cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26830049.v1
title: Quasi-experimental quality evaluation of educational-purposed user-generated contents under a stochastic multi-criteria environment

description: As social platforms experience an influx of diverse content from users, the need to determine high-quality contributions becomes crucial, especially for educational purposes. This paper highlights the pivotal role of quality in assessing how educational-purposed user-generated content (UGC) shapes user experiences, fosters engagement, and establishes credibility. This study proposes a computational framework using a quasi-experimental evaluation through the sorting-based ELimination Et Choice TRanslating Reality, termed ELECTRE-SORT, with a dataset randomly generated from normally distributed user evaluations. Considering the diverse nature of contents, the method evaluates 16 educational-purposed UGC videos from different online media platforms (i.e. Facebook, YouTube, TikTok). These videos were categorized based on their concordance and discordance to three (3) main criteria: content quality, design quality, and technology quality. Employing the ELECTRE-SORT reveals that most UGC videos (i.e. 14 out of 16) fall into the “medium quality” category, possessing a considerable standard for the quality of educational purpose content. Their characteristics generally satisfy the quality attributes and can be used to guide the development of future relevant UGC videos. Finally, to demonstrate the robustness of the proposed approach, we presented a sensitivity analysis by designing different weight assignments to the quality attributes. Practical insights are outlined in this work.
DOI: 10.6084/m9.figshare.25803126
title: A Human-In-One-Loop Active Domain Adaptation Framework for Digit Recognition

description: Domain adaptation can effectively enhance a model’s performance on target domain data with limited data. However, when some target domain labels are obtainable, training the model with both source and target domain data simultaneously can lead to worse performance due to the lower density of target domain data. If a large amount of target domain data is labeled without discrimination, it will necessitate a considerable expenditure of human resources. To address this issue, this paper proposes a human-in-one-loop active domain adaptation framework based on Target Domain Feature Generation to solve the problems. The oracle participates in only one iteration of data labeling, and a target domain classifier will take over the subsequent rest iterations. An image generator based on multiple CycleGANs forms an iterative co-training mechanism, which can continuously generate more high-quality labeled fake target domain data in iterations to improve the performance of the target domain classifier. The Top-N labeled data selection method with high confidence is devised to select the most accurately predicted data for labeling, reducing manual labeling workload. This framework can achieve an average accuracy of 0.8869 on six domain pairs, doubling the classical domain adaptation method DSN, requiring only a small amount of manual labeling.
DOI: 10.6084/m9.figshare.25803126.v1
title: A Human-In-One-Loop Active Domain Adaptation Framework for Digit Recognition

description: Domain adaptation can effectively enhance a model’s performance on target domain data with limited data. However, when some target domain labels are obtainable, training the model with both source and target domain data simultaneously can lead to worse performance due to the lower density of target domain data. If a large amount of target domain data is labeled without discrimination, it will necessitate a considerable expenditure of human resources. To address this issue, this paper proposes a human-in-one-loop active domain adaptation framework based on Target Domain Feature Generation to solve the problems. The oracle participates in only one iteration of data labeling, and a target domain classifier will take over the subsequent rest iterations. An image generator based on multiple CycleGANs forms an iterative co-training mechanism, which can continuously generate more high-quality labeled fake target domain data in iterations to improve the performance of the target domain classifier. The Top-N labeled data selection method with high confidence is devised to select the most accurately predicted data for labeling, reducing manual labeling workload. This framework can achieve an average accuracy of 0.8869 on six domain pairs, doubling the classical domain adaptation method DSN, requiring only a small amount of manual labeling.
DOI: 10.6084/m9.figshare.25902891
title: Performance Evaluation of Hybrid Machine Learning Algorithms for Online Lending Credit Risk Prediction

description: Peer-to-Peer systems are still in the early stages of development when it comes to the processing of credit and the appraisal of the risk associated with it. In this study, we used a hybrid convolutional neural network with logistic regression, a gradient-boosting decision tree, and a k-nearest neighbor to predict the credit risk in a P2P lending club. The lending clubs publicly available P2P loan data was used to train the model. In order to address the issue of data imbalance within the dataset, specifically between the non-defaulter and defaulter classes, the synthetic minority oversampling technique sampling approach is utilized. We developed the architecture of our hybrid model by removing the fully connected layer with the soft-max, which is the final layer of the fully connected CNN model and replaced by LR, GBDT, and k-NN algorithms. The experimental results show that the hybrid CNN-kNN model outperforms the CNN-GBDT and CNN-LR models based on the performance metrics accuracy, recall, F1-score, and area under the curve for both all input and important features. This shows that hybrid machine learning models effectively identify and categorize credit risk in peer-to-peer lending clubs, hence assisting in financial loss prevention.
DOI: 10.6084/m9.figshare.25902891.v1
title: Performance Evaluation of Hybrid Machine Learning Algorithms for Online Lending Credit Risk Prediction

