Applied Science and Engineering Journal for Advanced Research
Not a member yet
146 research outputs found
Sort by
Design and Implementation of an Automated Patch Management and Compliance Framework for Institutional IT Systems
Get PDFDelays in patching and uneven adherence to legal requirements make institutional IT systems more susceptible to security risks. The design and implementation of an automated patch management and compliance framework suited to institutional environments was the main emphasis of this project. The framework was implemented and tested in a simulated IT infrastructure with a variety of operating systems and device roles using a design science research methodology. According to the findings, there were notable gains in patch deployment success rates (96% vs. 78%), remediation time (3.2 vs. 14.5 hours), compliance (98% vs. 72%), and system downtime. The automated system was a strong and scalable paradigm for institutional IT governance since it also improved administrative efficiency and offered real-time compliance reports. These results imply that patch management automation improves cybersecurity and simplifies IT operations in both academic and business contexts
Retrieval-Augmented Generation Enhanced with Feature Stores: A Hybrid Architecture for Enterprise Knowledge Management
No full textThis research paper presents a novel hybrid Retrieval-Augmented Generation (RAG) architecture enhanced with enterprise stores to enhance accuracy, personalization, and contextual relevance of knowledge retrieval in contemporary organizations. Unlike traditional RAG systems that rely solely on semantic similarity, the proposed approach addresses the critical limitations: context-unaware retrieval, limited personalization, and inconsistent result quality. To fill this gap, the paper combines real-time structured elements including user role, expertise, behavioral patterns, and document metadata to the retrieval process in a hybrid similarity scoring framework. This study employed a simulation-based experimental design encompassing 10,000 documents, 500 users and three distinct system configurations, which is the semantic-only baseline and two hybrid models with 10 and 50 user/context features. The experimental results demonstrate significant performance improvements in the hybrid models which include 15-20% higher Top-K retrieval accuracy, 36% improvement in Mean Reciprocal Rank (MRR) and 41% enhancement in user satisfaction metrics. Although the computational steps were added, the latency was still acceptable to the enterprise and even when the feature became stale the retrieval accuracy did not change. All in all, the results prove the integrative process between feature stores and RAG systems to be a strong direction to the more correct, personal, and context-varying enterprise knowledge management
Gesture Control Revolution: Enhancing Automotive Infotainment through Advanced Hand Gesture Recognition
Get PDFIn the ever-developing industry of automobiles, a focus should be made on the innovation of the car’s user experience while keeping the driver safe. The following paper therefore aims at proposing a new hand gesture recognition system to be implemented in car infotainment, which employs a modified CNN model enhanced with KNN for enhanced gesture mapping. The efficiency of the system was tested on a data of samples consisting of 10000 images of 10 different gestures performed by different users under different lighting conditions. The results obtained for the experimental evaluation proved that the used CNN reached the accuracy of 92,5% with the validation set and the further use of KNN for post-processing increased the classification accuracy up to 95,2%. Resource consumption was low, the CNN occupied roughly 50 MB of memory, that is why it is possible to use it for the in-vehicle system. A similar survey that targeted users showed that 85% of them were comfortable with the system as it was easy to learn and did not interfere with the control of infotainment functions. This research discusses the possibility of using gesture recognition technology to improve the user experience in vehicles making infotainment systems safer and more efficient
Forecasting Health Vulnerabilities through Machine Learning
No full textMachine Learning Model to Forecast Patient Disease Vulnerability using Data Cleanroom Methodology enable to join Patient data anonymously coming from Patient diagnosed with different disease. Cross Organization data analytics helped to identify pattern in multiple dieses in Patients to train the model. So model can forecast if Patient has one dieses what are chances of having other dieses in future and take preventive actions Based on this project the aim is to investigate the effectiveness of deep learning techniques in the early identification of disease. Two things must be done to accomplish this goal: first, it must be made clear how important it is to promptly identify disease outbreaks in the current global health setting, and second, deep learning techniques must be subjected to a thorough evaluation of how well they perform in this critical job. These goals are part of the study\u27s overall effort to get a thorough understanding of the range of deep-learning approaches that can assist and improve disease outbreak investigation processes. The cutting-edge technology that has supported early intervention and lessened the effects of disease epidemics in the global community is improved by this research
Transforming Network Architectures with VMware NSX-T Data Centre: A Deep Dive into Software-Defined Networking for Multi-Cloud Environments
Get PDFThe rapid evolution of network architectures has increased the demand for scalable, secure, and automated solutions to manage complex multi-cloud environments. VMware NSX-T Data Center is a leading software-defined networking (SDN) platform that offers enhanced network virtualization, micro-segmentation, and Zero Trust security frameworks. This paper presents a comprehensive analysis of NSX-T’s transformative impact on modern networks. Results show that NSX-T reduces lateral attack surfaces by 95%, improves cloud resource provisioning times by 50%, and lowers manual configuration efforts by 60%. Additionally, latency reductions of 50% in 5G networks and throughput increases of 50% in large-scale data centers highlight its performance benefits. Despite deployment complexities and cost challenges, NSX-T demonstrates significant potential in advancing 5G, edge computing, and AI-driven network automation
