Machine Learning Classification Algorithms Research Articles

Faulty cell prediction accuracy comparison of machine learning algorithms using temperature sensor placement optimization approach in immersion cooled Li-ion battery modules

Immersion cooling is a promising thermal management system for LiBs where the cells are submerged in a thermally conductive coolant, thus improving heat dissipation and prolonging battery life. The present study investigates an aligned arranged 4S4P immersion-cooled LiB module to develop a best-fit machine learning (ML) model that could predict the fault position in the module depending on the inputs from the temperature sensors that are optimized under different operating and fault conditions. The Pearson Correlation Coefficient (PCC) feature selection approach is used to optimize the sensors, while the data is generated from numerical simulations on a 4S4P immersion-cooled LiB module. The model validation through internal experimental trials is performed on a 2S2P battery module. The four ML classification algorithms, which include the K Nearest Neighbors, Random Forest (RF), Extreme Gradient, and Long Short Term Memory (LSTM), are trained on the optimized sensors data and are further tested internally and externally to assess their performance on unseen data within and outside the training range. A 5-fold cross-validation process is also implemented, and following a comprehensive comparison of the predictions based on the accuracies and model elapsed time, the best-fit model is identified. The results conclude that while the LSTM model slightly outweighs the other models with an accuracy of 98.18% for the specific external test cases, the RF model is chosen as the best-fit model with a prediction accuracy of 99.7% based on the error metrics and low training time for the internal testing. The outcomes of this present work contribute to the early identification of battery module failures, enhancing safety and reducing costs.

Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.

Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B-cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier.

Quantum Support Vector Machine for Multi Classification

Abstract Classical machine learning algorithms seem to be totally incapable of processing tremendous data, while quantum machine learning algorithms could deal with big data unhurriedly and provide exponential acceleration over classical counterpart. In this paper, we propose two quantum support vector machine algorithms for multi classification. One is the quantum version of directed acyclic graph support vector machine. The other one is to use the Grover search algorithm before measurement, which amplifies the amplitude of the phase stored in the classification result. For $k$ classification, the former provides quadratic reduction in computational complexity when classifying. The latter accelerates the training speed significantly and more importantly, the classification result can be read out with a probability of at least 50\% using only one measurement. We conduct numerical simulations on two algorithms, and their classification success rates are 96\% and 88.7\%, respectively.

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products. Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learning techniques were used. The results indicated that a significant correlation (p < 0.05) could only observed between surface weathering and the glass types (high-potassium and lead–barium). Based on the random forest and logistic regression models, the primary chemical components that signify glass types and weathering status were determined using PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented a superior ability to identify glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.

Enhancing Transparency and Trust in Turbofan Engine Classification: Leveraging Explainable AI

Implementing predictive maintenance is paramount in guaranteeing the dependability and efficiency of intricate industrial systems. Classification approaches have been extensively utilized to detect and anticipate potential malfunctions in acknowledged systems. Numerous machine learning algorithms exhibit effectiveness, yet they encounter interpretability issues, posing a challenge in comprehending the fundamental factors contributing to their predictions. This study investigates the utilization of machine learning algorithms for classification in the domain of predictive maintenance. This research focuses on applying Random Forest, XGBoost, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms for Turbofan Engine classification into multiple categories. We use Explainable Artificial Intelligence (XAI) methodologies to mitigate the interpretability challenges commonly associated with black-box models. We aim to utilize XAI algorithms on the most effective models to elucidate the essential characteristics and decision-making procedures underlying the classification outcomes. The findings of our experiment indicate that the Random Forest algorithm based on ensemble learning and the XGBoost algorithm based on gradient boosting exhibit superior classification accuracy. Additionally, the utilization of Explainable Artificial Intelligence (XAI) algorithms amplifies the transparency and interpretability of said models, thereby facilitating a more profound comprehension of the determinants that impact the classification results. The results of this study make a valuable contribution to predictive maintenance by demonstrating the efficacy of machine learning algorithms in multiclass classification and emphasizing the significance of explainable artificial intelligence (XAI) in furnishing practical insights for decision-making.

