Partial Decision Tree Forest: A Machine Learning Model for the Geosciences

Kiyak, Elife Ozturk; Tuysuzoglu, Goksu; Birant, Derya

doi:10.3390/min13060800

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Partial decision trees (PART) [20], this approach integrates the divide-and-conquer methodology of RIPPER with the decision tree approach of C4.5. In greater detail, PART functions by generating a collection of rules using the divide-and-conquer strategy.…”

Section: Partial Decision Treesmentioning

confidence: 99%

Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning

Swain,

Pattnayak,

Mohanty

et al. 2024

IJEECS

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Cattle diseases can significantly impact on livestock health and agricultural productivity is substantial. Timely detection and prognosis of these diseases are essential for prompt interventions and preventing their spread within the herd. This study delved into employing machine learning models to anticipate cattle diseases based on relevant parameters. These parameters encompass milk fever, milk clots, milk watery, milk flake, blisters, lameness, stomach pain, gaseous stomach, dehydration, diarrhea, vomiting, abdominal issues, and alkalosis. A dataset of 2,000 samples from diverse cattle populations was amassed, each tagged with the presence or absence of specific diseases. The primary goal was to compare the efficacy of five well-known machine learning models: Naïve Bayes multinomial (NBM), lazy-IBk, partial tree (PART), random forest (RF), and support vector machine (SVM). The findings underscored the consistent superiority of RF in comparison to the other models, boasting the highest accuracy in predicting cattle diseases. The RF model exhibited an accuracy rate of 88% on the test dataset. This achievement can be ascribed to its capacity to handle intricate interactions among input features and mitigate over fitting through ensemble learning. These insights can furnish valuable information about early indicators and risk factors associated with diverse cattle diseases.

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Section: Partial Decision Treesmentioning

confidence: 99%

Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning

Swain,

Pattnayak,

Mohanty

et al. 2024

IJEECS

View full text Add to dashboard Cite

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Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction

Chen,

Ma,

Wang

et al. 2024

Earth Sci Inform

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The integration of machine learning (ML) and deep learning (DL) into geoscience has experienced a pronounced uptick in recent years, a trend propelled by the intricate nature of geosystems and the abundance of data they produce. These computational methods have been harnessed across a spectrum of geoscientific challenges, from climate modeling to seismic analysis, exhibiting notable efficacy in extracting valuable insights from intricate geological datasets for applications such as mineral prediction. A thorough analysis of the literature indicates a marked escalation in AI-centric geoscience research starting in 2018, characterized by a predictive research orientation and a persistent focus on key computational terms. The thematic network and evolution analyses underscore the enduring prominence of “deep learning” and “machine learning” as pivotal themes, alongside progressive developments in “transfer learning” and “big data”. Despite these advancements, other methodologies have garnered comparatively lesser focus. While ML and DL have registered successes in the realm of mineral prediction, their amalgamation with domain-specific knowledge and symbolic reasoning could further amplify their interpretability and operational efficiency. Neuro-Symbolic AI (NSAI) emerges as a cutting-edge approach that synergizes DL’s robust capabilities with the precision of symbolic reasoning, facilitating the creation of models that are both powerful and interpretable. NSAI distinguishes itself by surmounting traditional ML constraints through the incorporation of expert insights and delivering explanatory power behind its predictive prowess, rendering it particularly advantageous for mineral prediction tasks. This literature review delves into the promising potential of NSAI, alongside ML and DL, within the geoscientific domain, spotlighting mineral prediction as a key area of focus. Despite the hurdles associated with infusing domain expertise into symbolic formats and mitigating biases inherent in symbolic reasoning, the application of NSAI in the realm of critical mineral prediction stands to catalyze a paradigm shift in the field. By bolstering prediction accuracy, enhancing decision-making processes, and fostering sustainable resource exploitation, NSAI holds the potential to significantly reshape geoscience’s future trajectory.

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Computer Modeling and Machine Learning in Chemistry and Materials Science: From Properties and Reactions of Small Organic and Inorganic Molecules to the Smart Design of Polymers and Composites

Novikov

2023

Compounds

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Computer modeling, machine learning, and artificial intelligence are currently considered cutting-edge topics in chemistry and materials science. The application of information technologies in natural sciences can help researchers collect big data and understand patterns that are not obvious to humans. In this perspective, I would like to highlight the recent achievements of our research group and other researchers in relation to computer modeling and machine learning in chemistry and materials science.

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Partial Decision Tree Forest: A Machine Learning Model for the Geosciences

Cited by 4 publications

References 23 publications

Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning

Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning

Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction

Computer Modeling and Machine Learning in Chemistry and Materials Science: From Properties and Reactions of Small Organic and Inorganic Molecules to the Smart Design of Polymers and Composites

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