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The increasing development of the competitive market has forced organisations to make great efforts in the processes of supply, production, and distribution to meet customer demand in the shortest time and at the lowest cost. A cross-docking (CD) system is one of the successful and practical strategies in this field considered by researchers in various fields. Also, business process management plays a key role in continuous improvement and increased productivity. In today's digital age, due to the ability to record all activities, process mining is an important method to identify the current situation and improve productivity. In this research, a newly established CD belonging to a chain store is studied to improve the current situation, in which different goods enter and then exit after different processes. The purpose of this study is to obtain the optimal number of doors and loaders as sources. First, helping an RFID system, all activities are recorded, and the current situation of the processes is monitored, and then, the real process model is identified using heuristic and inductive miner algorithms. After adapting to the event log by using the simulation process in Arena software, different scenarios are examined, and the best possible case is presented. [ABSTRACT FROM AUTHOR]

This paper summarizes librarian research on information visualization as well as general trends in the broader field, highlighting the most recent trends, important journals, and which subject disciplines are most involved with information visualization. By comparing librarian research to the broader field, the paper identifies opportunities for libraries to improve their information visualization support services. [ABSTRACT FROM AUTHOR]

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patients body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0-2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3-4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0-4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.

This study addresses the potential for improving the accuracy, scope, and value of sales research through the application of data mining and machine learning algorithms. By examining prior research, identifying opportunities for improvement, and assessing gaps that can benefit from machine learning, research and application are made more accessible for sales researchers and managers. Machine learning can address important sales research questions that cannot be answered with the same accuracy or efficiency as traditional research methods. This study demonstrates the benefits of the methods through an example of application to the prediction of salesforce performance based on behavioral, attitudinal, and demographic data. This includes future research ideas, usage cases, and applications where machine learning could advance sales research and management. Machine learning and predictive analytics methods have multiple applications, including in B2C and B2B market contexts and for companies and independent sales teams. [ABSTRACT FROM AUTHOR]

With the rapid development of big data technology, electronic documents are more and more widely used. Aiming at the characteristics of large amount of data, complex format and non-standard information data of electronic documents, this paper uses data mining technology to realize the correlation construction between documents. Firstly, on the basis of dynamic incremental association rules, according to the characteristics of electronic document business, the association rules are improved. Secondly, the advantages of Boolean matrix operation, original data association rules and frequent 2-item set are used to improve the acquisition ability of the system for frequent items of document features. The experimental results show: that algorithm in this paper has higher computational efficiency in the process of file increment change and deletion change, and is suitable for electronic file processing in the case of big data. [ABSTRACT FROM AUTHOR]

The bitterling fish is a prime example of intelligent behavior in nature for survival. The bitterling fish uses the oyster spawning strategy as their babysitter. The female bitterling fish looks for a male fish stronger than other fish to find the right pair. In order to solve optimization issues, the Bitterling Fish Optimization (BFO) algorithm is modeled in this manuscript based on the mating behavior of these fish. The bitterling fish optimization algorithm is more accurate than the gray wolf optimization algorithm, whale optimization algorithm, butterfly optimization algorithm, Harris Hawks optimization algorithm, and black widow optimization algorithm, according to experiments and implementations on various benchmark functions. Data mining and machine learning are two areas where meta-heuristic techniques are frequently used. In trials, the MLP artificial neural network and a binary version of the BFO algorithm are used to lower the detection error for intrusion traffic. The proposed method's accuracy, precision, and sensitivity index for detecting network intrusion are 99.14%, 98.87%, and 98.85%, respectively, according to experiments on the NSL KDD data set. Compared to machine learning approaches like NNIA, DT, RF, XGBoot, and CNN, the proposed method is more accurate at detecting intrusion. The BFO algorithm is used for feature selection in the UNSW-NB15 dataset, and the tests showed that the accuracy of the proposed method is 96.72% in this dataset. The proposed method of the BFO algorithm is also used to improve Kmeans clustering, and the tests performed on the dataset of covid 19, diabetes, and kidney disease show that the proposed method performs better than iECA*, ECA*, GENCLUST + + (G + +) methods. Deep has KNN, LVQ, SVM, ANN, and KNN. [ABSTRACT FROM AUTHOR]

The railway sector has witnessed a significant surge in condition-based maintenance, thanks to the proliferation of sensing technologies and data-driven methodologies, such as machine learning. However, despite the plethora of algorithms designed to detect and classify track irregularities and wheel out-of-roundness, they often fall short when put to the test in real-world scenarios. These shortcomings typically stem from their inability to meet all four critical requirements for constructing an effective maintenance plan: (R1) suitability of the condition-based maintenance strategy, (R2) availability of relevant data, (R3) proper problem formulation, and (R4) accurate evaluation of data mining methods. In response to the absence of a unified framework and standardized guidelines, this survey delves into the realm of time series sensor data and wheel-track interface components for railway structural health monitoring. This survey aims to bridge this gap by offering an extensive categorization, pinpointing existing challenges, and outlining potential directions for future research. Through these efforts, this survey provides a more thorough and targeted exploration of the subject matter, contributing to the advancement of this field. [ABSTRACT FROM AUTHOR]