Machine Learning Classification Algorithms for Traffic Stops—A Comparative Study

The application of machine learning algorithms across various fields is gaining momentum, and the results increasingly emphasize the need for further testing and implementation. This is driven by the potential to streamline and expedite numerous processes. In this paper, we have employed five algorithms: KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes, and these algorithms have been tested in three large datasets. On average, their performance ranges from a minimum of 80% to a maximum of 90%. Data preprocessing has been completed, and concurrently, we have implemented the SMOTE algorithm to address the challenge of unbalanced data in this research. Simultaneously, the Naïve Bayes algorithm yields the most favorable results of Accuracy, Precision, Recall, and F1 Score, for the “is_arrested” class. Furthermore, to assess the performance of each algorithm, we employed metrics including Accuracy, Precision, Recall, and F1 Score. These metrics allowed us to decide which algorithm achieved the most effective classification.

Algorithmically Generated Domain Names Detection Using Gated Recurrent Unit Deep Learning

The modern malware increasingly employs domain generation algorithms (DGAs) to evade traditional DNS query detection methods, such as blacklisting or reverse engineering of suspicious domain names. These algorithms generate vast numbers of random domain names to establish communication with Command and Control (C&C) servers, posing significant challenges for detection. Previous research has predominantly relied on classical machine learning algorithms, necessitating manual feature extraction and classification, which is both time-consuming and labour-intensive this paper, we propose a deep learning-based architecture for detecting DGA-generated domain names. Our model utilizes recurrent networks with gated recurrent units (GRUs) for domain name detection. By converting domain names into vectors and employing GRUs, the model autonomously learns features, eliminating the need for manual intervention in feature extraction. Compared to traditional methods, our approach reduces time costs associated with feature extraction. The experimental result demonstrates the effectiveness of our proposed GRU achieving 98% accuracy, 94% recall rate, 93% precision, and an Area Under the Curve (AUC) of 99.6%. The GRUarchitecture outperforms LSTM models in terms of recall rate and accuracy while requiring less computational resources, indicating significant performance enhancement.

μDOSE+: Environmental radioactivity and dose rate measurement system with active shielding boosted by machine learning

The μDOSE+ is a novel measurement system designed for environmental radioactivity measurements and trapped charge dating applications. This study describes improvements integrated into the μDOSE+ system. These enhancements include a redesigned counting chamber equipped with an active radon removal system, passive shielding and an active shielding module that is further refined by machine learning algorithms for precise pulse classification. These advancements collectively lead to a significant reduction in background in the β counting window, improved α and β counting efficiency and easier sample handling. This is demonstrated through a comparative analysis with its predecessor, the classical μDOSE system.

Sentiment analysis of coronavirus data with ensemble and machine learning methods

The coronavirus pandemic has distanced people from social life and increased the use of social media. People's emotions can be determined with text data collected from social media applications. This is used in many fields, especially in commerce. This study aims to predict people's sentiments about the pandemic by applying sentiment analysis to Twitter tweets about the pandemic using single machine learning classifiers (Decision Tree-DT, K-Nearest Neighbor-KNN, Logistic Regression-LR, Naïve Bayes-NB, Random Forest-RF) and ensemble learning methods (Majority Voting (MV), Probabilistic Voting (PV), and Stacking (STCK)). After vectorizing the tweets using two predictive methods, Word2Vec (W2V) and Doc2Vec, and two traditional word representation methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BOW), classification models built using single machine learning classifiers were compared to models built using ensemble learning methods (MV, PV and STCK) by heterogeneously combining single machine classifier algorithms. Accuracy (ACC), F-measure (F), precision (P), and recall (R) were used as performance measures, with training/test separation rates of 70%-30% and 80%-20%, respectively. Among these models, the ACC of ensemble learning models ranged from 89% to 73%, while the ACC of single classifier models ranged from 60% to 80%. Among the ensemble learning methods, STCK with Doc2Vec text representation/embedding method gave the best ACC result of 89%. According to the experimental results, ensemble models built with heterogeneous machine learning classifier algorithms gave better results than single machine learning classifier algorithms.

