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The leading cause of cancer-related deaths worldwide is skin cancer. Effective therapy depends on the early diagnosis of skin cancer through the precise classification of skin lesions. However, dermatologists may find it difficult and time-consuming to accurately classify skin lesions. The use of transfer learning to boost skin cancer classification model precision is a promising strategy. In this work, we proposed a hybrid CNN with a transfer learning model and a random forest classifier for skin cancer disease detection. To evaluate the efficacy of the proposed model, it was verified over two datasets of benign skin moles and malignant skin moles. The proposed model is able to classify images with an accuracy of up to 90.11%. The empirical results and analysis assure the feasibility and effectiveness of the proposed model for skin cancer classification. [ABSTRACT FROM AUTHOR]

Objective: The objective of the study was to establish an AI-driven decision support system by identifying the most important features in the severity of disease for Intensive Care Unit (ICU) with Mechanical Ventilation (MV) requirement, ICU, and InterMediate Care Unit (IMCU) admission for hospitalized patients with COVID-19 in South Florida. The features implicated in the risk factors identified by the model interpretability can be used to forecast treatment plans faster before critical conditions exacerbate. Methods: We analyzed eHR data from 5371 patients diagnosed with COVID-19 from South Florida Memorial Healthcare Systems admitted between March 2020 and January 2021 to predict the need for ICU with MV, ICU, and IMCU admission. A Random Forest classifier was trained on patients' data augmented by SMOTE, collected at hospital admission. We then compared the importance of features utilizing different model interpretability analyses, such as SHAP, MDI, and Permutation Importance. Results: The models for ICU with MV, ICU, and IMCU admission identified the following factors overlapping as the most important predictors among the three outcomes: age, race, sex, BMI, diarrhea, diabetes, hypertension, early stages of kidney disease, and pneumonia. It was observed that individuals over 65 years ('older adults'), males, current smokers, and BMI classified as 'overweight' and 'obese' were at greater risk of severity of illness. The severity was intensified by the co-occurrence of two interacting features (e.g., diarrhea and diabetes). Conclusions: The top features identified by the models' interpretability were from the 'sociodemographic characteristics', 'pre-hospital comorbidities', and 'medications' categories. However, 'pre-hospital comorbidities' played a vital role in different critical conditions. In addition to individual feature importance, the feature interactions also provide crucial information for predicting the most likely outcome of patients' conditions when urgent treatment plans are needed during the surge of patients during the pandemic. [ABSTRACT FROM AUTHOR]

Background: Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration. Objectives: The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2. Methods: A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations. Results: The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45–89%. During 32 years (1990–2022), about 1280.47 km2 (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96). Conclusions: This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover. [ABSTRACT FROM AUTHOR]

In the realm of credit card fraud detection, the landscape is continually evolving, demanding innovative approaches to stay ahead of increasingly sophisticated fraudulent activities. Our research pioneers a groundbreaking methodology that amalgamates the power of bipartite graph visualization with advanced machine learning techniques. This fusion yields a comprehensive framework capable of effectively evaluating the efficacy of a random forest classifier in uncovering fraudulent credit card transactions. Our study showcases the compelling application of this methodology, offering a paradigm shift in how we analyze and understand credit card fraud detection systems. By seamlessly integrating machine learning algorithms with network analysis, we provide a holistic view of the data, unveiling intricate patterns hidden within. At the heart of our approach lies the innovative use of bipartite graphs, which serve as a dynamic visual bridge between model predictions and real-world outcomes. This visual representation not only enhances interpretability but also facilitates a deeper understanding of the classifier’s performance. By visually mapping the relationships between transactions and their respective classifications, our methodology offers actionable insights into both successful detection and potential areas for improvement. Empowering analysts and stakeholders, our approach facilitates informed decisionmaking by enabling them to fine-tune model parameters and enhance the overall effectiveness of fraud detection systems. Through this synergy between cutting-edge machine learning and network analysis techniques, we provide a powerful tool to combat the critical challenge of credit card fraud prevention. Step into the future of fraud detection with our innovative methodology, where every transaction is scrutinized with precision, and where security is not just a possibility, but a promise fulfilled. [ABSTRACT FROM AUTHOR]

