23 papers · ranked by Valyu relevance
Suleyman Alpaslan Sulak
This study analyzes university students’ attitudes towards artificial intelligence. Within the scope of the research, the data obtained from 1379 students through scale application were classified into three classes as “Insufficient”, “Sufficient” and “Strongly Sufficient” according to their attitudes towards…
Yuri Reina Marín, Lenin Quiñones Huatangari, Omer Cruz Caro, Einstein Sánchez Bardales + 3 more
There is concern about the levels of stress faced by college students and their effects on mental health and academic performance. This study aimed to characterize academic stress levels in college students, using data mining algorithms to classify and predict risk patterns. Data were collected from 287 students using…
Nadella Sunil, G Narsimha
The emergence of digital information has raised many issues over the release of sensitive personal information in data mining activities due to the rapid expansion of the digital information. Privacy-Preserving Data Mining (PPDM) has the goal of allowing a significant analysis of data, as well as safeguarding…
Avinash Kadimisetty, C. Oswald, B. Sivalselvan
The paper focuses on Image Compression, explaining efficient approaches based on Frequent Pattern Mining(FPM). The proposed compression mechanism is based on clustering similar pixels in the image and thus using cluster identifiers in image compression. Redundant data in the image is effectively handled by replacing…
Young-Chae Hong, Yangho Chen
Pattern discovery in data plays a crucial role across diverse domains, including healthcare, risk assessment, and machinery maintenance. In contrast to black-box deep learning models, symbolic rule discovery emerges as a key data mining task, generating human-interpretable rules that offer both transparency and…
Authors not listed
Iron, the most abundant element on Earth by mass (34.6%), primarily exists as iron minerals due to its inherent reactivity. The study of iron mineral phase transformations under changing environmental conditions remains an important research focus due to its geological, environmental, and industrial significance. Yet…
Rongjie Mao, Yuncheng Zhu
3## Data Mining and Machine Learning Methods This section outlines the historical development of data-mining and machine-learning approaches in ASD speech analytics, highlighting key methodological advances, representative applications, and their respective strengths and limitations across different stages of…
Matthias Stierle, Karsten Kraume, Martin Matzner
Data-driven analysis of business processes has a long tradition in research. However, recently the term of process mining is mostly used when referring to data-driven process analysis. As a consequence, awareness for the many facets of process analysis is decreasing. In particular, while an increasing focus is put onto…
Jef Caers
The energy transition through increased electrification has put the world's attention on critical mineral exploration. Despite the promise of a growing the demand, the global exploration industry is money-losing enterprise. Even with increased investments a decrease in new discoveries has taken place over the last two…
Authors not listed
The materials-science literature is the richest reservoir of domain knowledge, yet converting its unstructured text—especially narrative passages and complex tables—into machine-readable data for analysis and ML model training remains challenging. To address this, we present KnowMat, an agentic, multi-stage pipeline…
Muhammad Junaid, Karolina Hanna Prazanowska, Ha-Eun Jeong, Yebin Ryu + 3 more
The rapid expansion of the oncology literature has outpaced manual curation of clinically relevant gene-cancer-drug associations and oncogenic driver evidence. Existing automated approaches often lack transparency or are difficult to scale across heterogeneous data sources. To address this gap, we developed megaMine, a…
Felix Moorhoff, David Medina-Ortiz, Alicja Kotnis, Ahmed Hassanin + 1 more
The rapid expansion of protein sequence databases continues to outpace functional characterization, creating a persistent bottleneck in enzyme discovery and mining—particularly in large, heterogeneous, and sparsely annotated sequence spaces. This gap is amplified by visualization challenges and the lack of informed…
Deepshikha Singh, Shashank Jatav, Shefali Lathwal, Arman Kazmi + 1 more
Public biomedical repositories contain extensive, valuable datasets, yet identifying datasets that precisely match specific study requirements remains inefficient. Conventional keyword- and schema-based systems frequently fall short when queries encompass multiple biological and experimental facets. To address this, we…
Bum Chul Kwon, Natasha Mulligan, Joao Bettencourt-Silva, Ta-Hsin Li + 4 more
Formulating hypotheses about gene-disease associations requires logical inference from prior data, followed by a laborious literature review. AI models trained on curated datasets (e.g., GWAS Catalog) can suggest SNP–disease links, but validating these predictions still demands manual evidence extraction. To streamline…
Sayed Asaduzzaman, Benu Bansal, Parker Combs, Jie Zhang + 4 more
The expansion of biomedical literature demands systematic ontology-guided discovery of gene interactions, vaccine mechanisms, drug associations, and adverse events. Existing platforms such as STRING, DisGeNET, and PubTator fall short of providing a unified, freely accessible system that integrates ontology-based…
Authors not listed
Large Language Models have demonstrated impressive capabilities in natural language understanding and processing. However, as AI and LLMs continue to evolve, their ability to accurately and efficiently interpret data from scientific figures and plots remains obscure. In this study, we test and evaluate the ability of…
Andile Mkhohlakali, Mothwethwi Priscilla Toona, Tumelo Mogashane, Tshilidzi Rampfumedzi + 8 more
The mining sector is undergoing a major transformation, as it moves shifting from traditional, labor-intensive methods to adopting digital technologies within the framework of Industry 4.0. Machine learning (ML), artificial intelligence (AI), and robotics are emerging as key innovative tools to improve safety…
Oscar Morton, Christopher G. Bousfield, Prince Dégny Valé, Ieuan Lamb + 3 more
Demand for minerals sourced from sub-Saharan Africa is expanding rapidly1-5. If poorly managed, mining expansion poses a key threat to tropical forests across the continent6,7. Here we present a spatiotemporal assessment of mining-driven deforestation of dense forests across Africa, using continent-wide data on…
Alexey Shlyonskikh, Michael Sinelnikov, Daniil Nikolaev, Yurii Litvinov + 1 more
Matching dependency is a generalization of the functional dependency concept, which allows users to apply custom similarity functions for matching individual attributes. Matching dependencies have a wide range of applications for solving various data quality problems, such as entity resolution, data deduplication, data…
Richard Arthur, Virginia DiDomizio, Louis Hoebel
In some complex domains, certain problem-specific decompositions can provide advantages over monolithic designs by enabling comprehension and specification of the design. In this paper we present an intuitive and tractable approach to reasoning over large and complex data sets. Our approach is based on Active Data…
Authors not listed
Mass spectrometry (MS) generates large datasets that are stored in increasingly optimized and complex file types, demanding technical expertise to extract information rapidly and easily. We wondered whether a simple structured query language (SQL) database could hold raw MS data and allow for easily readable queries…
Matthias Kahl, Zhaiyu Chen, Sudipan Saha, Mrinalini Kochupillai + 2 more
Mining operations are of utmost importance to the economy of some nations. However, such operations result in land-use change, very high energy consumption, and negative impacts on the environment, including soil erosion and deforestation. The mining process can impact an area much larger than the mining site itself.…
Anthony Wu, Devon Kohler, Pruthvi Prakash Navada, Julia E. Robbins + 9 more
A common outcome of quantitative mass spectrometry-based proteomic and phosphoproteomic experiments is a list of proteins that are differentially abundant between conditions. However, biological interpretation requires evaluation in the context of prior knowledge of biological mechanisms and protein function. One…