Challenges In Mining Research Articles

Integrating AI with medical industry chain data: enhancing clinical nutrition research through semantic knowledge graphs.

In clinical nutrition research, the medical industry chain generates a wealth of multidimensional spatial data across various formats, including text, images, and semi-structured tables. This data's inherent heterogeneity and diversity present significant challenges for processing and mining, which are further compounded by the data's diverse features, which are difficult to extract. To address these challenges, we propose an innovative integration of artificial intelligence (AI) with the medical industry chain data, focusing on constructing semantic knowledge graphs and extracting core features. These knowledge graphs are pivotal for efficiently acquiring insights from the vast and granular big data within the medical industry chain. Our study introduces the Clinical Feature Extraction Knowledge Mapping ( ) model, designed to augment the attributes of medical industry chain knowledge graphs through an entity extraction method grounded in syntactic dependency rules. The model is applied to real and large-scale datasets within the medical industry chain, demonstrating robust performance in relation extraction, data complementation, and feature extraction. It achieves superior results to several competitive baseline methods, highlighting its effectiveness in handling medical industry chain data complexities. By representing compact semantic knowledge in a structured knowledge graph, our model identifies knowledge gaps and enhances the decision-making process in clinical nutrition research.

‘New mining scheme, same old problems?’; risk factors and health challenges in underground community mining in Ghana

Abstract Ghana’s small-scale mining sector has long been a cornerstone of its economy and a vital source of livelihood for many communities. However, the sector is plagued by significant challenges, including environmental degradation, health and safety risks, and illegal practices. In response, the Ghanaian government introduced the Community Mining Scheme (CMS), a new mining model designed to promote local community participation in artisanal small-scale gold mining (ASGM). This study examines the workplace risk factors and associated health challenges faced by ASGM miners under the CMS, drawing on interviews with fifty underground CMS miners in Ghana. Our findings reveal that miners continue to face numerous health and safety challenges, including inadequate lighting, poor ventilation, exposure to noise and odors, unsupported or poorly supported loose materials, non-use of personal protective equipment, high dust levels, and unsafe blasting practices. Additionally, health concerns such as skin rashes, musculoskeletal injuries, and the risk of mercury poisoning persist. These challenges mirror those reported in the literature on unregulated and illegal ASGM in Ghana, indicating that existing issues remain unresolved under the CMS. To prevent the undermining of this initiative, it is crucial to adopt progressive practices, such as localized coping strategies and the recruitment of trained professionals to lead ASGM operations within the CMS. Enhancing miners’ risk perception through targeted outreach and awareness campaigns is also essential. Further recommendations for mitigating safety risks and health challenges are discussed in the article.

Heavy Metal Levels in Insect Species Collected from Enyigba Community with Mining Activities in Southeast Nigeria

Insects are the largest and most diverse group of organisms which are typically the overwhelming dominant invertebrate faunal group extensively used in biomonitoring and bioassessment. The aim of this paper was to evaluate the heavy metals levels in insect species collected from Enyigba Community with mining activities in Southeast Nigeria using appropriate standard methods. In this study, three sites namely: Upper Inyia-(SA), Lower Inyia (SB) and the Control Site (SC) were selected and insect specimens were collected and evaluated from July 2022 to February, 2023. The insect groups used were Reticulitermes flavipes (termite), Zonocerus elegans (grasshopper), Acraea acrita (butterfly) and Cremtogaster sp. (Ant). The selected insects’ samples were analyzed using atomic absorption spectrophotometer (AAS). The results showed heavy metal accumulations on the selected insects particularly the toxic metals such as Cadmium (Cd), Copper (Cu), Manganese (Mn) and Mercury (Hg). Insects examined were Reticulitermes flavipes, Zonocerus elegans, Acraea acrita, and Crematogaster sp. collected from Enyigba mines. High concentrations of Cd was recorded in Zonocerus elegans at SA (0.13±0.042) and SB (0.17±0.007) which is far above WHO/FAO permissible limit. The result further revealed that there was also high Cd concentration (0.21±0.011) in Reticulitermes flavipes, which is also far above WHO/FAO permissible limit. Grasshopper, termites and butterfly had the highest accumulation of heavy metals among the studied insect groups, highlighting the challenges of mining on agriculture and food security in the region.