Hyperparameters play a critical role in analyzing predictive performance in machine learning models. They serve to strike a balance between overfitting and underfitting of research-independent features to prevent extremes. Manual tuning and automated techniques are employed to identify the optimal combination and permutation to achieve the best model performance. This study explores the pursuit of the best fit through various hyperparameters. Following Logistic Regression analysis, this research compared Random Forest, Randomized search, Grid search, Genetic, Bayesian, and Optuna machine learning model tuning for the best accuracy of prediction the student The model accuracy was further assessed using confusion matrices and Receiver Operating Characteristic—Area Under the Curve (ROC-AUC) curves for student grade classification. The genetic algorithm's recommended hyperparameter tuning yielded the highest accuracy (82.5%) and AUC-ROC score (90%) for student result classification. Manual tuning with an estimator of 300, criterion entropy, max features of sqrt, and a minimum sample leaf of 10 achieved an accuracy of 81.1%, which closely resembled the performance randomized search cross-validation algorithm. The default random forest model scored the least accuracy (78%). However, this manual tuning process took a lesser time (3.66 s) to fit the model while grid search CV tuned 941.5 s. Hence, this research made significant contributions to optimizing various machine learning models using a range of hyperparameters for grade classification. [ABSTRACT FROM AUTHOR]

Background: Predicting mortality and relapse in children with acute lymphoblastic leukemia (ALL) is crucial for effective treatment and follow-up management. ALL is a common and deadly childhood cancer that often relapses after remission. In this study, we aimed to apply and evaluate machine learning-based models for predicting mortality and relapse in pediatric ALL patients. Methods: This retrospective cohort study was conducted on 161 children aged less than 16 years with ALL. Survival status (dead/alive) and patient experience of relapse (yes/no) were considered as the outcome variables. Ten machine learning (ML) algorithms were used to predict mortality and relapse. The performance of the algorithms was evaluated by cross-validation and reported as mean sensitivity, specificity, accuracy and area under the curve (AUC). Finally, prognostic factors were identified based on the best algorithms. Results: The mean accuracy of the ML algorithms for prediction of patient mortality ranged from 64 to 74% and for prediction of relapse, it varied from 64 to 84% on test data sets. The mean AUC of the ML algorithms for mortality and relapse was above 64%. The most important prognostic factors for predicting both mortality and relapse were identified as age at diagnosis, hemoglobin and platelets. In addition, significant prognostic factors for predicting mortality included clinical side effects such as splenomegaly, hepatomegaly and lymphadenopathy. Conclusions: Our results showed that artificial neural networks and bagging algorithms outperformed other algorithms in predicting mortality, while boosting and random forest algorithms excelled in predicting relapse in ALL patients across all criteria. These results offer significant clinical insights into the prognostic factors for children with ALL, which can inform treatment decisions and improve patient outcomes. [ABSTRACT FROM AUTHOR]

High utility itemset mining is an essential new task in data mining, which is obtained from the extension of frequent itemset mining problems. The difference between these two groups is that in high utility itemset mining, the utilities of the items are essential in addition to the number of items. To find the itemsets with more utilities, defining a minimum utility threshold is necessary by testing different solutions to obtain the desired outcome. This is challenging and needs more time to reach the appropriate value. In this article, we propose a method called TKU-BChOA to explore top-k high utility itemsets according to the number of required itemsets based on the Chimp meta-heuristic algorithm. Because, in comparison with the meta-heuristic methods, the deterministic methods require more time and memory. Experimental results on five datasets show that the proposed approach outperforms the previous deterministic methods in memory usage. The proposed method performs better in terms of time and memory on large datasets than other approximate methods and is more accurate. [ABSTRACT FROM AUTHOR]

Bipolar disorder (BD) is associated with increased morbidity/mortality. Adverse outcome prediction might help with the management of patients with BD. We systematically reviewed the performance of machine learning (ML) studies in predicting adverse outcomes (relapse or recurrence, hospital admission, and suicide-related events) in patients with BD. Demographic, clinical, and neuroimaging-related poor outcome predictors were also reviewed. Three databases (PubMed, Scopus, and Web of Science) were explored from inception to July 2023. Eighteen studies, accounting for >30,000 patients, were included. Support vector machine, decision trees, random forest, and logistic regression were the most frequently used ML algorithms. ML models' area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity ranged from 0.71 to 0.98, 72.7–92.8 %, and 59.0–95.2 % for relapse/recurrence prediction (4 studies (3 on relapses and 1 on recurrences). The corresponding values were 0.78–0.88, 21.4–100 %, and 77.0–99.7 % for hospital admissions (3 studies, 21,266 patients), and 0.71–0.99, 44.4–97.9 %, and 38.9–95.0 % for suicide-related events (10 studies, 5558 patients). Also, one study addressed a combination of the interest outcomes. Adverse outcome predictors included early onset BD, BD type I, comorbid psychiatric or substance use disorder, circadian rhythm disruption, hospitalization characteristics, and neuroimaging parameters, including increased dynamic amplitude of low-frequency fluctuation, decreased frontolimbic functional connectivity and aberrant dynamic functional connectivity in corticostriatal circuitry. ML models can predict adverse outcomes of BD with relatively acceptable performance measures. Future studies with larger samples and nested cross-validation validation should be conducted to reach more reliable results. • Adverse outcome prediction aids in the management of patients with BD. • We reviewed studies assessing ML models in predicting relapses/recurrences, hospitalizations, and suicide-related events in BD. • ML predicts adverse outcomes of BD with various performance measures across different models. • Future research is needed to integrate this knowledge into the clinical management of in BD. [ABSTRACT FROM AUTHOR]

