Standard Machine Learning Algorithms Research Articles

Entropy‐based hybrid sampling (EHS) method to handle class overlap in highly imbalanced dataset

AbstractClass imbalance and class overlap create difficulties in the training phase of the standard machine learning algorithm. Its performance is not well in minority classes, especially when there is a high class imbalance and significant class overlap. Recently it has been observed by researchers that, the joint effects of class overlap and imbalance are more harmful as compared to their direct impact. To handle these problems, many methods have been proposed by researchers in past years that can be broadly categorized as data‐level, algorithm‐level, ensemble learning, and hybrid methods. Existing data‐level methods often suffer from problems like information loss and overfitting. To overcome these problems, we introduce a novel entropy‐based hybrid sampling (EHS) method to handle class overlap in highly imbalanced datasets. The EHS eliminates less informative majority instances from the overlap region during the undersampling phase and regenerates high informative synthetic minority instances in the oversampling phase near the borderline. The proposed EHS achieved significant improvement in F1‐score, G‐mean, and AUC performance metrics value by DT, NB, and SVM classifiers as compared to well‐established state‐of‐the‐art methods. Classifiers performances are tested on 28 datasets with extreme ranges in imbalance and overlap.

Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions

Abstract For safety, reliability, and uninterrupted output of gas turbines, aviation engines, power-generating equipment, pumps, gears, compressors etc., rotor mass imbalance must be detected and diagnosed to avoid catastrophic failure. Industry 4.0 relies on predictive digital maintenance and deep learning-based convolutional neural network (CNN), which predicts defects but fails if the operating conditions change. Research studies in various fields indicate that the domain shift issue occurs due to source and target samples being from different domains, which reduces prediction capability. Moreover, research studies are scarce in examining prediction capability under varying operating speeds for rotor mass imbalance. Hence, this research proposes the adversarial discriminative domain adaptation (ADDA) technique which predicts machine failures under various operational conditions. The efficacy of ADDA has been explored by introducing 1D-CNN as a source and a target encoder inside ADDA's architecture to take advantage of CNN's feature extraction capability. Further, this research effectively tackles CNN's inherent issues of overfitting and hyperparameters value selection. Furthermore, The real-world scenario has more healthy samples than fault condition samples, causing a multiclass imbalance in sample data, which affects the classification decision boundary and causes biased prediction. Hence, the proposed methodology first addresses the class imbalance through synthetic minority oversampling (SMOTE), then genetic algorithm optimizes 1D-CNN's hyperparameters, and the effective dropout layer positioning solves the overfitting. Finally, the deep learning-based SMOTE_ADDA_GO-1D-CNN decreases domain discrepancy with adversarial discriminative domain adaptation. The proposed methodology's efficacy has been explored through F1-Score, which is used as multiclass evaluation metrics, and it has been benchmarked against standard machine learning and deep learning algorithms. The test results of the proposed methodology surpassed all of them with maximum prediction accuracy. Thus, this study contributes to rotor mass imbalance detection and diagnosis for multiclass imbalanced data under varying operational conditions by successfully overcoming potential challenges during fault prediction.

A dynamic snow depth retrieval model based on time-series clustering optimization for GPS-IR

Due to the influence of environmental factors (i.e., terrain and surface coverage) around the GPS receivers, the snow depth retrieval results obtained by the existing global positioning system interferometric reflection (GPS-IR) method show significant variability. The resulting loss of reliability and accuracy limits the broad application of this technology. Therefore, this paper proposes a dynamic snow depth retrieval model based on time-series clustering optimization for GPS-IR to fully leverage multi-source satellite observation data for automatic and high-precision snow depth retrieval. The model employs Dynamic Time Warping distance measurement combined with the K-Medoids clustering algorithm to categorize frequency sequences obtained from various satellite trajectories, facilitating effective integration of multi-constellation data and acquisition of optimal datasets. Additionally, Long Short-Term Memory networks are integrated to capture and process the long-term dependencies in snow depth data, enhancing the model’s adaptability in handling time-series data. Validated against SNOTEL measured data and standard machine learning algorithms (such as BP Neural Networks, RBF, and SVM), the model’s retrieval capability is confirmed. For P351 and AB39 sites, the correlation coefficients for L1 band data retrieval were both 0.996, with RMSEs of 0.051 and 0.018 m, respectively. The experiment results show that the proposed model demonstrates superior precision and robustness in snow depth retrieval compared to the previous method. Then, we analyze the accuracy loss caused by sudden snowfall events. The proposed model and methodology offer new insights into the in-depth study of snow depth monitoring.

Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.

Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and transcatheter interventions. Recent advances in heart valve engineering, medical device and patch design are built upon these models. Furthermore, understanding vascular growth and remodeling in native and tissue-engineered vascular biomaterials, as well as designing and testing drugs on soft tissue, are crucial aspects of predictive regenerative medicine. Traditional nonlinear optimization methods and finite element (FE) simulations have served as biomaterial characterization tools combined with soft tissue mechanics and tensile testing for decades. However, results obtained through nonlinear optimization methods are reliable only to a certain extent due to mathematical limitations, and FE simulations may require substantial computing time and resources, which might not be justified for patient-specific simulations. To a significant extent, machine learning (ML) techniques have gained increasing prominence in the field of soft tissue mechanics in recent years, offering notable advantages over conventional methods. This review article presents an in-depth examination of emerging ML algorithms utilized for estimating the mechanical characteristics of soft biological tissues and biomaterials. These algorithms are employed to analyze crucial properties such as stress-strain curves and pressure-volume loops. The focus of the review is on applications in cardiovascular engineering, and the fundamental mathematical basis of each approach is also discussed. The review effort employed two strategies. First, the recent studies of major research groups actively engaged in cardiovascular soft tissue mechanics are compiled, and research papers utilizing ML and deep learning (DL) techniques were included in our review. The second strategy involved a standard keyword search across major databases. This approach provided 11 relevant ML articles, meticulously selected from reputable sources including ScienceDirect, Springer, PubMed, and Google Scholar. The selection process involved using specific keywords such as "machine learning" or "deep learning" in conjunction with "soft biological tissues", "cardiovascular", "patient-specific," "strain energy", "vascular" or "biomaterials". Initially, a total of 25 articles were selected. However, 14 of these articles were excluded as they did not align with the criteria of focusing on biomaterials specifically employed for soft tissue repair and regeneration. As a result, the remaining 11 articles were categorized based on the ML techniques employed and the training data utilized. ML techniques utilized for assessing the mechanical characteristics of soft biological tissues and biomaterials are broadly classified into two categories: standard ML algorithms and physics-informed ML algorithms. The standard ML models are then organized based on their tasks, being grouped into Regression and Classification subcategories. Within these categories, studies employ various supervised learning models, including support vector machines (SVMs), bagged decision trees (BDTs), artificial neural networks (ANNs) or deep neural networks (DNNs), and convolutional neural networks (CNNs). Additionally, the utilization of unsupervised learning approaches, such as autoencoders incorporating principal component analysis (PCA) and/or low-rank approximation (LRA), is based on the specific characteristics of the training data. The training data predominantly consists of three types: experimental mechanical data, including uniaxial or biaxial stress-strain data; synthetic mechanical data generated through non-linear fitting and/or FE simulations; and image data such as 3D second harmonic generation (SHG) images or computed tomography (CT) images. The evaluation of performance for physics-informed ML models primarily relies on the coefficient of determination . In contrast, various metrics and error measures are utilized to assess the performance of standard ML models. Furthermore, our review includes an extensive examination of prevalent biomaterial models that can serve as physical laws for physics-informed ML models. ML models offer an accurate, fast, and reliable approach for evaluating the mechanical characteristics of diseased soft tissue segments and selecting optimal biomaterials for time-critical soft tissue surgeries. Among the various ML models examined in this review, physics-informed neural network models exhibit the capability to forecast the mechanical response of soft biological tissues accurately, even with limited training samples. These models achieve high values ranging from 0.90 to 1.00. This is particularly significant considering the challenges associated with obtaining a large number of living tissue samples for experimental purposes, which can be time-consuming and impractical. Additionally, the review not only discusses the advantages identified in the current literature but also sheds light on the limitations and offers insights into future perspectives.

