Random Forest Tree Research Articles

Analysis of banana plant health using machine learning techniques

The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.

Inspection data‐based prediction on fatigue crack of orthotropic steel deck using interpretable machine learning method

AbstractThe prediction of fatigue cracks on orthotropic steel decks is of great significance to the maintenance of bridges. However, fatigue cracks are affected by various uncertainties in reality, which encourages a data‐driven study for the sake of reliability and accuracy of predictions. Based on the crack inspection data from orthotropic steel decks on actual bridges in China, the feature engineering is conducted considering fatigue crack behaviors, and the machine learning models are trained and tested for predicting cracks, including XGBoost, random forest, and multiple decision trees. According to the receiver operating characteristic curves of the three models, the XGBoost model has the best performance, whereas the average AUC is about 0.75, limited by the insufficient data volume of positive samples. With the SHAP values of all features, the interpretation of the machine learning model is presented, indicating that the global effects, that is, the longitudinal position, the loading condition, and the bridge age, are always influential factors for fatigue cracks. The local features concerning the interactions between cracks have an effect on crack behaviors to a certain extent, but less important. Accordingly, the interpretable machine learning model can provide conservative predictions in a rather transparent way on this issue, which can benefit decision‐making in bridge designs, maintenance, and management.

Improvement of drought forecasting by means of various machine learning algorithms and wavelet transformation

AbstractDrought, which is defined as a decrease in average rainfall amounts, is one of the most insidious natural disasters. When it starts, people may not be aware of it, which is why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods, such as drought indices, one of which is the Standardized Precipitation Index (SPI). In this study, SPI was used to detect droughts, and machine learning algorithms, including support vector machines (SVM), artificial neural networks, random forest, and decision tree, were used to predict droughts. In addition, 3 different statistical criteria, which are correlation coefficient (r), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE), were used to investigate model performance values. The wavelet transform (WT) was also applied to improve model performance. One of the areas most impacted by droughts in Turkey is the Konya Closed Basin, which is geographically positioned in the center of the country and is among the top grain-producing regions in Turkey. The Apa Dam is one of the most significant water resources in the area. It provides water to many fertile fields in its vicinity and is affected by droughts which is why it was selected as a study area. Meteorological data, such as monthly precipitation, that could represent the region were obtained between 1955 and 2020 from the general directorate of state water works and the general directorate of meteorology. According to the findings, the M04 model, whose input structure was developed using SPI, various time steps, data delayed up to 5 months, and monthly precipitation data from the preceding month (time t − 1), produced the best results out of all the models examined using machine learning algorithms. Among machine learning algorithms, SVM has achieved the most successful results not only before applying WT but also after WT. The best results were obtained from M04, in which SVM with WT was used (NSE = 0.9942, RMSE = 0.0764, R = 0.9971).

Cloud-Based Transaction Fraud Detection: An In-depth Analysis of ML Algorithms

Context: Cloud-based services are increasingly central in financial technology, enabling scalable and efficient transactions. However, they also heighten vulnerability to fraud, challenging the security of online financial activities. Traditional fraud detection struggles against sophisticated tactics, highlighting the need for advanced, cloud-compatible solutions. Objectives: This study assesses machine learning (ML) algorithms' ability to detect fraud in cloud environments, focusing on Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and XGBoost (XGB). It uses a comprehensive dataset to determine which ML model best identifies fraudulent transactions, aiming to optimize these models for accuracy, precision, and efficiency in real-time detection. Results: The XGBoost model outperformed others in fraud detection accuracy, with Random Forest and Decision Trees also showing high effectiveness. These models were particularly good at balancing precision and recall, minimizing false positives, and accurately identifying fraud in complex transaction patterns. Conclusion: ML, especially ensemble and boosting models like XGBoost and Random Forest, offers a strong approach to detecting transaction fraud in cloud-based financial systems. Their capacity to handle vast data volumes and adapt to new fraud patterns enhances cloud transaction security. Implication: The study provides a guide for financial and cloud services to implement sophisticated fraud detection systems. It emphasizes the importance of continual ML innovation to tackle digital finance fraud, suggesting that adopting these advanced ML models can significantly reduce transaction fraud risks, ensuring a secure, efficient, and trustworthy platform for users.

