K-nearest Neighbor Algorithm Research Articles

Investigation of Personality Traits Using Data from Facebook, Tweet's and Handwriting Samples

Personality characteristics are characterized as persistent habits of mind that have been related to a wide range of consequential choices and events. Personality qualities are correlated with one's level of happiness in relationships, career paths pursued, and other areas of life. As a result, there was surge in recent years in desire to create models that can anticipate personality characteristics in an automated fashion. With use of social media, anybody may establish a virtual identity & connect with others via the sharing of personal information and user-generated material. One kind of user behavior which socioeconomic and psychological factors often shape is how they present themselves. Furthermore, there has been lot of interest in the ability to anticipate a person's personality based upon their physiological signals, which may be analyzed in real time. Personality can be predicted considerably more accurately, which can be quite beneficial for society as a whole. And many of the researchers used the provided data for the methodical creation of health care, social marketing networks, and individually recognized advice. And after a brief discussion of the admiration of social networks like Twitter, Facebook, & Linked In, researchers were able to conduct their studies using the public data that was made available through these platforms as well as social behavior traits that could be used to predict personality traits for friends and followers. Online social media platforms networks offer increasing opportunities for knowledge discovery and the public's data in relation to their views. The social media networking content's structured information can be used to forecast the key and most significant personality traits. These days, people frequently communicate some of their ideas, facts, and emotions—and especially their personality traits—on social media platforms, which is how data is produced. Proposed the Myers Briggs Type Indicator (MBTI system) is a personality predicting type system that system that divides people into 16 components of personality types based on four axes: Extroversion(E) – Introversion(I) Thinking(T) – Feeling(F) using KNN and Logistic regression machine learning algorithms.

An Individual Tree Detection and Segmentation Method from TLS and MLS Point Clouds Based on Improved Seed Points

Individual Tree Detection and Segmentation (ITDS) is a key step in accurately extracting forest structural parameters from LiDAR (Light Detection and Ranging) data. However, most ITDS algorithms face challenges with over-segmentation, under-segmentation, and the omission of small trees in high-density forests. In this study, we developed a bottom–up framework for ITDS based on seed points. The proposed method is based on density-based spatial clustering of applications with noise (DBSCAN) to initially detect the trunks and filter the clusters by a set threshold. Then, the K-Nearest Neighbor (KNN) algorithm is used to reclassify the non-core clustered point cloud after threshold filtering. Furthermore, the Random Sample Consensus (RANSAC) cylinder fitting algorithm is used to correct the trunk detection results. Finally, we calculate the centroid of the trunk point clouds as seed points to achieve individual tree segmentation (ITS). In this paper, we use terrestrial laser scanning (TLS) data from natural forests in Germany and mobile laser scanning (MLS) data from planted forests in China to explore the effects of seed points on the accuracy of ITS methods; we then evaluate the efficiency of the method from three aspects: trunk detection, overall segmentation and small tree segmentation. We show the following: (1) the proposed method addresses the issues of missing segmentation and misrecognition of DBSCAN in trunk detection. Compared to using DBSCAN directly, recall (r), precision (p), and F-score (F) increased by 6.0%, 6.5%, and 0.07, respectively; (2) seed points significantly improved the accuracy of ITS methods; (3) the proposed ITDS framework achieved overall r, p, and F of 95.2%, 97.4%, and 0.96, respectively. This work demonstrates excellent accuracy in high-density forests and is able to accurately segment small trees under tall trees.

