Advancements in artificial intelligence and machine learning for poultry farming: Applications, challenges, and future prospects

Poultry Farming is crucial to feeding the world's growing population. Birds' abnormal behaviour can harm the birds, and disease detection relies on poultry behaviour. Integrating Internet of Things (IoT) technology into poultry farming can revolutionize the way to monitor and manage poultry health. Feeding, preening, and dustbathing are poultry's daily routines. In response to the problem of detecting correct poultry behaviour and health status, this paper proposes a smart poultry monitoring system that leverages IoT sensors to detect and monitor chicken behaviour in poultry farms and provides valuable information to industry stakeholders for management decisions and individual poultry health status. The phases of the proposed system are data preprocessing, feature extraction, feature selection, and detection of poultry behaviour via different classification algorithms. An optimized synthetic minority over-sampling technique (SMOTE) via an artificial hummingbird algorithm (AHA) is applied to solve the data imbalance problem. The experimental results show that an optimized SMOTE obtains better accuracy with 97 % than other algorithms. Further, to attain accuracy in predicting poultry behaviours, Random Forest (RF) achieves superiority compared to other machine learning algorithms with an accuracy of 98 %.

The convergence of healthcare with the Internet of Things (IoT) and Artificial Intelligence (AI) is reshaping medical practice with promising enhanced data-driven insights, automated decision-making, and remote patient monitoring. It has the transformative potential of these technologies to revolutionize diagnosis, treatment, and patient care. This study aims to explore the integration of IoT and AI in healthcare, outlining their applications, benefits, challenges, and potential risks. By synthesizing existing literature, this study aims to provide insights into the current landscape of AI, IoT, and AIoT in healthcare, identify areas for future research and development, and establish a framework for the effective use of AI in health. A comprehensive literature review included indexed databases such as PubMed/Medline, Scopus, and Google Scholar. Key search terms related to IoT, AI, healthcare, and medicine were employed to identify relevant studies. Papers were screened based on their relevance to the specified themes, and eventually, a selected number of papers were methodically chosen for this review. The integration of IoT and AI in healthcare offers significant advancements, including remote patient monitoring, personalized medicine, and operational efficiency. Wearable sensors, cloud-based data storage, and AI-driven algorithms enable real-time data collection, disease diagnosis, and treatment planning. However, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure responsible deployment of these technologies. Integrating IoT and AI in healthcare holds immense promise for improving patient outcomes and optimizing healthcare delivery. Despite challenges such as data privacy concerns and algorithmic biases, the transformative potential of these technologies cannot be overstated. Clear governance frameworks, transparent AI decision-making processes, and ethical considerations are essential to mitigate risks and harness the full benefits of IoT and AI in healthcare.

There has been a rapid development of information and communications technologies and, with the advent of the Internet of Things (IoT), artificial intelligence, and machine learning, massive disruption has been observed in various sectors, including education and libraries. These new technologies are interconnected and have revolutionized how electronic devices interact, exchange data, enable seamless automation, and provide exceptional user experiences by learning through user data. There is a huge market for IoT technologies, which has never been seen before with artificial intelligence integration. Despite this potential, the adoption of IoT in academic libraries faces several challenges, including high initial investment costs, a lack of standardized protocols, and security and privacy concerns. This study investigates the extent of adoption of IoT technologies and IoT-based services, and the challenges associated with their implementation and integration in university libraries across the United Arab Emirates. The study uses a descriptive quantitative research design with an online survey covering 66 university libraries. The findings reveal that there is a presence of all IoT technologies in university libraries across the United Arab Emirates, with radio-frequency identification and user-centric IoT applications such as self-checkout systems and mobile applications for various library services being widely used. On the otherhand, advanced technologies like artificial-intelligenceequipped IoT applications which includes augmented reality, virtual reality, drones, robots, and energy management systems are underutilized and are in a nascent stage. The key challenges identified include financial constraints, cybersecurity risks, organizational barriers, and a shortage of skilled personnel. The study provides various recommendations for policymakers and library administrators, including the need for a clear IoT adoption framework and policies, phased implementation, robust security measures, and staff training. By addressing these challenges, university libraries in the United Arab Emirates and other libraries in the Gulf Cooperation Council region and beyond can enhance their operational efficiency, users’ experiences, and sustainability with the adoption of IoT technologies.

