Handling Of Ambiguities Research Articles

Multi-criteria assessment of climate change due to green house effect based on Sugeno Weber model under spherical fuzzy Z-numbers

Using the multi-criteria decision-making (MCDM) approach, this research piece addresses the urgent problems of environmental degradation and climate change. The method provides a structured way to examine and compare different criteria and options, which improves the precision of decision-making. In order to make this method even better, we combine Zadeh's Z´-numbers with limitations and reliability factors in an effort to fill the current knowledge vacuum about their maximum potential. We investigate Z´-numbers, which are unexpected and fuzzy, in an effort to use them to our advantage by combining them with spherical fuzzy sets (SFSs). We present the idea of spherical fuzzy Z´-numbers (SFZ´Ns) that allow for flexible and adaptive handling of uncertain data by facilitating fast pairwise comparisons of decision-making options. We develop a full set of operational rules and create aggregation operators (AOs) based on the Sugeno Weber Γ¯-norm and Γ¯-conorm to formalize this new method. We use the average and geometric AOs, two of these operators, to show how our suggested technique works and how practical it is. In decision-making situations including climate change assessment, our comparison study highlights the importance of the suggested operators and approaches, especially in relation to the greenhouse effect. Our study helps us understand Z´-numbers better and how they might be used to handle uncertain data by tackling the interaction between fuzziness and randomness. By emphasizing clarity and specificity, our research provides a solid basis for better decision-making and handling of ambiguity in several fields.

A Semantics-enhanced Topic Modelling Technique: Semantic-LDA

Topic modelling is a beneficial technique used to discover latent topics in text collections. But to correctly understand the text content and generate a meaningful topic list, semantics are important. By ignoring semantics, that is, not attempting to grasp the meaning of the words, most of the existing topic modelling approaches can generate some meaningless topic words. Even existing semantic-based approaches usually interpret the meanings of words without considering the context and related words. In this article, we introduce a semantic-based topic model called semantic-LDA that captures the semantics of words in a text collection using concepts from an external ontology. A new method is introduced to identify and quantify the concept–word relationships based on matching words from the input text collection with concepts from an ontology without using pre-calculated values from the ontology that quantify the relationships between the words and concepts. These pre-calculated values may not reflect the actual relationships between words and concepts for the input collection, because they are derived from datasets used to build the ontology rather than from the input collection itself. Instead, quantifying the relationship based on the word distribution in the input collection is more realistic and beneficial in the semantic capture process. Furthermore, an ambiguity handling mechanism is introduced to interpret the unmatched words, that is, words for which there are no matching concepts in the ontology. Thus, this article makes a significant contribution by introducing a semantic-based topic model that calculates the word–concept relationships directly from the input text collection. The proposed semantic-based topic model and an enhanced version with the disambiguation mechanism were evaluated against a set of state-of-the-art systems, and our approaches outperformed the baseline systems in both topic quality and information filtering evaluations.

Analysis of inter- and intragranular variations based on 3DXRD data and crystal plasticity

The synchrotron-based technique of 3DXRD/HEDM has proven a powerful tool to investigate individual grains in the bulk of a polycrystal during plastic deformation. Comparisons between such experimental data and crystal plasticity simulations typically show agreement with respect to overall qualitative trends. However, comparisons at the level of individual grains are in general less successful. Experimental observations and crystal plasticity simulations for a number of representative grains are analysed for similarities and differences in order to elucidate the origin of these. The comparison points at insufficient modelling of grain interactions, hardening and possibly also in the handling of ambiguities. Spatially resolved experimental data on the orientation spread is expected to provide the information needed to improve the predictive capacity at the level of individual grains.

A Method for Webpage Classification Based on URL Using Clustering

Background: Pattern mining is the mechanism of extracting useful information from a large dataset of information. A sub-field of web mining is sequential Noisy data extraction from user query, which is considered along with redundancy handling. This redundancy handling mechanism employed in the existing literature is known as ambiguity handling. The clustering mechanism employed in the existing system includes k means, semantic search and incremental growth of the internet. Aims: The proposed works comprise an analysis of techniques used to extract useful URLs to replace noisy data. Methods: We consider noisy data extraction from user query considered along with the redundancy handling. This redundancy handling mechanism employed in the existing literature is known as ambiguity handling. The clustering mechanism used in the existing system includes k means and semantic search. These mechanisms are static, causing performance degradation in terms of execution time. It suggests the performance improvement mechanism in this literature. Results: The methods MPV (Most-Probable-Values) clustering and N-gram techniques for improvement considered in existing literature can further be improved using the research methodology specified through this literature. Conclusion: In the proposed system, results are based on MPV clustering with N-grams techniques. N-gram analyzes the instances of a word or phrase across all query data. The parameters fetch the results in terms of execution time and the number of URLs retrieves for web page classification.