Comparison of the K Nearest Neighbor Algorithm with Apriori Algorithm to Analyze Lifestyle Patterns in Hypertensive Patients

Hypertension is one of the most influential cardiovascular diseases that can result in organ disorders such as heart dysfunction or stroke, and hypertension is often found by chance. Hypertension is a condition in which blood pressure increases drastically to above-normal limits and forces the heart to work harder in pumping blood to meet the body's oxygen needs. This disease can interfere with the work of other organs if left untreated, especially the heart and kidneys. Not paying attention to diet, exercise, stress, smoking, and drinking alcohol can all be responsible for an increased risk of hypertension. An a priori algorithm was used in the Sei Semayang Health Center case study to predict hypertensive patients and compare their behavioural patterns and lifestyles. So, the results of rapid-miner using a priori algorithm to analyse the Comparison of the K-Nearest Neighbor Algorithm with the Apriori Algorithm to Analyze Lifestyle Patterns in Hypertensive Patients the results obtained U1, which means that there are hypertensive patients aged 25-38 years who have more hypertension. The result is H2, which means that the community has but does not control the doctor with a pattern style such as consuming alcohol, smoking, lack of exercise, consumption of sugar, consumption of saturated fats and foods that contain much salt and rarely consuming vegetables or fruits and foods that contain mucin, so more. More people have hypertension by way of an unhealthy lifestyle.

A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities.

This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it's important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD.

Analisis Sentimen Review ChatGPT di Play Store menggunakan Support Vector Machine dan K-Nearest Neighbor

The ChatGPT application for Android was launched on July 25, 2023, and the language model from OpenAI achieved a rating of 4.8 until early 2024. Despite the majority of positive reviews, user reports stating that ChatGPT provides inaccurate answers raise concerns about the reliability of this application. This research aims to compare the models of the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms in analyzing the sentiment of ChatGPT application reviews. Utilizing text mining methods to extract information from text, data was collected from Google Play Store reviews using data scraping techniques and analyzed with Support Vector Machine and K-Nearest Neighbor algorithms. Cross-validation with 5 folds and data split using 80% training and 20% testing data were applied to evaluate the performance of both algorithms. The sentiment classification results showed that the Support Vector Machine algorithm achieved an average accuracy of 80%, while K-Nearest Neighbor reached 71%. SVM excels due to its ability to overcome KNN's limitations regarding less relevant features that do not significantly contribute to predictions. The findings of this study are expected to help developers understand and respond to user feedback regarding the reliability of ChatGPT.

Prediksi Kelulusan Mata Kuliah Mahasiswa Teknologi Informasi Menggunakan Algoritma K-Nearest Neighbor

This research aims to develop a predictive model using the K-Nearest Neighbor (KNN) method to forecast the course completion of students in the Information Technology program. The issue at hand is the uncertainty in predicting student success based on historical data and specific attributes. This study focuses on the importance of understanding the factors that influence student success in the Database Management Systems course to provide accurate predictions and help improve student pass rates in this course. The objective of this research is to build a predictive model using the KNN algorithm and to implement this model using the PHP programming language. The study aims to offer valuable insights for educational institutions to enhance teaching and learning processes and to expand understanding of data mining concepts in specific case studies. The prediction aims to determine whether a student will pass or fail the Database Management Systems course based on predetermined training and testing data. By calculating the nearest distance between the training data and the test data. The results showed an accuracy rate of 90% for predicting course completion using k=5, with a dataset consisting of 40 training data points and 20 testing data points.

Implementasi Tunelling pada Perancangan Sistem Peringkasan dan Klasifikasi Berita Otomatis menggunakan Textrank dan KNN

News summarization is very important in the news analysis process. However, in the summarization process, there are often obstacles such as the large number of news stories and the need for news classification. This research aims to build a simple web-based system that can be used to summarize and classify news which will be very useful in the news analysis process. The proposed summarization method is Textrank, and the news classification method that will be used is KNN. This system is expected to provide an automatic summarization function to make it easier to analyze news content. The data that will be used as the basis for classification modeling is sports news in 3 months, and the classification that will be used to determine whether the news includes sports news in three branches, namely football, rackets or basketball. Testing of the summarization model using textrank was carried out by applying ROUGE-1 and ROUGE-2, with results of 0.79 and 0.67. Meanwhile, testing the classification model using KNN with k=3 and k=5 is 0.9866 and 0.9666 so k=3 will be used. This system will be built using the web scrapping library, textrank, stopword from PySastrawi, scikit-learn for the classification module using the KNN algorithm, and ngrok for publishing web-based applications. By using ngrok, we can expose the application through internet with a temporary public url without hosting required