Optimization of FDM Process Parameters for Minimizing Specific Wear Rate Using a GA- ANFIS Hybrid Model
Get PDFThis study investigates the optimization of tribological performance in Fused Deposition Modeling (FDM) fabricated components by focusing on the specific wear rate (SWR) of Polylactic Acid (PLA) specimens. A total of 30 samples were fabricated using a MakerBot Method X 3D printer following ASTM G99 standards, considering four key process parameters: nozzle temperature, infill density, layer height, and printing speed. Wear behavior was evaluated using a Pin-on-Disc apparatus under dry sliding conditions. To predict and minimize SWR, a hybrid GA-ANFIS (Genetic Algorithm–Adaptive Neuro-Fuzzy Inference System) model was employed. The ANFIS framework effectively captured nonlinear relationships among input variables, while GA optimized membership functions to improve prediction accuracy. Experimental results demonstrated that nozzle temperature and layer height had the most significant influence on SWR. The optimized parameter combination achieved a minimum SWR of 8.26 × 10⁻⁴ mm³/N·m, representing a 25.12% reduction compared to non-optimized settings. The proposed hybrid approach proved to be a robust tool for process parameter optimization, enabling enhanced wear resistance and mechanical integrity in FDM-printed parts
The Evolution of Middleware in Enterprise Architectures: A Future Outlook
Get PDFMiddleware technologies serve as critical enablers for seamless integration, communication, and management of distributed systems across diverse enterprise environments. As technological paradigms evolve, the future of middleware is set to be transformed by innovations in artificial intelligence (AI), hybrid and multi-cloud orchestration, zero-trust security frameworks, data-centric architectures, and low-code development platforms. AI-powered middleware will revolutionize system management by automating complex tasks such as anomaly detection, predictive maintenance, and dynamic traffic routing. In hybrid cloud contexts, advanced orchestration layers will abstract cloud complexities, enabling interoperability and portability. Middleware aligned with zero-trust models will embed context-aware security and fine-grained policy enforcement into core communication layers. Additionally, data-centric middleware will support real-time integration, predictive analytics, and automated governance, enhancing decision-making processes. Finally, low-code middleware abstractions will democratize integration capabilities, empowering non-technical users to build complex workflows with ease. This paper explores these emerging trends and highlights the pivotal role of middleware in future enterprise IT ecosystems
AeroTurbineX – Advanced Framework for Turbomachinery Innovation
No full textTurbomachinery is at the heart of aerospace propulsion systems, transforming chemical energy into kinetic energy for thrust and sustained flight. In pursuit of greater fuel efficiency, reduced emissions, and improved reliability, the aerospace industry continuously innovates in the design and optimization of compressors, turbines, and fans. This paper provides a comprehensive exploration of the fundamental design principles, engineering challenges, advanced computational tools, and optimization strategies used in modern high-performance turbomachinery. Emerging technologies such as additive manufacturing, AI-based optimization, and hybrid-electric integration are also discussed, offering a forward-looking perspective on next-generation propulsion systems
Optimizing Real-Time Telemetry and Diagnostics with Azure SignalR and Redis Cache Integration
No full textThis study looked into how to integrate Azure SignalR and Redis Cache to optimize real-time telemetry and diagnostics. A basic system without Redis integration was contrasted with a prototype system that integrated Redis with Azure SignalR. Metrics such as latency, throughput, scalability, error rates, and cache effectiveness were used to assess performance using simulated telemetry data streams under various client loads. The findings showed that Redis integration enhanced throughput by about 60%, decreased average and peak latency by over 50%, and preserved system stability with up to 10,000 concurrent clients. Redis\u27 dependability for handling frequent queries was confirmed by cache hit ratios that continuously above 90%. The results demonstrated that integrating Redis Cache with Azure SignalR offered a fault-tolerant, low-latency, and scalable solution for real-time telemetry settings. For crucial fields like industrial monitoring, medical diagnostics, and Internet of Things applications needing effective large-scale data processing, this strategy provided significant benefits
Next-Generation SAAS Transformation: Blending AI-Driven Analytics with Agile IT Operations and Agentic AI
No full textIn this work, the researcher explored how AI-based analytics, Agile IT operations, and agentic AI features can transform the next-generation SaaS environments. In its study, the research adopted both qualitative and quantitative methods comprising of a survey of 120 technology professionals using a set questionnaire, and interviewing 15 industry experts, to understand the adoption trends, operational performance, and strategic consequences. The results showed that integrated organizations employing AI and Agile methods had substantial improvement in the velocity of deployment, level of automation, efficiency of incident resolution and customer satisfaction. As a result of autonomous decision-making, proactive incident management, and optimisation of intelligent systems, agentic AI became an important contributor to overall resilience in the process of operation. Nevertheless, the research also identified issues associated with the talent preparedness, data-governance, and AI-supervisory demands. In general, the study has found that the intersection of AI-powered analytics with Agile frameworks allowed SaaS companies to transform into autonomous, scalable, and innovation-related digital ecosystems and place them in a position of continued competitive development in fast-growing cloud ecosystems