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A well-configured spare parts supply chain (SC) can reduce costs and increase the competitiveness of spare parts retailers. A structured method for configuring spare parts SCs should be used to determine whether to centralise or decentralise inventory management, also considering hybrid configurations. Moreover, such a method should define whether or not to switch the production of spare parts from Conventional Manufacturing (CM) technologies to Additive Manufacturing (AM) ones. Indeed, AM is considered the next revolution in the field of spare parts, and the adoption of AM technologies strongly affects the characteristics of SCs. However, the choice between centralisation and decentralisation is not the subject of much scientific research, and it is also not clear when AM would be the preferable manufacturing technology for spare parts. This paper aims to assist managers and practitioners in determining how to design their spare parts SCs, thus defining both the spare parts SC configuration and the manufacturing technology to adopt through the development of a decision support system (DSS). The proposed DSS is a user-friendly decision tree, and, for the first time, it allows comparison of the total costs of SCs characterised by different degrees of centralisation with both AM and CM spare parts. [ABSTRACT FROM AUTHOR]

AimTo assess, from a United States (US) payer's perspective, the cost-effectiveness of gels designed to separate the endometrial surfaces (intrauterine spacers) placed following intrauterine surgery.Materials and methodsA decision tree model was developed to estimate the cost-effectiveness of intrauterine spacers used to facilitate endometrial repair and prevent the formation (primary prevention) and reformation (secondary prevention) of intrauterine adhesions (IUAs) and associated pregnancy- and birth-related adverse outcomes. Event rates and costs were extrapolated from data available in the existing literature. Sensitivity analyses were conducted to corroborate the base case results.ResultsIn this model, using intrauterine spacers for adhesion prevention led to net cost savings for US payers of $2,905 per patient over a 3.5-year time horizon. These savings were driven by the direct benefit of preventing procedures associated with IUA formation ($2,162 net savings) and the indirect benefit of preventing pregnancy-related complications often associated with IUA formation ($3,002). These factors offset the incremental cost of intrauterine spacer use of $1,539 based on an assumed price of $1,800 and the related increase in normal deliveries of $931. Model outcomes were sensitive to the probability of preterm and normal deliveries. Budget impact analyses show overall cost savings of $19.96 per initial member within a US healthcare plan, translating to $20 million over a 5-year time horizon for a one-million-member plan.LimitationsThere are no available data on the effects of intrauterine spacers or IUAs on patients' quality of life. Resultingly, the model could not evaluate patients' utility related to treatment with or without intrauterine spacers and instead focused on costs and events avoided.ConclusionThis analysis robustly demonstrated that intrauterine spacers would be cost-saving to healthcare payers, including both per-patient and per-plan member, through a reduction in IUAs and improvements to patients' pregnancy-related outcomes.

Background: Despite the proven positive effects of clinical decision support systems (CDSSs) on general practitioners' (GPs') performance and patient management, their adoption remains slow. Several factors have been proposed to explain GPs' reluctance to adopt these tools. This study hypothesizes that the influence of CDSSs on patient-physician interactions could be a determining factor. To explore this hypothesis, we utilized the FeverTravelApp, designed to assist GPs in managing patients presenting with fever after returning from the tropics. A case–control study was conducted, observing and analyzing fourteen consultations between seven physicians and three simulated patients. Each physician conducted consultations both with and without the FeverTravelApp. The consultations were video-recorded and analyzed using a custom analysis grid based on three existing tools. Simulated patients completed the Communication Assessment Tool (CAT) after each consultation, and each physician participated in a semistructured interview following the use of the app. Results: The use of the FeverTravelApp influenced multiple aspects of the consultation, particularly communication. Both patient and GP speaking times decreased, while active silence (no one talking while the GP actively performed a task) increased. GPs focused more on the app, which reduced direct patient interaction. However, this influence seemed to bother GPs more than simulated patients, who rated their GPs equally whether the app was used or not. This could be because patients felt better understood when GPs asked fewer but more specific questions related to travel medicine, thus effectively addressing their concerns. Conclusions: This study supports the hypothesis that CDSSs influence consultation dynamics, which may contribute to their slow adoption. It is essential to involve clinicians early in the development of CDSSs to adapt them to clinical workflows and ensure system interoperability. Additionally, tools that allow clinicians to follow the entire clinical reasoning process, such as decision trees, are needed. Further research is necessary to confirm these findings in real patient settings and to develop CDSSs that meet both patients' and GPs' expectations. [ABSTRACT FROM AUTHOR]

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

Banana is a widely cultivated fruit, 114 million tonnes produced worldwide every year. Almost one-third of the weight of banana fruit is made up of banana peel, which is a kind of agro-waste. The weight of banana peel trash generated each year globally is around 39.9 million tonnes. In the present investigation, the microbial fuel cell (MFC) was operated using banana peel slurry as substrate with baker's yeast as external inoculum. Decision tree algorithms were applied for optimizing the input parameters of MFC. Input variables including temperature, pH, resistance, pretreatment of slurry and slurry concentration were optimized for achieving the maximum power density in MFC. Total five combination were obtained by the decision tree model that led to high power density. All five combinations were also tested for validation. Experimental validation of decision tree models showed accuracy in range of 77 to 99%. In order to obtain high power density, the best combination was determined by accuracy level and experimental validation. The best set of rules for high power density was 41.47 mL/L < slurry concentration < 87.5 mL/L, 22.44 °C ≤ temperature < 36.25 °C, 5.23 ≤ pH < 7.25, slurry was pretreated and resistance < 285 Ω. It was also observed that temperature, resistance and pretreatment were the most influential input parameters to achieve high power density. Results obtained in this work can be directly implemented at pilot and industrial scale without further experimental trails. [ABSTRACT FROM AUTHOR]