NÂNG CAO HIỆU QUẢ AN NINH MẠNG DỰA TRÊN MỘT SỐ KỸ THUẬT GIẢM CHIỀU DỮ LIỆU

This paper focuses on proposing a network intrusion detection model applying fundamental machine learning techniques to enhance early detection of network intrusions (rapid detection of attack behaviors) for improved efficiency in preventing network attacks. The system must still ensure technical accuracy in providing high-precision alerts. The research employs several dimensionality reduction techniques to detect abnormal network intrusions caused by Distributed Denial of Service (DDoS) attacks. The proposed model aims to reduce computation time for early attack detection. The results show that the proposed system performs best across all three datasets through the combination of the KNN algorithm and the Feature Importance dimensionality reduction technique. After calculating and returning the number of important features in attack detection using the Importance technique, the performance of the KNN algorithm is enhanced. By retaining only important features, as the dimensionality of the data decreases, the computation speed of KNN increases. Therefore, although the accuracy may slightly decrease, the computation time is significantly reduced. This is acceptable for practical purposes.

Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors

This research utilized machine learning to analyze experiments conducted in an open channel laboratory setting to predict microplastic transport with varying discharge, velocity, water depth, vegetation pattern, and microplastic density. Four machine learning (ML) models, incorporating Random Forest (RF), Decision Tree (DT), Extreme Gradient Boost (XGB) and K-Nearest Neighbor (KNN) algorithms, were developed and compared with the Linear Regression (LR) statistical model, using 75% of the data for training and 25% for validation. The predictions of ML algorithms were more accurate than the LR, while XGB and RF provided the best predictions. To explain the ML results, Explainable artificial intelligence (XAI) was employed by using Shapley Additive Explanations (SHAP) to predict the global behavior of variables. RF was the most reliable model, with a coefficient of correlation of 0.97 and a mean absolute percentage error of 1.8% after hyperparameter tuning. Results indicated that discharge, velocity, water depth, and vegetation all influenced microplastic transport. Discharge and vegetation enhanced and reduced microplastic transport, respectively, and showed a response to different vegetation patterns. A strong linear positive correlation (R2 = 0.8) was noted between microplastic density and retention. In the absence of dedicated microplastic transport analytical models and infeasibility of using classical sediment transport models in predicting microplastic transport, ML proved to be helpful. Moreover, the use of XAI will reduce the black-box nature of ML models with effective interpretation enhancing the trust of domain experts in ML predictions. The developed model offers a promising tool for real-world open channel predictions, informing effective management strategies to mitigate microplastic pollution.

Performance Analysis of the Ensemble Model in Anaemia Detection from Unmodified Smartphone-Captured Conjunctiva Images

Anaemia is a blood-related disorder that is sometimes a harbinger of a much more severe condition like cancer. WHO recommends context-specific diagnosing methods for anaemia in the case of in-feasibility of traditional diagnosing methods, including a haemoglobin colour scale method that is based on the colour of the blood. In this manifesto, we take it one step further and diagnose anaemia using images of lower palpebral conjunctiva captured in consumer-grade cameras. Data that include lower palpebral conjunctiva images, basic details, and haemoglobin values are collected from participants. Pixel parameters of collected conjunctiva images are extracted in various colour spaces and used as input features. An ensemble learning model was developed with K-Nearest Neighbour (KNN), Random Forest and Decision Tree algorithms which showed better classification performance in classifying anaemic and non-anaemic samples. This Ensemble Model-Based Anaemia Classifier (EMBAC) performed well with both training and test sets and produced 90.91% sensitivity, 89.06% specificity, 89.69% accuracy, and area under the Receiver Operating Characteristics(ROC) curve of 0.90 for the unseen test data. EMBAC also outperformed the conventional ensemble algorithms in terms of sensitivity and accuracy. EMBAC will classify the samples more correctly irrespective of the image acquisition device under any lighting conditions without any additional hardware. This research work attempts to lay the foundation for a smartphone application to detect anaemia in real-life conditions which will enable access to a mobile healthcare instrument to diagnose anaemia for the proletariat.

