Mining Applications Research Articles

Data mining applications in risk research: A systematic literature review

Despite the rising literature on data mining (DM) approaches, there is a lack of a complete literature review and categorization system within risk research. This paper presents the first recognized academic literature review on the application of data mining tools in risk research provides an up-to-date SCOPUS literature database. Based on bibliometric analysis, 5422 papers related torisk were identified from a total of 77,410 studies on data mining and thoroughly analyzed. Each of the selected 5422 papers was classified into four risk categories: global risk, public health risk, molecular and biomedical risk, and pharmaceutical risk. Each primary risk category was further subdivided to highlight the specific research focuses within each domain. Global risks encompass business, environmental, and social risks. Scholars have predominantly focused on the banking, market, and construction sectors within business risk, while environmental risk includes catastrophe-related risks. Social risks encompass areas such as education, traffic safety, and transportation concerns. Clinical data is usually employed in public health risk research, while various radiomic databases are utilized in genetic and molecular biology research. In pharmaceutical research, DM is primarily used to detect adverse drug effects. According to the findings of this review, the fields of computer science and medicine received the most significant research attention. The review also discusses limitations and provides a roadmap to guide future research, aiming to enhance knowledge development related to the application of data mining techniques in risk-related studies.

Big Data Visualization for Black Sigatoka Disease of Bananas and Pathogen–Host Interactions (PHI) of Other Plants

Black sigatoka is a leaf spot disease affecting banana plants that has caused significant yield reductions of up to 50% (Arman et al., 2023). This research presents data visualizations of 8,761 data points related to black sigatoka in banana plants, encompassing attributes such as time, canopy temperature, and relative humidity. The paper also reviews related work, including the application of data mining to plant studies, the use of deep learning neural networks for plant disease data, and the use of machine learning for predicting crop yields and detecting disease. Additionally, it presents big data visualizations for 20,952 values obtained from the web-accessible Pathogen–Host Interactions database (PHI-base), covering various plant disease categories and providing an epidemiological analysis of prevalent causative agents in specific plant diseases.

Fully Dynamic Submodular Maximization over Matroids

Maximizing monotone submodular functions under a matroid constraint is a classic algorithmic problem with multiple applications in data mining and machine learning. We study this significant problem in the fully dynamic setting, where elements can be both inserted and deleted in real-time. Our main result is a randomized algorithm that maintains an efficient data structure with an \({\tilde{O}(\frac{{k^{2}}}{{\varepsilon}})}\) amortized update time (in the number of insertions and deletions) and yields a \({(4+O(\varepsilon))}\) -approximate solution with respect to the dynamic optimum, where \(k\) is the rank of the matroid.

Efficient Integration of CNN and LSTM for Data Mining Observation Algorithms: A Systematic Review

An extensive analysis of the combination of Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN) is presented in this research. within data mining applications. CNNs, optimized for spatial data processing, and LSTMs, made to manage data in a sequential fashion, are combined to leverage their respective strengths in feature extraction and temporal pattern recognition. By analyzing over 60 published studies, we evaluate the effectiveness of CNN-LSTM models across diverse domains such as medical image analysis, financial time series forecasting, stock price prediction, and sentiment analysis. The review reveals that CNN-LSTM hybrids significantly outperform traditional methods in activities requiring both temporally and spatially understanding. However, challenges such as model interpretability, hyperparameter optimization, and the need for diverse datasets remain. Future research should focus on optimizing these models for specific applications, improving scalability, and addressing limitations like data scarcity. This review underscores the significant potential of CNNLSTM integration in advancing data mining techniques and solving complex problems in various fields. We present the related work of some of the most important publication in the field and classify over 60 published papers from different perspective. This review intends to classify the major techniques, methods, algorithms in the domain of medical images, financial time series analysis, stock price and social media.

