Classification And Regression Tree Research Articles

Effective combinations of features in predicting the range of incident ions using machine learning

Machine learning techniques were used to predict the range of ions in material, even when stopping power data is unknown. Simple features such as atomic and mass numbers were used in learning process. Two different machine learning models, Decision Tree (DT) and Kernel Ridge Regression (KRR), were used to assess each feature’s importance and the effect of combining them. The results showed that the chosen features were sufficient for accurate prediction. Furthermore, it was found that the combination and features are essential for accurate results. Analysis by KRR established a relationship between the range of the implanted ions and the energy of the incident ions, showing that the range is approximately proportional to the energy in the dataset used in this work.

Vocational opportunities for agricultural migrants in northern India: Insights from grassroots

The livelihood of Indian farmers is primarily shaped by agriculture and its allied components. The degree of intricacy and contributions of each agricultural activity, although, vary with different social-ecological systems. Migration is considered an approach to livelihood adaptation; however, the migrants face challenges in new social-ecological settings due to their limited access to resources. This research aimed to develop a predictive model for sustainable agricultural employment options in Rajasthan and Uttar Pradesh (UP). In this study, states were selected purposively, while respondent farmers were chosen using a stratified multistage sampling design. A total of 480 resident and migrant farmers were selected to collect data. The machine learning algorithm, based on classification and regression tree (CART) analysis, was applied that could help to identify factors and prospects for migrant farmers in the agricultural sector. Results indicated that milk yield, operational land holding, and rabi crop yield were significant predictors. Further, milk yield, rabi crop yield, and kharif crop yield were observed to be the essential factors contributing to profitable ventures. The recommendations provided by Krishi Vigyan Kendra’s professionals led to the identification of key income-generating activities, such as livestock management, vegetable production, and organic farming. These prospects are tailored to the location-specific context where migrant farmers reside. Overall, this research shed light on viable employment opportunities, ultimately contributing to the well-being of migrant workers in northern India, in addition to the policy interventions focused on capacity building and providing an enabling environment to the migrants.

Geospatial Assessment of Urban Sprawl: A Case Study of Herat City, Afghanistan

This study aims to investigate the spatial and temporal dynamics of urban sprawl in Herat City, Afghanistan, from 2000 to 2021 using GIS and remote sensing data (Landsat 7 and 8). In this study, three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART), were employed to classify the study area, and the accuracy of each algorithm for each study period was assessed. Based on the assessment results, the RF algorithm demonstrated higher accuracy and was selected as the classification algorithm. The Google Earth Engine cloud platform was utilized to classify the study area, and the GIS environment was employed for the creation of thematic layers. The analysis revealed a 30.06% increase in built-up areas from 2000 to 2021. Conversely, vegetation, water bodies, and bare land decreased by 8.51%, 1.08%, and 20.53%, respectively, during the same period. The findings indicated that Herat City experienced high-speed expansion between 2000 and 2013, while from 2013 to 2021; it developed at a medium speed. The Relative Shannon's entropy statistical algorithm was employed to quantify urban sprawl, and the results suggest a dispersed urban sprawl pattern. Internal migration to major cities due to conflicts, limited employment opportunities, and inadequate living amenities in rural areas has been a primary driver of urban sprawl in Herat City, Afghanistan.

Design and performance analysis of decision tree learning model for classification of dry and cooked rice samples

Design and performance analysis of decision tree learning model for classification of dry and cooked rice samples

A Hybrid Method Based on CRITIC Method and Machine Learning Models for Effective Fake News Detection in Thai Language

Fake news has emerged as a pervasive issue within the modern information ecosystem, leading to widespread dissemination of misinformation and erosion of trust. This paper introduces a novel hybrid approach for effectively detecting fake news in the Thai language by combining the CRITIC method with multiple machine learning models. The initial step involves collecting Thai-language fake news data from websites. Subsequently, the data undergoes a preprocessing phase. In the second step, the preprocessed data is used for validation through three basic machine learning models, namely, Naive Bayes, Decision Tree, and K-Nearest Neighbors. In the third step, the accuracy results from these three models are employed to calculate the significance weights of each model using the CRITIC method. In the final step, predictions are recalculated using the proposed method. The proposed method achieves an 83.37% accuracy, surpassing Naive Bayes (80.83%), Decision Tree (80.37%), and K-Nearest Neighbors (75.75%). This indicates a significant enhancement in performance, with the proposed method outperforming the established models by up to 7.62%.  Consequently, the proposed method can enhance the performance of fake news detection in Thai language by utilizing an ensemble of the original models. A significant advantage of this approach is its simplicity coupled with high efficacy. It is postulated that this method can be adapted for detecting fake news in other languages as well.

