This paper explores the relationship between register transfer level (RTL) automata (ASARTL) and looping algorithm specifications (ASASPEC) using semantics of algebra in the synthesis of stream calculus-based computing for big data in livestream (BDL). Specifically, it examines the verification of looping algorithm and RTL specifications through semantics of algebra. This paper introduces the concept of an algebraic semantics-based model (ASM), which is essentially a Chu space. A Chu space is represented as a triple (X, r, A), where X is the set of states, A is the set of events, and r is the matrix relation function. This model serves as a fundamental basis for formalizing RTL. This paper also defines ASASPEC for ensuring the correctness of RTL synthesis results. The formal verification method used is aimed at verifying functional equivalence to determine if the ASASPEC automata are equivalent to the ASARTL automata.

This is a consideration paper on the applying of the stream theory for the era of big data analytics. The stream theory that includes the stream algebra and the stream coalgebra, and this theory plays a very important role in mathematics and computer science, particularly in the analytics of big data in generally and big data in livestream (BDL) in particularly. Both the stream algebra and the stream coalgebra are applied to solving problems involving structured, semi-structured, and unstructured data. This research focuses on analyzing and comparing the stream theory based on characteristic criteria. This research constructs eight tables to analyze and compare the characteristic criteria that the stream theory has contributed from the past to the present. Through these analytical and comparative tables, we can gain an extensive understanding of the contributions of the stream theory and help them envision the development of the stream theory in the future.

The integration of privacy-preserving mechanisms into artificial intelligence has become essential for the deployment of trustworthy context-aware systems. This paper presents a systematic review of Federated Graph Neural Networks (FedGNNs) with Differential Privacy (DP), highlighting their mathematical foundations and practical applications. We formalize federated optimization and differential privacy guarantees in the context of graph-structured data, demonstrating how these frameworks protect both node features and graph topology. A structured methodology is applied to analyze recent contributions, evaluating trade-offs in privacy, scalability, accuracy, and fairness. Application domains including healthcare, finance, and ubiquitous computing illustrate the effectiveness of FedGNNs with DP in real-world scenarios. Open challenges are identified, such as handling dynamic graphs, ensuring fairness, and designing hybrid privacy mechanisms. This review contributes a unified mathematical and system-level perspective on privacy-preserving graph learning, offering guidance for the development of secure and context-aware AI systems.

Big data in livestream (BDL) is topic of interesting today, due to BDL appearing in the most of Internet of Mobile Things (IoMT). Algebraic aspects refer to applying algebraic structures and concepts to area of BDL in IoMT. They involve studying algebraic structures such as monoids by using them to analyze and solve problems in BDL in IoMT. Algebraic structures and comprehensive concepts are used to study and classify algebraic monoids where their properties are explored. Algebraic aspects have diverse applications. They are used in formal methods for software verification and program analysis, where algebraic structures are employed to model the behavior of programs and reason about their correctness. This article studies some algebraic aspects of BDL in IoMT. In particular, this research represents the computing stream calculus in BDL in IoMT and formalizes BDL monoids, BDL transformations, BDL automata, and combinators for BDL automata in IoMT. Generally, algebraic aspects play a crucial role in BDL in IoMT, provide powerful tools and frameworks for understanding and solving complex problems in BDL in IoMT.

In this paper, we introduce a graphical method for modeling and reasoning with linguistic expressions. The former represents a graph called a conceptual graph, and the latter involves graph transformations. In our conceptual graphs, nodes represent linguistic concepts and edges links between these concepts. This model facilitates reasonining with linguistic concepts by making direct consequences easy to infer.

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.

Nowadays, big data in livestream (BDL) is becoming increasingly prominent in computing systems (CS). Algebraic structures apply algebraic concepts to BDL in CS, aiding in the analysis and resolution of BDL-related problems. This research focuses on algebraic structures related to BDL, such as monoids, to analyze and categorize their properties. These algebraic monoids are essential in formal methods for verifying and analyzing applications of BDL, as they model program behavior and ensure program correctness. This paper examines the algebraic structure of BDL in CS, specifically detailing BDL and formalizing the properties of BDL monoids. Generally, algebraic structures are crucial for computing BDL, offering fundamental frameworks for comprehending and addressing complex issues of BDL in CS.

In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2% on the "Breast Ultrasound Images Dataset."

This paper studies on linguistic topological sapces which are generate from Hedge algebra. We also indicate homotopy classes of homotopic functions on this spaces as well as its equivalence relations.

This paper focuses on addressing the challenge of maintaining information coherence and robustness within a multi-agent system (MAS) that aggregates information from distributed sources, some of which may be defective intentionally or unintentionally. We propose a self-organizational approach in this context, emphasizing a systemic perspective that considers structural coupling across two levels: direct information gathering and communication. Specifically, we integrate a trust mechanism with local behavioral rules and selective environmental pressures to facilitate the emergence of two co-evolving organizations: one at the social level and the other at the spatial level. The social organization mirrors the trust relationships developed among the agents, while the spatial organization represents the deployment of agents in the environment to encourage exploration. The local behavioral rules encompass three categories: deployment rules, communication rules, and retro-action rules governing communication and deployment. We conduct simulations to experiment with the combination of these behavioral rules, observing the emergence of organizational structures and roles within the system.