The article explains the economic dynamics of the sports industry with adoption of deep learning algorithms and data mining methodology. Despite outstanding improvements in research of sports industry, a significant gap prevails with regard to proper quantification of economic benefits of this industry. Therefore, the current research is an attempt to filling this gap by proposing a specific economic model for the sports sector. This paper examines the data of sports industry covering the time span of 2012 to 2022 by using data mining technology for quantitative analyses. Deep learning algorithms and data mining techniques transform the gained information from sports industry databases into sophisticated economic models. The developed model then makes the efficient analysis of diverse datasets for underlying patterns and insights, crucial in realizing the economic trajectory of the industry. The findings of the study reveal the importance of sports industry for economic growth of China. Moreover, the application of deep learning algorithm highlights the importance of continuous learning and training on the economic data from the sports industry. It is, therefore, an entirely novel approach to build up an economic simulation framework using deep learning and data mining, tailored to the intricate dynamics of the sports industry. [ABSTRACT FROM AUTHOR]

As essential components of human society, buildings serve a multitude of functions and significance. Convolutional Neural Network (CNN) has made remarkable progress in the task of building extraction from detailed satellite imagery, owing to the potent capability to capture local information. However, CNN performs suboptimal in extracting larger buildings. Conversely, Transformer has excelled in capturing global information through self-attention mechanisms but are less effective in capturing local information compared to CNN, resulting in suboptimal performance in extracting smaller buildings. Therefore, we have designed the hybrid model STransU2Net, which combines meticulously designed Transformer and CNN to extract buildings of various sizes. In detail, we designed a Bottleneck Pooling Block (BPB) to replace the conventional Max Pooling layer during the downsampling phase, aiming to enhance the extraction of edge information. Furthermore, we devised the Channel And Spatial Attention Block (CSAB) to enhance the target location information during the encoding and decoding stages. Additionally, we added a Swin Transformer Block (STB) at the skip connection location to enhance the model's global modeling ability. Finally, we empirically assessed the performance of STransU2Net on both the Aerial imagery and Satellite II datasets, The IoU achieved state-of-the-art results with 91.04% and 59.09%, respectively, outperforming other models. [ABSTRACT FROM AUTHOR]

Real-world data (RWD) in the medical field, such as electronic health records (EHRs) and medication orders, are receiving increasing attention from researchers and practitioners. While structured data have played a vital role thus far, unstructured data represented by text (e.g., discharge summaries) are not effectively utilized because of the difficulty in extracting medical information. We evaluated the information gained by supplementing structured data with clinical concepts extracted from unstructured text by leveraging natural language processing techniques. Using a machine learning-based pretrained named entity recognition tool, we extracted disease and medication names from real discharge summaries in a Japanese hospital and linked them to medical concepts using medical term dictionaries. By comparing the diseases and medications mentioned in the text with medical codes in tabular diagnosis records, we found that: (1) the text data contained richer information on patient symptoms than tabular diagnosis records, whereas the medication-order table stored more injection data than text. In addition, (2) extractable information regarding specific diseases showed surprisingly small intersections among text, diagnosis records, and medication orders. Text data can thus be a useful supplement for RWD mining, which is further demonstrated by (3) our practical application system for drug safety evaluation, which exhaustively visualizes suspicious adverse drug effects caused by the simultaneous use of anticancer drug pairs. We conclude that proper use of textual information extraction can lead to better outcomes in medical RWD mining. [ABSTRACT FROM AUTHOR]

Background: Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease. Methods: In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations. Results: Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated. Conclusions: Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases. [ABSTRACT FROM AUTHOR]

ABSTRACT The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user‐friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large‐scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user‐friendly, full‐functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large‐scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant‐specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co‐expression network analysis strategy for efficient biomarker metabolite screening in large‐scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease. [ABSTRACT FROM AUTHOR]

Microvascular angina (MVA) is the most common cause of cardiac ischemic chest pain in patients without obstructive coronary artery disease (CAD) and lacks of effective treatment means. Medicine food homology (MFH) involves substances with both nutritional and medicinal qualities that have the potential to improve MVA symptoms as medicines, dietary supplements. However, research on MFH formula (MFHF) for MVA is not available. The study aims to generate a core MFHF for MVA through data mining and offer scientific backing for the utilization of edible medications in the prevention and alleviation of MVA. 11 databases were utilized to construct a database of MFH drugs, and the MFHF was generated through frequency analysis, association rule analysis, and clustering analysis. The composition of the formula is Codonopsis Radix, Astragali Radix, Platycodonis Radix, Persicae Semen, Glycyrrhizae Radix Et Rhizoma, Angelicae Sinensis Radix, and Allii Macrostemonis Bulbus. Through network pharmacology and molecular docking, we identified five major active components of MFHF: Adenosine, Nonanoic Acid, Lauric Acid, Caprylic Acid, and Enanthic Acid, along with nine core targets (NFKB1, ALB, AKT1, ACTB, TNF, IL6, ESR1, CASP3, and PTGS) for the improvement of MVA. These 5 active components have various biological activities, such as reducing oxidative stress, anti-inflammation, analgesia effect, inhibiting platelet aggregation, vasodilatation, vascular endothelial protection, and cardio-protection. GO and KEGG enrichment analyses revealed that MFHF mainly acted on the response to xenobiotic stimulus, integrative component of the plasma membrane, RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding, pathways in cancer, lipid and atherosclerosis, human cytomegalovirus infection, and the PI3K-Akt signaling pathway, which are the main pathogenesis of MVA. [ABSTRACT FROM AUTHOR]

