Vector Machine Research Articles

Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering

Network security is crucial in today’s digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.Intrusion detection systems (IDSs) are one of the significant aspect of cybersecurity that involve the monitoring and analysis, with the intention of identifying and reporting of dangerous activities that would help to prevent the attack.Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN) are the vector figures incorporated into the study through the results. These models are subjected to various test to established the best results on the identification and prevention of network violation. Based on the obtained results, it can be stated that all the tested models are capable of organizing data originating from network traffic. thus, recognizing the difference between normal and intrusive behaviors, models such as SVM, KNN, RF, and DT showed effective results. Deep learning models LSTM and ANN rapidly find long-term and complex pattern in network data. It is extremely effective when dealing with complex intrusions since it is characterised by high precision, accuracy and recall.Based on our study, SVM and Random Forest are considered promising solutions for real-world IDS applications because of their versatility and explainability. For the companies seeking IDS solutions which are reliable and at the same time more interpretable, these models can be promising. Additionally, LSTM and ANN, with their ability to catch successive conditions, are suitable for situations involving nuanced, advancing dangers.

Analysis of Malware Detection Using Various Machine Learning Approach

The number one goal of this research is to decorate existing methodologies for malware detection via developing a robust and scalable version that robotically identifies malware via the analysis of difficult styles inside both records and code, moving beyond traditional signature-primarily based methods. constructing on previous studies that have efficaciously implemented more than a few devices getting to know techniques, this technique will integrate each supervised and unsupervised studying algorithm. especially, category strategies consisting of choice bushes, random forests, and help vector machines, which have validated accuracies starting from 85% to 95%, could be utilized along superior deep getting to know frameworks, which includes neural networks, which have said accuracies exceeding 96% in positive contexts. by means of education these fashions on an in depth and various dataset that consists of both benign and malicious files, this study aims to improve the version's generalization abilities, consequently allowing it to efficiently perceive new, previously unknown malware variants. The overall performance of the proposed model can be rigorously evaluated against installed benchmarks and metrics, consisting of accuracy, precision, bear in mind, and the false tremendous fee, making sure its efficacy in actual-time malware detection eventualities. This multifaceted technique not best seeks to develop the sphere of cybersecurity but also builds on the foundational paintings of others, offering a greater adaptive and proactive way of malware identification that aligns with present day developments in gadget studying and cybersecurity studies

Flood susceptibility mapping in river basins: a risk analysis using AHP-TOPISIS-2 N support and vector machine

Flood susceptibility mapping in river basins: a risk analysis using AHP-TOPISIS-2 N support and vector machine

Sediment source tracing in a Brazilian subtropical catchment using diffuse reflectance: Effect of spectral ranges, pre-processing techniques, and multivariate model

ABSTRACT Agriculture intensification in Southern Brazil’s subtropical regions combined with the frequent occurrence of erosive rainfall has rendered the area a global water erosion hotspot. In this scenario, understanding and regulating erosion processes at the river catchment scale is critical for mitigating soil and water resource degradation. Traditional methods for tracing sediment sources are expensive and time-consuming and justify the development of alternative approaches. Therefore, in this study, we employed diffuse reflectance spectroscopy analyses in the ultraviolet-visible (UV-VIS), near-infrared (NIR), and mid-infrared (MIR) ranges, combined with multivariate models and spectral pre-processing techniques to estimate sediment source contributions in a homogeneous subtropical catchment (Conceição River, 804 km²). Soil samples (n = 181) were collected to characterize the four potential sediment sources, including: cropland (n = 78), stream bank (n = 36), unpaved road (n = 40) and pasture (n = 27). Moreover, 44 sediment samples were collected, including suspended sediment (n = 8), fine sediment deposited on the riverbed (n = 15), and suspended sediment samples collected in the water column during storm events (n = 21). Vector machine (SVM) model outperformed the others, with better accuracy and reliability. While UV-VIS spectra proved less effective due to soil homogeneity across the catchment, NIR and MIR spectra provided valuable information for discriminating sediment sources. Furthermore, reducing the number of potential sources (from four to three or two) improved model predictions, especially when distinguishing between surface sources (cropland and pasture) and subsurface sources (unpaved roads and stream banks). The study’s findings shed light on the power of efficient and cost-effective alternative methods for assessing sediment sources, which are vital for promoting effective erosion control and sustainable land management in similar regions.

