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Breast cancer prediction and diagnosis are critical for timely and effective treatment, significantly impacting patient outcomes. Machine learning algorithms have become powerful tools for improving the prediction and diagnosis of breast cancer. The Breast Cancer Prediction and Diagnosis Model (BCPM), which utilises machine learning techniques to improve the precision and efficiency of breast cancer diagnosis and prediction, is presented in this paper. BCPM collects comprehensive and high-quality data from diverse sources, including electronic medical records, clinical trials, and public datasets. Through rigorous pre-processing, the data is cleaned, inconsistencies are addressed, and missing values are handled. Feature scaling techniques are applied to normalize the data, ensuring fair comparison and equal importance among different features. Furthermore, feature-selection algorithms are utilized to identify the most relevant features that contribute to breast cancer projection and diagnosis, optimizing the model's efficiency. The BCPM employs numerous machine learning methods, such as logistic regression, random forests, decision trees, support vector machines, and neural networks, to generate accurate models. Area under the curve (AUC), sensitivity, specificity, and accuracy are only some of the metrics used to assess a model's performance once it has been trained on a subset of data. The BCPM holds promise in improving breast cancer prediction and diagnosis, aiding in personalized treatment planning, and ultimately taming patient results. By leveraging machine learning algorithms, the BCPM contributes to ongoing efforts in combating breast cancer and saving lives. [ABSTRACT FROM AUTHOR]

In E-learning, evaluating students' comprehension of lecture video content is significant. The surge in online platform usage due to the pandemic has been remarkable, but the pressing issue is that learning outcomes still need to match the growth. Addressing this, a scientific system that gauges the comprehensibility of lecture videos becomes crucial for the effective design of future courses. This research paper is based on a cognitive approach utilizing EEG signals to determine student's level of comprehension. The study involves the design, evaluation, and comparison of multiple machines learning models, aiming to contribute to developing an efficient learning system. Fifteen distinct machine learning (ML) classifiers were implemented, among them AdaBoost (ADA), gradient boosting (GBC), extreme gradient boosting (XGboost), extra trees (ET), random forest (RF), light gradient boosting machine (light gum), and decision tree (DT) algorithms standouts. The DT exhibited exceptional performance across metrics such as area under the curve (AUC), accuracy, recall, F1 score, Kappa, precision, and matthews correlation coefficient (MCC). It achieved nearly 1.0 in these metrics while taking a short training time of only 1.7 seconds. This reveals its potential as an efficient classifier for electroencephalography (EEG) datasets and highlights its viability for practical implementation. [ABSTRACT FROM AUTHOR]

The world was affected by a novel coronavirus in December 2019 that changed human life. Several types of research have been done, substantial scientific advances have been made, and millions of dollars have been spent on bringing scholars and scientists to one platform to end this critical pandemic. Ascertaining COVID-19 diagnoses in the initial stage of the pandemic was critical, specifically for patients with no manifestations. In this case, artificial intelligence-based systems were proposed to identify the virus at an earlier phase. Thus, the present study suggests a machine vision scheme to identify COVID-19 from chest X-ray images. Three machine learning approaches, such as logistic regression (LR), decision tree (DT), and random forest (RF), were implemented with more than 95% accuracy. The deep convolutional neural network (CNN) architecture was also proposed and implemented with a 99.99% detection rate. Therefore, the present work can effectively detect COVID-19 cases in the early stages. [ABSTRACT FROM AUTHOR]

A stroke is a dangerous, life-threatening disease that mostly affects people over 65, but an unhealthy diet is also contributing to the development of strokes at younger ages. Strokes can be treated successfully if they are identified early enough, and suitable therapies are available. The purpose of this study is to develop a stroke prediction model that will improve stroke prediction effectiveness as well as accuracy. Predicting whether someone is suffering from a stroke or not can be accomplished with this proposed machine learning algorithm. In this research, various machine learning techniques are evaluated for predicting stroke on the healthcare stroke dataset. The feature selection algorithms used here are gradient boosting and random forest, and classifiers include the decision tree classifier, Support Vector Machine (SVM) classifier, logistic regression classifier, gradient boosting classifier, random forest classifier, K neighbors classifier, and Xtreme gradient boosting classifier. In this process, different machine-learning approaches are employed to test predictive methods on different data samples. As a result obtained from the different methods applied, and the comparison of different classification models, the random forest model offers an accuracy rate of 98%. [ABSTRACT FROM AUTHOR]

In order to increase the likelihood of obtaining treatment and achieving a complete recovery, early illness identification and diagnosis are crucial. Artificial intelligence is helpful with this process by allowing us to rapidly start the necessary protocol for treatment in the early stages of disease development. Artificial intelligence is a major contributor to the improvement of medical treatment for patients. In order to prevent and foresee this problem on the individual, family, and generational levels, Monitoring the patient's therapy and recovery is crucial. This study's objective is to outline a non-invasive method for using mammograms to detect breast abnormalities, classify breast disorders, and identify cancerous or benign tumor tissue in the breast. We used classification models on a dataset that has been pre-processed so that the number of samples is balanced, unlike previous work on the same dataset. Identifying cancerous or benign breast tissue requires the use of supervised learning techniques and algorithms, such as random forest (RF) and decision tree (DT) classifiers, to examine up to thirty features, such as breast size, mass, diameter, circumference, and the nature of the tumor (solid or cystic). To ascertain if the tissue is malignant or benign, the examination's findings are employed. These features are mostly what determines how effectively anything may be categorized. The DT classifier was able to get a score of 95.32%, while the RF satisfied a far higher 98.83 percent. [ABSTRACT FROM AUTHOR]

A wearable textile bra-tenna system based on dual-polarization sensors for breast cancer (BC) detection is presented in this paper. The core concept behind our work is to investigate which type of polarization is most effective for BC detection, using the combination of orthogonal polarization signals with machine learning (ML) techniques to enhance detection accuracy. The bra-tenna sensors have a bandwidth ranging from 2–12 GHz. To complement the proposed system, detection based on machine learning algorithms (MLAs) is developed and tested to enhance its functionality. Using scattered signals at different polarizations, the bra-tenna system uses MLAs to predict BC in its early stages. Classification techniques are highly effective for data classification, especially in the biomedical field. Two scenarios are considered: Scenario 1, where the system detects a tumor or non-tumor, and Scenario 2, where the system detects three classes of one, two, and non-tumors. This confirms that MLAs can detect tumors as small as 10 mm. ML techniques, including eight algorithms such as the Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Methods (GBMs), Decision Tree (DT) classifier, Ada Boost (AD), CatBoost, Extreme Gradient Boosting (XG Boost), and Logistic Regression (LR), are applied to this balanced dataset. For optimal analysis of the BC, a performance evaluation is performed. Notably, SVM achieves outstanding performance in both scenarios, with metrics such as its F1 score, recall, accuracy, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), and precision all exceeding 90%, helping doctors to effectively investigate BC. Furthermore, the Horizontal-Horizontal (HH) sensor configuration achieved the highest accuracy of 98% and 99% for SVMs in the two scenarios, respectively. [ABSTRACT FROM AUTHOR]