Classification of transparent materials with various roughness types has been widely used in the field of computer vision. However, the surface roughness of a transparent material affects the extraction effect of classification features, thus affecting the performance of transparent material classification. In this study, a classification method of transparent materials with various surface roughness types and transparencies, which uses the microfacet shape factor, reflectivity, and transmissivity as classification characteristics, is proposed. First, a transparent material feature extraction method based on microfacet distribution function is proposed for the first time, and the microfacet shape factor, reflectivity, and transmissivity are extracted by our model as classification features. The microfacet distribution model ground glass unknown is combined with the time-of-flight imaging model to achieve an accurate classification of surfaces with various roughness types. Then, according to the nonlinear and discrete characteristics of data, an appropriate classifier is selected to realize the transparent material classification. The transparent material classification experiments are performed using four types of material appearances, and the proposed method is compared with the methods of Shim et al. and Lang et al. The average classification accuracy of the proposed method for the transparent materials with four material appearances is 92.62%, which represents improvements of 56.18% and 11.58% compared with the methods of Shim et al. and Lang et al., respectively. These improvements are achieved because the proposed method uses the microfacet shape factors as classification features, which ensures that the classification effect of transparent materials is not affected by surface roughness. Finally, the proposed method is suitable for all transparent materials. [ABSTRACT FROM AUTHOR]

Conducting high‐quality overviews of reviews (OoR) is time‐consuming. Because the quality of systematic reviews (SRs) varies, it is necessary to critically appraise SRs when conducting an OoR. A well‐established appraisal tool is A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2, which takes about 15–32 min per application. To save time, we developed two fast‐and‐frugal decision trees (FFTs) for assessing the methodological quality of SR for OoR either during the full‐text screening stage (Screening FFT) or to the resulting pool of SRs (Rapid Appraisal FFT). To build a data set for developing the FFT, we identified published AMSTAR 2 appraisals. Overall confidence ratings of the AMSTAR 2 were used as a criterion and the 16 items as cues. One thousand five hundred and nineteen appraisals were obtained from 24 publications and divided into training and test data sets. The resulting Screening FFT consists of three items and correctly identifies all non‐critically low‐quality SRs (sensitivity of 100%), but has a positive predictive value of 59%. The three‐item Rapid Appraisal FFT correctly identifies 80% of the high‐quality SRs and correctly identifies 97% of the low‐quality SRs, resulting in an accuracy of 95%. The FFTs require about 10% of the 16 AMSTAR 2 items. The Screening FFT may be applied during full‐text screening to exclude SRs with critically low quality. The Rapid Appraisal FFT may be applied to the final SR pool to identify SR that might be of high methodological quality. [ABSTRACT FROM AUTHOR]

Insurance plays an important role in the financial system, impacting various financial variables and vice versa. Many studies have analyzed the long- and short-term relationship between insurance and financial variables, particularly economic growth, using classical and benchmark methods. Given its impact on financial indicators, it is critical for policymakers to predict the insurance penetration rate, which can, in turn, forecast trends in other financial variables. This paper uses decision trees, random forests, and XGBoost to predict the insurance penetration rates for 30 OECD countries, identifying the most effective method for each country. The mean squared error for predicting the total insurance penetration rate with the decision tree and XGBoost is 2.32, and 1.461, respectively, while the loss function decreases to 1.1 with random forest. Additionally, XGBoost outperforms the other models in predicting non-life insurance penetration rate with an RMSE of 0.92, while for life insurance penetration rate, the random forest model has the highest accuracy, with an RMSE of 1.8. Using the prediction, for countries where the insurance penetration rate is going to decrease, policymakers could implement strategies that foster growth in the insurance sector and, consequently, boost other financial variables, particularly economic growth. [ABSTRACT FROM AUTHOR]

Increasing levels of oil spills in both the terrestrial and aquatic environments destroy aquatic life and crops, fill the air with dangerous fumes, and change the physical and chemical features of the soil. Numerous authors have researched oil spills, but the application of supervised machine-learning models has been incredibly under-explored. Therefore, this study compared four supervised machine learning algorithms (Naïve Bayes Classifier, Decision Tree, Random Forest, and Support Vector Machine) to model the geotechnical properties of oil-polluted soil samples. The soil samples were collected from Engenni Ahoada in Rivers State and Izombe, Imo State, Nigeria. The samples were tested for the following parameters according to the ASTM standard method of measurement: shear strength (SS), liquid limit (LL), plastic limit (PL), bulk density (BD), maximum dry density (MDD), optimum moisture content (OMC), California bearing ratio (CBR), and plasticity index (PI). These measurements and the kind of soil (clay or laterite) constituted the inputs for the machine learning algorithms for predicting pollutant type (crude or diesel). The data from the thirty-two (32) sample size was split into train (70%) and test (30%) sets. The models were trained using the train set, and the test sets were used to validate the models. The support vector machine outperformed the competition, with prediction accuracy in the train and test sets of 96% and 89%, respectively. The findings demonstrated that the plastic and liquid limits are the most crucial factors for determining the pollutants in a sample of polluted soil. Also, plastic limit values less than or equal to 22 are likely contaminated by crude oil, while those above 22 are contaminated by diesel. The results therefore recommend using a support vector machine learning model for predicting the pollutant type of polluted soil samples in the study area. [ABSTRACT FROM AUTHOR]

Featured Application: This study presents a novel approach to use ML models to estimate the hygroscopic properties of a natural fiber: Borassus fruit fibres (BNF). This knowledge is helpful to engineers designing and choosing materials for various applications in the building sector, as they can better understand how the material absorbs moisture. The ML models could potentially replace more expensive and time-consuming methods for testing these properties. This research focuses on the environmental-friendly production of Borassus fruit fibers (BNF), its characterization, and hygroscopic properties determination via Dynamic Vapor Sorption (DVS). The experimental results obtained from the hygroscopic behavior analysis were used to create a primary dataset to train and test Decision Tree Regression (DTR) and Gradient Boosting Regressor (GBR) models. The created primary dataset comprised 294 observations, from which 80% were used to train the models, and the remaining 20% were used for the testing of the two models. The models exhibited high accuracy, easy interpretability on the small-size dataset, and flexibility with regards to the nature of the relationship between the input and output variable. Both models successfully predicted the hygroscopic behavior with the Gradient Boosting Regressor outperforming Decision Tree Regression by indicating values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R2, respectively, during the desorption of the BNF, and values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R2, respectively, during the desorption of the BNF. This suggests that the Gradient Boosting Regressor illustrated the maximum accuracy. The outcomes can be utilized to provide an alternative for traditional methods, which can often be costly and time-consuming by improving the engineering properties of BNF. The models can be used in the construction sector to lower costs as they are able to pinpoint elements influencing the characteristics for specific applications to grasp its various properties through the prediction of its hygroscopic properties. [ABSTRACT FROM AUTHOR]

