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Life Cycle Assessment (LCA) has been used to evaluate the life-cycle Greenhouse Gas (GHG) emissions of pulp and paper production, and most previous studies rely on process-based models for specific product types (e.g., printing paper), industry-average data, or information from a few mills. In this work, a data-mining approach is used to quantify GHG emissions intensities of different paper products manufactured by the U.S. mills. Facility-level emission data collected from publically available governmental databases and mill-level production data collected from the private sector were integrated to track the GHG emissions for different product lines and paper products in mills (in total, 165 mills were matched and analyzed). The results highlight the ranges of GHG emissions intensities by different product groups and categories, and can be used as a transparent data source for LCA practitioners, policymakers, and the pulp and paper industry to perform further analysis on carbon accounting and strategic planning for GHG mitigation. [ABSTRACT FROM AUTHOR]

The growing number of scientific papers and document sources underscores the need for methods capable of evaluating the quality of publications. Researchers who are looking for relevant papers for their studies need ways to assess the scientific value of these documents. One approach involves using semantic search engines that can automatically extract important knowledge from the growing body of text. In this study, we introduce a new metric called "MAATrica," which serves as the foundation for an innovative method designed to evaluate research papers. MAATrica offers a new way to analyze and categorize text, focusing on the consistency of research documents in the life sciences, particularly in the fields of medicinal and nutraceutical chemistry. This method utilizes semantic descriptions to cover in silico experiments, as well as in vitro and in vivo essays. Created to aid in evaluation processes like peer review, MAATrica uses toolkits and semantic applications to build the proposed measure, identify scientific entities, and gather information. We have applied MAATrica to roughly 90,000 papers and present our findings here. [Display omitted] • MAATrica is a novel metric for assessing the coherence of methodologies in research papers within the fields of medicinal and nutraceutical chemistry. • MAATrica utilizes SciWalker (SW) as semantic search engine and ontologies to automate knowledge extraction and evaluate research papers. • MAATrica metric has been tested and validated using a dataset comprising approximately 90,000 papers within the SW platform. • MAATrica's reliability has undergone testing through comparisons with manual evaluations, revealing strong agreement and potential support for peer review. • MAATrica employs a user-controlled and customizable ontology, enabling personalized analysis of research papers in the fields of medicinal and nutraceutical chemistry. [ABSTRACT FROM AUTHOR]

Abstract: There is a large value in making Pulp and Paper mills more energy efficient. ABB has developed an energy assessment service where opportunities to save energy in the paper machine are identified. The energy assessment is done by quantifying energy flows, benchmarking energy users, data mining and steam sensitivity analysis and by experiments and additional measurements at the paper machine. Energy quantification helped in identifying main energy consumer, benchmarking was useful to assess the gap between operating performance and best performance whereas data mining and steam sensitivity analysis helped in studying the impact of key operating variables on performance of paper machines. After the assessment an action plan was presented to the mill for energy efficiency improvement together with a return on investment. [Copyright &y& Elsevier]

Breakthrough research may signal shifts in science, technology, and innovation systems. Early identification of breakthrough research is important not only for scientists, but also for policy makers and R&D experts in developing R&D strategies and allocating R&D resources. Researchers mostly use scientific papers data to identify potential breakthrough research, but they rarely make use of Twitter data related to scientific research and machine learning methods. Analysis of Twitter data is of great significance for us to understand the public's perception of potential breakthrough research and to identify potential breakthrough research. Machine learning methods can assist us in predicting the trend of events by utilizing prior knowledge and experience. Therefore, this paper proposes a framework for identifying potential breakthrough research using machine learning methods with scientific papers and Twitter data. We select solar cells as a case study to verify the valid and flexible of this framework. In this case, we use machine learning method to discover potential breakthrough research from scientific papers, and we use Twitter data mining to analyze Twitter users' sense of and response to the discovered potential breakthrough research, which aims to achieve a more extensive and diverse assessment of the discovered potential breakthrough research. This paper contributes to identifying potential breakthrough research, as well as understanding the emergence and development of breakthrough research. It will be of interest to R&D experts in the field of solar cell technology. • We proposed a framework for identifying potential breakthrough research using machine learning method. • We found 8 potential breakthrough researches in the field of solar cell technology in 2015. • Twitter data mining could be used to assist in identifying potential breakthrough research. [ABSTRACT FROM AUTHOR]

Although software has helped researchers conduct research, little is known of the impact of software on science. To fill this gap, this article proposes an improved bootstrapping method to extract software entities from full-text papers and assess their impact on science. Evaluation results show that the proposed entity extraction system outperforms three baseline methods on extracting software entities from full-text papers. The proposed method is then used to learn software entities from all papers published in PLoS ONE in 2014. More than 2000 unique software entities are obtained which accounted for more than 20,000 mentions and more than 7000 citations. The paper finds that software is commonly used in the scientific community along with a substantial uncitedness. [ABSTRACT FROM AUTHOR]

The fact-condition statement plays a significant role in the geological papers, where geological theory or phenomenon as a fact is constrained by its precondition. This constraint relation conveys a lot of key information in geological papers, but it has been ignored in most of studies on the information extraction. Although the rule-based algorithms have been proposed in the previous studies to extract the relationship between facts and conditions, but their low accuracy limit usability. Therefore, this paper introduces a series of mechanisms and methods to improve the extraction accuracy. We firstly utilize three kinds of pooling for extracting the semantic features of fact-condition tuples to find that balanced semantic representations can more accurately reflect their relations. In addition, semantic correlation between facts and conditions is also designed to mine their relations via the measurement of semantic similarity or semantic distance. Based on the above, the Deep Structured Semantic Model with Multi-Grained Representation (MGR-DSSM) is proposed. In this model, the Deep Structured Semantic Model (DSSM) is utilized two individual projections for denoising semantic correlation between facts and conditions, while the Multi-Grained Representation (MGR) is designed for encoding their semantic correlation at multiple granularities. Furthermore, the linear MGR-DSSM with SciBERT achieves the best result with 70.41% of accuracy and 74.55% of F1 score, obviously outperforming other baseline models. Finally, the linear MGR-DSSM with SciBERT is adopted to extract super relation from 722 geological paper abstracts, and the geological knowledge graph with fact-condition statement is built. With more accurate relation between fact and condition, the information validity of geological knowledge graph is enhanced. • The relation between facts and conditions play an essential role in the geological paper, the extraction of which is studied for the first time. • A series of mechanisms are proposed to improve the accuracy of fact-condition relation extraction. • The MGR-DSSM is proposed, which obviously outperforms other baselines in super relation extraction. [ABSTRACT FROM AUTHOR]

Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V's) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V's) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision. [ABSTRACT FROM AUTHOR]

Lower-dimensional representation of scientific text has attracted much attention among researchers due to its impact on many data mining and recommendation tasks. This paper studies two main research streams in scientific literature representation. First, both local and distributed representation viewpoints are reviewed and their advantages and disadvantages in lower dimensional representation are discussed. The paper then proposes a novel hybrid distributed technique for text representation. Using scientific articles as the major source of textual information, both the article's content and citation network are used to build a distributed and universal lower dimensional representation. The superiority of the new technique to the traditional methods is then justified in predicting the existence of links in large citation graphs. [ABSTRACT FROM AUTHOR]

Knowledge discovery for data streaming requires online feature selection to reduce the complexity of real-world datasets and significantly improve the learning process. This is achieved by selecting highly relevant subsets and minimising irrelevant and redundant features. However, researchers have difficulties in addressing various forms of data. The goal of this article is to present a state-of-the-art review of feature subset selection based on the data form for the high-dimensional data used in online streaming. Through a systematic literature review assessing journal and conference papers from the past five years, detailed discussions on traditional feature selection and online feature selection were presented. Subsequently, a taxonomy of the challenges related to OFS provides a comprehensive review of state-of-the-art OFS and the benchmark methods. Several data forms were identified based on the extensive review: group stream, multi-label, capricious, imbalance, and feature drift. Using critical analysis, the evaluation metrics of online feature selection methods were compared from the perspectives of threshold initialisation, accuracy, high dimensionality, running time, relevancy, and redundancy for the optimal feature subset. An online feature selection framework was derived to illustrate the relationship between the application area, data form, online feature selection methods, evaluation metrics, and tools. Finally, the findings and potential directions for future research were thoroughly discussed. It is suggested that future researchers explore the derived framework and aim to advance each method. • Online feature selection methods for high dimensional online streaming data according to data forms and correlation measures. • A taxonomy of OFS challenges and the OFS framework was derived to illustrate the relationship of its ecosystems. • Significant findings in the adjustment of threshold dimensionality of data, relevancy and redundancy state. [ABSTRACT FROM AUTHOR]

This paper puts forward a new mental health evaluation model based on data mining technology, and analyzes the factors that affect mental health. The model understands the influencing factors according to the mental health status of different student groups, then reduces the dimensionality of the original data through principal component analysis and discriminant analysis, and finally establishes a dependent variable prediction model through regression analysis. The experimental results show that the method proposed in this paper can effectively predict students' mental health status. [ABSTRACT FROM AUTHOR]

The existing forensics systems are developing in a more and more perfect direction in terms of forensic steps, legal constraints, audit supervision, etc., but they are still not fully applicable to legal forensics. A general legal forensics system must have a complete forensics process to ensure the smooth progress of the forensics work. One of the purposes of legal forensics is to use legal means to bring criminals to justice. Only by ensuring the legality of the evidence can it be accurately identified case, and therefore also to ensure the legality of the evidence collection process. In order to design a more powerful legal forensics system, this paper introduces a data mining algorithm to extract valuable information from massive data through association rules to improve the efficiency of forensics. At the same time, this paper also proposes three coding schemes for the integrity of the evidence object, and compares the forensic effects of the three schemes. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet finance, the problem of financial fraud has become increasingly prominent, which has brought severe challenges to the security and stability of the financial industry. This paper aims to use artificial intelligence (AI) technology to empower data mining models to build an efficient financial fraud detection and prevention system. First of all, through the analysis of the development trend of Internet finance and the current situation of financial fraud, the limitations of traditional prevention and control measures in the face of big data and complex fraud modes are revealed. Secondly, the paper introduces the application prospect of AI technology in the financial field, especially the advantages and potential in data mining and fraud detection. Then, the data mining techniques adopted are discussed in detail, including the application of machine learning-based model recognition and artificial intelligence algorithms, as well as data preprocessing and feature engineering in big data environment. Further, the paper describes the system architecture design and key technology implementation, including model training and optimization, real-time monitoring and early warning solutions. Finally, through the in-depth analysis and evaluation of the experimental results, the paper verifies the remarkable effect and feasibility of the system in financial fraud detection and prevention, and provides a new technical guarantee and risk management strategy for the financial industry. The experimental results show that the model has achieved good results in the actual data in the financial field, and provides effective technical guarantee and support for financial security. [ABSTRACT FROM AUTHOR]

The main aim of the study was to effects evaluation of physical and chemical indicators on water turbidity by data mining algorithm. In order to perform the evaluation experimental measurements were performed to acquire data for the statistical analysis. The data represents the physical and chemical properties of clean water. Adaptive neuro fuzzy inference system (ANFIS) was used as data mining algorithm to determine the effects of chemical and physical indicators on water turbidity. The obtained results could be used to improve and to maintain the quality of the clean water. • To analyze water turbidity by data mining approach. • Experimental investigation was made to acquire data for the statistical analysis. • The data represents the chemical and physical properties of the clean water. • To maintain the quality of the clean water. [ABSTRACT FROM AUTHOR]