Background: This study aims to assess systemic risk factors in diabetes mellitus (DM) patients and predict diabetic retinopathy (DR) using a Random Forest (RF) classification model. Methods: We included DM patients presenting to the retina clinic for first-time DR screening. Data on age, gender, diabetes type, treatment history, DM control status, family history, pregnancy history, and systemic comorbidities were collected. DR and sight-threatening DR (STDR) were diagnosed via a dilated fundus examination. The dataset was split 80:20 into training and testing sets. The RF model was trained to detect DR and STDR separately, and its performance was evaluated using misclassification rates, sensitivity, and specificity. Results: Data from 1416 DM patients were analyzed. The RF model was trained on 1132 (80%) patients. The misclassification rates were 0% for DR and ~20% for STDR in the training set. External testing on 284 (20%) patients showed 100% accuracy, sensitivity, and specificity for DR detection. For STDR, the model achieved 76% (95% CI-70.7%–80.7%) accuracy, 53% (95% CI-39.2%–66.6%) sensitivity, and 80% (95% CI-74.6%–84.7%) specificity. Conclusions: The RF model effectively predicts DR in DM patients using systemic risk factors, potentially reducing unnecessary referrals for DR screening. However, further validation with diverse datasets is necessary to establish its reliability for clinical use. [ABSTRACT FROM AUTHOR]

Supply chain attacks are potent cyber attacks for widespread ramifications by compromising supply chains. Supply chain attacks are difficult to detect as the malware is installed through trustworthy supply chains, missing signs of infection and making deployed security controls ineffective. Recent increases in supply chain attacks warrant a Zero-trust model and innovative solutions for detecting supply chain attacks. Supply chain malware need to establish a Command and Control (C2) connection as a communication link with the attacker to proceed on the privileged pathway. Discovery of the C2 channel between the attacker and supply chain malware can lead to detection of the attack. The most promising technique for detecting supply chain attacks is monitoring host-based indicators and correlating these with associated network activity for early discovery of C2 connection. Proposed framework has introduced a novel approach of detecting C2 over DNS by incorporating host-based activity with corresponding network activity coupled with threat intelligence. C2-Eye integrates process-specific host-based features, correlated network activity, DNS metadata, DNS semantic analysis, and real time threat intelligence from publicly available resources for detecting C2 of supply chain attacks. Besides, C2-Eye monitors the exploitation of C2 channel for probable data exfiltration. C2-Eye has introduced a distinctive featureset with 22 novel features specific to supply chain attack, enabling detection of the attack with F1-score of 98.70%. [ABSTRACT FROM AUTHOR]

One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal–external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts. [ABSTRACT FROM AUTHOR]

Taste sensation recognition is a core for taste-related queries. Most prior research has been devoted to recognizing the basic taste sensations using the Brain–Computer Interface (BCI), which includes EEG, MEG, EMG, and fMRI. This research aims to recognize electronic taste (E-Taste) sensations based on surface electromyography (sEMG). Silver electrodes with platinum plating of the E-Taste device were placed on the tongue's tip to stimulate various tastes and flavors. In contrast, the electrodes of the sEMG were placed on facial muscles to collect the data. The dataset was organized and preprocessed, and a random forest classifier was applied, giving a five-fold accuracy of 70.43%. The random forest classifier was used on each participant dataset individually and in groups, providing the highest accuracy of 84.79% for a single participant. Moreover, various feature combinations were extracted and acquired 72.56% accuracy after extracting eight features. For a future perspective, this research offers guidance for electronic taste recognition based on sEMG. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to assess the effectiveness of the best performing interpretable machine learning models in the diagnoses of leukoplakia and oral squamous cell carcinoma (OSCC). Methods: A total of 237 patient cases were analysed that included information about patient demographics, lesion characteristics, and lifestyle factors, such as age, gender, tobacco use, and lesion size. The dataset was preprocessed and normalised, and then separated into training and testing sets. The following models were tested: K-Nearest Neighbours (KNN), Logistic Regression, Naive Bayes, Support Vector Machine (SVM), and Random Forest. The overall accuracy, Kappa score, class-specific precision, recall, and F1 score were used to assess performance. SHAP (SHapley Additive ExPlanations) was used to interpret the Random Forest model and determine the contribution of each feature to the predictions. Results: The Random Forest model had the best overall accuracy (93%) and Kappa score (0.90). For OSCC, it had a precision of 0.91, a recall of 1.00, and an F1 score of 0.95. The model had a precision of 1.00, recall of 0.78, and F1 score of 0.88 for leukoplakia without dysplasia. The precision for leukoplakia with dysplasia was 0.91, the recall was 1.00, and the F1 score was 0.95. The top three features influencing the prediction of leukoplakia with dysplasia are buccal mucosa localisation, ages greater than 60 years, and larger lesions. For leukoplakia without dysplasia, the key features are gingival localisation, larger lesions, and tongue localisation. In the case of OSCC, gingival localisation, floor-of-mouth localisation, and buccal mucosa localisation are the most influential features. Conclusions: The Random Forest model outperformed the other machine learning models in diagnosing oral cancer and potentially malignant oral lesions with higher accuracy and interpretability. The machine learning models struggled to identify dysplastic changes. Using SHAP improves the understanding of the importance of features, facilitating early diagnosis and possibly reducing mortality rates. The model notably indicated that lesions on the floor of the mouth were highly unlikely to be dysplastic, instead showing one of the highest probabilities for being OSCC.