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

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

With the extensive technological advancements and expansion, the persistent issues regarding the creation and rapid dissemination of fake news have become a prevalent and recurrent concern. The manipulation of news content has critical repercussions, such as causing public mistrust, fear, harm, and misinformation. Addressing that, this study developed a supervised machine learning algorithm that can accurately classify social media data as fake news. The methodology of the proposed fake news detection model involved five main components: data acquisition from Twitter, data preprocessing, data transformation, model development using Naïve Bayes, decision tree, and support vector machine (SVM) and model evaluation using accuracy, precision, recall and F1-score. The results revealed that decision tree recorded the highest accuracy for both textual data (100%) and metadata (94.54%) and consistently outperformed both Naïve Bayes and SVM in terms of precision, recall, and F1-score metrics, with a score of 100% for the classification of textual data-based datasets. Regarding the metadata-based classification, decision tree also demonstrated excellent performance, with the highest F1-score of 94% for fake news data. Meanwhile, SVM exhibited the highest precision and recall performance for the metadata-based classification. Overall, the application of the decision tree classifier was deemed the most effective in Twitter fake news detection. [ABSTRACT FROM AUTHOR]

The study explores the potential of machine learning (ML) in predicting the thermal and mechanical properties of earth-based composites reinforced with natural Borassus fruit fiber. The limited availability of large datasets for accurate predictions is a challenge in material science research, which this study addresses. The authors collected data on thermal conductivity, compressive and flexural strength through experiments and employed four ML techniques suitable for small datasets: linear regression (LR), random forest (RF), decision tree regressor (DTR), and gradient boosting (GB). Evaluation metrics were used to assess the performance of the ML techniques. Linear regression emerged as the most efficient, exhibiting significantly lower error values compared to the others (e.g., RMSE of 0.066 for thermal conductivity, 0.119 for compressive strength, and 0.04 for flexural strength), followed by random forest and decision tree. However, gradient boosting showed relatively poor predictive accuracy. This study demonstrates the successful application of ML for predicting the properties of earth-based composites with limited data, which could significantly reduce the cost and time associated with developing new building materials and products. Manufacturers can gain a competitive edge by using ML to streamline material development, leading to lower costs, faster innovation, and the creation of more environmentally friendly building materials for a greener construction sector. [ABSTRACT FROM AUTHOR]

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

Obesity is an abnormal and potentially dangerous condition caused by excess body fat accumulation. The number of people with obesity is increasing worldwide. Obesity is the primary cause of various diseases; therefore, it is crucial to make efforts to control body weight. Identifying the factors that influence men with obesity to attempt to control and not control their weight is essential. The objective of this study was to create a prediction model for weight control experience among Korean men in their 30 s and 40 s. We analyzed data from the 2022 Community Health Survey and included 12,311 men who were overweight or obese. The men were divided into two groups based on their weight control experience: (1) Yes group (n = 9405) and (2) No group (n = 2906). Chi-square and independent t-tests were used to compare general and health-related characteristics between the groups. Decision tree analysis was used to build a prediction model for weight control experience. A split-sample test was conducted to validate the model. From the results of this study, various models predicting weight control experience were derived. From the decision tree model without setting the first node, those who weighed below average, had a high school diploma or less, and did not know their blood sugar levels had the highest probability of not controlling their weight at 55.3%. In the prediction model where the first node was set to age, those in their 40 s who thought their weight was below average and were unaware of their blood sugar levels had the highest rate of not trying to control their weight at 50.1%. In the prediction model where the first node was set to BMI, those who were overweight but thought their weight was below average and had a high school diploma or less had the highest rate of not trying to control their weight at 51.5%. There is an urgent need to provide obesity prevention and management education to those who have no weight control experience, particularly those at high risk, as identified in this study. [ABSTRACT FROM AUTHOR]

Background: Skin prick tests (SPTs), or intraepidermal tests, are often the first diagnostic approach for people with a suspected allergy. Together with the clinical history, SPTs allow doctors to draw conclusions on allergies based on the sensitization pattern. The purpose of this study is to investigate the potential cost consequences that would accrue to a Swiss University hospital after the adoption of computer vision-based SPTs. Methods: We conducted a cost-consequence analysis from a hospital's perspective to evaluate the potential cost consequences of using a computer vision-based system to read SPT results. The patient population consisted of individuals who were referred to the allergology department of one of the five university hospitals in Switzerland, Inselspital, whose allergology department averages 100 SPTs a week. We developed an early cost-consequence model comparing two SPT techniques; computer vision-based SPTs conducted with the aid of Nexkin DSPT and standard fully manual SPTs. Probabilistic sensitivity analysis and additional univariate sensitivity analyses were used to account for uncertainty. Results: The difference in average cost between the two alternatives from a hospital's perspective was estimated to be CHF 7 per SPT, in favour of the computer vison-based SPTs. Monte Carlo probabilistic simulation also indicated that SPTs conducted using the computer vision-based system were cost saving compared to standard fully manual SPTs. Sensitivity analyses additionally demonstrated the robustness of the base case result subject to plausible changes in all the input parameters, with parameters representing the costs associated with both SPT techniques having the largest influence on the incremental cost. However, higher sensitization prevalence rates seemed to favour the more accurate standard fully manual SPTs. Conclusion: Against the backdrop of rising healthcare costs especially in Switzerland, using computer-aided or (semi) automated diagnostic systems could play an important role in healthcare cost containment efforts. However, results should be taken with caution because of the uncertainty associated with the early nature of our analysis and the specific Swiss context adopted in this study. [ABSTRACT FROM AUTHOR]

