Using Supervised Machine Learning Algorithms for Automated Lithology Prediction from Wireline Log Data

Due to the rapid growth of android applications and mobile users in this technological era, there is a large increase in cyber attacks through mobile phones. During Pandemic period, mobile malware attacks are one of the top most cyber attacks observed in android mobile users to steal the user personal credentials by intrusion of adware, spyware, banking malware, SMS malware, riskware, viruses, Trojan horse, worms, keylogger and many more. Machine learning methods are very useful and amicable to detect mobile malwares. Automation of the mobile malware detection is the need of the hour and it is imperative to identify the most suitable machine learning techniques. This book entitled “Dynamic Analysis based Mobile Malware Classification using Supervised Machine Learning Methods” investigates the evaluation of supervised machine learning algorithms that are applied to detect and classify the mobile malwares. A systematic method of evaluation of supervised machine learning model to detect the malware data points and to classify them into binary classification as malware or benign is essential. The purpose of evaluating the supervised machine learning algorithms is to identify the best supervised machine learning model for mobile malware detection with high efficacy rate.  All important performance measures like Precision, Recall, F1 score, R2 score, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error are applied and the entire experiments are conducted using benchmark dataset taken from kaggle community. Nine Supervised Machine Learning methods such as Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Machine, Naïve Bayes, AdaBoost, Multi-layer Perceptron, Logistic Regression and Linear Discriminant Analysis are experimented and the results are discussed. +

The quality of source code is negatively impacted by code smells. Since the term "code smell" originated, numerous attempts have been made to comprehend it by identifying it using various techniques, such as metric-based, heuristic-based, optimization-based, machine learning (ML)-based, etc. Among these, supervised machine learning (SML) has shown effectiveness in detecting code smells. However, SML techniques have significant limitations, including the dependency on expensive and high-quality labeled data, the need for representative training datasets, and the risk of introducing biases in labeled examples that lead to skewed predictions. To overcome these challenges, this study introduces a method that leverages unsupervised machine learning (UnML) along with feature engineering. Unlike SML, UnML does not require labeled data and minimizes potential biases. The proposed method was evaluated using four datasets containing different types of code smells and was compared with a previous study that used SML techniques. The results indicate that the UnML-based method is effective, achieving outcomes closely aligned with those from the SML approach. This method is especially beneficial in situations where labeled data is scarce or unavailable and can be used to identify new code smells, generate labeled data for SML and detect multiple code smells simultaneously within a codebase.

Many lung diseases like Chronic Obstructive Pulmonary Disease (COPD) and Asthma are chronic diseases and worldwide considered as third main cause of death. COPD remains a forefront issue in global health, presenting significant challenges due to its complex nature and the imperative for precise classification to effectively guide treatment protocols. Traditional diagnostic methods, while effective, often come with limitations such as invasiveness, cost, and the requirement for specialized expertise. Recent advancements in machine learning (ML), particularly supervised ML algorithms, have shown promise in enhancing COPD classification through the analysis of clinical data, imaging, and physiological data. This review paper aims to systematically examine the current state of research on the application of supervised ML algorithms for the classification of COPD. We evaluate the effectiveness, accuracy, and clinical applicability of these algorithms by analyzing studies published in the last decade. Our review covers a range of supervised ML techniques, including K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), Extreme Gradient Boosting (EGB), emphasizing their effectiveness by employing evaluating metrics such as accuracy, specificity, AUC scores and sensitivity. The observations of this review indicate that supervised machine learning algorithms can significantly enhance the classification and prediction of COPD stages, offering prospects for customized treatment plans and enhanced patient prognosis.

Energy parameter modeling in plug-in hybrid electric vehicles using supervised machine learning approaches

Specific gene regions in DNA, such as cytochrome <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{c}$</tex> oxidase I (COI) in animals, are defined as DNA barcodes and can be used as identifiers to distinguish species. The standard length of a DNA barcode is approximately 650 base pairs (bp), However, because of the challenges associated with sequencing technologies and the unavailability of high-quality genomic DNA, it is not always possible to obtain the full-length barcode sequence of an organism. Recent studies suggest that mini-barcodes, which are shorter (100–300 bp) barcode sequences, can contribute significantly to species identification. Among various methods proposed for the identification task, supervised machine learning methods are effective. However, any prior work indicating the efficacy of mini-barcodes in species identification under a machine learning approach is elusive to find. In this study, we analyzed the effect of different barcode lengths on species identification using supervised machine learning and proposed a general approximation of the required length of the mini-barcode. Since Naïve Bayes is seen to generally outperform other supervised methods in species identification in other studies, we implemented this classifier and showed the effectiveness of the mini-barcode by demonstrating the accuracy responses obtained after varying the length of the DNA barcode sequences.