P2P credit risk management with KG-GNN: a knowledge graph and graph neural network-based approach

Credit risk management is crucial for the credit loan decision-making process on P2P platforms, essential for mitigating credit defaults. The rapid decline of China’s P2P platform market highlights the urgent need for Internet financial institutions to enhance their credit risk management strategies. Previous studies have applied machine learning to assess credit risk; however, their effectiveness is often hampered by a lack of consideration for semantic information and individual correlation. In response, we propose an innovative approach, KG-GNN, which integrates knowledge graph (KG) and graph neural network (GNN). KG-GNN leverages KG to encapsulate semantic information within complex categorical features and explore potential relationships between borrowers. Utilizing GNN, our framework extracts representation features from the KG to build comprehensive and accurate credit risk models. Our findings indicate that KG-GNN not only can predict credit risk more accurately than conventional machine learning models but also improves the stream model performance by over 20% through KG and GNN-based data augmentation techniques. By integrating KG with GNN, our approach enriches the methodologies for credit risk management and can be adapted to other data mining challenges that require processing complex semantic and relational information, thereby enhancing model learning capabilities.

Application and investigation of knowledge graph in biomedical field

A large amount of drug and disease research knowledge is scattered in unstructured literature data, presenting significant challenges in text mining within the field of biomedicine. These challenges include handling professional knowledge, integrating related knowledge, and disambiguating different meanings of the same words. Therefore, constructing a biomedical knowledge graph can significantly save expert human resources and make efficient use of medical literature resources. This review paper aims to summarize the construction methods used during the development of Biomedical Knowledge Graphs. It also outlines the latest models and frameworks, such as BioBERT and LSTM+CRF, highlighting their contributions and applications. In addition, this paper points out the limitations of current biomedical knowledge graphs, such as scalability issues and the need for large annotated datasets. To address these limitations, it proposes the use of Apache Spark for improved processing capabilities and transfer learning to enhance model performance and adaptability in diverse biomedical contexts.

An efficient ranking based binary salp swarm optimization for feature selection in high dimensional datasets

Feature selection is a major challenge in data mining which involves complex searching procedure to acquire relevant feature subset. The effectiveness of classification approaches is greatly susceptible to data dimensionality. The Higher dimensionality intricate numerous problems like higher computational costs and over fitting problem. The essential key factor to mitigate the problem is feature selection. The main motive is to minimize the number of features through eliminating noisy, insignificant, and redundant features from the original data. The Metaheuristic algorithm attains excellent performance for solving this kind of problems. In this paper, the grading based binary salp swarm optimization has been proposed to solve various complex problems with lesser computational time. The grading system has been used to maintain the balance among exploitation and exploration. The proposed method is examined using ten benchmark real datasets. The comparative result exhibits the promising performance of our proposed method and surpasses with other optimization interms of investigating evaluation measures.

Effectiveness Spatiotemporal of Pressure Unloading and Gas Extraction in Remote Upper Protected Seam Mining.

Severe gas hazards are increasing in deep mining areas, and there exists an enormous traditional challenge for protected seam mining. In this work, we conducted a multifaceted investigation into the spatiotemporal effectiveness exerted by pressure unloading from a distant safeguarded stratum in concert with the application of gas extraction methodologies. The stress and displacement evolution in the protected coal seam (PCS) are analyzed by applying Flac3D, and then positives are verified by the measurement of coal seam deformation and investigation of the unloading boundary. The results show that diminution coefficient of 0.62, and record an expansive deformation rate of 5.83%. The opportune temporal window for effective gas drainage is discerned, with its time window spans from 58 to 67 days as the expanse between 350 and 400 m progresses at the working face. Continuous and effective gas extraction occurs when transverse cracks in the PCS reach an optimal state of pressure relief. The final cumulative gas extraction was 320,300 m3 with an extraction efficiency of 34.3%. A residual gas volume of 3.68 m3/t and a residual gas pressure of 0.5 MPa reflect the efficient gas extraction.

A framework of Polar CanisFel optimization-based deep ensemble classifier with graph embedding for imbalanced data classification

Imbalanced data classification (IDC) presents a significant challenge in data mining (DM), as it frequently occurs in various real-world areas with profound implications for highly skewed databases. IDC revolves around the task of learning from data characterized by a substantial imbalance in the number of samples across its different classes. Hence the Polar-CanisFel (PCF) Optimization-deep ensemble model is designed to address imbalanced big data issues, incorporating the SMOTE technique for rebalancing the dataset. This ensemble classifier leverages a deep convolutional neural network (DCNN), Long Short-Term Memory (LSTM), and Gated Recurrent Neural Network (GRNN) architectures for effective data classification. For the Heart Failure Prediction Dataset, the model reaches an accuracy of 96.35%, sensitivity of 94.54%, and specificity of 96.11%. Further, the accuracy of 95.91%, sensitivity of 95.87%, and specificity of 94.79% are obtained concerning the Stroke Prediction dataset. Finally, when applied to the Hepatitis-C prediction dataset, the model attains an accuracy of 92.79%, sensitivity of 92.90%, and specificity of 92.63% during 90% of training.