Machine Learning Techniques in Enhanced Oil Recovery Screening Using Semisupervised Label Propagation

Summary Efficiently choosing the optimal enhanced oil recovery (EOR) technique is a critical requirement in reservoir engineering. Machine learning (ML) methods, with a well-established history of application, serve as a swift and dependable tool for EOR screening. In this paper, we aim to evaluate the effectiveness of various ML algorithms for EOR screening, utilizing a comprehensive database of nearly 1,000 EOR projects. This study delves into a comprehensive evaluation of regression and classification-based algorithms to develop a reliable screening system for EOR predictions and address challenges such as limited labeled data and missing values. Our analysis considered various EOR processes, including gas injection, chemical, and thermal EOR techniques. Various ML methods such as random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), shallow artificial neural networks (SANN), naive Bayes classifier (NBC), logistic regression (LR), and decision tree (DT) are applied, enabling both intermethod comparisons and evaluations against advanced methods, multiobjective deep artificial neural networks (MDANN), and multiobjective artificial neural networks (MANN). These advanced techniques provide the unique capability to concurrently address both regression and classification tasks. Considering that conventional methods can only be implemented on a single task, the RF, MANN, MDANN, and KNN algorithms demonstrated top-tier performance in our classification analysis. Regarding the regression task, KNN, RF, and MDANN displayed exceptional performance, signifying their prowess in predictive accuracy. However, MANN exhibited moderate performance in regression analysis. In addition, our study identified areas where certain algorithms, such as support vector regression (SVR), exhibited weaker performance, highlighting the importance of comprehensive model evaluation. This paper contributes novel insights into the application of ML techniques for EOR screening in the petroleum industry. By addressing challenges such as limited labeled data and missing values and by providing a thorough evaluation of various ML algorithms, our study offers valuable information for decision-makers in the oil and gas sector, aiding in the selection of suitable algorithms for EOR projects. In addition, the use of semisupervised label propagation and advanced techniques like KNN imputation adds to the existing body of literature, enhancing the state of knowledge in this domain.

Mantis Search Algorithm Integrated with Opposition-Based Learning and Simulated Annealing for Feature Selection

Feature selection (FS) plays a vital role in minimizing the high-dimensional data as much as possible to aid in enhancing the classification accuracy and reducing computational costs. The purpose of the FS techniques is to extract the most effective subset features, which might enable the machine learning (ML) algorithms to better grasp the input data’s patterns and improve their classification performance. Although several metaheuristic algorithms have been recently presented to solve this problem, they still suffer from several disadvantages, such as getting stuck in local optima, slow convergence speed, and a lack of population diversity, which prevent them from achieving the desired solutions in an acceptable time. Therefore, this study is presented to propose a new feature selection approach, namely OBMSASA, based on integrating the recently published mantis search algorithm with the opposition-based learning (OBL) method and simulated annealing (SA) to strengthen its exploration and exploitation operators. The OBL method aims to improve the exploration operator, making the algorithm able to avoid stagnation into local minima; meanwhile, the SA is used as a local search to further strengthen the exploration operator, thereby improving the convergence speed. The K-nearest neighbor algorithm is used to compute the accuracy of the selected feature. The proposed algorithm is assessed using 21 common datasets and compared to several rival optimizers in terms of several performance metrics, including convergence curve, average fitness, computational cost, length of selected features, and standard deviation, to observe its effectiveness and efficiency. The source code is publicly accessible at https://drive.mathworks.com/OBMSASA.