Detection of Pleuro Pulmonary Blastoma using Machine Learning Models

Pleura Pulmonary Blastoma (PPB) is a type of lung cancer seen in children. PPB needs to be detected earlier when treating children. The mortality rate of PPB is higher if left untreated. It can be detected from CT images through various machine learning and classification algorithms. The earlier detection of PPB can save children's lives, for which several research works have proposed several machine learning models. Several researchers adopt traditional classification algorithms like random forest and decision tree algorithms for detecting PPB. However, these techniques provided lesser accuracy and were difficult for earlier detections. This paper considered several machine learning algorithms like SVM, LR and MP and experimented with CT images and DICER-1 data to understand their betterness and overcome such issues. The architecture of the following algorithms is discussed in detail, and the results are compared. Through this, the ideal machine learning algorithm for detecting PPB is found. All the algorithms are implemented with the Python software, and the performance metrics of the respective algorithms are recorded. The results show that the SVM algorithm provides better accuracy (96%) for the DICER-1 dataset, which is higher than CT images (95.60%).

Enhanced Anomaly-Based Detection of the Distributed Denial of Service Attack using Modular Memetic Behavioral Analysis

The Internet’s popularity has proven to be an effective mode for data dissemination, and also advance the proliferation of adversaries whose exploits network for personal gain via unauthorized access that compromises a user device. Adversaries have achieved such feats via socially-engineered, subterfuge schemes – some of which deny users of network resources. These distributed denial of service (DDoS) attacks are carefully crafted to impact a large magnitude with the capability to wreak havoc at high levels of network infrastructures. This study posits a deep learning approach to distinguish between benign exchange of data and malicious attacks from data traffic. With benchmark ensemble such as XGBoost, Random Forest and Decision Tree – the results shows our proposed ensemble yields F1 of 0.9945, and outperforms XGBoost, RF and DT (with F1 of 0.9925, 0.9881 and 0.9805 respectively); And with an Accuracy of 0.9984 to outperform XGBoost, RF and DT (with 0.9981, 0.9964 and 0.9815 respectively). The proposed ensemble incorrectly classified only 283-instances with 13,418 correctly classified test instances with a 99.84% accuracy. Result shows our use of the deep learning memetic model effectively differentiate between genuine and malicious packets via anomaly-based detection. Keywords: Memetic Algorithm, Random Forest, XGBoost, feature selection, imbalanced dataset Aims Research Journal Reference Format: Sulu, G.A., Akazue, M.I., Edje, A.E., (2024): Enhancing computer network intrusion detection using the network behaviour analysis technique: a pilot study. Advances in Multidisciplinary and Scientific Research Journal Vol. 10. No. 2. Pp 125-142 www.isteams.net/aimsjournal. dx.doi.org/10.22624/AIMS/V10N2P11

Uyku Sağlığı ile Yaşam Tarzı Arasındaki İlişkinin PCA, Naive Bayes ve Rastgele Orman Ağaçları Yöntemleri ile İncelenmesi ve Karşılaştırılması

Uyku, günlük yaşamın temel bir unsuru olarak kabul edilir ve genel sağlık ile refahın sürdürülmesinde önemli bir rol oynar. Bu araştırma, Kaggle platformundan elde edilen "Uyku Sağlığı Yaşam Tarzı" veri setini kullanarak bir tahmin modeli oluşturmayı, bu modeli Principal Component Analysis (PCA) yöntemi, Naive Bayes yöntemi ve Rastgele Orman Ağaçları yöntemiyle değerlendirmeyi ve görselleştirmeler gerçekleştirmeyi amaçlamaktadır. İncelenen veri seti, KNIME platformunda PCA modülü ile boyut azaltma işlemine tabi tutulmuş ve elde edilen çıktılar sunulmuştur. Uyku kalitesini etkilediği düşünülen öznitelikler arasındaki ilişkiler, korelasyon hesaplamaları ile belirlenmiştir. Ayrıca, veri seti Naive Bayes ve Rastgele Orman Ağaçları yöntemleriyle analiz edilmiş, tahmin sonuçları KNIME ortamında değerlendirilmiştir. Elde edilen sonuçlar tablolar halinde sunulmuştur. Bu karşılaştırmaların dağılım matrisleri, KNIME platformundaki Scatter Plot modülü kullanılarak görselleştirilmiştir. Bu çalışmanın en önemli katkısı uyku verileri içeren veri setlerinde kullanılabilecek metodolojiler arasında en etkili olan yöntemi belirlemektir. Bulgular, tartışma ve sonuçlar bölümünde detaylı bir şekilde ele alınmıştır.

