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This paper aims to investigate the possibility of lowering the time taken during the aircraft design for unmanned aerial vehicles by using machine learning (ML) for the configuration selection phase. In this work, a database of unmanned aircraft is compiled and is proposed that decision tree classifiers (DTC) can understand the relations between mission and operational requirements and the resulting aircraft configuration.

This paper presents a ML-based approach to configuration selection of unmanned aircraft. Multiple DTC are built to predict the overall configuration. The classifiers are trained with a database of 118 unmanned aircraft with 57 characteristics, 47 of which are inputs for the classification problem, and 10 are the desired outputs, such as wing configuration or engine type.

This paper shows that DTC can be used for the configuration selection of unmanned aircraft with reasonable accuracy, understanding the connections between the different mission requirements and the culminating configuration. The framework is also capable of dealing with incomplete databases, maximizing the available knowledge.

This paper increases the computational usage for the aircraft design while retaining requirements’ traceability and increasing decision awareness.

The purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict efficient collaboration between the different teammates, allowing a smartly sharing knowledge in the Smart University environment.

A random forest (RF) approach is proposed, which is based on semantic modelization of the learner and the problem-solving allowing multidisciplinary collaboration, and heuristic completeness processing to build complementary teams. To achieve that, this paper established a Konstanz Information Miner workflow that integrates the main steps for building and evaluating the RF classifier, this workflow is divided into: extracting knowledge from the smart collaborative learning ontology, calculating the completeness using a novel heuristic and building the RF classifier.

The smart collaborative learning service enables efficient collaboration and democratized sharing of knowledge between learners, by using a semantic support decision support system. This service solves a frequent issue related to the composition of learning groups to serve pedagogical perspectives.

The present study harmonizes the integration of ontology, a new heuristic processing and supervised machine learning algorithm aiming at building an intelligent collaborative learning service that includes a qualified classifier of complementary teams of learners.

This paper aims to address the pressing problem of prediction concerning shipment times of therapeutics, diagnostics and vaccines during the ongoing COVID-19 pandemic using a novel artificial intelligence (AI) and machine learning (ML) approach.

The present study used organic real-world therapeutic supplies data of over 3 million shipments collected during the COVID-19 pandemic through a large real-world e-pharmacy. The researchers built various ML multiclass classification models, namely, random forest (RF), extra trees (XRT), decision tree (DT), multilayer perceptron (MLP), XGBoost (XGB), CatBoost (CB), linear stochastic gradient descent (SGD) and the linear Naïve Bayes (NB) and trained them on striped datasets of (source, destination, shipper) triplets. The study stacked the base models and built stacked meta-models. Subsequently, the researchers built a model zoo with a combination of the base models and stacked meta-models trained on these striped datasets. The study used 10-fold cross-validation (CV) for performance evaluation.

The findings reveal that the turn-around-time provided by therapeutic supply logistics providers is only 62.91% accurate when compared to reality. In contrast, the solution provided in this study is up to 93.5% accurate compared to reality, resulting in up to 48.62% improvement, with a clear trend of more historic data and better performance growing each week.

The implication of the study has shown the efficacy of ML model zoo with a combination of base models and stacked meta-models trained on striped datasets of (source, destination and shipper) triplets for predicting the shipment times of therapeutics, diagnostics and vaccines in the e-pharmacy supply chain.

The novelty of the study is on the real-world e-pharmacy supply chain under post-COVID-19 lockdown conditions and has come up with a novel ML ensemble stacking based model zoo to make predictions on the shipment times of therapeutics. Through this work, it is assumed that there will be greater adoption of AI and ML techniques in shipment time prediction of therapeutics in the logistics industry in the pandemic situations.

This study aimed to assess the potential of the Clinical Dementia Rating (CDR) Scale in the prognosis of dementia in elderly subjects.

Dementia staging severity is clinically an essential task, so the authors used machine learning (ML) on the magnetic resonance imaging (MRI) features to locate and study the impact of various MR readings onto the classification of demented and nondemented patients. The authors used cross-sectional MRI data in this study. The designed ML approach established the role of CDR in the prognosis of inflicted and normal patients. Moreover, the pattern analysis indicated CDR as a strong cohort amongst the various attributes, with CDR to have a significant value of p < 0.01. The authors employed 20 ML classifiers.

The mean prediction accuracy varied with the various ML classifier used, with the bagging classifier (random forest as a base estimator) achieving the highest (93.67%). A series of ML analyses demonstrated that the model including the CDR score had better prediction accuracy and other related performance metrics.

The results suggest that the CDR score, a simple clinical measure, can be used in real community settings. It can be used to predict dementia progression with ML modeling.

