Accuracy Of Machine Learning Algorithms Research Articles

Classification accuracy of machine learning algorithms for Chinese local cattle breeds using genomic markers.

Accurate breed classification is required for the conservation and utilization of farm animal genetic resources. Traditional classification methods mainly rely on phenotypic characterization. However, it is difficult to distinguish between the highly similar breeds due to the challenges in qualifying the phenotypic character. Machine learning algorithms show unique advantages in breed classification using genomic information. To evaluate the classification methods for Chinese cattle breeds, this study utilized genomic SNP data from 213 individuals across seven Chinese local breeds and compared the classification accuracies of three feature selection methods (FST value sorting and screening, mRMR, and Relief-F) and three machine learning algorithms (Random Forest, Support Vector Machine, and Naive Bayes). Results showed that: 1) using the FST method to screen more than 1500 SNPs, or using the mRMR algorithm to screen more than 1000 SNPs, the SVM classification algorithm can achieve more than 99.47% classification accuracy; 2) the most effective algorithm was SVM, followed by NB, while the best SNP selection method was FST and mRMR, followed by Relief-F; 3) species misclassification often occurs between breeds with high similarity. This study demonstrates that machine learning classification models combined with genomic data are effective methods for the classification of local cattle breeds, providing a technical basis for the rapid and accurate classification of cattle breeds in China.

The Role of Machine Learning in Enhancing Risk Management Strategies in Financial Institutions

Purpose: The aim of the study was to examine the role of machine learning in enhancing risk management strategies in financial institutions. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study revealed that integration of machine learning into risk management strategies within financial institutions has demonstrated significant potential for enhancing decision-making processes and mitigating various risks. The study have consistently shown that machine learning algorithms outperform traditional statistical methods in areas such as credit risk assessment, fraud detection, market risk management, and loan portfolio optimization. These advancements have led to improved accuracy, efficiency, and timeliness in risk assessment, enabling financial institutions to make more informed decisions while reducing losses and enhancing overall performance. Unique Contribution to Theory, Practice and Policy: Modern Portfolio Theory (MPT), Efficient Market Hypothesis (EMH) & Agency Theory may be used to anchor future studies on role of machine learning in enhancing risk management strategies in financial institutions. Invest in building robust data infrastructure and governance frameworks to support the implementation of machine learning models in risk management practices. High-quality data is crucial for training accurate and reliable machine learning algorithms. Establish regulatory guidelines and standards for the responsible use of machine learning in risk management within the financial industry. These guidelines should address issues such as model transparency, fairness, and accountability to ensure ethical and responsible practices.

AI Assisted Deceptive Content Analysis System Using Bi-LSTM

Abstract: In an age of widespread misinformation, creating accurate machine learning algorithms is vital for ensuring the integrity of information sharing. This project aims to develop a machine learning model capable of distinguishing between fake and true news articles. [1]The model utilizes a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network architecture. Exploratory data analysis techniques are employed to gain in-sights into the characteristics and distributions within the dataset. Data visualization techniques aid in understanding patterns and relationships within the dataset. Additionally, unigram analysis is conducted to extract meaningful features from the text data. The datasets are then prepared for model training, involving preprocessing steps such as tokenization and vectorization. Fi-nally, the Bi-LSTM model is constructed, leveraging its ability to capture long-range dependencies in sequential data. The model is trained on the prepared datasets, optimized using appropri-ate techniques, and evaluated using metrics such as accuracy, precision, recall, and F1-score. The Bi-LSTM architecture offers the advantage of capturing long-range dependencies in sequential data, thereby enhancing the model’s ability to discern nuanced patterns in news articles. The primary objective of this project is to develop a robust and accurate system for automatically detecting fake news, thus playing a pivotal role in enhancing the dissemination of reliable information in the digital age.