3D human pose estimation has achieved rapid progress in virtual fashion shows. Loose clothing and personalized poses in virtual fashion shows often led to unclear body joint positions and frequent joint self-occlusion. Moreover, the current methods usually focus on salient features, ignoring multi-scale high-resolution features in virtual fashion shows. This paper proposes a residual semantic graph convolutional network (GCN) with high-resolution representation, which contains high-resolution driven 2D feature extraction and semantic guidance 3D pose regression, for 3D human pose estimation in virtual fashion shows. To obtain enough valuable 2D features, we design a multi-scale module to extract global semantic information and local semantic information from high-resolution and low-resolution data respectively. Meanwhile, the feature heat map is regressed into 2D joint coordinates by integral regression. For 3D pose regression, a residual semantic GCN with non-local module, which views the 2D joint coordinates as input, is devised to explore the neighbor relationship of body joints to further improve the vague joint positions and joint self-occlusion. In the above network, each local joint is given a learnable weight to learn local semantic information gradually. Subsequently, the local semantic information is extended to the global level and is blended with obtained global semantic information via non-local operation. The experiments on COCO2017, Human3.6 M, and joint occlusion datasets demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

AbstractTensors have broad applications in neuroimaging, data mining, digital marketing, etc. CANDECOMP/PARAFAC (CP) tensor decomposition can effectively reduce the number of parameters to gain dimensionality-reduction and thus plays a key role in tensor regression. However, in CP decomposition, there is uncertainty about which rank to use. In this article, we develop a model averaging method to handle this uncertainty by weighting the estimators from candidate tensor regression models with different ranks. When all candidate models are misspecified, we prove that the model averaging estimator is asymptotically optimal. When correct models are included in the set of candidate models, we prove the consistency of parameters and the convergence of the model averaging weight. Simulations and empirical studies illustrate that the proposed method has superiority over the competition methods and has promising applications. [ABSTRACT FROM AUTHOR]

Three-dimensional (3-D) stereo images can be generated via computer-based image processing of CORONA stereo pairs. To a certain extent, important terrain and surface feature data extracted from these stereo images can improve the survey of archaeological sites and the identification and mapping of major landscapes. In this study, we focused on the identification of the archaeological ruins of Liangzhu City. An optical stereo model (red/blue stereo image) of the Liangzhu site was created through computer-based mosaicking and processing of CORONA remote-sensing stereo pairs taken in the 1960s and 1970s. By importing the optical stereo model into mobile phones, tablet computers, and other mobile devices, the research team undertook real-time locating of ruins via human observation, on-site investigation, and image overlay during a field survey and identified several Liangzhu-period dams, some of which have been confirmed via archaeological field investigations. The research team later applied the same method to the identification of tombs at the site of the mausoleums of the six emperors of the Southern Song dynasty. The results further prove that this method is feasible and reliable and can be widely promoted and used for the identification of archaeological ruins. [ABSTRACT FROM AUTHOR]

Steganography, the use of algorithms to embed secret information in a carrier image, is widely used in the field of information transmission, but steganalysis tools built using traditional steganographic algorithms can easily identify them. Steganography without embedding (SWE) can effectively resist detection by steganography analysis tools by mapping noise onto secret information and generating secret images from secret noise. However, most SWE still have problems with the small capacity of steganographic data and the difficulty of extracting the data. Based on the above problems, this paper proposes image steganography without embedding carrier secret information. The objective of this approach is to enhance the capacity of secret information and the accuracy of secret information extraction for the purpose of improving the performance of security network communication. The proposed technique exploits the carrier characteristics to generate the carrier secret tensor, which improves the accuracy of information extraction while ensuring the accuracy of secret information extraction. Furthermore, the Wasserstein distance is employed as a constraint for the discriminator, and weight clipping is introduced to enhance the secret information capacity and extraction accuracy. Experimental results show that the proposed method can improve the data extraction accuracy by 10.03% at the capacity of 2304 bits, which verifies the effectiveness and universality of the method. The research presented here introduces a new intelligent information steganography secure communication model for secure communication in networks, which can improve the information capacity and extraction accuracy of image steganography without embedding. [ABSTRACT FROM AUTHOR]

In the domain of adaptable educational environments, our study is dedicated to achieving three key objectives: forecasting the adaptability of student learning, predicting and evaluating student performance, and employing aspect-based sentiment analysis for nuanced insights into student feedback. Using a systematic approach, we commence with an extensive data preparation phase to ensure data quality, followed by applying efficient data balancing techniques to mitigate biases. By emphasizing higher education or educational data mining, feature extraction methods are used to uncover significant patterns in the data. The basis of our classification method is the robust WideResNeXT architecture, which has been further improved for maximum efficiency by hyperparameter tweaking using the simple Modified Jaya Optimization Method. The recommended WResNeXt-MJ model has emerged as a formidable contender, demonstrating exceptional performance measurements. The model has an average accuracy of 98%, a low log loss of 0.05%, and an extraordinary precision score of 98.4% across all datasets, demonstrating its efficacy in enhancing predictive capacity and accuracy in flexible learning environments. This work presents a comprehensive helpful approach and a contemporary model suitable for flexible learning environments. WResNeXt-MJ's exceptional performance values underscore its capacity to enhance pupil achievement in global higher education significantly. [ABSTRACT FROM AUTHOR]

In part two of this mini-series, we evaluate the range of machine-learning tools now available for application to veterinary clinical text-mining. These tools will be vital to automate extraction of information from large datasets of veterinary clinical narratives curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) and VetCompass, where volumes of millions of records preclude reading records and the complexities of clinical notes limit usefulness of more "traditional" text-mining approaches. We discuss the application of various machine learning techniques ranging from simple models for identifying words and phrases with similar meanings to expand lexicons for keyword searching, to the use of more complex language models. Specifically, we describe the use of language models for record annotation, unsupervised approaches for identifying topics within large datasets, and discuss more recent developments in the area of generative models (such as ChatGPT). As these models become increasingly complex it is pertinent that researchers and clinicians work together to ensure that the outputs of these models are explainable in order to instill confidence in any conclusions drawn from them. [ABSTRACT FROM AUTHOR]

