A hybrid machine learning approach for analysis of stegomalware

The demand for wearable devices that can detect anxiety and stress when driving is increasing. Recent studies have attempted to use multiple biosignals to detect driving stress. However, collecting multiple biosignals can be complex and is associated with numerous challenges. Determining the optimal biosignal for assessing driving stress can save lives. To the best of our knowledge, no study has investigated both longitudinal and transitional stress assessment using supervised and unsupervised ML techniques. Thus, this study hypothesizes that the optimal signal for assessing driving stress will consistently detect stress using supervised and unsupervised machine learning (ML) techniques. Two different approaches were used to assess driving stress: longitudinal (a combined repeated measurement of the same biosignals over three driving states) and transitional (switching from state to state such as city to highway driving). The longitudinal analysis did not involve a feature extraction phase while the transitional analysis involved a feature extraction phase. The longitudinal analysis consists of a novel interaction ensemble (INTENSE) that aggregates three unsupervised ML approaches: interaction principal component analysis, connectivity-based clustering, and K-means clustering. INTENSE was developed to uncover new knowledge by revealing the strongest correlation between the biosignal and driving stress marker. These three MLs each have their well-known and distinctive geometrical basis. Thus, the aggregation of their result would provide a more robust examination of the simultaneous non-causal associations between six biosignals: electrocardiogram (ECG), electromyogram, hand galvanic skin resistance, foot galvanic skin resistance, heart rate, respiration, and the driving stress marker. INTENSE indicates that ECG is highly correlated with the driving stress marker. The supervised ML algorithms confirmed that ECG is the most informative biosignal for detecting driving stress, with an overall accuracy of 75.02%.

Health care includes automatic analysis and diagnosis with the help of various learning approaches, like machine learning, deep learning, natural language processing (NLP), etc. Nowadays, machine learning plays a vital role in the field of health care analysis, with its simplified approach. In addition, machine learning algorithms represent the state of the art in the field of diagnosis. Therefore, machine learning approaches to analysis in the field of healthcare informatics will help in analysis and early diagnosis, which will reduce the processing time. Machine learning strategies improve the medical image processing, especially in healthcare informatics. Up until the past decade, it was a challenging task to achieve accurate results in healthcare informatics, but machine learning has changed the scenario and made it easier. Healthcare is one the rapidly growing areas, where technology plays a vital role in the end-to-end process, especially in analysis and diagnosis. These strategies give better results and are user friendly. This chapter will describe the different machine learning modules used in the field of health care, with informatics based on medical image processing.

The success of Machine Learning (ML) approaches as promising solutions has encouraged their widespread implementation across different fields. Owing to the high accident rate, the construction industry embraced ML in the risk assessment procedure. What if the machine produces knowledge of the relationship between the risk features and accident outcomes contained in the safety dataset? What if machines can explain an accident dataset without human intervention? Unsupervised ML techniques offer several advantages over supervised approaches, including their explainability to analyze and understand complex datasets. This chapter demonstrates the practical implementation of the unsupervised learning method, clustering, and dimensionality reduction to explain the similarities, differences, variances, and patterns that exist between the feature spaces of an occupational safety risk dataset. Principal component analysis (PCA) and K-means clustering with silhouette analysis were selected as two unsupervised ML approaches to demonstrate their implementation in enhancing data-centric decision-making during the construction risk assessment procedure.

Customer relationship management (CRM) is the tool to enhance customer relationship in any business. Due to the exponential growth of data volume, in any field, it is significant to develop new techniques to discover the customer knowledge, automation of the system and moreover customer satisfaction to win customer lifetime value. CRM with machine learning could bring a catalytic change in business. Several supervised and unsupervised machine learning techniques are utilized to improve the customer experience and profitability of business. This paper reviews the available literature on the CRM with machine learning techniques for customer identification, customer attraction, and customer retention and customer development. This study reveals that supervised learning techniques are 48.48% utilized, unsupervised learning techniques are utilized 15.15%, and 9.09% utilized other techniques in CRM. Paradigm is also shifted toward the deep learning from machine learning as 28.28% text has been reported to deep learning. Decision tree-based algorithm and support vector machine algorithms are most utilized algorithm of supervised learning. E-commerce and telecommunication sectors are the most important areas identified with the exponential growth of the users and hence need a suitable machine learning techniques for customer satisfaction and business profitability.