Reviewing the Impact of Machine Learning on Disease Diagnosis and Prognosis: A Comprehensive Analysis

Aim This study aimed to explore how machine learning algorithms can enhance medical diagnostics through the analysis of illness imagery and patient data, assessing their effectiveness and potential to improve diagnostic accuracy and early disease detection. Background This study highlights the critical role of machine learning in healthcare, particularly in medical diagnostics. By leveraging advanced algorithms to analyse medical data and images, machine learning enhances disease detection and diagnosis, contributing significantly to improved patient outcomes and the advancement of precision medicine. Objective The objective of this study was to thoroughly analyse and evaluate the efficacy of machine learning algorithms in medical diagnostics, focusing on their application in interpreting illness images and patient data. The goal was to ascertain the algorithms' accuracy in disease diagnosis and prognosis, aiming to demonstrate their potential in revolutionizing healthcare through improved diagnostic precision and early disease detection. Methods A systematic approach has been used in this study to evaluate machine learning algorithms' effectiveness in diagnosing diseases from medical images and data. It involved selecting pertinent datasets, applying and comparing models, like SVM and K-nearest neighbors, and assessing their diagnostic accuracy and performance, aiming to identify the most effective methodologies in medical diagnostics. Results The results have highlighted the varying accuracy of machine learning algorithms in medical diagnostics, with a focus on the performance of models, such as SVM and K-nearest neighbors. A comparative analysis has illustrated the differential effectiveness of these algorithms across various diseases and datasets, underscoring their potential to enhance healthcare diagnostics. Conclusion The study has concluded that machine learning algorithms have significantly improved medical diagnostics, offering varied effectiveness across different conditions. Their potential to revolutionize healthcare is evident, with enhanced diagnostic accuracy and efficiency. Ongoing research and clinical application are essential to harness these technologies' full benefits.

Comparison of Two Methods, Gradient Boosting and Extreme Gradient Boosting to Pre- dict Survival in Covid-19 Data

Introduction: The present study discusses the importance of having a predictive method to determine the prognosis of patients with diseases like Covid-19. This method can assist physicians in making treatment decisions that improve survival rates and avoid unnecessary treatments. This research also highlights the importance of calibration, which is often overlooked in model evaluation. Without proper calibration, incorrect decisions can be made in disease treatment and preventive care. Therefore, the current study compares two highly accurate machine learning algorithms, Gradient boosting and Extreme gradient boosting, not only in terms of prediction accuracy but also in terms of model calibration and speed. Methods: This study involved analyzing data from Covid-19 patients who were admitted to two hospitals in Mashhad city, Razavi Khorasan province, over a span of 18 months. The k-fold cross-validation method was employed on the training dataset (K=5) to conduct the study. The accuracy and calibration of two methods (Gradient boosting and Extreme gradient boosting) in predicting survival were compared using the Concordance Index and calibration. Results: The Concordance Index values obtained for gradient boosting and Extreme gradient boosting models were 0.734 and 0.736, in the imbalanced and In the balanced data, the Concordance Index values were 0.893 for gradient boosting and 0.894 for Extreme gradient boosting. The surv.calib_beta index, the gradient boosting model had an estimated value of 0.59 in the imbalanced data and 0.66 in the balanced data. The Extreme gradient boosting model had an estimated value of 0.86 in the balanced data and 0.853 in the imbalanced data. The Extreme gradient boosting model was faster in the learning process compared to the gradient boosting model. Conclusion: The Gradient boosting and Extreme gradient boosting models exhibited similar prediction accuracy and discrimination power, but the Extreme gradient boosting model demonstrated relatively good calibration compare to Gradient boosting model.