Dates are essential fruits loaded with vital nutrients that keep bones healthy and prevent bone-related disorders. Approximately 8.46 million tons of different types of dates are cultivated and produced annually around the globe. There are more than 400 types of dates that are time-consuming and expensive to produce. Classifying them using conventional methods is labor-intensive, and this is one of the biggest problems for the date industry. Dataset fruit classification plays a vital role in the food industry. Dates can be classified from a luxury class to a less quality class. Accordingly, the food industry needs an automotive date fruit classifier that can work in food factories. This study proposes a pioneering method to classify date fruit that relies on extracting features from the texture of dates using Scattering Wavelet Transformation (SWT). The SWT yields in numeric coefficients were found to be immune to the deformation of invariants. This feature set trains an ensemble classifier that combines a voting mechanism to eliminate overfitting. The ensemble classifier consists of a random forest, a support vector machine classifier, and a logistic regression hyper-learner. Our novel approach was tested on two benchmarked datasets. The first data set scored F1 between 0.95 and 1.0 at the same time. The second dataset registered F1 between 0.96 and 1.0 in each of the 20 date classes. Some dates are close to each other in texture, resulting in high false positives or recall, causing a lower F1 score accuracy degree. The novelty of this approach comes from the featured representative of each date class, relying on the texture of the fruit as a discriminative feature, not on the fruit shape or color, which may not be robust enough as distinguishable features, especially in date classes that are close to each other in shape. Doi: 10.28991/HIJ-2024-05-02-010 Full Text: PDF

Metastatic breast cancer (MBC) continues to be a leading cause of cancer-related deaths among women. This work introduces an innovative non-invasive breast cancer classification model designed to improve the identification of cancer metastases. While this study marks the initial exploration into predicting MBC, additional investigations are essential to validate the occurrence of MBC. Our approach combines the strengths of large language models (LLMs), specifically the bidirectional encoder representations from transformers (BERT) model, with the powerful capabilities of graph neural networks (GNNs) to predict MBC patients based on their histopathology reports. This paper introduces a BERT-GNN approach for metastatic breast cancer prediction (BG-MBC) that integrates graph information derived from the BERT model. In this model, nodes are constructed from patient medical records, while BERT embeddings are employed to vectorise representations of the words in histopathology reports, thereby capturing semantic information crucial for classification by employing three distinct approaches (namely univariate selection, extra trees classifier for feature importance, and Shapley values to identify the features that have the most significant impact). Identifying the most crucial 30 features out of 676 generated as embeddings during model training, our model further enhances its predictive capabilities. The BG-MBC model achieves outstanding accuracy, with a detection rate of 0.98 and an area under curve (AUC) of 0.98, in identifying MBC patients. This remarkable performance is credited to the model's utilisation of attention scores generated by the LLM from histopathology reports, effectively capturing pertinent features for classification. [ABSTRACT FROM AUTHOR]