Missing data imputation is a critical task in the data pre-processing stage to ensure the quality, stability, and reliability of machine learning models. If missing values are imputed incorrectly, it can result in erroneous predictions and inconsistent model performance. Traditional imputation methods often struggle with complex data patterns having non-linearity and uncertainties. By integrating soft clustering principles, the proposed work provides a flexible framework for imputing missing data by taking into account the underlying inherent data pattern. To address the missing data challenges, this paper presents a novel imputation technique called Radial Basis Kernel Intuitionistic Fuzzy Ⅽ-Means Imputation (KIFCMI), which builds upon the standard Intuitionistic Fuzzy Ⅽ-Means (IFCM) technique. KIFCMI explores the application of centroid-based imputation using IFCM by incorporating the RBF kernel-induced metric in the data space as a replacement for the original Euclidean norm metric. By employing a kernel function, KIFCMI enables the clustering of data that lacks linear separability within the original space, enabling the formation of homogeneous clusters in a space of higher dimensionality. The effectiveness of the proposed imputation technique is validated on ten diverse real-world datasets obtained from the Public Library UCI with 10% and 20% missing data. The comparative analyses of the proposed technique KIFCMI, is carried out with three conventional techniques, namely fuzzy c-means imputation (FCMI), kernelized fuzzy c-means imputation (KFCMI), and intuitionistic fuzzy c-means imputation (IFCMI). The experimental results using two performance measures, namely RMSE and MAE, showcase the robustness and versatility of the proposed technique across other imputation outcomes. This research paper contributes to the evolving landscape of missing data imputation, offering insights into the practical applications of fuzzy clustering techniques. [ABSTRACT FROM AUTHOR]

We examine trends in words/terms published in Health & Place between 1995 and 2018. Terms used have diversified over this period, including a decline in 'health' overall, as well as greater usage of 'diet', 'alcohol' and 'depression'. There was a noticeable increase in using 'neighbourhood' to describe context. Topic modelling reveals that papers are increasingly concerned with specific aspects of place (e.g. the built environment). Our findings emphasise that Health & Place has matured into the leading outlet for examining the geographical dimensions of health with important and enduring insights that continue to inform research and policy. [ABSTRACT FROM AUTHOR]

Condition monitoring and predictive maintenance applications receive ongoing scientific attention in production technology. Larger companies, especially machine and component manufacturers, already offer related products. Small and medium-sized enterprises (SMEs) in particular show interest in developing and offering solutions in this market themselves to gain economic advantages, to improve resource utilization of their machines or to be able to offer these advantages to their own customers. In the development process, however, they often encounter problems already in the digitization of the machines. The first hurdle is to obtain an analysis-capable data set. This is due to the fact that common and established general data mining development process models, such as CRISP-DM, do not focus on production technology, causing difficulties for engineers during deployment. A problem with existing process models is the limited practicality in the engineering domain due to restricted adaptability. In a previous paper, a guideline for engineers for data mining suitable digitization of production machines was developed in order to solve these problems. The related results were provided in the context of a project for condition monitoring of mixing machines. In this paper, the proposed method is applied to components of a 5-axis CNC milling machine in three different monitoring use cases. A complete workflow is presented, including effect analysis, sensor selection, formulation of predictive scenarios, data preparation, training of machine learning algorithms and vizualization. Data and documentation are provided alongside this publication. [ABSTRACT FROM AUTHOR]

Access points to Heliophysics information are often poorly inter-linked to one another, making it challenging for researchers to integrate information, mature their understanding, and incorporate more complex ideas and relationships into their analyses. The authors reason this behavior to be a direct conseaquence of the lack of infrastructure for knowledge and data discovery in Heliophysics. Building an online library resource for Heliophysics addresses this gap in resources, and can also address other troublesome gaps related to community-building, such as access to help from other scientists, example analysis tutorials, and collaboration opportunities. This paper envisions a resource that better connects existing Heliophysics information and knowledge sources, the LIbrary for KnowledgE and Discovery (LIKED), which will be an online resource that interlinks information across databases (e.g., observational and publication), researcher interactions (e.g., conferences and discussion boards), and educational materials. The result is a rich and searchable environment to provide users a more productive experience for research and collaboration. In this paper, we provide the conceptual model for LIKED. We expand to detail how LIKED will serve to connect the various knowledge and research components available internationally together in an uniformly searchable manner. Finally, this work outlines various contributions the community can make towards this goal. [ABSTRACT FROM AUTHOR]

Real-time semantic segmentation, as a key technique for scene understanding, has been an important research topic in the field of computer vision in recent years. However, existing models are unable to achieve good segmentation accuracy on mobile devices due to their huge computational overhead, which makes it difficult to meet actual industrial requirements. To address the problems faced by current semantic segmentation tasks, this paper proposes a lightweight bilateral asymmetric residual network (LBARNet) for real-time semantic segmentation. First, we propose the bilateral asymmetric residual (BAR) module. This module learns multi-scale feature representations with strong semantic information at different stages of the semantic information extraction branch, thus improving pixel classification performance. Secondly, the spatial information extraction (SIE) module is constructed in the spatial detail extraction branch to capture multi-level local features of the shallow network to compensate for the lost geometric information in the downsampling stage. At the same time, we design the attention mechanism perception (AMP) module in the jump connection part to enhance the contextual representation. Finally, we design the dual branch feature fusion (DBF) module to exploit the correspondence between higher-order features and lower-order features to fuse spatial and semantic information appropriately. The experimental results show that LBARNet, without any pre-training and pre-processing and using only 0.6M parameters, achieves 70.8% mloU and 67.2% mloU on the Cityscapes dataset and Camvid dataset, respectively. LBARNet maintain a high segmentation accuracy while using a smaller number of parameters compared to most existing state-of-the-art models. [Display omitted] • This paper proposes a Bilateral Asymmetric Residual (BAR) module, a Spatial Information Extraction (SIE) module, an Attention Mechanism Perception (AMP) module and a Dual Branch Feature Fusion (DBF) module. • A Lightweight Bilateral Asymmetric Residual Network (LBARNet) for real-time image semantic segmentation is proposed in this article. • The experimental results show that LBARNet achieves 70.8% and 67.2% segmentation accuracy on two challenging datasets (Cityscapes and CamVid) using only 0.6M parameters. [ABSTRACT FROM AUTHOR]