Advances in genomics and transcriptomics accompanying the rapid accumulation of omics data have provided new tools that have transformed and expanded the traditional concepts of model fungi. Evolutionary genomics and transcriptomics have flourished with the use of classical and newer fungal models that facilitate the study of diverse topics encompassing fungal biology and development. Technological advances have also created the opportunity to obtain and mine large datasets. One such continuously growing dataset is that of the Sordariomycetes, which exhibit a richness of species, ecological diversity, economic importance, and a profound research history on amenable models. Currently, 3,574 species of this class have been sequenced, comprising nearly one-third of the available ascomycete genomes. Among these genomes, multiple representatives of the model genera Fusarium, Neurospora, and Trichoderma are present. In this review, we examine recently published studies and data on the Sordariomycetes that have contributed novel insights to the field of fungal evolution via integrative analyses of the genetic, pathogenic, and other biological characteristics of the fungi. Some of these studies applied ancestral state analysis of gene expression among divergent lineages to infer regulatory network models, identify key genetic elements in fungal sexual development, and investigate the regulation of conidial germination and secondary metabolism. Such multispecies investigations address challenges in the study of fungal evolutionary genomics derived from studies that are often based on limited model genomes and that primarily focus on the aspects of biology driven by knowledge drawn from a few model species. Rapidly accumulating information and expanding capabilities for systems biological analysis of Big Data are setting the stage for the expansion of the concept of model systems from unitary taxonomic species/genera to inclusive clusters of well-studied models that can facilitate both the in-depth study of specific lineages and also investigation of trait diversity across lineages. The Sordariomycetes class, in particular, offers abundant omics data and a large and active global research community. As such, the Sordariomycetes can form a core omics clade, providing a blueprint for the expansion of our knowledge of evolution at the genomic scale in the exciting era of Big Data and artificial intelligence, and serving as a reference for the future analysis of different taxonomic levels within the fungal kingdom. [ABSTRACT FROM AUTHOR]

Objective: Data imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification models. We focus on quantifying the effects of different imbalance degrees and sample sizes on model performance, identifying optimal cut-off values, and evaluating the efficacy of various methods to enhance model accuracy in highly imbalanced and small sample size scenarios. Methods: We collected medical records of patients receiving assisted reproductive treatment in a reproductive medicine center. Random forest was used to screen the key variables for the prediction target. Various datasets with different imbalance degrees and sample sizes were constructed to compare the classification performance of logistic regression models. Metrics such as AUC, G-mean, F1-Score, Accuracy, Recall, and Precision were used for evaluation. Four imbalance treatment methods (SMOTE, ADASYN, OSS, and CNN) were applied to datasets with low positive rates and small sample sizes to assess their effectiveness. Results: The logistic model's performance was low when the positive rate was below 10% but stabilized beyond this threshold. Similarly, sample sizes below 1200 yielded poor results, with improvement seen above this threshold. For robustness, the optimal cut-offs for positive rate and sample size were identified as 15% and 1500, respectively. SMOTE and ADASYN oversampling significantly improved classification performance in datasets with low positive rates and small sample sizes. Conclusions: The study identifies a positive rate of 15% and a sample size of 1500 as optimal cut-offs for stable logistic model performance. For datasets with low positive rates and small sample sizes, SMOTE and ADASYN are recommended to improve balance and model accuracy. [ABSTRACT FROM AUTHOR]

Educational Data Mining (EDM) holds promise in uncovering insights from educational data to predict and enhance students' performance. This paper presents an advanced EDM system tailored for classifying and improving tertiary students' programming skills. Our approach emphasizes effective feature engineering, appropriate classification techniques, and the integration of Explainable Artificial Intelligence (XAI) to elucidate model decisions. Through rigorous experimentation, including an ablation study and evaluation of six machine learning algorithms, we introduce a novel ensemble method, Stacking-SRDA, which outperforms others in accuracy, precision, recall, f1-score, ROC curve, and McNemar test. Leveraging XAI tools, we provide insights into model interpretability. Additionally, we propose a system for identifying skill gaps in programming among weaker students, offering tailored recommendations for skill enhancement. [ABSTRACT FROM AUTHOR]

Background: Antibody-drug conjugates (ADCs) have emerged as the focus and hotspots in the cancer field, yet the accompanying ocular toxicity has often been underestimated. We aimed to comprehensively and comparatively analyze the risk of ocular toxicity associated with various ADCs using the FDA Adverse Event Reporting System (FAERS) database. Methods: Data were extracted from the FAERS database from Q3 2011 to Q3 2023. We analyzed the clinical characteristics of ADCs-related ocular adverse events (AEs). These data were further mined by proportional analysis and Bayesian approach to detect signals of ADCs-induced ocular AEs. Moreover, the time to onset of ocular toxicity was also evaluated. Results: A total of 1,246 cases of ocular AEs were attributed to ADCs. Ocular toxicity signals were observed in patients treated with belantamab mafodotin, brentuximab vedotin, enfortumab vedotin, mirvetuximab soravtansine, sacituzumab govitecan, trastuzumab deruxtecan, and trastuzumab emtansine. Of these, belantamab mafodotin, trastuzumab emtansine, and mirvetuximab soravtansine, whose payloads are microtubule polymerization inhibitors, were more susceptible to ocular toxicity. The ten most common ADCs-related ocular AEs signals are keratopathy [ROR = 1,273.52, 95% CI (1,129.26–1,436.21)], visual acuity reduced [ROR = 22.83, 95% CI (21.2–24.58)], dry eye [ROR = 9.69, 95% CI (8.81–10.66)], night blindness [ROR = 259.87, 95% CI (228.23–295.89)], vision blurred [ROR = 1.78, 95% CI (1.57–2.02)], photophobia [ROR = 10.45, 95% CI (9.07–12.05)], foreign body sensation in eyes [ROR = 23.35, 95% CI (19.88–27.42)], ocular toxicity [ROR = 144.62, 95% CI (117.3–178.32)], punctate keratitis [ROR = 126.21, 95% CI (101.66–156.69)], eye disorder [ROR = 2.71, 95% CI (2.21–3.32)]. In terms of onset time, sacituzumab govitecan displayed an earlier onset of 21 days, while trastuzumab deruxtecan exhibited the latest onset of 223 days. Conclusion: ADCs may increase the risk of ocular toxicity in cancer patients, leading to serious mortality. With the widespread application of newly launched ADCs, combining the FAERS data with other data sources is crucial for monitoring the ocular toxicity of ADCs. In addition, novel ocular toxicity signals not documented in product labeling were detected. Further research will be necessary to validate our findings in the future. [ABSTRACT FROM AUTHOR]

