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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]

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]

Experiential theories of cognition propose that the external environment shapes cognitive processing, shifting emphasis from internal mechanisms to the learning of environmental structure. Computational modelling, particularly distributional models of lexical semantics (e.g., Landauer & Dumais, 1997) and models of lexical organization (e.g., Johns, 2021a), exemplifies this, highlights the influence of language experience on cognitive representations. While these models have been successful, comparatively less attention has been paid to the training materials used to train these models. Recent research has explored the role of social/communicatively oriented training materials on models of lexical semantics and organization (Johns, 2021a, 2021b, 2023, 2024), introducing discourse- and user-centred text training materials. However, determining the optimal training materials for these two model types remains an open question. This article addresses this problem by using experiential optimization (Johns, Jones, & Mewhort, 2019), which selects the materials that maximize model performance. This study will use experiential optimization to compare user-based and discourse-based corpora in optimizing models of lexical organization and semantics, offering insight into pathways towards integrating cognitive models in these areas. [ABSTRACT FROM AUTHOR]

Identifying drug–drug interactions (DDIs) is an important aspect of drug design research, and predicting DDIs serves as a crucial guarantee for avoiding potential adverse effects. Current substructure‐based prediction methods still have some limitations: (i) The process of substructure extraction does not fully exploit the graph structure information of drugs, as it only evaluates the importance of different radius substructures from a single perspective. (ii) The process of constructing drug representations has overlooked the significant impact of relation embedding on optimizing drug representations. In this work, we propose a substructure‐aware graph neural network incorporating relation features (RFSA‐DDI) for DDI prediction, which introduces a directed message passing neural network with substructure attention mechanism based on graph self‐adaptive pooling (GSP‐DMPNN) and a substructure‐aware interaction module incorporating relation features (RSAM). GSP‐DMPNN utilizes graph self‐adaptive pooling to comprehensively consider node features and local drug information for adaptive extraction of substructures. RSAM interacts drug features with relation representations to enhance their respective features individually, highlighting substructures that significantly impact predictions. RFSA‐DDI is evaluated on two real‐world datasets. Compared to existing methods, RFSA‐DDI demonstrates certain advantages in both transductive and inductive settings, effectively handling the task of predicting DDIs for unseen drugs and exhibiting good generalization capability. The experimental results show that RFSA‐DDI can effectively capture valuable structural information of drugs more accurately for DDI prediction, and provide more reliable assistance for potential DDIs detection in drug development and treatment stages. [ABSTRACT FROM AUTHOR]

Optimal extraction of cosmological information from observations of the cosmic microwave background (CMB) critically relies on our ability to accurately undo the distortions caused by weak gravitational lensing. In this work, we demonstrate the use of denoising diffusion models in performing Bayesian lensing reconstruction. We show that score-based generative models can produce accurate, uncorrelated samples from the CMB lensing convergence map posterior, given noisy CMB observations. To validate our approach, we compare the samples of our model to those obtained using established Hamiltonian Monte Carlo methods, which assume a Gaussian lensing potential. We then go beyond this assumption of Gaussianity, and train and validate our model on non-Gaussian lensing data, obtained by ray-tracing N -body simulations. We demonstrate that in this case, samples from our model have accurate non-Gaussian statistics beyond the power spectrum. The method provides an avenue towards more efficient and accurate lensing reconstruction, which does not rely on an approximate analytical description of the posterior probability. The reconstructed lensing maps can be used as an unbiased tracer of the matter distribution, and to improve delensing of the CMB, resulting in more precise cosmological parameter inference. [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]

Ensemble learning synthesizes the advantages of different models and has been widely applied in the field of spatial prediction. However, the nonlinear constraints of spatial heterogeneity on the model ensemble process make it difficult to adaptively determine the ensemble weights, greatly limiting the predictive ability of the ensemble learning model. This paper therefore proposes a novel geographical spatial heterogeneous ensemble learning method (GSH-EL). Firstly, the geographically weighted regression model, geographically optimal similarity model, and random forest model are used as three base learners to express local spatial heterogeneity, global feature correlation, and nonlinear relationship of geographic elements, respectively. Then, a spatially weighted ensemble neural network module (SWENN) of GSH-EL is proposed to express spatial heterogeneity by exploring the complex nonlinear relationship between the spatial proximity and ensemble weights. Finally, the outputs of the three base learners are combined with the spatial heterogeneous ensemble weights from SWENN to obtain the spatial prediction results. The proposed method is validated on the PM2.5 air quality and landslide dataset in China, both of which obtain more accurate prediction results than the existing ensemble learning strategies. The results confirm the need to accurately express spatial heterogeneity in the model ensemble process. [ABSTRACT FROM AUTHOR]

