Precision Matrix Research Articles

Application of quantum technology Variational Quantum Classifier in agriculture for classification of wheat varieties

This study proposes the use of Variational Quantum Classifier for automated classification of wheat varieties. A model trained on a large data set will be able to identify unique patterns and relationships between seed characteristics and cultivar membership. This will allow farmers and researchers to more accurately identify wheat varieties, which in turn can improve growing and crop management processes. This approach is justified not only by the need to optimize agricultural production, but also in the context of the use of advanced technologies to achieve precision and efficiency in the agricultural sector. As a result of this research, it is expected that the quality and sustainability of wheat production will improve, which is important for food security and sustainable agricultural development. The goal of the problem is to classify wheat varieties based on seed characteristics. VQC is trained on the training dataset and then evaluated on the test dataset. To evaluate the performance of the model, various metrics are used, such as accuracy, precision, recall, F1-score and Confusion Matrix.

Enhanced Recognition of Handwritten Marathi Compound Characters using CNN-SVM Hybrid Approach

This study presents a hybrid recognition system for multi-class compound Marathi characters, which addresses the problem of handwritten Marathi character recognition. The methodology efficiently bridges the gap between feature extraction and classification by integrating a Convolutional Neural Network (CNN) and Support Vector Machine (SVM). The first step is gathering and preprocessing a wide range of handwritten Marathi compound characters that are written in different styles. Using conventional supervised learning methods, the CNN is trained on this dataset, paying special attention to data augmentation and validation in order to reduce overfitting. High-level features taken from the final fully connected layer of the CNN are fed into an SVM classifier in the next step. By using these features in its training, the SVM improves prediction accuracy. For multi-class classification, the one-vs-all method is used. The hybrid CNN-SVM algorithm demonstrates its effectiveness in the crucial phases of feature extraction and classification by identifying handwritten compound Marathi characters with remarkable accuracy. Evaluation metrics, such as accuracy, precision, recall, F1-score, and confusion matrix analysis, are employed in the process of evaluating the effectiveness of the model. This assessment is carried out on a different testing dataset, offering a thorough examination of the model's functionality. The proposed algorithm demonstrates its superior performance and potential for improved character recognition by achieving training accuracy of 98.60% and validation accuracy of 97.69%. The development of handwriting recognition systems has benefited greatly from this research, especially when it comes to intricate scripts like Marathi. The suggested hybrid algorithm shows encouraging outcomes and has a great deal of potential for use in document processing, natural language comprehension, and character recognition in languages that use the Marathi script. Subsequent efforts will centre on refining the model and investigating ensemble methods to increase the robustness and accuracy of recognition.

Multi-response Regression for Block-missing Multi-modal Data without Imputation.

Multi-modal data are prevalent in many scientific fields. In this study, we consider the parameter estimation and variable selection for a multi-response regression using block-missing multi-modal data. Our method allows the dimensions of both the responses and the predictors to be large, and the responses to be incomplete and correlated, a common practical problem in high-dimensional settings. Our proposed method uses two steps to make a prediction from a multi-response linear regression model with block-missing multi-modal predictors. In the first step, without imputing missing data, we use all available data to estimate the covariance matrix of the predictors and the cross-covariance matrix between the predictors and the responses. In the second step, we use these matrices and a penalized method to simultaneously estimate the precision matrix of the response vector, given the predictors, and the sparse regression parameter matrix. Lastly, we demonstrate the effectiveness of the proposed method using theoretical studies, simulated examples, and an analysis of a multi-modal imaging data set from the Alzheimer's Disease Neuroimaging Initiative.

Precision matrix estimation under the horseshoe-like prior–penalty dual

Precision matrix estimation under the horseshoe-like prior–penalty dual

COMPARISON OF LOGISTIC REGRESSION, MULTINOMIALNB, SVM, AND K-NN METHODS ON SENTIMENT ANALYSIS OF GOJEK APP REVIEWS ON THE GOOGLE PLAY STORE

Today's community activities cannot be separated from the name of transportation because it makes it very easy for people's daily activities. With the existence of transportation, people can more quickly reach their destination. With the Gojek application on the Google Play Store, it will help people travel or deliver goods. To determine service quality, sentiment analysis can be used to classify reviews. The purpose of this study is to compare which method has the best accuracy, so that it can classify reviews into positive or negative sentiments. The methods tested in this study are Logistic Regression, MultinomialNB, SVM, and K-NN. Performance assessment methods include score accuracy, recall, and precision, classification reports, and confusion matrix to determine the appropriate method for classifying reviews into positive or negative categories. Of the four methods tested, the one with the highest performance is the Logistic Regression method. Accuracy, recall and precision scores of the Logistic Regression method were 82.45%, 82.49%, 82.45% and 82.43%, respectively. Classification report also shows good results. In the confusion matrix, there are 111 and 124 True positives and True negatives. There are only 22 and 28 False positive and False negative results respectively. The method that has the lowest score is K-NN, with score accuracy, recall, and precision respectively were 52.28%, 59.43%, 93, 52%, and 65.65%. Classification report shows quite bad results. In the connection matrix, it produces True positives and True negatives 130 and 19. There are only 127 and 9 False positive and False negative results respectively. The results of this study state that using the Logistic Regression method is suitable for use in classifying positive and negative reviews in the review dataset on the Gojek application on the Google Play Store.

Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data

AbstractThis study addresses the critical challenge of accurately classifying brain tumors using artificial intelligence. Early detection is crucial, as untreated tumors can be fatal. Despite advances in AI, accurately classifying tumors remains a challenging task. To address this challenge, we propose a novel optimization approach called PSCS combined with deep learning for brain tumor classification. PSCS optimizes the classification process by improving Particle Swarm Optimization (PSO) exploitation using Cuckoo search (CS) algorithm. Next, classified gene expression data of brain tumor using Deep Learning (DL) to identify different groups or classes related to a particular tumor along with the PSCS optimization technique. The proposed optimization technique with DL achieves much better classification accuracy than other existing DL and Machine learning models with the different evaluation matrices such as Recall, Precision, F1‐Score, and confusion matrix. This research contributes to AI‐driven brain tumor diagnosis and classification, offering a promising solution for improved patient outcomes.

IEEE-754 Precision- p base-β Arithmetic Implemented in Binary

We show how an IEEE-754 conformant precision- p base-β arithmetic can be implemented based on some binary floating-point and/or integer arithmetic. This includes the four basic operations and square root subject to the five IEEE-754 rounding modes, namely the nearest roundings with roundTiesToEven and roundTiesToAway, the directed roundings downwards and upwards, as well as rounding towards zero. Exceptional values like ∞ of NaN are covered according to the IEEE-754 arithmetic standard. The results of the precision- p base-β operations are computed using some underlying precision- q binary arithmetic. We distinguish two cases. When using a precision- q binary integer arithmetic, the base-β precision p is limited for all operations by β 2 p ≤ 2 q , whereas using a precision- q binary floating-point arithmetic imposes stronger limits on the base-β precision, namely β 2 p ≤ 2 q for addition and multiplication, β 2 p ≤ 2 q-1 for division and β 2 p ≤ 2 q -3 for the square root. Those limitations cannot be improved. The algorithms are implemented in a Matlab/Octave flbeta-toolbox with the choice of using uint64 or binary64 as underlying arithmetic. The former allows larger precisions, the latter is advantageous for the square root, whereas computing times are similar. The flbeta-toolbox offers precision- p base-β scalar, vector and matrix operations including sparse matrices as well as corresponding interval operations. The base β can be chosen in the range β ∊ [2,64]. The flbeta-toolbox will be part of Version 13 of INTLAB [ 18 ], the Matlab/Octave toolbox for reliable computing.

A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection

Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of classification algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer classification. First, we made alteration of the fitness of spider monkey optimization (SMO) with cuckoo search algorithm (CSA) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using deep learning (DL) to identify different groups or classes related to a particular cancer disease. Eight different benchmark microarray gene expression datasets of cancer have been utilized to analyze the performance of the proposed approach with different evaluation matrix such as recall, precision, F1-score, and confusion matrix. The proposed gene selection method with DL achieves much better classification accuracy than other existing DL and machine learning classification models with all large gene expression dataset of cancer.

Novel ferrofluid based on water-based deep eutectic solvents: application in dispersive liquid-liquid microextraction of naphthalene-derived plant growth regulators in edible oil

A new ferrofluid extractant (Fe3O4 @SiO2-WDES) was ingeniously prepared by coating magnetic Fe3O4 @SiO2 microspheres with water-based deep eutectic solvent (WDES) and used for dispersive liquid-liquid microextraction (DLLME) and HPLC determination of naphthalene-derived plant growth regulators (PGRs, i.e., 1-naphthylacetic acid, 2-naphthylacetic acid, 1-naphthoxy acetic acid, 2-naphthyloxyacetic acid and 1-naphthylacetamide) in edible oil. Herein, mass transfer of the analytes in DLLME was significantly enhanced via increasing the contact surface by employing the microspheres as the supporter and dispersant of WDES, and phase separation can be efficiently achieved using an external magnet rather than centrifugation in traditional DLLME. Response surface methodology (RSM) based on Box-Behnken design (BBD) was employed for the optimization of core experimental conditions, and Analytical Eco-Scale and Analytical GREEnness Metric Approaches were adopted to evaluate the degree of greenness of the procedure. Under the optimal conditions, satisfactory performances of linearity ranged from 5 to 100 μg/L (R2 ≥ 0.9982), limit of detection (0.58–0.91 μg/L), limit of quantitation (1.9–3.0 μg/L), precision (RSDs ≤ 5.5%), recovery (81.3%−108.1%) and negligible matrix effect were achieved, which introduced a promising alternative route for the determination of naphthalene-derived plant growth regulators in edible oil.

