Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part supply chain. SCRM often depend rapid adaptive decision-making based potentially large, multidimensional data sources. These characteristics make suitable application area for artificial intelligence (AI) techniques. The aim this paper is provide comprehensive review literature...

The importance of transformations and normal forms in logic programming, generally computer science, is well documented. This paper investigates the context Defeasible Logic, a simple but efficient formalism for nonmonotonic reasoning based on rules priorities. described this have two main benefits: one hand they can be used as theoretical tool that leads to deeper understanding formalism, other been development an implementation defeasible logic.

Abstract Ontologies play a key role in the advent of Semantic Web. An important problem when dealing with ontologies is modification an existing ontology response to certain need for change. This complex and multifaceted one, because it can take several different forms includes related subproblems, like heterogeneity resolution or keeping track versions. As result, being addressed by different, but closely often overlapping research disciplines. Unfortunately, boundaries each such discipline...

Defeasible reasoning is a simple but efficient rule-based approach to nonmonotonic reasoning. It has powerful implementations and shows promise be applied in the areas of legal modelling business rules. This paper establishes significant links between defeasible argumentation. In particular, Dung-like argumentation semantics provided for two key logics, which one ambiguity propagating other blocking. There are several reasons significance this work: (a) establishing formal systems leads...

Abstract Ontology evolution aims at maintaining an ontology up to date with respect changes in the domain that it models or novel requirements of information systems enables. The recent industrial adoption Semantic Web techniques, which rely on ontologies, has led increased importance research. Typical approaches are designed as multiple-stage processes combining techniques from a variety fields (e.g., natural language processing and reasoning). However, few existing surveys this topic lack...

Heart disease, caused by low heart rate, is one of the most significant causes mortality in world today. Therefore, it critical to monitor health identifying deviation rate very early, which makes easier detect and manage heart's function irregularities at a early stage. The fast-growing use advanced technology such as Internet Things (IoT), wearable monitoring systems artificial intelligence (AI) healthcare has continued play vital role analysis huge amounts health-based data for accurate...

Defeasible reasoning is a rule-based approach for efficient with incomplete and inconsistent information. Such is, among others, useful ontology integration, where conflicting information arises naturally; the modeling of business rules policies, exceptions are often used. This paper describes these scenarios reports on implementation system defeasible Web. The system, DR-DEVICE, capable about RDF metadata over multiple Web sources using logic rules. It implemented top CLIPS production rule...

Nonmonotonic rule systems are expected to play an important role in the layered development of semantic Web. Defeasible reasoning is a direction nonmonotonic that based on use rules may be defeated by other rules. It simple, but often more efficient approach than for with incomplete and inconsistent information. This paper reports implementation system defeasible The 1) syntactically compatible RuleML, 2) features strict rules, priorities, two kinds negation, 3) translation logic programming...

Abstract Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental that includes symptoms such as inattentiveness, and impulsiveness. It considered an important public health issue prevalence of, well demand for diagnosis, has increased awareness of the disease grew over past years. Supply specialist medical experts not kept pace with increasing assessment, both due to financial pressures on systems difficulty train new experts, resulting in growing waiting lists. Patients are...

Apart from the need for superior accuracy, healthcare applications of intelligent systems also demand deployment interpretable machine learning models which allow clinicians to interrogate and validate extracted medical knowledge. Fuzzy rule-based are generally considered that able reflect associations between conditions associated symptoms, through use linguistic if-then statements. Systems built on top fuzzy sets particular appealing since they enable tolerance vague imprecise concepts...

Large language models (LLMs) such as ChatGPT have risen in prominence recently, leading to the need analyze their strengths and limitations for various tasks. The objective of this work was evaluate performance large model checking, which is used extensively critical tasks software hardware verification. A set problems were proposed a benchmark three LLMs (GPT-4, Claude, Gemini) evaluated with respect ability solve these problems. evaluation conducted by comparing responses gold standard...

For many years, the non-montonic reasoning community has focussed on highly expressive logics. Such logics have turned out to be computationally expensive, and given little support practical use of non-monotonic reasoning. In this work we discuss defeasible logic, a less-expressive but more efficient logic. We report two new implemented systems for logic: query answering system employing backward-chaining approach, forward-chaining implementation that computes all conclusions. Our...

Defeasible reasoning is a simple but efficient approach to nonmonotonic that has recently attracted considerable interest and found various applications. logic its variants are an important family of defeasible methods. So far no relationship been established between mainstream approaches. In this paper we establish close links known semantics programs. particular, give translation theory , instead.

The imperfect nature of context in Ambient Intelligence environments and the special characteristics entities that possess share available information render contextual reasoning a very challenging task. accomplishment this task requires formal models handle involved as autonomous logic-based agents provide methods for handling distributed context. This paper proposes solution based on Multi-Context Systems paradigm which local knowledge ambient is encoded rule theories (contexts), flow...

The representation of temporal information has been in the center intensive research activities over years areas knowledge representation, databases and more recently, Semantic Web. proposed approach extends existing framework representing ont ologies by allowing for concepts evolving time (referred to as "dynamic" information) their properties terms qualitative descriptions addition quantitative ones (i.e., dates, instants intervals). For this purpose, we advocate use natural language...