Certainty of outlier and boundary points processing in data mining

Data certainty is one of the issues in the real-world applications which is caused by unwanted noise in data. Recently, more attentions have been paid to overcome this problem. We proposed a new method based on neutrosophic set (NS) theory to detect boundary and outlier points as challenging points in clustering methods. Generally, firstly, a certainty value is assigned to data points based on the proposed definition in NS. Then, certainty set is presented for the proposed cost function in NS domain by considering a set of main clusters and noise cluster. After that, the proposed cost function is minimized by gradient descent method. Data points are clustered based on their membership degrees. Outlier points are assigned to noise cluster and boundary points are assigned to main clusters with almost same membership degrees. To show the effectiveness of the proposed method, two types of datasets including 3 datasets in Scatter type and 4 datasets in UCI type are used. Results demonstrate that the proposed cost function handles boundary and outlier points with more accurate membership degrees and outperforms existing state of the art clustering methods.Comment: Conference Paper, 6 page

Synthesis of current evidence on factors influencing the suitability of synthetic biodegradable mulches for agricultural applications: A systematic review

Mulching practice offers farmers an opportunity to minimize the effects of drought, water loss, and soil erosion on crop production. Plastic film is widely used as a mulching material; however, contamination of arable lands by residual plastic has become a serious concern. Synthetic biodegradable mulch films and sprays may offer a more sustainable alternative to plastic films, however current evidence on the factors that influence the suitability of these products for agricultural applications is fragmented, making it unclear under what conditions these products meet agronomic, environmental, and societal needs. We address this gap by conducting a systematic review of studies that evaluate the use of synthesized biodegradable mulch for agricultural applications and extract data from 151 primary studies on factors that directly and indirectly influence the suitability of its use. Like others, we find that using biodegradable mulches nearly always provides agronomic benefits over not mulching but rarely provides agronomic benefits over conventional plastic films. However, we also find that reported benefits vary across climate conditions, mulch type, and crop and agronomic factors tested, highlighting the context-specificity of biodegradable mulch benefits which is not yet well understood. In addition, we identify a need for studies that experimentally evaluate the secondary environmental and social benefits of biodegradable mulch use to provide a better understanding of the full potential of these products for sustainable agriculture

Conversational Ontology Alignment with ChatGPT

This study evaluates the applicability and efficiency of ChatGPT for ontology alignment using a naive approach. ChatGPT's output is compared to the results of the Ontology Alignment Evaluation Initiative 2022 campaign using conference track ontologies. This comparison is intended to provide insights into the capabilities of a conversational large language model when used in a naive way for ontology matching, and to investigate the potential advantages and disadvantages of this approach

The KnowWhereGraph Ontology:A Showcase

KnowWhereGraph is one of the largest fully publicly available spatially enabled knowledge graphs. It includes data on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, among other themes. These have been leveraged through the graph by a variety of applications to address challenges in food security and agricultural supply chains; sustainability related to soil conservation practices and farm labor; and delivery of emergency humanitarian aid following a disaster. This paper showcases the KnowWhereGraph ontology, which acts as the schema for the KnowWhereGraph. We discuss how it enables the powerful spatial and semantic integration across these datasets, our validation paradigm, and the applications it supports.</p