The increasing complexity of software systems, especially in Free and Open-Source Software (FOSS) ecosystems, there has been a growing requirement for the adoption of automated software quality prediction tools. In this context, this study attempts to present a hybrid Neuro-Fuzzy Machine Learning (NF-ML) model to predict software quality by identifying defective software modules. In this study, the proposed model combines the efficient learning capability offered by neural networks and the malleable reasoning capabilities offered by fuzzy logic to create a robust model with high interpretability. In this experiment, the NASA Metrics Data Program (MDP) dataset sourced from Kaggle was used to train and test the proposed model. Additionally, data pre-processing was conducted to address missing data points and normalize the data points using chi-squared feature selection. The proposed model was developed using TensorFlow programming from Python with various performance metrics such as accuracy, precision, recall, F1-measure, and AUC-ROC accuracy to determine efficiency. The experimental outcome of this research study suggests that the hybrid NF-ML model attains better efficiency than ML models to predict software quality while increasing reliability.

The research presents a Privacy-Preserving Data Exchange (PPDE) framework for electronic health records (EHRs) that integrates elliptic-curve cryptography (ECC), zero-knowledge proofs (ZKPs), and capability tokens on a permissioned blockchain to create a cohesive, policy-compliant data-sharing workflow. In this system, IoT gateways establish ECC-based session keys to encrypt measurements at the edge, while only cryptographic hashes and pointers to the encrypted data are stored on a consortium blockchain, ensuring data confidentiality with tamper-evident integrity and auditable provenance. Healthcare providers issue Access Request Transactions that include ECC signatures and ZKPs to prove policy compliance (such as patient consent and role-based access) without revealing sensitive details. Off-chain ZKPs validate authorization decisions, with on-chain records providing verifiable evidence of conformance and access history, and a capability token is granted to authorize future data access within defined scopes and time windows. The framework emphasizes end-to-end confidentiality, privacy-preserving authorization, and tamper resistance through the blockchain. Our evaluation analyzes network throughput, end-to-end delay, packet delivery ratio, scalability, and energy efficiency, with particular attention to the overhead from blockchain operations, ECC, and ZKP verification. Results show improved privacy and security for EHR data, with only nominal overhead relative to the privacy and integrity gains, and favorable scalability and energy performance in realistic deployments.

In the present work, the analysis of the 4 state-of-the-art algorithms of visual-inertial SLAM is detailed and compared to each other: ORB-SLAM3, VINS-Fusion, RTAB-Map and DROID-SLAM. The base set used in the test, the TUM RGB-D Freiburg1_xyz benchmark data (along with its synchronized RGB, depth and ground-truth trajectory data) was the basis set. We ran the four algorithms using the same set of conditions and ran the Umeyama transformation in the case of rigid alignment. Then, we analyzed the resultant trajectories using Absolute Trajectory Error (ATE), Relative Pose Error (RPE), drift and scale consistency. The quantitative findings showed that the performance of the tested methods introduced is very variable among the algorithms of different samples. ORB-SLAM3 is the best localization with the ATE RMSE of 0.0091 m and RPE RMSE of 0.0385 m. The same but almost worse results were obtained with VINS-Fusion with an ATE_RMSE= 0.0115 m and RPE-RMSE=0.0458 m. The accuracy of RTAB-Map was slightly above a medium (ATE_RMSE=0.0192 m., RPE_RMSE=0.0778 m.). DROID-SLAM had the greatest errors (ATE_RMSE=0.0365 m, RPE_RMSE=0.1598 m), and a high bias in scale (scale ratio=14.99) that was great in drift (end drift=0.0325 m, segment drift=0.535 m.). Visual (geometric) simulations, in terms of cumulative distribution functions (CDFs), per-axis error plots, and radar (radar visual representations), showed that ORB-SLAM3 and VINS-Fusion exhibited superior trajectory stability, minimum drifts and more uniform scale maintenance as compared to the rest of the frameworks. But DROID-SLAM exhibited a large range of over-scaling as well as instability in motion estimation. In the final evaluation, ORB-SLAM3 showed the most optimal performance as well as the balanced performance of all the algorithms which were tested. Therefore, it is optimal with the real-time robotic navigation and mapping applications. VINS-Fusion is a solid option to use in the case of tightly coupled visual-inertial systems.