Background Older adults (≥65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predicting hospitalization and mortality, there remains a significant need to optimize ML models further. Accurately predicting hospitalization may tremendously affect the clinical care of older adults as preventative measures can be implemented to improve clinical outcomes for the patient. Methods In this retrospective cohort study, a data set of 14 198 community-dwelling older adults (≥65 years) with complex care needs from the International Resident Assessment Instrument-Home Care database was used to develop and optimize 3 ML models to predict 30-day hospitalization. The models developed and optimized were Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR). Variable importance plots were generated for all 3 models to identify key predictors of 30-day hospitalization. Results The area under the receiver-operating characteristics curve for the RF, XGB, and LR models were 0.97, 0.90, and 0.72, respectively. Variable importance plots identified the Drug Burden Index and alcohol consumption as important, immediately potentially modifiable variables in predicting 30-day hospitalization. Conclusions Identifying immediately potentially modifiable risk factors such as the Drug Burden Index and alcohol consumption is of high clinical relevance. If clinicians can influence these variables, they could proactively lower the risk of 30-day hospitalization. ML holds promise to improve the clinical care of older adults. It is crucial that these models undergo extensive validation through large-scale clinical studies before being utilized in the clinical setting. [ABSTRACT FROM AUTHOR]

Background: In randomized clinical trials, treatment effects may vary, and this possibility is referred to as heterogeneity of treatment effect (HTE). One way to quantify HTE is to partition participants into subgroups based on individual's risk of experiencing an outcome, then measuring treatment effect by subgroup. Given the limited availability of externally validated outcome risk prediction models, internal models (created using the same dataset in which heterogeneity of treatment analyses also will be performed) are commonly developed for subgroup identification. We aim to compare different methods for generating internally developed outcome risk prediction models for subject partitioning in HTE analysis. Methods: Three approaches were selected for generating subgroups for the 2,441 participants from the United States enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) randomized controlled trial. An extant proportional hazards-based outcomes predictive risk model developed on the overall ASPREE cohort of 19,114 participants was identified and was used to partition United States' participants by risk of experiencing a composite outcome of death, dementia, or persistent physical disability. Next, two supervised non-parametric machine learning outcome classifiers, decision trees and random forests, were used to develop multivariable risk prediction models and partition participants into subgroups with varied risks of experiencing the composite outcome. Then, we assessed how the partitioning from the proportional hazard model compared to those generated by the machine learning models in an HTE analysis of the 5-year absolute risk reduction (ARR) and hazard ratio for aspirin vs. placebo in each subgroup. Cochran's Q test was used to detect if ARR varied significantly by subgroup. Results: The proportional hazard model was used to generate 5 subgroups using the quintiles of the estimated risk scores; the decision tree model was used to generate 6 subgroups (6 automatically determined tree leaves); and the random forest model was used to generate 5 subgroups using the quintiles of the prediction probability as risk scores. Using the semi-parametric proportional hazards model, the ARR at 5 years was 15.1% (95% CI 4.0–26.3%) for participants with the highest 20% of predicted risk. Using the random forest model, the ARR at 5 years was 13.7% (95% CI 3.1–24.4%) for participants with the highest 20% of predicted risk. The highest outcome risk group in the decision tree model also exhibited a risk reduction, but the confidence interval was wider (5-year ARR = 17.0%, 95% CI= -5.4–39.4%). Cochran's Q test indicated ARR varied significantly only by subgroups created using the proportional hazards model. The hazard ratio for aspirin vs. placebo therapy did not significantly vary by subgroup in any of the models. The highest risk groups for the proportional hazards model and random forest model contained 230 participants each, while the highest risk group in the decision tree model contained 41 participants. Conclusions: The choice of technique for internally developed models for outcome risk subgroups influences HTE analyses. The rationale for the use of a particular subgroup determination model in HTE analyses needs to be explicitly defined based on desired levels of explainability (with features importance), uncertainty of prediction, chances of overfitting, and assumptions regarding the underlying data structure. Replication of these analyses using data from other mid-size clinical trials may help to establish guidance for selecting an outcomes risk prediction modelling technique for HTE analyses. [ABSTRACT FROM AUTHOR]

Campus placement is an essential aspect of a student's academic career, as it determines their entry into the workforce. Predicting students' campus placement can help universities and colleges identify students who are likely to be successful in their chosen career paths and provide them with the necessary support to secure a job. Application of machine learning algorithms to forecast students' placement on campuses has gained traction in recent years. This research paper will explore the different approaches to predicting students' campus placement, the factors that influence campus placement, and the benefits and limitations of using machine learning algorithms for prediction. Machine learning is capable of adaptability and with the use of statistical models and algorithms they are able to draw inferences from patterns in data. Using ML algorithms forecasting can be done about the campus placement of students. Three ML algorithms viz, Naïve Bayes, Random Forest and Decision Trees are used to forecast the job/campus placement of students and evaluation of the aforesaid algorithms are performed with respect to accuracy of the classifier[11]. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 virus, which induces an acute respiratory illness commonly referred to as COVID-19, had been designated as a pandemic by the World Health Organization due to its highly infectious nature and the associated public health risks it poses globally. Identifying the critical factors for predicting mortality is essential for improving patient therapy. Unlike other data types, such as computed tomography scans, x-radiation, and ultrasounds, basic blood test results are widely accessible and can aid in predicting mortality. The present research advocates the utilization of machine learning (ML) methodologies for predicting the likelihood of infectious disease like COVID-19 mortality by leveraging blood test data. Age, LDH (lactate dehydrogenase), lymphocytes, neutrophils, and hs-CRP (high-sensitivity C-reactive protein) are five extremely potent characteristics that, when combined, can accurately predict mortality in 96% of cases. By combining XGBoost feature importance with neural network classification, the optimal approach can predict mortality with exceptional accuracy from infectious disease, along with achieving a precision rate of 90% up to 16 days before the event. The studies suggested model's excellent predictive performance and practicality were confirmed through testing with three instances that depended on the days to the outcome. By carefully analyzing and identifying patterns in these significant biomarkers insightful information has been obtained for simple application. This study offers potential remedies that could accelerate decision-making for targeted medical treatments within healthcare systems, utilizing a timely, accurate, and reliable method. [ABSTRACT FROM AUTHOR]