The accurate classification of human activities in crime scenes during forensics (criminalistics) is of utmost importance in classifying suspicious and unlawful activities, easing their acceptability and interpretability by judges during legal procedures in courts or by other non-experts in the field of forensics. This paper implements machine learning (ML) algorithms: support vector machine (SVM) and decision tree (DT), to demonstrate with a high accuracy, how data emanating from smartphones' sensors reveal and isolate relevant information about static and dynamic human activities in criminalistics. Smartphones' data from five different sensors (accelerometer, gravity, orientation, Gyroscope and light), related to ten recurrent crime scenes activities, grouped into three classes of events (normal, felony and none-felony events) are classified by the proposed algorithms, with novelty being the classification decisions based on the entire period of the events and not instantaneous decision makings. Three independent data-subsets were made, with permutations done between them and at each time, two sets used for training and the third set used for testing. Time- and frequency-domain features were initially used separately and then combined for the model training and testing. The best average training accuracies of 100% and 97.8% were obtained for the DT and SVM, respectively, and the testing accuracies of 89.1% were obtained for both algorithms. We therefore believe that these results will serve as a solid persuasive and convincing argument to judges and non-experts of the field of forensics to accept and easily interpret computer-aided classification of suspicious activities emanating from criminalistic studies. [ABSTRACT FROM AUTHOR]

The potential of generalized deep learning models developed for crop water estimation was examined in the current study. This study was conducted in a semiarid region of India, i.e., Karnataka, with daily climatic data (maximum and minimum air temperatures, maximum and minimum relative humidity, wind speed, sunshine hours, and rainfall) of 44 years (1976–2020) for twelve locations. The Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), and Random Forest (RF) are three ensemble deep learning models that were developed using all of the climatic data from a single location (Bengaluru) from January 1976 to December 2017 and then immediately applied at eleven different locations (Ballari, Chikmaglur, Chitradurga, Devnagiri, Dharwad, Gadag, Haveri, Koppal, Mandya, Shivmoga, and Tumkuru) without the need for any local calibration. For the test period of January 2018–June 2020, the model's capacity to estimate the numerical values of crop water requirement (Penman-Monteith (P-M) ETo values) was assessed. The developed ensemble deep learning models were evaluated using the performance criteria of mean absolute error (MAE), average absolute relative error (AARE), coefficient of correlation (r), noise to signal ratio (NS), Nash–Sutcliffe efficiency (ɳ), and weighted standard error of estimate (WSEE). The results indicated that the WSEE values of RF, GB, and XGBoost models for each location were smaller than 1 mm per day, and the model's effectiveness varied from 96% to 99% across various locations. While all of the deep learning models performed better with respect to the P-M ETo approach, the XGBoost model was able to estimate ETo with greater accuracy than the GB and RF models. The XGBoost model's strong performance was also indicated by the decreased noise-to-signal ratio. Thus, in this study, a generalized mathematical model for short-term ETo estimates is developed using ensemble deep learning techniques. Because of this type of model's accuracy in calculating crop water requirements and its ability for generalization, it can be effortlessly integrated with a real-time water management system or an autonomous weather station at the regional level. [ABSTRACT FROM AUTHOR]

In today’s interconnected digital world, email remains a primary mode of digital interaction valued for its suitability in official, academic and business communications. However, despite its utility, email faces significant challenges due to the widespread presence of spam in various forms, such as phishing, suspicious attachments and deceptive content. This issue not only affects the efficiency and security of email communication but also poses a barrier to its reliability. Therefore, it is essential to devise effective methods to tackle the escalating count of spam emails. This research work presents an intriguing methodology to combat the persistent problem of email spam. The proposed method, abbreviated as AIGSADT, is an amalgamation of the intelligent variant of the gravitational search algorithm (IGSA) and decision trees (DTs). The machine learning-based DT algorithm is individually inadequate for dealing with the large amount of constructive data on a certain attribute due to its instability and ineffectiveness. The proposed AIGSADT approach integrates the IGSA algorithm, which is effective in handling large amounts of data to detect email spam. This is achieved by constructing decision trees employing gravitational forces as the means of information transfer through mass agents. Here, the intelligent factor of the IGSA algorithm prevents the trapping of GSA agents in local optima by updating their positions based on information provided by the best and worst agents. The performance of the presented AIGSADT approach is analyzed through experiments conducted on various categories available in the Ling spam dataset. These experimental evaluations aim to analyze the significance of different pre-processing modules across different dataset categories. The comparative analysis indicates the supremacy of the proposed approach compared to state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

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

This study highlights the drawbacks of current quantum classifiers that limit their efficiency and data processing capabilities in big data environments. The paper proposes a global decision tree paradigm to address these issues, focusing on designing a complete quantum decision tree classification algorithm that is accurate and efficient while also considering classification costs. The proposed method integrates the Bayesian algorithm and the quantum decision tree classification algorithm to handle incremental data. The proposed approach generates a suitable decision tree dynamically based on data objects and cost constraints. To handle incremental data, the Bayesian algorithm and quantum decision tree classification algorithm are integrated, and kernel functions obtained from quantum kernel estimation are added to a linear quantum support vector machine to construct a decision tree classifier using decision directed acyclic networks of quantum support vector machine nodes (QKE). The experimental findings demonstrate the effectiveness and adaptability of the suggested quantum classification technique. In terms of classification accuracy, speed, and practical application impact, the proposed classification approach outperforms the competition, with an accuracy difference from conventional classification algorithms being less than 1%. With improved accuracy and reduced expense as the incremental data increases, the efficiency of the suggested algorithm for incremental data classification is comparable to previous quantum classification algorithms. The proposed global decision tree paradigm addresses the critical issues that need to be resolved by quantum classification methods, such as the inability to process incremental data and the failure to take the cost of categorization into account. By integrating the Bayesian algorithm and the quantum decision tree classification algorithm and using QKE, the proposed method achieves high accuracy and efficiency while maintaining high performance when processing incremental sequences and considering classification costs. Overall, the theoretical and experimental findings demonstrate the effectiveness of the suggested quantum classification technique, which offers a promising solution for handling big data classification tasks that require high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Drought has a significant impact on plants, affecting their growth, development and survival. This study focuses on evaluating the impact of drought stress, a significant abiotic factor, on the agronomic and fiber parameters of potential cotton (Gossypium hirsutum L.) lines with the aim of developing drought-tolerant varieties. The experiment involved two irrigation regimes—well-watered (100% field capacity) and deficit irrigation (50% field capacity)—conducted on F9–F10 generations. Key fiber parameters, including fiber length, boll weight, fiber strength, and lint percentage, were identified as crucial selection criteria under both well-watered and deficit irrigation conditions. Notably, boll number emerged as the decisive parameter in both F9 and F10 generations. The study employed univariate and multivariate analyses, such as PCA, heat map cluster, correlation analysis, and decision tree, which consistently highlighted fiber length, boll weight, fiber strength and lint (ginning) percentage the key factor. In the F10 generation, the integration of decision tree and heat map cluster results led to the identification of 8 promising lines. These selected genotypes have potential for inclusion in future cotton breeding programmes, offering the opportunity to increase drought tolerance and improve cotton yield and productivity. Their resilience to environmental stresses makes them promising candidates for improving overall cotton performance under challenging conditions. [ABSTRACT FROM AUTHOR]