Utilizing an Ensemble of Extra Tree Model for Classifying Mesothelioma Cancer

Objectives: Explore the potential of ensemble learning techniques like Bagging Tree, Random Forest, and Ensemble Extra Tree in transforming mesothelioma diagnosis.Overcome challenges associated with late-stage detection and limited treatment options using advanced machine learning algorithms.Enhance predictive power and feature extraction capabilities through the combination of diverse ensemble algorithms.

Machine learning–assisted prediction of mechanical properties of high-entropy alloy/graphene nanocomposite

The addition of graphene (Gr) to high-entropy alloys (HEAs) has recently been demonstrated to endow the alloys with increased strength and plasticity in experiments and simulations. However, determining the effects of various factors on the mechanical properties of Gr during the experimental process is difficult due to its special structure and the complexity of its components. Investigation of the mechanical properties (Young's modulus, yield strength and toughness) of HEA/Gr under different simulation conditions through experiments and simulations is not only costly in terms of raw materials but also time-consuming. This study aimed to address the aforementioned problem and investigate the mechanical properties of HEA/Gr under different conditions. First, the dataset necessary for the machine learning model was constructed using molecular dynamics simulation and downscaled from 59 to 21 features using principal component analysis. Then, five machine learning models, including XGBoost, light gradient boosting (LGBoost) machine, random forest, AdaBoost, and decision tree, were employed. Finally, 10-fold cross-validation and grid search were utilized during the training process to search for the best parametric model, and the coefficient of determination R2 and mean square error MSE were used as metrics. Results indicate the superiority of the XGBoost and LGBoost models to other models in predicting HEA/Gr mechanical properties. Among the three properties studied, the models predicting Young’s modulus and toughness revealed an R2 value above 85 %. Two models realized an R2 greater than 90 % for Young’s modulus, while all three models achieved an R2 greater than 85 % for toughness.

Predicting Student Achievement via Machine Learning: Evidence from Turkish Subset of PISA

This study seeks to identify the determinants of academic performance in mathematics, science, and reading among Turkish secondary school students. Using data from the OECD's PISA 2018 survey, which includes several student- and school-level variables as well as test scores, we employed a range of supervised machine learning methods specifically ensemble decision trees to assess their predictive performance. Our results indicate that the boosted regression tree (BRT) method outperforms other methods bagging and random forest regression trees. Notably, the BRT highlights the importance of general secondary education programs over vocational and technical (VAT) education in predicting academic achievement. Moreover, both characteristics specific to student and school environment are demonstrated to be significant predictors of academic performance in all subject areas. These findings contribute to the development of evidence-based educational policies in Turkey.

Explorative prediction of novel superhard carbon allotropes with lager cell: Density functional theory-assisted deep learning

Searching for superhard materials with excellent properties has been a key challenge in materials science over the past decades. In this study, based on high throughput and density functional theory (DFT), we identified 24 new stable carbon allotropes from 50 initially identified candidates through structural optimization by removing repetitive structures and mechanically, molecularly dynamic, and thermally unstable structures. In addition, we built a crystal convolution residual neural network (CCRNN) to predict the elastic properties and mechanical hardness of the carbon allotropes and used chemical formulas as inputs to the model. The model used nearly 9979 target compounds, monomers, and 143 element-based features such as covalent radius, electronegativity, volume, and magnetic moment. To accurately predict carbon allotropes, we added lattice constants (a,b,c) and lattice angles (alpha,beta,gamma) as inputs after the feature descriptors. Random forest (RF) and gradient-boosted decision tree (GBDT) regression algorithms were constructed, and the r of the CCRNN model was 0.978 and 0.955 for the bulk and shear moduli, respectively, and the best model (CCRNN) was chosen to predict the bulk and shear moduli of the stabilized carbon allotropes obtained from high-throughput calculations. Density functional theory validated the machine learning results. This study not only revealed 7 new superhard carbon allotropes, but also proposed a new deep learning model, and these newly discovered superhard carbon allotropes had wide-ranging potential applications in the fields of industry, electronics, aerospace, geology, and biomedicine. Our research has provided an important theoretical and experimental basis for the development of new superhard materials and applications and was significant in advancing the field of materials science and engineering.

Using Machine Learning for Non-Invasive Detection of Kidney Stones Based on Laboratory Test Results: A Case Study from a Saudi Arabian Hospital.