Introduction: Mechanical damage significantly reduces the market value of fruits, making the early detection of such damage a critical aspect of agricultural management. This study focuses on the early detection of mechanical damage in blueberries (variety: Sapphire) through a non-destructive method. Methods: The proposed method integrates hyperspectral image fusion with amultistrategy improved support vector machine (SVM) model. Initially, spectral features and image features were extracted from the hyperspectral information using the successive projections algorithm (SPA) and Grey Level Co-occurrence Matrix (GLCM), respectively. Different models including SVM, RF (Random Forest), and PLS-DA (Partial Least Squares Discriminant Analysis) were developed based on the extracted features. To refine the SVM model, its hyperparameters were optimized using a multi-strategy improved Beluga Whale Optimization (BWO) algorithm. Results: The SVM model, upon optimization with the multi-strategy improved BWO algorithm, demonstrated superior performance, achieving the highest classification accuracy among the models tested. The optimized SVM model achieved a classification accuracy of 95.00% on the test set. Discussion: The integration of hyperspectral image information through feature fusion proved highly efficient for the early detection of bruising in blueberries. However, the effectiveness of this technology is contingent upon specific conditions in the detection environment, such as light intensity and temperature. The high accuracy of the optimized SVM model underscores its potential utility in post-harvest assessment of blueberries for early detection of bruising. Despite these promising results, further studies are needed to validate the model under varying environmental conditions and to explore its applicability to other fruit varieties. [ABSTRACT FROM AUTHOR]

Occupational accidents occur in many sectors in Türkiye every year. In this study, which aims to evaluate occupational accidents occurring in the oil sector, accident estimation was made using artificial neural networks. Data on 2210 work accidents that occurred between 2020 and 2023 in a company operating in the oil sector were used. In this study; Artificial neural network modeling was done with monthly accident data. In the study, Multilayer Perceptron Artificial Neural Networks (MLPANN) and Radial Basis Function Artificial Neural Networks (RBFANN), which are feed-forward networks, were used. 70% of the data is divided as training data and 30% as test data. As a result of the analysis; An 84.1% correct classification rate was obtained with the MLPANN method, and an 86.4% correct classification rate was obtained with the RBFANN method. It can be said that the RBFANN method performs more successfully than the MLPANN method. lt is suggested to use the methods in order to estimate the occupational accidents . [ABSTRACT FROM AUTHOR]

Safety signal detection is an integral component of Pharmacovigilance (PhV), which is defined by the World Health Organization as "science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other possible drug related problems". The purpose of safety signal detection is to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. While post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals, there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS) demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data. [ABSTRACT FROM AUTHOR]

With the unprecedented growth of the Internet, online evaluations of teaching have emerged as a pivotal tool in assessing the quality of university education. Leveraging data mining technology, we can extract invaluable insights from these evaluations, offering a robust scientific foundation for enhancing both teaching quality and administrative oversight. This study utilizes teaching evaluation data from a mathematics course at a university in Beijing to propose a comprehensive data mining framework covering both subjective and objective evaluations. The raw data are first cleaned, annotated, and preprocessed. Subsequently, for subjective evaluation data, a model combining Bidirectional Encoder Representations from Transformers (BERT) pre-trained models and Long Short-Term Memory (LSTM) networks is constructed to predict sentiment tendencies, achieving an accuracy of 92.76% and validating the model's effectiveness. For objective evaluation data, the Apriori algorithm is employed to mine association rules, from which meaningful rules are selected for analysis. This research effectively explores teaching evaluation data, providing technical support for enhancing teaching quality and devising educational reform initiatives. [ABSTRACT FROM AUTHOR]