Smart cities are centres of economic opulence and hope for standardized living. Understanding the shades of urban resilience and livability in smart city models is of paramount importance. This study presents a novel two-stage data-driven framework combining a multivariate metric-distance analysis with machine learning (ML) techniques for resilience and livability assessment of smart cities. A longitudinal dataset for 35 top-ranked European smart cities from 2015 till 2020 applied as the case study under the proposed framework. Initially, a metric distance-based weighting approach is used to weight the indicators and quantify the scores across each aspect under city resilience and urban livability. The key aspects under city resilience include social, economic, infrastructure and built environment and, institutional resilience, while under urban livability, the aspects include accessibility, community well-being, and economic vibrancy. Fuzzy c-means clustering as an unsupervised machine learning technique is used to sort smart cities based on the degree of performance. In addition, an intelligent approach is presented for the prediction of the degree of livability, resilience, and aggregate performance of smart cities based on various supervised ML techniques. Classification models such as Naïve Bayes, k-nearest neighbors (kNN), support vector machine (SVM), Classification and Regression Tree (CART) and, ensemble models including Random Forest (RF) and Gradient Boosting machine (GBM) were used. Three coefficients (accuracy, Cohen's Kappa (κ) and average area under the precision-recall curve (AUC-PR)) along with confusion matrix were used to appraise the performance of the classifier ML models. The results revealed GBM as the best classification and predictive model for the resilience, livability, and aggregate performance assessment. The study also revealed Copenhagen, Geneva, Stockholm, Munich, Helsinki, Vienna, London, Oslo, Zurich, and Amsterdam as the smart cities that co-create resilience and livability in their development model with superior performance.

Diabetes is a major chronic syndrome caused by a series of metabolic abnormalities in which blood glucose levels are abnormally high for an indeterminate amount of time. It influences various organs in the human body, resulting in a variety of complex diseases such as stroke, renal disease, pulmonary embolism, eyesight, and so on. Diabetes Disorders (DD) are presently one of the healthcare top causes of mortality. Predictive analytics in the health care system is a huge obstacle, but if accurate early prediction is achieved, the potential risk and degree of diabetes may be significantly decreased. Machine learning (ML) techniques are now used to analyze medical datasets at an earlier stage of life in keeping people safe. In this research, we utilized several ML approaches notably Logistic Regression, Decision Tree (DT), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) on PIMA Indian Diabetes Dataset in order to monitor and evaluate their performances in diabetes prediction. The performance of the various ML algorithms employed in this research suggests which algorithm is most suitable in diabetes prediction. It is observed that among all the models XGBoost had outperformed the other ML techniques with an accuracy of 80.73% while SVM was the second-best performing model with a classification accuracy of 80.21%. Thus, employing ML techniques, this study aims to assist doctors as well as clinicians in the early detection of diabetes.

Smartphone-based apps are increasingly used to prevent relapse among those with substance use disorders (SUDs). These systems collect a wealth of data from participants, including the content of messages exchanged in peer-to-peer support forums. How individuals self-disclose and exchange social support in these forums may provide insight into their recovery course, but a manual review of a large corpus of text by human coders is inefficient. The study sought to evaluate the feasibility of applying supervised machine learning (ML) to perform large-scale content analysis of an online peer-to-peer discussion forum. Machine-coded data were also used to understand how communication styles relate to writers' substance use and well-being outcomes. Data were collected from a smartphone app that connects patients with SUDs to online peer support via a discussion forum. Overall, 268 adult patients with SUD diagnoses were recruited from 3 federally qualified health centers in the United States beginning in 2014. Two waves of survey data were collected to measure demographic characteristics and study outcomes: at baseline (before accessing the app) and after 6 months of using the app. Messages were downloaded from the peer-to-peer forum and subjected to manual content analysis. These data were used to train supervised ML algorithms using features extracted from the Linguistic Inquiry and Word Count (LIWC) system to automatically identify the types of expression relevant to peer-to-peer support. Regression analyses examined how each expression type was associated with recovery outcomes. Our manual content analysis identified 7 expression types relevant to the recovery process (emotional support, informational support, negative affect, change talk, insightful disclosure, gratitude, and universality disclosure). Over 6 months of app use, 86.2% (231/268) of participants posted on the app's support forum. Of these participants, 93.5% (216/231) posted at least 1 message in the content categories of interest, generating 10,503 messages. Supervised ML algorithms were trained on the hand-coded data, achieving F1-scores ranging from 0.57 to 0.85. Regression analyses revealed that a greater proportion of the messages giving emotional support to peers was related to reduced substance use. For self-disclosure, a greater proportion of the messages expressing universality was related to improved quality of life, whereas a greater proportion of the negative affect expressions was negatively related to quality of life and mood. This study highlights a method of natural language processing with potential to provide real-time insights into peer-to-peer communication dynamics. First, we found that our ML approach allowed for large-scale content coding while retaining moderate-to-high levels of accuracy. Second, individuals' expression styles were associated with recovery outcomes. The expression types of emotional support, universality disclosure, and negative affect were significantly related to recovery outcomes, and attending to these dynamics may be important for appropriate intervention.