Edge storage is driven by the emerging edge computing and application intelligence, which makes the data anonymization become essential to guarantee the security of the data. The risk with data anonymization is that it can be re-identified, where anonoymized data is matched with available information to discover the individual. Appropriate data anonymization techniques can help on reducing the risk of re-identification. It is very challenging to protect data privacy against re-identification attacks without significantly reduce usefulness of the data. This work proposes an evaluation framework for data anonymization techniques to measure the risks of re-identification and usefulness of anonymized data. [ABSTRACT FROM AUTHOR]

Diabetes is one of the most deadly chronic diseases because most sufferers do not realize they have it. A more accurate prediction of diabetes disease must be made to reduce the risk of bad things happening to sufferers. This research will optimize the decision tree (DT) classification method for diabetes prediction. Optimization is done by splitting criteria, splitting data, particle swarm optimization (PSO), and parameter optimization to find the highest and most accurate forecast of diabetes. Splitting criteria is done by comparing the results of three criteria, namely gain ratio (GR), information gain (IG), and gini index (GI). Splitting data is done by dividing training data and testing data into three comparison groups, namely 70:30, 80:20, and 90:10. The application of PSO and parameter optimization is carried out to increase the accuracy value. The processed data is taken from the UCI machine learning repository with 520 records and 17 attributes (1 class/label attribute). From the experiments, the GI criterion with splitting data 90:10 obtained the greatest accuracy of 98.08%, and the combination with PSO resulted in an accuracy of 97.66%. Meanwhile, parameter optimization with splitting data 90:10 combined with GR criteria resulted in the highest accuracy of 97.90%. [ABSTRACT FROM AUTHOR]

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

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

Wireless sensor network (WSN) consists of large number of sensor nodes that are deployed in geographical locations to collect sensed information, process data and communicate it to the control station for further processing. Due the unfriendly environment where the sensors are deployed, there exist many possibilities of malicious nodes which performs malicious activities in the network. Therefore, the security threats affect performance and life time of sensor networks, whereas various security aspects are there to address security issues in WSN namely Cryptography, Trust Management, Intrusion Detection System (IDS) and Intrusion Prevention Systems (IPS). However, IDS detect the malicious activities and produce an alarm. These malicious activities exploit vulnerabilities in the network layer and affect all layers in the network. Existing feature selection methods such as filter-based methods are not considering the redundancy of the selected features and wrapper method has high risk of overfitting the classification of intrusion. Due to overfitting, the classification algorithm fails to detect the intrusion in better manner. The main objective of this paper is to provide the efficient feature selection algorithm which was suitable for any type classification algorithm to detect the intrusion in an effective manner. This paper, the security of the network is addressed by proposing Feature Selection Algorithm using Chi Squared with Ensemble Method (FSChE). The proposed scheme employs the combination of decision tree along with the random forest classification algorithm to form ensemble classifier. The experimental results justify the feasibility of the proposed scheme in terms of attack detection, packet delivery ratio and time analysis by employing NSL KDD cup data Set. The obtained results shows that the proposed ensemble method increases the overall performance by 10% to 25% with respect to mentioned parameters. [ABSTRACT FROM AUTHOR]

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In a construction project, especially in risky environments, subcontractors are considered as important as the main contractor since most work is carried out by them. A crucial step in building projects is choosing the finest subcontractor. When choosing subcontractors, a variety of considerations must be taken into account. A poor choice of subcontractors might cause a lot of issues when the job is being done. These include subpar work and project length delays. Many things influence this process. From earlier studies, variables that affect the choice of a subcontractor have been compiled. The main aim of the paper is to predict the failure of the subcontractor by using a mix of several advanced technology as BIM, machine leaning and building a system. The methodology of the paper is first to use BIM to see the mistakes and unqualified quantity estimation by the subcontractors which lead to increase the cost of the projects and hence failures of the projects. The second step is to identify primary reasons for subcontractors' failure and identify and evaluate the major reasons why contractors fail on industrial projects in Iraq. The third step is to predict the performance of the subcontractor using three algorithms (logistic regression, decision tree and Naive Bayes) with data collected from 234 projects and see how the wrong selection of the subcontractors leads to incomplete projects in the future. The variables that used in the paper include the most effective one, as change order effect directly on the projects performance as well as cost, time, deviation, type of projects and the rate of project completion, the low the percentage of completion the worse the performance. The final step is to develop a system to manage the subcontract in different projects stage. The results show that there is a different in the quantity estimated between the actual and the estimated range between (5% to 15%) in several item of the building, which consider high. the best algorithms for the is Naive Bayes as give accuracy about 97%. [ABSTRACT FROM AUTHOR]