Magnetic resonance imaging-based prediction models for differentiating intraspinal schwannomas from meningiomas: classification and regression tree and random forest analysis.

Due to the variations in surgical approaches and prognosis between intraspinal schwannomas and meningiomas, it is crucial to accurately differentiate between the two prior to surgery. Currently, there is limited research exploring the implementation of machine learning (ML) methods for distinguishing between these two types of tumors. This study aimed to establish a classification and regression tree (CART) model and a random forest (RF) model for distinguishing schwannomas from meningiomas. We retrospectively collected 88 schwannomas (52 males and 36 females) and 51 meningiomas (10 males and 41 females) who underwent magnetic resonance imaging (MRI) examinations prior to the surgery. Simple clinical data and MRI imaging features, including age, sex, tumor location and size, T1-weighted images (T1WI) and T2-weighted images (T2WI) signal characteristics, degree and pattern of enhancement, dural tail sign, ginkgo leaf sign, and intervertebral foramen widening (IFW), were reviewed. Finally, a CART model and RF model were established based on the aforementioned features to evaluate their effectiveness in differentiating between the two types of tumors. Meanwhile, we also compared the performance of the ML models to the radiologists. The receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the models and clinicians' discrimination performance. Our investigation reveals significant variations in ten out of 11 variables in the training group and five out of 11 variables in the test group when comparing schwannomas and meningiomas (P<0.05). Ultimately, the CART model incorporated five variables: enhancement pattern, the presence of IFW, tumor location, maximum diameter, and T2WI signal intensity (SI). The RF model combined all 11 variables. The CART model, RF model, radiologist 1, and radiologist 2 achieved an area under the curve (AUC) of 0.890, 0.956, 0.681, and 0.723 in the training group, and 0.838, 0.922, 0.580, and 0.659 in the test group, respectively. The RF prediction model exhibits more exceptional performance than an experienced radiologist in discriminating intraspinal schwannomas from meningiomas. The RF model seems to be better in discriminating the two tumors than the CART model.

Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?

Staging liver fibrosis is important, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning-Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C patients. We propose a method based on a selection of machine learning classification methods including multilayer perceptron (MLP) neural network, Naive Bayesian (NB), decision tree, and deep learning. Initially, the synthetic minority oversampling technique (SMOTE) is performed to address the imbalance in the dataset. Afterward, the integration of MLP and TLBO is implemented. We propose a novel algorithm that reduces the number of required patient features to seven inputs. The accuracy of MLP using 12 features is 0.903, while that of the proposed MLP with TLBO is 0.891. Besides, the diagnostic accuracy of all methods, except the model designed with the Bayesian network, increases when the SMOTE balancer is applied. The decision tree-based deep learning methods show the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and seven features, MLP reached an accuracy rate of 0.891, which is quite satisfactory when compared with those of similar studies. The proposed model provides high diagnostic accuracy, while reducing the required number of properties from the samples. The results of our study show that the recruited algorithm of our study is more straightforward, with a smaller number of required properties and similar accuracy.

Experimental road safety study of the actual driver reaction to the street ads using eye tracking, multiple linear regression and decision trees methods

The article describes the results of a naturalistic driving study done in Kuwait performed by 34 participants wearing a mobile eye tracker to monitor the effect of roadside advertisements on user attention. Eye-tracking (fixations) are the main dependent variable and examined as a function of driving/roadside characteristics, particularly billboards, speed, and so forth. The results obtained were analyzed using traditional statistics (ANOVA test and multiple linear regression) and machine learning (decision tree estimation methods). From the results, it was found that road advertisements negatively affect driver attention and thus road safety. How the level of safety varies with the type and size of advertisement was also investigated. As a consequence, all of the estimates revealed that different aspects of advertising have a detrimental impact on drivers' behavior, and that the duration of fixation and the rate of acceleration before viewing an ad are impacted by advertising type and size, respectively.