COVID-19 has been affecting the entire world from the year 2019 and in order to handle this pandemic situation, it is necessary to follow the measures till valid medicine has been found. The proposed approach helps in detecting as well as monitoring the COVID-19 on real time basis. Data is collected with the help of IoT devices for detecting this disease at the early stage. The components of the system are, (i) Collection of symptom data, (ii) Center of Isolation, (iii) Machine Learning approaches for analysis, (iv) Healthcare analysts and (v) Cloud. The three algorithms in machine learning used for detection of the virus are Decision tree, Support vector machine and Neural Network. These three algorithms are tested with the real time dataset and it is observed that all these algorithms have accuracy greater than 91%. Identifying the disease accurately with the three machine learning algorithms, effective results are produced. The response of treatment for every person who gets affected by the virus is then documented.

Machine learning is a branch of artificial intelligence that enables computer systems to learn from data and analyze data without being explicitly programmed. Interest in machine learning has grown rapidly in clinical settings because the diagnosis of diseases or disorders can be automated by computer systems with high accuracy and minimum human intervention. However, the use of machine learning to identify postural control patterns for people with Parkinson’s disease (PD) is not well established. PURPOSE: The purpose of the study was to develop and validate an automated identification of PD postural control patterns using a machine learning approach. METHODS: 12 participants with PD (age = 75.3 ± 6.6 yr, height = 1.71 ± 0.12 m, mass = 83.1 ± 12.4 kg) and 18 healthy controls (age = 83.3 ± 5.5 yr, height = 1.62 ± 0.08 m, mass = 73.1 ± 16.2 kg) were recruited. Participants were instructed to stand on a force plate and maintain still for 2 minutes during eyes-open and eyes-closed conditions. The center of pressure (COP) data were collected at 50 Hz; sway area, linear displacements, total distances, standard deviations of COP positions and average velocities were calculated. 3 supervised machine learning algorithms (i.e., logistic regression (LR), k-nearest neighbors (KNN) and naïve Bayes (NB)) were used to identify PD postural control patterns. All participants were divided into two datasets: 70% for training and 30% for testing. RESULTS: KNN achieved the highest overall accuracy rate (0.90) to identify PD postural control. LR and NB also exhibited satisfactory performance. The overall accuracy of LR ranged was 0.86; and the overall accuracy of NB was 0.81. Though all three models are capable of analyzing small-sample data, model performance to identify PD postural control could be potentially improved by recruiting a larger sample size and exploring other machine learning models in future research. CONCLUSIONS: Computer-aided machine learning models successfully identified postural control patterns of PD patients with high accuracy. The use of machine learning may provide a valid and efficient approach to better understand PD postural control features and thus, could be beneficial for the early diagnosis and early intervention in individuals with PD.

Objective To address the calibration and procedural challenges inherent in remote audiogram assessment for rehabilitative audiology, this study investigated whether calibration-independent adaptive categorical loudness scaling (ACALOS) data can be used to approximate individual audiograms by classifying listeners into standard Bisgaard audiogram types using machine learning (ML). Design Three classes of ML approaches—unsupervised, supervised, and explainable—were evaluated. Principal component analysis (PCA) was performed to extract the first two principal components, which jointly explained more than 50% of the variance. Seven supervised multi-class ML classifiers were trained and compared, alongside unsupervised and explainable methods. Study Sample A large auditory reference database (n = 847 ears) containing ACALOS data was used for model development and evaluation. Results The factor map showed substantial overlap between listeners, indicating that cleanly separating participants into six Bisgaard classes based solely on their loudness patterns is challenging. Nevertheless, the ML models demonstrated reasonable classification performance. Among the supervised classifiers, logistic regression achieved the highest accuracy. In addition, the SHAP and feature permutation analyses showed that the highest predictive power of the ML models was attributable to the minimum loudness levels at 1.5 and 4 kHz. Conclusions The findings demonstrate that ML models can predict standard Bisgaard audiogram types—within certain limits—from calibration-independent loudness perception data. This approach may support future hearing aid fitting in remote or resource-limited settings without requiring a traditional audiogram.