Rapid and non‐destructive determination of total phenolic contents using UV–NIR spectroscopy of dehydrated mushroom (Lentinus edodes)

AbstractMultispectral imaging (MSI) is an emerging technique that ranges from light spectrum of UV–NIR (405–970 nm) used for rapid determination of phenolic contents. The current study focuses on the determination of total phenolic content (TPC) in a fast and non‐invasive way in hot‐air dehydrated Lentinus edodes. The spectral information of MSI has been combined with various chemometrics like partial least squares (PLS), back propagation neural networks (BPNN), and least squares‐support vector machines (LS‐SVM) for the quantitative prediction of phenolic contents. Fresh mushrooms possessed 1.49 and 1.73 GAE g kg−1 TPC in processed samples at 10% moisture content. Comparing models, PLS acquired significant Rp with a lower RMSEP of 0.9980 and 0.1039 and was considered an outstanding algorithm, followed by LS‐SVM of 0.9777 and 0.1417 coefficient of determination and error of prediction data set, respectively. It is concluded that MSI spectroscopy produces highly significant results to determine functional food properties in a rapid and non‐destructive way. The present study will provide a strong platform for the fast online determination of phenolic profile of agricultural commodities.Practical applicationsUtilization of spectroscopy for non‐destructive detection of phenolic contents is defined through the combination of chemometrics with spectra of multispectral imaging system and emphasizes control processing of postharvest horticulture produces. Prediction results R2 above 80% describe that these chemometrics are successfully capable of estimating total phenolic contents in control processing for quality preservation.

Voltage‐based fault arc detection based on PCA‐RF

SummaryThe arc fault characteristics of certain loads lack significance, making it difficult to efficiently detect the line current characteristics. This research presents a novel approach for detecting arc faults using a combination of principalc analysis (PCA) and Random Forest (RF) based on voltage measurements. The time‐domain eigenvalues of the load terminal voltages of single and mixed loads are initially extracted during both arc fault and normal operation. Principal component analysis is then conducted on a subset of these eigenvalues. The skewness and magnitude features of the resulting principal components and load terminal voltages are utilized as inputs for the Random Forest algorithm. After training the model, classification results are obtained. Ultimately, it is contrasted with techniques such as rime optimization algorithm‐multilayer perceptron (RIME‐MLP), convolutional neural network‐gated recurrent unit‐SE attention (CNN‐GRU‐SE), and Kepler optimization algorithm‐support vector machine (KOA‐SVM). The results demonstrated that the approach exhibits superior accuracy and a reduced false alarm rate.

Improving prediction of 12-months suicidal attempts in bipolar disorder: a machine learning study

IntroductionBipolar disorder (BD) is a recurrent disorder, causing functional impairment and raised mortality, particularly due to suicide. However, the difficulty in predicting suicidal behaviors relies in the lack of clear biomarkers.Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, most ML studies focused on lifetime attempts, without having a predictive time window, and did not employ time-dependent variables. Moreover, most studies lie on cross-sectional databases, without including more than one time-point.Objectives First, we aimed to predict 12-months suicide attempts in a naturalistic sample of BD patients, using clinical and demographic data.Second, we aimed to improve the prediction by including information from intermediate visits (1, 3, and 6 months), mimicking more closely the clinician’s way of thinking and the multiple observations a patient receives.MethodsA sample of 163 BD patients (53% females, mean age 44.7, SD 15.3) were recruited.Based on EHR, 56 clinical and demographic features were extracted, including hospitalizations, suicidal behaviors lifetime and in the last 12 months, along with comorbidity, family history, work, and therapies. Patients were followed up for 12 months.Support Vector Machine (SVM) was used to differentiate subjects who attempted suicide versus those who did not in a 12-month time window, within a repeated nested Cross-Validation. The SVM was optimized weighting the hyperplane for uneven group sizes.Then, we repeated the analysis including information from intermediate visits (1, 3, 6 months after the first contact). For each visit, we created a composite score based on current therapy, new admissions, and ER presentations. To avoid circularity, all the information (ER, admission etc.) related to a suicide attempt were not included.ResultsDuring the 12-months follow-up, 9.8% of patients attempted suicide. The results from the 12-months suicide prediction model obtained an Area Under the Curve of 0.71(with a Balanced Accuracy (BAC) of 68%).After incorporating the composite scores based on intermediate visits in the model, the prediction raised to an Area Under the Curve of 0.78 (BAC 73%), suggesting that including intermediate visits is a valid method to improve prediction.The features that contributed the most to the prediction were the composite score at 6-month visit, lifetime number of suicide attempts, suicide attempts in the last 12 months, substance of abuse (other than cannabis), and antipsychotics.ConclusionsML proved a good prediction accuracy for suicide in a 12-months time window, and the prediction was improved by including data from intermediate visits. The model showed the importance of time-dependent features, such as attempts in the last 12 months. Our analysis might help in identifying early clinical risk factors and underlies the importance of multiple evaluations in populations at risk.Disclosure of InterestNone Declared