Pastoralists' livelihood on natural rangelands is increasingly influenced by the destructive impacts of climate change. While the phenomenon of climate change continues globally, it is expected to put more pressure on unfavourable rangelands and pastoral households whose livelihood source is only based on pastoralism activities. Thus, it is vital to reduce the livelihood vulnerability of pastoralists to climate change impacts through appropriate adaptation strategies. Accordingly, the aim of the current study was to assess the compatibility of adaptation strategies with the vulnerabilities posed by climate change to pastoralists' livelihoods. The evaluation was based on a spatial survey that was conducted with a random sample of 393 Iranian pastoralists distributed in the counties of Eghlid, Sepidan, and Shiraz in the northern Fars province. The results showed that pastoralists were commonly vulnerable in terms of high-risk exposure and low adaptive capacity. About 27 livelihood vulnerability patterns were detected using data mining based on the relationships among the sub-components of exposure, sensitivity, and adaptation in three counties. The scores of the livelihood vulnerability were high in most of the livelihood vulnerability patterns. Only four patterns obtained relatively low vulnerability scores through accurate decisions on adopting the appropriate set of adaptation strategies. According to the low compatibility of pastoralists' livelihood with climate change, the results of this study can assist policymakers to introduce appropriate adaptation strategies for pastoralists. In this regard, appropriate adaptation strategies in each vulnerability pattern are able to reduce livelihood vulnerability to climate change. [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]

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]

The problem of quantifying the distance between distributions arises in various fields, including cryptography, information theory, communication networks, machine learning, and data mining. In this article, the analogy with the cumulative Jensen–Shannon divergence, defined in Nguyen and Vreeken (2015), we propose a new divergence measure based on the cumulative distribution function and call it the cumulative α-Jensen–Shannon divergence, denoted by CJS (α) . Properties of CJS (α) are studied in detail, and also two upper bounds for CJS (α) are obtained. The simplified results under the proportional reversed hazard rate model are given. Various illustrative examples are analyzed. [ABSTRACT FROM AUTHOR]

Mesostructure-based simulation technology offers an effective approach to modeling the mesomechanical responses of asphalt concrete and studying its mesomechanisms. However, current exploration depth and utilization level of the simulation data are both very limited. The intricacies of the vast mesomechanical response data necessitate the development and adoption of advanced data-processing methods that can gain valuable insights. To this end, this study proposed a data-mining framework that integrates a three-dimensional (3D) random aggregate method and the finite-element (FE) method for mesoscopic simulation of asphalt concrete. This framework mainly consists of two stages. In the first stage, a random aggregate method is used to establish 3D mesostructures of asphalt concrete, and FE simulations are performed on these mesostructures to model their mesomechanical responses. Based on the simulated responses, several procedures for data export, processing, and mining are developed and sequentially performed in the second stage. The data-export procedure extracts the mesomechanical responses from the FE result files and stores them into a database for efficient management. The required response data are accessed via SQL and then processed preliminarily using statistical and computational methods. Machine-learning methods and variable importance analysis techniques are used to conduct in-depth mining analyses on the processed data. The efficacy of this framework was demonstrated by applying it to investigating the effects of the volume fractions of coarse aggregates (larger than 2.36 mm) of different sizes on 129 simulated mesostructures of asphalt concrete with a nominal maximum aggregate size of 13.2 mm. The results indicated that the coarse aggregates with the particle sizes between 4.75 and 9.5 mm may make the highest contribution to the overall dynamic modulus of asphalt concrete. The coarse aggregates with larger size differences could cause more significant stress concentrations in the surrounding asphalt mortar. [ABSTRACT FROM AUTHOR]