The fundamental method for pre-processing speech or text data that enables computers to comprehend human language is known as natural language processing. Numerous models have been developed to date to pre-process data in the English language; however, the Hindi language does not support these models. India's national tongue is Hindi. In order to help the locals, the authors of this study used supervised learning methods like Linear Regression, SVM, and Naive Bayes algorithm to investigate a dataset of complicated terms in the Hindi language. Additionally, a sophisticated Hindi word classification model is suggested employing several methods based on the forecasts as well as collective learning strategies like Random Forest, Adaboost, and Decision Tree. Depending on how well the user's language is understood, the suggested model will assist in simplifying Hindi text. Authors attempt to classify the uncharted dataset using deep learning algorithms like Bi-LSTM and GRU approaches in further processing. [ABSTRACT FROM AUTHOR]

Heart diseases are a major cause of death worldwide, highlighting the need for early detection. The electrocardiogram (ECG) records the heart’s electrical activity using electrodes. Our research focuses on the ECG data to diagnose heart disorders, particularly arrhythmias. We utilized the MIT-BIH arrhythmia dataset for comparative analysis of various machine learning techniques, including random forest, K-Nearest Neighbor, and Decision Tree, along with deep learning algorithms like Long short-term memory and Convolutional Neural Networks. This required employing various preprocessing methods like filtering and normalization and feature selection techniques such as chi-square and sequential feature selectors to improve the performance of heart disease prediction. Therefore, hybrid machine and deep learning models are proposed, and the results reveal that hybrid models perform better than conventional models. [ABSTRACT FROM AUTHOR]

Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death. [ABSTRACT FROM AUTHOR]

Summary: The promising results of ML (machine learning) methods in various disciplines have led to the frequent use of these methods in health fields such as disease diagnosis, personalized medicine, medical image‐based diagnosis, and predicting the number of deaths and cases in a pandemic. However, a neglected area in the field of healthcare is the lack of study with ML to predict treatment outcomes for tuberculosis (TB) patients, particularly children experiencing failed treatment. This need has become more apparent as the coronavirus pandemic has reversed the gains of health institutions with TB disease, especially in children. Therefore, this article conducted a study using the stacked ensemble ML method to early predict the risk for children experiencing a failed treatment outcome of TB. To fulfill this need and determine the most appropriate technique, a two‐stage methodology was followed in this work. First, predictions were obtained by combining the information gain feature selection (IGFS) approach with a variety of single‐based ML algorithms, including logistic regression (LR), deep belief neural networks (DBN), random forest (RF), and decision tree (DT). Second, the proposed method, which includes a stacked ensemble ML technique, was used. The latter model uses LR as a meta‐learner and the aforementioned single‐based ML algorithms (DBN, LR, RF, and DT). The performance results of ML models used in the two stages were compared, and the proposed model which is the combination of the stack‐based ensemble learning model and the IGFS technique provided better ROC curves, accuracy, precision, and recall results. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) has become increasingly popular in various fields of activity. Cloud platforms have also grown in popularity, as they offer services that are more secure and accessible worldwide. In this context, cloud-based technologies emerged to support ML, giving rise to the machine learning as a service (MLaaS) concept. However, the clients accessing ML services in order to obtain classification results on private data may be reluctant to upload sensitive information to cloud. The model owners may also prefer not to outsource their models in order to prevent model inversion attacks and to protect intellectual property. The privacy-preserving evaluation of ML models is possible through multi-key homomorphic encryption (MKHE), that allows both the client data and the model to be encrypted under different keys. In this paper, we propose an MKHE evaluation method for decision trees and we extend the proposed method for random forests. Each decision tree is evaluated as a single lookup table, and voting is performed at the level of groups of decision trees in the random forest. We provide both theoretical and experimental evaluations for the proposed method. The aim is to minimize the performance degradation introduced by the encrypted model compared to a plaintext model while also obtaining practical classification times. In our experiments with the proposed MKHE random forest evaluation method, we obtained minimal (less than 0.6%) impact on the main ML performance metrics considered for each scenario, while also achieving reasonable classification times (of the order of seconds). [ABSTRACT FROM AUTHOR]

Early in the 20th century, as a result of technological advancements, the importance of digital marketing significantly increased as the necessity for digital customer experience, promotion, and distribution emerged. Since the year 1988, in the case when the term "Digital Marketing" first appeared, the business sector has undergone drastic growth, moving from small startups to massive corporations on a global scale. The marketer must navigate a chaotic environment caused by the vast volume of generated data. Decision-makers must contend with the fact that user data is dynamic and changes every day. Smart applications must be used within enterprises to better evaluate, classify, enhance, and target audiences. Customers who are tech-savvy are pushing businesses to make bigger financial investments and use cutting-edge technologies. It was only natural that marketing and trade could be one of the areas to move to such development, which helps to move to the speed of spread, advertisements, along with other things to facilitate things for reaching and winning customers. In this study, we utilized machine learning (ML) algorithms (Decision tree (DT), K-Nearest Neighbor (KNN), CatBoost, and Random Forest (RF) (for classifying data in customers to move to development. Improve the ability to forecast customer behavior so one can gain more business from them more quickly and easily. With the use of the aforementioned dataset, the suggested system was put to the test. The results show that the system can accurately predict if a customer will buy something or not; the random forest (RF) had an accuracy of 0.97, DT had an accuracy of 0. 95, KNN had an accuracy of 0. 91, while the CatBoost algorithm had the execution time 15.04 of seconds, and gave the best result of highest f1 score and accuracy (0.91, 0. 98) respectively. Finally, the study's future goals involve being created a web page, thereby helping many banking institutions with speed and forecast accuracy. Using more techniques of feature selection in conjunction with the marketing dataset to improve diagnosis. [ABSTRACT FROM AUTHOR]

Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting-edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic gradient descent, logistic regression, random forest, decision trees, k-nearest neighbor, and naive Bayes. The system was trained using Common Objects in Context (COCO), the most challenging publicly available dataset. Notably, a yearly object detection challenge is held using COCO. The resulting technology is quick and precise, making it ideal for applications requiring an object detection accuracy of 97%. [ABSTRACT FROM AUTHOR]