Short-Term Forecasting of Photovoltaic Power Using Multilayer Perceptron Neural Network, Convolutional Neural Network, and k-Nearest Neighbors’ Algorithms

Governments and energy providers all over the world are moving towards the use of renewable energy sources. Solar photovoltaic (PV) energy is one of the providers’ favourite options because it is comparatively cheaper, clean, available, abundant, and comparatively maintenance-free. Although the PV energy source has many benefits, its output power is dependent on continuously changing weather and environmental factors, so there is a need to forecast the PV output power. Many techniques have been employed to predict the PV output power. This work focuses on the short-term forecast horizon of PV output power. Multilayer perception (MLP), convolutional neural networks (CNN), and k-nearest neighbour (kNN) neural networks have been used singly or in a hybrid (with other algorithms) to forecast solar PV power or global solar irradiance with success. The performances of these three algorithms have been compared with other algorithms singly or in a hybrid (with other methods) but not with themselves. This study aims to compare the predictive performance of a number of neural network algorithms in solar PV energy yield forecasting under different weather conditions and showcase their robustness in making predictions in this regard. The performance of MLPNN, CNN, and kNN are compared using solar PV (hourly) data for Grahamstown, Eastern Cape, South Africa. The choice of location is part of the study parameters to provide insight into renewable energy power integration in specific areas in South Africa that may be prone to extreme weather conditions. Our data does not have lots of missing data and many data spikes. The kNN algorithm was found to have an RMSE value of 4.95%, an MAE value of 2.74% at its worst performance, an RMSE value of 1.49%, and an MAE value of 0.85% at its best performance. It outperformed the others by a good margin, and kNN could serve as a fast, easy, and accurate tool for forecasting solar PV output power. Considering the performance of the kNN algorithm across the different seasons, this study shows that kNN is a reliable and robust algorithm for forecasting solar PV output power.

Artificial intelligence robots based on machine learning and visual algorithms for interactive experience assistance in music classrooms

This article aims to study the application of artificial intelligence robots based on machine learning and visual algorithms in music classroom interactive experience assistance. In artificial intelligence robots, mobile adaptive networks can be used to optimize the perception and decision-making abilities of robots. By continuously learning and adapting to environmental changes, robots can better understand and respond to the interactive needs of music classrooms, providing more accurate and targeted auxiliary services. By learning and analyzing rich training data, robots can possess higher-level cognitive and comprehension abilities. In terms of music recommendation, the K-nearest neighbor algorithm is used to recommend music works that are suitable for students. By analyzing students’ music preferences and learning needs, robots provide personalized music recommendations to students based on this information, helping them better participate in and enjoy music classes. By applying machine learning and visual algorithms to music classroom interaction experiments, artificial intelligence robots based on machine learning and visual algorithms have the potential to assist in music classroom interaction experience, and teaching optimization strategies for music classrooms have been proposed.

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.

This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors. 242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model. The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility. The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.

Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.

Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features. This was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023. The results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects. This review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.

Improved Particle Filter in Machine Learning-Based BLE Fingerprinting Method to Reduce Indoor Location Estimation Errors

Indoor position fingerprint-based location estimation methods have been widely used by applications on smartphones. In these localization estimation methods, it is very popular to use the RSSI (Received Signal Strength Indication) of signals to represent the position fingerprint. This paper proposes the design of a particle filter for reducing the estimation error of the machine learning-based indoor BLE location fingerprinting method. Unlike the general particle filter, taking into account the distance, the proposed system designs improved likelihood functions, considering the coordinates based on fingerprint points using mean and variance of RSSI values, combining the particle filter with the k-NN (k-Nearest Neighbor) algorithm to realize the reduction in indoor positioning error. The initial position is estimated by the position fingerprinting method based on the machine learning method. By comparing the fingerprint method based on k-NN with general particle filter processing, and the fingerprint estimation method based on only k-NN or SVM (Support Vector Machine), experiment results showed that the proposed method has a smaller minimum error and a better average error than the conventional method.