This paper aims to examine the impact of the COVID-19 pandemic on the private label food products, specifically assessing the stability and changes in factors influencing purchasing decisions, and comparing pre-pandemic and post-pandemic datasets.

The study employs the Extra Tree Classifier method, a robust quantitative approach, to analyse data collected from questionnaires distributed among two distinct consumer samples. This methodological choice is explicitly adopted to provide a clear classification of factors influencing consumer preferences for private label products, surpassing conventional qualitative methods.

Despite the profound disruptions caused by the COVID-19 pandemic, this research underscores the persistent hierarchy of factors shaping consumer choices in the private label food market, showing an overall stability in consumer behaviour. At the same time, the analysis of individual variables highlights the positive increase in those related to product quality, health, taste, and communication.

The use of online surveys for data collection may introduce a self-selection bias, and the non-probabilistic sampling method could limit the generalizability of the results.

Practical implications suggest that managers in the private label industry should prioritize enhancing quality control, ensuring effective communication, and dynamically adapting strategies to meet evolving consumer preferences, with a particular emphasis on quality and health attributes.

This study contributes to the existing body of literature by providing insights into the profound transformations induced by the COVID-19 pandemic on consumer behaviour, specifically in relation to their preferences for private label food products.

The purpose of this study is to develop tree-based binary classification models to predict the likelihood of employee attrition based on firm cultural and management attributes.

A data set of resumes anonymously submitted through Glassdoor’s online portal is used in tandem with public company review information to fit decision tree, random forest and gradient boosted tree models to predict the probability of an employee leaving a firm during a job transition.

Random forest and decision tree methods are found to be the strongest attrition prediction models. In addition, compensation, company culture and senior management performance play a primary role in an employee’s decision to leave a firm.

This study may be used by human resources staff to better understand factors which influence employee attrition. In addition, techniques developed in this study may be applied to company-specific data sets to construct customized attrition models.

This study contains several novel contributions which include exploratory studies such as industry job transition percentages, distributional comparisons between factors strongly contributing to employee attrition between those who left or stayed with the firm and the first comprehensive search over binary classification models to identify which provides the strongest predictive performance of employee attrition.

The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.

The researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.

The proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.

The implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.

E-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.

The present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.

Worker activity identification and classification is the most crucial and difficult stage in work sampling studies. Manual methods of recording are tedious and prone to error and, hence automating the task of observing and classifying worker activities is an important step towards improving the current practice. Very recently, accelerometer-based systems have been explored to automate activity recognition in construction, but it had been carried out in controlled environment. The purpose of this paper is to cover the evaluation of the system in field situations.

Experimental investigation was carried out on crews of iron workers and carpenters with accelerometer data loggers worn at selected locations on the human body. The accelerometer data collection was spread over a time period of two weeks, and video recording of the worker activities was concurrently carried out to serve as ground truth, the reference used for comparison. The activity recognition analysis was carried out on accelerometer data features using a decision tree algorithm.

It was found that the classification using the individual training scheme performed better when compared with the collective training scheme for both the trades. The field studies results showed that the classification accuracies for iron work and carpentry are 90.07 and 77.74 per cent, respectively, using decision tree classifier. It was found that similarities of movements were a major cause for lower accuracy of recognition.

The work being preliminary in nature has used the basic classifier and pre-processing methods and, standard settings of algorithms.

The paper has investigated accelerometer-based method for construction labour activity classification in field situations.

The purpose of the food traceability is used to retain the good quality of raw material supply, diminish the loss and reduced system complexity.

The proposed hybrid algorithm is for food traceability to make accurate predictions and enhanced period data. The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers.

In order to survive with the existing financial circumstances and the development of global food supply chain, the authors propose efficient food traceability techniques using the internet of things and obtain a solution for data prediction.

The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers. The experimental analysis depicts that proposed algorithm has high accuracy rate, less execution time and error rate.

Predictive analytics is the science of decision-making that eliminates guesswork out of the decision-making process and applies proven scientific procedures to find right solutions. Predictive analytics provides ideas on the occurrences of future downtimes and rejections thereby aids in taking preventive actions before abnormalities occur. Considering these advantages, predictive analytics is adopted in various diverse fields such as health care, finance, education, marketing, automotive, etc. Predictive analytics tools can be used to predict various behaviors and patterns, thereby saving the time and money of its users. Many open-source predictive analysis tools namely R, scikit-learn, Konstanz Information Miner (KNIME), Orange, RapidMiner, Waikato Environment for Knowledge Analysis (WEKA), etc. are freely available for the users. This chapter aims to reveal the best accurate tools and techniques for the classification task that aid in decision-making. Our experimental results show that no specific tool provides the best results in all scenarios; rather it depends upon the datasets and the classifier.