Ahmad F. Subahi

In this research, a comprehensive framework employing large language models (LLMs) and prompt engineering techniques is developed with the aim of optimizing the creation of domain-specific languages (DSLs). Domain experts utilize DSLs to create precise, high-level specifications that can be customized to their application domains. However, challenges regarding usability, semantic correctness, and syntax design are often encountered in the development process. With recent advances in LLaMA, Claude, DeepSeek, Gemini, and ChatGPT, it is now possible to automate and refine several DSL development stages through carefully written prompts. The proposed framework provides structured prompt patterns that are customized to each successive stage of the DSL life cycle: domain knowledge acquisition, grammar definition, syntax construction, and validation. A case study involving a hospital monitoring system is used to explore the effectiveness of the models and the prompting strategies. This study also classifies the prompting techniques and assesses five LLMs in terms of readability, expressiveness, accuracy, syntax clarity, and completeness. The findings suggest that prompt engineering significantly contributes to the development of DSLs, with the five LLMs excelling in different evaluation measurements. For example, DeepSeek and LLaMA display superior syntactic precision, while Claude and ChatGPT perform better in terms of expressiveness and readability. The need to align the choice of LLM with stakeholder expectations, whether they be technical or domain specific, is highlighted by expert assessments. This research contributes to the body of knowledge relating to LLM-assisted DSL development and provides a basis for further study in adaptive prompting and multimodal integration.

In the digital era, cybersecurity is integral for preserving national security, digital privacy, and social sustainability. This research emphasizes the role of non-functional equirements (NFRs) in developing secure software systems that enhance societal wellbeing by ensuring data protection, user privacy, and system robustness. Specifically, this study introduces a proof-of-concept approach by leveraging machine learning (ML) models to classify NFRs and identify security-related issues early in the software development lifecycle. Two experiments were conducted to assess the effectiveness of different models for binary and multi-class classification tasks. In Experiment 1, BERT-based models and artificial neural networks (ANNs) were fine-tuned to classify NFRs into security and non-security categories using a dataset of 803 statements. BERT-based models outperformed ANNs, achieving higher accuracy, precision, recall, and ROC-AUC scores, with hyperparameter tuning further enhancing the results. Experiment 2 assessed logistic regression (LR), a support vector machine (SVM), and XGBoost for the multi-class classification of security-related NFRs into seven categories. The SVM and XGBoost showed strong performance, achieving high precision and recall in specific categories. The findings demonstrate the effectiveness of advanced ML models in automating NFR classification, improving software security, and supporting social sustainability. Future work will explore hybrid approaches to enhance scalability and accuracy.

In recent years, the integration of Internet of Things technologies in smart agriculture has become critical for sustainability and efficiency, to the extent that recent improvements have transformed greenhouse farming. This study investigated the complexity of IoT architecture in smart greenhouses by introducing a greenhouse language family (GreenH) that comprises three domain-specific languages designed to address various tasks in this domain. The purpose of this research was to streamline the creation, simulation, and monitoring of digital twins, an essential tool for optimizing greenhouse operations. A three-stage methodology was employed to develop the GreenH DSLs, a detailed metamodel for enhanced smart monitoring systems. Our approach used high-level metamodels and extended Backus–Naur form notation to define the DSL syntax and semantics. Through a comprehensive evaluation strategy and a selected language usability metrics, the expressiveness, consistency, readability, correctness, and scalability of the DSL were affirmed, and areas for usability improvement were highlighted. The findings suggest that GreenH languages hold significant potential for advancing digital twin modeling in smart agriculture. Future work should be aimed at refining usability and extending its application range. The anticipated integration with additional model-drive engineering and code generation tools will improve interoperability and contribute to digital transformation in the smart greenhouse domain and promote more sustainable food production systems.

