Random Reflectance: A New Hyperspectral Data Preprocessing Method for Improving the Accuracy of Machine Learning Algorithms

This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R2) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R2 and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R2 values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient.

In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer’s Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms –when applied to different biomarkers and other conditions– is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed –with the voting procedure– to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms’ performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions.

In sustainable and competitive business management, it is crucial for organizations to consider organizational change and transformational leadership in human resource (HR) management to adapt to the changes in their environment. This capability enables large-scale enterprises to maintain their presence in an increasingly competitive environment through enhanced management capacity. Enterprises that adopt transformational leadership in HR management must equip leadership candidates with competencies such as creating a shared vision, providing appropriate role models, encouraging the adoption of group goals, meeting high performance expectations, and providing individual support and the development of intellectual stimulation. By identifying potential leadership candidates using a decision support model, the necessary competencies can be developed through in-service training and experiential learning during their careers. Innovative and effective approaches to identifying leadership candidates can be developed by analyzing complex big data using advanced artificial intelligence (AI) techniques. In this article, a forecast model using machine learning (ML) algorithms for a human resource management career planning approach was developed for the Turkish Post Corporation (PTT) and it was tested to predict potential leadership candidates by analyzing the big data of 5000 employees. The Turkish Post Corporation ML algorithms were applied to 100 randomly selected data points using the k-Nearest Neighbor (kNN), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) algorithms to predict the types of titles held by the staff employed at PTT. The kNN, GB, RF, and SVM algorithms achieved accuracy rates of 96%, 91%, 73%, and 41%, respectively. The case study results indicate that promotion decisions in large-scale and rooted enterprises can be effectively modeled using big data and ML algorithms, highlighting significant potential for HR management and leadership development practices in the public sector.

Racial and Ethnic Disparities in Predictive Accuracy of Machine Learning Algorithms Developed Using a National Database for 30-Day Complications Following Total Joint Arthroplasty

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify—CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup.Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools.Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from “slight” to “significant” in 80% of the cases.Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.

This study presents a comparative analysis of the classification performance of facial and emotion recognition systems using Machine Learning (ML) and Deep Learning (DL) algorithms. The primary objective of this work is to evaluate the applicability of emotion recognition in fields such as psychotherapy and crime analysis, using the FER-2013 dataset.The study was conducted with ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Trees, and Gradient Boosting, as well as the DL algorithm, Convolutional Neural Network (CNN). Supported by the application of feature selection, preprocessing, and feature extraction techniques, the model’s performance was measured using standard metrics such as accuracy, precision, F1-score, and AUC-ROC.The experimental results showed that the highest classification accuracy (46.61%) was achieved with the CNN model. While the ML models generally offered lower accuracy, they provided advantages in terms of computational efficiency in specific scenarios.This study aims to contribute to the literature by providing a comparative analysis of ML and DL algorithms and by highlighting the effect of data preprocessing on performance. The findings set targets for future work, such as real-time system integration and hybrid model development.

Paper aims This study seeks to investigate the accuracy of machine learning algorithms for estimation of the effort required for software development in the manufacturing sector to identify the most effective algorithms according to the nature and complexity of the data and the number of available attributes. Originality This work distinguishes itself from other studies in the field of effort prediction by utilizing a data repository that consists exclusively of projects from the manufacturing sector. This approach ensures that the specific characteristics of manufacturing projects are reflected in the predictions, addressing a gap in the existing literature. Another notable contribution of this study is the comparative analysis of various machine learning algorithms assessed under different dimensionality scenarios (three and five variables). Although this factor is crucial for enhancing effort estimation accuracy, it has received limited attention in the literature. Research method The investigated techniques in this work were (i) Support Vector Regression, (ii) Gradient Boosting Machines (GBM), (iii) eXtreme Gradient Boosting (XGBoost), (iv) Random Forest (RF), (v) Extreme Learning Machine (ELM); and (vi) Linear Regression (LR). Performance measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) were used to compare the results achieved by each model, considering a dataset of 230 records originating from various countries. Main findings The comparison among machine learning models revealed significant performance variations depending on the number of variables and the evaluation metrics adopted. GBM stood out for its robustness in complex scenarios, while SVR achieved the lowest mean absolute error. ELM, in turn, proved effective with fewer variables but showed sensitivity to outliers and less stability in more complex contexts. Among all the techniques evaluated, XGB yielded the worst performance across all parameters. Implications for theory and practice This study contributes by applying these models to the manufacturing sector and comparing scenarios with three and five variables. The results support a more informed selection of models based on project complexity and data dimensionality. The more research conducted in this area, the stronger the theoretical and practical conclusions can be drawn.

Predicting undernutrition among elementary schoolchildren in the Philippines using machine learning algorithms

Estimation of asphaltene adsorption on MgO nanoparticles using ensemble learning

BackgroundNearly 20% of pregnancies in patients with Systemic lupus erythematosus (SLE) result in an adverse pregnancy outcome (APO); early identification of women with SLE who are at high risk of...