<p>In the rapidly evolving technological landscape, modern innovations such as blockchain (BC), artificial intelligence (AI), and the Internet of Things (IoT) are playing a pivotal role in realizing the vision of Society 5.0. This concept envisions a harmonious integration of physical, digital, and human elements to create a sustainable and progressive future. Emerging technologies like robotics, biotechnology, quantum computing, 5G/6G networks, smart cities, and the metaverse are expected to further amplify the impact of Society 5.0, driving transformative changes across industries and societal structures. The integration of BC, AI, and IoT within cloud-based frameworks offers a robust solution to enhance security, privacy, and data management. The integration of blockchain, AI, and IoT within a cloud-based framework offers a transformative solution to address the challenges of modern societies. By leveraging these technologies, the proposed platform aims to create a secure, efficient, and sustainable ecosystem that enhances quality of life and drives progress toward the vision of Society 5.0. This approach not only tackles current societal issues but also paves the way for a more interconnected and innovative future. This article introduces a platform that seamlessly combines these technologies to provide a secure and reliable foundation for data collection, sharing, and analysis while enabling the development of smart applications that improve efficiency, productivity, and overall quality of life (QoL).</p>

The digital transformation of healthcare is revolutionizing the management of medical institutions, improving operational efficiency, patient outcomes, and data security. With the increasing complexity of healthcare systems, the integration of cutting-edge technologies such as Artificial Intelligence. The COVID-19 pandemic significantly accelerated digital transformation, compelling healthcare institutions to adopt telemedicine, AI-assisted diagnostics, and cloud-based medical records to meet growing patient demands and resource constraints. The rapid digital transformation of healthcare is driven by advancements in Artificial Intelligence (AI), blockchain, the Internet of Things (IoT), and Big Data. This review article aims to analyze the objectives and implications of digital transformation in medical institutions, focusing on the integration of AI, blockchain, and IoT in hospital management. The methodological approach for this review article focuses on synthesizing existing literature to examine the role of Artificial Intelligence (AI), blockchain, the Internet of Things (IoT), and Big Data in the digital transformation of healthcare management. The integration of Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), and Big Data has demonstrated significant improvements in healthcare management, enhancing efficiency, patient outcomes, and data security.

This book chapter explores the intersection of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), highlighting their transformative impact on various industries and applications. As IoT devices generate massive amounts of real-time data, AI and ML algorithms provide the analytical power necessary to derive actionable insights, automate processes, and enhance decision-making. The chapter covers key AI and ML techniques, such as supervised and unsupervised learning, reinforcement learning, and deep learning, and discusses their integration into IoT systems for improved efficiency, scalability, and real-time performance. Specific use cases, including smart agriculture, industrial IoT, and healthcare, are examined to demonstrate the practical applications of these technologies. Furthermore, the chapter addresses the challenges in data processing, security, and privacy that arise from IoT and AI integration, as well as the future potential of these systems in driving innovation across sectors. This comprehensive analysis serves as a foundational resource for understanding the synergistic relationship between AI, ML, and IoT in creating intelligent, connected systems.

This paper reviews the emerging and critical role of Artificial Intelligence (AI) and the Internet of Things (IoT) in transforming modern agriculture into a more sustainable and efficient practice. Faced with escalating global population demands and a myriad of challenges such as climate change, labor costs, and market fluctuations, the agricultural sector is at a pivotal juncture. The integration of AI and IoT offers a beacon of hope, enabling smarter farming methods through real-time monitoring, data analysis, and management of agricultural processes. This review delves into the application of these technologies in various facets of farming, including precision agriculture, crop yield forecasting, and the management of resources. It explores how AI's analytical prowess can predict and address crop diseases, automate farming operations, and assist in decision-making, while IoT's network of sensors and devices facilitates the real-time tracking and monitoring of farm conditions. Additionally, the synergy of these technologies with blockchain and cloud computing is examined, highlighting their potential in enhancing transparency, data security, and supply chain management. Collectively, these technological innovations are not just reshaping the agricultural landscape but are instrumental in steering it towards a more sustainable, productive, and resilient future.

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has revolutionized various industries, enabling automation, real-time decision-making, and enhanced connectivity. However, these advancements have also introduced new cybersecurity threats, increasing the vulnerability of interconnected systems. The proliferation of IoT devices and AI-driven applications has expanded the attack surface, making them prime targets for cyber adversaries. Traditional security mechanisms, which often rely on signature-based threat detection, struggle to address sophisticated attacks such as adversarial AI manipulations, IoT botnet infiltrations, and real-time data breaches. This study examines emerging cybersecurity risks in AI and IoT environments, emphasizing the limitations of existing security frameworks in detecting and mitigating evolving threats. One of the key challenges is the inability of conventional methods to adapt to novel attack patterns in dynamic and complex networks. To address this issue, we introduce a machine learning-based risk assessment framework designed for proactive threat mitigation. This framework leverages anomaly detection, behavioral analytics, and predictive threat modeling to identify potential cybersecurity risks in real time. By integrating adaptive learning algorithms and continuous monitoring, the proposed system enhances resilience against AI-driven cyberattacks and IoT-based vulnerabilities. The findings highlight the critical need for AI-driven cybersecurity solutions capable of evolving alongside emerging threats, ensuring the safety and reliability of interconnected digital ecosystems.