Background and Objectives: Metastatic castration-sensitive prostate cancer (mCSPC) represents a critical juncture in the management of prostate cancer, where the accurate prediction of the onset of castration resistance is paramount for guiding treatment decisions. Methods: In this study, we underscore the power and efficiency of auto-ML models, specifically the Random Forest Classifier, for their low-code, user-friendly nature, making them a practical choice for complex tasks, to develop a predictive model for the occurrence of castration resistance events (CRE( . Utilizing a comprehensive dataset from MSK (Clin Cancer Res 2020), comprising clinical, genetic, and molecular features, we conducted a comprehensive analysis to discern patterns and correlations indicative of castration resistance. A random forest classifier was employed to harness the dataset's intrinsic interactions and construct a robust predictive model. Results: We used over 18 algorithms to find the best model, and our results showed a significant achievement, with the developed model demonstrating an impressive accuracy of 75% in predicting castration resistance events. Furthermore, the analysis highlights the importance of specific features such as 'Fraction Genome Altered ‘and the role of prostate specific antigen (PSA( in castration resistance prediction. Conclusion: Corroborating these findings, recent studies emphasize the correlation between high 'Fraction Genome Altered' and resistance and the predictive power of elevated PSA levels in castration resistance. This highlights the power of machine learning in improving outcome predictions vital for prostate cancer treatment. This study deepens our insights into metastatic castrationsensitive prostate cancer and provides a practical tool for clinicians to shape treatment strategies and potentially enhance patient results. [ABSTRACT FROM AUTHOR]

The reliability of data-driven approaches in generating landslide susceptibility maps depends on data quality, analytical method selection, and sampling techniques. Selecting optimal datasets and determining the most effective analytical methods pose significant challenges. This study assesses the performance of seven machine learning classifiers in the Himalayan region of the China–Pakistan Economic Corridor, utilizing statistical techniques and validation metrics. Thirteen geo-environmental variables were analyzed, including topographic (8), land cover (1), hydrological (1), geological (2), and meteorological (1) factors. These variables were evaluated for multicollinearity, feature importance, and their influence on landslide incidences. Our findings indicate that Support Vector Machines and Logistic Regression were highly effective, particularly near fault zones and roads, due to their effectiveness in handling complex, non-linear terrain interactions. Conversely, Random Forest and Logistic Regression demonstrated variability in their results. Each model distinctly identified landslide susceptibility zones ranging from very low to very high risk. Significant conditioning variables such as elevation, rainfall, lithology, slope, and land use were identified, reflecting the unique geomorphological conditions of the Himalayas. Further analysis using the Variance Inflation Factor and Pearson correlation coefficient showed minimal multicollinearity among the variables. Moreover, evaluations of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) values confirmed the strong predictive capabilities of the models, with the Random Forest Classifier performing exceptionally well, achieving an AUC of 0.96 and an F-Score of 0.86. This study shows the importance of model selection based on dataset characteristics to enhance decision-making and strategy effectiveness. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) has shown a significant impact in tackling various complicated problems in different application domains, including healthcare, economics, ecological, stock market, surveillance, and commercial applications. Machine Learning techniques are good enough to deal with a wide range of data, uncover fascinating links, offer insights, and spot trends. ML can improve disease diagnosis accuracy, predictability, performance, and reliability. This paper reviews various machine learning techniques applied to different medical datasets and proposes an ensemble method for helping in the early diagnosis of different diseases. The study compares existing machine learning techniques with the proposed ensemble method. The ensemble method uses the AdaBoost algorithm to combine the traits of choice trees, random forests, and support vector machines. Three feature selection techniques, Fisher’s score, information gain, and genetic algorithm, are used to select appropriate dataset features. The ensemble method also uses the K-fold cross-validation technique (where k=15) for validating results. SMOTE was employed to balance some of the datasets because they were quite unbalanced. All the methods used in this study are evaluated based on accuracy, AU Curve, Recall, Precision, and F1-score. The paper uses different medical datasets at the University of California Irvine and the Kaggle directory to compare machine-learning models with the proposed ensemble method. The encouraging results show that the ensemble method outperforms the existing machine-learning techniques. The paper thoroughly analyzes how machine learning is used in the medical industry, covering established technologies and their impact on medical diagnosis. An early diagnosis is needed to prevent people from deadly diseases. Hence, this study proposes an ensemble method that may be used to diagnose different diseases early. [ABSTRACT FROM AUTHOR]

