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Preterm births have been seen to have psychological and financial implications; current surveys suggest that amongst the various methods of preterm prediction, there is yet to exist a reliable and standard means of predicting preterm births. This study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester, alongside information from the patients' medical health record regarding the pregnancy, towards preterm prediction and an associated delivery imminency timeline. In addition to this, the impact of both linear and non‐linear dimensional embedding methods towards the preterm prediction is explored. The classification exercises were carried out using a support vector machine and decision tree, both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.

Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific crop types, cropland, and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures. This study applied a methodology to identify cropland and specific crop types, including tobacco, wheat, barley, and gram, as well as the following cropping patterns: wheat-tobacco, wheat-gram, wheat-barley, and wheat-maize, which are common in Gujranwala District, Pakistan, the study region. The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning (ML) methods, namely a Decision Tree Classifier (DTC) and a Random Forest (RF) algorithm. The best time-periods for differentiating cropland from other land cover types were identified, and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms. The methodology was subsequently evaluated using Landsat images, crop statistical data for 2020 and 2021, and field data on cropping patterns. The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images, together with ML techniques, for mapping not only the distribution of cropland, but also crop types and cropping patterns when validated at the county level. These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan, adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.

INTRODUCTION: Cardiovascular disease is a major concern and pressing issue faced by the healthcare sector globally. According to a survey conducted by the WHO every year, CVDs cause 17.9 million deaths worldwide. Lack of pre-prediction of CVDs is a significant factor contributing to the death of patients. Predicting CVDs is a challenging task for medical practitioners as it requires a high level of medical analysis skills and extensive knowledge. OBJECTIVES: We believe that the improvement in the accuracy of prediction can significantly reduce the risk caused by CVDs and help medical practitioners better diagnose patients . METHODS: In this study, We created a CVD prediction model. using a ML approach. We utilized various algorithms, including logistic regression, Gaussian Naive Baye, Bernoulli Naive Baye, SVM, KNN, optimized KNN, X Gradient Boosting, and random forest algorithms to analyze and predict CVDs. RESULTS: Our developed prediction model achieved an accuracy of 96.7%, indicating its effectiveness in predicting CVDs. DL algorithms can also assist in identifying, classifying, and quantifying patterns of medical images, improving patient evaluation and diagnosis based on prior medical history and evaluation patterns. CONCLUSION: Furthermore, deep learning algorithms can help in developing new drugs with minimum cost by reducing the number of clinical research trials, using prior prediction of the drug's efficacy.

Fake social media profiles are responsible for various cyber-attacks, spreading fake news, identity theft, business and payment fraud, abuse, and more. This paper aims to explore the potential of Machine Learning in detecting fake social media profiles by employing various Machine Learning algorithms, including the Dummy Classifier, Support Vector Classifier (SVC), Support Vector Classifier (SVC) kernels, Random Forest classifier, Random Forest Regressor, Decision Tree Classifier, Decision Tree Regressor, MultiLayer Perceptron classifier (MLP), MultiLayer Perceptron (MLP) Regressor, Naïve Bayes classifier, and Logistic Regression. For a comprehensive evaluation of the performance and accuracy of different models in detecting fake social media profiles, it is essential to consider confusion matrices, sampling techniques, and various metric calculations. Additionally, incorporating extended computations such as root mean squared error, mean absolute error, mean squared error and cross-validation accuracy can further enhance the overall performance of the models.

This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market.

In this paper, the performance of machine learning methods for squirrel cage induction motor broken rotor bar (BRB) fault detection is evaluated. Decision tree classification (DTC), artificial neural network (ANN), and deep learning (DL) methods are developed, applied, and studied to compare their performance in detecting broken rotor bar faults in squirrel cage induction motors. The training data were collected through experimental measurements. The BRB fault features were extracted from measured line-current signatures through a transformation from the time domain to the frequency domain using discrete Fourier Transform (DFT) of the frequency spectrum of the current signal. Eighty percent of the data were used for training the models, and twenty percent were used for testing. A confusion matrix was used to validate the models’ performance using accuracy, precision, recall, and f1-scores. The results evidence that the DTC is less load-dependent, and it has better accuracy and precision for both unloaded and loaded squirrel cage induction motors when compared with the DL and ANN methods. The DTC method achieved higher accuracy in the detection of the magnitudes of the twice-frequency sideband components induced in stator currents by BRB faults when compared with the DL and ANN methods. Although the detection accuracy and precision are higher for the loaded motor than the unloaded motor, the DTC method managed to also exhibit a high accuracy for the unloaded current when compared with the DL and ANN methods. The DTC is, therefore, a suitable candidate to detect broken rotor bar faults on trained data for lightly or thoroughly loaded squirrel cage induction motors using the characteristics of the measured line-current signature.