A web GIS based integrated water resources assessment tool for Javeh Reservoir

The creation of tools and services for tracking, estimating, and predicting water resource indices that might provide authorities access to information in close to real-time is essentially a must. The considerable development of Web-GIS DSS (Web-Geographic Information System Decision Support System) allows for the analysis of geospatial data and scenarios via the internet without a traditional desktop GIS. The web GIS technology is coupled to techniques which determine water resources Carlson, and OECD (Organization for Economic Corporation and Development) Trophic Status Indices (TSIs), the water resource vulnerability index (WRVI), and social water stress index (SWSI). The water quality indices were determined by helping machine learning (ML) techniques, Decision Tree, Random Forest, and XGBoost. The performances of aforementioned MLs were compared, and the best efficient one, XGBoost, was included in the Web-GIS DSS tool. The developed Web-GIS DSS was examined in Javeh Reservoir, Kurdestan, IranThe developed Web-GIS DSS allows the users to define the relevant features of the Javeh Reservoir system in pre-defined time horizons such as meteorological, hydrological, inflow water quality, reservoir operation strategies, downstream water demands, geographical external water transfers, etc. The results indicate that the Javeh Reservoir experiences eutrophication status according to the Carlson and OECD indices over time horizons. The water quantity assessment reveals the Javeh Reservoir's susceptibility based on WRVI, although no stress is indicated by SWSI. This susceptibility may be attributed to limited precipitation and water inflow during the simulation period, suggesting a need for comprehensive water resources management.The conclusion drawn from this research underscores the essential role of the developed Web-GIS DSS in decision-making processes for water and environment management, substantiating its practicality with accurate and user-friendly water and environmental data analysis. The tool exemplifies the potential of integrating ML and Web-GIS technologies to promote sustainable environmental practices, thereby establishing a model for future research and policy-making initiatives.

An iterative and subsequent proximation method to map historical crop information with satellite images

Mapping agricultural information such as cropping area and type is of great significance for land use and food security and a common method to retrieve such information is satellite imagery classification. The current image classification techniques require a statistically large number of training samples to obtain representative spectral signatures, ideally collected during the satellite overpass time/date. Although this prerequisite sounds simple and easy in principle, it is extremely challenging and sometimes impossible when classifying historical satellite images, as one simply cannot go back in time to collect training datasets or ground truthing. In this paper, we introduce an iterative and subsequent proximation (ISP) method to circumvent this problem for historical image classifications for historical crop mapping. This method assumes that the same crops grown two years apart have similar spectral properties within the same growing season, with limited variation. This allows the use of the crop information classified in any given year (n) to be used as training samples for the next year (n + 1) or the previous year (n-1). Iteratively repeating n-1, n-2… process leads to the mapping of historical cropping information without a priori ground truthing data. To demonstrate the ISP feasibility, we first used the historical Landsat time-series data to map four major stable crops (rice, maize, soybean, and wheat) in Hailun County, Heilongjiang Province, China, and then further expanded the ISP application to the entire Heilongjiang Province to map cropping areas from 1982 to 2020, using the classification and regression trees (CART) algorithm. The results were validated for those years when actual cropping measurement data were available and further verified with statistical data for other years. The results indicated that the proposed ISP method was appropriate for the historical mapping of four major crops, with a mapping accuracy of approximately 80 % when validated with field data, and correlation coefficients of 0.86, 0.85, 0.91, and 0.94, respectively when compared with historical statistical cropping data. The crop mapping results showed distinct trends of northward expansion in each of the four crops in Heilongjiang Province, which agreed well with previous studies. In conclusion, the ISP method is quick, easy, and convenient for historical crop mapping, which is important in understanding the agricultural production history.

High-performance grating-like SERS substrate based on machine learning for ultrasensitive detection of Zexie-Baizhu decoction

Rapid, universal and accurate identification of chemical composition changes in multi-component traditional Chinese medicine (TCM) decoction is a necessary condition for elucidating the effectiveness and mechanism of pharmacodynamic substances in TCM. In this paper, SERS technology, combined with grating-like SERS substrate and machine learning method, was used to establish an efficient and sensitive method for the detection of TCM decoction. Firstly, the grating-like substrate prepared by magnetron sputtering technology was served as a reliable SERS sensor for the identification of TCM decoction. The enhancement factor (EF) of 4-ATP probe molecules was as high as 1.90 × 107 and the limit of detection (LOD) was as low as 1 × 10−10 M. Then, SERS technology combined with support vector machine (SVM), decision tree (DT), Naive Bayes (NB) and other machine learning algorithms were used to classify and identify the three TCM decoctions, and the classification accuracy rate was as high as 97.78 %. In summary, it is expected that the proposed method combining SERS and machine learning method will have a high development in the practical application of multi-component analytes in TCM.