One of the most common causes of blindness, particularly in the elderly, is cataracts. Nearly half of India's elderly population has cataracts by the age of 80 or has had surgery to treat them. According to surveys conducted by the WHO and NPCB, there are over 12 million blind persons in India, and 80.1% of them are blind due to cataracts. Early detection of cataract cases is necessary to prevent total blindness. The World Health Organization (WHO) states that cataracts are the most frequent cause of blindness and visual loss. The risk of blindness among cataract patients can be decreased with prompt detection and treatment. On the other hand, clinical cataract identification requires the expertise of ophthalmologists. Therefore, the broad adoption of cataract detection to avert blindness may be hampered by the prospective expenses. Researchers are becoming more and more interested in artificial intelligence assisted diagnosis based on medical imagery. This study suggests an automated cataract detection procedure built on image processing and machine learning methods. A set of fundus retinal images serves as the input for the suggested model. The image dataset includes two kinds of images: healthy and images with cataracts to train the algorithm. This research consists of three primary phases: pre-processing, feature extraction and classification. The initial step of the method involves pre-processing the images to make them easier to process by extract the gray scale from the input image, then contrast-limited adaptive histogram equalization (CLAHE) is applied to improve the image and minimize noise, Finally ROI has been extracted. The second phase is feature extraction; by extract four kinds of texture features: (I) grey-level co-occurrence matrix (GLCM) to extract 11 features: 1) difference variance, 2) inverse difference moment, 3,4) information1\2, 5) entropy, 6) difference entropy, 7) correlation, 8) sum entropy, 9) maximal correlation coefficient, 10) contrast, and 11) angular second moment. (II) First Order Statistics (FOS) to extract 5 features: 1) entropy, 2) maximal gray level, 3) kurtosis, 4) skewness, and 5) energy. (III) Statistical Feature Matrix (SFM) to extract 4 features: 1) coarseness, 2) periodicity, 3) contrast, and 4) roughness. And finally (IV) Neighborhood Gray Tone Difference Matrix (NGTDM) to extract 4 features: 1) complexity, 2) coarseness, 3) strength, and 4) contrast. In the last phases, the extracted 24 features are put as input to the classifier, the classification was done by using Random Forest, support vector machines (SVM), Logistic Regression, K Neighbors (KNN), Decision Tree, and Naive Base Classifier. Classifies the retinal fundus images into two classes Cataracts or normal image. When compared to other current approaches, the experimental results of the suggested method show that its accuracy is 95%. [ABSTRACT FROM AUTHOR]

Introduction: The identification of microbe-drug associations can greatly facilitate drug research and development. Traditional methods for screening microbe-drug associations are time-consuming, manpower-intensive, and costly to conduct, so computational methods are a good alternative. However, most of them ignore the combination of abundant sequence, structural information, and microbe-drug network topology. Methods: In this study, we developed a computational framework based on a modified graph attention variational autoencoder (MGAVAEMDA) to infer potential microbedrug associations by combining biological information with the variational autoencoder. In MGAVAEMDA, we first used multiple databases, which include microbial sequences, drug structures, and microbe-drug association databases, to establish two comprehensive feature matrices of microbes and drugs after multiple similarity computations, fusion, smoothing, and thresholding. Then, we employed a combination of variational autoencoder and graph attention to extract low-dimensional feature representations of microbes and drugs. Finally, the lowdimensional feature representation and graphical adjacency matrix were input into the random forest classifier to obtain the microbe-drug association score to identify the potential microbe-drug association. Moreover, in order to correct the model complexity and redundant calculation to improve efficiency, we introduced a modified graph convolutional neural network embedded into the variational autoencoder for computing low dimensional features. Results: The experiment results demonstrate that the prediction performance of MGAVAEMDA is better than the five state-of-the-art methods. For the major measurements (AUC =0.9357, AUPR =0.9378), the relative improvements of MGAVAEMDA compared to the suboptimal methods are 1.76 and 1.47%, respectively. Discussion: We conducted case studies on two drugs and found that more than 85% of the predicted associations have been reported in PubMed. The comprehensive experimental results validated the reliability of our models in accurately inferring potential microbe-drug associations. [ABSTRACT FROM AUTHOR]