Natural Language Generation: Algorithms and Applications

Natural Language Generation (NLG) is a subfield of artificial intelligence and computational linguistics that focuses on the automatic generation of natural language text. NLG has a wide range of applications in various fields, including content generation, virtual assistants, business intelligence, and healthcare. This paper provides an overview of NLG techniques and algorithms, including rule-based NLG, template-based NLG, statistical NLG, and neural NLG. It also explores the applications of NLG in different fields, highlighting its role in automated journalism, personalized content creation, virtual assistants, and data storytelling. Furthermore, the paper discusses the current challenges in NLG, such as naturalness, ambiguity handling, and scalability, and examines emerging trends and future directions in NLG, including advancements in neural NLG models, integration with other AI technologies, and ethical considerations. Overall, this paper aims to provide a comprehensive understanding of NLG and its impact on modern society.

FlexiDot: highly customizable, ambiguity-aware dotplots for visual sequence analyses

FlexiDot is a cross-platform dotplot suite generating high quality self, pairwise and all-against-all visualizations. To improve dotplot suitability for comparison of consensus and error-prone sequences, FlexiDot harbors routines for strict and relaxed handling of ambiguities and substitutions. Our shading modules facilitate dotplot interpretation and motif identification by adding information on sequence annotations and sequence similarities. Combined with collage-like outputs, FlexiDot supports simultaneous visual screening of large sequence sets, enabling dotplot use for routine analyses. FlexiDot is implemented in Python 2.7. Software and documentation are freely available at http://github.com/molbio-dresden/flexidot. Supplementary data are available at Bioinformatics online.

Handling Ambiguities in Sindhi Named Entity Recognition (SNER)

Natural language processing is considered the least advanced field of artificial intelligence and the main reason is the challenges in form of differences of accent, variations in script and other problems. A working Sindhi Named Entity recognition impose a challenging task, ambiguity; where the word can be a name of person and the day of the week. It is very hard to understand the difference between two meanings whereas the word is same in writing as well as in pronunciation. This research work presents the handling of ambiguities in Sindhi Entity Recognition (SNER). A rule based approach with the help of indexing has been used to handle ambiguities in SNER. The Sindhi Entity Recognition System recognizes ambiguous words of Sindhi language and produces corresponding meaning successfully. The SNER system clearly understands from the sentence and produces the accurate meaning corresponding to the ambiguous word and its available meaning.

Hapl-o-Mat: open-source software for HLA haplotype frequency estimation from ambiguous and heterogeneous data

BackgroundKnowledge of HLA haplotypes is helpful in many settings as disease association studies, population genetics, or hematopoietic stem cell transplantation. Regarding the recruitment of unrelated hematopoietic stem cell donors, HLA haplotype frequencies of specific populations are used to optimize both donor searches for individual patients and strategic donor registry planning. However, the estimation of haplotype frequencies from HLA genotyping data is challenged by the large amount of genotype data, the complex HLA nomenclature, and the heterogeneous and ambiguous nature of typing records.ResultsTo meet these challenges, we have developed the open-source software Hapl-o-Mat. It estimates haplotype frequencies from population data including an arbitrary number of loci using an expectation-maximization algorithm. Its key features are the processing of different HLA typing resolutions within a given population sample and the handling of ambiguities recorded via multiple allele codes or genotype list strings. Implemented in C++, Hapl-o-Mat facilitates efficient haplotype frequency estimation from large amounts of genotype data. We demonstrate its accuracy and performance on the basis of artificial and real genotype data.ConclusionsHapl-o-Mat is a versatile and efficient software for HLA haplotype frequency estimation. Its capability of processing various forms of HLA genotype data allows for a straightforward haplotype frequency estimation from typing records usually found in stem cell donor registries.

Colligation in child welfare work: Decision-making in a case on the tipping point

This article reports on a single-case study of a decision-making process in child welfare. Based on analysis of field notes, research interviews with caseworkers and case documents, the study explored caseworkers’ handling of ambiguity and uncertainty in a case of possible neglect on the tipping point between home-based and out-of-home care. The prospective study followed events and activities in the case of a family consisting of a mother, a father and newborn twins, reflecting real process in real time. Data were gathered in a local frontline child welfare office in a Norwegian town, and decision-making was studied as a process of sequential colligation rather than as a linear and cumulative effort. The findings suggest that the caseworkers’ individual commitment to and felt responsibility for the outcome led to a quest for documentation, making the process of decision-making more challenging. The search for decisive evidence may contribute to prolonged casework and postponed closure in cases on the tipping point.

