This study introduces an innovative approach to network intrusion detection by harnessing the capabilities of adversarial networks and underscoring the importance of encoder components. Conventional intrusion detection systems frequently grapple with the real-time identification of intricate and evolving threats, rendering them susceptible to both false positives and negatives. Our proposed method aims to confront these inherent challenges. The proposed strategy, referred to as ID-ANWE, entails the integration of adversarial networks into the system. This augmentation enhances the system's ability to differentiate between typical network behavior and irregular, potentially malicious activities. Through a training process, these adversarial networks become proficient in distinguishing between legitimate network traffic and potential intrusion attempts, contributing to the establishment of a dynamic and adaptable security framework.

Anemia results from reduced red blood cell or hemoglobin levels, limiting oxygen transport. Sickle Cell Anemia arises from HbS polymerization under low oxygen, causing sickled cells, vaso-occlusion, hemolysis, and multi-organ complications with symptoms including pain, fatigue, and infection risk. Clinicians face challenges such as misdiagnosis, time pressure, and the need for rapid, accurate results, while conventional methods like blood smears and electrophoresis remain slow and variable. To address this, the work proposes a hybrid pipeline using DenseNet-121 for feature extraction, a Quantum Convolutional Neural Network for quantum-enhanced classification, and a Deep Q-Network for personalized clinical recommendations. The pipeline supports automated image interpretation and risk-aware decision support. Evaluated using accuracy, precision, recall, F1-score, and AUC-ROC, the model achieves 92% accuracy, demonstrating strong diagnostic capability overall. Future work includes extending to multi-class blood disorders and supporting real-time inference on edge-based quantum platforms.

This chapter explores the integration of digital twin technology (DTT) and artificial intelligence (AI) in advancing underwater wireless sensor networks (UWSN). The problem statement revolves around the challenges faced by UWSN in terms of data quality, real-time decision-making, and energy efficiency. Traditional UWSN systems lack the ability to adapt swiftly to changing underwater conditions and ensure reliable data transmission. This study addresses these challenges by proposing a novel approach that leverages DTT and AI for enhanced UWSN performance. Its methodology involves the design and implementation of a DTT-AI-based UWSN framework. DTT replicates the physical underwater environment, providing a virtual representation that continuously updates in real-time. AI algorithms process data from UWSN sensors within this digital twin, enabling intelligent decision-making and predictive analytics.

Air Quality Index (AQI) prediction and forecasting play pivotal roles in assessing and managing air pollution, contributing to public health and environmental sustainability. This paper provides a comprehensive review of recent advancements, methodologies, challenges, and future directions in AQI prediction and forecasting. Recent research has seen a surge in the development of machine learning, statistical, and hybrid models for AQI prediction. These models leverage various input data sources such as meteorological data, satellite imagery, and pollutant emissions data to enhance prediction accuracy. Furthermore, the integration of advanced techniques like deep learning and ensemble modeling has shown promising results in capturing complex nonlinear relationships and improving forecast precision. Challenges persist, including the need for real-time data integration, model interpretability, and addressing spatial and temporal variations in air quality. Additionally, the impact of emerging factors such as climate change and urbanization on AQI prediction requires further investigation. Future research directions focus on the development of hybrid models that integrate multiple data sources, including sensor networks and IoT devices, to improve spatial and temporal resolution. Moreover, there is a growing emphasis on the incorporation of uncertainty quantification techniques to provide probabilistic forecasts and enhance decision-making under uncertainty. In conclusion, this paper underscores the importance of AQI prediction and forecasting in addressing air pollution challenges and promoting public health. By advancing methodologies, addressing challenges, and exploring emerging research avenues, we can strive towards more accurate, reliable, and actionable AQI predictions for sustainable urban development and environmental stewardship.

The digital age has brought amazing levels of connectivity, but also new levels of exposure and risk to ever more sophisticated cyber threats. Old security is frequently no match for new attacks and AI is game-changing in cybersecurity. AI supports instant detection, identification of anomalies, and anticipated threat-intelligence using advanced machine learning applications, such as deep machine learning and behavioral algorithmics. They improve detection of intrusion, categorization of malware and automation of response, cutting reaction time and damage. AI can predictIdentify and Respond, beyond the detection of vulnerabilities, AI will enable organization to see something bad is happening before it does and allocate resources to respond. Yet, challenges including adversarial AI, ethical considerations, algorithmic bias and high implementation costs need to be formalized.