Impact and Challenges of Data Mining : A Comprehensive Analysis

Impact and Challenges of Data Mining : A Comprehensive Analysis

A dynamic graph structural framework for implicit sentiment identification based on complementary semantic and structural information

Implicit sentiment identification has become the classic challenge in text mining due to its lack of sentiment words. Recently, graph neural network (GNN) has made great progress in natural language processing (NLP) because of its powerful feature capture ability, but there are still two problems with the current method. On the one hand, the graph structure constructed for implicit sentiment text is relatively single, without comprehensively considering the information of the text, and it is more difficult to understand the semantics. On the other hand, the constructed initial static graph structure is more dependent on human labor and domain expertise, and the introduced errors cannot be corrected. To solve these problems, we introduce a dynamic graph structure framework (SIF) based on the complementarity of semantic and structural information. Specifically, for the first problem, SIF integrates the semantic and structural information of the text, and constructs two graph structures, structural information graph and semantic information graph, respectively, based on specialized knowledge, which complements the information between the two graph structures, provides rich semantic features for the downstream identification task, and helps to understanding of the contextual information between implicit sentiment semantics. To deal with the second issue, SIF dynamically learns the initial static graph structure to eliminate the noise information in the graph structure, preventing noise accumulation that affects the performance of the downstream identification task. We compare SIF with mainstream natural language processing methods in three publicly available datasets, all of which outperform the benchmark model. The accuracy on the Puns of day dataset, SemEval-2021 task 7 dataset, and Reddit dataset reaches 95.73%, 85.37%, and 65.36%, respectively. The experimental results demonstrate a good application scenario for our proposed method on implicit sentiment identification tasks.

Enhanced Data Mining and Visualization of Sensory-Graph-Modeled Datasets through Summarization.

The acquisition, processing, mining, and visualization of sensory data for knowledge discovery and decision support has recently been a popular area of research and exploration. Its usefulness is paramount because of its relationship to the continuous involvement in the improvement of healthcare and other related disciplines. As a result of this, a huge amount of data have been collected and analyzed. These data are made available for the research community in various shapes and formats; their representation and study in the form of graphs or networks is also an area of research which many scholars are focused on. However, the large size of such graph datasets poses challenges in data mining and visualization. For example, knowledge discovery from the Bio-Mouse-Gene dataset, which has over 43 thousand nodes and 14.5 million edges, is a non-trivial job. In this regard, summarizing the large graphs provided is a useful alternative. Graph summarization aims to provide the efficient analysis of such complex and large-sized data; hence, it is a beneficial approach. During summarization, all the nodes that have similar structural properties are merged together. In doing so, traditional methods often overlook the importance of personalizing the summary, which would be helpful in highlighting certain targeted nodes. Personalized or context-specific scenarios require a more tailored approach for accurately capturing distinct patterns and trends. Hence, the concept of personalized graph summarization aims to acquire a concise depiction of the graph, emphasizing connections that are closer in proximity to a specific set of given target nodes. In this paper, we present a faster algorithm for the personalized graph summarization (PGS) problem, named IPGS; this has been designed to facilitate enhanced and effective data mining and visualization of datasets from various domains, including biosensors. Our objective is to obtain a similar compression ratio as the one provided by the state-of-the-art PGS algorithm, but in a faster manner. To achieve this, we improve the execution time of the current state-of-the-art approach by using weighted, locality-sensitive hashing, through experiments on eight large publicly available datasets. The experiments demonstrate the effectiveness and scalability of IPGS while providing a similar compression ratio to the state-of-the-art approach. In this way, our research contributes to the study and analysis of sensory datasets through the perspective of graph summarization. We have also presented a detailed study on the Bio-Mouse-Gene dataset, which was conducted to investigate the effectiveness of graph summarization in the domain of biosensors.