• Distinctive crack patterns in 22 paintings by juel were linked to ground layer types. • Paintings from Denmark/Germany significantly vary from those from France/Switzerland. • Ground composition (lead white vs. calcium carbonate) influences crack morphology. • In-depth analysis was achieved by advanced statistical and machine learning methods. • The study links material choices and ageing, emphasising lead white resilience. Jens Juel (1745–1802) was a prominent Danish portrait painter, mainly known for creating over 600 portraits during his prolific four-decade career. This study investigated 22 canvas paintings by the artist, the majority of which are well preserved despite displaying typical signs of ageing like cracking. Crack formation in paintings results from a combination of external factors, such as environmental conditions, and inherent properties, including the composition of painting materials. Different materials used in the layers of a painting contribute to distinctive crack patterns, and this work focused on understanding how the composition of the ground layer and the density of the canvas influence these patterns. By employing a multi-analytical approach involving imaging, elemental and molecular analysis, along with data-driven methods such as dimensionality reduction, cluster analysis and statistical tests, the research established relationships between crack patterns and ground layer compositions in Juel's paintings. The findings unveiled significant physical and chemical differences between artworks created in Denmark (and partly Germany) and those from France and Switzerland, which can be attributed to different canvas preparation traditions. Crack patterns in Danish paintings are primarily characterised by orthogonal patterns with jagged lines, and these artworks have ground layers rich in calcium carbonate with minimal lead white and denser canvases. In contrast, French and Swiss paintings exhibit diagonal cracks in larger islands, and predominantly contain lead white with little to no presence of calcium carbonate in their ground layers. By integrating visual and chemical data, the investigation revealed four distinct crack pattern groups aligned with four ground types, underscoring the resilience of lead white-rich grounds, which are likely to be less susceptible to canvas-induced cracking compared to calcium-based grounds. Hence, the research uncovered a correlation between the proportion of these two components in the ground and the observed variations in crack patterns across different geographical and temporal contexts. These insights are crucial for conservators aiming to preserve not only Juel's artworks but also those of his contemporaries. The study highlights the significance of linking material choices to natural ageing patterns, and emphasises the importance of material analysis in predicting responses to ambient conditions and guiding conservation strategies for long-term stability of paintings. [Display omitted] [ABSTRACT FROM AUTHOR]

A common approach for biological assessment is to compare current observations of biota at a site to predictions of the biota that would occur if the site were in reference condition. To estimate these reference expectations, predictive bioassessment models use only data collected at reference sites and estimate relationships between biota and environmental variables that are unaffected by human activities (i.e., immutable variables). However, in some areas where human activities are pervasive, few reference sites are available. Here, we introduce a new approach for bioassessment, in which we first estimate relationships between widely available measurements of both immutable and human-influenced landscape variables and local environmental conditions (conductivity, dissolved total P, total suspended solids, percentage of sand and fines in the substrate, and dissolved organic C), and then we estimate the relationship between these local environmental variables and total macroinvertebrate richness. We then calculate the values of the local environmental variables under reference conditions by adjusting predictors that are strongly influenced by human activities to levels that are consistent with those observed at reference sites. Reference values of local environmental variables are then combined with the model for taxon richness to calculate taxon richness expected under reference conditions. Predictions of total taxon richness in validation data using the new approach are similar in accuracy and precision as predictions calculated using a traditional approach that focuses only on reference site data. The new approach complements existing bioassessment methods by highlighting types of sites in which estimates of reference expectations are uncertain and by improving our understanding of how instream stressors affect expected total taxon richness. [ABSTRACT FROM AUTHOR]

This article analyzes the citation network of the Babylonian Talmud, building on an earlier article that we published (Satlow and Sperling 2022). The article has three goals. Our first goal is to show how an ontological-based information extraction system combined with pattern matching can successfully extract structured data from a very complicated, unstructured text. Our second goal is to extend our previous analysis and demonstrate how citation data might lead to wider conclusions about redactional patterns. In addition to highlighting the citation tendencies of different tractates (which could indicate different redactors for those tractates), we hypothesize that there existed a source document originating in the circle of Rav Yehudah bar Yehezkel, used by at least some redactors, and that the character of Rabbi Zeira deserves further attention as an important figure connecting different nodes on the network. Finally, we seek to outline an analytical workflow that could be helpful to other historical projects in the digital humanities. [ABSTRACT FROM AUTHOR]

Documents contain abundant information available for managerial decision-making. However, manual methods of screening document information lack accuracy due to the heterogeneity of documents. To address the above issue, we propose a multimodal network combining multivariate semantic association graphs, MMIE, for accurately extracting information from documents. Firstly, the multivariate semantic graphs between multimodal data within each modality are constructed based on the semantic association of text contents, followed by the semantic relationships in the graphs to lead the fusion and embedding of the extracted multimodal data and improve the feature representation capability. Subsequently, the semantically linked multimodal information is fed into the newly constructed multimodal self-attention module to better establish inter-modal associations. Finally, a supervised comparison learning loss function is employed to reduce further the information loss due to sample imbalance. The experimental results on three real datasets show that the proposed model can extract feature information of different modal data more accurately, and the F1 scores reach 87.28 % , 82.53 % , and 81.17 % , respectively. [ABSTRACT FROM AUTHOR]

Industrial image anomaly detection achieves automated detection and localization of defects or abnormal regions in images through image processing and deep learning techniques. Currently, utilizing the approach of reverse knowledge distillation has yielded favourable outcomes. However, it is still a challenge in terms of the feature extraction capability of the image and the robustness of the decoding of the student network. This study first addresses the issue that the teacher network has not been able to extract global information more effectively. To acquire more global information, a vision transformer network is introduced to enhance the model's global information extraction capability, obtaining better features to further assist the student network in decoding. Second, for anomalous samples, to address the low similarity between features extracted by the teacher network and features restored by the student network, Gaussian noise is introduced. This further increases the probability that the features decoded by the student model match normal sample features, enhancing the robustness of the student model. Extensive experiments were conducted on industrial image datasets AeBAD, MvtecAD, and BTAD. In the AeBAD dataset, under the PRO performance metric, the result is 89.83%, achieving state‐of‐the‐art performance. Under the AUROC performance metric, it reaches 83.35%. Similarly, good results were achieved on the MvtecAD and BTAD datasets. The proposed method's effectiveness and performance advantages were validated across multiple industrial datasets, providing a valuable reference for the application of industrial image anomaly detection methods. [ABSTRACT FROM AUTHOR]

