for future studies.Several methods for gender classification utilizing fingerprints have been presented in the literature,including ANN,KNN,Naive Bayes,the Gaussian mixture model,and deep learning-based classifiers.Although these classifiers have shown good classification accuracy,gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy,computation,and running time.In this paper,a CNN-SVM hybrid framework for gender classification from fingerprints is proposed,where preprocessing,feature extraction,and classification are the three main components.The main goal of this study is to use CNN to extract fingerprint information.These features are then sent to an SVM classifier to determine gender.The hybrid model’s performance measures are examined and compared to those of the conventional CNN model.Using a CNN-SVM hybrid model,the accuracy of gender classification based on fingerprints was 99.25%.

DIMENSIONALITY REDUCTION BASED ON SVM AND LDA,AND ITS APPLICATION TO CLASSIFICATION TECHNIQUE

Some dimensionality reduction （DR） approaches based on support vector machine （SVM） are proposed. But the acquirement of the projection matrix in these approaches only considers the between-class margin based on SVM while ignoring the within-class information in data. This paper presents a new DR approach, call- ed the dimensionality reduction based on SVM and LDA （DRSL）. DRSL considers the between-class margins from SVM and LDA, and the within-class compactness from LDA to obtain the projection matrix. As a result, DRSL can realize the combination of the between-class and within-class information and fit the between-class and within-class structures in data. Hence, the obtained projection matrix increases the generalization ability of subsequent classification techniques. Experiments applied to classification techniques show the effectiveness of the proposed method.

A chaotic neural network mimicking an olfactory system and its application on image recognition

Based on the research of a biological olfactory system, a novel chaotic neural network model - K set model has been es- tablished. This chaotic neural network not only simulates the real brain activity of an olfactory system, but also presents a novel chaotic concept for signal processing and pattern recognition. The characteristics of the K set models are investigated and show that a KIII model can be used for image pattern classification.

Feature selection algorithm for text classification based on improved mutual information

In order to solve the poor performance in text classification when using traditional formula of mutual information （MI） , a feature selection algorithm were proposed based on improved mutual information. The improved mutual information algorithm, which is on the basis of traditional improved mutual information methods that enbance the MI value of negative characteristics and feature＇ s frequency, supports the concept of concentration degree and dispersion degree. In accordance with the concept of concentration degree and dispersion degree, formulas which embody concentration degree and dispersion degree were constructed and the improved mutual information was implemented based on these. In this paper, the feature selection algorithm was applied based on improved mutual information to a text classifier based on Biomimetic Pattern Recognition and it was compared with several other feature selection methods. The experimental results showed that the improved mutu- al information feature selection method greatly enhances the performance compared with traditional mutual information feature selection methods and the performance is better than that of information gain. Through the introduction of the concept of concentration degree and dispersion degree, the improved mutual information feature selection method greatly improves the performance of text classification system.

Modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition Model

Traditional indoor human activity recognition(HAR)is a timeseries data classification problem and needs feature extraction.Presently,considerable attention has been given to the domain ofHARdue to the enormous amount of its real-time uses in real-time applications,namely surveillance by authorities,biometric user identification,and health monitoring of older people.The extensive usage of the Internet of Things(IoT)and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing.The more commonly utilized inference and problemsolving technique in the HAR system have recently been deep learning(DL).The study develops aModifiedWild Horse Optimization withDLAided Symmetric Human Activity Recognition(MWHODL-SHAR)model.The major intention of the MWHODL-SHAR model lies in recognition of symmetric activities,namely jogging,walking,standing,sitting,etc.In the presented MWHODL-SHAR technique,the human activities data is pre-processed in various stages to make it compatible for further processing.A convolution neural network with an attention-based long short-term memory(CNNALSTM)model is applied for activity recognition.The MWHO algorithm is utilized as a hyperparameter tuning strategy to improve the detection rate of the CNN-ALSTM algorithm.The experimental validation of the MWHODL-SHAR technique is simulated using a benchmark dataset.An extensive comparison study revealed the betterment of theMWHODL-SHAR technique over other recent approaches.