Credit Card Fraud Detection Using Machine Learning

Credit Card Fraud Detection Using Machine Learning

A pose estimation for motion tracking of infants cerebral palsy

AbstractThe General Movements Analysis (GMA) has demonstrated noteworthy promise in the early detection of infantile Cerebral Palsy (CP). However, it is subjective and requires highly trained clinicians, making it costly and time-consuming. Automation of GMA could potentially enhance accessibility and further our comprehension of infants’ full-body movements. This paper investigates the feasibility of using 2D and 3D pose estimation strategies to observe and scrutinize the infant’s comprehensive body movement attributes to improve our perspective to consider joint movement and positions over time as an alternative to GMA for early CP prediction. The study includes comprehensive movement analysis from video recordings for accurate and efficient analysis of infant movement by computing various metrics such as angle orientations at different predicted joint locations, postural information, postural variability, movement velocity, movement variability, and left–right movement coordination. Along with antigravity movements are assessed and tracked as indicators of CP. We employed a variety Machine Learning (ML) algorithms for CP classification based on a series of robust features that have been developed to enhance the interpretability of the model. The proposed approach is assessed through experimentation using the MINI-RGBD and RVI-38 datasets with a classification accuracy of 92% and 97.37% respectively. These results substantiate the efficacy of employing pose estimation techniques for the precocious prediction of infantile CP, highlighting the importance of monitoring changes in joint angles over time for accurate diagnosis and treatment planning.

Exploring inertial sensor-based balance biomarkers for early detection of mild cognitive impairment.

Dementia is characterized by a progressive loss of cognitive abilities, and diagnosing its early stages Mild Cognitive Impairment (MCI), is difficult since it is a transitory state that is different from total cognitive collapse. Recent clinical research studies have identified that balance impairments can be a significant indicator for predicting dementia in older adults. Accordingly, the current research focuses on finding innovative postural balance-based digital biomarkers by using wearable inertial sensors and pre-screening of MCI in home settings using machine learning techniques. For this research, sixty subjects (30 cognitively normal and 30 MCI) with waist-mounted inertial sensor performed balance tasks in four different standing postures: eyes-open, eyes-closed, right-leg-lift, and left-leg-lift. The significant balance biomarkers for MCI identification are discovered by our research, demonstrating specific characteristics in each of these four states. A robust feature selection approach is ensured by the multi-step methodology that combines the strengths of Filter techniques, Wrapper methods, and SHAP (Shapley Additive exPlanations) technique. The proposed balance biomarkers have the potential to detect MCI (with 75.8% accuracy), as evidenced by the results of machine learning algorithms for classification. This work adds to the growing body of literature targeted at enhancing understanding and proactive management of cognitive loss in older populations and lays the groundwork for future research efforts aimed at refining digital biomarkers, validating findings, and exploring longitudinal perspectives.

On the Generalizability of Machine Learning Classification Algorithms and Their Application to the Framingham Heart Study

The use of machine learning algorithms in healthcare can amplify social injustices and health inequities. While the exacerbation of biases can occur and be compounded during problem selection, data collection, and outcome definition, this research pertains to the generalizability impediments that occur during the development and post-deployment of machine learning classification algorithms. Using the Framingham coronary heart disease data as a case study, we show how to effectively select a probability cutoff to convert a regression model for a dichotomous variable into a classifier. We then compare the sampling distribution of the predictive performance of eight machine learning classification algorithms under four stratified training/testing scenarios to test their generalizability and their potential to perpetuate biases. We show that both extreme gradient boosting and support vector machine are flawed when trained on an unbalanced dataset. We then show that the double discriminant scoring of type 1 and 2 is the most generalizable with respect to the true positive and negative rates, respectively, as it consistently outperforms the other classification algorithms, regardless of the training/testing scenario. Finally, we introduce a methodology to extract an optimal variable hierarchy for a classification algorithm and illustrate it on the overall, male and female Framingham coronary heart disease data.

Analysis of Gradient Boosting, XGBoost, and CatBoost on Mobile Phone Classification

In the ever-evolving landscape of mobile phone technology, accurately classifying device specifications is paramount for market analysis and consumer decision-making. This research conducts a comprehensive analysis of mobile phone specification classification using three prominent machine learning algorithms: Gradient Boosting, XGBoost, and CatBoost. Through meticulous dataset acquisition and preprocessing steps, including resolution normalization and price categorization, features essential for classification analysis were standardized. Robust cross-validation techniques were employed to assess model performance effectively. The study demonstrates the significant impact of normalization techniques on improving model performance across all algorithms and fold variations. CatBoost consistently emerges as the top-performing algorithm, followed closely by XGBoost, with Gradient Boosting displaying respectable performance. Notably, CatBoost consistently achieves the highest AUC values and accuracy scores, demonstrating superior performance in accurately classifying mobile phone specifications. These findings underscore the importance of preprocessing methods and algorithm selection in achieving optimal classification results. For mobile phone manufacturers, leveraging machine learning algorithms for effective classification can inform product development strategies, optimizing offerings based on consumer preferences. Similarly, for data analysts, employing appropriate preprocessing techniques and algorithmic approaches can lead to more accurate predictions and informed decision-making. Future research avenues include exploring advanced preprocessing methods, investigating alternative algorithms, and incorporating additional features or datasets to enrich the classification process. Overall, this research contributes to understanding mobile phone specification classification through machine learning methodologies, offering actionable insights for industry practitioners and researchers to address evolving market dynamics and consumer preferences.