In the Internet of Medical Things, due to the sensitivity of medical information, data typically need to be retained locally. The training model of heart disease data can predict patients' physical health status effectively, thereby providing reliable disease information. It is crucial to make full use of multiple data sources in the Internet of Medical Things applications to improve model accuracy. As network communication speeds and computational capabilities continue to evolve, parties are storing data locally, and using privacy protection technology to exchange data in the communication process to construct models is receiving increasing attention. This shift toward secure and efficient data collaboration is expected to revolutionize computer modeling in the healthcare field by ensuring accuracy and privacy in the analysis of critical medical information. In this paper, we train and test a multiparty decision tree model for the Internet of Medical Things on a heart disease dataset to address the challenges associated with developing a practical and usable model while ensuring the protection of heart disease data. Experimental results demonstrate that the accuracy of our privacy protection method is as high as 93.24%, representing a difference of only 0.3% compared with a conventional plaintext algorithm. [ABSTRACT FROM AUTHOR]

Since cooling load estimation directly impacts air conditioning control and chiller optimization, it is essential for increasing the energy efficiency of cooling systems. Machine learning outshines traditional regression analysis by efficiently managing vast datasets and discerning complex patterns influenced by various factors like occupancy, building materials, and meteorology. These capabilities greatly enhance building management and energy optimization. The primary objective of this study is to introduce a framework based on ML algorithms to accurately predict cooling loads in buildings. The Decision Tree model was chosen as the core algorithm for this purpose. Furthermore, as an innovative approach, four metaheuristic algorithms – namely, the Improved Arithmetic Optimization Algorithm, Prairie Dog Optimization, Covariance Matrix Adaptation Evolution Strategy, and Coyote Optimization Algorithm – were employed to enhance the predictive capabilities of the Decision Tree model. The dataset which utilized in this study derived from previous studies, the data comprised of eight input parameters, including Relative Compactness, Surface Area, Wall Area, Roof Area, Overall Height, Orientation, Glazing Area, and Glazing Area Distribution. With an astonishing R2 value of 0.995 and a lowest Root Mean Square Error value of 0.660, the DTPD (DT + PDO) model performs exceptionally well. These astounding findings demonstrate the DTPD model's unmatched precision in forecasting the results of cooling loads and point to its potential for useful implementation in actual building management situations. Properly predicting and managing cooling loads ensures that indoor environments remain comfortable and healthy for occupants. Maintaining optimal temperature and humidity levels not only enhances comfort but also supports good indoor air quality. [ABSTRACT FROM AUTHOR]

In this study, we demonstrate the benefits of using a decision tree to learn the rules for exchanges of nonmonetary assets. Our results indicate that students using a decision tree performed significantly better on both true/false questions and an open-ended application problem than students who were not provided the decision tree. Most notably, lower-performing students who used the decision tree were able to bridge the gap with their higher-performing peers on the true/false questions. Furthermore, the results for the survey of usefulness show that lower-performing students valued the decision tree marginally better than other students. The success of this graphic organizer implies that similar decision trees should be adopted or created to engage meaningful learning within other accounting topics. Overall, our findings indicate that lower-performing students benefit the most from integrating decision trees in the accounting curriculum. [ABSTRACT FROM AUTHOR]

The development of diagnostic systems for rotating machines such as induction motors (IMs) is a task of utmost importance for the industrial sector. Reliable diagnostic systems allow for the accurate detection of different faults. Different methods based on the acquisition of thermal images (TIs) have emerged as diagnosis systems for the detection of IM faults to prevent the further generation of faults. However, these methods are based on artisanal feature selection, so obtaining high accuracy rates is usually challenging. For this reason, in this work, a new system for fault detection in IMs based on convolutional neural networks (CNNs) and thermal images (TIs) is presented. The system is based on the training of a CNN using TIs to select and extract the most salient features of each fault present in the IM. Subsequently, a classifier based on a decision tree (DT) algorithm is trained using the features learned by the CNN to infer the motor conditions. The results of this methodology show an improvement in the accuracy, precision, recall, and F1-score metrics for 11 different conditions. [ABSTRACT FROM AUTHOR]

Wire-breakage (WB) during Wire electric discharge machine (WEDM) is a critical issue since it halts the machining process and leads to increased energy consumption, lower productivity, and diminished product quality. Current study applies machine learning-based classification approaches: logistic regression, random forest, decision tree, and Gaussian Naïve Bayes for prediction of wire-breakage instances during WEDM of nimonic 263 executed under varying cutting conditions of peak current (IP), spark-gap voltage (SV), pulse-on time (Ton) and pulse-off time (Toff). The decision tree and Gaussian Naïve Bayes' accuracy have equal accuracy (96%), whereas the logistic regression has a comparatively lesser accuracy (92%). Random forest has the highest accuracy of a hundred percent for the testing data. This research demonstrates that binomial classification algorithms may be employed offline to anticipate input parameters contributing to wire breaking. Avoiding the parametric combination leading to wire breakage during machining may enhance productivity and improve product quality. [ABSTRACT FROM AUTHOR]

Purpose: Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling. Design/methodology/approach: The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems. Findings: In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation. Originality/value: This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects. [ABSTRACT FROM AUTHOR]

Initial Public Offerings (IPOs) provide great opportunities for companies to grow and expand, and they allow investors to invest their money wisely to get a decent Return on Investment (ROI) in the short term. Nevertheless, the intricate nature of the stock market is susceptible to several influences such as a company's financial statement, governmental regulations, and public sentiment, which hinders the attainment of a satisfactory ROI. This study aims to address this challenge by developing a model that combines public sentiment analysis and machine learning approaches to optimize the ROI for IPO trends. The study gives a novel approach that uses multiple different features associated with IPOs like the public opinion, grey market price (GMP), issue prices, lot size etc. and leverages the use of various machine learning techniques like Random Forest, Decision Tree, Naive Bayes, and K-Nearest Neighbour (KNN) to make well-informed investment recommendations. The testing results demonstrate that the Decision Tree method surpasses the other algorithms, with an accuracy rate of 82.3%. This discovery emphasizes the effectiveness of our method in forecasting the success of IPOs by utilizing a combination of sentiment analysis and crucial financial indicators. [ABSTRACT FROM AUTHOR]

Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures. This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems. The objectives include Investigating the performance of various classifiers, such as Gaussian Naïve Bayes, K Nearest Neighbors (KNN), Stochastic Gradient Descent Classifier (SGDC), and Decision Tree, in detecting Windows malware. Evaluating the accuracy, efficiency, and suitability of each classifier for real-world malware detection scenarios. Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers. Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence. The study employs a structured methodology consisting of several phases: exploratory data analysis, data preprocessing, model training, and evaluation. Exploratory data analysis involves understanding the dataset's characteristics and identifying preprocessing requirements. Data preprocessing includes cleaning, feature encoding, dimensionality reduction, and optimization to prepare the data for training. Model training utilizes various supervised classifiers, and their performance is evaluated using metrics such as accuracy, precision, recall, and F1 score. The study's outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection. Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware. Additionally, insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses. Overall, this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats. [ABSTRACT FROM AUTHOR]

The software maintenance process is costly, accounting for up to 70% of the total cost in the software development life cycle (SDLC). The difficulty of maintaining software increases with its size and complexity, requiring significant time and effort. One way to alleviate these costs is to automate parts of the maintenance process. This research focuses on the automation of the classification phase using decision trees (DT) to sort, rank, and accept/reject maintenance requests (MRs) for mobile applications. Our dataset consisted of 1,656 MRs. We found that DTs could automate sorting and accepting/rejecting MRs with accuracies of 71.08% and 64.15%, respectively, though ranking accuracy was lower at 50%. While DTs can reduce costs, effort, and time, human verification is still necessary. [ABSTRACT FROM AUTHOR]

Packet classification is a core function of network devices for providing advanced services, with the key challenge being to optimize classification speed while maintaining low memory usage. So far, many have proposed software-based packet classification solutions, with most of them adopting a multi-classifier architectures to accommodate the distribution of rule sets. Unfortunately, the need to perform lookups on each classifier during the packet classification stage significantly increases overhead, severely limiting classification speed. To address this shortfall, an efficient packet classification framework based on decision tree algorithms named MultiSplit is proposed. By leveraging the relationships of coverage and priority within the rule set, a new attribute can be abstracted for each rule, termed equivalent priority. Through this preprocessing, MultiSplit significantly reduces redundant lookup overhead while supporting the multi-classifier framework. Additionally, MultiSplit introduces a novel decision tree algorithm that combines multiple splits and intra-level binary search, significantly improving rule separation efficiency. The experimental results show that MultiSplit reduces memory consumption by 49% and decreases memory access by 63%, on average, compared with state-of-the-art packet classification algorithms. [ABSTRACT FROM AUTHOR]

Background: The identification of factors involved in the conversion across the different Alzheimer's disease (AD) stages is crucial to prevent or slow the disease progression. We aimed to assess the factors and their combination associated with the conversion across the AD stages, from mild cognitive impairment to dementia, at a mild, moderate or severe stage and to identify profiles associated with earliest/latest conversion across the AD stages. Methods: In this study conducted on the real-life MEMORA cohort data collected from January 1, 2013, and December 31, 2019, three cohorts were selected depending on the baseline neurocognitive stage from a consecutive sample of patients attending a memory center, aged between 50 and 90 years old, with a diagnosis of AD during the follow-up, and with at least 2 visits at 6 months to 1 year of interval. A machine learning approach was used to assess the relationship between factors including socio-demographic characteristics, comorbidities and history of diseases, prescription of drugs, and geriatric hospitalizations, and the censored time to conversion from mild cognitive impairment to AD dementia, from the mild stage of dementia to the moderate or severe stages of AD dementia, and from the moderate stage of AD dementia to the severe stage. Profiles of earliest/latest conversion compared to median time to conversion across stages were identified. The median time to conversion was estimated with a Kaplan-Meier estimator. Results: Overall, 2891 patients were included (mean age 77±9 years old, 65% women). The median time of follow-up was 28 months for mild cognitive impairment (MCI) patients, 33 months for mild AD dementia and 30 months for moderate AD dementia. Among the 1264 patients at MCI stage, 61% converted to AD dementia (median time to conversion: 25 months). Among the 1142 patients with mild AD dementia, 59% converted to moderate/severe stage (median time: 23 months) and among the 1332 patients with moderate AD dementia, 23% converted to severe stage (Q3 time to conversion: 22 months). Among the studied factors, cardiovascular comorbidities, anxiety, social isolation, osteoporosis, and hearing disorders were identified as being associated with earlier conversion across stages. Symptomatic treatment i.e. cholinesterase inhibitors for AD was associated with later conversion from mild stage of dementia to moderate/severe stages. Conclusion: This study based on a machine learning approach allowed to identify potentially modifiable factors associated with conversion across AD stages for which timely interventions may be implemented to delay disease progression. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) and inflammatory bowel disease (IBD) are chronic diseases affecting the central nervous system and gastrointestinal tract, respectively. Recent research suggests a bidirectional relationship between neurodegeneration in PD and intestinal inflammation in IBD. PD patients may experience gastrointestinal dysfunction over a decade before motor symptom onset, and IBD may increase the risk of developing PD. Despite the "gut-brain axis" concept, the underlying pathophysiological mechanisms of this potential association remain unclear. This study aimed to investigate the biological mechanisms of differentially expressed genes in PD and IBD using bioinformatics tools, providing novel insights into the co-diagnosis and treatment of these diseases. We constructed a gene marker for disease diagnosis and identified five important genes (BTK, NCF2, CRH, FCGR3A and SERPINA3). Through nomogram and decision tree analyses, we found that both the IBD and PD required only the expression levels of BTK and NCF2 for accurate discrimination. Additionally, small molecule drugs RO-90-7501 and MST-312 may be useful for the treatment of both IBD and PD. These findings offer new perspectives on the co-diagnosis and treatment of PD and IBD, and suggest that targeting BTK may be a promising therapeutic strategy for both diseases. [ABSTRACT FROM AUTHOR]