The Triple Ionosophere PhotoMeter (TRIPM) is designed to make the disk observations of the Earth airglow emissions at OI 135.6 nm and N 2 Lyman-Birge-Hopfield (LBH) band that flew aboard the newly launched early morning satellite Fengyun-3E (FY-3E) which was launched on July 5th, 2021. This is the first attempt to obtain the global twilight airglow data in the far ultraviolet band using Ionosophere PhotoMeter. The seasonal behavior of the twilight airglow at the OI 135.6 nm and N 2 LBH band is exhibited and interpreted in the paper. Validity of the TRIPM data is analyzed by comparison with the simulation results of the Global Airglow (GLOW) and the simulation results reveal good consistency with the observational results. Furthermore, based on the ionospheric contributions calculated by GLOW model, we extract the ionospheric information from the TRIPM 135.6 nm emissions. This means the ionospheric signature in the 135.6 nm radiance at the dawn and dusk times can be provided. Comparison between the ionospheric 135.6 nm radiance due to O+ and electron radiative recombination with GPS TEC data shows a similar morphology of equatorial arcs and seasonal variation of the ionosphere. [ABSTRACT FROM AUTHOR]

This paper explores the utilization of randomized SVD (rSVD) in the context of kernel matrices arising from radial basis functions (RBFs) for the purpose of solving interpolation and Poisson problems. We propose a truncated version of rSVD, called trSVD, which yields a stable solution with a reduced condition number in comparison to the non-truncated variant, particularly when manipulating the scale or shape parameter of RBFs. Notably, trSVD exhibits exceptional proficiency in capturing the most significant singular values, enabling the extraction of critical information from the data. When compared to the conventional truncated SVD (tSVD), trSVD achieves comparable accuracy while demonstrating improved efficiency. Furthermore, we explore the potential of trSVD by employing scale parameter strategies, such as leave-one-out cross-validation and effective condition number. Then, we apply trSVD to solve a 2D Poisson equation, thereby showcasing its efficacy in handling partial differential equations. In summary, this study offers an efficient and accurate solver for RBF problems, demonstrating its practical applicability. The code implementation is provided to the scientific community for their access and reference. [ABSTRACT FROM AUTHOR]

With the development of big data technology, it is necessary to quickly and accurately mine the information contained in data. In this paper, we study the selection of kernel function types and the selection of kernel function parameters for support vector machines under classification and regression problems, and experimentally verify their regression prediction performance and classification performance on scientific datasets. Firstly, the scientific data mining model is constructed, the model design principle is clarified, and then linear kernel function, Gaussian kernel function, and polynomial kernel function are selected for the comparison experiments, and the performance classification and regression prediction of the data are finally obtained. [ABSTRACT FROM AUTHOR]

Multi-label classification has recently attracted research interest as a data mining task. Many current applications in data mining address problems that have instances belonging to more than one class. This requires the development of new efficient methods. Instance selection has been used in multi-label learning to improve the execution time and classification performance of many learning methods. Following the single-label approach, instance selection has been applied by selecting or unselecting the same instances for all labels. In this paper, we present a different and novel approach. An instance might be useful for some labels and harmful for others; therefore, our algorithm allows each instance to be discarded, selected, or only partially selected for use in the classification of certain labels. An extensive comparison using 45 datasets shows the usefulness of our approach in improving the current instance selection methods for multi-label problems, as well as the ability of our algorithm to compete with other more complex multi-label classification methods. • This paper presents a new framework for instance selection for multi-label datasets. • The method improves several state-of-the-art methods. • The method is scalable to large multi-label datasets. • Interesting new research lines are opened by the framework. [ABSTRACT FROM AUTHOR]

According to the demand of sports athletes' scientific training, combined with data mining technology, a set of sports training model which is suitable for China's national conditions and has a higher level of scientific research is established. Starting from the data mining technology, this paper expounds its connotation and several common methods in detail. Based on the demand of sports training, this paper studies the quality control of sports training and the statistical system of tactical combat. On this basis, an improved output method of Aprilio algorithm is introduced to enhance the correctness of systematic evaluation. On this basis, three algorithms, classic, DC-Apriori and modified Apriori, are simulated and compared. The results show that the improved Apriori algorithm studied in this project can provide better decision support for the training model. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, knowledge management has become an important means for enterprises, organizations, and individuals to gain advantages in competition. The design and implementation of Knowledge management is one of the key links of knowledge management. This paper discussed the design of Knowledge management based on Support Vector Machine (SVM), in order to improve the efficiency and quality of knowledge management. This paper introduced the application of SVM in Knowledge management, and discussed how to design an excellent Knowledge management. The research results showed that the response time of the Knowledge management designed in this paper was 87 seconds when the number of users was 300. When the number of users exceeded 300, although the response time was longer, its growth rate slowed down. SVM can be used in knowledge classification, entity recognition and relationship extraction, and can greatly improve the efficiency of Information extraction and knowledge management. [ABSTRACT FROM AUTHOR]

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper. • This pre-Editorial provides an overview of some hot topics in AI for life. • The benefit for the reader is to get a short, crisp, concise intro into AI. • Authors interested in this Special Issue will get a scope of topics. • The take away message is that in the future no biotechnology can do without AI. [ABSTRACT FROM AUTHOR]

In this paper, we examine the possibility of employing the idea of progressive-inductive (π) aggregation in the k-means algorithm. We base our work on the interactive visualization framework called Skydive which is a tightly coupled system that combines data aggregation and visual aggregation for smooth user interaction. It is based on an aggregate pyramid data structure (π-cube), allowing a visualization client to quickly filter the data. Skydive was designed for interactive data visualization in terms of expressiveness and efficiency. Therefore, the underlying data structure enabled access to data on desired levels of granularity. In this paper, we utilize this concept to speed up the process of clustering proposing π-means - a modified version of the k-means algorithm. This will allow us to explore further possible applications of π-cube towards interactive data exploration. [ABSTRACT FROM AUTHOR]

