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Biosensors integrated with artificial intelligence (AI) hold immense potential for transforming healthcare through rapid, automated diagnostics and precision therapeutics. This paper reviews the convergence of biosensing and AI towards developing smart biomedical systems. The fundamentals, historical evolution, and classification of biosensors are presented, highlighting key applications across infections, chronic illnesses, and environmental monitoring. Core AI concepts, including machine learning, neural networks, computer vision, and natural language processing, are discussed, along with their implementation to augment biosensor functionality, connectivity, point-of-care adoption, and laboratory automation. Promising research directions and real-world case studies applying AI-integrated biosensors for early diagnosis and drug delivery are discussed. The opportunities and challenges in advancing this synergistic technology are contemplated, underscoring the need for cross-disciplinary collaboration, clinical validation, ethical vigilance and supportive policy environments to successfully translate AI-biosensors into practical healthcare solutions. [ABSTRACT FROM AUTHOR]

Biosensors that incorporate a biological recognition element with a transducer offer a rapid, simple approach for selective detection of analytes. However, conventional rigid biosensors have limited capabilities for continuous on-body monitoring applications. The emergence of flexible electronics has opened new possibilities for developing conformal biosensing platforms that can intimately interface with soft, curvilinear surfaces like skin for non-invasive health tracking. This review covers the fundamentals of biosensor technologies and recent progress in flexible biosensors aimed at point-of-care diagnostics and medical monitoring. Key developments in materials, electrochemical sensing interfaces, microfluidics integration and wireless connectivity are highlighted. Examples of flexible biosensors for metabolites, electrolytes, proteins and nucleic acids are presented along with capabilities and limitations. Current challenges include improving biocompatibility, correlation with standard assays, wireless range, manufacturability, and regulatory requirements. Further research on translating flexible biosensor devices from initial proof-of-concepts to validated medical products is warranted. Overall, flexible biosensors hold immense promise to enable continuous personalized health monitoring. [ABSTRACT FROM AUTHOR]

Alcohol misuse has become a major public health concern worldwide. The development of an accurate, non-invasive, and cost-effective method for real-time alcohol detection would have important applications for monitoring and preventing alcohol misuse. This paper proposes a novel wearable biosensor for real-time alcohol detection in sweat. The biosensor uses alcohol oxidase immobilized on a screen-printed electrode to amperometrically detect ethanol in sweat samples. An artificial sweat solution was used to optimize the sensor design and evaluate its analytical performance. The optimized biosensor demonstrated a linear response to ethanol in the concentration range of 0.1-1.0 g/dL, with a limit of detection of 0.02 g/dL. On-body trials of the wearable biosensor provided rapid measurements that strongly correlated with blood alcohol content. This approach could offer a convenient solution for continuous, non-invasive alcohol monitoring to help reduce alcohol misuse and promote healthy behaviors. Further research on long-term use, alternate electrode geometries, and fully integrated wearable designs is warranted. [ABSTRACT FROM AUTHOR]

The integration of Virtual Reality (VR) environments with Internet of Things (IoT) infrastructure can enable more intuitive and immersive interactions. However, realizing the potential of this convergence requires overcoming technical constraints and implementation challenges. This paper reviews the motivations, architectures, applications, and open issues associated with combining VR and IoT. This study also aims to provide a comprehensive analysis of architectures, use cases and technical challenges involved in integrating VR and IoT to guide further research and real-world deployment. Use cases in manufacturing, energy, retail, entertainment and other sectors highlight the benefits like remote monitoring, training and collaboration unlocked by interfacing VR's realistic 3D visualizations with real-time IoT sensor data. This paper also discusses VR simulation of IoT systems for testing, limitations in interoperability, and security considerations. The outlook for VR-IoT solutions is promising, with 5G and edge computing advancements expected to address current bottlenecks to adoption. Human-centric design approaches focused on high-value use cases can enable impactful deployments across domains. [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) overlays digital information and virtual objects on real environments to deliver enhanced, contextual experiences. Meanwhile, the Internet of Things (IoT) enables an ecosystem of interconnected smart devices and sensors. This paper reviews the synergistic integration of AR and IoT in developing next-generation intelligent systems and human-machine interfaces. A detailed analysis is presented on, integrating AR software with IoT devices and dataflows, enhanced visualization, training, navigation and contextual user experiences enabled by AR-IoT convergence, and key challenges around interfaces, latency, spatial registration, security, privacy and scalability. The use cases across manufacturing, healthcare and smart environments illustrate the transformational potential of AR and IoT. However, standardized interfaces, edge computing, tailoring security with usability, and responsible development focused on human benefits are crucial for realizing the promise of hybrid physical-digital ecosystems. This paper undertakes a systematic review of Augmented Reality (AR) and Internet of Things (IoT) convergence encompassing integration approaches, emerging use cases, and technical obstacles. A cross-disciplinary analysis strategy centered on real-world viability synthesizes opportunities and challenges for shaping responsible research directions across application domains. [ABSTRACT FROM AUTHOR]

