Indoor air quality (IAQ) has a crucial impact on health, yet many spaces suffer from unnoticed pollution. This study introduces “Indoor Air Wellness”, a predictive model that utilizes advanced AI techniques, specifically integrating Kalman filtering (KF) and artificial neural networks (ANN), to enhance indoor air pollution prediction and management. The model excels in dynamic environments by refining the accuracy of the proposed Kal-ANN algorithm achieving a predictive accuracy (R²) of up to 96. 55%, a root mean square error (RMSE), a Mean Absolute Error (MAE), and a Mean Squared Prediction Error (MSPE) as low as 9.85, 6.12, and 3.15 g/m. ‘‘Indoor AirWellness" operates in three phases: IAQ prediction, control, and energy-efficient green building design. IAQ prediction consists of Learning and Analysis subphases, continuously monitoring and forecasting indoor parameters such as temperature, humidity, and CO levels while considering external factors. The Analysis phase implements control measures based on these predictions, leading to a 60% improvement in energy utilization in green buildings compared to traditional methods. Simulations and real-world applications demonstrate its effectiveness in reducing pollutants by up to 23% and minimizing energy consumption by up to 48%. Overall, the “Indoor Air Wellness” model enhances air quality and promotes energy efficiency, positioning it as a valuable tool for residential, commercial, and industrial applications. This study contributes to advancing smart environmental management, advocating for healthier indoor environments through AI-driven solutions.

A new problem is how to adjust farming operations to shifting climatic conditions while using cutting edge agricultural technologies and novel seed varieties. IoT offers a fresh method for adjusting to future problems as a result. Historically, agricultural production was significantly influenced by the weather. The current climate catastrophe has dramatically changed this norm, though. Older seed kinds still generate superior yields when cared for correctly, whereas newer seed varieties have evolved in response to certain climatic and moisture requirements. In this study, we set out to train the machinery to recognize significant developmental stages in the life cycle of the crop, so that it can advise the farmer on how to best care for the machine depending on the circumstances under which the crop thrives. Instead of striving to create a system with predetermined thresholds, this improved agricultural initiative seeks to protect the environment while also meeting the crop's shifting needs throughout the course of its whole life cycle. Depending onwhere the crop is in its lifecycle and how long it is expected to last, the farmer may search for new approaches. Any device having an internet connection and the capability to attach sensorsmay be utilized to carry out all of these procedures.

The Smart City (SC) framework has garnered global recognition for its transformative influence on society through innovative solutions. However, the extensive use of Internet of Things (IoT) devices in SCs raises concerns regarding electronic waste and resource consumption. Addressing this challenge necessitates integrating smart grid systems to safeguard SC residents’ environment and well-being. Accurate air quality prediction is essential for informed societal decisions, safe transportation, and disaster preparedness. This study introduces a novel approach: Towards Cleaner Industries: Smart Cities’ Impact on Predictive Air Quality Management (SPAM). The SPAM model utilizes a bidirectional stacking mechanism of long short-term memory neural networks, considering spatiotemporal correlations to forecast future air pollutant concentrations. Surpassing conventional methods, SPAM enhances accuracy while reducing computational complexity. Experimental findings demonstrate enhanced efficiency and accuracy, underscoring its practicality in industrial contexts. The SPAM model represents a significant advancement in promoting environmental sustainability within the SC framework.

According to global data on visual impairment from the World Health Organization in 2010, an estimated 285 million individuals, including 39 million who are blind, face visual impairments. These individuals use non-contact methods such as voice commands and hand gestures to interact with user interfaces. Recognizing the significance of hand gesture recognition for this vulnerable population and aiming to improve user usability, this study employs a Generative Adversarial Network (GAN) coupled with Convolutional Neural Network (CNN) techniques to generate a diverse set of hand gestures. Recognizing hand gestures using HaCk typically involves a two-step approach. First, the GAN is trained to generate synthetic hand gesture images, and then a separate CNN is employed to classify gestures in real-world data. The evaluation of HaCk is demonstrated through a comparative analysis using Leave-One-Out Cross-Validation (LOO CV) and Holdout Cross-Validation (Holdout CV) tests. These tests are crucial for assessing the model’s generalization, robustness, and suitability for practical applications. The experimental results reveal that the performance of HaCk surpasses that of other compared ML/DL models, including CNN, FTCNN, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models. Specifically, the improvement percentages for the LOO CV Test are 17.03%, 20.27%, 15.76%, 13.76%, 10.16%, 5.90%, and 15.90%, respectively. Similarly, for the Holdout CV Test, HaCk outperforms HU, ZM, GB, GB-ZM, GB-HU, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models, with improvement percentages of 56.87%, 15.91%, 13.97%, 24.81%, 23.52%, 17.72%, 15.72%, 12.12%, 7.94%, and 17.94%, respectively.

E-learning controlling quality ready to demonstrate the achievement of the conveyance and plan of the framework by understanding the user satisfaction. Besides, the qualification between learning substance and site content should be parallel whereby both will impact one another and influence user satisfaction t and fulfillment. After the examination has closed, concern emerges on how the elements of e-learning in service, system and information quality influence the nature of e-learning framework in keeping fit. Besides, realizing that client is a mainstay of e-learning framework, this exploration expected to see if the user fulfillment has any impact on e-learning framework quality. Framework plan, framework conveyance and framework result been utilized as a stage for estimating the accomplishment of e-learning framework.

Malware classification presents a pressing cybersecurity challenge, necessitating advanced pattern recognition techniques. This paper introduces a novel framework that integrates supervised machine learning with deep learning methodologies, augmented by advanced image processing techniques. Beyond traditional methods, our framework pioneers dynamic feature extraction with adversarial resilience, continuously adapting to evolving malware behaviors in real-time. Additionally, we propose a graph-based behavioral analysis for malware graphs, capturing intricate relationships and dependencies between system entities to enhance detection accuracy. By modeling malware behavior as a graph structure and employing graph neural networks, our framework offers a comprehensive approach to malware analysis, uncovering sophisticated attack patterns with unparalleled efficacy. Furthermore, our framework embraces privacy-preserving collaborative malware analysis, enabling secure knowledge sharing across organizations without compromising sensitive data. Through federated learning and secure multiparty computation, participating entities can collaboratively train detection models while preserving the confidentiality of their proprietary datasets. Through extensive experimentation on diverse malware datasets, our framework demonstrates superior classification accuracy and performance metrics, outperforming existing methods significantly. We highlight its utility for real-world cyber protection, emphasizing scalability and adaptability to changing malware environments. By presenting a unified and innovative approach to malware classification through comprehensive pattern recognition and incorporating dynamic feature extraction, graph-based analysis, and privacy-preserving collaboration, this paper advances the field and provides valuable insights for securing our digital world.