Advanced MRI metrics improve the prediction of baseline disease severity for individuals with degenerative cervical myelopathy

BACKGROUND CONTEXTDegenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. Degeneration of spinal discs, bony osteophyte growth and ligament pathology results in physical compression of the spinal cord contributing to damage of white matter tracts and grey matter cellular populations. This results in an insidious neurological and functional decline in patients which can lead to paralysis. Magnetic resonance imaging (MRI) confirms the diagnosis of DCM and is a prerequisite to surgical intervention, the only known treatment for this disorder. Unfortunately, there is a weak correlation between features of current commonly acquired MRI scans (“community MRI, cMRI”) and the degree of disability experienced by a patient. PURPOSEThis study examines the predictive ability of current MRI sequences relative to “advanced MRI” (aMRI) metrics designed to detect evidence of spinal cord injury secondary to degenerative myelopathy. We hypothesize that the utilization of higher fidelity aMRI scans will increase the effectiveness of machine learning models predicting DCM severity and may ultimately lead to a more efficient protocol for identifying patients in need of surgical intervention. STUDY DESIGN/SETTINGSingle institution analysis of imaging registry of patients with DCM. PATIENT SAMPLEA total of 296 patients in the cMRI group and 228 patients in the aMRI group. OUTCOME MEASURESPhysiologic measures: accuracy of machine learning algorithms to detect severity of DCM assessed clinically based on the modified Japanese Orthopedic Association (mJOA) scale. METHODSPatients enrolled in the Canadian Spine Outcomes Research Network registry with DCM were screened and 296 cervical spine MRIs acquired in cMRI were compared with 228 aMRI acquisitions. aMRI acquisitions consisted of diffusion tensor imaging, magnetization transfer, T2-weighted, and T2*-weighted images. The cMRI group consisted of only T2-weighted MRI scans. Various machine learning models were applied to both MRI groups to assess accuracy of prediction of baseline disease severity assessed clinically using the mJOA scale for cervical myelopathy. RESULTSThrough the utilization of Random Forest Classifiers, disease severity was predicted with 41.8% accuracy in cMRI scans and 73.3% in the aMRI scans. Across different predictive model variations tested, the aMRI scans consistently produced higher prediction accuracies compared to the cMRI counterparts. CONCLUSIONSaMRI metrics perform better in machine learning models at predicting disease severity of patients with DCM. Continued work is needed to refine these models and address DCM severity class imbalance concerns, ultimately improving model confidence for clinical implementation.

Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects

In this study, the failure pressure of submarine oil and gas pipelines was predicted by using five commonly used machine learning models and derivative models. Firstly, an efficient local time-intensive finite element algorithm is constructed and used to generate a machine learning database. Secondly, in order to improve the accuracy of machine learning algorithm, the frontier optimization algorithm is improved to form a new predictive regression model IAEO-SVM model. To compare the accuracy of commonly used machine learning models and IAEO-SVM models, a comprehensive evaluation standard consisting of k-fold cross-validation and three statistical indicators was used. Finally, the influence of seawater depth and geometric factors of corrosion defects on the blasting pressure of submarine oil and gas pipelines was comprehensively analyzed through the high-dimensional surface built by the IAEO-SVM model. The IAEO-SVM model shows superior stability and accuracy compared to the comparison model, as demonstrated by its MSE’ of 0.0482, R2’ of 0.9982, MAE’ of 0.1295, and SD of 0.2198. The high-dimensional surface obtained through inversion shows a linear relationship between seawater depth and failure pressure. Meanwhile, the width of corrosion defects has a significant impact on failure pressure, accounting for up to 23% and thus cannot be overlooked.