The reliance on networks and systems has grown rapidly in contemporary times, leading to increased vulnerability to cyber assaults. The Distributed Denial-of-Service (Distributed Denial of Service) attack, a threat that can cause great financial liabilities and reputation damage. To address this problem, Machine Learning (ML) algorithms have gained huge attention, enabling the detection and prevention of DDOS (Distributed Denial of Service) Attacks. In this study, we proposed a novel security mechanism to avoid Distributed Denial of Service attacks. Using an ensemble learning methodology aims to it also can differentiate between normal network traffic and the malicious flood of Distributed Denial of Service attack traffic. The study also evaluates the performance of two well-known ML algorithms, namely, the decision tree and random forest, which were used to execute the proposed method. Tree in defending against Distributed Denial of Service (DDoS) attacks. We test the models using a publicly available dataset called TIME SERIES DATASET FOR DISTRIBUTED DENIAL OF SERVICE ATTACK DETECTION. We compare the performance of models using a list of evaluation metrics developing the Model. This step involves fetching the data, preprocessing it, and splitting it into training and testing subgroups, model selection, and validation. When applied to a database of nearly 11,000 time series; in some cases, the proposed approach manifested promising results and reached an Accuracy (ACC) of up to 100 % in the dataset. Ultimately, this proposed method detects and mitigates distributed denial of service. The solution to securing communication systems from this increasing cyber threat is this: preventing attacks from being successful. [ABSTRACT FROM AUTHOR]

This research presents a comparative analysis of sentiment analysis techniques applied to user comments on YouTube, with a specific focus on TED talks. The proliferation of social media platforms has provided individuals with unprecedented opportunities to express their opinions and emotions. YouTube, as a leading video-sharing platform, has become a significant hub for user-generated content and discussions on a wide range of topics. In light of the exponential growth of unstructured and semi-structured data, sentiment analysis plays a critical role in extracting valuable emotional insights from online interactions. To evaluate sentiments expressed in YouTube comments, a self-created and meticulously labeled dataset comprising user comments was employed. The study compared the performance of five ML techniques: NB, SVM, RF, KNN, and DT. The performance of the classifiers was evaluated using key evaluation metrics such as Precision, Recall, and F1- score. The findings of this research offer valuable insights into the efficacy of various machine learning techniques for sentiment analysis in the context of YouTube comments on TED talks. Among the classifiers, SVM demonstrated the highest Precision, Recall, and F1-score, indicating its effectiveness in accurately identifying sentiment in YouTube comments. Random Forest and Decision tree also displayed competitive performance, while KNN and Naïve Bayes exhibited slightly lower accuracy. These results provide researchers and practitioners with valuable information to make informed decisions regarding the selection of appropriate ML techniques for SA tasks on social media platforms. [ABSTRACT FROM AUTHOR]

Functional genomic data has recently been used to aid in the effective and early detection of cancer. The Microarray in genomic data evidence has two main problems, according to previous research, one is high dimensionality and another is limited sample size. Several researchers have used various statistical and machine learning-based methods to study and assess the cancer classification challenge but attaining the highest accuracy is still remaining as a future scope. While classifying the cancer type, leaving a large number of non-informative genes in the study might lead to skewed results and lower power which hinders the methodology overall accuracy. So to overcome it, the dimensional reduction takes place by t-SNE with Kullback divergence and Shannon entropy for an effective prediction. Generally, for classification, decision trees are employed which are prone to over fitting, especially when a tree is particularly deep. Hence, a Decisive random forest classifier is utilized for the cancer prediction with the resources updated and classify them efficiently. The performance of the proposed method was compared with that of other SOTA approaches such as BERT, XLNet, RoBERTa and BART and the results showed that the proposed method outperformed the other methods. The result obtained by the proposed model efficiently predicted the cancer with high accuracy of 99%, high Recall is 99.8%, high precision is 99% and high F1-Score is 99.6%. [ABSTRACT FROM AUTHOR]

Background: New patient referrals are often processed by practice coordinators with little‐to‐no medical background. Treatment delays due to incorrect referral processing, however, have detrimental consequences. Identifying variables that are associated with a higher likelihood of surgical oncological resection may improve patient referral processing and expedite the time to treatment. The study objective is to develop a supervised machine learning (ML) platform that identifies relevant variables associated with head and neck surgical resection. Methods: A retrospective cohort study was conducted on 64 222 patient datapoints from the SEER database. Results: The random forest ML model correctly classified patients who were offered head and neck surgery with an 81% accuracy rate. The sensitivity and specificity rates were 86% and 71%. The positive and negative predictive values were 85% and 73%. Conclusions: ML modeling accurately predicts head and neck cancer surgery recommendations based on patient and cancer information from a large population‐based dataset. ML adjuncts for referral processing may decrease the time to treatment for patients with cancer. [ABSTRACT FROM AUTHOR]

This study estimates both hourly and daily Downward Surface Solar Radiation (SSR) in Istanbul while determining the importance of variables on SSR using tree-based machine learning methods, namely Decision Tree (DT), Random Forest (RF), and Gradient Boosted Regression Tree (GBRT). The hourly and daily data of climatic factors for the period between January 2016 and December 2020 are gathered from the European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. In addition to the meteorology data, hourly data of selected aerosols are obtained from the Ministry of Environment, Urbanization and Climate Change. Temperature, cloud coverage, ozone level, precipitation, pressure, and two components of wind speeds, PM10, PM2.5, and SO2 are utilized to train and test the established models. The model performances are determined with the out-of-bag errors by calculating R-squared, MSE, RMSE, and MBE. The GBRT model is found to be the most accurate model with the lowest error rates. Furthermore, this study provides the variable importance in determining the SSR. Although all models provide different values for the variable importance; temperature, ozone level, cloud coverage, and precipitation are found to be the most important variables in estimating daily SSR. For the hourly estimation, the time of day (hour) becomes the most important factor in addition to temperature, ozone level, and cloud coverage. Finally, this study shows that the tree-based machine learning methods used with these variables to estimate hourly and daily SSR results are very accurate when it is not possible to measure the SSR values directly. [ABSTRACT FROM AUTHOR]

The work presents a tool enabling the selection of powder for 3D printing. The project focused on three types of powders, such as steel, nickel- and cobalt-based and aluminum-based. An important aspect during the research was the possibility of obtaining the mechanical parameters. During the work, the possibility of using the selected algorithm based on artificial intelligence like Random Forest, Decision Tree, K-Nearest Neighbors, Fuzzy K-Nearest Neighbors, Gradient Boosting, XGBoost, AdaBoost was also checked. During the work, tests were carried out to check which algorithm would be best for use in the decision support system being developed. Cross-validation was used, as well as hyperparameter tuning using different evaluation sets. In both cases, the best model turned out to be Random Forest, whose F1 metric score is 98.66% for cross-validation and 99.10% after tuning on the test set. This model can be considered the most promising in solving this problem. The first result is a more accurate estimate of how the model will behave for new data, while the second model talks about possible improvement after optimization or possible overtraining to the parameters. [ABSTRACT FROM AUTHOR]