description: Peer-to-Peer systems are still in the early stages of development when it comes to the processing of credit and the appraisal of the risk associated with it. In this study, we used a hybrid convolutional neural network with logistic regression, a gradient-boosting decision tree, and a k-nearest neighbor to predict the credit risk in a P2P lending club. The lending clubs publicly available P2P loan data was used to train the model. In order to address the issue of data imbalance within the dataset, specifically between the non-defaulter and defaulter classes, the synthetic minority oversampling technique sampling approach is utilized. We developed the architecture of our hybrid model by removing the fully connected layer with the soft-max, which is the final layer of the fully connected CNN model and replaced by LR, GBDT, and k-NN algorithms. The experimental results show that the hybrid CNN-kNN model outperforms the CNN-GBDT and CNN-LR models based on the performance metrics accuracy, recall, F1-score, and area under the curve for both all input and important features. This shows that hybrid machine learning models effectively identify and categorize credit risk in peer-to-peer lending clubs, hence assisting in financial loss prevention.
DOI: 10.6084/m9.figshare.26304269
title: An algorithmic multiple attribute decision-making context to model uncertainty associated with hospital site selection problem using complex sv-neutrosophic soft information

description: Decision-making approaches are often used in uncertain environments by people who must make difficult judgments in daily life, including elements of varied qualities and costs. These methods assist decision-makers in managing ambiguity and uncertainty, allowing for more informed and risk-reduced decisions. This research introduces an advanced framework called a complex single-valued neutrosophic soft set (csvNSS) to address uncertainties inherent in decision-making. The csvNSS framework is capable of managing information periodicity by introducing two components: amplitude and phase. The first deals with fuzzy membership, while the second manages periodicity within a complex plane. Some rudiments of csvNSS like properties, set operations and aggregations, are investigated. To make these ideas practically applicable in choosing an appropriate location for the hospital, an algorithm for handling csvNSS is proposed. An enhanced strategy is validated through the use of a specific example that takes site selection for hospital into account. The outcome demonstrates the efficacy of the suggested strategy. The method can be used in other domains where selection issues arise.
DOI: 10.6084/m9.figshare.26304269.v1
title: An algorithmic multiple attribute decision-making context to model uncertainty associated with hospital site selection problem using complex sv-neutrosophic soft information

description: Decision-making approaches are often used in uncertain environments by people who must make difficult judgments in daily life, including elements of varied qualities and costs. These methods assist decision-makers in managing ambiguity and uncertainty, allowing for more informed and risk-reduced decisions. This research introduces an advanced framework called a complex single-valued neutrosophic soft set (csvNSS) to address uncertainties inherent in decision-making. The csvNSS framework is capable of managing information periodicity by introducing two components: amplitude and phase. The first deals with fuzzy membership, while the second manages periodicity within a complex plane. Some rudiments of csvNSS like properties, set operations and aggregations, are investigated. To make these ideas practically applicable in choosing an appropriate location for the hospital, an algorithm for handling csvNSS is proposed. An enhanced strategy is validated through the use of a specific example that takes site selection for hospital into account. The outcome demonstrates the efficacy of the suggested strategy. The method can be used in other domains where selection issues arise.
DOI: 10.6084/m9.figshare.26936240
title: A Red Teaming Framework for Securing AI in Maritime Autonomous Systems

description: Artificial intelligence (AI) is being ubiquitously adopted to automate processes in science and industry. However, due to its often intricate and opaque nature, AI has been shown to possess inherent vulnerabilities which can be maliciously exploited with adversarial AI, potentially putting AI users and developers at both cyber and physical risk. In addition, there is insufficient comprehension of the real-world effects of adversarial AI and an inadequacy of AI security examinations; therefore, the growing threat landscape is unknown for many AI solutions. To mitigate this issue, we propose one of the first red team frameworks for evaluating the AI security of maritime autonomous systems. The framework provides operators with a proactive (secure by design) and reactive (post-deployment evaluation) response to securing AI technology today and in the future. This framework is a multi-part checklist, which can be tailored to different systems and requirements. We demonstrate this framework to be highly effective for a red team to use to uncover numerous vulnerabilities within a real-world maritime autonomous systems AI, ranging from poisoning to adversarial patch attacks. The lessons learned from systematic AI red teaming can help prevent MAS-related catastrophic events in a world with increasing uptake and reliance on mission-critical AI.
DOI: 10.6084/m9.figshare.26304264
title: Generalized Performance of LSTM in Time-Series Forecasting