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback.

Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step Non-Line-of-Sight Identification.

The effective identification and mitigation of non-line-of-sight (NLOS) ranging errors are essential for achieving high-precision positioning and navigation with ultra-wideband (UWB) technology in harsh indoor environments. In this paper, an efficient UWB ranging-error mitigation strategy that uses novel channel impulse response parameters based on the results of a two-step NLOS identification, composed of a decision tree and feedforward neural network, is proposed to realize indoor locations. NLOS ranging errors are classified into three types, and corresponding mitigation strategies and recall mechanisms are developed, which are also extended to partial line-of-sight (LOS) errors. Extensive experiments involving three obstacles (humans, walls, and glass) and two sites show an average NLOS identification accuracy of 95.05%, with LOS/NLOS recall rates of 95.72%/94.15%. The mitigated LOS errors are reduced by 50.4%, while the average improvement in the accuracy of the three types of NLOS ranging errors is 61.8%, reaching up to 76.84%. Overall, this method achieves a reduction in LOS and NLOS ranging errors of 25.19% and 69.85%, respectively, resulting in a 54.46% enhancement in positioning accuracy. This performance surpasses that of state-of-the-art techniques, such as the convolutional neural network (CNN), long short-term memory-extended Kalman filter (LSTM-EKF), least-squares-support vector machine (LS-SVM), and k-nearest neighbor (K-NN) algorithms.

Image‐Feature Based Deep Learning Model for Plant Leaf Disease Detection

AbstractCurrently, everyone is facing significant difficulties with food scarcity. There may be several causes for this, but food loss is a well‐known issue. Specifically, crop losses bring on by plant and leaf diseases during farming operations. Plant disease is typically identified visually or through laboratory testing, which delays detection and reduces crop yields by the time it is finished. The wide use of smartphones along with recent advancements in computer vision has made it viable to diagnose any ailment by applying machine learning techniques. Smartphone‐assisted disease diagnosis is now a reality. In this study, a multi‐Support Vector Machine (SVM) model is used to detect four different diseases as well as healthy conditions of plant leaves. Using 13 feature vectors for each input, a total of 2400 leaves representing four distinct classes (four varieties) have been used to train and test the model. The experimental results give an average accuracy of 91.25% for diseased leaves detection and 99% accuracy for healthy leaves detection.

