In this paper, we introduce two algorithms for reasoning with words on fuzzy dynamic system. The systems that use linguistic variables which are variables whose values may be expressed in terms of a specific natural or artificial language, for example 𝕃= very less true; less true; true; more true; very true; very very true .... In language of hedge algebra ( ℍ𝔸 ), 𝕃 set which is generated from ℍ𝔸 is the POSET (partial order set). The algorithms are Static reasoning and Dynamic reasoning. The former traverses the branch of the fuzzy graph whereas the later transform according to the equation of state and create a space of states of the system. Algorithms performed on linguistic variables and applied labeling techniques. And finally, the application of the algorithm on the mobile network model is also investigated.
This paper represents verification algorithms and register transfer level (RTL) specification as algebraic aspects proposed to validate the results of RTL synthesis. Major properties of this approach, the conception of an algebraic semantics-based model (ASM), to be interpreted as a Chu space, is viewed as an algebraic semantics foundation for the RTL formalization and the conception of algebraic semantics-based specification automata ( ASA_SPEC ) are given for formal correctness of the results of RTL synthesis. Approaching formal verification is focused on functional equivalence examining to define if the algebraic RTL automata ( ASA_RTL ) are equivalent to ASA_SPEC . To put it another way, the comparison is determined as an assessing that examines the synthesis algorithm is produced an effective RTL specification.
The application of Artificial Intelligence (AI) and ontologies to the COVID-19 pandemic has been an active area of research and development. AI techniques such as machine learning, computer vision, and natural language processing have been used to analyze vast amounts of data generated by the pandemic, such as medical records, scientific literature, and social media posts. Ontologies, on the other hand, provide a structured representation of knowledge, which can be used to standardize data and facilitate data integration, enabling more efficient and effective data analysis.
English is the most common language globally, and it is increasingly important. English has been compiled in most online documents, information, and contents. However, with a considerable vocabulary, learning English is difficult for many people to remember. Therefore, many modern technologies have been proposed to support English learning, such as English learning technology through word-matching games to help children become excited and easily approach English from an early age. In addition, translation tools can help users look up vocabularies, antonyms, synonyms, and examples. This study presents a method to support learning English via object detection in videos, images, or even live-stream videos in real-time using deep learning architectures such as You Look Only Once (YOLO) - one of the finest families of object detection models with state-of-the-art performances. The method to obtain an mAP is 55.6 with 17GFlops. The results are vocabulary, meaning, and making sentences with that. Our method has good accuracy in data of 2786 images belonging to 59 classes.
Online learning has gained significant popularity, but maintaining learner focus remains a challenge, especially in financial enterprise training systems. The need for training has increased with banking and finance digitalization trends, yet high learning curves and prolonged sessions often lead to distractions. This research introduces an online learning tool that monitors and quantifies learner attention in real-time. Using the MobileNet Convolutional Neural Network, we detect seven core emotions, which, combined with attention scores, form a Concentration Index (CI). Learners are then categorized as “Highly-engaged,” “Normally Engaged,” or “Disengaged.” With 70