Due to the highly variable clinical phenotype, Klinefelter Syndrome is underdiagnosed. Assessment of supervised machine learning based prediction models for identification of Klinefelter Syndrome among azoospermic patients, and comparison to expert clinical evaluation. Retrospective patient data (karyotype, age, height, weight, testis volume, follicle-stimulating hormone, luteinizing hormone, testosterone, estradiol, prolactin, semen pH and semen volume) collected between January 2005 and June 2019 were retrieved from a patient data bank of a University Centre. Models were trained, validated and benchmarked based on different supervised machine learning algorithms. Models were then tested on an independent, prospectively acquired set of patient data (between July 2019 and July 2020). Benchmarking against physicians was performed in addition. Based on average performance, support vector machines and CatBoost were particularly well-suited models, with 100% sensitivity and >93% specificity on the test dataset. Compared to a group of 18 expert clinicians, the machine learning models had significantly better median sensitivity (100% vs. 87.5%, p=0.0455) and fared comparably with regards to specificity (90% vs. 89.9%, p=0.4795), thereby possibly improving diagnosis rate. A Klinefelter Syndrome Score Calculator based on the prediction models is available on http://klinefelter-score-calculator.uni-muenster.de. Differentiating Klinefelter Syndrome patients from azoospermic patients with normal karyotype (46,XY) is a problem that can be solved with supervised machine learning techniques, improving patient care. Machine learning could improve the diagnostic rate of Klinefelter Syndrome among azoospermic patients, even more for less-experienced physicians.

Microfinance institutions in Kenya play a unique role in promoting financial inclusion, loans, and savings provision, especially to low-income individuals and small-scale entrepreneurs. However, despite their benefits, most of their products and programs in Machakos County have been reducing due to repayment challenges, threatening their financial ability to extend further credit. This could be attributed to ineffective credit scoring models which are not able to establish the nuanced non-linear repayment behavior and patterns of the loan applicants. The research objective was to enhance credit risk scoring for microfinance institutions in Machakos County using supervised machine learning algorithms. The study adopted a mixed research design under supervised machine learning approach. It randomly sampled 6771 loan application account records and repayment history. Rstudio and Python programming languages were deployed for data pre-processing and analysis. Logistic regression algorithm, XG Boosting and the random forest ensemble method were used. Metric evaluations used included the performance accuracy, Area under the Curve and F1-Score. Based on the study findings: XG Boosting was the best performer with 83.3% accuracy and 0.202 Brier score. Development of legal framework to govern ethical and open use of machine learning assessment was recommended. A similar research but using different machine learning algorithms, locations, and institutions, to ascertain the validity, reliability and the generalizability of the study findings was recommended for further research.

Millions of people worldwide are affected by Parkinson's disease (PD), which significantly worsens their quality of life. Currently, the diagnosis is based on assessment of motor symptoms, but interest toward non-motor symptoms is increasing, as well. Among them, idiopathic hyposmia (IH) is associated with an increased risk of developing PD in healthy adults. In this work, a wearable inertial device, named SensFoot V2, was used to acquire motor data from 30 healthy subjects, 30 people with IH, and 30 PD patients while performing tasks from the MDS-UPDRS III for lower limb assessment. The most significant and non-correlated extracted parameters were selected in a feature array that can identify differences between the three groups of people. A comparative classification analysis was performed by applying three supervised machine learning algorithms. The system resulted able to distinguish between healthy and patients (specificity and recall equal to 0.967), and the people with IH can be identified as a separate class within a three-group classification (accuracy equal to 0.78). Thus, the system could support the clinician in objective assessment of PD. Further, identification of IH together with changes in motor parameters could be a non-invasive two-step approach to investigate the early onset of PD.