The secure transmission of information is receiving more and more attention nowadays. Aeolus is a novel platform designed to enhance the development of distributed applications by preventing unauthorized disclosure of information. And one of the most representative systems for information transmission is the file system, therefore it is of great significance to formally analyze the Aeolus-based file system. In this paper, we use Communicating Sequential Processes (CSP) to model and formalize the file system based on Aeolus. Moreover, we utilize the Process Analysis Toolkit (PAT) to simulate and verify the CSP description of our established model. We specifically verify the validity of five properties: Deadlock Freedom, Divergence Freedom, Reachability, Secrecy, and Integrity. The verification results demonstrate that the model successfully satisfies these properties, affirming the effectiveness of the framework in ensuring file operations and guaranteeing the secure transmission of information.

A new cognitive paradigm in internet of mobile things (IOMT) is currently on spot: Context-Awareness (CA), which is inspired by the activity of the human autonomous nervous system. The comprehensive target of CA is to realize IOMT, that can self-govern without direct human interventions. To solve this enormous challenge of CA requires a basic solution for CA concept. For this purpose, in this paper, the categorical approach is used to establish a strong formal basis for modeling CA in order to achieve the formal aspects of CA.

Biometric and identification patterns have gained extensive research and application, particularly in iris recognition. The iris harbors a wealth of individual-specific information, making it a vital element in biometric authentication. This article presents a comprehensive study encompassing iris segmentation and identification. We introduce the Weighted Adaptive Hough Ellipsopolar Transform Convolutional Neural Network (WAHET-CNN) as a novel approach for classifying pattern images. Our experimental outcomes demonstrate a commendable 90% accuracy achieved by the proposed WAHET-CNN on the CASIA dataset Version 4.

A new perception model in the Internet of Mobile Things (IoMT) is now underway: Contextual Perception (CP), which is encouraged by the mankind autonomic neural system functioning. CP’s general goal is to understand clearly IoMT, which can be self-managed without direct mankind intervention. To address this huge challenge of the CP expects a fundamental solution of the CP notion. For this purpose, in this article, a categorical method is used to construct a robust formal foundation for the CP model in order to obtain the CP formal semantics. Furthermore, the categorical structure of CP in IoMT is formally specified in which IoMT products and co-products, finite limits and colimits in IoMT are formally modeled and argued.

The Coronavirus Disease 2019 (COVID-19) has rapidly spread globally, causing a significant impact on public health. This study proposes a predictive model employing machine learning techniques to distinguish between influenza-like illness and COVID-19 based on clinical symptoms and diagnostic parameters. Leveraging a dataset sourced from BMC Med Inform Decis Mak, comprising cases of influenza and COVID-19, we explore a diverse set of features, including clinical symptoms and blood assay parameters. Two prominent machine learning algorithms, XGBoost and Random Forest, are employed and compared for their predictive capabilities. The XGBoost model, in particular, demonstrates superior accuracy with an AUC under the ROC curve of 98.8%, showcasing its potential for clinical diagnosis, especially in settings with limited specialized testing equipment. Our model's practical applicability in community-based testing positions it as a valuable tool for efficient COVID-19 detection. This study advances the field of predictive modeling for disease detection, offering promising prospects for improved public health outcomes and pandemic response strategies. The model's reliability and effectiveness make it a valuable asset in the ongoing fight against the COVID-19 pandemic.

Human always has the ability to view things and phenomena, but to understand about it that depends on the ability to perceive as well as combine with existing experiences and knowledge which is available in every human to understand it. This research studies the human vision organs, infographics capture characteristics of the human eye, human vision awareness progress, and human visual viewing - thinking mechanisms to design visual analytics system of multivariate data (mdVAS), model of viewing - thinking visually analyzing, and visual graphs for discovering knowledge from multivariate data (mD). This mdVAS will support to upgrade human vision awareness progress that includes stages such as viewing - perceiving - cognizing - analyzing - understanding - remembering (vpcaurHs). This progress, especially the analysis stage plays a key that opens the door for human to understand the profound significance of things. Humans who want to understand the profound insights of things must bring out questions, at that time the mdVAS will enhance knowledge for humans to answer questions that humans themselves brought out. This approach will help humans, especially leaders, who have a profound insight into mD. This research illustrates experiments on educational data to manage the student intakes to help leadership on policy and decision making.

The big data in livestream (BDL) are existing at various forms everywhere in the world. Based on these facts, we find that not yet mathematical foundations to explain and prove about working mechanisms of BDL. This article presents the overview of related research of stream theory-based BDL (including the stream algebra and coalgebra in BDL) and analyzes briefly the types of big data technologies in the Hadoop ecosystem compared with the existing technologies in the different properties. Moreover, we also indicate the challenges for stream theory-based BDL and propose the major objectives of stream theory-based BDL which have to be performed.

Facial emotion recognition technology is used to analyze and recognize human emotions based on facial expressions. This technology uses deep learning models to classify facial expressions, eyes, eyebrows, mouth, and other facial expressions to determine a person's emotions. The application of facial emotion recognition in the field of education is a potential way to evaluate the level of student absorption after each class period. Using cameras and emotion recognition technology, the system can record and analyze students' facial expressions during class. In this paper, we use the Convolutional Neural Network (CNN) algorithm combined with the linear regression analysis method to build a model to predict students' facial emotions over a period of time camera recorded.

This article will perform the formally specifying and coinductive approach to verifying synthesis of stream calculus-based computing big data in livestream (BDL). Specifically, this article specifies register transfer level (RTL) synthesis without looping and including looping in stream calculus as behavioral functions. This approach assists the RTL synthesis semantics and is advantageous in putting coinduction in an application for comparing the behavioral functions using bisimulation. In other words, this article describes the principle of coinductive approach to verifying synthesis of stream calculus-based computing BDL. The specification enables pipelining, where all hardware resources including registers along with functional units (FUs) are reused during different control steps (CSteps), to be interpreted as, different scheduling.