Some considerations on archival material oriented towards the assessment of documents generated in the ambit of technical/construction activities. The analysis of what is produced by operative interventions (renovation, maintenance, etc.) underlines the qualitative differences perceptible in the diverse types of documents. These last ones are included in categories defined for material produced in project circumstances and also in a typological context not dissimilar to what is preserved in archives with different characteristics. The verification of some executive itineraries allows the evidence of specific documents regarding the technical/construction sector and also of archival material belonging to a more generic ambit.

Modern business is characterized by exponential growth in the amount of data, where the availability of data alone does not guarantee better decision-making. This paper explores the synergistic relationship between visual business intelligence (BI), decision support systems (DSS) and artificial intelligence (AI), with a focus on the practical application of Tableau Public software. The aim of this paper is twofold: (1) to demonstrate the use of no-code visualization tools in the function of decision support on the example of sales data analysis, and (2) to analyze the degree and specifics of the application of BI systems in Bosnia and Herzegovina (BiH) through case studies from retail, manufacturing, banking and higher education. The paper uses a combined research design – a practical demonstration in Tableau Public on the Superstore Sales dataset, and a qualitative analysis of secondary data for four global (Walmart, Netflix, Coca-Cola, Siemens) and five domestic business cases. The results of the practical analysis show that visual BI significantly reduces the time needed to identify key performance (KPIs), trends and anomalies compared to traditional tabular presentations. Case studies from BiH point to a growing awareness of the importance of BI tools, but also to the barriers present: incomplete data, lack of staff and limitations of free software versions in terms of security and predictive functionalities. The paper contributes to the domestic literature with the first systematic analysis of the application of Tableau tools in the context of BiH, and offers company management concrete guidelines for the transition from descriptive to predictive and prescriptive analytics. It is concluded that visual BI, despite its limitations, has become an unavoidable factor of competitiveness and that its adoption in BiH is a strategic necessity for companies, not a technical choice.

The present research paper is an extensive quantitative investigation of both daily and monthly production rates, and defect patterns of production in manufacturing of vertical fridges with a height of 13 feet and referred to as the General Company of Light Industry, Iraq in the year 2024. This study used statistical process control (SPC) techniques and Pareto analysis to test the data on production in four selected months (February, April, September, and October). The analysis indicates that the fixed production quantity of 40 units daily with the defect rates that are between 0.38 percent and 1.15 percent throughout the duration of the sample. Instability in power supply became the leading causal factor with 55.6 percent of all defects (n=15), then there was welding process errors (18.5% n=5), worker negligence (14.8% n=4), machine malfunctions (7.4% n=2), and material defects (3.7% n=1). Using the control charts, it was found out that the months of April 2024 were a statistically significant period that was out of control and that urgent corrective actions were needed. This study provides empirical data to the industry of industrial engineering to establish the functionality of the combined production-quality control systems in the appliance production situations. This paper ends with evidence-based proposals to apply the Six Sigma methodologies, preventive maintenance measures, and infrastructure stabilization solutions in order to meet the goal of operational excellence.

Global logistics networks are undergoing a profound transformation driven by technological convergence—the integration of digital, physical, and analytical technologies into unified operational ecosystems. Innovations in artificial intelligence (AI), the Internet of Things (IoT), blockchain, cloud computing, robotics, big data analytics, and advanced telecommunications are no longer evolving independently; instead, they increasingly operate as interdependent systems reshaping how goods, information, and value move across borders. This convergence promises unprecedented efficiency, visibility, resilience, and sustainability in global supply chains. However, it also introduces systemic risks, including cybersecurity vulnerabilities, operational fragility, workforce disruption, regulatory complexity, and data governance challenges. From the review of related literature, the study reveal that technological convergence enables enhanced end-to-end visibility, predictive risk management, operational efficiency, sustainability improvements, and the emergence of platform-based business models. At the same time, the study highlights significant risks, including cybersecurity vulnerabilities, systemic and cascading failures, over-automation, workforce displacement, data governance challenges, and ethical concerns.