The incorporation of sustainability principles during the requirements engineering phase of the development life cycle constitutes greening software requirements. This incorporation can have a variety of effects on the software design employed in modern and cutting-edge information technology (IT) systems. When sustainability principles are incorporated into requirements engineering, software design priorities can change and address current design issues such as energy and resource consumption, modularity, maintainability, and adaptability. In contrast to other green approaches that consider sustainable development, there is a further need to investigate the relationship between software development and the relevant green principles of sustainability during the requirements engineering phase. We present a new mechanism for mapping software nonfunctional requirements (NFRs) to defined dimensions of green software sustainability, consisting of two mapping steps: 1) between NFRs and sustainability dimensions; and 2) between sustainability dimensions and two clusters of green IT aspects defined in this work. The overall architecture of the promising approach is based on the use of the Bidirectional Encoder Representations from Transformers (BERT) language model with an expanded dataset. We consider transfer learning and domain-specific fine-tuning capabilities for constructing and evaluating a model specifically tailored for developing a proof of concept of the greening software requirements engineering task, as language models have recently emerged as a potent technique in the field of software engineering, with numerous applications in code analysis, automated documentation, and code generation. In addition, we test the model’s performance using an extended version of the PROMISE_exp dataset after adding a new binary classification column for categorizing sustainability dimensions into two defined clusters: Eco-technical and Socioeconomic, and having a selected domain expert label the raw data. The model’s efficiency is evaluated using four matrices—1) accuracy; 2) precision; 3) recall; and 4) F1 score—across a variety of epoch and batch sizes. Our numerical results demonstrate the viability of the approach in text classification tasks via performing well in mapping NFRs to software sustainability dimensions. This acts as a proof of concept for automating the sustainability measurement of software awareness at the early development stage. In addition, the results emphasize the importance of domain-specific fine-tuning and transfer learning for obtaining high performance in classification tasks in requirements engineering.

With the rapid growth in the availability of digital health-related data, there is a great demand for the utilization of intelligent information systems within the healthcare sector. These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks. They can also provide various sustainable health services such as medical error reduction, diagnosis acceleration, and clinical services quality improvement. The intensive care unit (ICU) is one of the most important hospital units. However, there are limited rooms and resources in most hospitals. During times of seasonal diseases and pandemics, ICUs face high admission demand. In line with this increasing number of admissions, determining health risk levels has become an essential and imperative task. It creates a heightened demand for the implementation of an expert decision support system, enabling doctors to accurately and swiftly determine the risk level of patients. Therefore, this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs, as a proof-of-concept, for tackling this healthcare-related issue. The system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to hospitals. The proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system.

Heart disease (HD) has surpassed all other causes of death in recent years. Estimating one’s risk of developing heart disease is difficult, since it takes both specialized knowledge and practical experience. The collection of sensor information for the diagnosis and prognosis of cardiac disease is a recent application of Internet of Things (IoT) technology in healthcare organizations. Despite the efforts of many scientists, the diagnostic results for HD remain unreliable. To solve this problem, we offer an IoT platform that uses a Modified Self-Adaptive Bayesian algorithm (MSABA) to provide more precise assessments of HD. When the patient wears the smartwatch and pulse sensor device, it records vital signs, including electrocardiogram (ECG) and blood pressure, and sends the data to a computer. The MSABA is used to determine whether the sensor data that has been obtained is normal or abnormal. To retrieve the features, the kernel discriminant analysis (KDA) is used. By contrasting the suggested MSABA with existing models, we can summarize the system’s efficacy. Findings like accuracy, precision, recall, and F1 measures show that the suggested MSABA-based prediction system outperforms competing approaches. The suggested method demonstrates that the MSABA achieves the highest rate of accuracy compared to the existing classifiers for the largest possible amount of data.