• Deep auto-encoder model is used to handle the non-linear and redundant information in dam key parameters. • A hybrid system based on intelligent optimization algorithms and deep learning for predicting tailings dam parameters. • The uncertainty analysis provides more comprehensive and precise interval predictions. • The prediction accuracy of monitoring parameters of tailings dam is significantly improved. Tailings dams, as critical infrastructure, play a vital role in ensuring the safety and reliability of tailings pond systems. Predicting the trend of tailings dam monitoring parameters helps monitor its operational status and support safety management and decision-making. The traditional time series prediction model focuses on point prediction while neglecting the nonlinear feature of data and the uncertainty of prediction results, which is far from meeting the requirements for risk monitoring and safety management of tailings dam systems. To address this, Deep Auto-Encoder is used to extract and optimize features of multi-dimensional time series; Whale Optimization Algorithm, Long Short Term Memory, and Kernel Density Estimation are used to build a monitoring parameters trend interval prediction model. It can effectively predict and analyze the uncertainty of tailings dam monitoring parameters. Time series data of monitoring parameters are collected from a tailings dam in Anhui, China and modeled to verify the effectiveness of the model. The results show that the hybrid prediction model performs the best in monitoring parameter prediction accuracy and trend. The Root Mean Square Error values of the Whale Optimization Algorithm and Long Short Term Memory prediction for reservoir water level, surface displacement, internal displacement, phreatic line, and beach width are 0.0160, 1.0405, 0.0389, 0.0176, and 0.7525, with errors reduced by 0.37 % to 17.92 % compared to other models. The uncertainty analysis confirms the high reliability of the model. The Kernel Density Estimation effectively forecasts the fluctuation range of predicted values, significantly improving the prediction effectiveness of the monitoring parameters. [ABSTRACT FROM AUTHOR]

Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the traditional machine learning approaches rely on the time-consuming process of creating corpora and cannot extract information for specific needs and locations in certain situations. This paper presents a clinical NER approach based on a convolutional neural network (CNN) for better use of specific features around medical entities and analyzes the performance of the proposed approach through fine-tuning NER with effective pre-training on the BC5CDR dataset. The proposed method uses annotation of entities for various medical concepts. The second stage develops a clinically NER method. This proposed method shows interesting results on the performance measures achieving a precision of 92.57%, recall of 92.22%, and F1- measure of 91.6% [ABSTRACT FROM AUTHOR]

The Internet of Things massive adoption in many industrial areas in addition to the requirement of modern services is posing huge challenges to the field of data mining. Moreover, the semantic interoperability of systems and enterprises requires to operate between many different formats such as ontologies, knowledge graphs, or relational databases, as well as different contexts such as static, dynamic, or real time. Consequently, supporting this semantic interoperability requires a wide range of knowledge discovery methods with different capabilities that answer to the context of distributed architectures (DA). However, to the best of our knowledge there is no general review in recent time about the state of the art of Concept Analysis (CA) and multi-relational data mining (MRDM) methods regarding knowledge discovery in DA considering semantic interoperability. In this work, a systematic literature review on CA and MRDM is conducted, providing a discussion on the characteristics they have according to the papers reviewed, supported by a clusterization technique based on association rules. Moreover, the review allowed the identification of three research gaps toward a more scalable set of methods in the context of DA and heterogeneous sources. [ABSTRACT FROM AUTHOR]

Today, the rapid development of online social networks, as well as low costs, easy communication, and quick access with minimal facilities have made social networks an attractive and very influential phenomenon among people. The users of these networks tend to share their sensitive and private information with friends and acquaintances. This has caused the data of these networks to become a very important source of information about users, their interests, feelings, and activities. Analyzing this information can be very useful in predicting the behavior of users in dealing with various issues. But publishing this data for data mining can violate the privacy of users. As a result, data privacy protection of social networks has become an important and attractive research topic. In this context, various algorithms have been proposed, all of which meet privacy requirements by making changes in the information as well as the graph structure. But due to high processing costs and long execution times, these algorithms are not very appropriate for anonymizing big data. In this research, we improved the speed of data anonymization by using the number factorization technique to select and delete the best edges in the graph correction stage. We also used the chaotic krill herd algorithm to add edges, and considering the effect of all edges together on the structure of the graph, we selected edges and added them to the graph so that it preserved the graph's utility. The evaluation results on the real-world datasets, show the efficiency of the proposed algorithm in comparison with the state-of-the-art methods to reduce the execution time and maintain the utility of the anonymous graph. [ABSTRACT FROM AUTHOR]