In the modern world, condition monitoring is crucial to the predictive maintenance of machinery. Gearboxes are widely used in machineries and auto motives to achieve the variable speeds. The major problem in gearbox is catastrophic failure due to heavy loads, corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. The main purpose of this study to use Machine Learning (ML) algorithms and Artificial Neural Network (ANN) which are very powerful and reliable tool for fault detection and its most important attribute is its ability to efficiently detect non-stationary, non-periodic, transient features of the vibration signal. To do the vibration study, an experimental setup was created, and various faults were induced faults of various kinds that usually occurred in the gearbox. The gear in the gear train was subjected to vibration analysis which was captured via a sensor. Signal processing was carried out using MATLAB Toolbox. To automatically identify the flaws in the helical gearbox, an artificial neural network (ANN) and several machines learning methods, including KNN, decision tree, random forest, and SMV, were trained by creating a database from the experiment conducted. The outcomes showed potential in accurately classifying the faults. The results show that ANN has the highest accuracy of 99.6% with a 6.5662 seconds computational time while SVM has the lowest accuracy of 96% among them along with the highest computational time of 21.324 seconds. [ABSTRACT FROM AUTHOR]

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle's position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position. [ABSTRACT FROM AUTHOR]

The principal goal of this study was to explore machine-learning techniques deployed for the early detection of breast cancer in the United States. Specifically, three algorithms were trained on a breast cancer dataset: Decision Tree, Random Forest, and Linear Regression. Each model was further evaluated for its performance, to ascertain the best model. Upon review, the Random Forest provided higher classification performance. It was postulated that the Random Forest offered higher accuracy models on the test data because Decision Trees and Linear Regression require more extensive data for them to be more precise in making high-precision predictions. Out of all the models, the Random Forest provided suitable accuracy on test data. Therefore, in this research scope, Random Forest was the most successful and proved effective in accurately identifying breast cancer malignancies. In that light, the proposed random forest can benefit healthcare organizations by facilitating in detection of breast cancer disease by identifying patients in high-risk groups at an early and more treatable stage of disease for improved outcomes and lower healthcare costs. Besides, Random Forest models can assist in identifying high-risk patients in advance for prompt treatment. In that regard, such detection saves lives and decreases long-term healthcare costs for the US government. [ABSTRACT FROM AUTHOR]

Understanding consumer churn is pivotal for companies in the USA to develop efficient strategies for consumer retention and reduce its negative effects on revenue and profitability. To start with, understanding client churn entails pinpointing the factors that contribute to it. This research paper delved into the application of machine learning algorithms such as Random Forests and Decision Trees for designing churn prediction models and exploring key factors that churn probabilities. The dataset used in this study was sourced from the prominent UCI repository of machine learning databases, preserved at the University of California, Irvine. This dataset provided extensive information on a total of 3333 clients, facilitating in-depth analysis and insights. Models performance evaluation comprised examining the model's efficiency using a confusion matrix. Random Forest seemed to be a relatively better performing model than Decision Tree for this specific classification task. In particular, Random Forest attained higher accuracy (96.25%), precision (91.49), Recall (83.49%), F-measure (0.87), and Phi coefficient (0.85). By deploying Random Forest and Decision Tree models, government companies can get an in-depth comprehension of the factors that lead to consumer churn. As a result, this information may enable them to tailor targeted retention strategies and interventions. By effectively retaining consumers, government organizations can maintain a stable customer base, leading to sustained revenue and economic growth. [ABSTRACT FROM AUTHOR]

Player performance is the most critical parameter for a match's outcome. The selection of a certain set of players according to various parameters, including consistency, Form, performance against the particular opponent, performance in the specific venue, the tournament in which the match is being played, the pressure of the type of match, etc., elevates the probability of a team winning the game. The following research aims to analyze and predict the player's performance based on the player's performance parameters. The problem is segmented into two parts, i.e., batting performance and bowling performance. The problem is presumed to be a classification problem. Runs scored, and wickets taken are classified in distinct ranges. Naïve Bayes', Decision Tree, Random Forest, and Support Vector Machine (SVM) are the algorithms used in the research. Random Forest and Decision Tree were almost identical and, hence, the most accurate for the result. [ABSTRACT FROM AUTHOR]

With the advancement of network technology, multimedia videos have emerged as a crucial channel for individuals to access external information, owing to their realistic and intuitive effects. In the presence of high frame rate and high dynamic range videos, the coding efficiency of high-efficiency video coding (HEVC) falls short of meeting the storage and transmission demands of the video content. Therefore, versatile video coding (VVC) introduces a nested quadtree plus multi-type tree (QTMT) segmentation structure based on the HEVC standard, while also expanding the intra-prediction modes from 35 to 67. While the new technology introduced by VVC has enhanced compression performance, it concurrently introduces a higher level of computational complexity. To enhance coding efficiency and diminish computational complexity, this paper explores two key aspects: coding unit (CU) partition decision-making and intra-frame mode selection. Firstly, to address the flexible partitioning structure of QTMT, we propose a decision-tree-based series partitioning decision algorithm for partitioning decisions. Through concatenating the quadtree (QT) partition division decision with the multi-type tree (MT) division decision, a strategy is implemented to determine whether to skip the MT division decision based on texture characteristics. If the MT partition decision is used, four decision tree classifiers are used to judge different partition types. Secondly, for intra-frame mode selection, this paper proposes an ensemble-learning-based algorithm for mode prediction termination. Through the reordering of complete candidate modes and the assessment of prediction accuracy, the termination of redundant candidate modes is accomplished. Experimental results show that compared with the VVC test model (VTM), the algorithm proposed in this paper achieves an average time saving of 54.74%, while the BDBR only increases by 1.61%. [ABSTRACT FROM AUTHOR]

This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These variables were fed to three tree-based machine learning algorithms: decision trees, random forest, and XGBoost. Furthermore, a variable importance measure (VIM) technique was applied to identify and rank the most important explanatory variables. The optimal model, a trained random forest, achieves a mean absolute percent error (MAPE) in the out-of-sample of 3.23% for the New York and 3.78% for the Los Angeles gasoline spot prices. The first lag, AR (1), of gasoline is the most important variable in both markets; the top five variables are all energy-related. This paper can strengthen the understanding of price determinants and has the potential to inform strategic decisions and policy directions within the energy sector, making it a valuable asset for both industry practitioners and policymakers. [ABSTRACT FROM AUTHOR]