Fifth-Generation (5G) cellular networks target substantial gains over 4G networks to meet the increasing data rate, capacity and low-latency demands of emerging mobile applications. This research investigates the potential of largescale antenna system architectures named Massive Multiple-Input Multiple-Output (MIMO) to enhance the spectral efficiency and reliability of 5G new radio (NR) wireless access. Comprehensive system-level modeling and simulations quantify the throughput, capacity and transmit power savings realizable under various realistic propagation environments. Results demonstrate significant performance gains over contemporary network configurations, supporting Massive MIMO as a key enabler on the road to 5G. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has emerged as a transformative technology, connecting a wide range of devices and sensors to the internet, enabling data-driven decision-making and automation across various domains. Deep learning, a subset of machine learning, has played a pivotal role in advancing IoT applications by providing the means to process and interpret the massive volumes of data generated by IoT devices. This comprehensive survey provides an extensive review of the integration of deep learning techniques with IoT, aiming to offer a holistic understanding of the state-of-the-art research and practical implementations. The key motivations include handling complex IoT data and enabling smart applications across domains like healthcare, transportation, and industry 4.0. Real-world case studies demonstrate significant improvements on metrics like prediction accuracy, operational efficiency, and energy savings. The adoption of edge computing is further driven by the need to reduce latency in data processing and enhance real-time decision)making capabilities in these critical sectors. However, challenges remain around model interpretation, robustness, and computational complexity. Future IoT systems must focus on lightweight deep learning architectures tailored for edge devices, distributed and privacy-preserving analytics, and improved model transparency and fairness. Additionally, these future IoT systems should prioritize robust security measures to protect sensitive data and ensure the reliability and integrity of connected devices and networks. A systematic framework is proposed covering data curation, model development, deployment, and monitoring to guide adoption. The comprehensive nature of this survey aims to bridge the gap between theoretical knowledge and practical deployment, enabling a smoother integration of deep learning into IoT systems. By offering a structured overview of best practices and emerging trends, it empowers professionals to make informed decisions in their IoT projects. [ABSTRACT FROM AUTHOR]

Databases are critical for storing structured data, but deriving insights remains challenging. This paper investigates integrating classification, clustering, association rules, and anomaly detection within database architectures to enable intelligent analytics. A unified architecture is proposed along with an asynchronous incremental learning technique to efficiently handle dynamic data. Comprehensive experiments on diverse real-world datasets demonstrate 10-25% improvements in metrics like query latency, accuracy, and costs compared to conventional integration approaches. Emerging applications in multimedia, spatiotemporal, and IoT mining are discussed. The holistic convergence of multiple techniques is highlighted as the key innovation in progressing towards next-generation intelligent databases powered by analytics. [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) and Virtual Reality (VR) technologies have been used to transform surgical operating systems by enhancing visualization, preoperative planning, intraoperative navigation, and surgical training. AR and VR are simulated imaging tools used in surgery. Using 2D and 3D image rendering from VR/AR tools, surgeons can mimic real surgical procedures and anatomy, thereby boosting preparedness and efficiency in the operating room. However, validity, cost, training requirements, and ethical concerns remain. This study provided a comprehensive review of AR/VR applications across surgical workflows. A conceptual framework was proposed which included safety, accuracy, regulation, education, and workflow integration. Future research must balance innovation with clinical prudence to realize the potential of AR/VR while managing risks. This review synthesizes insights that serve as a knowledge base for transforming surgical systems using immersive technologies. [ABSTRACT FROM AUTHOR]

A biosensor is an analytical device containing immobilized biological materials (enzymes, antibodies, nucleic acids, hormones, organelles, or whole cells) that can specifically interact with an analyte and produce physical, chemical, or electrical signals that can be measured, and then report these data and use them for medical applications. Biosensors have transformed diverse fields, including medical diagnostics to diagnose infectious diseases, environmental monitoring, and food safety, by enabling rapid, sensitive, and selective detection of target analytes. This study provides a comprehensive review of biosensor technology, covering the fundamental principles and practical applications. Recent advances in biological techniques and instrumentation involving fluorescence tags for nanomaterials have increased the sensitivity of biosensors. The current challenges and future prospects are also discussed in this study. [ABSTRACT FROM AUTHOR]

The use of Wireless Sensor Networks (WSNs) has become increasingly popular in agriculture for real-time monitoring of environmental parameters. This study presents the development and evaluation of a WSN implemented using Arduino, Raspberry Pi, and XBee modules for real-time monitoring of temperature, humidity, soil moisture, and light intensity in a greenhouse. The results demonstrate that the system provides reliable and accurate data with low power consumption, thereby highlighting the feasibility of using WSNs for agricultural monitoring. This substantiates the feasibility and practicality of employing WSNs as invaluable tools for agricultural monitoring. As agriculture undergoes a transformative shift towards precision and sustainability, this research underscores the potential of WSNs to revolutionize farming practices, enhance resource allocation, increase crop yields, and ultimately contribute to the promotion of environmentally responsible agriculture. This study serves as a significant step in the convergence of technology and agriculture, fostering more efficient and eco-conscious farming methodologies. [ABSTRACT FROM AUTHOR]