Abstract: As the world moves towards digital transformation and sustainable development, the concept of Smart Villages is revolutionizing rural communities by leveraging the Internet of Things (IoT) and Artificial Intelligence (AI) to bridge the urban-rural divide. "Smart Villages: IoT and AI for Sustainable Rural Development" explores how cutting-edge technologies are transforming agriculture, healthcare, energy management, education, and governance in rural areas. The book examines AI-driven precision farming, IoT-powered smart irrigation, AI-based telemedicine, renewable energy microgrids, and blockchain-enabled governance as key enablers for sustainable rural development. It highlights the role of digital connectivity, real-time data analytics, and AI-driven decision-making in optimizing resource allocation, improving infrastructure, and fostering economic resilience. The book also delves into policy frameworks, investment strategies, and global initiatives that support scalable and replicable Smart Village models. Through real-world case studies, expert insights, and technological advancements, this book serves as a comprehensive guide for policymakers, researchers, technology developers, and entrepreneurs seeking to implement smart and inclusive rural transformation. Keywords: Smart Villages, IoT in rural development, AI for sustainable agriculture, precision farming, smart irrigation, AI-driven telemedicine, remote healthcare, wearable health sensors, renewable energy microgrids, smart energy management, blockchain in rural governance, digital rural transformation, AI in education, rural connectivity, fintech for rural development, AI-powered decision-making, smart supply chains, predictive analytics in farming, AI and IoT integration, sustainable rural development, cloud computing in rural areas, AI-powered infrastructure, digital inclusion, rural economic resilience, data-driven agriculture, smart public services.

This research focuses on the high urgency of utilizing Artificial Intelligence (AI) and the Internet of Things (IoT) to enhance Smart Tourism Destinations (STDs). The integration of AI and IoT technologies offers unprecedented opportunities to revolutionize various aspects of tourism, from personalized recommendations to real-time data collection. The research aims to provide a comprehensive analysis of the current state, challenges, and future direction of STDs in the context of AI and IoT integration. It explores various AI techniques and IoT-enabled data collection mechanisms that can enrich the traveler experience and improve destination management. However, challenges such as privacy and data security issues need to be addressed. The research also provides foresight into future technologies like Augmented Reality (AR) and Virtual Reality (VR) that can further enhance STDs. The ultimate goal is to contribute to the development of smarter, visitor-oriented tourism destinations. The research highlights the significance of AI in shaping STDs and emphasizes the importance of addressing ethical considerations, data quality, interpretability, and human-AI collaboration to ensure responsible and effective use of AI in the tourism industry.

The fusion of wireless networks, the Internet of Things (IoT), and artificial intelligence (AI) holds transformative potential, ushering in an era where ubiquitous connectivity and intelligent decision-making reshape our world. This paper delves into the profound implications of this convergence, examining the interactions between wireless networks, IoT devices, and AI algorithms. Through an exploration of the evolution of wireless technologies, including the advent of 5G and beyond, the paper highlights the foundation upon which this convergence is built. The synergy of IoT, encompassing sensor-laden objects and data-driven insights, intertwines seamlessly with AI, empowering machines to emulate human cognition. The intricate relationships and dynamics between these paradigms unveil novel applications and unprecedented opportunities. From smart cities optimizing urban living to healthcare systems providing remote patient monitoring, the integration of wireless networks, IoT, and AI is witnessed across diverse domains. This paper navigates through real-world use cases that showcase how this convergence enhances efficiency, accuracy, and resource management. However, with these benefits come challenges. Data privacy, security, and ethical considerations are magnified in this interconnected landscape. Scalability and interoperability pose technical hurdles, necessitating collaborative efforts to ensure responsible and sustainable development. As this convergence advances, emerging trends like edge computing, AI-enabled autonomy, and quantum computing chart the course for the future. These innovations herald a world where seamless connectivity, intelligent decision-making, and exponential computation reshape our understanding of what's possible. In conclusion, the intersection of wireless networks, IoT, and AI transcends mere technological integration. It represents a paradigm shift that bridges the virtual and physical worlds, fostering a connected ecosystem with immense promise and a host of responsibilities. Through exploration, innovation, and ethical considerations, we are poised to harness the full potential of this convergence, shaping a future that is both technologically advanced and ethically aligned.