Feature selection for relative poverty classification in Morocco using supervised machine learning algorithms

A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.

The aim of this study was to assess measurements of the maxillary canines using Cone Beam Computed Tomography (CBCT) and develop a machine learning model for sex estimation. CBCT scans from 610 patients were screened. The maxillary canines were examined to measure total tooth length, average enamel thickness, and mesiodistal width. Various supervised machine learning algorithms were employed to construct predictive models, including Decision Tree, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Logistic Regression, Multi-Layer Perceptron (MLP), Random Forest Classifier, Support Vector Machine (SVM), XGBoost, LightGBM, and CatBoost. Validation of each model was performed using a 10-fold cross-validation approach. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were computed, with ROC curves generated for visualization. The total length of the tooth proved to be the variable with the highest predictive power. The algorithms that demonstrated superior performance in terms of AUC were LightGBM and Logistic Regression, achieving AUC values of 0.77 [CI95% = 0.65-0.89] and 0.75 [CI95% = 0.62-0.86] for the test data, and 0.74 [CI95% = 0.70-0.80] and 0.75 [CI95% = 0.70-0.79] in cross-validation, respectively. Both models also showed high precision values. The use of maxillary canine measurements, combined with supervised machine learning techniques, has proven to be viable for sex estimation. The machine learning approach combined with is a low-cost option as it relies solely on a single anatomical structure.

Investigation of optimal feature for milling chatter identification using supervised machine learning techniques

Technology adoption in the Indian dairy industry is significantly less than in other sectors. There is a need for technology adoption in dairy farming like artificial intelligence (AI), machine learning (ML), and the internet of things (IoT). Manual handling in dairy operations is becoming a tough job, reducing the efficiency in milk production. This paper aims to determine whether the dairy industry can adopt ML techniques for better milk production. This paper applied supervised ML techniques to improve dairy management. The data was recorded and collected from smart devices that the cow wears. Those data can be integrated by ordering from the innovative wearable database and analyzed by using supervised machine learning algorithms like SVM, bagging and boosting techniques, K-nearest neighbor and hybridization techniques, which classifies the feature of the data with at most accuracy and can provide ultimate results to predict the nature of a herd of cows for the best production and the evaluation can be done by calculating precision, sensitive, recall, f-Measure and accuracy.

Accurately identifying primary biological aerosol particles (PBAPs) using analytical techniques poses inherent challenges due to their resemblance to other atmospheric carbonaceous particles. We present a study of an enhanced method for detecting PBAPs by combining single-particle measurement with advanced supervised machine learning (SML) techniques. We analyzed ambient particles from a variety of environments and lab-generated standards, focusing on chemical composition for traditional rule-based and clustering approaches and incorporating morphological features into the SML approaches, neural networks and XGBoost, for improved accuracy. This study demonstrates that SML methods outperform traditional methods in quantifying PBAPs, achieving significant improvements in precision, recall, F1-score, and accuracy, leading to an increased number of detected PBAPs by at least 19%. The adaptability of the proposed XGBoost-based SML model is showcased in comparison to traditional methods in categorizing PBAPs for blind data sets from different geographical locations. Two field case studies were investigated, over agricultural land and Amazonia rain forest, representing relatively low and high concentrations of PBAPs, respectively, where XGBoost consistently detected up to 3.5 times more PBAPs than traditional methods. Precise detection of PBAPs in the atmosphere could significantly improve the prediction of climatic impacts by them.

Botanists study in general the characteristics of leaves to give to each plant a scientific name; such as shape, margin...etc. This paper proposes a comparison of supervised plant identification using different approaches. The identification is done according to three different features extracted from images of leaves: a fine-scale margin feature histogram, a Centroid Contour Distance Curve shape signature and an interior texture feature histogram. First represent each leaf by one feature at a time in, then represent leaves by two features, and each leaf was represented by the three features. After that, the authors classified the obtained vectors using different supervised machine learning techniques; the used techniques are Decision tree, Naïve Bayes, K-nearest neighbour, and neural network. Finally, they evaluated the classification using cross validation. The main goal of this work is studying the influence of representation of leaves' images on the identification of plants, and also studying the use of supervised machine learning algorithm for plant leaves classification.