Post-pandemic student performance prediction has gained considerable attention within the academic community. Educational institutions worldwide have adopted blended learning approaches for delivering education and assessments. In this study, we harnessed data from the 'Offee' assessment platform based in Mumbai, India, spanning from September 2020 to October 2022. This dataset allows us to analyze the real impact of the Covid-19 pandemic on examinations. To tackle the challenges posed by high-dimensional data, we employed feature selection techniques to retain only the most significant features for predicting student performance. Specifically, we assessed the effectiveness of two decision tree-based methods, J48 classifier and Random Forest, in conjunction with the ranker search method using wrapper subset evaluation. The resulting feature set was then utilized to train and test a Support Vector Machine model for predicting student performance. Our findings reveal that both J48 and Random Forest feature selection methods yield impressive precision accuracy rates for student performance prediction, achieving 93.14% and 86.24%, respectively. In contrast, using the dataset without feature selection resulted in lower precision accuracy, standing at 79.9%, and consumed more time during model training and development. In summary, this study underscores the significance of feature selection in enhancing the accuracy of student performance prediction compared to utilizing the dataset without such feature curation. [ABSTRACT FROM AUTHOR]

Metabolic syndrome increases cardiovascular risk, necessitating accurate prediction models. However, prior studies demonstrate key gaps including small datasets limiting generalizability, insufficient model comparisons constraining algorithm selection, and lack of model interpretation impeding biomarker identification. This study addresses these limitations through a robust methodology. Using a dataset of 2400 patients, larger than previous studies, we evaluated nine machine learning classifiers including Logistic Regression, KNN, SVC, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, AdaBoost Classifier, XGBoost Classifier, and LightGBM Classifier, after optimized preprocessing like hyperparameter tuning to tackle overfitting. The XGBoost model outperformed other algorithms with 95% training accuracy and 88.97% testing accuracy owing to advance nonlinear data handling. It achieved high precision, recall, and a 0.913 F1 score. Feature importance analysis revealed waist circumference as the most predictive biomarker. Our extensive model comparisons over sizable data significantly advance metabolic syndrome prediction capabilities compared to prior work. The optimized and interpretable XGBoost model enables early risk identification to guide timely interventions mitigating associated complications. Additional validation across diverse populations can further confirm generalizability. This study successfully demonstrates a multifaceted machine learning approach to address key gaps in literature related to model evaluation, selection, and interpretation. [ABSTRACT FROM AUTHOR]

An enormous number of deaths occur every year as a result of heart disease, making it a major concern in world health. Improving patient outcomes and lowering death rates, early detection and correct diagnosis of cardiac disease play a key role. When the heart's arteries become blocked, oxygen-poor blood cannot reach the heart properly, resulting in coronary heart disease. Early detection of cardiac disease is viable because it reduces medical costs and potentially saves the patient's life. Recently presented methods have improved heart failure detection accuracy on testing data without sacrificing accuracy on training data, yet most of these algorithms are suffering from the issue of overfitting. Models that were created end up fitting the test data too well. In this study, we create a novel diagnostic system to address this issue, and the resulting system demonstrates high intelligence and excellent performance on both training and testing data. Machine learning (ML) algorithms have demonstrated promising potential in assisting healthcare professionals with timely and accurate diagnosis. In this paper, is based on supervised machine learning methods are decision tree (DT), random forest (RF), Support vector Machine (SVM), Principal Component Analysis(PCA). We compare their accuracy with each other by using bar plot. [ABSTRACT FROM AUTHOR]

Purpose: This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets. Design/methodology/approach: The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics. Findings: Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities. Practical implications: The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure. Originality/value: Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market. [ABSTRACT FROM AUTHOR]

In contemporary society, encountering individuals afflicted with various diseases is a common occurrence, emphasizing the critical need for accurate disease prediction as an integral facet of effective treatment. This paper focuses on leveraging classification algorithms such as Naive Bayes, Random Forest, Decision Tree, and KNN to predict diseases based on patient symptoms. This system enables users to input symptoms and, through meticulous analysis, accurately forecast the disease the patient may be suffering from. The prediction model extends to specific diseases like heart disease and diabetes, providing the outcome of the presence or absence of a particular ailment. The potential impact of such a predictive system on the future of medical treatment is substantial. Upon disease prediction, the system not only identifies the ailment but also recommends the appropriate type of doctor for consultation. This paper reviews recent advancements in utilizing machine learning for disease prediction and emphasizes the creation of an interactive interface as the front-end for user-friendly symptom input. By leveraging machine learning algorithms, this system extracts valuable insights from medical databases, aiding in early disease prediction, patient care, and community services. A comprehensive analysis was conducted using a dataset comprising 4920 patient records with 41 diseases. This integrated machine learning-based disease prediction system represents a significant step forward in leveraging advanced technologies for enhancing healthcare outcomes. [ABSTRACT FROM AUTHOR]

Heart disease is one of the most dangerous diseases in the world. People with these diseases, most of them end up losing their lives. Therefore, machine learning algorithms have proven to be useful in this sense to help decision-making and prediction from the large amount of data generated by the healthcare sector. In this work, we have proposed a novel method that allows increasing the performance of the classical random forest technique so that this technique can be used for the prediction of heart disease with its better performance. We used in this study other classifiers such as classical random forest, support vector machine, decision tree, Naïve Bayes, and XGBoost. This work was done in the heart dataset Cleveland. According to the experimental results, the accuracy of the proposed model is better than that of other classifiers with 83.5%.This study contributed to the optimization of the random forest technique as well as gave solid knowledge of the formation of this technique. [ABSTRACT FROM AUTHOR]

Spare-parts provisioning strategy is crucial for airlines or Maintenance Repair Overhauls (MRO). The strategy is divided into multiple classes that refer to several criteria, then uses Material Requirements Planning (MRP) to run those strategies. However, classifying spare parts into a strategy is onerous and requires innovation. Therefore, we employ three algorithms: Decision Tree, Random Forest, and Support Vector Machine (SVM), and use spare parts and aircraft utilization data to classify the strategy and optimize spare parts provisioning of MRO over time. The results showed Random Forest performs best by accuracy, sensitivity, and specificity with a score of more than 97%. [ABSTRACT FROM AUTHOR]