In this work, we propose a deployment-oriented intrusion detection framework for enterprise networks, combining a multi-branch convolutional neural network (CNN) with channel attention and a fine-tuned decision-tree (DT) classifier. Our system offers transparent, human-interpretable rules with minimal inference overhead. We evaluate the proposed model on two public benchmarks: the CIC-IDS2017 dataset, consisting of over 2 million labeled network flows with 80 + features, and the NSL-KDD dataset, containing 125,000 connection records with 41 features. These datasets challenge the model with multiple flow classification tasks, including both known and unknown attack types. Our evaluation shows that the proposed model outperforms strong CNN-based baselines, achieving 99.28% accuracy and 99.30% ROC-AUC on CIC-IDS2017, with a 5.7% improvement over CNN + DT baselines. On NSL-KDD, the model attains a 99.10% accuracy and 0.997 ROC-AUC, marking a 5.7% gain compared to CNN + DT approaches. Furthermore, we report a cross-dataset transfer improvement, with a + 0.97-point increase in macro-F1 score, demonstrating the model’s ability to generalize across temporal and dataset shifts. These results underline the system’s effectiveness in both classification accuracy and interpretability for real-world enterprise network security deployment.

The Internet of Things (IoT) is a transformative technology that is reshaping industries and daily life, leading us towards a connected future that is full of possibilities and innovations. In this paper, we present a robust framework for the application of Internet of Things (IoT) technology in the agricultural sector in Bangladesh. The framework encompasses the integration of IoT, data mining techniques, and cloud monitoring systems to enhance productivity, improve water management, and provide real-time crop forecasting. We conducted rigorous experimentation on the framework. We achieve an accuracy of 87.38% for the proposed model in predicting data harvest. Our findings highlight the effectiveness and transparency of the framework, underscoring the significant potential of the IoT in transforming agriculture and empowering farmers with data-driven decision-making capabilities. The proposed framework might be very impactful in real-life agriculture, especially for monsoon agriculture-based countries like Bangladesh.

Background In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. Results To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Conclusion Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.

Floods, as natural disasters, impose significant human and financial burdens, necessitating stringent mitigation measures. The recurrent annual incidence of floods precipitates considerable economic setbacks and tragic human casualties. In the realm of disaster management, flood susceptibility mapping has evolved into an indispensable instrument for preemptive intervention. In recent years, the amalgamation of machine learning (ML) methodologies and geographic information systems (GIS) has demonstrated remarkable promise in the realm of flood susceptibility mapping. Nonetheless, the inherent limitations of standalone ML models have constrained their predictive efficacy. Several shortcomings are evident in prior research. These include the failure to utilize contemporary ensemble approaches capable of enhancing performance and the limited exploration of diverse classifier combinations, which are instrumental in augmenting reliability. Simultaneously, there is an absence of current and up-to-date flood susceptibility maps on recent floods within the study area. Hence, this study endeavors to enhance the precision of flood susceptibility mapping, within the Haraz-Neka River basin across Mazandaran province, by harnessing an ensemble of ML models. The research methodology encompassed several pivotal phases. Initially, data about 240 flood sites were meticulously compiled. Subsequently, 70% of this dataset was allocated for training and cartographic elucidations, whereas the remaining 30%, selected at random, served to validate the resultant maps. The analytical framework incorporated a spectrum of influential parameters, encompassing Elevation, Slope, Aspect, Rainfall, land use, Vegetation Differentiation Index (NDVI), Soil Hydrology Groups, Proximity to the River, Distance from Landslides, Topographic Wetness Index (TWI), Stream Power Index (SPI), and Sediment Transport Index (STI) for spatial modeling. The results undeniably highlight the superior performance of the ensemble model compared to its individual counterparts. Validation exercises, leveraging historical flood data, prominently endorsed the AdaBoost algorithm integrated with the Decision Tree classifier as the most efficacious. Garnering an Area Under ROC curve surpassing 0.96, accompanied by an accuracy of 0.93%, a sensitivity of 0.95%, and a specificity of 0.92%, this amalgamation substantiates its prowess. The proposed framework stands poised to empower decision-makers in identifying vulnerable regions and devising efficacious flood risk mitigation strategies.