A cognitive map framework to support integrated environmental assessment

In this work we present the rational and design of a methodology to support Integrated Environmental Assessment using the DPSIR (Driving Forces–Pressures–State–Impact–Response) causal-effect framework and non-monotonic Fuzzy Cognitive Maps. The methodology is based on key pillars in environmental management, namely connecting the socioeconomic and the natural environment dimensions into a policy oriented context; integration of stakeholders with inter-sectorial synergies and tradeoffs; handling of ambiguities and uncertainties intrinsic to environmental modeling and representation of complex non-linear cause-effect relationships in the form of Fuzzy Inference Systems, capable of adapting dynamically the influence between indicators. The methodology has the potential to support the development of informed policies and improves reliability through transparent, traceable and reproducible results. The illustrative example assesses the impact of air pollution abatement policies according to expert perceptions using proactive scenarios; the results revealed that, despite some positive changes, air protection activities are missing an overall strategic vision.

Integrated foreground segmentation and boundary matting for live videos.

The objective of foreground segmentation is to extract the desired foreground object from input videos. Over the years, there have been significant amount of efforts on this topic. Nevertheless, there still lacks a simple yet effective algorithm that can process live videos of objects with fuzzy boundaries (e.g., hair) captured by freely moving cameras. This paper presents an algorithm toward this goal. The key idea is to train and maintain two competing one-class support vector machines at each pixel location, which model local color distributions for both foreground and background, respectively. The usage of two competing local classifiers, as we have advocated, provides higher discriminative power while allowing better handling of ambiguities. By exploiting this proposed machine learning technique, and by addressing both foreground segmentation and boundary matting problems in an integrated manner, our algorithm is shown to be particularly competent at processing a wide range of videos with complex backgrounds from freely moving cameras. This is usually achieved with minimum user interactions. Furthermore, by introducing novel acceleration techniques and by exploiting the parallel structure of the algorithm, near real-time processing speed (14 frames/s without matting and 8 frames/s with matting on a midrange PC & GPU) is achieved for VGA-sized videos.

The limits of checklists: handoff and narrative thinking

Concerns about the role of communication failures in adverse events coupled with the success of checklists in addressing safety hazards have engendered a movement to apply structured tools to a...

Responding to ambiguity, responding to change the value of a responsive approach to evaluation

Loosely organised teams and flexible arranged programs offer a new answer to improve the practice of palliative health care. Given the ambiguity of this care concept and the innovative organisational arrangement it is possible for confusion to emerge over the goals, core activities and target groups. In such a context it is not sufficient to determine the degree to which goals have been attained because the search for goals is part of the developmental process. An evaluation of a palliative team in a Dutch health region shows that it is more appropriate to acknowledge ambiguity and to go on to facilitate the handling of ambiguity as a vital element in dynamic learning processes. The utility of a responsive approach to evaluation, methodological considerations and learning experiences are described. The article is educational for workers in the field of health care but also to scholars and practitioners in an evaluation community.

Medical-concept models and medical records: an approach based on GALEN and PEN&PAD.

To investigate the issues raised in applying a preliminary version of the GALEN compositional concept reference (CORE) model to a series of radiographic reports, and to demonstrate that the same underlying concept model could be used in conjunction with both a detailed, fine-grained model of medical records based on that used in the PEN&PAD project and with other more conventional medical-record models. Following analysis and representation of concepts from a set of reports, a single report was taken as a "case study." This report was analyzed in detail in its entirety and represented using each of the medical-record models. The reports were successfully represented within the limits of the study, but a number of significant issues were raised. The compositional approach plus the PEN&PAD medical-record model allowed detailed information in the radiographic report to be represented, including information about the inferences and the clinical process. The resulting representation was large, and more compact representations may be necessary for some systems. Alternative encapsulations of the information as might be used in such systems were successfully prepared. The compositional approach avoided many issues that often cause controversy in the design of traditional coding and classification systems, but it raised other issues, including the handling of ambiguity and underspecification, linkage to information not explicitly present in the report, and questions concerning the focus of individual concepts. All work is preliminary and definitive conclusions await further studies and systematic evaluation.