Extreme floods threaten lives, assets and ecosystems, with the largest impacts occurring in urbanised areas. However, flood mitigation schemes generally neglect the fact that urban floods carry a considerable amount of solid load. In this study, we define urban flood drifters (UFDs) as loose objects present in the urban landscape that can become mobile under certain flow conditions, thereafter blocking drainage infrastructure and endangering both downstream and upstream communities. Based on 270 post‐flood photographic records from 63 major inundations of the past quarter‐century across 46 countries, we provide a comprehensive analysis of UFDs and their flood‐hazard implications. We show that a variety of vehicles, furniture and a heterogeneous mixture of drifters are present in post‐flooding scenarios. Plastic, construction debris and wood (natural or anthropogenic) dominate the statistics of transported drifters in urban floods (with frequencies of roughly 50%–60% each), followed by cars (present in 31.5% of post‐flood images). Other heavy vehicles are readily observed in post‐flood imagery and furniture such as bins, garden sheds or water tanks also appear occasionally, therefore suggesting that they can play a relevant role in extreme floods. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are the leading contributor of mortality worldwide. Accurate cardiovascular disease prediction is crucial, and the application of machine learning and data mining techniques could facilitate decision-making and improve predictive capabilities. This study aimed to present a model for accurate prediction of cardiovascular diseases and identifying key contributing factors with the greatest impact. The Cleveland dataset besides the locally collected dataset, called the Noor dataset, was used in this study. Accordingly, various data mining techniques besides four ensemble learning-based models were implemented on both datasets. Moreover, a novel model for combining individual classifiers in ensemble learning, wherein weights were assigned to each classifier (using a genetic algorithm), was developed. The predictive strength of each feature was also investigated to ensure the generalizability of the outcomes. The ultimate ensemble-based model achieved a precision rate of 88.05% and 90.12% on the Cleveland and Noor datasets, respectively, demonstrating its reliability and suitability for future research in predicting the likelihood of cardiovascular diseases. Not only the proposed model introduces an innovative approach for specifying cardiovascular diseases by unraveling the intricate relationships between various biological variables but also facilitates early detection of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

In machine learning and data mining, concept factorization (CF) has achieved great success for its powerful capability in data representation. To learn an adaptive inherent graph structure of data space, and to ease the burden brought by the explicit orthogonality constraint, we propose a concept factorization with adaptive graph learning on the Stiefel manifold (AGCF-SM). The method essentially integrates concept factorization and manifold learning into a unified framework. Therein the adaptive similarity graph is learned by iterative locally linear embedding, which is free from dependence on neighbor sets. An iterative updating algorithm is developed and the convergence and complexity analyses of the algorithm are provided. The numerical experiments on ten benchmark datasets have demonstrated that the proposed algorithm outperforms other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on document-level RE in English, Korean document-level RE research is still at the very beginning due to the absence of a dataset. To accelerate the studies, we present TREK (Toward Document-Level Relation Extraction in Korean) dataset constructed from Korean encyclopedia documents written by the domain experts. We provide detailed statistical analyses for our large-scale dataset and human evaluation results suggest the assured quality of TREK. Also, we introduce the document-level RE model that considers the named entity-type while considering the Korean language's properties. In the experiments, we demonstrate that our proposed model outperforms the baselines and conduct qualitative analysis. [ABSTRACT FROM AUTHOR]

In response to the demand for scientific training of sports athletes, the author combined data mining technology to study an improved sports training mode decision support evaluation system. In this regard, the author analyzed the characteristics of association rule algorithms and elaborated on their functions in data preprocessing, data mining, and pattern evaluation. Based on the software design of decision support systems, the characteristics of system operation were analyzed. At the same time, the author focused on explaining the data fusion processing of association rules in sports evaluation decision support systems, and proposed an improved Apriori algorithm output mode to improve the effectiveness of system evaluation. Compared with other algorithms such as Apriori, DC Apriori and Apriori, this algorithm has higher reliability. When the minimum confidence is increased, the advantage of prior information will gradually disappear, and the final result will be obtained. Experimental results show that this method can effectively provide support for sports training decision-making. [ABSTRACT FROM AUTHOR]

