Search Results (26,398)

In embedded software for critical domains such as medical devices and defense, requirement traceability is essential for ensuring quality attributes. Standards and regulations mandate traceability between requirements and artifacts such as design elements and code. However, existing methods often overlook the hardware-dependent nature of embedded systems or conduct traceability retroactively, which may affect consistency. This study introduces a structured feature-based approach to component architecture design, bridging the gap between requirements and design to ensure traceability. The structured feature model supports traceability between functional requirements, software components, and hardware elements in embedded systems. A case study demonstrates that structured features can effectively map the requirements to design artifacts, helping to visualize relationships through a traceability matrix. Although the process is manual, structured features improve efficiency in the early stages of design and create traceable links between requirements and architectural elements. Full article

As the world shifts towards a low-carbon economy, the strategic deployment of renewable energy sources (RESs) is critical for maximizing energy output and ensuring sustainability. This study introduces GREENIA, a novel artificial intelligence (AI)-powered framework for optimizing RES placement that holistically integrates machine learning (gated recurrent unit neural networks with swish activation functions and attention layers), evolutionary optimization algorithms (Jaya), and Shapley additive explanations (SHAPs). A key innovation of GREENIA is its ability to provide natural language explanations (NLEs), enabling transparent and interpretable insights for both technical and non-technical stakeholders. Applied in Greece, the framework addresses the challenges posed by the interplay of meteorological factors from 10 different meteorological stations across the country. Validation against real-world data demonstrates improved prediction accuracy using metrics like root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). SHAP analysis enhances transparency by identifying key meteorological influences, such as temperature and humidity, while NLE translates these insights into actionable recommendations in natural language, improving accessibility for energy planners and policymakers. The resulting strategic plan offers precise, intelligent, and interpretable recommendations for deploying RES technologies, ensuring maximum efficiency and sustainability. This approach not only advances renewable energy optimization but also equips stakeholders with practical tools for guiding the strategic deployment of RES across diverse regions, contributing to sustainable energy management. Full article

The Industrial Internet of Things (IIoT) revolutionizes both industrial and residential operations by integrating AI (artificial intelligence)-driven analytics with real-time monitoring, optimizing energy usage, and significantly enhancing energy efficiency. This study proposes a secure IIoT framework that simultaneously predicts both active and reactive loads while also incorporating anomaly detection. The system is optimized for real-time deployment on an edge server, such as a single-board computer (SBC), as well as on a cloud or centralized server. It ensures secure and reliable industrial operations by integrating smart data acquisition systems with real-time monitoring, control, and protective measures. We propose a Temporal Convolutional Networks-Gated Recurrent Unit-Attention (TCN-GRU-Attention) model to predict both active and reactive loads, which demonstrates superior performance compared to other conventional models. The performance metrics for active load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Mean Absolute Error (MAE), and 0.1354 Root Mean Squared Error (RMSE), while for reactive load forecasting, the metrics are 0.0202 (MSE), 0.1077 (MAE), and 0.1422 (RMSE). Furthermore, we introduce an optimized Isolation Forest model for anomaly detection that considers the transient conditions of appliances when identifying irregular behavior. The model demonstrates very promising performance, with the average performance metrics for all appliances using this Isolation Forest model being 95% for Precision, 98% for Recall, 96% for F1 Score, and nearly 100% for Accuracy. To secure the entire system, Transport Layer Security (TLS) and Secure Sockets Layer (SSL) security protocols are employed, along with hash-encoded encrypted credentials for enhanced protection. Full article

Nowadays, driving accidents are considered one of the most crucial challenges for governments and communities that affect transportation systems and peoples lives. Unfortunately, there are many causes behind the accidents; however, drowsiness is one of the main factors that leads to a significant number of injuries and deaths. In order to reduce its effect, researchers and communities have proposed many techniques for detecting drowsiness situations and alerting the driver before an accident occurs. Mostly, the proposed solutions are visually-based, where a camera is positioned in front of the driver to detect their facial behavior and then determine their situation, e.g., drowsy or awake. However, most of the proposed solutions make a trade-off between detection accuracy and speed. In this paper, we propose a novel Visual-based Alerting System for Detecting Drowsy Drivers (VAS-3D) that ensures an optimal trade-off between the accuracy and speed metrics. Mainly, VAS-3D consists of two stages: detection and classification. In the detection stage, we use pre-trained Haar cascade models to detect the face and eyes of the driver. Once the driver’s eyes are detected, the classification stage uses several pre-trained Convolutional Neural Network (CNN) models to classify the driver’s eyes as either open or closed, and consequently their corresponding situation, either awake or drowsy. Subsequently, we tested and compared the performance of several CNN models, such as InceptionV3, MobileNetV2, NASNetMobile, and ResNet50V2. We demonstrated the performance of VAS-3D through simulations on real drowsiness datasets and experiments on real world scenarios based on real video streaming. The obtained results show that VAS-3D can enhance the accuracy detection of drowsy drivers by at least $7.5\%$ (the best accuracy reached was $95.5\%$) and the detection speed by up to $57\%$ (average of $0.25$ ms per frame) compared to other existing models. Full article