ETL-FEXIC Model For Secured Heart Rate Abnormality Healthcare Framework

In traditional methods, it is critical for an effective continuous pulse monitor for humans prone to heart rate abnormalities. This paper proposes a secured heartrate abnormality detector which continuously monitors human pulse rate and SpO2 level. The current studies proposes that machine learning (ML) models performs well in classification; also, TinyML model shows better performance for data from resource constrained IoT devices. Hence, the research first analyses abnormal heart rate detection and spam data identification using standard ML algorithms such as SVM, Random Forest, Decision Tree, and TinyML. Though ML models are superior in classification, deep learning approaches outperforms them in feature learning. Hence, our proposed framework combines the merits of both ML and DL models. In our approach, the generated healthcare dataset is fed to DL models such as ANN, and autoencoder and also to SHAP XAI (eXplainable Artificial Intelligence) for feature extraction and learning. These learnt features are fed to ML models for classification. In this experiment, the proposed ETL-FEXIC (Enhanced Tiny Machine Learning with Automated Feature Extraction) outperforms the other ML models where the extracted features from XAI is fed to optimized TinyML classification model.

An ensemble-based stage-prediction machine learning approach for classifying fetal disease

Fetal diseases often lead to the death of many babies during pregnancies. Machine learning and deep learning are promising technologies providing efficient and effective detection and treatment of various fetal diseases. We contribute to the medical field by addressing the critical challenge of fetal disease classification, a concern affecting females and infants. This study utilizes 22 features associated with fetal heart rate extracted from 2126 patient records within the Cardiotocography(CTG) datasets. Our classification system offers a cost-effective, efficient, and accurate solution. It classifies fetal diseases into three categories: Normal, Suspect, and Pathological, based on preprocessed data that underwent MinMax Scaling and employed dimensionality reduction techniques, including Principal Component Analysis(PCA) and Autoencoders. By incorporating dimensionality reduction techniques, the computation time has been reduced from 9 to 26 s to just 4 and 15 s, which is less than half of the original computation time. We assessed the performance of 11 standard machine learning algorithms and various performance metrics to identify the best classification model. We have applied the K-fold Cross-Validation technique to validate our model to improve machine learning models and identify the most effective algorithm. When the results are compared, it is observed that Extreme Gradient Boosting (XGBoost) gained the highest accuracy of 0.99% also highest precision 0.93% and outperformed all the other machine learning algorithms.

Machine learning approach to detect android malware using feature-selection based on feature importance score

The hazards posed by malware are proliferating along with technology’s rapid advancement and the use of online services. Specifically, attacks on Android devices are growing enormously because of the boost in the popularity of Smartphones. Existing research confirms that identifying benign or malware applications on the Android platform is possible by analysing the permissions through the machine-learning classifier. There are machine-learning techniques that create models to detect Android malware using permission-based attributes. However, further research is still needed to develop effective feature selection strategies for malware detection mechanisms in Android. In this study, a machine-learning-based Android malware detection mechanism is proposed, and standard machine-learning algorithms are used on multiple permission-based datasets to classify malware. This study suggests a feature selection method that uses the feature importance score computed using Gradient boosting to identify the essential permissions. The proposed methodology decreases the feature vector’s dimension, reducing the model’s training time. We also compare the classifier’s performance with the complete feature set and with the reduced feature set. Examining the results, we notice that the algorithm’s execution time improved significantly for all datasets with negligible loss in accuracy.

Combining machine learning and econometrics: Application to commercial real estate prices

AbstractIn this article, we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will be used to estimate a common trend, property type trends, location value, and property random effects for properties that sold more than once. The machine learning algorithm will fit the observed characteristics (features) in a complex nonlinear fashion. The model is applied to a small sample of 2652 transactions in Phoenix (AZ) between 2001 and 2021. We only observe a limited number of property characteristics. The average out‐of‐sample MAPE is below 11%, which is as good or even better compared to the average appraisal error found in literature. The out‐of‐sample MAPE is even 9% for properties that sold more than once in the training set. In addition, our model provides indexes and locational heatmaps. These have their own uses and cannot be obtained with standard machine learning algorithms.

Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data

Background:For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces. New Method:We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable. Results:Through a rigorous cross-validation procedure on an Alzheimer’s disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis. Comparison with Existing Method(s):Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information. Conclusions:As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD.