Goal Orientation for Fair Machine Learning Algorithms

A key challenge facing the use of machine learning (ML) in organizational selection settings (e.g., the processing of loan or job applications) is the potential bias against (racial and gender) minorities. To address this challenge, a rich literature of Fairness-Aware ML (FAML) algorithms has emerged, attempting to ameliorate biases while maintaining the predictive accuracy of ML algorithms. Almost all existing FAML algorithms define their optimization goals according to a selection task, meaning that ML outputs are assumed to be the final selection outcome. In practice, though, ML outputs are rarely used as-is. In personnel selection, for example, ML often serves a support role to human resource managers, allowing them to more easily exclude unqualified applicants. This effectively assigns to ML a screening rather than a selection task. It might be tempting to treat selection and screening as two variations of the same task that differ only quantitatively on the admission rate. This paper, however, reveals a qualitative difference between the two in terms of fairness. Specifically, we demonstrate through conceptual development and mathematical analysis that miscategorizing a screening task as a selection one could not only degrade final selection quality but also result in fairness problems such as selection biases within the minority group. After validating our findings with experimental studies on simulated and real-world data, we discuss several business and policy implications, highlighting the need for firms and policymakers to properly categorize the task assigned to ML in assessing and correcting algorithmic biases.

Multi-objective optimization and machine learning-based prediction of tensile properties of an armchair graphene sheet

Graphene is a widely used nanoparticle in different industries, especially in nanocomposite applications. Prediction of its properties is of great importance for engineers. Therefore, a comprehensive study is first performed in this study to investigate the effect of temperature, strain rate, number of layers, and dimensions on the tensile properties of graphene nanosheets including elastic modulus (E), yield strength (YS), and ultimate tensile strength (UTS) using molecular dynamics simulation. Then, E, YS, and UTS are simultaneously optimized via response surface methodology. To determine the simplest and the most accurate machine learning algorithm for the prediction of tensile properties, three algorithms of Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT) are compared and the GBT algorithm is introduced as the best one. Furthermore, the architecture of each algorithm was optimized via the Taguchi design of experiment method to enhance the prediction accuracy. The DT algorithm with a maximum depth of 8 was obtained as the most accurate one.

Machine learning in the classification of asian rust severity in soybean using hyperspectral sensor

Traditional monitoring of asian soybean rust severity is a time- and labor-intensive task, as it requires visual assessments by skilled professionals in the field. Thus, the use of remote sensing and machine learning (ML) techniques in data processing has emerged as an approach that can increase efficiency in disease monitoring, enabling faster, more accurate and time- and labor-saving evaluations. The aims of the study were: (i) to identify the spectral signature of different levels of Asian soybean rust severity; (ii) to identify the most accurate machine learning algorithm for classifying disease severity levels; (iii) which spectral input provides the highest classification accuracy for the algorithms; (iv) to determine a sample size of leaves that guarantees the best accuracy for the algorithms. A field experiment was carried out in the 2022/2023 harvest in a randomized block design with a 6x3 factorial scheme (ML algorithms x severity levels) and four replications. Disease severity levels assessed were: healthy leaves, 25 % severity, and 50 % severity. Leaf hyperspectral analysis was carried out over a wide range from 350 to 2500 nm. From this analysis, 28 spectral bands were extracted, seeking to distinguish the spectral signature for each severity level with the least input dataset. Data was subjected to machine learning analysis using Artificial Neural Network (ANN), REPTree (DT) and J48 decision trees, Random Forest (RF), and Support Vector Machine (SVM) algorithms, as well as a traditional classification method (Logistic Regression - LR). Two different input datasets were tested for each algorithm: the full spectrum (ALL) provided by the sensor and the 28 spectral bands (SB). Tests with different sample sizes were also conducted to investigate the algorithms' ability to detect severity levels with a reduced sample size. Our findings indicate differences between the spectral curves for the severity levels assessed, which makes it possible to differentiate between healthy plants with low and high severity using hyperspectral sensing. SVM was the most accurate algorithm for classifying severity levels by using all the spectral information as input. This algorithm also provided high classification accuracy when using smaller leaf samples. This study reveals that hyperspectral sensing and the use of ML algorithms provide an accurate classification of different levels of Asian rust severity, and can be powerful tools for a more efficient disease monitoring process.