Pneumonia Detection Using Machine Learning

An enormous amount of morbidity and mortality cases are caused by pneumonia, which is still a major global health concern. Pneumonia must be accurately and quickly detected in order to manage patients effectively and achieve better results. Machine learning (ML) algorithms have become effective instruments in recent years for automating the detection and diagnosis of pneumonia from medical imaging data. The goal of this review paper is to give a thorough overview of recent developments in ML-based pneumonia detection. It includes the various ML algorithms used, the training and testing datasets, and the evaluation metrics used to rate the effectiveness of these models. Additionally, this review highlights the difficulties encountered in the field and suggests possible directions for improvement in order to create a more reliable and robust pneumonia detection system. Healthcare professionals place a high value on pneumonia detection, and machine learning (ML)-based automation of There's been a lot of attention paid to this process. The importance of pneumonia detection and the part that ML techniques play in automating this process are highlighted in the introduction to this review paper. In the following section, it examines different machine learning (ML) The various system used for the discernment of pneumonia. Such include supervised understanding algorithms like logistic statistics, vector machine and randomization. forests, and convolutional neural networks. The review also discusses pneumonia detection using unsupervised learning techniques like clustering, dimensionality reduction, and autoencoders. In order to develop them, an assessment of pneumonia detection models is essential. The study has examined several appraisal metrics which are commonly used for that purpose, such as sensitivity, specificity, precision and the operational status of receivers. characteristic (ROC) curve, recall, precision, and F1-score. The selection of suitable metrics, which considers specific requirements for pneumonia detection, is main factor to be taken into consideration. The main obstacles is that there are no annotation data. to creating reliable pneumonia detection models. Accurate ML algorithms must be trained on high-quality labelled datasets. However, since chest X-ray images must be annotated by qualified radiologists, obtaining a sizable annotated dataset for pneumonia is frequently challenging. The creation of efficient ML models for pneumonia detection is hampered by the limited availability of annotated data.

A Smart Machine Learning Model for Detection of Brain Hemorrhage Using IoT

Abstract: For the most part, the development of Web of Things empowered applications propelled the world during the most recent couple of years, giving cutting edge and novel-based answers for various issues. This developmental field is fundamentally lead by remote sensor organization, radio recurrence ID, and brilliant versatile advances. The Internet of Things, or IoT, plays a crucial role in smart medical devices and wearables that can collect a wide range of longitudinal patient-generated health data while also providing options for preliminary diagnosis. As far as endeavors made for aiding the patients utilizing IoT-based arrangements, specialists exploit capacities of the AI calculations to give proficient arrangements in drain conclusion. To decrease the demise rates and propose precise treatment, this paper presents a savvy IoT-based application utilizing AI calculations for the human cerebrum drain finding. In view of the modernized tomography check pictures for intracranial dataset, the help vector machine and feedforward brain network have been applied for the grouping purposes. Generally, characterization consequences of 80.67% and 86.7% are determined for the help vector machine and feedforward brain organization, separately. It is finished up from the resultant examination that the feedforward brain network beats in ordering intracranial pictures. The classification tool's output provides information about the kind of brain hemorrhage that, in the end, helps validate the expert's diagnosis. It is used as a learning tool for trainee radiologists to reduce system errors.

Sentiment Analysis of Consumer Reviews: Unveiling Perspectives and Building a Machine Learning Model for Product Evaluation

The goal of this paper is to classify the unusual and dreadful critiques of the clients over one-of-a-kind products and put up a control analysis version to polarize big amounts of analysis. Our dataset comprises consumer reviews and ratings, which are information we have obtained through user evaluations of Amazon goods. We took the skills from our dataset and developed a large supervised version entirely based on them. These modes not great consist of conventional algorithms mutually with naive bays, linear helping vector machines, and k-nearest fellow resident, but also deep studying metrics which encompass frequent Neural Networks and complication neural networks. We evaluated the correctness of those fashions and got a higher know-how of the inverse attitude earlier to the produce. The initial objective is to describe the situation from the client's perspective and assess the intensity of the emotion. The goal of the second assignment is to build and train a machine learning system that can be used to divide customer evaluations into two categories: excellent and terrible. Regardless of the information that Amazon does now not have an API like Twitter to download evaluation with, it does have links for every assessment on every item for consumption, to theoretically traverse the web page through product IDs. We used Perl script written by way of Andrea Eula to get hold of the evaluation for the stimulation and some poles apart products. The most important script downloads the entire HTML page for the product, and the second searches the record for data about the appraisal, such as the product ID, rating, assessment date, and estimate passage. This study shows that the model had a classification accuracy of 78% and a precision similar to the sympathy and genuine score, but the Kappa do curve was rather low. In the future, more work can be done by improving the key parameters.

