Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques.

Due to the rising popularity of the Internet of Things (IoT), interconnected infrastructures with better flexibility and efficiency are being developed for industry 4.0. The use of IoT in industry 4.0 introduces a new term “Industrial IoT (IIoT)”, which allows remote monitoring of wide-ranging industrial processes. IIoT tends to function with a large number of heterogeneous devices, therefore, data interoperability is the biggest challenge in achieving seamless real-time communication. To achieve data interoperability, there is a need to integrate various IoT application layer protocols; separately, as well as, with other data acquisition protocols along with hardware/software platforms. Although such integrations are frequently presented in the state-of-the-art, a comprehensive investigation and analysis of such approaches in single research work are hard to find. Therefore, in this article, a Systematic Literature Review (SLR) is presented by investigating 34 influential research works, published during 2014-2020, where interoperability of application layer protocols is performed for IIoT. Consequently, the selected research studies are divided into three categories: 1) Integration of IoT application layer protocols— 13 papers; 2) Integration of IoT and data acquisition protocols—6 papers; 3) Integration of IoT protocols with hardware/software platforms—15 papers to resolve interoperability issues. Moreover, nineteen (19) tools used for integration in the selected research works are identified. Furthermore, leading approaches to perform such integrations are recognized, e.g., Gateways. It has been concluded that although researchers frequently propose centralized (e.g., gateway or protocol converter) and distributed intermediator (e.g., Middleware) to achieve required interoperability for IIoT, it is a need of a day to develop a device level solution without the involvement of intermediator. This will solve different interoperability problems in industries like scalability, load balancing, and a single point of failure.

The improved productivity and reduced time-to-market are essential requirements for the development of modern embedded systems and, therefore, the comprehensive as well as timely design verification is critical. Assertion Based Verification (ABV) is a renowned paradigm to timely achieve an optimum test coverage, either through static or dynamic techniques. However, the major limitation with ABV is its inherited low-level implementation complexity. In order to simplify its execution, various Model Based System Engineering approaches provide a higher abstraction layer. Nevertheless, the complete verification requirements, targeting the static as well as dynamic ABV at the same time in a unified framework, are not being addressed. Furthermore, the dynamic verification support is provided through some traditional languages (like C, Verilog) where the advanced ABV features cannot be exploited. Consequently, this article introduces the MODEVES (MOdel-based DEsign Verification for Embedded Systems) framework to simultaneously support the static and dynamic ABV. Particularly, the UML (Unified Modeling Language) and SysML (Systems Modeling Language) diagrams are used to model the structural and behavioral requirements. Moreover, the NLCTL (Natural Language for Computation Tree Logic) is proposed to include the verification requirements for static ABV while the SVOCL (SystemVerilog in Object Constraint Language) is used to represent the dynamic verification constraints. An open source transformation engine is developed to automatically generate the SystemVerilog Register Transfer Level (RTL) code, Timed Automata model, SystemVerilog assertions and Computation Tree Logic (CTL) assertions with minimum transformation losses. The significance of the MODEVES framework is established through several case studies and the quantitative analysis shows an improvement of almost 100% in design productivity, as compared to the conventional low-level implementations.

Legacy systems are large applications which are significant in performing daily organizational operations and cannot be upgraded easily especially in the absence of architectural and design documentation. Software modernization is an emerging field of software engineering, which transforms the legacy systems into new one according to the specified requirements of stakeholders. It mainly deals with improving the architecture, features, rules and data sources of existing system. It always remained a challenging task to achieve high-level representation of legacy systems. In order to achieve this high-level representation, Reverse Engineering (RE) plays an integral role. The issues of traditional RE are overcome with the help of Model Driven Reverse Engineering (MDRE) such as it generates model-based view of the legacy systems, which is comprehensible and easy to understand for practitioners. MDRE is an active research area but it provides limited tool support to extract and model both structural and behavioral aspects of legacy systems. In this paper, a novel MDRE framework named as “Source to Model Framework (Src2MoF)”is proposed to generate Unified Modeling Language (UML) structural (class) and behavioral (activity) diagrams from the Java source code. Src2MoF is based on the principles of Model Driven Engineering (MDE), which use models as first-class citizens alleviating the complexity of software systems. The contributions of this paper are as follows; first, an Intermediate Model Discoverer (IMD) is developed using source code parser to get the intermediate representation of the system from the existing Java code. Second, an open source transformation engine named “UML model generator” is implemented using Java, which takes these intermediate models as input, and produce high-level UML models of the subject legacy system. Finally, the two benchmark case studies are presented to depict the relevance and usability of Src2MoF.

The liver is the largest organ of the human body with more than 500 vital functions. In recent decades, a large number of liver patients have been reported with diseases such as cirrhosis, fibrosis, or other liver disorders. There is a need for effective, early, and accurate identification of individuals suffering from such disease so that the person may recover before the disease spreads and becomes fatal. For this, applications of machine learning are playing a significant role. Despite the advancements, existing systems remain inconsistent in performance due to limited feature selection and data imbalance. In this article, we reviewed 58 articles extracted from 5 different electronic repositories published from January 2015 to 2023. After a systematic and protocol‐based review, we answered 6 research questions about machine learning algorithms. The identification of effective feature selection techniques, data imbalance management techniques, accurate machine learning algorithms, a list of available data sets with their URLs and characteristics, and feature importance based on usage has been identified for diagnosing liver disease. The reason to select this research question is, in any machine learning framework, the role of dimensionality reduction, data imbalance management, machine learning algorithm with its accuracy, and data itself is very significant. Based on the conducted review, a framework, machine learning‐based liver disease diagnosis (MaLLiDD), has been proposed and validated using three datasets. The proposed framework classified liver disorders with 99.56%, 76.56%, and 76.11% accuracy. In conclusion, this article addressed six research questions by identifying effective feature selection techniques, data imbalance management techniques, algorithms, datasets, and feature importance based on usage. It also demonstrated a high accuracy with the framework for early diagnosis, marking a significant advancement.