Hybrid Machine Learning Approach for predicting E-wallet Adoption among Higher Education Students in Malaysia

In today’s digitised world, e-wallets have been sprouting thick and fast in Malaysia as they contribute significantly to expediting onlinetransactions. The E-wallet system is not only a mechanism for businesses to acquire profit but is also one of the most secure paymentoptions for customers, particularly during the COVID-19 pandemic. However, the adoption of e-wallets among higher education students remains unfavourable, eliciting only minimal response. This study aims to analyse higher education students’ adoption of e-wallets using a hybrid machine learning method, combining clustering and decision trees. This approach provides deep insights into user behaviour, improving prediction accuracy and enabling personalised strategies for enhanced user experiences. It profiles and classifies students based on demographics and traits such as age, year of study, gender, frequency of use, future use intention, lifestyle compatibility, perceived trust, risk perception, convenience, and security factors. The analysis reveals the segmentation of the dataset into four distinct clusters, each characterised by shared attributes. These clusters are subsequently labelled descriptively and incorporated into the dataset. The dataset, now enriched with cluster information, serves as the foundation for constructing a decision tree model. The outcome of the decision tree indicates that Cluster 2 and Cluster 3 are hesitant towards e-payment. In contrast, Cluster 1 and Cluster 4 are more receptive despite security concerns, as e-wallets offer convenience despite lacking full trust, with security being a prominent concern amidst rising cyber threats. This study helps the Malaysian government and service providers promote cashless transactions and shape students’ financial independence based on their traits.

Application of Machine Learning Decision Tree Algorithm Based on Big Data in Intelligent Procurement

Application of Machine Learning Decision Tree Algorithm Based on Big Data in Intelligent Procurement

A Comparative Analysis of Machine Learning Algorithms for Predicting Paddy Production

For countries with large populations, such as Indonesia, food security is a very important issue. The majority of Indonesia's population depends on rice as their main food, and paddy is one of the most widely cultivated food commodities. The very good and accurate national paddy production prediction results really support decisions regarding national paddy production targets for the coming period. Therefore, to ensure supply and price stability, paddy availability must be predicted. Many studies have used machine learning to predict crop yields. By learning important patterns and relationships from input data, machine learning can combine the advantages of other methods to make better predictions of paddy yields. The aim of this research is to conduct a comparative analysis between three machine learning algorithms, namely, random forest, decision tree, and k-nearest neighbors, in predicting paddy production. To determine which algorithm is the best, a model evaluation is carried out using the coefficient of determination (R2-score), mean absolute error (MAE), and mean squared error (MSE). This research goes through methodological stages, starting from collecting datasets, data preprocessing, training and testing split datasets, applying algorithms, and evaluating the model. From this research, results were obtained for the random forest algorithm with an R2-score of 82.38%, MAE of 261726.20, and MSE of 2.19495E+11. For the decision tree, the R2-score was 79.62%, MAE was 323257.99, and MSE was 2.49304E+11. Meanwhile, k-nearest neighbors obtained an R2-score of 76.25%, MAE of 318433.42, and MSE of 2.90577E+11. The results of this research show that the random forest algorithm is the best for predicting paddy production because it obtains a larger R2-score as well as smaller MAE and MSE results.

Do sanctions trigger financial crises?

We investigate the effect of sanctions on the occurrence of financial crises. We use the Classification and Regression Tree (CART) algorithm to check whether binary classification mechanism selects sanctions as a predictive factor for the different types of financial crises. We find that trade sanctions matter for the increased probability of banking crises, while military sanctions are associated with currency crises. We find no evidence of the effect of sanctions on sovereign debt crises. We furthermore indicate which variables and their respective thresholds serve as potential harbingers of financial crises.