The present study reviews leachate models useful for proactive and rehab actions to safeguard surface and subsurface soft water, which have become even more scarce. Integrated management plans of water basins are of crucial importance since intensively cultivated areas are adding huge quantities of fertilizers to the soil, affecting surface water basins and groundwater. Aquifers are progressively being nitrified on account of the nitrogen-based fertilizer surplus, rendering water for human consumption not potable. Well-tested solute leaching models, standalone or part of a model package, provide rapid site-specific estimates of the leaching potential of chemical agents, mostly nitrates, below the root zone of crops and the impact of leaching toward groundwater. Most of the models examined were process-based or conceptual approaches. Nonetheless, empirical prediction models, though rather simplistic and therefore not preferrable, demonstrate certain advantages, such as less demanding extensive calibration database information requirements, which in many cases are unavailable, not to mention a stochastic approach and the involvement of artificial intelligence (AI). Models were categorized according to the porous medium and agents to be monitored. Integrated packages of nutrient models are irreplaceable elements for extensive catchments to monitor the terrestrial nitrogen-balanced cycle and to contribute to policy making as regards soft water management. Full article

Medical imaging plays an essential role in modern healthcare, providing non-invasive tools for diagnosing and monitoring various medical conditions. However, the resolution limitations of imaging hardware often result in suboptimal images, which can hinder the precision of clinical decision-making. Single image super-resolution (SISR) techniques offer a solution by reconstructing high-resolution (HR) images from low-resolution (LR) counterparts, enhancing the visual quality of medical images. In this paper, we propose an enhanced Residual Feature Learning Network (RFLN) tailored specifically for medical imaging. Our contributions include replacing the residual local feature blocks with standard residual blocks, increasing the model depth for improved feature extraction, and incorporating enhanced spatial attention (ESA) mechanisms to refine the feature selection. Extensive experiments on medical imaging datasets demonstrate that the proposed model achieves superior performance in terms of both quantitative metrics, such as PSNR and SSIM, and qualitative visual quality compared to existing state-of-the-art models. The enhanced RFLN not only effectively mitigates noise but also preserves critical anatomical details, making it a promising solution for high-precision medical imaging applications. Full article

The health, safety, and well-being of household pets such as cats has become a challenging task in previous years. To estimate a cat’s behavior, objective observations of both the frequency and variability of specific behavior traits are required, which might be difficult to come by in a cat’s ordinary life. There is very little research on cat activity and cat disease analysis based on real-time data. Although previous studies have made progress, several key questions still need addressing: What types of data are best suited for accurately detecting activity patterns? Where should sensors be strategically placed to ensure precise data collection, and how can the system be effectively automated for seamless operation? This study addresses these questions by pointing out whether the cat should be equipped with a sensor, and how the activity detection system can be automated. Magnetic, motion, vision, audio, and location sensors are among the sensors used in the machine learning experiment. In this study, we collect data using three types of differentiable and realistic wearable sensors, namely, an accelerometer, a gyroscope, and a magnetometer. Therefore, this study aims to employ cat activity detection techniques to combine data from acceleration, motion, and magnetic sensors, such as accelerometers, gyroscopes, and magnetometers, respectively, to recognize routine cat activity. Data collecting, data processing, data fusion, and artificial intelligence approaches are all part of the system established in this study. We focus on One-Dimensional Convolutional Neural Networks (1D-CNNs) in our research, to recognize cat activity modeling for detection and classification. Such 1D-CNNs have recently emerged as a cutting-edge approach for signal processing-based systems such as sensor-based pet and human health monitoring systems, anomaly identification in manufacturing, and in other areas. Our study culminates in the development of an automated system for robust pet (cat) activity analysis using artificial intelligence techniques, featuring a 1D-CNN-based approach. In this experimental research, the 1D-CNN approach is evaluated using training and validation sets. The approach achieved a satisfactory accuracy of 98.9% while detecting the activity useful for cat well-being. Full article