Kidney stone disease is a widespread urological disorder affecting millions globally. Timely diagnosis is crucial to avoid severe complications. Traditionally, renal stones are detected using computed tomography (CT), which, despite its effectiveness, is costly, resource-intensive, exposes patients to unnecessary radiation, and often results in delays due to radiology report wait times. This study presents a novel approach leveraging machine learning to detect renal stones early using routine laboratory test results. We utilized an extensive dataset comprising 2156 patient records from a Saudi Arabian hospital, featuring 15 attributes with challenges such as missing data and class imbalance. We evaluated various machine learning algorithms and imputation methods, including single and multiple imputations, as well as oversampling and undersampling techniques. Our results demonstrate that ensemble tree-based classifiers, specifically random forest (RF) and extra tree classifiers (ETree), outperform others with remarkable accuracy rates of 99%, recall rates of 98%, and F1 scores of 99% for RF, and 92% for ETree. This study underscores the potential of non-invasive, cost-effective laboratory tests for renal stone detection, promoting prompt and improved medical support.

Applying machine learning to characterize and extrapolate the relationship between seismic structure and surface heat flow

SUMMARY Geothermal heat flow beneath the Greenland and Antarctic ice sheets is an important boundary condition for ice sheet dynamics, but is rarely measured directly and therefore is inferred indirectly from proxies (e.g. seismic structure, magnetic Curie depth, surface topography). We seek to improve the understanding of the relationship between heat flow and one such proxy—seismic structure—and determine how well heat flow data can be predicted from the structure (the characterization problem). We also seek to quantify the extent to which this relationship can be extrapolated from one continent to another (the transportability problem). To address these problems, we use direct heat flow observations and new seismic structural information in the contiguous United States and Europe, and construct three Machine Learning models of the relationship with different levels of complexity (Linear Regression, Decision Tree and Random Forest). We compare these models in terms of their interpretability, the predicted heat flow accuracy within a continent and the accuracy of the extrapolation between Europe and the United States. The Random Forest and Decision Tree models are the most accurate within a continent, while the Linear Regression and Decision Tree models are the most accurate upon extrapolation between continents. The Decision Tree model uniquely illuminates the regional variations of the relationship between heat flow and seismic structure. From the Decision Tree model, uppermost mantle shear wave speed, crustal shear wave speed and Moho depth together explain more than half of the observed heat flow variations in both the United States [$r^2 \approx 0.6$ (coefficient of determination), $\mathrm{RMSE} \approx 8\, {\rm mW}\,{\rm m}^{-2}$ (Root Mean Squared Error)] and Europe ($r^2 \approx 0.5, \mathrm{RMSE} \approx 13\, {\rm mW}\,{\rm m}^{-2}$), such that uppermost mantle shear wave speed is the most important. Extrapolating the U.S.-trained models to Europe reasonably predicts the geographical distribution of heat flow [$\rho = 0.48$ (correlation coefficient)], but not the absolute amplitude of the variations ($r^2 = 0.17$), similarly from Europe to the United States ($\rho = 0.66, r^2 = 0.24$). The deterioration of accuracy upon extrapolation is caused by differences between the continents in how seismic structure is imaged, the heat flow data and intrinsic crustal radiogenic heat production. Our methods have the potential to improve the reliability and resolution of heat flow inferences across Antarctica and the validation and cross-validation procedures we present can be applied to heat flow proxies other than seismic structure, which may help resolve inconsistencies between existing subglacial heat flow values inferred using different proxies.

Advancing Skin Cancer Prediction Using Ensemble Models

There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits. Dermoscopy and other advanced imaging techniques have enhanced early detection by providing detailed images of lesions. However, accurately interpreting these images to distinguish between benign and malignant tumors remains a difficult task. Improved predictive modeling techniques are necessary due to the frequent occurrence of erroneous and inconsistent outcomes in the present diagnostic processes. Machine learning (ML) models have become essential in the field of dermatology for the automated identification and categorization of skin cancer lesions using image data. The aim of this work is to develop improved skin cancer predictions by using ensemble models, which combine numerous machine learning approaches to maximize their combined strengths and reduce their individual shortcomings. This paper proposes a fresh and special approach for ensemble model optimization for skin cancer classification: the Max Voting method. We trained and assessed five different ensemble models using the ISIC 2018 and HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, and Extra Trees. Their combined predictions enhance the overall performance with the Max Voting method. Moreover, the ensemble models were fed with feature vectors that were optimally generated from the image data by a genetic algorithm (GA). We show that, with an accuracy of 95.80%, the Max Voting approach significantly improves the predictive performance when compared to the five ensemble models individually. Obtaining the best results for F1-measure, recall, and precision, the Max Voting method turned out to be the most dependable and robust. The novel aspect of this work is that skin cancer lesions are more robustly and reliably classified using the Max Voting technique. Several pre-trained machine learning models’ benefits are combined in this approach.