A fast ADMM algorithm for sparse precision matrix estimation using lasso penalized D-trace loss

Sparse precision matrix estimation, also known as the estimation of the inverse covariance matrix in statistical contexts, represents a critical challenge in numerous multivariate analysis applications. This challenge becomes notably complex when the dimension of the data is far greater than the capacity of samples. To address this issue, we introduce a convex relaxation model that employs the first-order optimality conditions associated with the lasso-penalized D-trace loss for the purpose of estimating sparse precision matrix. The proposed model is effectively solved through the widely recognized alternating direction method of multipliers. Additionally, we provide closed-form solutions to subproblems in each iteration with a computational complexity of O(np2), and establish the convergence of our proposed algorithm. Numerical investigations demonstrate that our algorithm exhibits the capability to handle large-scale datasets and significantly outperforms the existing methods, particularly when dealing with high-dimensional scenarios characterized by a large dimension p.

Thermal monitoring and deep learning approach for early warning prediction of rock burst in underground structures

The occurrence of rockburst has the potential to result in significant economic and human losses in underground mining and excavation operations. The accuracy of traditional methods for early prediction is considerably affected by factors such as site conditions, noise levels, accessibility, and other variables. This study proposes a methodology for identifying the most defected region in a hard rock sample by integrating motion thermogram data obtained from the laboratory monitoring of rock burst phenomena with a cutting-edge deep neural network approach based on a regional convolutional network (i.e. Mask RCNN). The efficacy of the suggested approach was evaluated by determining the F1 score and average precision matrices based on a specific intersection over union value. The findings demonstrate that the proposed approach possesses satisfactory precision with respect to detection, localization, and segmentation, thereby establishing its potential utility as an autonomous predictor of rock bursts.

Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning.

The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice. ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD. A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%. Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.

Local Whittle estimation of high-dimensional long-run variance and precision matrices

Local Whittle estimation of high-dimensional long-run variance and precision matrices

Bayesian adaptive Lasso estimation of large graphical model based on modified Cholesky decomposition

In this paper, based on the modified Cholesky decomposition of the precision matrix, we propose Bayesian adaptive Lasso estimation and maximum adaptive posterior estimation for graphical model. We also recover the graph by minimizing the decoupled shrinkage and selection loss function.

Improvement of Malicious Software Detection Accuracy through Genetic Programming Symbolic Classifier with Application of Dataset Oversampling Techniques

Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling automated pattern recognition, anomaly detection, and continuous learning, allowing security systems to adapt to evolving threats and identify complex, polymorphic malware that may exhibit varied behaviors. This synergy of hybrid features with AI empowers malware detection systems to efficiently and proactively identify and respond to sophisticated cyber threats in real time. In this paper, the genetic programming symbolic classifier (GPSC) algorithm was applied to the publicly available dataset to obtain symbolic expressions (SEs) that could detect the malware software with high classification performance. The initial problem with the dataset was a high imbalance between class samples, so various oversampling techniques were utilized to obtain balanced dataset variations on which GPSC was applied. To find the optimal combination of GPSC hyperparameter values, the random hyperparameter value search method (RHVS) was developed and applied to obtain SEs with high classification accuracy. The GPSC was trained with five-fold cross-validation (5FCV) to obtain a robust set of SEs on each dataset variation. To choose the best SEs, several evaluation metrics were used, i.e., the length and depth of SEs, accuracy score (ACC), area under receiver operating characteristic curve (AUC), precision, recall, f1-score, and confusion matrix. The best-obtained SEs are applied on the original imbalanced dataset to see if the classification performance is the same as it was on balanced dataset variations. The results of the investigation showed that the proposed method generated SEs with high classification accuracy (0.9962) in malware software detection.

Sparse precision matrix estimation under lower polynomial moment assumption

Precision matrix (inverse covariance matrix) estimation is a rising challenge in contemporary applications while dealing with high‐dimensional data. This paper focuses on large‐scale precision matrix of the random vector that only has lower polynomial moments. We mainly investigate upper bounds of the proposed estimator under the spectral norm in terms of the probability and mean estimation respectively. It is shown that the data‐driven estimator is fully adaptive and achieves the same optimal convergence order as under Gaussian assumption on the data. Simulation studies further support our theoretical claims.