Artificial Intelligence (AI) is poised to revolutionize numerous aspects of human life, with healthcare among the most critical fields set to benefit from this transformation. Medicine remains one of the most challenging, expensive, and impactful sectors, with challenges such as information retrieval, data organization, diagnostic accuracy, and cost reduction. AI is uniquely suited to address these challenges, ultimately improving the quality of life and reducing healthcare costs for patients worldwide. Despite its potential, the adoption of AI in healthcare has been slower compared to other industries, highlighting the need to understand the specific obstacles hindering its progress. This review identifies the current shortcomings of AI in healthcare and explores its possibilities, realities, and frontiers to provide a roadmap for future advancements. Full article

Prefabricated construction has become a significant trend in the international building industry, yet its promotion in China faces cost challenges. This study explores the effect of building information modelling (BIM) technology on the various phases of prefabricated buildings, focusing on the entire lifecycle cost to reduce the overall cost. Key factors influencing the lifecycle as the whole cost control of prefabricated buildings are identified via the top 35 highly cited BIM papers; 15 experts were invited to evaluate the factors influencing the lifecycle cost control of prefabricated buildings, and 22 factors were identified to construct the surveys. The results of 364 valid questionnaires were analysed. Research indicates that BIM significantly impacts cost control across various stages of the lifecycle of prefabricated buildings. BIM’s impact on cost control, ranked from highest to lowest, is as follows: construction and installation phase, production and transportation phase, operational maintenance phase, and design phase. By minimising costs at each stage, BIM enhances design efficiency, simulates production and logistics, reduces rework during construction, and, when integrated with artificial intelligence, BIM optimises operation and maintenance management. Leveraging BIM technology to its full potential effectively reduces the lifecycle costs of prefabricated buildings. Full article

The increasing prevalence of disinformation has become a global challenge, exacerbated by the rapid dissemination of information in online environments. The present study conducts a bibliometric analysis of scholarly efforts made over time in the research papers associated with the disinformation field. Thus, this paper aims to understand and help combat disinformation by focusing on methodologies, datasets, and key metadata. Through a bibliometric approach, the study identifies leading authors, affiliations, and journals and examines collaboration networks in the field of disinformation. This analysis highlights the significant growth in research on disinformation, particularly in response to events such as the 2016 U.S. election, Brexit, and the COVID-19 pandemic, with an overall growth rate of 15.14% in the entire analyzed period. The results of the analysis underscore the role of social media and artificial intelligence in the spread of disinformation, as well as the importance of fact-checking technologies. Findings reveal that the most prolific contributions come from universities in the United States of America (USA), the United Kingdom (UK), Spain, and other global institutions, with a notable increase in publications since 2018. Through thematic maps, a keyword analysis, and collaboration networks, this study provides a comprehensive overview of the evolving field of disinformation research, offering valuable insights for future investigations and policy development. Full article

Background: Parkinson’s disease (PD) diagnosis benefits significantly from advancements in artificial intelligence (AI) and image processing techniques. This paper explores various approaches for processing hand-drawn Archimedean spirals in order to detect signs of PD. Methods: The best approach is selected to be integrated in a neurodegenerative disease management platform called NeuroPredict. The most innovative aspects of the presented approaches are related to the employed feature extraction techniques that convert hand-drawn spirals into a frequency spectra, so that frequency features may be extracted and utilized as inputs for various classification algorithms. A second category of extracted features contains information related to the thickness and pressure of drawings. Results: The selected approach achieves an overall accuracy of 95.24% and allows acquiring new test data using only a pencil and paper, without requiring a specialized device like a graphic tablet or a digital pen. Conclusions: This study underscores the clinical relevance of AI in enhancing diagnostic precision for neurodegenerative diseases. Full article

Non-destructive testing (NDT) is essential for evaluating the integrity and safety of structures without causing damage. The integration of artificial intelligence (AI) into traditional NDT methods can revolutionize the field by automating data analysis, enhancing defect detection accuracy, enabling predictive maintenance, and facilitating data-driven decision-making. This paper provides a comprehensive overview of AI-enhanced NDT, detailing AI models and their applications in techniques like ultrasonic testing and ground-penetrating radar. Case studies demonstrate that AI can improve defect detection accuracy and reduce inspection times. Challenges related to data quality, ethical considerations, and regulatory standards were discussed as well. By summarizing established knowledge and highlighting advancements, this paper serves as a valuable reference for engineers and researchers, contributing to the development of safer and more efficient infrastructure management practices. Full article