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as a transformative approach to enhancing industrial safety by enabling intelligent decision-making, predictive analytics, and real-time monitoring. Industrial environments are inherently exposed to risks such as equipment failure, toxic exposure, and human error, all of which can lead to accidents, injuries, and financial losses. AI-driven predictive analytics, when combined with IoT-enabled sensor networks, offers a proactive framework to mitigate these hazards before they escalate into critical incidents. Smart sensors continuously capture data related to equipment performance, environmental conditions, and worker behavior, which is then analyzed by AI algorithms to detect anomalies, predict failures, and trigger timely alerts. This integration enhances workplace safety, minimizes unplanned downtime, optimizes resource utilization, and ensures compliance with regulatory standards. Moreover, AI-powered automation and robotics can take over high-risk tasks, reducing direct human exposure to hazardous conditions. Wearable IoT devices further strengthen worker protection by monitoring vital health indicators and adherence to safety protocols. Overall, the integration of AI and IoT transforms reactive safety systems into proactive and predictive strategies, fostering an industrial ecosystem that is safer, more intelligent, and environmentally sustainable.

The integration of artificial intelligence (AI) with cardiac implantable electronic devices (CIEDs) has revolutionized remote monitoring, predictive diagnostics, and personalized patient care. Traditional device management relies on periodic clinical evaluations, which may lead to delayed detection of malfunctions or performance degradation. AI-driven analytics, in combination with Internet of Things (IoT) and cloud computing, enable real-time assessment of electrophysiological signals, device telemetry, and patient-specific risk factors. Machine learning and deep learning models enhance the accuracy of anomaly detection, reducing false positives and false negatives in ECG interpretation. Hybrid AI frameworks combining rule-based and neural network architectures improve predictive maintenance, ensuring timely interventions and extending device longevity. Blockchain technology strengthens data security by establishing a decentralized, immutable ledger for managing sensitive patient information and device performance metrics. The integration of AI, IoT, and blockchain in cardiac device monitoring fosters a secure, transparent, and efficient healthcare ecosystem, minimizing clinical workload while optimizing patient outcomes. This chapter explores advanced AI methodologies, predictive analytics, and cybersecurity frameworks that enhance the reliability, efficiency, and security of AI-enabled cardiac device management. Future advancements in AI-driven diagnostics and regulatory considerations will be critical in achieving widespread clinical adoption of intelligent cardiac monitoring systems.

Hospital waste management is a critical aspect of healthcare operations, influencing public health, environmental sustainability, and operational efficiency. Traditional methods often rely on manual processes prone to errors and inefficiencies. This paper explores the transformative potential of Artificial Intelligence (AI) and the Internet of Things (IoT) in optimizing hospital waste management. AI facilitates predictive analytics for waste generation trends and enhances waste segregation, while IoT enables real-time monitoring and data-driven decision-making. Case studies and comparative analyses demonstrate significant improvements in efficiency, sustainability, and regulatory compliance with AI and IoT integration. The findings underscore the role of technology in creating safer, more sustainable healthcare environments.

This paper discusses a crucial component of contemporary agriculture: using Big Data, Artificial Intelligence (AI), and the Internet of Things (IoT) to monitor and reduce carbon emissions from agriculture. We concentrate on how these cutting-edge technologies augment Climate-Smart Agriculture (CSA) and advance sustainable farming methods. A thorough analysis of the ways in which IoT, Big Data, and AI can be integrated to track carbon footprints and promote more general sustainability goals in agriculture is given in this research. As a significant contribution, we provide a practical, all-encompassing system architecture that integrates real-time data analytics, predictive modeling, and IoT-enabled sensors created especially for carbon footprint assessment. This paper assesses how well these technologies work to enhance emission monitoring, operational effectiveness, and environmental compliance while highlighting their observable advantages through real-world case studies. The rigorous analysis of issues including data interoperability, device energy efficiency, and implementation costs also sheds light on current research gaps. The report also outlines potential future paths, such as blockchain-based carbon credit trading platforms, scalable IoT-based carbon markets, and machine learning (ML) algorithms for precision farming. The goal is to offer helpful guidance on how to apply cutting-edge technology in the agricultural sector to achieve carbon neutrality and environmental sustainability.