Social media platforms generate vast amounts of data that can be mined to gain insights into user behavior, preferences, and opinions. With the widespread adoption of mobile devices and the Android platform, social media data mining on Android has become an important area of research. In this paper, we present a systematic review of existing research related to social media data mining on Android. We conducted a comprehensive literature search and identified relevant research papers published between 2015 and 2022. We analyzed the papers to identify key themes and trends, and we discuss the strengths and weaknesses of current approaches. Our review revealed that most of the research in this area focuses on text data mining, and that machine learning techniques are commonly used for data analysis. We also found that there is a lack of research on image and video data mining, and that privacy and ethical issues are important considerations in this field. Based on our review, we propose future research directions for social media data mining on Android, including the development of more efficient and accurate machine learning algorithms, and the integration of multimedia data analysis. Our paper provides a valuable overview of existing research in this field and offers insights for researchers and practitioners interested in social media data mining on Android. [ABSTRACT FROM AUTHOR]

This paper focuses on home smart voice devices to address the problems of traditional mental health diagnosis and evaluation (e.g., the inability to monitor, analyze and assess mental health in real-time and the heavy reliance on psychologists and counselors for judgment). It constructs a model for monitoring, examining, and evaluating children's mental health using interactive communication and voice recordings between children and intelligent voice devices. The model uses techniques such as natural language processing and data mining based on a random forest algorithm. Meanwhile, this paper explores and investigates the feasibility of implementing the model on the ground in terms of technology development and technology preference. [ABSTRACT FROM AUTHOR]

Recent developments in the field of artificial intelligence has led to renewed interest in natural language processing. Named entity recognition (NER) is a classic problem in natural language processing. Investigating named entities is a continuing concern within information extraction. Names of people, organizations, locations, events, etc. are named entities (NEs). Although extensive research has been carried out on NER, limited study exists that explores NER in the Marathi language. The paper aims to provide a conceptual theoretical framework based on the gazetteer matching technique for named entity recognition system development for the Marathi language. A combination of rules and regular expressions is considered for temporal and numerical pattern recognition. The techniques presented in this paper are systematic, clear, and effective for morphologically rich language processing tool development. The system described in the paper reports a satisfactory performance with 62.64% NE identification and 72.27% NE classification accuracy. [ABSTRACT FROM AUTHOR]

COVID-19 is a pandemic that has resulted in numerous fatalities and infections in recent years, with a rising tendency in both the number of infections and deaths and the pace of recovery. Accurate forecasting models are important for making accurate forecasts and taking relevant actions. As a result, accurate short-term forecasting of the number of new cases that are contaminated and recovered is essential for making the best use of the resources at hand and stopping or delaying the spread of such illnesses. This paper shows the various techniques for forecasting the covid-19 cases. This paper classifies the various models according to their category and shows the merits and demerits of various fore-casting techniques. The research provides insight into potential issues that may arise during the forecasting of covid-19 instances for predicting the positive, negative, and death cases in this pandemic. In this paper, numerous forecasting techniques and their categories have been studied. The goal of this work is to aggregate the findings of several forecasting techniques to aid in the fight against the pandemic. [ABSTRACT FROM AUTHOR]

The domain of Social Collaboration Analytics (SCA) is gathering momentum due to the increasing importance of Enterprise Collaboration Systems (ECS) that are used to build performant digital workplaces. ECS collect and store large amounts of digital trace data, which reflect the collaborative work of employees. Collaborative work activity is usually supported by multiple systems, which complicates the analysis of the digital traces. The cross-system analysis of collaborative work is challenging because of the nature of the log files that are stored in heterogeneous formats and at different levels of log granularity. These challenges cannot easily be overcome with the help of existing approaches for data preprocessing. In this paper, approaches and frameworks for data mining, data preprocessing and SCA are adapted to design a special method for Social Process Mining (SPM). The DaProXSA approach was developed to obtain interpretable, high-quality event logs of multiple ECS and builds on existing literature as well as on findings from an analysis of leading ECS. DaProXSA contributes to the aim of discovering and interpreting patterns and processes that occur in computer-mediated collaborative work. [ABSTRACT FROM AUTHOR]

Today, higher education institutions are focused on understanding which factors are associated with the failure or success of students to, early on, be able to implement measures that can reduce the low performance of students and even dropout. The retention rate is positively and negatively influenced by factors belonging to several dimensions (personal, environmental, and institutional). We aim to use information from those dimensions to identify students enrolled in a Computer Networks course at risk of failing the subject. Besides, this needs to happen as early as possible, to be able to provide the students, for example, with extra support or resources to try to prevent that negative outcome. For predicting the grade level on the first test, the best accuracy obtained was 55%. However, most C-level grades were correctly classified, with 63% accuracy in predicting the students that are most at risk of failing, which is one of our main objectives. As for the prediction of the second test's grade level, the best accuracy obtained was 89% and concerned data regarding the students' interaction with the LMS together with students' grades history. All the C-level grades were correctly classified (100% accuracy) and so we were able to correctly predict every student at a high risk of failing. Using the procedure described in this paper, we are able to anticipate the students needing extra support, and provide them with different resources, to try to prevent their negative outcome. [ABSTRACT FROM AUTHOR]

