IIRJET

IoT-Based Real-Time Monitoring and Testing of AI Applications for Analysis and Forecasting

2026-07-08T11:05:10+00:00

One of the most essential elements for the continuation of life on Earth is air. Air pollution is continuously rising due to industrial factors and the use of fossil fuels. Because these elements have an impact on health and prosperity of life on Earth, it is necessary to constantly examine the quality of the air in our surroundings. The implementation and strategy of IoT based air pollution tracking and projecting using AI techniques are presented in this study. Due to high levels of dangerous chemicals, air pollution in industrial settings, especially during the chrome coating process, puts workers' health at serious risk. The demand for effective testing and monitoring procedures to guarantee system reliability, safety, and efficiency has grown due to the quick development of automated production. A real-time tracking and assessment method for IoT-based autonomous systems is presented in this paper. The proposed system integrates IoT-enabled sensors, online information technology, and AI approaches to continuously collect, process, and evaluate real-time data from connected devices and automated settings. Since the system consistently detects possible issues long before they become serious mistakes, our results show a large rise in the early detection of abnormal trends. This study demonstrates how IoT and AI may be successfully integrated to enhance industrial management. It also emphasizes the concrete advantages of this integration process, such as the system's flexibility and ongoing learning, which guarantee its long-term efficacy.

Intelligent Avionics: A Deep Learning Approach for Next-Generation Aircraft Systems and Flight Automation

2026-07-08T11:14:47+00:00

The rapid growth of global air transportation and the increasing demand for safer, more efficient, and environmentally sustainable aviation systems have accelerated the adoption of intelligent avionics technologies. Deep learning has emerged as a transformative approach for enhancing aircraft autonomy, flight control, predictive maintenance, navigation, and real-time decision-making. This study examines the application of deep learning techniques in next-generation aircraft systems and flight automation through a comprehensive review of recent research and technological developments. The analysis highlights the role of neural networks, reinforcement learning, and data-driven predictive models in improving situational awareness, fault detection, trajectory optimization, and autonomous flight operations. The accomplishment of energy-optimized operations depends on intelligent energy managing systems, where AI is emerging as a disruptive solution. These AI-driven systems enable distributed energy flow oversight, adaptive engine control, and real-time decision-making spanning mission targets such as maximum distance traveled, minimum energy consumption, or minimum carbon footprint levels. The paper concludes with future strategies for integrating AI-driven control, scalable standardized infrastructure, and flight-ready energy alternatives to enable the next generation of intelligent hybrid electric VTOL aircraft and eco air mobility systems.

Advanced Machine Learning Approaches for Biotechnology Quality Compliance in Future Prospects

2026-07-08T11:25:25+00:00

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies in healthcare, biotechnology, and vaccine development, offering significant potential to improve process efficiency, decision-making, and resource utilization. This study presents Bio-MARL, an advanced machine learning framework for biotechnology quality compliance and biological process optimization that integrates multi-objective management with time-series prediction techniques. The proposed framework employs Long Short-Term Memory (LSTM) and Transformer-based models for temporal forecasting, combined with predictive maintenance strategies and multi-objective optimization to effectively manage operational trade-offs. And the Productivity rose by 29.9% across datasets (Yeast 26.9%, E. coli 34.2%, and CHO 28.5%). and 94.8% of the batch was successful. Depending on the type of procedure, resource usage dropped by 20–25%. The design combines multi-objective methods that manage practical trade-offs, LSTM and Transformer models for temporal prediction, and predictive upkeep that cuts unscheduled downtime by 43%. Our method is validated by three industrial data sets: yeast generating enzymes on a large scale, E. coli producing the use of re and CHO cell cultures expressing monoclonal proteins. Together, the three datasets show steady gains in robustness, quality, and output. These findings show that smart automation may significantly boost supply-chain resilience and bio manufacturing profitability.

The Role of Augmented Reality in Enhancing Architectural Design and Spatial Experience

2026-07-08T11:38:24+00:00

This paper presents a novel approach for building an immersive multi-modal virtual exhibit hall integrating virtual reality, augmented reality, and artificial intelligence in order to transform how people perceive art and national heritage. These days, the architecture and design sectors need more user-friendly visualization systems for effective utilization of digital information. Currently, existing constructed environments account for more than half of all building activity in the German building sector. The proposed approach integrates AR-based spatial visualization, exploration, and interactive meaning-making to enhance users’ understanding of architectural and landscape spaces. A practical experiment was conducted using a portable AR system to evaluate users’ experiences within an outdoor landscape architecture design. Three different exploratory scenarios were implemented, each providing varying levels of user freedom and interaction. The study assessed participants’ ability to explore, interpret, and establish spatial relationships within the augmented environment. This idea was supported by a hands-on experiment that used portable augmented reality to experience an outdoor landscape architecture design while taking into account and contrasting three different exploring situations. Viewers have different combinations of levels of freedom to watch the action taken in these situations. The findings demonstrated that users saw exploration as a good thing, that they were successful in giving space meaning, and that, based on the exploratory scenario, they could accurately define the spatial linkages within the AR intervention process.

An Intelligent Framework for Environmental Impact Assessment Using Artificial Intelligence

2026-07-08T11:52:02+00:00

The restoration of terrestrial ecosystems promotes sustainable land resource development and aids in the preservation of the natural world. For increasingly severe land degradation, contemporary and effective strategies for the preservation of ecological purposes must be developed The proposed framework integrates Natural Language Processing (NLP) and Machine Learning (ML) techniques to analyze environmental data obtained from Environmental Product Declarations (EPDs) and Life Cycle Assessment (LCA) reports. NLP is employed to extract and process relevant environmental information, while a Random Forest algorithm is utilized to develop predictive models for environmental impact assessment. The framework is trained using product-specific data and seven environmental impact categories and subsequently validated using an independent testing dataset. Our findings show that the model had an accuracy of 85%, 72%, 65%, and 71% in predicting the values of the following impact categories: global warming possibility, abiotic depleting potential for fossil fuels, acidity capacity, and the photochemical ozone generation potential. Our approach shows that by learning from the outcomes of the earlier LCA research, sustainability can be predicted with a defined variability. The quantity of data provided for training also affects the model's efficacy.

Deep Learning-Based Intelligent Framework for Cloud-Native 5G Core Network Management and Optimization

2026-07-08T11:57:39+00:00

Cloud native technology, which offers previously unheard-of levels of operational robotics, scalability, and versatility, has completely transformed 5G and 6G communications networks. However, distributing resources for fluid cloud computing environments faces a new difficulty due to the wide range of cloud native services and apps. The proposed framework, based on the CygNet MaSoN architecture, integrates real-time monitoring, data aggregation, predictive analytics, and deep learning models to optimize resource utilization and detect anomalous network behavior. The system enables proactive identification of service degradation, network performance issues, and security threats while supporting self-organizing and closed-loop automation capabilities required for autonomous 5G networks. Furthermore, the framework incorporates sequence-aware learning algorithms and synthetic data generation techniques to improve model performance in dynamic and context-dependent network environments. The components of a system and architecture are described in detail. After that, three actual use cases that have been performed on this structure are explained. The features taken into consideration are discussed together with machine learning in general models created and synthetic data production techniques used. These findings support the significance of sequence-aware algorithms for protecting roaming environments, which frequently involve context-dependent and fleeting dangers. The suggested paradigm offers a route for robust security in networks beyond 5G and 6G as well as a basis for intelligent, adaptive security monitoring in 5G.