Building Single Sample Predictors (SSPs) from gene expression profiles presents challenges, notably due to the lack of calibration across diverse gene expression measurement technologies. However, recent research indicates the viability of classifying phenotypes based on the order of expression of multiple genes. Existing SSP methods often rely on Top Scoring Pairs (TSP), which are platform-independent and easy to interpret through the concept of "relative expression reversals". Nevertheless, TSP methods face limitations in classifying complex patterns involving comparisons of more than two gene expressions. To overcome these constraints, we introduce a novel approach that extends TSP rules by constructing rank-based trees capable of encompassing extensive gene-gene comparisons. This method is bolstered by incorporating two ensemble strategies, boosting and random forest, to mitigate the risk of overfitting. Our implementation of ensemble rank-based trees employs boosting with LogitBoost cost and random forests, addressing both binary and multi-class classification problems. In a comparative analysis across 12 cancer gene expression datasets, our proposed methods demonstrate superior performance over both the k-TSP classifier and nearest template prediction methods. We have further refined our approach to facilitate variable selection and the generation of clear, precise decision rules from rank-based trees, enhancing interpretability. The cumulative evidence from our research underscores the significant potential of ensemble rank-based trees in advancing disease classification via gene expression data, offering a robust, interpretable, and scalable solution. Our software is available at https://CRAN.R-project.org/package=ranktreeEnsemble. [ABSTRACT FROM AUTHOR]

Gait analysis has been studied over the last few decades as the best way to objectively assess the technical outcome of a procedure designed to improve gait. The treating physician can understand the type of gait problem, gain insight into the etiology, and find the best treatment with gait analysis. The gait parameters are the kinematics, including the temporal and spatial parameters, and lack the activity information of skeletal muscles. Thus, the gait analysis measures not only the three-dimensional temporal and spatial graphs of kinematics but also the surface electromyograms (sEMGs) of the lower limbs. Now, the shoe-worn GaitUp Physilog® wearable inertial sensors can easily measure the gait parameters when subjects are walking on the general ground. However, it cannot measure muscle activity. The aim of this study is to measure the gait parameters using the sEMGs of the lower limbs. A self-made wireless device was used to measure the sEMGs from the vastus lateralis and gastrocnemius muscles of the left and right feet. Twenty young female subjects with a skeletal muscle index (SMI) below 5.7 kg/m2 were recruited for this study and examined by the InBody 270 instrument. Four parameters of sEMG were used to estimate 23 gait parameters. They were measured using the GaitUp Physilog® wearable inertial sensors with three machine learning models, including random forest (RF), decision tree (DT), and XGBoost. The results show that 14 gait parameters could be well-estimated, and their correlation coefficients are above 0.800. This study signifies a step towards a more comprehensive analysis of gait with only sEMGs. [ABSTRACT FROM AUTHOR]

The rapid proliferation of new technologies such as Internet of Things (IoT), cloud computing, virtualization, and smart devices has led to a massive annual production of over 400 zettabytes of network traffic data. As a result, it is crucial for companies to implement robust cybersecurity measures to safeguard sensitive data from intrusion, which can lead to significant financial losses. Existing intrusion detection systems (IDS) require further enhancements to reduce false positives as well as enhance overall accuracy. To minimize security risks, data analytics and machine learning can be utilized to create data-driven recommendations and decisions based on the input data. This study focuses on developing machine learning models that can identify cyber-attacks and enhance IDS system performance. This paper employed logistic regression, support vector machine, decision tree, and random forest algorithms on the UNSW-NB15 network traffic dataset, utilizing in-depth exploratory data analysis, and feature selection using correlation analysis and random sampling to compare model accuracy and effectiveness. The performance and confusion matrix results indicate that the Random Forest model is the best option for identifying cyber-attacks, with a remarkable F1 score of 97.80%, accuracy of 98.63%, and low false alarm rate of 1.36%, and thus should be considered to improve IDS system security. [ABSTRACT FROM AUTHOR]

Intensity modulation/direct detection (IM/DD) optical interconnect systems are susceptible to waveform distortion caused by complex nonlinearities. To reduce the effect of waveform distortion on signal transmission accuracy, in this paper, a random forest (RF) equalization algorithm based on decision tree (DT) integration is proposed for cost-sensitive IM/DD systems, which has the characteristics of nonlinear classification and regression. The performance of the RF equalizer is verified by simulation experiments on a 120 Gbps 8-level pulse amplitude modulation (PAM8) transmission system using an electro-absorption-modulated laser (EML). Simulation studies show that whether it is a back-to-back (B2B) transmission system or a 10 km optical fiber transmission system, the RF equalization scheme can achieve much better bit error rate (BER) performance than the traditional equalization scheme. In addition, in both transmission cases, the RF equalization scheme enables efficient classification of signals and is much less simpler than the artificial neural network (ANN) equalization scheme. [ABSTRACT FROM AUTHOR]

Software effort estimation has constituted a significant research theme in recent years. The more important provocation for project managers concerns reaching their targets within the fixed time boundary. Machine learning strategies can lead software management to an entire novel stage. The purpose of this research work is to compare an optimized long short-term memory neural network, based on particle swarm optimization, with six machine learning methods used to predict software development effort: K-nearest neighbours, decision tree, random forest, gradient boosted tree, multilayer perceptron, and long short-term memory. The process of effort estimation uses five datasets: China and Desharnais, for which outputs are expressed in person-hours; and Albrecht, Kemerer, and Cocomo81, for which outputs are measured in person-months. To compare the accuracy of these intelligent methods four metrics were used: mean absolute error, median absolute error, root mean square error, and coefficient of determination. For all five datasets, based on metric values, it was concluded that the proposed optimized long short-term memory intelligent method predicts more accurately the effort required to develop a software product. Python 3.8.12 programming language was used in conjunction with the TensorFlow 2.10.0, Keras 2.10.0, and SKlearn 1.0.1 to implement these machine learning methods. [ABSTRACT FROM AUTHOR]

Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model. [ABSTRACT FROM AUTHOR]

In such an advancing world, telecommunication companies confront the relentless problems of customer churn. Churning can be defined as cancelling the present company's subscription and buying a new service plan from another company. Churn Prediction is the technique that is used to detect these customers before they churn. For example, when JIO was launched, there was a significant change in the number of customers of Airtel, Idea, etc. Many of them churned and shifted to Jio as they offered economical plans. This paper focuses on utilizing machine learning algorithms like logistic regression, Extreme Gradient Boosting (XGBoost), Random Forest, etc to predict the customers who are likely to churn. We initiated the project by thoroughly exploring a Telecom Customer Churn dataset (WA_Fn-UseC_-Telco-Customer- Churn) available on Kaggle. This dataset includes several features like customer characteristics, service usage patterns and contract details. The insights from these features are important for the prediction of customer churn. Furthermore, we emphasize the development of a user-friendly front-end interface using StreamLit. This tool will enable telecom service providers to input customer information and receive real-time churn predictions, improving decision-making processes. [ABSTRACT FROM AUTHOR]