Generation Z represents a significant portion of the current workforce and is poised to become dominant in the engineering field. As the new generation arises, employee retention becomes a crucial topic in the Philippines. Hence, this study explored the factors influencing employee retention among Generation Z engineers in the Philippines using machine learning feature selection (filter method's permutation, wrapper method's backward elimination, and embedded method's Least Absolute Shrinkage and Selection Operator) and classifiers (support vector and random forest). A total of 412 participants were gathered through a purposive sampling technique. The results showed that six out of seven investigated features were found to be significant factors impacting Generation Z engineers' intention to remain in a company. These six features were supervisor support, company attachment, job satisfaction, contribution, emotional support, and shared value, organized in descending order of feature importance. These were further explained by fifteen significant subfeatures representing each feature. Only one feature, servant leadership, was deemed insignificant. These findings were extracted from the optimal combination of machine learning algorithms. Particularly, feature selection's backward elimination brought 85.66% accuracy, and the random forest classifier further enhanced the accuracy value to 90.10%. In addition, the model's precision, recall, and F1-score values were 89.50%, 90.10%, and 88.90%, respectively. This research also provided practical insights for the company executives, organizational leaders, and human resources department seeking to enhance employee retention strategies. These implications were based on the significant features influencing Generation Z engineers' retention, ultimately contributing to the long-term success and competitiveness of organizations. [ABSTRACT FROM AUTHOR]

Summary: The Optimization‐Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi‐step optimisation process that categorises grey‐scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub‐regions. Followed by each group has undergone individually optimised base enhancement. This process enhances the contrast between cancerous and normal tissues, eliminates over‐ and under‐enhancement, and supports breast tumour diagnosis. The optimised‐based enhancement images serve as a reference point for the histogram specification of the V component of the thermograms in the HSV (Hue, Saturation, and Value) model. Further, we evaluated the proposed model using both qualitative and quantitative measures. Finally, using dimension‐reduced significant Grey‐Level Co‐occurrence Matrix (GLCM) features, we validated the results with a Random Forest (RF) classifier. The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. The subjective comparison confirmed the proposed method's superior resolution in normal and malignant cases. The classifier results showed an accuracy of 96.4%, sensitivity of 98.1%, and specificity of 96.9%. [ABSTRACT FROM AUTHOR]

This study focused on the impact of Netflix's interactive entertainment on Filipino consumers, seamlessly combining vantage points from consumer behavior and employing data analytics. This underlines the revolutionary aspect of interactive entertainment in the quickly expanding digital media ecosystem, particularly as Netflix pioneers fresh content distribution techniques. The main objective of this study was to find the factors impacting the real usage of Netflix's interactive entertainment among Filipino viewers, filling a critical gap in the existing literature. The major goal of using advanced data analytics techniques in this study was to understand the subtle dynamics affecting customer behavior in this setting. Specifically, the random forest classifier with hard and soft classifiers was assessed. The random forest compared to LightGBM was also employed, alongside the different algorithms of the artificial neural network. Purposive sampling was used to obtain responses from 258 people who had experienced Netflix's interactive entertainment, resulting in a comprehensive dataset. The findings emphasized the importance of hedonic motivation, underlining the requirement for highly engaging and rewarding interactive material. Customer service and device compatibility, for example, have a significant impact on user uptake. Furthermore, behavioral intention and habit emerged as key drivers, revealing interactive entertainment's long-term influence on user engagement. Practically, the research recommends strategic platform suggestions that emphasize continuous innovation, user-friendly interfaces, and user-centric methods. This study was able to fill in the gap in the literature on interactive entertainment, which contributes to a better understanding of consumer consumption and lays the groundwork for future research in the dynamic field of digital media. Moreover, this study offers essential insights into the intricate interaction of consumer preferences, technology breakthroughs, and societal influences in the ever-expanding environment of digital entertainment. Lastly, the comparative approach to the use of machine learning algorithms provides insights for future works to adopt and employ among human factors and consumer behavior-related studies. [ABSTRACT FROM AUTHOR]