Porosity and cement controlling the response of artificially cemented tailings under hydrostatic loading to high pressures

Cemented tailings find various applications in mining, such as open-pit and underground backfill, dam decommissioning and filtered tailings stacking. This research investigates the compression behaviour of iron ore tailings (IOT) mixed with distinct amounts of Portland cement and compacted in different conditions through isotropic compression, pulse velocity, and unconfined compression tests. The results show the adequacy of the porosity/cement index (η/Civ) in predicting elastic and plastic characteristics of compacted filtered IOT - Portland cement blends, an original correlation that has not been reported by previous work. This index is useful in selecting the cement content and target density for essential parameters required to design cemented IOT stacks. Besides, both cement addition and compaction have promoted the tailings isotropisation (conversion of an anisotropic system to an isotropic one). The evolution of the Post-Yield Compression Line (PYCL) with cementation is shown. Finally, it is demonstrated that distinct initial (after compaction) porosities of uncemented specimens reach a unique PYCL after isotropic pressures above 100 MPa, and cemented specimens do not reach a unique PYCL even at 120 MPa of isotropic pressures. The results underscore the requirement of rigorous compaction control in the field and offer a methodology for the dosage and technological control of artificially cemented tailings.

Resilience as a Concept for Convergence Across Health, Systems, and Well-Being: An AI-Augmented Mapping of 50 Years of Resilience Research

Resilience has become a focal point of academic research investigating the impact of adverse disruption to the well-being of people, systems, the built environment, ecosystems, and climate. However, the proliferation of this work has not been accompanied by increasing clarity about the core meaning of resilience as a singular construct, threatening its relevance and complicating its use in practice. To improve the application of resilience in cross-disciplinary and convergence approaches to sustainability and well-being research, this work synthesized resilience conceptualizations across disciplines with novel artificial intelligence (AI)-augmented approaches. Using open-source applications for text mining and machine-learning-based natural language processing algorithms for the examination of text-as-data, this work mapped the content of 50 years of academic resilience work (24,732 abstracts). Presented as thematic and statistical textual associations in a series of network maps and tables, the findings highlight how specific measurements, components, and terminologies of resilience relate to one another within and across disciplines, emphasizing what concepts can be used to bridge disciplinary boundaries. From this, a converged conceptualization is derived to answer theoretical questions about the nature of resilience and define it as a dynamic process of control through the stages of disruption and progression to an improved state thereafter. This conceptualization supports a cross-disciplinary meaning of resilience that can enhance its shared understanding among a variety of stakeholders, and ultimately, the rigor and uniformity of its application in addressing sustainability and well-being challenges across multiple domains.

Innovative Teaching of AI-Based Text Mining and ChatGPT Applications for Trend Recognition in Tourism and Hospitality

This research applies a model-based teaching approach aimed at scrutinizing trends in the leisure, tourism, hospitality, recreation, and sport (LTHRS) field by integrating artificial intelligence (AI) along with ChatGPT, project-based learning (PBL), systems thinking, and industrial analysis tools to foster trend recognition skills. The study employs a quasi-experimental design to compare the efficacy of two instructional approaches (exploratory vs. confirmatory) concerning AI literacy and learning outcomes. Notably, the exploratory group exhibits marked improvements in AI knowledge, while the confirmatory group demonstrates enhanced trend recognition ability. Additionally, the research delves into the application effects of AI-based text mining and ChatGPT (AITM) as content analysis tools through four distinct projects (5G’s impact on tourism industries, travel trends caused by metaverse, daylily tour in Huatan Township, and Taiwanese elements in spectator sports), underscoring the substantial efficacy of AITM in capturing diverse themes, albeit with challenges in discerning subtle and subjective labels. These findings highlight the effectiveness of the model-based teaching approach and the multifaceted utility of AI and automated text mining in augmenting trend recognition skills.

Nitrogen, phosphorus and potassium budget in crop production in South-Asia: regional and country trends during the last five decades

Nutrient budgeting for cropland is a crucial tool for assessing nutrient mining or excess application. We estimated the nutrient budget of nitrogen (N), phosphorus (P), and potassium (K) in cropland for South Asia during the last five decades (from 1970 to 2018) using equation-based empirical methods. Nutrient budget for the last five decades shows a negative balance of N (3.94 million tons, Mt), P (23.87 Mt), and K (247.23 Mt). Inorganic fertilizer remained the major input source for N and P, and its decadal average share increased for N (from 27.9% to 72.8%) and P (from 72.1% to 94.5%) from 1970 to 2010s and the share of manure, deposition, and crop residue to N, P and K input decreased. Deposition remained a major source of K input and its share decreased from 64.0% to 35.5% during the period. The share of crop removal to the decadal output of N (58.6% to 53.4%) and P (49.0% to 23.1%) decreased, and K (72.5% to 76.0%) increased from 1970 to 2010s. The higher losses of fertilizer N, and accumulation of P and K fertilizers in soils, resulted in decreasing partial factor productivity of N (from 72.2% to 16.9%), P (from 217.0% to 42.2%), and K (from 480.3% to 113.8%) from 1970 to 2018. Nutrient budget helps in identifying the regional imbalance (mining/accumulation) of the major nutrients, it will provide valuable information on the present status of country-level nutrient use for reorientation of their nutrient/fertilizer use policies.