Pedotransfer function (PTFs) is widely developed using machine learning algorithms (MLA) and non-linear regression (NLR). Saturated hydraulic conductivity (Ks) is obviously used to regulate water movement in the soil. The objective of the work is to evaluate the accuracy of MLA to predict Ks. Moreover, is to determine the best class of predictors that can be used to predict Ks with little estimation error. Saturated hydraulic conductivity was measured by direct method using the constant head method. In addition, Ks was predicted using PTFs developed by machine learning algorithms including Random Forest Algorithm (RFA), and Gradient Boosting Algorithm (GBA) models with three categories of soil physical predictors. Furthermore, PTFs developed by RFA, and GBA were compared with PTFs developed by NLR models using the same three categories of soil physical predictors. The three categories of soil physical predictors were the following: category-1 refers to sand, silt, and clay “SSC”; category-2 refers to category-1, in addition to bulk density “SSC+BD”; and category-3 refers to category-2, in addition to organic matter “SSC+BD+OM”. The results of error analysis observed that PTFs developed by RFA were more accurate than GBA and NLR models, respectively, for Ks prediction with the three categories of soil physical predictors. Moreover, the category-3 which takes into account sand, silt, clay, bulk density, and organic matter was the best category used to predict Ks using RFA, and NLR models. PTFs developed by machine learning algorithms including RFA, and GBA models can be utilized to predict Ks with little calculation error. However, RFA was greater than GBA for calculating Ks. The efficiency of RFA was associated with the number of soil variables predictors and building classification and regression trees which is more robust than decision trees such as GBA to optimize Ks prediction.

The prevalence of urinary tract infections (UTIs) is a significant concern, particularly among women. This study aims to adopt machine learning models for predicting UTIs using gradient boosting, support vector machine (SVM), and random forest algorithms. The dataset used was obtained from the Kaggle repository, consisting of UTI articles. The performance of the models was evaluated using metrics such as accuracy, precision, recall, and F1-score. The UTI dataset was preprocessed by removing irrelevant columns and handling missing values. The dataset was then divided into training and testing sets. The three models were trained using the training data. The results showed that both random forest and gradient boosting classification achieved an accuracy of 0.8750, while SVM achieved an accuracy of 0.8333. Precision values were 0.8000 for random forest, 1.0000 for SVM, and 0.9000 for gradient boosting. F1-scores were 0.8421 for both random forest and SVM, and 0.8571 for gradient boosting. Recall values were 0.8889 for both random forest and SVM, and 0.8182 for gradient boosting. Comparing the results with previous studies, the models showed competitive performance. The ROC curves were plotted to visualize the models' performance across different classification thresholds. In conclusion, the study demonstrates the effectiveness of gradient boosting, SVM, and random forest algorithms in predicting UTIs. The adopted models provide valuable insights for early detection and management of UTIs, contributing to improved healthcare outcomes.

The objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, viz., multi-layer perceptron (MLP), random forest (RF), extra trees regressor (ETR), CatBoost, and gradient boost (GB), considering the impact of variation in sample sizes for theprediction of soil nutrients. In this context, this study evaluates the impact of sample size on the prediction performance of five ML algorithms for mapping 14 soil properties, including key soil physico-chemical properties (soil organic carbon, pH, and electrical conductivity), and multiple macro (available nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur) and micronutrients (available iron, manganese, copper, zinc, and boron) at a geographical extent of 8303 km2 located in eastern India. A total of 1024 surface soil samples were collected, of which 800 were used for model training, while the remaining 224 were reserved for independent validation (IV) of the resultant maps. The original training data set (800 samples) was reduced by random selection into six different sample sizes, i.e., 800, 400, 200, 100, 50, and 25. An exhaustive set of 574 environmental variables, derived from digital terrain derivatives and Sentinel-2 satellite imagery, was used as predictors. Popular statistical indicators, such as Lin's concordance correlation coefficient (CCC) and root mean squared error (RMSE) were employed to evaluate the predictive capability of the algorithms under different sample size scenarios. The results showed that prediction accuracy and reliability of prediction performance across multiple target variables improved with sample size. However, beyond a certain point, improvement in predictive performance became substantially negligible compared to the efforts in additional sampling. All the ML algorithms performed well (mean IV-CCC varied between 0.26 and 0.64 for different soil properties) to increase insample size, except MLP, which exhibited a poorer prediction performance(mean IV-CCC varied between 0.14 and 0.29 for different soil properties). Micronutrients in general responded well to the increase in sample sizes. The uncertainty analysis revealed that increasing sample size generally reduced prediction uncertainty, though the extent varied by soil property and ML algorithm. Concisely, the results presented herein showed an effective manner of selecting an appropriate sample size and a suitable ML algorithm to predict multiple soil nutrients accurately, which would be recommended to achieve optimal accuracy for a project.

The smart-phone has become a critical cybernetic victim especially of cellular botnets. The current exploration examines mobile botnet attacks for Android smart-phone launched from a Windows based PC and detects those attacks using an ensemble machine learning classification algorithm. This investigation is to breach the gap to develop malware framework in perspective of headers’ field examination of PE format with Machine Learning algorithm used. The Homogeneous architecture model proposed in this investigation chooses the most representative subset of the features toward accurate classification of botnet traffic. The ensemble classification technique used in this architecture consists of four machine learning algorithms namely; Random Forest, Gradient Boosting Algorithm, Extreme learning machine and eXtreme Gradient Boosting. After exhaustive literature survey, it is concluded that the architecture proposed in this investigation is unique homogeneous combination of four algorithm mention above. The efficiency of the proposed architecture is evaluated on existing data-set CLaMP (Classification of Malware with PE headers). It has been evaluated on a ClaMP data-set to achieve better botnet detection accuracy relative to its peer techniques.