Purpose: This paper aims to introduce the goal-oriented requirements extraction approach (GOREA). It is an elicitation approach that uses, specifically, healthcare business goals to derive the requirements of e-health system to be developed.Design/methodology/approach: GOREA consists of two major phases: (1) modelling e-health business requirements phase and (2) modelling e-health information technology (IT) and systems requirements phase. The modelling e-health business requirements phase is divided into two main stages: (1) model e-health business strategy stage and (2) model e-health business environment stage. The modelling e-health IT and systems requirements phase illustrates the process of obtaining requirements of e-health system from the organizational goals that are determined in the previous phase. It consists of four main steps that deal with business goals of e-health system: (1) modelling e-health business process (BP) step; (2) modelling e-health business goals step; (3) analysing e-health business goals step; and (4) eliciting e-health system requirements step. A case study based on the basic operations and services in hospital emergency unit for checking patient against COVID-19 virus and taking its diagnostic testing has been set and used to examine the validity of the proposed approach by achieving the conformance of the developed system to the business goals.Findings: The results indicate that (1) the proposed GOREA has a positive influence on the system implementation according to e-health business expectations; and (2) it can successfully fulfil the need of e-health business in order to save the citizens life by checking them against COVID-19 virus.Research limitations/implications: The proposed approach has some limitations. For example, it is only validated using one e-health business goal and thus it has to be authenticated with different e-health business goals in order to address different e-health problems.Originality/value: Many e-health projects and innovations are not established based on robust system requirements engineering phase. In order to ensure the success delivery of e-health services, all characteristics of e-health systems and applications must be understood in terms of technological perspectives as well as the all system requirements.

This work presents the design of an Internet of Things (IoT) edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places. A wireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design. A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design. A Neo4j graph database is used as a target implementation generated from the proposed transformational system to store all captured real-time IoT data about the distances between individuals in an indoor area and answer user predefined queries, expressed using Neo4j Cypher, to provide insights from the stored data for decision support. As proof of concept, a discrete-time simulation model was adopted for the design of a COVID-19 physical distancing measures case study to evaluate the introduced system architecture. Twenty-one weighted graphs were generated randomly and the degrees of violation of distancing measures were inspected. The experimental results demonstrate the capability of the proposed system design to detect violations of COVID-19 physical distancing measures within an enclosed area.

Network security and energy consumption are deemed to be two important components of wireless and mobile ad hoc networks (WMANets). There are various routing attacks which harm Ad Hoc networks. This is because of the unsecure wireless communication, resource constrained capabilities and dynamic topology. In order to cope with these issues, Ad Hoc On-Demand Distance Vector (AODV) routing protocol can be used to remain the normal networks functionality and to adjust data transmission by defending the networks against black hole attacks. The proposed system, in this work, identifies the optimal route from sender to collector, prioritizing the number of jumps, the battery life, and security, which are fundamental prerequisites. Researches have proposed various plans for detecting the shortest route, as well as ensuring energy conversions and defense against threats and attacks. In this regard, the packet drop attack is one of the most destructive attack against WMANet communication and hence merits special attention. This type of attack may allow the attacker to take control of the attacked hubs, which may lost packets or transmitted information via a wrong route during the packets journey from a source hub to a target one. Hence, a new routing protocol method has been proposed in this study. It applies the concept of energy saving systems to conserve energy that is not required by the system. The proposed method for energy aware detection and prevention of packet drop attacks in mobile ad hoc networks is termed the Ad Hoc On-Demand and Distance Vector–Packet Drop Battling Mechanism (AODV–PDBM).

The Kingdom of Saudi Arabia is known for its extreme climate where temperatures can exceed 50 °C, especially in summer. Improving agricultural production can only be achieved using innovative environmentally suitable solutions and modern agricultural technologies. Using Internet of Things (IoT) technologies in greenhouse farming allows reduction of the immediate impact of external climatic conditions. In this paper, a highly scalable intelligent system controlling, and monitoring greenhouse temperature using IoT technologies is introduced. The first objective of this system is to monitor the greenhouse environment and control the internal temperature to reduce consumed energy while maintaining good conditions that improve productivity. A Petri Nets (PN) model is used to achieve both monitoring of the greenhouse environment and generating the suitable reference temperature which is sent later to a temperature regulation block. The second objective is to provide an Energy-Efficient (EE) scalable system design that handles massive amounts of IoT big data captured from sensors using a dynamic graph data model to be used for future analysis and prediction of production, crop growth rate, energy consumption and other related issues. The design tries to organize various possible unstructured formats of raw data, collected from different kinds of IoT devices, unified and technology-independent fashion using the benefit of model transformations and model-driven architecture to transform data in structured form.