A machine learning (ML)-based random forest (RF) classification model algorithm was employed to investigate the main factors affecting the formation of the core-shell structure of BaTiO3-based ceramics and their interpretability was analyzed by using Shapley additive explanations (SHAP). An F1-score changed from 0.879 5 to 0.931 0, accuracy from 0.845 0 to 0.907 0, precision from 0.871 4 to 0.900 0, recall from 0.892 9 to 0.964 3, and ROC/AUC value of 0.97±0.03 was achieved by the RF classification with the optimal set of features containing only 5 features, demonstrating the high accuracy of our model and its high robustness. During the interpretability analysis of the model, it was found that the electronegativity, melting point, and sintering temperature of the dopant contribute highly to the formation of the core-shell structure, and based on these characteristics, specific ranges were delineated and twelve elements were finally obtained that metall the requirements, namely Si, Sc, Mn, Fe, Co, Ni, Pd, Er, Tm, Lu, Pa, and Cm. In the process of exploring the structure of the core-shell, the doping elements can be effectively localized to be selected by choosing the range of features. [ABSTRACT FROM AUTHOR]

This study evaluates the potential of machine learning algorithms for early-stage diabetes prediction. A dataset containing demographic information, medical history, and lab results was analyzed using Logistic Regression and Random Forest Classifier. The results showed that Random Forest algorithms were able to accurately predict diabetes at an early stage with high accuracy. The best-performing algorithm was found to be the Random Forest Classifier, with an accuracy of 98.0%. These findings suggest that machine-learning algorithms hold great promise for improving diabetes diagnosis and management. The results of this study provide valuable insights for future research in this area and may help inform the development of more effective and efficient screening and treatment strategies for diabetes. [ABSTRACT FROM AUTHOR]

A healthy body is the capital of success and supports human activities. To maintain health, humans need to avoid disease. A healthy life is everyone’s dream and should start early. Busy activities often hinder a healthy lifestyle. Nonetheless, it is important for every individual to lead a healthy lifestyle. Human activities determine health and the implementation of a healthy life. One method that can perform classification with machine learning is extreme gradient boosting (XGBoost). XGBoost is one of the techniques in machine learning for regression analysis and classification based on gradient boosting decision tree (GBDT). By using gradient descent to minimize the error when creating a new model, the algorithm is called gradient boosting. In determining a classification starting from determining the model to the results, usually only using one algorithm method, and combining other methods together with the method is an algorithm called random forest classifier. Among these merging methods are, stacking classifier, voting classifier, and bagging classifier. The conclusion obtained from the results of this research is that the test results show that the stacking classifier achieves the highest accuracy of 76.07%, making it the best method in this research. And the stacking classifier has a precision of 76.96%, recall of 75.83%, and F1-score of 75.81%. This shows that the model has a good balance between the ability to provide true positive results and the ability to recover positive data. [ABSTRACT FROM AUTHOR]

• Proposed a Hybrid Model for Paddy Crop prediction based on phenological parameters. • CNN-LSTM attained RMSE value of 0.219 t/ha compared to other existing methods. • Random Forest method achieved highest accuracy of 96.6% in paddy classification. Rice is one of the predominant food sources to fulfill the dietary requirements of well-being in India. Therefore, accurate and timely paddy crop yield prediction is crucial to ensure the food security of the country. In this direction, the present study proposed a hybrid deep-learning method based on Conv-1D and LSTM layers using the classification-derived phenological with meteorological parameters for paddy crop yield prediction. The paddy crop classification has been conducted using high-resolution (10 m) multispectral imagery based on GPS coordinates collected during the paddy field visits to extract the phenological parameters for input to the prediction model. In this context, the efficiency of Random Forest, Naïve Bayes, SVM, and Gradient Tree boost classifiers was assessed. Furthermore, we have also analyzed the accuracy of Landsat-8, Sentinel-1 GRD, and Sentinel-2 satellite imagery in paddy crop classification based on area estimation. The Statistical Abstract of Haryana was utilized to validate the paddy crop area estimation and yield prediction. The classification outcomes showed that the Random Forest method attained the highest accuracy of 96.6 % compared to other GEE-based classifiers. The proposed Hybrid Deep learning approach achieved an RMSE value of 0.219 t/ha compared to CNN, LSTM, CNN-Bi-LSTM, and Regression techniques for crop yield prediction. The study conclusion highlighted that the sentinel-2 satellite imagery performed well and found that the proposed hybrid approach provided an alternative for paddy crop yield prediction. [ABSTRACT FROM AUTHOR]