In order to better achieve the integration and innovation of sports social platforms and information technology, the author proposes an SFD (Sport Friend Discover) sports friend recommendation model based on physical testing big data. The core idea of this model is to use physical measurement data to match the similarity between athletes and recommend suitable exercise partners. Specifically, we collected a large amount of physical measurement data, including height, weight, body fat percentage, muscle mass, etc. Then, through data mining algorithms, these data are transformed into feature vectors of the movers. Next, we use a similarity algorithm to calculate the similarity between different athletes and find the most matching motion partner with the user. The results show that the SFD method outperforms the other two traditional recommendation methods on the dataset, with P @ 10, P @ 20, P @ 30, and P @ 40 of SFD reaching 0.099, 0.095, 0.085, and 0.591, respectively. SFD not only utilizes more neighboring information than FOAF based on local graph structure, but also compared to TRW based on global graph structure method, at the same time, the importance of the node itself is also considered, resulting in higher accuracy. It has been proven that the SFD sports friend recommendation model based on physical testing big data has achieved good results in recommending sports partners. Users can quickly find sports partners with similar body types and health conditions, improving the fun and effectiveness of exercise. [ABSTRACT FROM AUTHOR]

The extraction of webpage information is of paramount importance in the realm of competitive intelligence. This research is dedicated to the design and implementation of a visual webpage information extraction module within a competitive intelligence system, approached through the lens of research and development (R&D) technology and its practical applications. Initially, the study delineates the objectives and requirements for webpage information extraction, emphasizing the practical needs of competitive intelligence systems. By critically assessing the strengths and weaknesses of current theories and methodologies in webpage text information extraction, this paper introduces an innovative visual method for extracting webpage text information. Subsequently, the paper meticulously outlines the comprehensive architecture of the proposed module. Building upon this foundation, the study delves into the specifics of the extraction template, rule generation, optimization techniques, and the extraction algorithm pivotal to the process of visual webpage information extraction. The system's effectiveness and practical utility are substantiated through a series of confirmatory experiments, the results of which are thoroughly analyzed. The findings affirm that the developed system adeptly fulfills the webpage information extraction needs of competitive intelligence systems, contributing significantly to the R&D efforts in which the authors are engaged. [ABSTRACT FROM AUTHOR]

With the proliferation of digital platforms, the dissemination of literary works has encountered unprecedented challenges, particularly concerning copyright infringement and unauthorized use. This study introduces a comprehensive framework for copyright protection and risk assessment, specifically tailored to online literary works. The framework employs advanced CNN based information extraction (IE) techniques coupled with machine learning (ML) algorithms to identify, classify, and protect literary content against copyright violations. Firstly, we delineate a novel CNN-Decision tree-based IE methodology that systematically harvests metadata and textual content from various online repositories. This process is designed to detect and index online literary works, extracting pertinent features such as authorship, publication date, and textual patterns. Following the extraction, the study utilizes natural language processing (NLP) to analyze and compare content, pinpointing potential instances of copyright infringement by identifying significant overlaps and stylistic similarities with registered works. Subsequently, we introduce a risk assessment model developed through supervised machine learning. This model is trained on a labelled dataset comprising instances of both copyrighted and non-copyrighted works, along with known cases of copyright infringement. By analyzing the extracted features, the model assesses the probability of infringement, categorizing risks into high, medium, and low categories. This stratification allows stakeholders to prioritize enforcement actions and resources efficiently. The study further explores the implementation of various ML algorithms, including decision trees, support vector machines, and neural networks, to determine the most effective approach for copyright protection in the literary domain. We evaluate the models based on accuracy, precision, recall, and F1-score metrics, emphasizing their capacity to generalize and operate in dynamic, real-world environments. [ABSTRACT FROM AUTHOR]

Recently hybrid cloud solutions integrating public and private cloud is proposed to address the privacy and security concerns faced by Enterprises in their data offloading decisions. In these solutions, the transformed data is kept in public cloud while transformation keys are kept in private cloud. The existing works for data transformation used in hybrid clouds does not address multiple objectives of privacy, security, fine grained access control, utility preservation for mining and data retrieval efficiency. This work proposes a multi objective data transformation technique for hybrid cloud to address all these objectives. The proposed solution is built on attribute based hierarchical data access control with hierarchy selection based on joint consideration of security, utility preservation and retrieval efficiency. The proposed solution is able to provide 5% higher security strength, 1.34% higher clustering accuracy over perturbed data and 29.95 % higher data retrieval efficiency over perturbed data compared to existing works. [ABSTRACT FROM AUTHOR]