A novel approach in ECG beat recognition using adaptive neural fuzzy filter

Accurate and computationally efficient means of electrocardiography (ECG) arrhythmia detec-tion has been the subject of considerable re-search efforts in recent years. Intelligent com-puting tools such as artificial neural network (ANN) and fuzzy logic approaches are demon-strated to be competent when applied individu-ally to a variety of problems. Recently, there has been a growing interest in combining both of these approaches, and as a result, adaptive neural fuzzy filters (ANFF) [1] have been evolved. This study presents a comparative study of the classification accuracy of ECG signals using (MLP) with back propagation training algorithm, and a new adaptive neural fuzzy filter architec-ture (ANFF) for early diagnosis of ECG ar-rhythmia. ANFF is inherently a feed forward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules [1]. In this paper we used an adap-tive neural fuzzy filter as an ECG beat classifier. We combined 3 famous wavelet transforms and used them mid 4 the order AR model coefficient as features. Our results suggest that a new proposed classifier (ANFF) with these features can generalize better than ordinary MLP archi-tecture and also learn better and faster. The results of proposed method show high accu-racy in ECG beat classification (97.6%) with 100% specificity and high sensitivity.

基于光纤声发射的局部放电信号识别方法分析

局部放电(PD)是电气设备绝缘退化的主要原因,相较于传统的检测方法,光纤声发射检测技术具有不影响设备运行方式、抗电磁干扰能力强等优点。设计并制作了尖端放电、气泡放电、悬浮放电和沿面放电4种典型局部放电模型,搭建了基于光纤声发射的局部放电声发射信号采集试验平台,采集了梯度施加电压下的局部放电声发射信号并对其进行了参量统计分析,提出了将去噪后信号时频图作为卷积神经网络特征输入的识别模型。试验结果表明,该识别模型对于淹没在噪声中的微弱局部放电信号具有良好的识别效果,识别准确率可达98%。

A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of diabetes.At present,deep convolutional neural networks have achieved promising performance in automatic DR detection tasks.The convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for processing.However,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(e.g.hemorrhages and exudates)scattered throughout the whole image.The proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR detection.Patch-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as plaques.The Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained networks.Extensive experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

基于弹性振动和深度学习的变压器状态识别

针对当前传统的变压器状态识别算法需要人工干预的问题,研究了一种能够自动提取特征并分类的一维卷积神经网络算法。该算法通过3层卷积池化层自动提取信号特征,并通过全连接层展为一维矢量,最终通过Softmax层进行分类。鉴于弹性振动信号抗干扰能力较强,选择弹性振动信号作为信号处理研究对象,运用基于一维卷积神经网络和弹性振动的方法对变压器状态进行识别,并通过采集500kV变压器的弹性振动信号获取的数据集进行验证,结果表明该算法的准确率优于BP、SVM和SAE算法,能对变压器的不同状态实现自动有效识别。

Grade classification of neuroepithelial tumors using high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy and pattern recognition

Clinical data have shown that survival rates vary considerably among brain tumor patients,according to the type and grade of the tumor.Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS 1H NMRS) can provide important information on tumor biology and metabolism.These metabolic fingerprints can then be used for tumor classification and grading,with great potential value for tumor diagnosis.We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies,including two astrocytomas (grade I),12 astrocytomas (grade II),eight anaplastic astrocytomas (grade III),three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS 1H NMRS.The results were correlated with pathological features using multivariate data analysis,including principal component analysis (PCA).There were significant differences in the levels of N-acetyl-aspartate (NAA),creatine,myo-inositol,glycine and lactate between tumors of different grades (P<0.05).There were also significant differences in the ratios of NAA/creatine,lactate/creatine,myo-inositol/creatine,glycine/creatine,scyllo-inositol/creatine and alanine/creatine (P<0.05).A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%.HRMAS 1H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades.

A PATTERN RECOGNITION APPROACH FOR TONE CLASSIFICATION OF ISOLATED SYLLABLE IN PUTONGHUA

In this paper ,a new approach of pattern recognition for tone classification of Putonghua Which is important for speech recognition of Putonghua is discribed . In this method , four parameters of the fundamental frequency trajectory are selected based on a large number of statistical experiments . It is assumed that the four parameters satisfy multidimensional Gaussion distribution and a non-Euclidean distance function for each tone class is derived according to the rule of minimum probability of calssification error . the optimal decision results are obtained in a sense of statistics . It is proved that this method provides very satisfactory results by the experiments for speaker-independent tone classification of Putonghua .

脑电信号的稳定扩散样本增强方法

针对脑电信号样本稀缺导致的分类性能瓶颈问题,本文提出了一种脑电的稳定扩散模型样本增强方法。通过将脑电样本转换为时频图,并采用有效提示词微调图像稳定扩散模型以扩充脑电样本集,以提升分类识别准确率。实验结果表明,经过稳定扩散模型增强后的样本集,对其分类的准确率由72.94%提高到76.80%,说明本文方法有效地破除了分类性能瓶颈,为小样本脑电样本分析提供了新途径。