Forest age estimation using UAV-LiDAR and Sentinel-2 data with machine learning algorithms- a case study of Masson pine (Pinus massoniana)

ABSTRACT The assessment of ecological functions, such as those of forest structure zoning and carbon sinks, heavily relies on forest age classification. Therefore, monitoring forest age is a crucial element of forest resource surveys. With the increased availability of high-quality open-access satellite data and advancements in Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) technology, remote sensing has emerged as an essential method for acquiring accurate forest age information. In this study, Sentinel-2 remote sensing data, UAV-LiDAR data, and combined Sentinel-2 and LiDAR data are used as data sources. Three machine learning algorithms, Adaptive Boosting (AdaBoost), Random Forest (RF), and Extreme Random Tree (ERT), are used to predict forest age in a Masson pine (Pinus massoniana Lamb.) forest. The optimal model is used to predict the forest age and simulate the spatial age distribution. The machine learning models based on separate Sentinel-2 and LiDAR data accurately predict the age of the Masson pine forest. Nevertheless, the accuracy of the RF model with combined data was higher than that in other cases, with an accuracy R value of 0.81. The model displayed good stability, and the spatial uncertainty of age estimation was low. Compared with the RF model using only Sentinel-2 data (R = 0.43), the RF model with combined LiDAR and Sentinel-2 data achieved the highest accuracy, with R values 88.37% higher. In addition, the forest canopy structure parameters, such as the average height of the forest stand extracted from UAV-LiDAR data, had a significant impact on the estimation of forest age. Thus, when the combined Sentinel-2 and LiDAR data were used to establish these parameters, the highest accuracy in the estimation of Masson pine was obtained. The findings of this study provide new insights for forest age estimation based on multi-source remote sensing data.

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

ObjectiveRadio Frequency Time Series (RF TS) is a cutting-edge ultrasound approach in tissue typing. The RF TS does not provide dynamic insights into the propagation medium; when the tissue and probe are fixed. We previously proposed the innovative RFTSDP method in which the RF data are recorded while stimulating the tissue. Applying stimulation can unveil the mechanical characteristics of the tissue in RF echo. Materials and methodsIn this study, an apparatus was developed to induce vibrations at different frequencies to the medium. Data were collected from four PVA phantoms simulating the nonlinear behaviors of healthy, fibroadenoma, cyst, and cancerous breast tissues. Raw focused, raw, and beamformed ultrafast data were collected under conditions of no stimulation, constant force, and various vibrational stimulations using the Supersonic Imagine Aixplorer clinical/research ultrasound imaging system. Time domain (TD), spectral, and nonlinear features were extracted from each RF TS. Support Vector Machine (SVM), Random Forest, and Decision Tree algorithms were employed for classification. ResultsThe optimal outcome was achieved using the SVM classifier considering 19 features extracted from beamformed ultrafast data recorded while applying vibration at the frequency of 65 Hz. The classification accuracy, specificity, and precision were 98.44 ± 0.20 %, 99.49± 0.01 %, and 98.53± 0.04 %, respectively. Applying RFTSDP, a notable 24.45 % improvement in accuracy was observed compared to the case of fixed probe assessing the recorded raw focused data. ConclusionsExternal vibration at an appropriate frequency, as applied in RFTSDP, incorporates beneficial information about the medium and its dynamic characteristics into the RF TS, which can improve tissue characterization.