Big data and its analysis are crucial for other applications like the Internet of Things (IoT), data mining, cloud computing, etc. The volume of data is expanding along with the internet and other digital technologies. Traditional systems, however, cannot process this volume of data. Analyzing this much data will take a lot of work. However, there are several difficulties in assuring data security and privacy, such as safeguarding against data leakage and security breaches, flaws in open databases, and third-party data sharing, among others. This paper's goal is to analyze the benefits of blockchain in big data as well as its drawbacks and identify potential future approaches. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive exploration of the development of an AI-driven Gen-Alpha education guidance indicator for Oman, employing cutting-edge machine learning techniques. The primary objective is to deliver a highly personalized support system and guidance mechanism that accompanies students throughout their academic journey. The research places a spotlight on the pivotal role of Artificial Intelligence (AI) within the realm of education. It skillfully demonstrates AI's robust capabilities in accurately forecasting student performance through the effective use of data mining and advanced machine learning techniques. An integral aspect of this research is the application of AI in aiding students to navigate the involved process of subject selection for their higher education pursuits. This multidimensional process encompasses exhaustive data collection, the detailed creation of a sophisticated machine learning model, and the application of a diverse array of state-of-the-art algorithms. Among these algorithms, K-Nearest Neighbors (KNN), Decision Trees, Random Forest, and Support Vector Machines (SVM) stand out prominently. Notably, the SVM algorithm emerges as the outright winner, delivering an extraordinary accuracy rate of 77%. This remarkable achievement underscores the model's unwavering robustness and its potential to redefine the educational landscape in Oman. In essence, this paper transcends the boundaries of conventional research and offers conclusive validation of AI's revolutionary potential in reshaping educational paradigms. By facilitating data-driven decision-making, the AI-driven Gen-Alpha education guidance indicator empowers students to embark on their educational journeys well-informed and confident, thereby playing a pivotal role in advancing Oman's education system. [ABSTRACT FROM AUTHOR]

The paper presents advancements in healthcare data capture through the application of image-based extraction techniques, which include sophisticated image processing techniques such as resizing and adaptive thresholding, for prescription information. With the increasing digitization of medical records, automating the extraction of relevant data from prescription documents has become crucial. This research explores the utilization of image processing and optical character recognition (OCR) methodologies to extract prescription information accurately. By converting prescription documents into image format and employing OCR algorithms, the text content is extracted and parsed for critical details such as medication names, dosages, and patient instructions. Notably, our methodology excels in overcoming limitations associated with handwritten documents, achieving an impressive accuracy rate of 98%. This image-based approach offers a streamlined and efficient method for capturing prescription data, reducing manual data entry efforts, and minimizing potential errors. Experimental evaluations demonstrate the effectiveness and accuracy of the proposed approach, highlighting its potential to enhance healthcare data capture and improve patient care. [ABSTRACT FROM AUTHOR]

Financial anomalies must be detected in order for financial institutions and regulatory bodies to manage risks and avoid fraudulent behavior. Financial anomaly detection is the practice of identifying unexpected or irregular financial transactions or patterns that may indicate fraudulent behavior or errors. It is crucial in today's digital era to prevent fraud, limit financial losses, and maintain secure financial systems. Various types of financial anomalies, such as credit card fraud, money laundering, financial statement fraud, and cryptocurrency fraud, pose significant risks to individuals and organizations. This review critically evaluates a selected research article on the use of blockchain technology in conjunction with data mining techniques to detect financial anomalies. The paper employs the case study method to demonstrate how well the suggested integrated system works in spotting financial anomalies. This review evaluates the article's methodology and conclusions and discusses its implications for practice. The report claims that merging data mining methods with blockchain technology can increase the precision and effectiveness of financial anomaly identification. This research advances knowledge about how block-chain technology and data mining techniques can be used to find financial abnormalities while also offering suggestions for further study and use. [ABSTRACT FROM AUTHOR]

Techniques for classifying data using data mining are now a day prevalent in agriculture. The method of classifying seeds involves grouping various seed varieties according to their morphological characteristics. To accomplish categorization of the typical Charotar region (generally comprising Anand and Kheda districts of the Gujarat State of India) Gujarat Wheat (GW) varieties (TRITICUM – AESTIVUM) viz. GW 273, GW 496, GW 322, LOK-1, and GDW 1255 (TRITICUM – DURUM), Weka Explorer was used. The features used are area, perimeter, solidity, aspect ratio, major and minor axis of seed kernel, Hue, Saturation, Value, and SF1 (empirical). Features reduction was done using Information Gain (IG) and its modified version Gain Ratio (GR). This paper compares performance of Tree based data mining algorithms in classifying wheat varieties. For classification we used purely tree-based machine learning algorithms viz. J48, Random Forest, Hoeffding Tree, Logistic Model Tree (LMT), and REPTree. LMT- logistics regression method gives higher accuracy 96.4% compared to other classifiers. Hoeffding Tree classifiers stood second with 96% accuracy. For validation 10-fold cross validation was used. By reducing the number of folds in cross validation performance of most algorithms decreased except J48. The percentage of correctly classified instance increased for all algorithms when features were selected by GR except for J48. [ABSTRACT FROM AUTHOR]

Geographic spaces is a vast and complex system involving multiple elements and nonlinear interactions of these elements, and rich in geographical phenomena, processes and patterns. Artificial intelligence methods (AI) are increasingly utilized to extract information of interest, patterns and insights from massive remote sensing (RS) images. Among them, two representative paradigms for RS interpretation are knowledge-driven symbolism and data-driven connectionism. Knowledge-driven approaches are certain and theoretically sound, which consider the regularities, formulae with accompanying theorems, and expert knowledge. However, it is difficult to address huge-scale RS data and complex nonlinear problems. The data-driven paradigm has stronger data learning and representation ability, yet poor interpretability, low trustworthiness of interpretation results, and reliance on massive labeled data. To address these limitations, we argue that current RS intelligent interpretation requires the guidance of geographic ideas and a unified theoretical framework rather than the independent use of remotely sensed data, duplicative development of interpretation models, or isolated applications of geographic information extraction. Thus, we introduce the concepts of trustworthy RS interpretation (TRSI) with multi-granularity of space, time, and attribute to facilitate the understanding of the morphology, types, and indicators (visual perception) and the perspective of the structure, evolution, and trends (geographical cognition) of complex geographical space. Second, integrated technologies of the TRSI with multiple scales and stages are developed, including a pixel-level visual perception, a geo-parcel-level quantitative inversion, and a scene-level geographical cognition. Finally, a RS big data interpretation system with quantitative semantic parsing is designed to support precise applications with cloud–edge-end collaboration. Rather than a simple overview and accumulation of traditional RS intelligent interpretation methods, this paper analyzes current challenges in RS interpretation and proposes new concepts, technologies, and applications of TRSI. It will empower us to perceive the complex geographical space comprehensibly and reveal its structural composition and evolutionary mechanism, serving geospatial decision applications such as evaluation, planning, and forecasting. [ABSTRACT FROM AUTHOR]