With the development of network technology, online shopping is becoming more and more convenient. But the increasing number of products also makes it difficult for consumers to make the right decision. When there is no apparent market demand, how to recommend products with commercial potential to customers has become an urgent problem for businesses to solve. This paper proposes e-commerce product recommendation based on user preference and association rule algorithm aiming at the problems existing in e-commerce product recommendation. Firstly, this paper constructs a user interest modeling method. Through analyzing users' interests and preferences, to provide users with timely and accurate personalized services. Then, the FP_Growth algorithm is optimized and improved. A more effective CTE-MARM algorithm is designed, and an association rules database based on user benefit items is constructed and analyzed jointly. Analyze products with strong correlations. According to consumers' interest levels, TOP-N is the best product choice. Experiments show that the algorithm has higher prediction accuracy. The research results of this project can not only improve enterprises' ability to analyse data and provide data support for enterprises to carry out effective marketing management. [ABSTRACT FROM AUTHOR]

This paper uses the hybrid leapfrog algorithm to mine user information in encrypted advertisements effectively and intelligently. This method handles the nonlinearity of the original data by mapping it into kernel space. The representation of the original ciphertext in the kernel space is obtained by sparsely reconstructing the encrypted original advertising data. Build a corresponding scoring mechanism and select the best advertising data characteristics. The selected data were clustered using the data fuzzy clustering method based on the improved hybrid leapfrog. Set the adjustment coefficient to improve the local optimization performance of hybrid frog leaping. This algorithm uses the tightness and separation in genetic algorithms and constructs a fitness function to determine the clustering critical value. This enables the practical, intelligent mining of homomorphic passwords with privacy protection. Experimental results show that the method proposed in this article can effectively improve the convergence speed and accuracy of clustering. Improve Blowfish by combining multi-threading, sharing encryption and other methods. This enables encryption and decryption of large amounts of model data. The research of this project has very important research value in improving the security performance and effectiveness of cryptographic algorithms. [ABSTRACT FROM AUTHOR]

The main objective of the study is to address the lack of comprehensive management technology in student campus life in universities. Starting from the life trajectory data of students in campus life, a trajectory mining model combining data mining technology and university information system is designed. In addition, an applied clustering algorithm is designed to classify different trajectory feature types. The research results show that in actual trajectory analysis, the categories of action trajectories from dormitories to canteens, from 0 to 4, are 87.64%, 87.86%, 86.97%, 88.63%, and 88.71%, respectively, which are the most matched effective action trajectories. It can be seen that the trajectory analysis model designed in the study is effective and can provide assistance for the comprehensive academic management of college students. [ABSTRACT FROM AUTHOR]

Objective To determine reference intervals (RI) for fasting blood insulin (FBI) in Brazilian adolescents, 12 to 17 years old, by direct and indirect approaches, and to validate indirectly determined RI. Methods Two databases were used for RI determination. Database 1 (DB1), used to obtain RI through a posteriori direct method, consisted of prospectively selected healthy individuals. Database 2 (DB2) was retrospectively mined from an outpatient laboratory information system (LIS) used for the indirect method (Bhattacharya method). Results From DB1, 29345 individuals were enrolled (57.65 % female) and seven age ranges and sex partitions were statistically determined according to mean FBI values: females: 12 and 13 years-old, 14 years-old, 15 years-old, 16 and 17 years-old; and males: 12, 13 and 14 years-old, 15 years-old, 16 and 17 years-old. From DB2, 5465 adolescents (67.5 % female) were selected and grouped according to DB1 partitions. The mean FBI level was significantly higher in DB2, on all groups. The RI upper limit (URL) determined by Bhattacharya method was slightly lower than the 90 % CI URL directly obtained on DB1, except for group female 12 and 13 years old. High agreement rates for diagnosing elevated FBI in all groups on DB1 validated indirect RI presented. Conclusion The present study demonstrates that Bhattacharya indirect method to determine FBI RI in adolescents can overcome some of the difficulties and challenges of the direct approach. [ABSTRACT FROM AUTHOR]