Multimodal machine learning approach for emotion recognition using physiological signals

This study explores a novel approach to emotion recognition through machine learning, addressing the limitations of previous methods. While deep learning has shown promise in this field, it often requires significant computational resources and time. In response, we propose a multimodal approach utilizing Feature-level Fusion (FLF) and Decision-level Fusion (DLF) to enhance performance while reducing complexity. The study focuses on integrating electroencephalogram (EEG), electromyography (EMG), and electrooculogram (EOG) signals. Signal preprocessing involves extracting statistical features, power spectral density (PSD), and incremental entropy analysis. Recursive Feature Elimination (RFE) is employed as a feature selector, facilitating the fusion of different signal features. Three fusion strategies are explored: EEG with EOG, EEG with EMG, and a combination of EEG with EOG and EMG. For classification, the Bagging Classifier and K-Nearest Neighbors Algorithm are chosen. Results demonstrate promising accuracy rates, with 95.7% for arousal, 96.41% for valence in subject-dependent classification, 93.68% for arousal, and 93.23% for valence in subject-independent classification. This approach presents a viable alternative to deep learning methods, offering improved performance with reduced computational burden.

The Homology Determination System for APT Samples Based on Gene Maps

At present, there are fewer types of homology determination methods for advanced persistent threat (APT) samples detection, and most existing determination schemes have problems such as high cost, low accuracy, and difficulty in identifying unknown APT samples. Therefore, we proposed a homology determination system for APT samples based on gene maps by integrating deep learning and gene maps. Firstly, we extract the software gene features from the samples uploaded by the user and apply the TF-IDF algorithm to clean the extracted software genes. The Word2Vec algorithm is used to vectorize all the genes to construct the gene sample vectors. And we use a LSTM-based classifier to detect APT attack samples. Finally, the K-nearest neighbor algorithm is used to determine the homology of gene-sharing APT samples. The detailed construction process of the scheme is given in this paper, including APT sample gene extraction, cleaning, clustering, sample detection, and homology determination. Experimental validation showcases our model outperforming existing methodologies with an accuracy of 95%, precision of 94%, and recall of 95%. When compared to previous models, the superiority of our approach is evident. These results underscore our model’s high efficiency and accuracy, confirming its potential for significant application in the field of cybersecurity.

Detection of dried jujube from fresh jujube with different variety and maturity after hot air drying based on hyperspectral imaging technology

The consistent quality of dried jujube has a significant impact on its economic value and consumer acceptance. Currently, the evaluation of dried jujube quality heavily relies on agricultural experts and advanced instrument analysis, which is time costly and is limited by many uncertainties. This study aims to develop a rapid and accurate method for classifying dried jujube produced from fresh jujube at different stages of maturity using hyperspectral imaging. Dried jujube samples were collected from two varieties (Zhongyang Muzao jujube and Yuanling jujube) in Hebei Province, representing two maturity stages: Kimri and Rutab. Linear partial least squares-discriminant analysis (PLS-DA), K-Nearest Neighbor (KNN) and support vector machine (SVM)algorithms were combined with six pre-processing methods. the pretreatment spectra of Area Naturalization (AN), Baseline and Multiple scattering correction (MSC) were selected for further analysis. Successive projections algorithm (SPA), Competitive adaptive reweighted sampling （CARS）and Uninformative Variables Elimination (UVE)were combined to select characteristic variables. A total of 16 characteristic variables were selected by AN-UVE-SPA-SVM model. This model achieved the highest accuracies of 96.0 % and 93.1 % in the final discriminant accuracy for the calibration and Validation sets, respectively. The hardness, soluble solid content (SSC), and moisture of two categories of dried jujube derived from fresh jujube at various stages of maturity were analyzed. The results showed that dried jujube produced from fresh jujubes at the Rutab stage were more likely to exhibit characteristics of high SSC and low hardness' compared to those produced at the Kimri stage. The partial least squares regression (PLSR) model established based on characteristic wavelengths extracted by AN-UVE-SPA showed the best results in predicting SSC and moisture. This study showcased the feasibility of identifying and classifying dried jujube using hyperspectral imaging technology, laying a solid foundation for further research in the rapid non-destructive testing of dried fruits and vegetables through the application of hyperspectral technology.