Optimizing pool mining performance: A VIKOR‐based model for identifying reputed miners in blockchain networks

AbstractBlockchain networks continue to gain attraction in cutting‐edge applications and mining within these networks has become increasingly popular. To get rewards, miners solve cryptographic puzzles and add new blocks to blockchain networks using the proof‐of‐work (PoW) consensus mechanism. Numerous miners opt to participate in mining pools due to the challenges of solo mining. However, selecting reputed miners for pool mining poses a significant challenge, given the decentralized nature of the blockchain system. This paper addresses this challenge by introducing a new ranking model that evaluates miners' performance and reputation through trust scores. It provides a method for optimizing pool mining performance by identifying highly reputed miners within mining pools, enhancing overall pool profitability. This endeavor necessitates the development of ranking algorithms tailored to the unique dynamics of mining pools. The research offers a meticulously designed ranking model that identifies reputed miners. We extensively evaluate the proposed model using the hyperledger blockchain framework, guaranteeing strong performance across vital metrics like block authorization time, Processing time, block creation time, validation time, and confirmation time.

Linguistics-based formalization of the antibody language as a basis for antibody language models.

Apparent parallels between natural language and antibody sequences have led to a surge in deep language models applied to antibody sequences for predicting cognate antigen recognition. However, a linguistic formal definition of antibody language does not exist, and insight into how antibody language models capture antibody-specific binding features remains largely uninterpretable. Here we describe how a linguistic formalization of the antibody language, by characterizing its tokens and grammar, could address current challenges in antibody language model rule mining.

Object–attribute–relation model-based semantic coding for image transmission

The development of digital, networked and intelligent communication technology has led to significant growth in data and services. Especially in the communication scenario of monitoring data, a variety of downstream tasks bring significant technical challenges for high-performance data transmission, information mining, and data distribution. The problem to be solved is the coding and low bit-rate transmission of large amounts of surveillance data. Concepts and methods that utilize semantic communication are needed. This paper proposes a communication architecture based on the object–attribute–relation (OAR) semantic knowledge base. The sender uses the model parameters and prior knowledge in the knowledge base to drive the OAR extraction model to encode the image. After semantic coding and channel coding, the signal is transmitted to the receiver through the physical channel. At the receiver, the signal is decoded into a semantic vector. The OAR global vector is generated by structured processing, which is used for downstream tasks such as image retrieval or image reconstruction. Experimental comparison and analysis illustrate the effectiveness and superiority of each part of the communication framework. This approach provides a new solution for improving data utilization and processing efficiency.

Enhanced Measurement and Verification Practices in Deep-Level Mines: The Current State

This article explores operational challenges in mining, with a focus on energy management amid depleting ore grades and rising costs. The urgent need for innovative energy management systems and strategies is highlighted by analyzing the unexplored landscape of mine energy budgeting and forecasting and identifying gaps in current practices. Drawing from the literature, this paper offers new insights into energy budgeting and the evaluation of energy efficiency initiatives by integrating traditional and advanced measurement and verification (M&V) techniques. M&V practices are crucial for energy management, particularly in deep-level mines, with a focus on practical knowledge and advanced methodologies. Key findings from this study show that integrating advanced M&V techniques with unplanned events is crucial to improve the financial management of mines. By leveraging these key findings, this article proposes a roadmap of the next seven milestones needed in advanced M&V research to aid effective energy management in a mining environment. If executed successfully, a practical method for applying advanced M&V processes to deep-level mining operations can be constructed. Such a generic method will enhance mining companies’ energy efficiency initiatives and improve financial management practices on a global scale.

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software.

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

Artisanal and Small-Scale Gold Mining Impact on Soil and Agriculture: Evidence from Upper Denkyira East Municipality, Ghana

The gold mining industry in Ghana has made a substantial contribution to the country's economic growth by generating diverse employment opportunities, both directly and indirectly, in mining communities. This has the potential to mitigate poverty and improve the quality of life for individuals and households in rural areas, where climate change is having a disproportionate impact on small-scale farmers. Nevertheless, it is crucial to recognize that this sector has also resulted in detrimental environmental impacts. A total 150 key informants in Upper Denkyira East Municipality, comprising representatives from the Environmental Protection Agency, the Forestry Commission, the Mineral Commission, and other stakeholders, were interviewed using standardize questionnaire to ascertain the impact of mining on the soil, agriculture and the environment. The study also employed inductively coupled plasma to assess the concentration of heavy metals in soils at a small-scale mining site. The soil analyses indicated high concentrations of heavy metals, such as mercury, arsenic, nickel, zinc, and iron. The Weighted Average Index, which was used to assess the impact of mining, revealed that communities face challenges such as deforestation, low crop yields, water pollution, land tenure issues, and a high cost of living. The study's analyses of measures to address small scale mining challenges revealed that alternative livelihood, law enforcement, training and support, and access to mining concessions were perceived as suitable for addressing artisanal and small scale mining. The study concluded that stricter mining regulations, promoting sustainable mining practices, and providing comprehensive training to miners on environmental protection and land reclamation can help mitigate the negative impacts of artisanal and small-scale mining. Additionally, supporting alternative means of livelihood for mining communities, such as agriculture or eco-tourism, can reduce reliance on mining and promote sustainable development.