Detailed evaluation of prostate cancer glands is an essential yet labor-intensive step in grading prostate cancer. Gland segmentation can serve as a valuable preliminary step for machine-learning-based downstream tasks, such as Gleason grading, patient classification, cancer biomarker building, and survival analysis. Despite its importance, there is currently a lack of a reliable gland segmentation model for prostate cancer. Without accurate gland segmentation, researchers rely on cell-level or human-annotated regions of interest for pathomic and deep feature extraction. This approach is sub-optimal, as the extracted features are not explicitly tailored to gland information. Although foundational segmentation models have gained a lot of interest, we demonstrated the limitations of this approach. This work proposes a prostate gland segmentation framework that utilizes a dual-path Swin Transformer UNet structure and leverages Masked Image Modeling for large-scale self-supervised pretaining. A tumor-guided self-distillation step further fused the binary tumor labels of each patch to the encoder to ensure the encoders are suitable for the gland segmentation step. We united heterogeneous data sources for self-supervised training, including biopsy and surgical specimens, to reflect the diversity of benign and cancerous pathology features. We evaluated the segmentation performance on two publicly available prostate cancer datasets. We achieved state-of-the-art segmentation performance with a test mDice of 0.947 on the PANDA dataset and a test mDice of 0.664 on the SICAPv2 dataset. Full article

Background: Hyperkinetic movement disorders involve excessive, involuntary movements such as ataxia, chorea, dystonia, myoclonus, tics, and tremor. Recent advances in artificial intelligence (AI) allow investigators to integrate multimodal instrumented movement measurements and imaging techniques and to analyze these data together at scale. In this systematic review, we aim to characterize AI’s performance in diagnosing and quantitatively phenotyping these disorders. Methods: We searched PubMed and Embase using a semi-automated article-screening pipeline. Results: Fifty-five studies met the inclusion criteria (n = 11,946 subjects). Thirty-five studies used machine learning, sixteen used deep learning, and four used both. Thirty-eight studies reported disease diagnosis, twenty-three reported quantitative phenotyping, and six reported both. Diagnostic accuracy was reported in 36 of 38 and correlation coefficients in 10 of 23 studies. Kinematics (e.g., accelerometers and inertial measurement units) were the most used dataset. Diagnostic accuracy was reported in 36 studies and ranged from 56 to 100% compared to clinical diagnoses to differentiate them from healthy controls. The correlation coefficient was reported in 10 studies and ranged from 0.54 to 0.99 compared to clinical ratings for quantitative phenotyping. Five studies had an overall judgment of “low risk of bias” and three had external validation. Conclusion: There is a need to adopt AI-based research guidelines to minimize reporting heterogeneity and bolster clinical interpretability. Full article

The undertaking of traffic safety facility (TSF) surveys represents a significant labor-intensive endeavor, which is not sustainable in the long term. The subject of traffic safety facility recognition (TSFR) is beset with numerous challenges, including those associated with background misclassification, the diminutive dimensions of the targets, the spatial overlap of detection targets, and the failure to identify specific targets. In this study, transformer-based and YOLO (You Only Look Once) series target detection algorithms were employed to construct TSFR models to ensure both recognition accuracy and efficiency. The TSF image dataset, comprising six categories of TSFs in urban areas of three cities, was utilized for this research. The dimensions and intricacies of the Detection Transformer (DETR) family of models are considerably more substantial than those of the YOLO family. YOLO-World and Real-Time Detection Transformer (RT-DETR) models were optimal and comparable for the TSFR task, with the former exhibiting a higher detection efficiency and the latter a higher detection accuracy. The RT-DETR model exhibited a notable reduction in model complexity by 57% in comparison to the DINO (DETR with improved denoising anchor boxes for end-to-end object detection) model while also demonstrating a slight enhancement in recognition accuracy. The incorporation of the RepGFPN (Reparameterized Generalized Feature Pyramid Network) module has markedly enhanced the multi-target detection accuracy of RT-DETR, with a mean average precision (mAP) of 82.3%. The introduction of RepGFPN significantly enhanced the detection rate of traffic rods, traffic sign boards, and water surround barriers and somewhat ameliorated the problem of duplicate detection. Full article