The present study investigates the implementation of machine learning models on crop data to predict crop yield in Rajasthan state, India. The key objective of the study is to identify which machine learning model performs are better to provide the most accurate predictions. For this purpose, two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was used as an optimization algorithm. The result clarifies that using gradient boosting regression can reduce the yield prediction mean square error to 6%. Additionally, for the present data set, random forest regression performed better than other models. We reported the machine learning model's performance using Mean Squared Error, Mean Absolute Error and R-squared and identified that after the inclusion of gradient boosting regression, the accuracy increased to 92.77%. The MAE value decreased from 26.20 Mg/ha to 21.58 Mg/ha. The results indicate that machine learning models can improve the prediction of crop yield. [ABSTRACT FROM AUTHOR]

Estimating an individual's height from a two-dimensional (2D) image has emerged as a focal point of investigation within Computer Vision. Notably, the research landscape has witnessed endeavors dedicated to human height estimation from frontal-face and full-body images. Nevertheless, there exists an imperative need for further exploration within this domain, primarily aimed at enhancing the precision of such estimations. Within this context, our study seeks to delve into the nuanced task of height from a solitary full-body image. Leveraging cutting-edge AI methodologies, we have harnessed the capabilities of Yolov7 for human pose estimation, alongside the DeepFace pre-trained model for age, gender, and race estimation, to serve as pivotal feature extraction mechanisms. Notably, our model's efficacy is intrinsically linked to the performance benchmarks set by these two foundational models. Our empirical observations underscore the commendable performance of Yolov7; however, it is incumbent to acknowledge that the DeepFace model has, regrettably, not demonstrated commensurate levels of accuracy in our experimentation. In the present study, we have employed a set of regression and tree-based predictive models, including Linear Regression, AdaBoost Regressor, Decision Tree, and Random Forest. Notably, the AdaBoost Regressor model has exhibited superior performance compared to the other models under consideration. Specifically, it has demonstrated a remarkable mean absolute error of 6.2 cms and a mean squared error of 7.9 cms, thereby establishing its efficacy in minimizing prediction errors in this context. [ABSTRACT FROM AUTHOR]

Technological advancement is one of the factors contributing to a rise of susceptible cyberattacks. Distributed denial of service (DDoS) attack reduces the efficiency of network servers by saturating them with unwanted data and preventing authorized clients from accessing them. Due to the centralized architecture of Software Defined Network (SDN), it faces a number of security vulnerabilities. In SDN, DDoS attack is one of the main strikes on the control planes. A novel Optimized Dual Intrusion Detection System is proposed to identify DDoS and Non-DDoS attack more quickly with best proposed models. Hyper Tuned parameter optimization is carried on Logistic Regression, Decision Tree and Random Forest algorithms to find the best parameters. RFE with Repeated Stratified K-fold feature selection is used using the best parameters to reduce the 77 features to 4 features. A novel Deep Grid Network combines hyper-tuned classifiers with 7 other machine learning algorithms to produce 21 models. An ensemble technique uses 6 best models from 21 models for the best prediction of DDoS attack. A new dataset is also generated through Mininet for proper validation of the model. [ABSTRACT FROM AUTHOR]

Breakouts provide crucial information regarding evaluating in situ stresses and verifying the geomechanical model. One of the most common methods for identifying borehole deformation, such as breakouts and drilling-induced fractures, is a combination of image logs and multiple pad calipers to identify borehole elongation. However, image logs are suitable for geomechanical studies but are usually unavailable in most drilled wells due to technical and financial reasons. Conventional wireline logs, including gammaray, neutron porosity, density, resistivity, and single pad caliper, on the other hand, are widely used in drilled wells. Moreover, in recent years, machine learning has been widely applied to classification and optimization problems in the geology and petroleum industry. This research investigated the possibility of predicting the occurrence of borehole breakouts. The models employed encompass K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). The input values used in this study are 4099 subintervals from conventional logs and their corresponding identified breakouts from real data as classification labeling parameters for the same interval. The performance capacity of the classification models was evaluated based on Accuracy, Precision, Recall, and F1 Score. The results are promising, and RF classification performance is more reliable than the other models. Furthermore, the RF predictive model had Accuracy, Precision, Recall, and F1 Scores equal to 92.41%, 86.36%, 87.08%, and 86.7%, respectively, superseding the performance of the other models. Overall, this study shows that the application of machine learning classification models demonstrated a reasonable and fast performance in identifying borehole breakouts from conventional logs. [ABSTRACT FROM AUTHOR]

Background The entire world was combating COVID-19; however, a significant proportion of patients demonstrate the persistence of some COVID-19 symptoms, new symptom development, or exaggeration of pre-existing disease after a negative viral load. They are referred to as a post-COVID-19 syndrome. According to various researches, COVID-19 has a wide range of long-term effects on virtually all systems, including the respiratory, cardiovascular, gastrointestinal, neurological, mental, and dermatological systems. Finding the various symptoms of post-acute and chronic is critical since they might have a significant impact on the patients' everyday functioning. As a result, we aimed to distinguish the symptoms immediately after the initial phase in which the symptoms affected them for more than three weeks using data mining techniques. Methodology: Post-COVID conditions do not affect everyone the same way. They can cause various types and combinations of symptoms in different people. The purpose of this research is to analyse the complications of post covid-19 syndrome. The purpose of Data mining is for discovering the knowledge from vast amount of database. To classify the symptoms of post covid-19, data mining techniques is used. In this study, ranking method was used in preprocessing to select subset of attributes for strengthening the rate of accuracy of classifiers. The data were collected through Google form of 384 household of students from Public Health College in Jazan University. The WEKA open-source software is used for this research work under Windows7 environment. An experimental study is carried out using data mining technique such as J48 and Random Forest tree. The data records are classified as six categories such as General symptoms, Nervous symptoms, Respiratory symptoms, Heart symptoms, Digestive symptoms and normal. Result: The performances of classifiers are evaluated through the confusion matrix in terms of accuracy, time taken to build the Model and error rate. It has been concluded that Random Forest Tree gives better accuracy, minimum time taken to build the model and less error rate than the J48 classifier. [ABSTRACT FROM AUTHOR]

Credit scoring is a typical example of imbalanced classification, which poses a challenge to conventional machine learning algorithms and statistical classifiers when attempting to accurately predict outcomes for defaulting customers. In this paper, we propose a credit scoring classifier called Decision Tree Ensemble model (DTE). This model effectively addresses the challenge of imbalanced data and identifies significant features that influence the likelihood of credit status. An experiment demonstrates that DTE exhibits superior performance metrics in comparison to well-known based-tree ensemble classifiers such as Bagging, Random Forest, and AdaBoost, particularly when integrated with resampling techniques for handling imbalanced data. [ABSTRACT FROM AUTHOR]