Simulation-Based Performance Evaluation of Missing Data Handling in Network Analysis

Network analysis has gained popularity as an approach to investigate psychological constructs. However, there are currently no guidelines for applied researchers when encountering missing values. In this simulation study, we compared the performance of a two-step EM algorithm with separated steps for missing handling and regularization, a combined direct EM algorithm, and pairwise deletion. We investigated conditions with varying network sizes, numbers of observations, missing data mechanisms, and percentages of missing values. These approaches are evaluated with regard to recovering population networks in terms of loss in the precision matrix, edge set identification and network statistics. The simulation showed adequate performance only in conditions with large samples ( n ≥ 500 ) or small networks (p = 10). Comparing the missing data approaches, the direct EM appears to be more sensitive and superior in nearly all chosen conditions. The two-step EM yields better results when the ratio of n/p is very large – being less sensitive but more specific. Pairwise deletion failed to converge across numerous conditions and yielded inferior results overall. Overall, direct EM is recommended in most cases, as it is able to mitigate the impact of missing data quite well, while modifications to two-step EM could improve its performance.

Development of a novel UHPLC-MS/MS method for the quantification of corynoxeine: Application to pharmacokinetics and tissue distribution studies in normal and chronic unpredictable mild stress-induced depression rats

Corynoxeine, a natural active alkaloid found in Genus Uncaria, has been reported to have anti-depressant effects. In this study, a sensitive and efficient ultra-high performance liquid chromatography tandem mass spectrometry method for quantifying corynoxeine in rat plasma and tissues was established, validated and applied to investigate the pharmacokinetics and tissue distribution differences between normal rats and chronic unpredictable mild stress (CUMS)-induced depression model rats following oral administration. All bio-samples were prepared by methanol protein precipitation method with theophylline as internal standard (IS). Chromatographic separation was conducted on an Agilent ZORBAX Eclipse Plus C18 column using mobile phase A (acetonitrile) and B (0.1% formic acid in water) in gradient elution mode with a flow rate of 0.3 mL/min. Mass spectrometric detection was performed in multiple-reaction monitoring mode with positive electrospray ionization source. The transitions of m/z 383.0→160.2 for corynoxeine and m/z 181.1→124.0 for IS were chosen for quantification. The method showed good linearity, stability, accuracy, precision, recovery, and non-significant matrix effect, which were within the acceptable ranges. The pharmacokinetic results revealed that the absorption and bioavailability of corynoxeine in depression rats decreased compared to normal rats. The tissue distribution of corynoxeine trended to be mostly in the intestine and stomach and the distribution of this compound in intestine tissue of depression rats was significantly increased compared to the normal rats. The pharmacokinetics and tissue distribution profiles of corynoxeine were altered in CUMS-induced depression rats compared to normal rats and these experimental findings could provide beneficial information to the mechanism research and clinical applications of corynoxeine.

Scaling priors for intrinsic Gaussian Markov random fields applied to blood pressure data

An Intrinsic Gaussian Markov Random Field (IGMRF) can be used to induce conditional dependence in Bayesian hierarchical models. IGMRFs have both a precision matrix, which defines the neighborhood structure of the model, and a precision, or scaling, parameter. Previous studies have shown the importance of selecting the prior for this scaling parameter appropriately for different types of IGMRF, as it can have a substantial impact on posterior estimates. Here, we focus on cases in one and two dimensions, where tuning of the prior is achieved by mapping it to the marginal SD of an IGMRF of corresponding dimensionality. We compare the effects of scaling various IGMRFs, including an application to real two‐dimensional blood pressure data using MCMC methods.

Cyber-Attack Detection for Cloud-Based Intrusion Detection Systems

In this research, we delve into an exhaustive ex- amination of the usage of machine learning (ML) models for the identification of cyber threats within Cloud-Based Intrusion Detection Systems (IDS). With the escalating dependence on cloud services across various industries, the urgency to develop effective and resilient IDS to mitigate burgeoning cyber risks is paramount. Our study makes a significant contribution to this imperative field by analyzing the efficacy of diverse ML models in detecting cyber-attacks within a cloud context. We scrutinized an array of ML models, namely Decision Trees (DT), Random Forest (RF), XGBoost, and Support Vector Machines (SVM), utilizing key performance parameters such as accuracy, recall, precision, F1-s, and confusion matrix to understand their practical application in real-world IDS scenarios. XGBoost stood out as the most proficient model, showcasing not only an impressive accuracy but also a balanced performance in terms of precision and recall. This highlights the considerable potential of ensemble and gradient boosting techniques in optimizing cloud- based IDS detection capabilities. Our findings underscore the significant role of machine learning in fostering more dependable, robust, and efficient IDS in the cloud, thus significantly aiding in securing our digital ecosystems.