Atif Alvi

Software projects go through a cycle of development, the initial phase of this cycle is proposed by collecting requirements. A successful software has high quality requirements that are complete, clear, and concise. For these requirements, effective RE strategies are required. This paper is a systematic literature review discussing the methodology of gamification as a transformational RE compared with conventional methods used previously. This study is conducted on 97 peer‐reviewed literature articles published between 2012 and 2024 on topics of requirement elicitation, gamification, and stakeholder motivation. The article papers were collected from major scientific databases including IEEE Xplore, SpringerLink, ScienceDirect, and ProQuest. This review article based its findings by creating a comprehensive screening and quality assessment process. The assessment of relevant articles established in this theory was based on the objective of gamified requirement elicitation and stakeholder motivation. The findings contribute to quality synthesis of results in the form of answers to four questions. Additionally, this paper also discusses a Gamified Requirement Elicitation Model (GREM) in theory, which builds on limitations of previous findings. The paper also builds a domain taxonomy to structure the findings as an exploratory framework for expert analysis and industry integration. The review builds on gamification and also presents the considerations to fulfil when employing its use.

Smart agriculture, which is culturally specific, combines education with the celebratory potlatch of our agrarian heritage. Smart agriculture, also known as precision farming, refers to the application of advanced technologies to enhance the effectiveness and efficiency of agricultural practices. The concept of serious games has gained popularity across various fields, including smart agriculture. Serious gaming is currently being considered as a new approach to address challenging farming issues and enhance farmer training, thereby stimulating technological innovation in the agricultural sector. Despite the growing interest in serious games, there is a lack of research exploring how such games can effectively communicate practical farming knowledge, particularly across different cultural practices. Research is needed to assess whether these games can effectively teach key topics, such as soil management and crop selection while respecting cultural norms. It is also crucial to evaluate whether gameplay influences players’ attitudes toward sustainable farming and the extent to which they retain the knowledge in real‐world contexts. This study examines a culturally specific game design model for smart agriculture, integrating educational and cultural elements to enhance learning and engagement. The game allows players to experience the farming cycle, encompassing planting, tending, and harvesting. It is intended to educate children and anyone interested in learning about the origins of their food, as well as to explain why sustainable farming is essential for future generations. The players experience farming through seasonal cycles, from decisions on seed types to soil preparation, including dealing with weather calamities. A quasiexperimental design compared two groups: a traditional learning group and an experimental serious game group. A serious game developed based on the proposed model was implemented and evaluated through pre‐ and posttests, statistical analyses, and heuristic evaluations to measure improvements in agricultural knowledge. The results demonstrate significant knowledge retention in the game group, particularly in precision farming, sustainability, and IoT‐based solutions ( p < 0.05). These findings highlight the potential of serious games as an innovative tool for blending traditional agricultural knowledge with modern farming practices, promoting sustainable farming education in diverse cultural settings.

The growth in interest has shown great promise in game‐based learning, with particular potential found in healthcare for first aid training. This study indicates that the issues with conventional first aid training, such as with nonexperts and youth, can be resolved by games. The present study is aimed at designing, implementing, and evaluating a gamified first aid training system using the First Aid Serious Game Design Model (FASGDM) to enhance user motivation, knowledge retention, and practical application of first aid techniques. Feedback was incorporated in the design process to make sure the refinement of low‐fidelity and high‐fidelity prototypes to ensure usability and accuracy with an interrater reliability (IRR) coefficient of 90%, confirming the gamified system’s effectiveness and reliability. A quasiexperimental study was conducted with 40 students aged 12–15 years, divided into a control group ( n = 20) who received traditional lessons and an experimental group ( n = 20) trained using the gamified prototype. Pretest results showed equal performance in both groups, while posttest results revealed significant improvement in the experimental group’s performance through statistical analysis t = −23.77, df = 24, p < 0.05. The gamified approach increased engagement, improved knowledge retention, and increased learner’s confidence in applying first aid skills in real‐life scenarios, contributing to improved public health outcomes.