Cardiovascular Disease Prediction Using Machine Learning

Heart disease-related deaths have become a big issue in today's world, with one person dying from the disease every minute. It considers both male and female groups, and the ratio varies by location. It is also used for the 25-69 age group. This isn't to say that people of all ages will be affected by heart disease. This condition could start in the early stages of life, and predicting the source and sickness is currently a huge challenge. Heart disease is one of the world's most fatal problems, one that cannot be seen with the naked eye and manifests itself as soon as it reaches its limits. As a result, precise diagnosis at the right moment is necessary. Every day, the health-care business generates massive amounts of patient and illness-related data. Researchers and practitioners, on the other hand, do not make appropriate use of this data. Despite its lack of knowledge, the healthcare business now has a wealth of data. In data mining and machine learning, there are a variety of approaches and tools for extracting usable information from databases and using that information to make more accurate diagnoses and decisions. So, in order to detect such disorders in time for adequate treatment, a reliable, precise, and feasible approach is required. In the realm of medicine, machine learning algorithms and approaches have been used to process enormous data sets. Researchers employ a variety of data mining and machine learning approaches to analyse large data sets and aid in the accurate prediction of cardiac illnesses. This research compares and contrasts the Nave Bayes, Help Vector Machine, Random Forest, and supervised learning models to find the most successful algorithm. When compared to other algorithms, Random Forest has95.08 per cent more precision.

Approximate Solutions of Nonlinear Boundary Value Problems by Collocation Methods Compared to Newer Methods

A large variety of new methods are being developed for fast and efficient solutions of nonlinear boundary value problems. Some of these methods are, Adomian decomposition (ADM), differential transform (DTM), least squares vector machines (LSSVMM), and multiple variational iteration (MVIM). A natural question arises as to how efficient and simple to use these newer methods are compared to classical methods. One of the simplest and widely applicable classical methods is the collocation method. The overall performance of collocation method and the newer methods are compared on a number of problems, which were previously used to benchmark the newer methods. It is concluded that, at least for the problems considered, the collocation method performs as successfully as the newer methods.

Alpha Asymmetry as biomarker for Mild Cognitive Impairment

AbstractBackgroundEarly screening of dementia at its pre‐clinical stage is crucial, seeing as how deteriorated cognitive functions can be recovered through appropriate medicinal practices. The pre‐clinical stage, which we refer to as mild cognitive impairment (MCI) is known to affect the alpha rhythm of the brain activity. Hence, our study aims to establish quantitative EEG (qEEG)‐based alpha asymmetry biomarker that aids the diagnoses of MCI.MethodThe qEEG dataset were composed of age‐ and sex‐ matched subjects (N = 634, 317 healthy control (HC), 317 MCI), acquired in accordance with the 10‐20 system and under resting state with their eyes being closed. Subjects were recruited by Chung‐Ang University (153 HC, 111 MCI), iMediSync(164 HC, 76 MCI), Dong‐a Medical Center(84 MCI) and Pusan Medical Center(46 MCI).The features were classified into four types: the absolute and relative region alpha power (AAP/RAP) of six regions (Prefrontal, Frontal, Temporal, Central, Parietal, Occipital); alpha power asymmetry (APA) between regions that align in anterior‐posterior and lateral planes; occipital alpha peak frequency (OPF) that analyzes peak frequencies.In order to highlight the alpha variability, EEG signals were bandpass filtered (6.5‐12Hz). Filtered signal at each channel was categorized into slow (6.5‐8Hz), middle (8‐10Hz) and fast alpha (10‐12Hz). The mean and standard deviation of their FFT were computed through moving time window. Henceforth, the feature set were used to train machine learning algorithms. Data were split into 8(training N = 507) to 2(test N = 127) ratio. 101 subjects were randomly selected and used as validation set. The algorithm that exhibited the best classification performance were determined through 5‐fold cross validation (CV) results.ResultSupport vector machine (SVM) showed the best 5‐fold CV results – 84.61% average accuracy, 74.02% sensitivity, 95.25% specificity, 94.15% precision and F1‐Score of 0.8273. The RAP decreased for all regions and OPF were slower in MCI group when compared to HC. APA patterns of the right intra‐hemisphere exhibited most significant differences.ConclusionThis study illustrates the potential of APA as a biomarker for MCI. Current applications of APA are confined to emotional‐related studies, such as depressive disorder. The reported cases of MCI‐associated depression also uphold the validity of APA as a biomarker for MCI.