In the urban rail transportation industry, collecting various data, including the number of trips, the number of fleet breakdowns, the number of breakdowns of railway lines, etc., contributes significantly to future planning and optimization of resources and costs. We are faced with big data analysis due to the daily production of large volumes of data in the urban rail transport industry. This study aims to find a trip prediction model for Tehran Metro to do trip decision-making. This paper provides a brief introduction to knowledge discovery from failure databases and presents the data mining methodology. The relevancy of data mining for a trip prediction model will be depicted. In this sense, considering the relevance of data collected on the metro control center and intelligent trip prediction, this paper presents a functional architecture of a predictive trip model using data mining techniques. Data Mining will identify behavior patterns, allowing a more accurate early detection of faults in the daily operation. This research has investigated three situations, including delayed trips, incomplete trips, and canceled trips, using actual data recorded over the past four years, including non-programmed failures and their impact on trips. A model with more than 90% (90.48%) accuracy suggests a trip status forecast on this line. Also, the research has used Rapid Miner software and the Decision tree technique in data mining and its rules. Also, the kappa index of 0.792 is calculated for the validation model. Therefore, the proposed model enables the prediction of three types of incomplete traffic, delayed trips, and cancellation of trips due to emergency failure at the time of the operation of the Tehran Metro. [ABSTRACT FROM AUTHOR]

Numerous research on stunting supplementation interventions in Indonesia have been published. The information can be extracted through data mining, especially from academic research databases. In this paper, we presented a text mining-based literature review strategy to create a pipeline that researchers can use to accelerate the development of stunting supplementation intervention research in Indonesia. Utilizing various NLP (Natural Language Processing) techniques, data were crawled, processed, and visualized using Python. The crawling dataset used a module from the Pubmed API (Application Programming Interface) to collect literature papers. The NLTK (Natural Language Toolkit) module and itertools were used to process text data. The n-grams model was applied to process tokens into bigrams and trigrams. Text information was visualized using Matplotlib and Word cloud packages. There is an increasing number of publication in stunting supplementation intervention according to our result, which was observed from 2015 to 2021. West Java was the province where most of the stunting research has been conducted, as determined by research abstracts. Top occurrences obtained from the bigram and trigrams models calculation produced different terms. The word pairings that occurred the most frequently in the bigram and trigram model analyses were "child-aged" and "iron-folic-acid," respectively. The findings of this study are expected to help researchers to obtain the latest research topics related to stunting supplementation interventions in Indonesia. [ABSTRACT FROM AUTHOR]

The metaheuristics approach is important for solving many engineering applications, including healthcare. In the healthcare sector, many problems related to patients' treatments are not covered, and it is one of the issues for healthcare services across the globe. Healthcare support functions like clinical decision support, patient profiling, customized care, and health tracking could be the area where data analysis effectively provides end-user support. This paper focused on the metaheuristics algorithm concept with data mining technologies to provide a healthcare sector solution mechanism. This paper identifies the data analysis problem of the healthcare sector. It reviews various recent technologies used in healthcare and related sectors. It focused on the importance of metaheuristics algorithms, artificial intelligence, and implementation methodologies. [ABSTRACT FROM AUTHOR]

The concept of data lake was proposed more than a decade ago. Although progress has been made in data lake research and applications, there are also numerous issues and challenges need to be addressed. In this paper, we survey some recent developments, provide our observations, as well as our expectations on future research and practice in this area. We start with a discussion on general terminology in data lakes, then review various aspects of data lakes, including an examination on metadata and related issues, information granularities involved in data lakes, unique features such as mixed lazy and eager approach in data lake lifecycle, relationship between data lakes and data mining, interplay between data lakes and data warehousing techniques, as well as some notable developments related to applications of data lakes. We present our observations while we examine these aspects, and wrap up this paper by expressing our hope of developing a unified framework for data lake lifecycle evaluation. [ABSTRACT FROM AUTHOR]

• Gas turbine life management monitors and assesses turbine status and conditions. • Inlet and exit air passages impact gas turbine performance, not physical attributes. • Study gap: An incomplete gas-turbine model limits big data's predictive analysis. • Big data analytics drive optimized informed integrated health monitoring decisions for gas turbines. • Data mining extends gas turbine life by forecasting faults for the future. In addition to power generation, gas turbines (GT) are crucial components in many engineering applications, including those for aircraft, ships, and other machinery that needs a prime mover. One of the main concerns was predicting a comprehensive GT model, including intake, exit, and power-generator characteristics. Hence, this study provides a comprehensive review of GT's main components, parameters, faults, and predicting models using machine learning and data mining applications. Forty-four publications between 2014 and 2023 were chosen for this review study based on the primary keywords associated with the paper title. This study focuses solely on the GT model prediction aspects, considering the significance of field distribution, applications of data mining, research goals, benefits, constraints, and contributions to aviation, the oil and gas industry, and marine propulsion fields. Based on the examined studies, ANN, ELM, and KF applications show promise for potential GT life management. The typical limitations of GT life prediction, as observed in 93.2% of the review papers, involved the absence of a comprehensive GT model that incorporates all vibration factors and the use of substantial amounts of real data for predictive modelling. Another notable finding that underscores the knowledge gap in this area is the utilization of complete GT units to monitor unit life and facilitate life management. Apparently, the development of GT life management is still riddled with several limitations, which call for more improvement. [ABSTRACT FROM AUTHOR]