Performance analysis of genetically optimized 1D-convolutional neural network architecture for rotor system fault detection and diagnosis

Rotor mass imbalance is a critical problem in industries, which can lead to a breakdown or a catastrophic failure if unattended. Hence, Industry 4.0 uses deep learning approaches like convolutional neural network (CNN) for rotor fault detection and diagnosis of Industrial machines and turbomachinery where multifunctional structures are used. In machinery fault diagnosis, manual tuning of CNN hyperparameters is widely used, which is a tedious, time-consuming, and challenging task to achieve effective feature extraction. Additionally, overfitting of CNN during its training is an obstacle to achieving higher prediction accuracy. Hence, to address these issues, this research focuses on multi-class mass imbalance fault diagnosis using genetically optimized deep learning architecture of 1D-CNN to achieve higher diagnosis capability. The performance of the proposed methodology is evaluated through various case studies using experimental data from the in-house developed test rig. The test results are compared against various 1D-CNN architectures in which the hyperparameters and effective dropout layer positioning are genetically optimized. Also, the performance is benchmarked against standard machine learning algorithms. The results show that genetic algorithm (GA)-optimized 1D-CNN with dropout achieves highest fault prediction accuracy of 97.47% with reduced depth of CNN (with three convolution layers) compared to 84.16% of manually tunned CNN architecture (with seven convolutional layers) and outperformed standard machine learning algorithms. Best drop positioning (end of feature extraction part) reduced the learnable parameters to 95.9% with the highest prediction accuracy of 97.47% compared to adding dropout at all the CNN layers. The proposed GA-optimized 1D-CNN with effective dropout positioning eliminates human intervention and reduces the depth of CNN architecture; this, in turn, reduces computational load and time by reducing learnable parameters of CNN (network weights) with the highest prediction accuracy. Hence, the proposed approach shows promise in enhancing the performance and contributing to the advancement of 1D-CNN for rotor system fault detection and diagnosis.

Artificial intelligence-based forecasting model for incinerator in sulfur recovery units to predict SO2 emissions

Pollutant emissions from chemical plants are a major concern in the context of environmental safety. A reliable emission forecasting model can provide important information for optimizing the process and improving the environmental performance. In this work, forecasting models are developed for the prediction of SO2 emission from a Sulfur Recovery Unit (SRU). Since SRUs incorporate complex chemical reactions, first-principle models are not suitable to predict emission levels based on a given feed condition. Accordingly, artificial intelligence-based models such as standard machine learning (ML) algorithms, multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolution (1D-CNN), and CNN-LSTM models were tested, and their performance was evaluated. The input features and hyperparameters of the models were optimized to achieve maximum performance. The performance was evaluated in terms of mean squared error (MSE) and mean absolute percentage Error (MAPE) for 1 h, 3 h and 5 h ahead of forecasting. The reported results show that the CNN-LSTM encoder-decoder model outperforms other tested models, with its superiority becoming more pronounced as the forecasting horizon increased from 1 h to 5 h. For the 5-h ahead forecasting, the proposed model showed a MAPE advantage of 17.23%, 4.41%, and 2.83%, respectively over the 1D-CNN, Deep LSTM, and single-layer LSTM models in the larger dataset.

Harnessing Twitter: Sentiment Analysis for Predicting Election Outcomes in India

This paper explores the potential of Twitter, a popular social media platform, as a tool for predicting election outcomes. Sentiment analysis has emerged as a powerful tool for predicting election outcomes, with numerous studies showcasing its effectiveness in various countries. For instance, research has utilized sentiment analysis to forecast election results in nations like the USA, India, Pakistan and other countries, demonstrating the utility of social media data in gauging public opinion and predicting electoral results [1]. Elections in India are always considered important events that most people look forward to the rapid growth of social media in the past has provided end users with powerful tools to share their ideas. Twitter, which is one such platform, provides daily updates on political events through various hashtags and trends. People react to political events and give their opinions. Our approach is to collect tweets from top political parties contesting the Gujarat Assembly Elections 2022, and then calculate sentiment scores. The database includes a variety of recent and well-liked tweets about a specific political party. Party tweets are generated with specific keywords like “BJP”, “AAP”,“Congress” and so on. In the context of India, Twitter sentiment analysis tools and classification have been used to predict the outcomes of state assembly elections, underscoring the potential of social media data in forecasting electoral results within the country [2]. We used standard machine learning algorithms like VADER sentiment analyzer on Random Forest and Decision Tree for our classification and testing data to classify tweets as positive and negative. As a result, this work uses sentiment analysis to evaluate tweets gathered from Twitter and forecast election outcomes. This work shows the growing influence of social media on politics and the feasibility of using such platforms for predictive analysis. The findings of this study could provide valuable insights for political parties, policymakers, and researchers interested in the intersection of social media and politics. Random Forest and Decision Tree models performed well in predicting election outcomes based on sentiment analysis on Twitter data with 89% and 86% respectively.