Early detection of bovine respiratory disease in pre-weaned dairy calves using sensor based feeding, movement, and social behavioural data

Previous research shows that feeding and activity behaviours in combination with machine learning algorithms has the potential to predict the onset of bovine respiratory disease (BRD). This study used 229 novel and previously researched feeding, movement, and social behavioural features with machine learning classification algorithms to predict BRD events in pre-weaned calves. Data for 172 group housed calves were collected using automatic milk feeding machines and ultrawideband location sensors. Health assessments were carried out twice weekly using a modified Wisconsin scoring system and calves were classified as sick if they had a Wisconsin score of five or above and/or a rectal temperature of 39.5 °C or higher. A gradient boosting machine classification algorithm produced moderate to high performance: accuracy (0.773), precision (0.776), sensitivity (0.625), specificity (0.872), and F1-score (0.689). The most important 30 features were 40% feeding, 50% movement, and 10% social behavioural features. Movement behaviours, specifically the distance walked per day, were most important for model prediction, whereas feeding and social features aided in the model’s prediction minimally. These results highlighting the predictive potential in this area but the need for further improvement before behavioural changes can be used to reliably predict the onset of BRD in pre-weaned calves.

Airline Customer Satisfaction Prediction

As the aviation industry evolves, understanding customer satisfaction has become critical for airlines pursuing to thrive in a competitive market. This research paper investigates the dynamics of airline customer satisfaction by analyzing reviews posted on Skytrax, a popular online platform known for its extensive collection of airline reviews. The study analyzes the essential features that are required for customer satisfaction in airline business, as well as performs a comparative analysis of various machine learning classifier algorithms by employing different metrics. The purpose of this comparison was to determine the most effective algorithm among them. The dataset utilized in the research consisted of reviews and ratings given by customers obtained through web scraping data from SkyTrax. Before data was transmitted to algorithms, data was cleaned using various techniques, data imputation, and removal of outliers, and removal of dependable and comparable features using various statistical methods. SMOTE imbalance approach was used in analyzing the level of bias in data. The study employs a range of metrics including accuracy score, precision, recall, f1-score, and confusion matrix, in order to compare different machine learning classifier algorithms, such as KNN, Random Forest, Decision Trees, and Logistics Regression, amongst others.

Automatic Literature Mapping Selection: Classification of Papers on Industry Productivity

The academic community has witnessed a notable increase in paper publications, whereby the rapid pace at which modern society seeks information underscores the critical need for literature mapping. This study introduces an innovative automatic model for categorizing articles by subject matter using Machine Learning (ML) algorithms for classification and category labeling, alongside a proposed ranking method called SSS (Scientific Significance Score) and using Z-score to select the finest papers. This paper’s use case concerns industry productivity. The key findings include the following: (1) The Decision Tree model demonstrated superior performance with an accuracy rate of 75% in classifying articles within the productivity and industry theme. (2) Through a ranking methodology based on citation count and publication date, it identified the finest papers. (3) Recent publications with higher citation counts achieved better scores. (4) The model’s sensitivity to outliers underscores the importance of addressing database imbalances, necessitating caution during training by excluding biased categories. These findings not only advance the utilization of ML models for paper classification but also lay a foundation for further research into productivity within the industry, exploring themes such as artificial intelligence, efficiency, industry 4.0, innovation, and sustainability.