Knowledge graphs (KGs) are crucial in documenting bridge maintenance expertise. However, existing KG schemas lack integration of bridge design and practical inspection insights. Meanwhile, traditional methods for node feature initialization, relying on meticulous manual encoding or word embeddings, are inadequate for real-world maintenance textual data. To address these challenges, this paper introduces a bridge maintenance-oriented KG (BMKG) schema and approaches for graph data mining, including node-layer classification and link prediction. These methods leverage large language model (LLM)-based text encoding combined with GraphSAGE, demonstrating excellent performance in semantic enrichment and KG completion on deficient BMKGs. Additionally, ablation studies reveal the superiority of the pre-trained BERT text encoder and the L2 distance pairwise scoring calculator. Furthermore, a practical implementation framework integrating these approaches is developed for routine bridge maintenance, which can facilitate various practical applications, such as maintenance planning, and has the potential to enhance the efficiency of engineers' documentation work. • This paper introduces a hierarchical knowledge graph schema based on real-world bridge maintenance reports. • A node-layer classification approach is proposed for semantic enrichment by employing GraphSAGE and LLM-based text encoding. • A link prediction approach is proposed for graph completion by leveraging GraphSAGE and contrastive learning. • An implementation framework integrating the above schema and approaches is designed for practical bridge maintenance. [ABSTRACT FROM AUTHOR]

• This paper proposes a novel unsupervised feature selection called CPSLR. • CPSLR uses a specific formula to obtain the central point matrix. Construct a link graph through the central matrix and the Laplacian matrix to retain the similarity between the data. • The link graph and the data graph form a dual graph structure, which can not only preserve more complete data information but also holds on to the manifold structure of the data. • Feature selection is conducted in the latent representation space, and interconnection information among data is mined by using latent representation learning to preserve the connections among data itself. • CPSLR applies l 2,1/2-norm constraint on the feature transformation matrix to select robust and low-redundancy features. Before conducting unsupervised feature selection, it is usually assumed that these data are independent of each other. On the contrary, real data will influence each other. Therefore, traditional feature selection methods may lose information related to each other between data. This can lead to inaccurately generated pseudo-label information and may result in poor feature selection results. To find solutions to this issue, this paper proposes robust feature selection via central point link information and sparse latent representation (CPSLR). Firstly, structure a link graph by calculating the center matrix to store the distance information from the sample to the center point. If two samples have similar distances to the center point, it can be determined that they belong to the same class. Therefore, the similarity between samples is preserved, and more accurate pseudo-label information is obtained. Secondly, CPSLR uses data graph and link graph to form a dual graph structure. It can not only retain the link information between samples but also retain the manifold structures of the samples. Then, CPSLR saves the interconnection contents between samples by sparse latent representation. That is, the constraint l 2,1 -norm is exerted on the expression of latent representation, and sparse non-redundant interconnection information is preserved. And by combining central point link information with sparse latent representation makes the interconnections between data reserved more comprehensive. That is to say, the pseudo-labels obtained are more like the real labels of the classes. Finally, CPSLR constrains the feature transformation matrix by l 2,1/2 -norm constraint so as to select robust and sparse features. CPSLR uses l 2,1/2 -norm constraint to assure that the feature transformation matrix is sparse, selecting more discriminative features, thereby obtaining the feature selection that can improve its efficiency. The experiments demonstrate that the clustering result of CPSLR outperform six classical or latest compared algorithms on eight datasets. [ABSTRACT FROM AUTHOR]

This paper aims to present a comprehensive review and analysis to demonstrate the main papers, journals, authors, and trends significantly contributing to the scientific output in predicting the traffic incident duration using statistical and ML-based methods. We analyze new methods as well as data resources and characteristics such as incident time phases, data types, incident types, duration time distribution, available data resources, significant influencing factors, and unobserved heterogeneity and randomness. Also, this paper used the VOSviewer® software to conduct a visualization study of knowledge mapping on the literature of predicting traffic incident duration from 2010 to 2022 based on various databases. The contributions of this paper are three-fold. First, this paper undertakes a comprehensive comparison of previous studies in this field. Second, this paper identifies the key conceptual characteristics of analysis and prediction. Third, this paper explores the expected future trends in predicting the traffic incident duration. Furthermore, a key finding from this paper is that the usage of crowdsourcing, social media, and textual data is rare. Considering that future prediction methods of traffic incident duration will likely be utilized in all subfields related to traffic congestion, focusing on a review that summarizes such studies is a timely topic. • A comprehensive review of traffic incident duration prediction • Key papers, journals, authors, methods, and trends from 2010 to 2022 analyzed • Machine learning and HBDM are the most commonly used methods. • Rarely usage of crowdsourcing, social media, and textual data is revealed. • Potential challenges and future research on this topic indicated [ABSTRACT FROM AUTHOR]

Named Entity Recognition (NER) plays a pivotal role in knowledge extraction and improving the intelligence of edge computing. The effectiveness of span-based NER models predominantly depends on the representation of spans. Existing methods primarily utilize semantic features to represent spans, often neglecting other vital information. This paper proposes a method incorporating Part of Speech (POS) information into span representations to overcome this limitation. Central to this methodology is a span POS encoder designed to extract the POS feature of spans. For migrating the method to edge devices, this paper introduces a fast span POS encoder, which significantly reduces the time complexity of POS feature extraction. Building upon this innovation, a span-based NER model named IPSI (I ncorporating P art of S peech I nformation in span representation) is developed, exhibiting outstanding performance on nested and flat datasets. Comparison the original and fast span POS encoders reveals that while the fast encoder slightly compromises performance, it markedly accelerates the training and inference processes. Finally, through a series of experiments and sample analyses, this article explores the intrinsic mechanism through which the span POS feature influences entity recognition and further illustrates the importance of the POS feature. • A span POS encoder is proposed to extract the POS feature of a span. • An efficient variation of the span POS encoder is developed to cater to the limited computational capacity of edge devices. • A NER model is developed, which incorporates the POS feature to enhance span representation, outperforming competitor models. • The significance of the POS feature for NER is substantiated through a series of experiments. [ABSTRACT FROM AUTHOR]