Advance driver assistance systems (ADAS) swiftly and effectively detect oncoming cars’ lanes-changing intentions in intelligent transportation, supporting decision support and safety. Current techniques fail to account for vehicle interactions and trajectory data temporal dependencies; hence this research proposes a multi-model fusion-based lane-changing intention recognition framework for intelligent transportation. Using actual vehicle trajectory data from a dataset, the suggested model is verified and contrasted with several well used baseline models. According to the experimental findings, the lane change intention detection technique can greatly increase prediction accuracy by fusing attention processes, reinforcement learning-based CRF, and vehicle interaction data. The system’s main components are input processing and lane-changing intention recognition. Vehicle trajectory data is cleaned, labelled, sliced, and one-hot encoded during input processing BiLSTM-F model detects driver lane-change intent, enhanced by incorporating attention mechanism to the Bidirectional Long Short-Term Memory (BiLSTM) network, the model may give changing weights to input processing section output. This lets the model focus on lane-changing intention-affecting factors. Finally, a Reinforcement Learning-based Conditional Random Field (CRF) efficiently determines the globally optimal lane-changing intention. This field fully represents input data temporal interdependence. The model was trained and tested on the public NGSIM dataset. Validation results show it can achieve up to 97.19% accuracy and predict a vehicle’s lane change intention with 94.16% accuracy, two seconds before the actual maneuver occurs. The suggested model outperforms baseline lane-changing intention recognition models in terms of accuracy, loss performance, F1 score, and stability.

Generative Artificial Intelligence (GAI) is emerging as a transformative force that empowers transnational education (TNE) in engineering. Recent trends indicate a significant shift in the application of generative AI in engineering policies, academic research, business practices, and educational settings throughout TNE. Governments and organizations are transitioning from restrictive stances to developing guiding frameworks for its application, enabling cross-border collaboration in TNE. Numerous universities have permitted and even promoted the utilization of GAI. Furthermore, academic research around the world is looking into the pros and cons of GAI in engineering education, focusing on how it can help teachers and keep students interested. Industrial applications are diversifying, extending across disciplines, and TNE is occurring in engineering contexts, including cross-border programs. GAI possesses the capacity to transform TNE by revolutionizing talent development, reformulating engineering models, and facilitating scientific assessment across multinational frameworks. However, problems like the generative illusion, ethical and ideological risks, lack of trust between teachers and students, and new threats to TNE in engineering equity in global settings require substantial focus. This study examines these concerns and outlines potential strategies to leverage GAI for transnational education in engineering, offering stakeholders the opportunity to prioritize AI literacy among educators and learners. This work emphasizes that cross-disciplinary and collaborative R&D, following national and international standards, should tackle application hurdles while guaranteeing safety and inclusion. This study also addresses several future directions that can contribute to creating a unified framework and cost-effective solutions. These solutions, integrated with platforms like the National Smart Education Platform, can bridge digital divides, ensuring equitable access and enabling global TNE stakeholders to capitalize on the GAI revolution. We also provide several statistics and case studies to show the effectiveness of GAI over TNE in engineering and provide practical solutions for the incorporation of GAI into TNE within engineering frameworks, guaranteeing inclusivity and equity.

ABSTRACT The increasing complexity of software development processes has heightened the need for robust security measures. Although technical safeguards are essential, the role of human factors in securing software development remains underexplored. This paper presents a novel approach that integrates people's maturity with a fuzzy analytic hierarchy process (Fuzzy‐AHP) decision‐making framework to enhance the security in software development. The framework provides a systematic method for evaluating and prioritizing human factors that influence an organization's security posture, such as team‐expertized communication and adherence to security protocols. Using the decision‐making model allows the project managers and stakeholders to determine the appropriate areas for improvement and develop the right strategies and actions to nurture a secure and mature development culture. The paper identifies 24 human success factors (HSFs) and human security vulnerabilities (HSVs) and 38 practices for addressing these HSFs and HSVs through systematic literature review (SLR) and empirical survey. Furthermore, we discuss the local and global ranks of each HSF and HSV practice and categorize the identified practices into nine categories to determine the ranks and weight of each category. Based on collected data, Fuzzy‐AHP prioritized these practices; the category “C4: Skill development and stakeholder engagement” is ranked highest at rank‐1 and possesses the most significant weight of 0.12435. Similarly, the highest global weight is 0.051506, and the global ranked (rank‐1) HSF and HSV practice is “P15: Hands‐on practice and stakeholder communication.” The proposed approach complements existing technical methods by addressing the human element of security, making it adaptable to diverse organizational environments. Through this integration of people maturity and Fuzzy‐AHP, the paper contributes a new dimension to securing software development, emphasizing the critical role of human factors in achieving comprehensive security.