Text Indexing for Faster Gapped Pattern Matching

We revisit the following version of the Gapped String Indexing problem, where the goal is to preprocess a text T[1..n] to enable efficient reporting of all occ occurrences of a gapped pattern P=P1[α..β]P2 in T. An occurrence of P in T is defined as a pair (i,j) where substrings T[i..i+|P1|) and T[j..j+|P2|) match P1 and P2, respectively, with a gap j−(i+|P1|) lying within the interval [α..β]. This problem has significant applications in computational biology and text mining. A hardness result on this problem suggests that any index with polylogarithmic query time must occupy near quadratic space. In a recent study [STACS 2024], Bille et al. presented a sub-quadratic space index using space O˜(n2−δ/3), where 0≤δ≤1 is a parameter fixed at the time of index construction. Its query time is O˜(|P1|+|P2|+nδ·(1+occ)), which is sub-linear per occurrence when δ<1. We show how to achieve a gap-sensitive query time of O˜(|P1|+|P2|+nδ·(1+occ1−δ)+∑g∈[α..β]occg·gδ) using the same space, where occg denotes the number of occurrences with gap g. This is faster when there are many occurrences with small gaps.

Integrating environmental sustainability and social justice principles into South Africa's blue economy initiative: re-imagining the political economy of our ocean

South Africa has vigorously embraced the concept of the blue economy. However, the scope, scale and impacts of South Africa's blue economy initiative, have raised significant concerns amongst local communities, NGOs and scholar activists in South Africa who are challenging South Africa's conceptualization of, and approach to “blue growth.” This paper provides a critique of South Africa's approach to planning, developing and governing the ocean economy and argues that the principles of social justice and environmental sustainability have been sidelined in favor of pursuing rapid economic growth. The paper examines key issues in the literature, environmental assessment processes, various legal cases that deal with mining, oil and gas applications, as well as insights from participation in several workshops with small-scale coastal fishing communities where issues on the blue economy were explored. These issues are discussed in relation to the growing literature on inclusive governance and integration of social justice and equity principles as foundational for fostering a sustainable and equitable blue economy. We conclude with proposals for embedding environmental sustainability and social justice principles, in a co-created blue economy policy and governance framework for South Africa.

POTENTIALITY OF HIGH-SPEED LASER CLADDING (HSLC) PROCESS FOR METAL DEPOSITION IN THE BRAZILIAN SCENARIO

This study explores the potential of High-Speed Laser Cladding (HSLC) within the Brazilian context. Given the nascent stage of HSLC technology in Brazil, preliminary experiments were conducted to assess its technical feasibility. AISI 316L tubes served as substrates for AISI 316L steel coatings, with the thickness of the deposited layers thoroughly analyzed. Results showed consistently continuous and adherent coatings without delamination. The findings suggest that HSLC is well-suited for high-quality, efficient applications in Brazil's oil, mining, and automotive industries, highlighting its potential to meet the demands of these sectors for innovation and precision.

Efficiency of excavators application in open pit mines of Uzbekistan

Efficiency of excavators application in open pit mines of Uzbekistan

Application of social media data mining in biomechanical and tactical analysis of tennis tournament players

Application of social media data mining in biomechanical and tactical analysis of tennis tournament players

Development of age-specific population-based paediatric computational phantoms for image-based data mining and other radiotherapy applications