Delving into Machine Learning's Influence on Disease Diagnosis and Prediction

Introduction/ Background Medical diagnoses have increasingly depended on digitized images obtained through cutting-edge technology. These algorithms offer a promising avenue to transform diagnostic processes in healthcare, with their application scope continually widening due to ongoing advancements. This paper explores machine learning's role in clinical analysis and prediction, examining various studies that apply these techniques in clinical diagnosis, focusing on their use in analyzing images and providing accurate diagnoses. Materials and Methods This study employs a comparative analysis approach, utilizing diverse machine learning algorithms like SVM, K-nearest neighbors, Random Forests, and Decision Trees to analyze digitized medical images and patient records. We extracted data from several medical databases, ensuring a varied and comprehensive dataset. We also evaluated the impact of different data characteristics on the algorithms' effectiveness, aiming to understand the variability in their diagnostic precision across various conditions. Results The results indicate that machine learning algorithms, particularly SVM, K-nearest neighbors, Random Forests, and Decision Trees, demonstrate significant accuracy in diagnosing diseases from digitized images and medical records. SVM and Random Forests showed particularly high effectiveness in clinical diagnosis, suggesting their robustness across different medical conditions and datasets. These findings underscore the potential of machine learning to enhance diagnostic precision and predict illnesses early, aligning with the growing trend of technology-driven medical diagnostics. Discussion The findings reinforce the pivotal role of machine learning in transforming medical diagnostics. The variability in algorithm performance highlights the necessity for tailored approaches, considering dataset specifics and the medical condition being diagnosed. This study underscores the potential of machine learning to enhance diagnostic accuracy, yet it also emphasizes the need for continuous refinement and understanding of the underlying factors affecting algorithm performance. Future research should focus on optimizing these algorithms within diverse clinical settings to fully harness their diagnostic capabilities. Conclusion This study highlights the transformative potential of machine learning in medical diagnostics, demonstrating how various algorithms can effectively analyze digitized images and patient records to diagnose diseases. While the performance of these algorithms varies based on dataset characteristics, the overall high accuracy underscores machine learning's promise in healthcare. As the field continues to evolve, machine learning is poised to become an integral part of clinical diagnosis, enhancing the accuracy and efficiency of medical evaluations and treatments.

Robustness verification of k-nearest neighbors by abstract interpretation

AbstractWe study the certification of stability properties, such as robustness and individual fairness, of the k-nearest neighbor algorithm (kNN). Our approach leverages abstract interpretation, a well-established program analysis technique that has been proven successful in verifying several machine learning algorithms, notably, neural networks, decision trees, and support vector machines. In this work, we put forward an abstract interpretation-based framework for designing a sound approximate version of the kNN algorithm, which is instantiated to the interval and zonotope abstractions for approximating the range of numerical features. We show how this abstraction-based method can be used for stability, robustness, and individual fairness certification of kNN. Our certification technique has been implemented and experimentally evaluated on several benchmark datasets. These experimental results show that our tool can formally prove the stability of kNN classifiers in a precise and efficient way, thus expanding the range of machine learning models amenable to robustness certification.

Relationship Between Biophysical Properties of Antimicrobial Peptides (AMPs) and their Associated Drug Efficacies

Antibiotic resistance is a growing global health threat. One consequence is that patients with cystic fibrosis (CF) are prone to developing antibiotic resistant lung infections caused by multiple strains of bacteria, including Pseudomonas aeruginosa. Due to the limited number of treatment options for patients with chronic antibiotic resistant infections, there is a need for finding new antibiotics that allow for effective eradication of bacterial infections, such as those in the CF lung. Many antimicrobial peptides (AMPs) have been annotated in databases and are considered as potential alternatives for current antibiotics. However, in many instances, the suitability of AMPs as drug molecules has not been extensively explored. Here, we propose that certain molecular properties of AMPs favor high antibiotic efficacy. Using information from AMP databases, we combined statistical analyses and machine learning techniques to identify relationships between various biophysical properties of AMPs and their drug efficacies. Analyses from classification and regression trees (CART) and random forests suggest that net charge and maximum average hydrophobic moment are the most important properties in determining if a peptide is useful against P. aeruginosa infections in CF patients. Maximum average hydrophobic residue, average alpha helix propensity score, hydrophobic proportion, and peptide length still contribute to this determination but to lesser degrees. Cation-pi interactions, on the other hand, do not appear to factor into this decision at all. Based on these properties, our current work is focused on designing and experimentally testing new peptides that may have activity against P. aeruginosa infections.

Building semi-supervised decision trees with semi-cart algorithm

AbstractDecision trees are a fundamental statistical learning tool for addressing classification and regression problems through a recursive partitioning approach that effectively accommodates numerical and categorical data [1, 2]. The Classification and regression tree (CART) algorithm underlies modern Boosting methodologies such as Gradient boosting machine (GBM), Extreme gradient boosting (XGBoost), and Light gradient boosting machine (LightGBM). However, the standard CART algorithm may require improvement due to its inability to learn from unlabeled data. This study proposes several modifications to incorporate test data into the training phase. Specifically, we introduce a method based on Graph-based semi-supervised learning called “Distance-based Weighting,” which calculates and removes irrelevant records from the training set to accelerate the training process and improve performance. We present Semi-supervised classification and regression tree (Semi-Cart), a new implementation of CART that constructs a decision tree using weighted training data. We evaluated its performance on thirteen datasets from various domains. Our results demonstrate that Semi-Cart outperforms standard CART methods and contributes to statistical learning.

Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach.

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.