Numerical and theoretical analysis of multi-pillar instability under elastic beams

With the increase of mining scale and depth, the cascading pillar failure (CPF) disaster has gradually become one of the core technical challenges for safe mining. This paper studies the CPF disaster of multi-pillar (P1-2, P2-3, P3-4 and P4-5) at 848 m level of Alhada Lead-Zinc Mine based on the RFPA2D numerical simulation software and stiffness theory. The numerical simulation results indicate that the load transfer effect induces the domino instabilities of double pillar (P2-3 and P3-4) and double pillar (P1-2 and P4-5), which is characterized by the ‘2 + 2’ compound failure mode. The physical essence of load transfer effect is revealed through numerical simulation, which is the elastic rebound of surrounding rockmass (such as roof-floor). In addition, the theoretical model of rebound overload mechanism of roof-multi-pillar-floor system is established based on the catastrophe theory, and the instability criterion, sudden jump and energy release of roof-multi-pillar-floor system are derived. Finally, the main influencing factors of load transfer effect such as pillar spacing and damage fracture zone are quantitatively analyzed, and the load transfer law shows that the load transfer effect decreases with the increasing pillar spacing and is hindered by the structural plane in the surrounding rockmass.

Applied Machine Learning Predictive Analytics to SQL Injection Attack Detection and Prevention

Abstract: The back-end database is pivotal to the storage of the massive size of big data Internet exchanges stemming from cloud-hosted web applications to Internet of Things (IoT) smart devices. Structured Query Language (SQL) Injection Attack (SQLIA) remains an intruder’s exploit of choice on vulnerable web applications to pilfer confidential data from the database with potentially damaging consequences. The existing solutions of mostly signature approaches were all before the recent challenges of big data mining and at such lacks the functionality and ability to cope with new signatures concealed in web requests. An alternative Machine Learning (ML) predictive analytics provides a functional and scalable mining to big data in detection and prevention of SQLIA. Unfortunately, lack of availability of readymade robust corpus or data set with patterns and historical data items to train a classifier are issues well known in SQLIA research. In this project, we explore the generation of data set containing extraction from known attack patterns including SQL tokens and symbols present at injection points. Also, as a test case, we build a web application that expects dictionary word list as vector variables to demonstrate massive quantities of learning data. The data set is pre-processed, labelled and feature hashing for supervised learning. The trained classifier to be deployed as a web service that is consumed in a custom dot NET application implementing a web proxy Application Programming Interface (API) to intercept and accurately predict SQLIA in web requests thereby preventing malicious web requests from reaching the protected back-end database. This project demonstrates a full proof of concept implementation of an ML predictive analytics and deployment of resultant web service that accurately predicts and prevents SQLIA with empirical evaluations presented in Confusion Matrix (CM) and Receiver Operating Curve (ROC).

Hybrid-attention mechanism based heterogeneous graph representation learning

Heterogeneous graph refers to a type of graph data characterized by its diverse node types and relation types, containing rich structures, features and heterogeneous information. How to fully utilize and capture these key information to generate effective node representations poses a great challenge in heterogeneous graph analysis and mining. To better tackle this problem, a heterogeneous graph representation learning model based on hybrid-attention mechanism is proposed, namely Heterogeneous Graph Relation Attention Network (HGRAN). The main contributions of HGRAN are listed as follows. First, a novel framework was proposed for better representing heterogeneous information originating from various relations and comprehensive usage of both structural and feature information instead of employing meta-path based framework. Second, a novel hybrid-attention mechanism which combines relation attention and node attention was proposed within this framework. Third, a novel feature similarity based relation attention is proposed to capture heterogeneous information originating from different relations. Fourth, in order to better implement node attention in heterogeneous graphs, a new transforming method that transforms adjacency matrices of diverse relations into a unified manner is proposed. Finally, extensive experiments on multiple real-world heterogeneous graph datasets are conducted to verify HGRAN, and the results support its superiority in comparison with the state-of-the-art methods.