Objective.Computational anatomical models have many applications in paediatric radiotherapy. Age-specific computational anatomical models were historically developed to represent average and/or healthy individuals, where cancer patients may present with anatomical variations caused by the disease and/or treatment effects. We developed RT-PAL, a library of computational age-specific voxelized anatomical models tailored to represent the paediatric radiotherapy population.Approach.Data from patients undergoing craniospinal irradiation (CSI) were used (n = 74, median age 7.3y, range: 1-17y). The RT-PAL phantoms were generated using groupwise deformable image registration to spatially normalize and average a sub-set of twenty clinical CTs and contours (n = 74, median age 7.7y, range: 3-14 y). To assess their anatomical and age-dependency plausibility, the RT-PAL models were compared against clinical cancer patient data and two healthy population based libraries of phantoms: the International Commission on Radiological Protection (ICRP) pediatric reference computational phantoms (n = 8, median age 7.5y, range: 1-15y) and a range of 4D paediatric extended cardiac torso (XCAT) phantoms (n = 75, median age 9.1y, range: 1-18y). For each dataset, nineteen organs were segmented on all age models to determine their volume. Each set was evaluated through a linear fit of organ volume with age, where comparisons were made relative to the linear fit of the clinical data.Main Results.Overall good anatomical plausibility was found for the RT-PAL phantoms. The age-dependency reported was comparable to both the clinical data and other phantoms, demonstrating their efficacy as a library of age-specific phantoms. Larger discrepancies with the clinical, ICRP and XCAT organ data were attributable to differences in organ filling, segmentation strategy and age distribution of the datasets, limitations of RT-PAL generation methodology, and/or possible anatomical differences between healthy and cancer populations.Significance.The RT-PAL models showed potential in representing the paediatric radiotherapy cohort, who are most likely to benefit from dedicated, age-specific anatomical phantoms.

Analyzing Daytime/Nighttime Pedestrian Crash Patterns in Michigan Using Unsupervised Machine Learning Techniques and their Potential as a Decision-Making Tool

Background Data mining applications are becoming increasingly common across various fields. Numerous data mining methodologies have been utilized in pedestrian crash data analysis. However, association and correspondence analyses have yet to be extensively employed in pedestrian safety literature to support decision-making. Material and Methods Road lighting significantly affects pedestrian crashes, highlighting the importance of examining pedestrian crash patterns during daytime and nighttime. This study analyzed pedestrian fatal and injury crashes in Michigan from 2011 to 2021 under varying road lighting conditions. The study identified pedestrian crash patterns using unsupervised machine-learning techniques based on several multidimensional factors. The Association Rules Learning (ARL) technique was used to determine general associations of patterns leading to pedestrian crashes. At the same time, Multiple Correspondence Analysis (MCA) helped understand crash attribute patterns in two notable scenarios: elderly pedestrian crashes during daytime lighting and high-speed midblock crashes during nighttime. These methods revealed key patterns associated with contributing factors to pedestrian crashes in different lighting conditions. Results In the analysis of cloud combinations involving elderly pedestrians during daylight, it was found that: 1) elderly pedestrians were significantly involved in severe injury crashes at two-lane midblock locations with a speed limit between 45 and 65 mph; 2) improper actions by elderly drivers were significantly associated with elderly pedestrians on wide roads (>5 lanes); and 3) the complex interaction between city streets with 3-5 lanes was significantly correlated with drivers 'failed to yield' actions. Additionally, in the analysis of cloud combinations involving nighttime pedestrian crashes at high-speed midblock locations, it was found that 1) a specific age group of alcohol- or drug-involved pedestrians (19-30) was significantly associated with young drivers and improper driver actions; 2) during the winter season, young pedestrians were significantly involved in moderate injury crashes at undivided midblock locations; and 3) elderly drivers and 'failed to yield' actions were highly correlated with 3-5 lane roadways. Discussion The research findings are expected to raise awareness of Michigan pedestrian crash patterns under daytime and nighttime lighting conditions. They will also recommend safety countermeasures for practitioners to enhance pedestrian safety in walkable cities. The proposed methodologies can uncover relationships that need to be well-documented in the existing literature on pedestrian safety. Additionally, these methods can identify valuable relationships without restricting variables to being either dependent or independent, and they can reveal relationships that might be challenging to detect otherwise. The results will also provide decision rules and visualizations that are easy to understand. Conclusion The results showed that the proposed techniques can analyze pedestrian crash data in a way that aligns with understanding pedestrian crash characteristics. Traffic safety administrators could benefit from this methodology as a decision-support tool.