Effective modeling of metasurface‐loaded circularly polarized dielectric resonator‐based MIMO antenna for sub‐6.0‐GHz band using machine learning algorithms

SummaryIn this article, a dual‐port dielectric resonator antenna (DRA) is modeled using machine learning (ML) algorithms, that is, deep neural network (DNN), random forest, and XGBoost. The unique properties of the proposed article are as follows: (i) Two different diversity techniques, that is, pattern (with the help of metallic wall) and polarization (mirror image of the aperture), improve the isolation value between the ports; (ii) ML algorithms are used to optimize and predict the reflection coefficient as well as mutual coupling of the proposed antenna. The accuracy of ML algorithms is verified by using the HFSS EM simulator and experimental validation. Error is less than 1%–2% between the value predicted from ML algorithms and HFSS/experimental results. The proposed design is working well in between 2.4 and 4.02 GHz with a 3‐dB axial ratio from 2.84 to 2.95 GHz. All these features make the radiator employable to the sub‐6.0‐GHz frequency band.

Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms

Machine learning (ML) algorithms have been widely used in big data prediction and analysis in terms of their excellent data regression ability. However, the prediction accuracy of different ML algorithms varies between different regression problems and data sets. In order to construct a prediction model with optimal accuracy for fly ash concrete (FAC), ML algorithms such as genetic programming (GP), support vector regression (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) and adaptive network-based fuzzy inference system (ANFIS) were selected as regression and prediction algorithms in this study; the particle swarm optimization (PSO) algorithm was also used to optimize the structure and hyperparameters of each algorithm. The statistical results show that the performance of the assembled algorithms is better than that of an NN-based algorithm. In addition, PSO can effectively improve the prediction accuracy of the ML algorithms. The comprehensive performance of each model is analyzed using a Taylor diagram, and the PSO-XGBoost model has the best comprehensive performance, with R2 and MSE equal to 0.9072 and 11.4546, respectively.

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.

Medical tumor image classification based on Few-shot learning.

As a high mortality disease, cancer seriously affects people's life and well-being. Reliance on pathologists to assess disease progression from pathological images is inaccurate and burdensome. Computer aided diagnosis (CAD) system can effectively assist diagnosis and make more credible decisions. However, a large number of labeled medical images that contribute to improve the accuracy of machine learning algorithm, especially for deep learning in CAD, are difficult to collect. Therefore, in this work, an improved few-shot learning method is proposed for medical image recognition. In addition, to make full use of the limited feature information in one or more samples, a feature fusion strategy is involved in our model. On the dataset of BreakHis and skin lesions, the experimental results show that our model achieved the classification accuracy of 91.22% and 71.20% respectively when only 10 labeled samples are given, which is superior to other state-of-the-art methods.

Eucalyptus Species Discrimination Using Hyperspectral Sensor Data and Machine Learning

The identification of tree species is very useful for the management and monitoring of forest resources. When paired with machine learning (ML) algorithms, species identification based on spectral bands from a hyperspectral sensor can contribute to developing technologies that enable accurate forest inventories to be completed efficiently, reducing labor and time. This is the first study to evaluate the effectiveness of classification of five eucalyptus species (E. camaldulensis, Corymbia citriodora, E. saligna, E. grandis, and E. urophyla) using hyperspectral images and machine learning. Spectral readings were taken from 200 leaves of each species and divided into three dataset sizes: one set containing 50 samples per species, a second with 100 samples per species, and a third set with 200 samples per species. The ML algorithms tested were multilayer perceptron artificial neural network (ANN), decision trees (J48 and REPTree algorithms), and random forest (RF). As a control, a conventional approach by logistic regression (LR) was used. Eucalyptus species were classified by ML algorithms using a randomized stratified cross-validation with 10 folds. After obtaining the percentage of correct classification (CC) and F-measure accuracy metrics, the means were grouped by the Scott–Knott test at 5% probability. Our findings revealed the existence of distinct spectral curves between the species, with the differences being more marked from the 700 nm range onwards. The most accurate ML algorithm for identifying eucalyptus species was ANN. There was no statistical difference for CC between the three dataset sizes. Therefore, it was determined that 50 leaves would be sufficient to accurately differentiate the eucalyptus species evaluated. Our study represents an important scientific advance for forest inventories and breeding programs with applications in both forest plantations and native forest areas as it proposes a fast, accurate, and large-scale species-level classification approach.