description: Optimizing the time-series forecasting performance is a multi-objective problem which enables the comparison of general applicability of methods across multiple use cases such as finance and demographics. Libra, a time-series forecasting framework which shifts the problem of optimization from minimizing single to multiple evaluation measures and use cases, is used as a benchmark to evaluate the performance of the Long Short-Term Memory (LSTM) neural network. LSTMs with parameter tuning have been shown to perform well with time-series forecasting. This paper applies LSTMs (mostly with standard parameters and variations of some of them) to the Libra framework and concludes that due to data characteristic variance and without increased hardware and time constraints LSTMs do not outperform the median measures of Libra.
DOI: 10.6084/m9.figshare.26362802
title: Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data

description: Changeover events occur in every industrial production when a machine is prepared and setup for production of the next product variant. Changeover times must be acquired with a high degree of validity for product cost calculations, order sequencing, and work schedules. The novelty of this article is a <i>Machine Learning</i> (ML) approach to automatically detect changeover events in production on manufacturing machines without direct human feedback. The machine learning approach uses several algorithms to classify different phases of the changeover process. The changeover of a milling process was defined using different phase concepts (2-phases, 6-phases, 23-phases) to be applicable to other types of manufacturing machines. Different machine learning methods were compared. The best results for the F1 score were achieved with the <i>Random Forest</i>, the <i>CatBoost</i>, and the <i>Extra Trees</i> algorithm (2-phases: 99.4–99.7%, 6-phases: 85.2–85.9%, 23-phases: 77.7–79.4%). It is shown that detecting changeover events can be realized based on data from an NC of a manufacturing machine without data from external sensors (2-phases: 98.9%, F1 score).
DOI: 10.6084/m9.figshare.26362802.v1
title: Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data

description: Changeover events occur in every industrial production when a machine is prepared and setup for production of the next product variant. Changeover times must be acquired with a high degree of validity for product cost calculations, order sequencing, and work schedules. The novelty of this article is a <i>Machine Learning</i> (ML) approach to automatically detect changeover events in production on manufacturing machines without direct human feedback. The machine learning approach uses several algorithms to classify different phases of the changeover process. The changeover of a milling process was defined using different phase concepts (2-phases, 6-phases, 23-phases) to be applicable to other types of manufacturing machines. Different machine learning methods were compared. The best results for the F1 score were achieved with the <i>Random Forest</i>, the <i>CatBoost</i>, and the <i>Extra Trees</i> algorithm (2-phases: 99.4–99.7%, 6-phases: 85.2–85.9%, 23-phases: 77.7–79.4%). It is shown that detecting changeover events can be realized based on data from an NC of a manufacturing machine without data from external sensors (2-phases: 98.9%, F1 score).
DOI: 10.6084/m9.figshare.25858505
title: Handling Imbalanced Classification Problems by Weighted Generalization Memorization Machine

description: Imbalanced classification problems are of great significance in life, and there have been many methods to deal with them, e.g. eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Decision Trees (DT), and Support Vector Machine (SVM). Recently, a novel Generalization-Memorization Machine (GMM) was proposed to maintain good generalization ability with zero empirical for binary classification. This paper proposes a Weighted Generalization Memorization Machine (WGMM) for imbalanced classification. By improving the memory cost function and memory influence function of GMM, our WGMM also maintains zero empirical risk with well generalization ability for imbalanced classification learning. The new adaptive memory influence function in our WGMM achieves that samples are described individually and not affected by other training samples from different category. We conduct experiments on 31 datasets and compare the WGMM with some other classification methods. The results exhibit the effectiveness of the WGMM.
DOI: 10.6084/m9.figshare.25858505.v1
title: Handling Imbalanced Classification Problems by Weighted Generalization Memorization Machine

