Nawroz University

The information sharing on behalf of the delivery of the packet in the network router and the original information that is share to the correct person is enabled for the analysis and then the analysis is done. The traffic related problem based on the sharing the information is occur when two or more sis sent in the same routing path. So the packet or the information has been sent using the single router or an execution time delivery for the analysis. The method of novel QoS Routing, fault tolerance is used in this study for the analysis of the system to produce the formation and the analysis of the packet routing. Fault tolerance is used in the deliver the packets without fail and to deliver it in the fast manner. The QoS is used for managing the information or the packets which makes and send the data which is the high priority of the data and the analysis of the dependency is analysed. The finding of the CRAFT related protocol and the efficient routing has been found. Routing system the effective method for the analysis and the protocol transfer has been enabled using the CRAFT method. The suggested system explains the algorithms for enhancing fault detection, fault isolation, data redundancy, robustness against coalescing innovative QoS routing, and fault tolerance improving QoS parameters in the ad-hoc wireless network utilising CRAFT protocol. Compared to the early existing system, fault detection is now three times better. The execution time is 96% faster, and the decreased latency is less than 2 ms. The data redundancy rate is likely about 90% when restricted to the current services, which have improved in terms of resilience by 93%, jitter by 7%, and packet arrival time by over 50 ms per transmission.

Diabetes mellitus is a health issue that is rapidly increasing worldwide, and it affects more than 347 million people globally. It is important to note that the disease can be successfully detected in its early stages, enabling physicians to avoid complications and improve patient outcomes. Despite the fact that machine learning (ML) has been extensively used in diabetes classification, the available solutions tend to place little or no emphasis on feature selection and ensembles, which limits prediction accuracy and generalizability. In this study, we introduce a hybrid framework that is based on three feature-selection algorithms, specifically, genetic algorithm (GA), correlation-based feature selection (CFS) and recursive feature elimination (RFE), in single and hybrid forms, and three classifiers, namely, multi-layer perceptron (MLP), support vector machine (SVM) and random forest (RF), to achieve a greater predictive robustness with the aid of soft voting. Experimental findings obtained from a benchmark diabetes dataset indicate that the RFE + CFS + SVM combination achieves the best performance, with an accuracy of 98.0%, sensitivity of 97.43%, specificity of 99.03%, precision of 99.51% and F1-score of 98.72%. These results indicate that the suggested hybrid feature-selection and ensemble learning model can offer a robust and highly effective approach for early-stage diabetes diagnosis, one which clinicians may use to make timely and accurate decisions.

Drug–drug interactions (DDIs) can cause adverse reactions or reduce the efficiency of a drug. Using computers to predict DDIs is now critical in pharmacology, as this reduces risks, improves drug outcomes and lowers healthcare costs. Clinical trials are slow, expensive, and require a lot of effort. The use of artificial intelligence (AI), primarily in the form of machine learning (ML) and its subfield deep learning (DL), has made DDI prediction more accurate and efficient when handling large datasets from biological, chemical, and clinical domains. Many ML and DL approaches are bio-inspired, taking inspiration from natural systems, and are considered part of the broader class of biomimetic methods. This review provides a comprehensive overview of AI-based methods currently used for DDI prediction. These include classical ML algorithms, such as logistic regression (LR) and support vector machines (SVMs); advanced DL models, such as deep neural networks (DNNs) and long short-term memory networks (LSTMs); graph-based models, such as graph convolutional networks (GCNs) and graph attention networks (GATs); and ensemble techniques. The use of knowledge graphs and transformers to capture relations and meaningful data about drugs is also investigated. Additionally, emerging biomimetic approaches offer promising directions for the future in designing AI models that can emulate the complexity of pharmacological interactions. These upgrades include using genetic algorithms with LR and SVM, neuroevaluation (brain-inspired model optimization) to improve DNN and LSTM architectures, ant-colony-inspired path exploration with GCN and GAT, and immune-inspired attention mechanisms in transformer models. This manuscript reviews the typical types of data employed in DDI (pDDI) prediction studies and the evaluation methods employed, discussing the pros and cons of each. There are useful approaches outlined that reveal important points that require further research and suggest ways to improve the accuracy, usability, and understanding of DDI prediction models.

The advent of artificial intelligence to the field of education has triggered a change in the way an environment is conceived and built with the objective of allowing adaptive learning. A research focused on the power of AI on adaptive learning contexts are drawn in this study including empirical scenarios and case studies showing transformative concepts at the crossroads of technology and academia. Adaptive learning environments are dynamically personalised educational environments built upon the deployment of artificial intelligence by providing a personalised learning experience for learners. Consequently, these systems include the unique needs and preferences of each learner. Empowered by artificial intelligence, adaptive learning systems are making data-driven decisions based on student performance metrics and then adjust the instructional materials based on techniques such as machine learning, natural language processing, and data analytics. These systems function in real-time, dynamically adjusting not only the complexity of tasks, but also the modality of feedback, as well as additional resources depending on a learner’s progress in their learning and their learning style. This paradigm provides an easy, coherent and effective alternative to traditional one-size-fits-all educational models. Regulatory bodies have also mapped out the use of artificial intelligence in adaptive learning scenarios citing many case studies in this capacity. For example, Knewton and DreamBox offer AI-based personalised learning paths in learning areas such as reading and mathematics. Knewton use of analysis of student interaction to personalise classes and assessments which claim that this technological approach not only increases the engagement of students but also improves their academic performance.

Monetary policy and its instruments constitute the fundamental pillar for achieving economic stability, particularly in emerging economies facing persistent structural fluctuations. The problem of this study lies in the volatile effectiveness of traditional monetary policy tools in Turkey amid high inflation and frequent structural breaks, raising a fundamental question about the most influential channel for productive activity. Accordingly, the study aims to measure and analyze the impact of structural changes in monetary policy instruments on economic growth in Turkey during the period (1981–2025). To achieve this, the research adopted the Indicator Saturation Method (ISM) for its ability to identify structural breaks and integrate them into the econometric model, while analyzing variables such as broad money supply, real interest rates, and exchange rates. The key results revealed a weak impact of the broad money supply on growth, in contrast to the pivotal role of the exchange rate channel as a primary driver of economic activity. Furthermore, the model identified significant structural breaks in (2003, 2011, and 2022), reflecting the impact of radical political and economic shifts. The study’s main conclusions and recommendations highlight the limited effectiveness of the "liquidity channel" in high-inflation environments. Therefore, the study recommends that monetary policymakers focus on exchange rate stability and real interest rate management as more efficient tools for stimulating sustainable growth and enhancing economic resilience to future shocks.