Multi-Class Network Anomaly Detection Using Machine Learning Techniques

Computer networks rely on Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) to ensure the security, reliability, and availability of an organization. In recent years, various approaches were developed and implemented to create effective IDSs and IPSs. This paper specifically focuses on IDSs that utilize Machine Learning (ML) techniques for improved accuracy. ML-based IDSs have verified to be successful in discovering network attacks. However, their performance tends to decline when dealing with high-dimensional data spaces. It is essential to develop a suitable feature extraction strategy that could identify and remove irrelevant features that do not significantly classification process to address this issue. Additionally, many ML-based IDSs exhibit high false positive rates and poor detection accuracy when trained on unbalanced datasets. In this study, we analyze the UNSW-NB15 IDS, which will serve as the training and testing data for our models. In order to reduce the feature space and improve the efficiency of our analysis, we leverage a filter-based feature reduction method utilizing the Pearson correlation coefficient algorithm. By identifying and selecting only the most relevant features, we are able to streamline our dataset and focus on the variables that have the highest impact on our analysis. This approach not only reduces computational complexity but also improves the interpretability of our results by eliminating unnecessary noise from the data. After applying the feature reduction technique, we proceed to implement a range of machine learning methods to perform our classification task. These include well-known algorithms such as Stacking, Extra Trees, Multi-Layer Perceptron, XGBoost, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, Support Vector Machine, Random Forest, and Decision Tree. By employing a diverse set of algorithms, we are able to explore different modeling approaches and evaluate their effectiveness in accurately classifying the various types of assaults. In order to assess the performance of our classification models, we utilize a range of specialized evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R2-Score, Mean Squared Error (MSE), Precision, F1-Score, Recall, and Accuracy. These metrics provide us with a comprehensive understanding of how well our models are performing across different dimensions, including the accuracy of predictions, the level of precision in classifying different assault types, and the overall goodness-of-fit of our models. By considering multiple evaluation metrics, we are able to gain a more nuanced understanding of the strengths and weaknesses of each algorithm and make informed decisions about their suitability for our classification task. These metrics deliver a complete evaluation of the classifiers’ effectiveness in detecting community intrusions.

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

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

A Prediction Model for Identifying Seasonal Influenza Vaccination Uptake Among Children in Wuxi, China: Prospective Observational Study.

Predicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions. The aim of this study was to develop predictive models for influenza vaccination behavior among children in China. We obtained data from a prospective observational study in Wuxi, eastern China. The predicted outcome was individual-level vaccine uptake and covariates included sociodemographics of the child and parent, parental vaccine hesitancy, perceptions of convenience to the clinic, satisfaction with clinic services, and willingness to vaccinate. Bayesian networks, logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), naive Bayes (NB), random forest (RF), and decision tree classifiers were used to construct prediction models. Various performance metrics, including area under the receiver operating characteristic curve (AUC), were used to evaluate the predictive performance of the different models. Receiver operating characteristic curves and calibration plots were used to assess model performance. A total of 2383 participants were included in the study; 83.2% of these children (n=1982) were <5 years old and 6.6% (n=158) had previously received an influenza vaccine. More than half (1356/2383, 56.9%) the parents indicated a willingness to vaccinate their child against influenza. Among the 2383 children, 26.3% (n=627) received influenza vaccination during the 2020-2021 season. Within the training set, the RF model showed the best performance across all metrics. In the validation set, the logistic regression model and NB model had the highest AUC values; the SVM model had the highest precision; the NB model had the highest recall; and the logistic regression model had the highest accuracy, F1 score, and Cohen κ value. The LASSO and logistic regression models were well-calibrated. The developed prediction model can be used to quantify the uptake of seasonal influenza vaccination for children in China. The stepwise logistic regression model may be better suited for prediction purposes.

Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering

The classification of imbalanced datasets is a prominent task in text mining and machine learning. The number of samples in each class is not uniformly distributed; one class contains a large number of samples while the other has a small number. Overfitting of the model occurs as a result of imbalanced datasets, resulting in poor performance. In this study, we compare different oversampling techniques like synthetic minority oversampling technique (SMOTE), support vector machine SMOTE (SVM-SMOTE), Border-line SMOTE, K-means SMOTE, and adaptive synthetic (ADASYN) oversampling to address the issue of imbalanced datasets and enhance the performance of machine learning models. Preprocessing significantly enhances the quality of input data by reducing noise, redundant data, and unnecessary data. This enables the machines to identify crucial patterns that facilitate the extraction of significant and pertinent information from the preprocessed data. This study preprocesses the data using various top-level preprocessing steps. Furthermore, two imbalanced Twitter datasets are used to compare the performance of oversampling techniques with six machine learning models including random forest (RF), SVM, K-nearest neighbor (KNN), AdaBoost (ADA), logistic regression (LR), and decision tree (DT). In addition, the bag of words (BoW) and term frequency and inverse document frequency (TF-IDF) features extraction approaches are used to extract features from the tweets. The experiments indicate that SMOTE and ADASYN perform much better than other techniques thus providing higher accuracy. Additionally, overall results show that SVM with ’linear’ kernel tends to attain the highest accuracy and recall score of 99.67% and 1.00% on ADASYN oversampled datasets and 99.57% accuracy on SMOTE oversampled dataset with TF-IDF features. The SVM model using 10-fold cross-validation experiments achieved 97.40 mean accuracy with a 0.008 standard deviation. Our approach achieved 2.62% greater accuracy as compared to other current methods.