Hybrid prognosis of drill-bits based on direct inspection

Within the paradigm of industry 5.0, manufacturing systems are seeking for human-centred production, where the operator finds high-level supervision tasks. In this context, low-level decision making should be performed by machines themselves. In this paper, a hybrid prognosis algorithm is developed to automatically inspect the cutting edges of drill-bits and to predict their Remaining Useful Life (RUL) and the associated probability density function. The solution relies on the automatic measurement of flank wear through convolutional filtering and edge detection. Prognosis exploits particle filter, which updates multi-layer perceptron with online data, to adaptively predict drill-bits RUL. The solution reduces the experimental preliminary run-to-failures needed for training standard machine learning algorithms, exploiting them in a real-time adaptive scenario, while predicting tool RUL under untested and variable cutting process operations. The algorithm uses direct wear observations, taken during set-up times (e.g., tool changes, workpiece change), thus not interfering with the process.

On learning rates for regularized Nyström subsampling in unsupervised domain adaptation

The regularized Nyström subsampling is a popular approach for learning problems that deals with big data. We employ such technique in the context of the unsupervised domain adaptation problems with covariate shift assumption. Within the framework of the Reproducing Kernel Hilbert Space concept, an algorithm is constructed that is a combination of the Nyström subsampling and the two-steps iterated Tikhonov regularization. This approach allows significantly reduce the amount of computing resources involved and at the same time maintains the same learning rates as for the standard machine learning algorithms.

Development of a Simulator for Household Refrigerator Using Equation-Based Optimization Control with Bayesian Calibration

Conventional household refrigerators consist of a motor-driven compressor, evaporator, condenser, and expansion valve. To determine the optimal operation strategies of refrigerators, it is essential to investigate the overall system performance, using an appropriate simulator. This study proposed a data-driven simulator based on engineering features and machine learning algorithms for conventional household refrigerators. The most correlated variables for identifying the indoor temperature of refrigerators were identified using variable importance, and these were revealed to be the circulation fan speed, compressor operation status, and refrigerant flow direction. A data-driven simulator was constructed using Bayesian calibration, which considers the important variables, combined with a straightforward heat balance equation. The Markov Chain Monte Carlo approach was used to simultaneously calibrate three coefficients on the critical variables based on the heat balancing equation on each time step, which is consistent with the actual temperature of the container. The results revealed that the proposed approach (equation-based Bayesian calibration outperforms) standard machine learning algorithms, such as linear regression and random forest models, by 38.5%. Additionally, compared to the typical numerical analysis method, it can reduce the delivery time and effort required to develop a reliable simulator for household refrigerators.

Identifying Thyroid Dysfunction Using Standard Laboratory Testings – A Systematic Review

Thyroid dysfunction includes various thyroid-related illnesses, with subclinical hypothyroidism or hyperthyroidism at the initial stage. Blood TSH and T4 levels indicate a neutral ground between clinical conditions. Following guidelines for levothyroxine administration based on thyroid hormonal levels is beneficial for hypothyroid patients. TSH, a measurable signal, is crucial for assessing thyroid activity, with reference ranges determined by testing facilities. However, elevated TSH levels require consideration of the patient's history and lifestyle before intervention. A Machine Learning-based Thyroid Dysfunction Identification (ML-TDI) model was developed to screen individuals for medicinal intervention. Despite its prevalence and health consequences, thyroid dysfunction often goes undiagnosed. The study used standard laboratory data and machine learning algorithms to identify thyroid dysfunction, suggesting the potential for technology-driven screenings during routine medical procedures. However, assessing the benefits and risks of widespread thyroid illness evaluation requires well-conducted randomized trials.

Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach

Examining otoscopic images for ear diseases is necessary when the clinical diagnosis of ear diseases extracted from the knowledge of otolaryngologists is limited. Improved diagnosis approaches based on otoscopic image processing are urgently needed. Recently, convolutional neural networks (CNNs) have been carried out for medical diagnosis to obtain higher accuracy than standard machine learning algorithms and specialists' expertise. Therefore, the proposed approach involves using the Bayesian hyperparameter optimization with the CNN architecture for automatic diagnosis of ear imagery database including four classes: normal, myringosclerosis, earwax plug, and chronic otitis media (COM). The suggested approach was trained using 616 otoscopic images, and the performance of this approach was assessed using 264 testing images. In this paper, the performance of ear disease classification was compared in terms of accuracy, sensitivity, specificity, and positive predictive value (PPV). The results produced a classification accuracy of 98.10%, a sensitivity of 98.11%, a specificity of 99.36%, and a PPV of 98.10%. Finally, the suggested approach demonstrates how to locate optimal CNN hyperparameters for accurate diagnosis of ear diseases while taking time into account. As a result, the usefulness and dependability of the suggested approach will lead to the establishment of an automated tool for better categorization and prediction of different ear diseases.