description: Imbalanced classification problems are of great significance in life, and there have been many methods to deal with them, e.g. eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Decision Trees (DT), and Support Vector Machine (SVM). Recently, a novel Generalization-Memorization Machine (GMM) was proposed to maintain good generalization ability with zero empirical for binary classification. This paper proposes a Weighted Generalization Memorization Machine (WGMM) for imbalanced classification. By improving the memory cost function and memory influence function of GMM, our WGMM also maintains zero empirical risk with well generalization ability for imbalanced classification learning. The new adaptive memory influence function in our WGMM achieves that samples are described individually and not affected by other training samples from different category. We conduct experiments on 31 datasets and compare the WGMM with some other classification methods. The results exhibit the effectiveness of the WGMM.
DOI: 10.6084/m9.figshare.25487859
title: Multi-objective Optimization for Green Delivery Routing Problems with Flexible Time Windows

description: This paper presents a model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows. A scenario of new vehicle routing is analyzed in which customers are asked to provide alternative time windows to offer flexibility to help route planners find more fuel-efficient routes (“green delivery”). Customers can rank their preferred time windows as first, second, and third. The optimization model aims to reduce tour costs, promote electromobility over fossil fuels, such as diesel, and meet customer preferences when possible and affordable. The study incorporates a multi-objective optimization model with three objectives, which are overall cost, use of fossil fuel, and customer satisfaction. For the new problem, a set of realistic benchmark problems is created and four mainstream solvers are applied for the Pareto front approximation: NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA. These algorithms are compared in terms of their effectiveness in achieving the objectives of minimizing travel costs, promoting electromobility, and meeting customer preferences. The study uses five different problems of single-vehicle route planning. Two major findings are that the selection of the metaheuristic can make a big difference in terms of algorithm performance. The resulting 3-D Pareto fronts reveal the nature of this new class of problems: Interestingly, in the new model with flexible time windows, most users can still be delivered in their most preferred time windows with only small concessions to the other objectives. However, using only one time window per user can lead to an increasingly drastic cost and fossil fuel consumption.
DOI: 10.6084/m9.figshare.25487859.v1
title: Multi-objective Optimization for Green Delivery Routing Problems with Flexible Time Windows

description: This paper presents a model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows. A scenario of new vehicle routing is analyzed in which customers are asked to provide alternative time windows to offer flexibility to help route planners find more fuel-efficient routes (“green delivery”). Customers can rank their preferred time windows as first, second, and third. The optimization model aims to reduce tour costs, promote electromobility over fossil fuels, such as diesel, and meet customer preferences when possible and affordable. The study incorporates a multi-objective optimization model with three objectives, which are overall cost, use of fossil fuel, and customer satisfaction. For the new problem, a set of realistic benchmark problems is created and four mainstream solvers are applied for the Pareto front approximation: NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA. These algorithms are compared in terms of their effectiveness in achieving the objectives of minimizing travel costs, promoting electromobility, and meeting customer preferences. The study uses five different problems of single-vehicle route planning. Two major findings are that the selection of the metaheuristic can make a big difference in terms of algorithm performance. The resulting 3-D Pareto fronts reveal the nature of this new class of problems: Interestingly, in the new model with flexible time windows, most users can still be delivered in their most preferred time windows with only small concessions to the other objectives. However, using only one time window per user can lead to an increasingly drastic cost and fossil fuel consumption.
DOI: 10.6084/m9.figshare.26509595
title: Exploring Entropy Measures with Topological Indices on Subdivided Cage Networks via Linear Regression Analysis

description: In this study, we investigate entropy measurements for subdivided cage networks based on topological indices. We specifically calculate different entropy, redefining Zagreb entropy, HM(G),M1(G), M2(G) entropy, atom bond connection entropy, and Randic entropy. We examine the graphical behavior of various entropy measures using the line fit approach. The results highlight patterns in the distribution of entropy values and interactions between them, which shed light on the intricate connectivity and structural properties of segmented cage networks. This work improves our understanding of cage network dynamics and provides a visual framework for interpreting their behavior.
DOI: 10.6084/m9.figshare.26509595.v1
title: Exploring Entropy Measures with Topological Indices on Subdivided Cage Networks via Linear Regression Analysis

description: In this study, we investigate entropy measurements for subdivided cage networks based on topological indices. We specifically calculate different entropy, redefining Zagreb entropy, HM(G),M1(G), M2(G) entropy, atom bond connection entropy, and Randic entropy. We examine the graphical behavior of various entropy measures using the line fit approach. The results highlight patterns in the distribution of entropy values and interactions between them, which shed light on the intricate connectivity and structural properties of segmented cage networks. This work improves our understanding of cage network dynamics and provides a visual framework for interpreting their behavior.