Machine Learning in the Hyperspectral Classification of Glycaspis brimblecombei (Hemiptera Psyllidae) Attack Severity in Eucalyptus

Assessing different levels of red gum lerp psyllid (Glycaspis brimblecombei) can influence the hyperspectral reflectance of leaves in different ways due to changes in chlorophyll. In order to classify these levels, the use of machine learning (ML) algorithms can help process the data faster and more accurately. The objectives were: (I) to evaluate the spectral behavior of the G. brimblecombei attack levels; (II) find the most accurate ML algorithm for classifying pest attack levels; (III) find the input configuration that improves performance of the algorithms. Data were collected from a clonal eucalyptus plantation (clone AEC 0144—Eucalyptus urophilla) aged 10.3 months old. Eighty sample evaluations were carried out considering the following severity levels: control (no shells), low infestation (N1), intermediate infestation (N2), and high infestation (N3), for which leaf spectral reflectances were obtained using a spectroradiometer. The spectral range acquired by the equipment was 350 to 2500 nm. After obtaining the wavelengths, they were grouped into representative interval means in 28 bands. Data were submitted to the following ML algorithms: artificial neural networks (ANN), REPTree (DT) and J48 decision trees, random forest (RF), support vector machine (SVM), and conventional logistic regression (LR) analysis. Two input configurations were tested: using only the wavelengths (ALL) and using the spectral bands (SB) to classify the attack levels. The output variable was the severity of G. brimblecombei attack. There were differences in the hyperspectral behavior of the leaves for the different attack levels. The highest attack level shows the greatest distinction and the highest reflectance values. LR and SVM show better accuracy in classifying the severity levels of G. brimblecombei attack. For the correct classification percentage, the RL and SVM algorithms performed better, both with accuracy above 90%. Both algorithms achieved F-score values close to 0.90 and above 0.8 for Kappa. The entire spectral range guaranteed the best accuracy for both algorithms.

HEART DISEASE PREDICTION USING MACHINE LEARNING

Heart disease is a major cause of death worldwide, making early diagnosis and prevention essential. Predictive models have gained significant attention in recent years, with several algorithms being employed to develop these models. However, there are challenges in implementing heart disease prediction models, including data quality, model accuracy, ethical concerns, and limited data. Therefore, this project aims to develop a heart disease prediction model and analyse different algorithms used in disease prediction. In order to increase the predictive accuracy of machine learning algorithms, this study compares six algorithms, including KNN (K-Nearest Neighbour), Decision Tree, Random Forest, Support Vector Machines, Logistic Regression, and Neural Network. 13 attributes, including age, sex, and cholesterol, are used, and ensemble methods like boosting and bagging are used. The accuracy, recall, f1 score, and precision of each algorithm are calculated to determine the most accurate model. Additionally, this study identifies the limitations of heart disease prediction models and their implications for patient diagnosis and treatment, by developing and analysing heart disease prediction models. In conclusion, while heart disease prediction models have the potential to be financially feasible and be useful in the future, their current limitations and challenges mean that they cannot be relied upon as the sole means of diagnosis or treatment decisions Key Words: Heart Diseases, Machine Learning Algorithms, Logistic Regression, Random Forest, Decision Tree.