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

In malaria-endemic areas of Sub-Saharan Africa, overlap of clinical symptoms between malarial and non-malarial febrile illnesses can lead to empiric use of antibiotics among children. Our study aimed to illustrate the potential impact of decreasing malaria prevalence from malaria control efforts on antibiotic use. We constructed a probabilistic decision tree model representing antibiotic prescription in febrile children < 5 years. This model was used to predict change in absolute antibiotic use compared to baseline under levels of decreasing malaria prevalence. Model parameters were based on data from a hospital study in Ghana and validated via literature review. The baseline prevalence of malaria diagnoses was 52% among all hospitalized children. For our main results, we reported outcomes for a scenario representing a 50% decrease in malaria prevalence. Compared to baseline, absolute antibiotic prescription decreased from a baseline of 639 doses (95% CI 574–694) to 575 (95% CI 502–638). This reflected a 10% (95% CI 7%–13%) decrease in absolute antibiotic use. Our findings demonstrate that effective malaria control can reduce pediatric antibiotic use. However, until substantial progress is made in developing accurate diagnostics for non-malarial febrile illnesses, further reductions in antibiotic use will remain a challenge. [ABSTRACT FROM AUTHOR]

Debt collection companies buy overdue debts on the market in order to collect them and recover the highest possible amount of a debt. The pursuit of debt recovery by employees of collection agencies is a very demanding task. The aim of the article is to propose a rule-based model for managing the process of mass debt collection in a debt collection company, which will make the debt collection process more efficient. To achieve this, we have chosen a decision tree as a machine learning technique best suited for creating rules based on extensive data from the debt collection company. The classification accuracy of the decision tree, regardless of the possibility of acquiring rule-based knowledge, proved to be the highest among the tested machine learning methods, with an accuracy rate of 85.5%. Through experiments, we generated 16 stable rules to assist in the debt collection process. The proposed approach allows for the elimination of debts that are difficult to recover at the initial stage of the recovery process and to decide whether to pursue amicable debt collection or to escalate the debt recovery process to legal action. Our approach also enables the determination of specific actions during each stage of the proceedings. Abandoning certain actions or reducing their frequency will alleviate the burden on collection agency employees and help to avoid the typical burnout associated with this line of work. This is the path to making the organizational culture of a collection agency more sustainable. Our model also confirms the possibility of using data from debt collection companies to automatically generate procedural rules and automate the process of purchasing and collecting debts. However, this would require a larger set of attributes than what we currently possess. [ABSTRACT FROM AUTHOR]

Speech emotion recognition (SER) plays an important role in human-computer interaction (HCI) technology and has a wide range of application scenarios in medical medicine, psychotherapy, and other applications. In recent years, with the development of deep learning, many researchers have combined feature extraction technology with deep learning technology to extract more discriminative emotional information. However, a single speech emotion classification task makes it difficult to effectively utilize feature information, resulting in feature redundancy. Therefore, this paper uses speech feature enhancement (SFE) as an auxiliary task to provide additional information for the SER task. This paper combines Long Short-Term Memory Networks (LSTM) with soft decision trees and proposes a multi-task learning framework based on a decision tree structure. Specifically, it trains the LSTM network by computing the distances of features at different leaf nodes in the soft decision tree, thereby achieving enhanced speech feature representation. The results show that the algorithm achieves 85.6% accuracy on the EMO-DB dataset and 81.3% accuracy on the CASIA dataset. This represents an improvement of 11.8% over the baseline on the EMO-DB dataset and 14.9% on the CASIA dataset, proving the effectiveness of the method. Additionally, we conducted cross-database experiments, real-time performance analysis, and noise environment analysis to validate the robustness and practicality of our method. The additional analyses further demonstrate that our approach performs reliably across different databases, maintains real-time processing capabilities, and is robust to noisy environments. [ABSTRACT FROM AUTHOR]

One of the important factors of decision units and policymakers to develop successful policies is the correct estimation of macroeconomic variables for future periods. Inflation is among these macroeconomic indicators, and to realize successful policies, it is necessary to minimize the real effects and severity of inflation, determine future changes and their effects, and predict inflation reliably. Accurate forecasting of inflation is important both in terms of the policies to be implemented and the investment decisions to be taken by the public and private sectors. In this context, quarterly data were selected for Turkey using the 2008-2023 time period. In the study, machine learning methods were used instead of traditional econometric methods, in which the difference between prediction and reality was minimized. Brent oil, Rediscount advance interest rate, money supply, CPI, tax revenues, general budget revenues, policy rate, US Dollar/TL parity, and GDP attributes, which are thought to affect inflation, were selected by using random forest, decision tree and multi-layered detector, which is an artificial neural networks method, which are alternative machine learning methods. With the selected attributes, it is aimed to determine the method and influencer to make the correct estimation. The analysis results show that the decision tree model predicts the most accurate inflation rate compared to the random forest and multilayer sensor. Another finding from the study is that the highest determinant of inflation in Turkey is the US dollar. [ABSTRACT FROM AUTHOR]

Machine Learning and Data Mining algorithms are used extensively to enhance the performance of Intrusion Detection Systems. The number of training instances and the dimensionality of data are crucial factors affecting the performance of the model built during the training of any supervised learning algorithms. A sufficient proportion of instances having relevant features from all classes of attacks and normal traffic are considered most desirable while building the classification model that classifies the network traffic into attack and normal. This paper proposes a methodology to improve the accuracy of the model by giving importance to the relevant features that can contribute to model building. The feature selection using correlation‐based and information gain‐based techniques during training and testing contributes much to the detection of stealthier attacks and minority attacks. Then the features of the less detected attacks are identified as the second phase of the filter that is used to improve the performance. The relevant features of stealthy attacks are identified based on the correlation of corresponding features of the attack and normal data as the attacks are made stealthy mostly by making it resemble the normal traffic. Finally, the attacks that are rarely found in the training data are oversampled to improve their detection. CICIDS 2017 data set is employed as it comprises stealthier attacks generated using modern tools. NSL KDD data set is also used for evaluation to compare the proposed work with existing literature as it is used in most of the available literature. The results show superior performance with an accuracy of 99.8%, false positive rate of 0.2%, and a detection rate and 99.8%. [ABSTRACT FROM AUTHOR]