The focus of this paper is to use machine learning to create predictive models that detect the probable factors impacting Gestational Diabetes Mellitus (GDM) which is developed in some pregnant women. GDM is defined as any proportion of glucose intolerance developed during pregnancy. Several factors may cause GDM complications. Here, we aimed to identify factors predisposing to GDM and predict the occurrence based on several predictive models. The dataset used in this study is the Pima Indian. With the assistance of Machine Learning and Statistical Analysis, it is possible to develop intelligent models that are capable of making decisions on an autonomous basis. Seven machine learning models were tested to determine which model fits the dataset better. These models learn from past instances of data through Statistical Analysis and pattern matching. Based on the learned data, they provide us with the predicted results. This study establishes the feasibility of machine learning in the field of public health. It is observed that each technique gives different results of associated factors. The Cascade classifier model attained an accuracy of 98.58%, Random Forest (89%), SVM (69%), Logistic Regression (78%), K-NN (72%), and Decision Tree (78%). These models are validated and evaluated using several metrics. This work demonstrated that identifying risk factors must not consider one model. [ABSTRACT FROM AUTHOR]

Background and Objectives: Heart failure (HF) is a prevalent and debilitating condition that imposes a significant burden on healthcare systems and adversely affects the quality of life of patients worldwide. Comorbidities such as chronic kidney disease (CKD), arterial hypertension, and diabetes mellitus (DM) are common among HF patients, as they share similar risk factors. This study aimed to identify the prognostic significance of multiple factors and their correlation with disease prognosis and outcomes in a Jordanian cohort. Materials and Methods: Data from the Jordanian Heart Failure Registry (JoHFR) were analyzed, encompassing medical records from acute and chronic HF patients attending public and private cardiology clinics and hospitals across Jordan. An online form was utilized for data collection, focusing on three kidney function tests, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), and creatinine levels, with the eGFR calculated using the Cockcroft–Gault formula. We also built six machine learning models to predict mortality in our cohort. Results: From the JoHFR, 2151 HF patients were included, with 644, 1799, and 1927 records analyzed for eGFR, BUN, and creatinine levels, respectively. Age negatively impacted all measures (p ≤ 0.001), while smokers surprisingly showed better results than non-smokers (p ≤ 0.001). Males had more normal eGFR levels compared to females (p = 0.002). Comorbidities such as hypertension, diabetes, arrhythmias, and implanted devices were inversely related to eGFR (all with p-values <0.05). Higher BUN levels were associated with chronic HF, dyslipidemia, and ASCVD (p ≤ 0.001). Higher creatinine levels were linked to hypertension, diabetes, dyslipidemia, arrhythmias, and previous HF history (all with p-values <0.05). Low eGFR levels were associated with increased mechanical ventilation needs (p = 0.049) and mortality (p ≤ 0.001), while BUN levels did not significantly affect these outcomes. Machine learning analysis employing the Random Forest Classifier revealed that length of hospital stay and creatinine >115 were the most significant predictors of mortality. The classifier achieved an accuracy of 90.02% with an AUC of 80.51%, indicating its efficacy in predictive modeling. Conclusions: This study reveals the intricate relationship among kidney function tests, comorbidities, and clinical outcomes in HF patients in Jordan, highlighting the importance of kidney function as a predictive tool. Integrating machine learning models into clinical practice may enhance the predictive accuracy of patient outcomes, thereby supporting a more personalized approach to managing HF and related kidney dysfunction. Further research is necessary to validate these findings and to develop innovative treatment strategies for the CKD population within the HF cohort. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a non-intrusive and powerful tool for understanding the complex nature of the brain. Epileptic seizures, a neurological disorder, are characterized by excessively synchronized neuronal activity within the brain. This abnormal synchronization disrupts the normal functioning of neural networks, leading to seizures. This work proposes to use the Gaussian-based Stockwell Transform (GST) to segment EEG signals into various frequency bands such as delta, theta, alpha, beta, and gamma band for analysis. The feature extraction process on these sub-band signals generates a feature set, encompassing the fluctuation index, relative amplitude, holoentropy, logarithmic band power, and the newly introduced Hermite polynomial-based delta amplitude modulation spectrogram. To differentiate between epileptic and non-epileptic events in EEG signals, the previous features have been applied to the Random Forest Classifier (RFC) algorithm. The effectiveness of our proposed method is validated using a benchmark EEG dataset, with GST and RFC demonstrating accurate results in distinguishing seizure activity. Experimental results show an optimal classification accuracy of 96.4 %, a sensitivity of 97 %, and a specificity of 96.4 %.