Evaluating chemometric strategies and machine learning approaches for a miniaturized near-infrared spectrometer in plastic waste classification

Optimizing the sorting of plastic waste plays a crucial role in improving the recycling process. In this contribution, we report on a comparative study of multiple machine learning and chemometric approaches to categorize a data set derived from the analysis of plastic waste performed with a handheld spectrometer working in the Near-Infrared (NIR) spectral range. Conducting a cost-effective NIR study requires identifying appropriate techniques to improve commodity identification and categorization. Chemometric techniques, such as Principal Component Analysis (PCA) and Partial Least Squares - Discriminant Analysis (PLS - DA), and machine learning techniques such as Support- Vector Machines (SVM), fine tree, bagged tree, and ensemble learning were compared. Various pre-treatments were tested on the collected NIR spectra. In particular, Standard Normal Variate (SNV) and Savitzky-Golay derivatives as signal pre-processing tools were compared with feature selection techniques such as multiple Gaussian Curve Fit based on Radial Basis Functions (RBF). Furthermore, results were combined into a single predictor by using a likelihood-based aggregation formula. Predictive performances of the tested models were compared in terms of classification parameters such as Non-Error Rate (NER) and Sensitivity (Sn) with the analysis of the confusion matrices, giving a broad overview and a rational means for the selection of the approach in the analysis of NIR data for plastic waste sorting.

SVM‐ABC based cancer microarray (gene expression) hybrid method for data classification

AbstractMicroarray technology presents a challenge due to the large dimensionality of the data, which can be difficult to interpret. To address this challenge, the article proposes a feature extraction‐based cancer classification technique coupled with artificial bee colony optimization (ABC) algorithm. The ABC‐support vector machine (SVM) method is used to classify the lung cancer datasets and compared them with existing techniques in terms of precision, recall, F‐measure, and accuracy. The proposed ABC‐SVM has the advantage of dealing with complex nonlinear data, providing good flexibility. Simulation analysis was conducted with 30% of the data reserved for testing the proposed method. The results indicate that the proposed attribute classification technique, which uses fewer genes, performs better than other modalities. The classifiers, such as naïve Bayes, multi‐class SVM, and linear discriminant analysis, were also compared and the proposed method outperformed these classifiers and state‐of‐the‐art techniques. Overall, this study demonstrates the potential of using intelligent algorithms and feature extraction techniques to improve the accuracy of cancer diagnosis using microarray gene expression data.

Determination of Tibetan tea quality by hyperspectral imaging technology and multivariate analysis

Tibetan tea is a dark tea native to Ya'an, and its taste and quality are closely related to the contents of tea polyphenols (TPs) and free amino acids (FAAs). In this study, TPs and FAAs were determined by chemometrics and the grades of Tibetan tea were distinguished. Then, hyperspectral data were collected, a variety of preprocessing methods were used to preprocess the spectral data, and principal component analysis (PCA) was used for feature dimensionality reduction. Results showed that the combination of the preprocessing method and machine learning (ML) could achieve a higher prediction effect. Savitzky Golay (SG)-Standard Normal Variable (SNV)-PCA-Extratree had the best predictive ability for TPs (Rp2=0.9248, RMSEP=0.4842, and RPD=3.646). For detecting FAAs, SG-Multiplicative Scatter Correction (MSC)-PCA-Extratree had the best predictive ability (Rp2=0.8736, RMSEP=0.159, and RPD=2.813). In addition, tea grade can be determined by SG-MSC-PCA-Support Vector Machine (SVM) with 100% accuracy, recall, and precision. In sum, hyperspectral imaging technology (HSI) can be used as an alternative method for rapid, non-destructive testing of tea quality.

Applications of Neural Network-Based Plan-Cancer Method for Primary Diagnosis of Mesothelioma Cancer.

"Malignant mesothelioma (MM)" is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is "malignant mesothelioma." Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination "epithelial/mesenchymal appearance of MM," however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful "Malignant Mesothelioma illness" identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support "Vector Machine, Neural Network, and Decision Tree" are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.