Grain yield estimation of wheat-maize rotation cultivated land based on Sentinel-2 multi-spectral image: A case study in Caoxian County, Shandong, China

Establishing the remote sensing yield estimation model of wheat-maize rotation cultivated land can timely and accurately estimate the comprehensive grain yield. Taking the winter wheat-summer maize rotation cultivated land in Caoxian County, Shandong Province, as test object, using the Sentinel-2 images from 2018 to 2019, we compared the time-series feature classification based on QGIS platform and support vector machine algorithm to select the best method and extract sowing area of wheat-maize rotation cultivated land. Based on the correlation between wheat and maize vegetation index and the statistical yield, we screened the sensitive vegetation indices and their growth period, and obtained the vegetation index integral value of the sensitive spectral period by using the Newton-trapezoid integration method. We constructed the multiple linear regression and three machine learning (random forest, RF; neural network model, BP; support vector machine model, SVM) models based on the integral value combination to get the best and and optimized yield estimation model. The results showed that the accuracy rate of extracting wheat and maize sowing area based on time-series features using QGIS platform reached 94.6%, with the overall accuracy and Kappa coefficient were 5.9% and 0.12 higher than those of the support vector machine algorithm, respectively. The remote sensing yield estimation in sensitive spectral period was better than that in single growth period. The normalized differential vegetation index and ratio vegetation index integral group of wheat and enhanced vegetation index and structure intensify pigment vegetable index integral group of maize could more effectively aggregate spectral information. The optimal combination of vegetation index integral was difference, and the fitting accuracy of machine learning algorithm was higher than that of empirical statistical model. The optimal yield estimation model was the difference value group-random forest (DVG-RF) model of machine learning algorithm (R2=0.843, root mean square error=2.822 kg·hm-2), with a yield estimation accuracy of 93.4%. We explored the use of QGIS platform to extract the sowing area, and carried out a systematical case study on grain yield estimation method of wheat-maize rotation cultivated land. The established multi-vegetation index integral combination model was effective and feasible, which could improve accuracy and efficiency of yield estimation.

Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting

Accurate land use and land cover (LULC) is crucial for sustainable urban planning and for many scientific researches. However, the demand for accurate LULC maps is increasing; it is required to compare the classification algorithms to choose the best one. Though, machine and deep learning algorithms are widely used across the world their application is limited in Bangladesh. Accurate urban LULC mapping is challenging because urban heterogeneity affects image classification models in specific feature extraction. In this research, the accuracy of machine learning algorithms (MLA) of RF (Random Forest), SVM (Support Vector Machine), deep learning algorithm (DLA) of ANN (Artificial Neural Network) and traditional Maximum Likelihood (MaxL) method was compared in LULC classification of Dhaka city. Model accuracy of MLA and DLA was tested by statistical indices of sensitivity, specificity, precision, recall F1 etc. There is a high correlation between SVM and ANN models were found. The overall accuracy of the maps was 0.93, 0.94, 0.91 and 0.95 and kappa was 0.89, 0.91, 0.86 and 0.93 for the MaxL, RF, SVM and ANN models respectively. The user accuracy and producer accuracy largely varied according to LULC classes in the applied models. The lowest accuracy of the models was found for bare land classification followed by built-up and vegetation. The high mixture of LULC classes affects the accuracy of built up and bare land classification which produces the lowest accuracy in the MaxL model. The findings indicate that the most accurate and reliable model for urban LULC classification was the ANN model.

Performance prediction of dielectric resonator based MIMO antenna for sub-6.0 GHz using machine learning algorithms

ABSTRACTIn this article, a dual port dielectric resonator antenna is modeled using various machine learning algorithms i.e. deep neural network (DNN), Random Forest, and XG boost. The unique properties of proposed article are as follows: (i) two different diversity techniques i.e. pattern (with the help of metallic wall) and polarization (mirror image of the aperture) improves the isolation value between the ports; (ii) machine learning (ML) algorithms are used to optimize and predict the reflection coefficient as well as mutual coupling of proposed antenna. The accuracy of ML algorithms is verified by using HFSS EM simulator as well as experimental validation. Error is less than 1–2% between the value predicted from ML algorithms and HFSS/Experimental results. The proposed design is working well in between 2.4–4.02 GHz with 3-dB axial ratio from 2.84–2.95 GHz. All these features make the radiator employable to sub-6.0 GHz frequency band.