Aboveground Biomass Inversion Based on Object-Oriented Classification and Pearson–mRMR–Machine Learning Model

Cities play a crucial role in the carbon cycle. Measuring urban aboveground biomass (AGB) is essential for evaluating carbon sequestration. Satellite remote sensing enables large-scale AGB inversion. However, the apparent differences between forest and grassland biomass pose a significant challenge to the accurate estimation of urban AGB using satellite-based data. To address this limitation, this study proposed a novel AGB estimation method using the eastern part of the Zhahe mining area in Huaibei City as the study area, which integrates land cover classification, feature selection, and machine learning modelling to generate high quality biomass maps of different vegetation types in an urban area with complex feature distribution. Utilizing the GEE platform and Sentinel-2 image, we developed an object-oriented machine learning classification algorithm, combining SNIC and GLCM to extract vegetation information. Optimal feature variables for forest and crop-grass AGB inversion were selected using the Pearson–mRMR algorithm. Finally, we constructed nine machine learning models for AGB inversion and selected the model with the highest accuracy to generate the AGB map of the study area. The results of the study are as follows: (1) Compared with the pixel-based classification method, the object-oriented classification method can extract the boundaries of different vegetation types more accurately. (2) Forest AGB is strongly correlated with vegetation indices and physiological parameters, while agri-grass AGB is primarily associated with vegetation indices and vegetation physiological parameters. (3) For forest AGB modelling, the RF-R model outperforms other machine learning models with an R2 of 0.77. For agri-grass AGB modelling, the XGBoost-R model is more accurate, with an R2 of 0.86. (4) The mean forest AGB in the study area was 4.60 kg/m2, while the mean agri-grass AGB was 0.71 kg/m2. High AGB values were predominantly observed in forested areas, which were mainly distributed along roads, waterways, and mountain ranges. Overall, this study contributes to a better understanding of the health of local urban ecosystems and provides valuable insights for ecosystem protection and the sustainable use of natural resources.

CHRONIC KIDNEY DISEASE PREDICTION USING MACHINE LEARNING

The term ―chronic kidney disease‖ means lasting damage to the kidneys that can get worse over time. If the damage is very bad, your kidneys may stop working. This is called kidney failure, or end-stage Kidney disease (ESRD). Kidney disease patients have the potential to get into the chronic phase and chronic kidney disease (CKD) is a decrease in kidney function gradually. So, doctors can diagnose kidney disease patients. So, predicting whether patients with Kidney disease have entered a phase of chronic kidney disease or not by showing the best accuracy result of comparing supervised classification machine learning algorithms. The aim is to investigate machine learning- based techniques for CKD forecasting by predicting results in the best accuracy. The analysis of the dataset by supervised machine learning technique (SMLT) to capture several information like, variable identification, univariate analysis, bi-variate and multi-variate analysis, missing value treatments and analyze the data validation, data cleaning/preparation, and data visualization will be done on the entire given dataset. Additionally, to compare and discuss the performance of various machine learning algorithms from the given hospital dataset with an evaluation classification report, identify the confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with the best accuracy with precision, Recall and F1 Score. Key Words: Chronic kidney, Damage, Univariate, bi-variate, Multi-variate.

DETECTA – MULTIPLE DISEASE DETECTION

Machine Learning Techniques for Predictive Analytics in Healthcare There are multiple techniques in machine learning that can in a variety of industries, do predictive analytics on large amounts of data. Predictive analytics in healthcare is a difficult endeavor, but it can eventually assist practitioners in making timely decisions regarding patients' health and treatment based on massive data. Diseases like Breast cancer, diabetes, and heart-related diseases are causing many deaths globally but most of these deaths are due to the lack of timely check-ups of the diseases. The above problem occurs due to a lack of medical infrastructure and a low ratio of doctors to the population. The statistics clearly show the same, WHO recommended, the ratio of doctors to patients is 1:1000 whereas India's doctor-to- population ratio is 1:1456, this indicates the shortage of doctors. The diseases related to heart, cancer, and diabetes can cause a potential threat to mankind, if not found early. Therefore, early recognition and diagnosis of these diseases can save a lot of lives. This work is all about predicting diseases that are harmful using machine learning classification algorithms. In this work, breast cancer, heart, and diabetes are included. To make this work seamless and usable by the mass public, our team made a medical test web application that makes predictions about various diseases using the concept of machine learning. In this work, our aim to develop a disease-predicting web app that uses the concept of machine learning-based predictions about various diseases like Breast cancer, Diabetes, Heart diseases, Covid, Pneumonia, Alzheimer.