Mangroves are indispensable in the coastal ecosystem and are depleting due to man-made and natural causes. Attempts are being made to mitigate the impact on mangroves, and transplantation is considered a strategy for mangrove regeneration. However, it is essential to identify suitable regions and select mangrove species compatible with these regions to achieve maximum benefit from transplantation. Mangrove transplantation is increasingly adopted as a restoration strategy, yet its success hinges on selecting species adapted to site-specific environmental conditions. This study utilized an unsupervised machine learning (ML) approach, a Self-Organizing Map (SOM) model, to predict mangrove species distribution using readily available data, requiring minimal labor, time, and cost. The data used in this study were gathered from the literature and included climatic, hydrological, and solar insolation variables. An SOM model with an 11 × 8 neuron grid was developed to cluster species based on their ecological preferences. Model accuracy was assessed through Quantization error (0.0003) and Topographic error (0.0548), confirming strong predictive performance and topological preservation. The SOM model effectively predicts mangrove species across climatic zones, capturing their zonal preferences based on input variables. This approach offers a practical decision-support tool for restoration practitioners, improving site–species matching and thereby increasing transplantation success rates. Future refinement could be achieved by integrating additional environmental parameters such as soil salinity, tidal dynamics, and seasonal microclimatic variations to better capture temporal shifts in species distribution. Hence, findings highlight the potential of ML to guide cost-effective, evidence-based mangrove conservation and ecological restoration strategies.

Background To assess the feasibility of a machine learning (ML) approach using radiomics features of perfusion defects on rest myocardial perfusion imaging (MPI) to detect the presence of hibernating myocardium. Methodology Data of patients who underwent 99mTc-sestamibi MPI and 18F-FDG PET/CT for myocardial viability assessment were retrieved. Rest MPI data were processed on ECToolbox, and polar maps were saved using theNFile PMap tool. The reference standard for defining hibernating myocardium was the presence of mismatched perfusion-metabolism defect with impaired myocardial contractility at rest. Perfusion defects on the polar maps were delineated with regions of interest (ROIs) after spatial resampling and intensity discretization. Replicable random sampling allocated 80% (257) of the perfusion defects of the patients from January 2017 to September 2022 to the training set and the remaining 20% (64) to the validation set. An independent dataset of perfusion defects from 29 consecutive patients from October 2022 to January 2023 was used as the testing set for model evaluation. One hundred ten first and second-order texture features were extracted for each ROI. After feature normalization and imputation, 14 best-ranked features were selected using a multistep feature selection process including the Logistic Regression and Fast Correlation-Based Filter. Thirteen supervised ML algorithms were trained with stratified five-fold cross-validation on the training set and validated on the validation set. The ML algorithms with a Log Loss of <0.688 and <0.672 in the cross-validation and validation steps were evaluated on the testing set. Performance matrices of the algorithms assessed included area under the curve (AUC), classification accuracy (CA), F1 score, precision, recall, and specificity. To provide transparency and interpretability, SHapley Additive exPlanations (SHAP) values were assessed and depicted as beeswarm plots. Results Two hundred thirty-nine patients (214 males; mean age 56 ± 11 years) were enrolled in the study. There were 371 perfusion defects (321 in the training and validation sets; 50 in the testing set). Based on the reference standard, 168 perfusion defects had hibernating myocardium (139 in the training and validation sets; 29 in the testing set). On cross-validation, six ML algorithms with Log Loss <0.688 had AUC >0.800. On validation, 10 ML algorithms had a Log Loss value <0.672, among which six had AUC >0.800. On model evaluation of the selected models on the unseen testing set, nine ML models had AUC >0.800 with Gradient Boosting Random Forest (xgboost) [GB RF (xgboost)] achieving the highest AUC of 0.860 and could detect the presence of hibernating myocardium in 21/29 (72.4%) perfusion defects with a precision of 87.5% (21/24), specificity 85.7% (18/21), CA 78.0% (39/50) and F1 Score 0.792. Four models depicted a clear pattern of model interpretability based on the beeswarm SHAP plots. These were GB RF (xgboost), GB (scikit-learn), GB (xgboost), and Random Forest. Conclusion Our study demonstrates the potential of ML in detecting hibernating myocardium using radiomics features extracted from perfusion defects on rest MPI images. This proof-of-concept underscores the notion that radiomics features capture nuanced information beyond what is perceptible to the human eye, offering promising avenues for improved myocardial viability assessment.