Assessing tourists' social media perceptions is crucial in today's digital age. Due to its industrial reputation, Johor Bahru, Malaysia, has been overlooked for heritage tourism. This data mining study examines tourists' experiences and motivations for visiting Johor Bahru's heritage buildings of 2,369 Google Review and Tripadvisor comments using web crawlers scripted in Python. The content analysis identified six themes associated with tourist motivation: activity, accessibility, diversity, attachment, inclusivity, and appearance. Even though their aesthetics are subdued, Johor Bahru's public historic sites thrive on accessibility and active activities, unlike magnificent royal edifices. Despite their architectural value, royal structures attracted fewer tourists due to the absence of diversity, inclusivity, and activities and restrictive measures. Maintaining the city's legacy and architectural identity requires highlighting these buildings. This study accentuates social media-driven popular views. The findings may encourage local authorities and stakeholders to strengthen Johor Bahru's identity and promote heritage tourism. [ABSTRACT FROM AUTHOR]

Transportation infrastructures and operations can be severely impacted during flood events, leading to significant disruptions to the flow of goods and services. Although numerous studies have evaluated the direct impacts of flood events on the performance of transportation infrastructures, the indirect impacts or cascading effects have been rarely assessed. Hence, this paper examines the cascading effects of floods on transportation infrastructure using data mining algorithms. First, 33 effects of flood events on transportation infrastructure have been identified based on data collected for multiple flood events in New York and New Jersey. Second, association rule mining analysis was implemented to identify the key co-occurrences between flooding and the different events. Third, network analysis was conducted to quantify the co-occurrences or key combinations among the events. Fourth, cluster analysis was used to group or prioritize the cascading effects and co-occurring events into highly connected clusters to identify the most critical ones based on two scenarios: (1) without consideration of co-occurrences (Scenario 1); and (2) with consideration of co-occurrences (Scenario 2). The findings provided insights that while some cascading impacts could be individually critical/frequent (under Scenario 1), other cascading impacts could also result due to a combination of different effects that might not be perceived to be critical on the individual level but rather become critical when combined with other cascading events (under Scenario 2). The outcomes of this paper demonstrate the importance of considering the co-occurrences between the events and cascading effects, rather than analyzing them in isolation. This study adds to the body of knowledge by offering an analytical approach that could be used to identify and prioritize critical cascading effects of flood events on the operations and performance of transportation infrastructures. [ABSTRACT FROM AUTHOR]

The current research is based on the explanation of the optimal model for predicting the performance of companies using data mining techniques. The method of this research is of the applied type, in terms of the way of doing the work, it is of the descriptive-causal research type, and in terms of the time dimension, it is of the post-event research type. In the first step, by referring to databases such as thesis, articles and similar researches, the required literature was collected in order to write the theoretical foundations and background of the research. In the following, the information of the investigated companies selected as a statistical sample, whose information is available in the form of data banks on CDs and is under the supervision and review of the responsible institutions, was audited by referring to the financial statements and new implementation software was compiled. The mentioned information included the financial data of the companies admitted to the Tehran Stock Exchange for a period of 10 years from the beginning of 2011 to the end of 2014. Finally, the findings showed that the firefly algorithm, genetic algorithm and evolutionary algorithm were effective in predicting the ratio of QTobins, return on equity and return on equity, and the firefly algorithm had a higher power to predict the ratio of QTobins, return on equity and return. has shares. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this research is to develop a robust multicriteria method to classify suppliers into ordered categories and its validation in real contexts. The proposed technique is based on a property of net flows of the PROMETHEE method and uses global and local search concepts, which are common in the optimisation field. The results obtained are compared to those from the most cited sorting algorithm, and an empirical validation and sensitivity analysis is performed using real supplier evaluation data. Furthermore, it does not require additional information from decision‐makers as other sorting algorithms do for assigning incomparable or indifferent alternatives to groups. An extension of the silhouette concept from data mining is also contributed to measure the quality of ordered classes. Both contributions are easy to apply and integrate into decision support systems for automated decisions in the supply chain management. Finally, this practical approach is also useful to classify customers and any type of alternatives or actions into ordered categories, which have an increasing number of real applications. [ABSTRACT FROM AUTHOR]