ABSTRACT Information System Security (ISS) is the primary economic lever for the global economy. It is the cornerstone for value generation, and its absence undeniably affects technology, people, and finances. The emergence of the worldwide information society has introduced fresh economic and legal challenges attributed to the surge in Internet utilization and advancements in the digital economy. Ensuring the security of advancements within information systems has emerged as a primary concern in propelling the evolution of information processes within the software development industry. This study aims to develop and propose a Fuzzy Analytic Hierarchy Process (Fuzzy‐AHP) framework to enhance decision‐making for software maintenance and deployment in ISS. This framework aims to provide a systematic, flexible method for evaluating and prioritizing multiple conflicting criteria under conditions of uncertainty. The study initially adopts an empirical survey to identify software security maintenance and deployment risks and their practices for ISS organizations. Then adopts the Fuzzy‐AHP method to handle the imprecision of expert judgments and organizes decision‐making into a hierarchical structure. The framework is applied to evaluate key criteria related to software maintenance and deployment, including security risks, system performance, operational costs, and compliance requirements. Data from 50 ISS experts were collected and used to validate the framework. The paper identifies 52 security risks in maintenance and deployment (SRMD) processes in ISS and also identified 139 best practices for ensuring security, including regular updates, patch management, and adherence to industry‐standard security protocols. The Fuzzy‐AHP framework effectively structured the decision‐making process by prioritizing criteria and sub‐criteria. The results demonstrated that the framework helps mitigate the subjective biases in expert judgment and provides a more balanced assessment of maintenance and deployment strategies. Prioritizing security risks and compliance emerged as key factors in the decision‐making process. The proposed Fuzzy‐AHP framework provides an innovative and adaptable solution for optimizing ISS organizations' software maintenance and deployment decisions. It addresses the complexity and uncertainty involved in such decisions, offering a transparent and structured approach that improves the accuracy and reliability of outcomes. Future research should focus on empirical validation of the framework in real‐world case studies and expand its application to other industries with similar decision‐making needs.

The interactive attention graph convolution network (IAGCN), a novel model proposed in this article, will revolutionize aspect-level sentiment analysis (SA). IAGCN effectively addresses these key features, in contrast to prior research that ignored the meaning of aspect terms and their relationship with context. The model combines a modified dynamic weighting layer with bidirectional long short-term memory (BiLSTM) to accurately acquire context. It takes use of graph convolutional networks (GCNs) to encrypt syntactic information from the syntactic dependency tree. Furthermore, a method for interactive attention is employed to discover the intricate relationships between context and aspect terms, which results in the reconstruction of those terms’ representations. Comparing the proposed IAGCN model to baseline models, impressive gains are made. Across five datasets, the model beats previous methods with an amazing improvement in F1 scores that ranges from 1.34% to 4.04% and an impressive improvement in accuracy that ranges from 0.56% to 1.75%. Additionally, the IAGCN model outperforms the global vectors (GloVe)-based strategy when the potent pretrained model bidirectional encoder representations from transformers (BERT) is included in the challenge, resulting in even greater improvements. The F1 score considerably increases from 2.59% to 7.55%, and accuracy increases from 1.47% to 3.95%, making the IAGCN model a standout performer in aspect-level SA.

With the advancement in Tiny Machine Learning (ML) technologies, their application in enhancing unmanned aerial vehicles (UAVs) for hydraulic engineering surveying and mapping has become increasingly significant. TinyML’s integration offers a leap in processing efficiency and capabilities, particularly in addressing challenges such as UAV search and monitoring due to loss of contact or forced landings. The usage of medical cyber-physical systems in healthcare can revolutionize existing service delivery methods. The study focuses into the spatial grid mapping technique for three-dimensional information, the PTZ camera spatial grid target locking algorithm, and the UAV detection and image correction algorithm. The UAV target is processed using the surveying UAV target tracking method. TinyML techniques are essential for processing and analyzing these photos quickly. Precise UAV identification and tracking are made possible by the combination of image recognition and radar data, which are then processed using TinyML algorithms. This study explores the complexities of algorithms designed specifically for TinyML, such as tracking, UAV detection, grid mapping, and 3D grid space division. Experimental results validate the enhanced capability of this. The results show how well the proposed technique maps and surveys water conservation regions while promptly catching, locking onto, and tracking drones. The algorithm in this study betters than the YOLO, SSD, and RetinaNet algorithms in the recognition and detection of image-oriented surveying and mapping drones.

Traditional classification algorithms struggle to categorize hypertensive retinopathy (HR) lesions correctly because they lack obvious characteristics. A regional IoT-enabled federated learning-based HR categorization approach (IoT-FHR) incorporating global and local attributes is suggested as a solution to this issue. The local feature arterial and venous nicking (AVN) classification model is fused with the overall IoT-FHR classification model to enhance the effect of the classification of IoT-FHR. The AVN classification model’s local lesion characteristics and the IoT-FHR classification model’s global lesion characteristics were combined using feature mean. After that, the results of the global IoT-FHR classification model are averaged with the results of the local AVN classification model. An easy neural network receives its input from the final outcome. The probability value of IoT-FHR in the fundus image is output by the sigmoid classifier after the neural network’s two fully connected and one dropout layer. The AVN classification makes a new kind of intersection detection algorithm suggestion. To determine the intersection points, the algorithm applies a logical AND operation to the classified arteries and veins. It takes HR fundus pictures and extracts AVN image blocks using the region of interest extraction approach. The accuracy, sensitivity, and specificity of the suggested fusion model are 93.50%, 69.83%, and 98.33%, respectively, when tested on a private dataset. It is clear from the experiments and results that the suggested model leads the currently used methods when the single-stage classification model is compared with them.

There is a significant need to give careful consideration to the Capability Maturity Model Integration (CMMI) level 2 specific practices (i.e. SP 1.1 ‘understand requirements’ and SP 1.2 ‘obtain commitment to requirements’), especially in the context of small‐ and medium‐sized software development organisations, in order to assist such organisations in effectively managing their requirements engineering processes. In this study, the authors propose an abstract‐level model for each of these two specific practices as well as cover the initial evaluation of the models. In addition, necessary templates and checklists are also provided for each proposed model. The proposed models are based on a significant amount of research in software process improvement, CMMI and requirements engineering. The initial evaluation of the proposed models was executed using an expert panel review process. The results showed that the proposed models provide ease of learning and ease of use, provide stakeholder satisfaction and can be applied to small‐and medium‐sized software development organisations. It is important to highlight that this study contributes not only to the implementation of SP 1.1 and SP 1.2 of REQM process area in the context of small‐ and medium‐sized software development organisations but also to the body of knowledge on REQM.