Deteksi Dini Siswa Korban Bullying dalam Penerapan Data Mining Menggunakan Algoritma C4.5 pada SMP IDN Jonggol

This research discusses the application of data mining using the C4.5 algorithm for early detection of student victims of bullying at IDN Jonggol Junior High School. The problem studied is how to identify students who are victims of bullying quickly and accurately using the available data analysis. The expected solution is the development of an early detection system that can provide early warning to the school so that intervention can be done earlier. The object of the study was IDN Jonggol Junior High School students. The method used involves collecting demographic data, behavior records, and bullying reports, which are then analyzed using the C4.5 Algorithm. The results showed that the C4.5 algorithm was able to classify student victims of bullying with an accuracy of 78.6%. Preliminary conclusions show that the C4.5 Algorithm has great potential to be used in the early detection of bullying. The implication of this research is the improvement of student safety and well-being through faster and more appropriate intervention. Recommendations from this research are further development of the detection system and its application in other schools

Data mining in AI: Evolution, applications, and future directions

Abstract. This paper provides a comprehensive analysis of the evolution and impact of data mining in the field of artificial intelligence (AI), with a particular focus on its application within social and information networks. It traces the origins of AI back to the 1956 Dartmouth Conference, highlighting the subsequent advancements in technologies such as machine learning and data mining that have fueled AI's growth. The paper explores the multifaceted applications of data mining in various sectors including healthcare, transportation, and industrial manufacturing, and delves into the challenges and innovations in recommendation systems, matrix factorization, and intelligent control of autonomous vehicles in intelligent transportation systems. The study emphasizes the significance of distributed algorithms and big data processing frameworks in enhancing the efficiency and applicability of data mining techniques.

THE APPLICATION OF DATA MINING AND OLAP TECHNOLOGIES IN HEALTHCARE

The article examines the application of Data Mining and real-time Online Analytical Processing (OLAP) in healthcare. The calculation of analytical indicators and solving planning problems in healthcare are complex and multi-stage processes. The extraction of knowledge from large volumes of data accumulated in databases is referred to as Data Mining. Today, databases are measured in terabytes, containing strategically significant information. One of the most critical issues is determining methods for discovering important information within such vast amounts of data. Data Mining technology, by utilizing a set of analytical tools, helps to uncover relationships within large datasets and make predictions about future trends. Simply defined, Data Mining automatically identifies relevant patterns within databases. For many years, analysts manually recorded statistically significant relationships within databases. Today, Data Mining automates this process. The goal of Data Mining is to create decision-making models that predict future behavior based on the analysis of past actions. With the advent of computers and the rise of the digital information age, there have been changes in both the scale and complexity of existing problems in terms of analytical analysis. Advances in the storage, management, and examination of data have led to the emergence of a new field that promotes the development of bioinformatics and introduces analytical analysis and computational problems in the fields of biology and medicine. Keywords: Data Mining, OLAP, database, factor analysis, healthcare network, multidimensional models.

Fault-tolerant graph embeddings in Archimedean networks

Fault tolerance refers to the ability of a system to continue functioning even if some of its components fail. This concept is fundamental in several domains because reliability and continuous operation are essential in carrying out their functions. Graph embedding means mapping a graph into some other structure that preserves some of the properties or relations held by the original graph. Applications in machine learning, data mining, and network analysis make fault-tolerant embedding of graphs a crucial topic. In this paper, fault-tolerant embeddings are proposed in Archimedean tessellations. The path in the embeddings does not intersect or overlap.

Reducing Dust and Respirable Crystalline Silica Near Conveyors Using a Hybrid Dust Control System.

Occupational exposures to respirable dusts and respirable crystalline silica (RCS) is well established as a health hazard in many industries including mining, construction, and oil and gas extraction. The U.S. National Institute for Occupational Safety and Health (NIOSH) is researching methods of controlling fugitive dust emissions at outdoor mining operations. In this study, a prototype engineering control system to control fugitive dust emissions was developed combining passive subsystems for dust settling with active dust filtration and spray-surfactant dust suppression comprising a hybrid system. The hybrid system was installed at an aggregate production facility to evaluate the effectiveness of controlling fugitive dust emissions generated from two cone crushers and belt conveyors that transport crushed materials. To evaluate effectiveness of the system, area air measurements (n = 14 on each day for a total of 42 samples) for respirable dust were collected by NIOSH before, during, and after the installation of the dust control system in the immediate vicinity of the crushers and the nearby conveyor transfer point. Compared to pre-intervention samples, over short periods of time, geometric mean concentrations of airborne respirable dust were reduced by 37% using passive controls (p = 0.34) but significantly reduced by 93% (p < 0.0001) when the full hybrid system was installed. This proof-of-concept project demonstrated that the combined use of active and passive dust controls along with a spray surfactant can be highly effective in controlling fugitive dust emissions even with minimal use of water, which is desirable for many remote mining applications.