Glass fiber‐reinforced tapes are increasingly used in high‐performance sectors due to their high specific strength and dimensional stability. Ensuring quality requires accurate assessment of tensile strength (TS) and linear density (Tex), which are influenced by microstructural parameters such as fiber weight fraction (FWF) and fiber diameter (FD). Traditional experimental and linear statistical models, however, struggle to capture the complex nonlinear relationships governing these properties. To address this, the present study integrates experimental evaluation with machine learning (ML)‐based prediction of TS and Tex. A dataset of 100 samples was generated and analyzed using eight supervised ML algorithms. Results demonstrated that nonlinear ML models substantially outperformed linear approaches in predictive accuracy. Support vector regression (SVR) achieved the highest performance for both TS ( R 2 = 0.995, MAE = 1.524 MPa, RMSE = 1.892 MPa) and Tex ( R 2 = 0.979, MAE = 802.559, RMSE = 1024.195). Multilayer Perceptron (MLP) followed closely, while ensemble models, Random Forest Regressor (RFR) and XGBoost Regressor (XGBR) provided strong accuracy with moderate error. Feature attribution analyses revealed FWF as the dominant predictor, with FD exerting secondary influence. This study highlights the advantages of nonlinear ML approaches, establishing a predictive framework for quality control in industrial glass fiber‐reinforced tapes.

Software Bug Prediction (SBP) is an important issue in software development and maintenance processes, which concerns with the overall of software successes. This is because predicting the software faults in earlier phase improves the software quality, reliability, efficiency and reduces the software cost. However, developing robust bug prediction model is a challenging task and many techniques have been proposed in the literature. This paper presents a software bug prediction model based on machine learning (ML) algorithms. Three supervised ML algorithms have been used to predict future software faults based on historical data. These classifiers are Naïve Bayes (NB), Decision Tree (DT) and Artificial Neural Networks (ANNs). The evaluation process showed that ML algorithms can be used effectively with high accuracy rate. Furthermore, a comparison measure is applied to compare the proposed prediction model with other approaches. The collected results showed that the ML approach has a better performance.

Background: Critically ill patients frequently experience multi-organ dysfunction and unstable vital signs, increasing their risk of myocardial injury and elevated serum troponin levels. Myocardial injury from oxygen supply-demand mismatch, whether due to systemic disease or obstructive coronary artery disease (CAD), often leads to patients undergoing an invasive coronary angiogram (ICA) and potentially percutaneous coronary intervention (PCI) if obstructive CAD is identified. However, this procedure carries significant risks for critically ill patients. To address this challenge, we aimed to develop a machine learning (ML) model to predict the need for PCI in critically ill patients with myocardial injury. Methods: The study cohort included critically ill patients with elevated serum troponin levels who underwent ICA during their hospitalization at a single institution from 2012 to 2022. We defined critical illness as a life-threatening condition requiring intensive support for vital sign instability. Patients with atherothrombosis identified during ICA were excluded from the study, focusing only on patients with type 2 MI. We utilized Matplotlib to analyze patient data, including demographics, lab results, vital signs, and comorbidities. We then used XGBoost, a supervised ML algorithm, for binary classification tasks. An ensemble of decision trees was employed, with gradient boosting to correct errors from previous trees. XGBoost iteratively adjusted predictions, creating a model to predict which patients required PCI. Results: Out of 765 patients, 173 (22.6%) underwent PCI. The cohort had a mean age of 60.5 ± 13.5 years, with 41.7% being female. The ML model accurately identified non-PCI candidates at 75% and PCI candidates at 64%, achieving an overall accuracy of 73%. The three most important features identified by the model were a history of diabetes, diastolic heart failure, and whether the patient received a blood transfusion during hospitalization. Conclusion: A ML approach using XGBoost demonstrated moderate accuracy in predicting the need for PCI in critically ill patients with signs of myocardial injury. This prediction holds clinical significance due to the heightened risks associated with ICA in these patients. Given that PCI occurred in only 22.6% of the cohort, future models should include a higher proportion of patients who underwent PCI to improve the robustness and true positive accuracy of the model.