With computational evolution, there has been a growth in the number of machine learning algorithms and they became more complex and robust. A greater challenge is upon faster and more practical ways to find hyperparameters that will set up each algorithm individually. This article aims to use meta‐learning as a practicable solution for recommending hyperparameters from similar datasets, through their meta‐features structures, than to adopt the already trained XGBoost parameters for a new database. This reduced computational costs and also aimed to make real‐time decision‐making feasible or reduce any extra costs for companies for new information. The experimental results, adopting 198 data sets, attested to the success of the heuristics application using meta‐learning to compare datasets structure analysis. Initially, a characterization of the datasets was performed by combining three groups of meta‐features (general, statistical, and info‐theory), so that there would be a way to compare the similarity between sets and, thus, apply meta‐learning to recommend the hyperparameters. Later, the appropriate number of sets to characterize the XGBoost turning was tested. The obtained results were promising, showing an improved performance in the accuracy of the XGBoost, k = {4 − 6}, using the average of the hyperparameters values and, comparing to the standard grid‐search hyperparameters set by default, it was obtained that, in 78.28% of the datasets, the meta‐learning methodology performed better. This study, therefore, shows that the adoption of meta‐learning is a competitive alternative to generalize the XGBoost model, expecting better statistics performance (accuracy etc.) rather than adjusting to a single/particular model. [ABSTRACT FROM AUTHOR]

In the current investigation, an intriguing method called Firefly-XGBoost was put up to predict and integrate blast muck-pile results notably drop, throw, and lateral spread, which are instrumental in regulating and remedying the loading problems by loader as well as deceiving factors in type of loader selection. As a result, the particle swarm optimisation (PSO) algorithm and the firefly algorithm were relied on to shore up the efficacy of the XG Boost conventional model. A total of 164 blast experiments were executed in two different mines and the data collected from these trials data were used to train the model. This data includes specific features such as joints spanning height (JSH) as well as other blast characteristics such as the number of joint sets, decking length, total quantity of explosives, stemming length, decking length and firing pattern. The Firefly-XG Boost algorithm yielded better outcomes in terms of RMSE and R2 values when compared to the XG Boost, and PSO-XG Boost algorithms. The developed model was found to be suitable in predicting muck-pile parameters for practical application. [ABSTRACT FROM AUTHOR]

Service discovery and composition are crucial tasks in the development of Web services, it is designed to select the appropriate services for each task and ensure that services are called in the correct order. Unlike traditional methods, we aim to solve the service composition problem from a new perspective by developing a novel service composition using semantics based natural language descriptions. The proposed algorithm employs a combination of natural language processing and semantic-based techniques to extract the functional semantics of service datasets and understand the user context. By parsing the user's request, the method extracts possible sub-queries contained within the request and identifies the appropriate combination of sub-queries. Leveraging the BM25 algorithm and the ESIM semantic matching model, the method identifies atomic services with high correlation with sub-queries and determines the composable atomic services set according to the defined composition constraints. Experimental results validate the efficacy of our method. [ABSTRACT FROM AUTHOR]

With the continuous advancement of the global industrialization process, industrial cultural heritage has gradually attracted the attention of all parties, but its protection and excavation still face multiple challenges. The purpose of this study is to explore the conservation and mining strategies of global industrial cultural heritage based on big data. Through a comprehensive analysis of the value and status quo of industrial cultural heritage, this study integrates multi-source data and constructs a big data processing and analysis model by using the questionnaire method. The research results show that big data has significant application potential and practical value in the protection of industrial cultural heritage, and can provide more accurate and personalized protection strategies. Based on the data analysis, this study further proposes a series of practical conservation and excavation strategies to provide strong support for the sustainable development of industrial cultural heritage. This comprehensive analysis and practical recommendations are expected to advance scientific research and policy implementation in this area. [ABSTRACT FROM AUTHOR]

Due to the influence of illumination, noise, distortion and other factors on monocular vision images, the image quality is reduced, the difficulty of image information extraction is high, and there are often errors and uncertainties in background segmentation, which affect the effect of monocular vision image background virtualization. Therefore, a new depth information extraction monocular vision image automatic hierarchical background virtualization method is studied to improve the effect of background virtualization. The depth information map is extracted by anisotropic thermal diffusion equation. The morphology is used to fill the tiny holes in the depth information map, and its smoothing process is used to determine the image depth range, automatically layer the depth information map, and obtain the foreground layer and background layer. The background layer is virtualized by Gaussian blur operation. Pyramid image fusion method is used to fuse the foreground layer and the blurred background layer to complete the background virtualization of monocular vision image. Experimental results have shown that this method can effectively improve the clarity of depth information map edges, preserve a large amount of image edge information, and have high structural similarity, with an average value of 0.96. The efficiency is high, and the background virtualization time is only 15 ms. [ABSTRACT FROM AUTHOR]

Gaussian processes are popular in spatial statistics, data mining and machine learning because of their versatility in quantifying spatial variability and in propagating uncertainty. Although there has been a prolific research activity about Gaussian processes over Euclidean domains, only recently this research has extended to non-Euclidean manifolds. This paper digs into vector-valued Gaussian processes defined over the product of a hypersphere and a Euclidean space of arbitrary dimension, which are of interest in various disciplines of the natural sciences and engineering. Under mild regularity conditions, we establish a surprising one-to-one correspondence between matrix-valued kernels associated with vector Gaussian processes over the product space, and what we term partial ultraspherical and Fourier transforms that are taken over either the sphere or the Euclidean subspace. The properties of our approach are illustrated in terms of new parametric classes of matrix-valued kernels for product spaces of a hypersphere crossed with a Euclidean space. We also provide two algorithms that allow for fast simulation of approximately Gaussian (in the sense of the central limit theorem) processes in such product spaces. [ABSTRACT FROM AUTHOR]