Requirement elicitation is the first and foremost step in project development. A set of quality requirements anticipates utmost diligence as this carries the rest of the project. During requirement elicitation, the involvement and engagement of stakeholders determine the extent of the project’s success. Ensuring constant stakeholder motivation and engagement is challenging and requires adequate measures to keep them ambitious throughout. Gamification is an approach that helps sustain and enhance continuous stakeholder enthusiasm by using concepts and elements that appeal to human psychology. The accurate use of gamified concepts and elements ensures constant stakeholder motivation in requirement elicitation and leads to quality requirement generation by reducing ambiguity. Although the use of gamification has the potential to yield productive results, it is not guaranteed in every case. Therefore, there is a need for a comprehensive study of the characteristics, attributes, and elements of gamification in the context of software project development. This paper attempts to identify this problem, and its study is based on the detailed investigation of 74 research articles. This paper adopts a systematic review approach to previous studies and proposes research based on gamification and its elements and using gamified concepts in industrial software development. This detailed analysis and review assembles data, examines queries, and explores implications to develop a theoretical framework to contribute to the knowledge of the existing body of literature. A valuable contribution is made through the projection of the current standing of gamification in software project development that suggests that further research in contextual specificity of a project’s requirement elicitation step can guarantee successful software by yielding quality results. Overall, gamification has potential if used appropriately in requirement engineering, and its application can potentially revolutionize requirement elicitation for stakeholders and project managers.

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.

Software-defined network (SDNs) have fundamentally changed network infrastructure by decoupling the data plane and the control plane. This architectural shift rejuvenates the network layer by granting the re-programmability and centralized management of networks which brings about exciting challenges. Although an SDN seems to be a secured network when compared to conventional networks, it is still vulnerable and faces rigorous deployment challenges. Moreover, the bifurcation of data and control planes also opens up new security problems. This systematic literature review (SLR) has formalized the problem by identifying the potential attack scenarios and highlighting the possible vulnerabilities. Eighty-six articles have been selected carefully to formulize the SLR. In this SLR, we have identified major security attacks on SDN planes, including the application plane, control plane, and data plane. Moreover, this research also identifies the approaches used by industry experts and researchers to develop security solutions for SDN planes. In this research, we have introduced an attack taxonomy and proposed a collaborative security model after comprehensively identifying security attacks on SDN planes. Lastly, research gaps, challenges, and future directions are discussed for the deployment of secure SDNs.

Internet of Things (IoT) is considered the upcoming industrial and academic revolution in the technological world having billions of things and devices connected to the Internet. These connected devices are heterogeneous. They have different standards and technologies which communicate through different protocols. Therefore, the implementation of IoT on a large scale is difficult due to these heterogeneity challenges. This motivated us to overcome the scaling problem of IoT by identifying the challenges from the literature and providing solutions. This study is based on the identification of the heterogeneous challenges with solutions via a systematic literature review. A total of 81 primary sources were selected. After extracting and synthesizing the data, we identified 14 different IoT heterogeneity challenges. Some of the identified challenges are “heterogeneity of devices,” “heterogeneity in formats of data,” “heterogeneity in communication,” and “interoperability issue due to heterogeneity.” The identified challenges have been analyzed from digital libraries and timeframe perspectives. Furthermore, we have found a total of 81 solutions for those challenges, with at least 5 unique solutions for each challenge. In the future, we will categorize the challenges and prioritize the solutions by using a multi-criteria decision-making problem.

Major issues currently restricting the use of learning analytics are the lack of interpretability and adaptability of the machine learning models used in this domain. Interpretability makes it easy for the stakeholders to understand the working of these models and adaptability makes it easy to use the same model for multiple cohorts and courses in educational institutions. Recently, some models in learning analytics are constructed with the consideration of interpretability but their interpretability is not quantified. However, adaptability is not specifically considered in this domain. This paper presents a new framework based on hybrid statistical fuzzy theory to overcome these limitations. It also provides explainability in the form of rules describing the reasoning behind a particular output. The paper also discusses the system evaluation on a benchmark dataset showing promising results. The measure of explainability, fuzzy index, shows that the model is highly interpretable. This system achieves more than 82% recall in both the classification and the context adaptation stages.

In Alzheimer’s disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) in the future. We used robust predictors from multivariate MRI‐derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset‐based technique to forecast the future marker value using readings from at least two previous follow‐up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI‐to‐AD progressors amongst the MCI patients enrolled in the Alzheimer’s disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5‐fold cross‐validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6‐month‐ and 1‐year‐ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI‐to‐AD conversion prediction.