Purpose: Sacubitril/valsartan is extensively used in heart failure; however, there are few long-term safety studies of it in a wide range of populations. The aim of this study was to evaluate sacubitril/valsartan-induced adverse events (AEs) through data mining of the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: Reports in the FAERS from the third quarter of 2015 (FDA approval of sacubitril/valsartan) to the fourth quarter of 2023 were collected and analyzed. Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and empirical Bayesian geometric mean (EBGM) algorithms were adopted in data mining to quantify signals of sacubitril/valsartanassociated AEs. Results: A total of 12,001,275 reports of sacubitril/valsartan as the "primary suspected (PS)" and 99,651 AEs induced by sacubitril/valsartan were identified. More males than females reported AEs (59.95% vs. 33.31%), with the highest number of reports in the 60-70 years age group (8.11%), and most AEs occurred < 7 days (14.13%) and = 60 days (10.69%) after dosing. Sacubitril/valsartan-induced AE occurrence targeted 24 system organ classes (SOCs) and 294 preferred terms (PTs). Of these, 4 SOCs were strongly positive for all four algorithms, including cardiac disorders, vascular disorders, ear and labyrinth disorders, and respiratory, thoracic and mediastinal disorders. Among all PTs, consistent with the specification, hypotension (n = 10,078) had the highest number of reports, and dizziness, cough, peripheral swelling, blood potassium increased, and renal impairment were also reported in high numbers. Notably, this study also discovered a high frequency of side effects such as death, dyspnea, weight change, feeling abnormal, hearing loss, memory impairment, throat clearing, and diabetes mellitus. Conclusion: This study identified potential new AE signals and gained a more general understanding of the safety of sacubitril/valsartan, promoting its rational adoption in the cardiovascular system. [ABSTRACT FROM AUTHOR]

A tiebreak in tennis is one of the critical moments where players are expected to excel under mental pressure and maintain high level of performance. Despite the importance of tiebreak points, research exploring the performance of male and female players during such match phrase remains limited. This study aimed to investigate i) the overall tiebreak performance of male and female players in relation to the outcome, ii) to examine their point-level performance by considering different contextual variables. A total of 535 tiebreaks comprising 6380 points from the 2016–2021 US Open men’s and women’s singles matches were collected. The difference in match performance between winning and losing players within the entire tiebreak game was explored. A subsequent decision tree analysis was then used to analyse the effect of the contextual and performance variables on tiebreak point-by-point outcome. The results showed that male and female Winning players outperformed the Losing players in 1st Serve, Serve Width and Net approach performance. The analysis of point-level performance showed that Net point, Score scene, and Point server substantially impacted tennis players’ tiebreak outcome. These findings provide valuable insight for coaches and players, informing tiebreak tactics tailoring and training in relevance to different match status. [ABSTRACT FROM AUTHOR]

Predictive modelling in the education domain can be utilised to significantly improve teaching and learning experiences. Massive Open Online Courses (MOOCs) generate a large volume of data that can be exploited to predict and evaluate student performance based on various factors. This paper has two broad aims. Firstly, to develop and tune several Machine Learning (ML) models to perform classification tasks on the dataset to predict student performance, including Linear Regression, Logistic Regression, Random Forests, K-Nearest Neighbours, and more. Secondly, to evaluate the efficacy of these ML models and identify those which are best suited to this task. The categories of data utilised in achieving these aims include (i) demographic information, (ii) academic background, and (iii) interaction with MOOC course materials. The research procedure comprises five phases: data exploration to analyse the dataset, feature engineering which involves discerning the most important features and converting them into a format decipherable by the ML models, model building, model evaluation by measurement of accuracy, and subsequent comparative evaluation between the different models. The results achieved in this study are expected to have implications on how MOOC platforms utilise data to improve user experience. As indicated by the findings of this study, the data collected by these platforms may be used to predict performance with accuracy of over 77%; this extracted information can be exploited to enhance educational theory or practices in the context of MOOCs, for instance by implementing varying teaching methodologies or providing different types of resources based on predicted performance. [ABSTRACT FROM AUTHOR]