Osteoporosis detection with microwave signals: An investigation into natural resonance frequencies

This study analyses the dispersion behavior of microwave signals passing through biological tissues by considering various electromagnetic relaxation phenomena. The investigation concentrates on the impact of this dispersion behaviour on human bone tissues causing the health conditions of osteoporosis. Based on the attenuation profile through biological tissues, an optimum frequency range of operation is identified to capture the comprehensive target tissue profiles without minimal loss of signal strength in surrounding tissues. The dispersion through a human wrist model is analysed by extracting the late-time response. A pioneering aspect of this study lies in its utilization of the natural resonant frequency plane as a tool to gauge the dielectric properties of bone tissue, offering insights into its mineral content. To improve the ease of use and diagnostic accuracy, an automated multi-class classification using standard machine learning algorithms is developed during the final response analysis. The proposed approach effectively and accurately classifies the progression of osteoporosis.

Predicting Survival of End-Stage Heart Failure Patients Receiving HeartMate-3: Comparing Machine Learning Methods.

HeartMate 3 is the only durable left ventricular assist devices (LVAD) currently implanted in the United States. The purpose of this study was to develop a predictive model for 1 year mortality of HeartMate 3 implanted patients, comparing standard statistical techniques and machine learning algorithms. Adult patients registered in the Society of Thoracic Surgeons, Interagency Registry for Mechanically Assisted Circulatory Support (STS-INTERMACS) database, who received primary implant with a HeartMate 3 between January 1, 2017, and December 31, 2019, were included. Epidemiological, clinical, hemodynamic, and echocardiographic characteristics were analyzed. Standard logistic regression and machine learning (elastic net and neural network) were used to predict 1 year survival. A total of 3,853 patients were included. Of these, 493 (12.8%) died within 1 year after implantation. Standard logistic regression identified age, Model End Stage Liver Disease (MELD)-XI score, right arterial (RA) pressure, INTERMACS profile, heart rate, and etiology of heart failure (HF), as important predictor factors for 1 year mortality with an area under the curve (AUC): 0.72 (0.66-0.77). This predictive model was noninferior to the ones developed using the elastic net or neural network. Standard statistical techniques were noninferior to neural networks and elastic net in predicting 1 year survival after HeartMate 3 implantation. The benefit of using machine-learning algorithms in the prediction of outcomes may depend on the type of dataset used for analysis.

DeepMDFC: A deep learning based android malware detection and family classification method

AbstractUnprecedented growth and prevalent adoption of the Android Operating System (OS) have triggered a substantial transformation, not only within the smartphone industry but across various categories of intelligent devices. These intelligent devices store a wealth of sensitive data, making them enticing targets for malicious individuals who create harmful Android applications to steal this data for malicious purposes. While numerous Android malware detection methods have been proposed, the exponential growth in sophisticated and malicious Android apps presents an unprecedented challenge to existing detection techniques. Some of the researchers have attempted to classify malicious Android applications into families through static analysis of applications but most of them are evaluated on applications of previous API levels. This paper introduces a novel dataset compromising of 2019 to 2021 applications and proposes a Deep Learning based Malware Detection and Family Classification method (DeepMDFC) to detect and classify emerging malicious Android applications through static analysis and deep Artificial Neural Networks. Experimental findings indicate that DeepMDFC surpasses standard machine learning algorithms, achieving accuracy rates of 99.3% and 96.7% for Android malware detection and classification, respectively, with a limited size feature set. The performance of DeepMDFC is also assessed using the benchmark dataset (DREBIN) and results showed that DeepMDFC surpasses these methods in terms of performance. Furthermore, it leverages the proposed dataset to construct a prediction model that adeptly identifies malicious Android applications from both the years 2022 and 2023. This process the potency and resilience of DeepMDFC against emerging Android applications.