Special Issue on: Advances in Secure, Smart and Intelligent Technology based systems

This chapter introduces data science with its history and importance in this modern era briefly. This chapter also elaborates the discussion by relating data science to various modern fields like big data analytics, artificial intelligence, deep learning, and machine learning. This chapter also discuss the necessary of data analytics in this big data era. This chapter also briefly introduces another emerging field, Internet of Things (IoT) and explores the contribution IoT towards big data analytics and data science in research perspective. It also briefly introduces the programming and non-programming tools used in the data science field.

The rapid development in the field of artificial intelligence is bringing advances in many research fields including the renewable electricity‐driven CO 2 and CO reduction, which features as a promising approach to mitigate carbon emission and electrically synthesize value‐added chemicals. This review focuses on the recent progresses on artificial intelligence and machine learning in the field of CO (2) reduction based on the three aspects: the catalyst screening and design, reaction mechanism investigation, and the knowledge graph construction. For the catalyst development, this work discusses the acceleration of catalyst optimization and the generative artificial intelligence frameworks based on machine learning model predictions, which represent an emerging paradigm for designing efficient catalysts. For the investigation of reaction mechanisms, the combination of machine learning and in situ spectroscopies can present detailed information on the structure evolution and the electric field effects, enabling to bridge experiments and theories. For constructing the knowledge graph in the CO (2) electroreduction, the natural language processing technologies are demonstrating strong capabilities. Finally, the current challenges and potential perspectives on the interdisciplinary fields of CO (2) RR and artificial intelligence are proposed to provide inspiration for the continuous developments of this field.

Exploring the nexus of smart technologies and sustainable ecotourism: A systematic review

Wearable devices are gaining popularity in the fields of health monitoring, diagnosis, and drug delivery. Recent advances in wearable technology have enabled real-time analysis of biofluids such as sweat, interstitial fluid, tears, saliva, wound fluid, and urine. The integration of microfluidics and emerging smart technologies, such as artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT), into wearable devices offers great potential for accurate and non-invasive monitoring and diagnosis. This paper provides an overview of current trends and developments in microfluidics and smart technologies in wearable devices for analyzing body fluids. The paper discusses common microfluidic technologies in wearable devices and the challenges associated with analyzing each type of biofluid. The paper emphasizes the importance of combining smart technologies with microfluidics in wearable devices, and how they can aid diagnosis and therapy. Finally, the paper covers recent applications, trends, and future developments in the context of intelligent microfluidic wearable devices.

Smart technologies in today's Internet of Things (IoT) era cover a wide spectrum, including smart identification technologies, robotics, virtual reality (VR), augmented reality (AR), wearable technologies, cloud, artificial intelligence (AI), machine learning (ML), and blockchain. This study aims at revealing the role and importance of smart technologies in service systems which encompass a wide spectrum of sectors. The scope of this study covers the main applications in transportation and logistics, retail, health care, hospitality, and financial services sectors. A broad overview is provided for the potentials and benefits of smart technology utilization in these service areas, and future research agenda is suggested. Findings reveal that simultaneous use of smart technologies bring tremendous opportunities in terms of efficiencies and automation, reshaping and transforming operations and business processes of the service enterprises. The study highlights that although core proven applications of smart technologies are in manufacturing, generally lagging service sector applications promise radical productivity improvements, performance enhancements, and increased service quality. By radically upgrading the customer satisfaction to entirely new levels and providing unique customer experiences, smart technologies lead to bottom line profitability improvements as well competitiveness.

• Integration of AI and IoT enhances poultry health management and operational efficiency • YOLOv8 and CNN models improve early disease detection in poultry farming • AI-driven behavioral analysis supports better welfare monitoring and disease prevention • Real-time IoT sensor data optimizes poultry environmental and health management • Edge-AI solutions reduce latency and enable on-site poultry farm monitoring Rapid advancements in Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) have transformed modern poultry farming by enabling precision management and real-time decision-making. This systematic literature review investigates how integrating AI/ML with sensor-based IoT technologies can address key challenges in poultry production, including disease outbreaks, welfare monitoring, and environmental management. The findings reveal that Convolutional Neural Networks (CNN), especially YOLOv8, offer superior performance in visual-based poultry health detection, achieving over 90% accuracy for conditions like bumblefoot and woody breast. IoT-enabled sensors continuously track temperature, humidity, and flock activity, allowing automated systems to adjust ventilation, lighting, and feed distribution. Edge-AI solutions further reduce latency and dependence on cloud computing, making on-site monitoring feasible in low-connectivity environments. This review introduces a structured taxonomy of AI/ML applications grouped into five domains: disease detection, behaviour analysis, environmental control, automation, and productivity optimization. A comparative table of state-of-the-art techniques and their respective performance metrics is provided to highlight the strengths and limitations of each approach. Challenges such as limited infrastructure, high initial costs, and digital illiteracy among smallholders hinder broader adoption. Research gaps are identified in dataset generalizability, multi-modal sensor fusion, and the lack of scalable real-world trials. Future directions include developing lightweight, energy-efficient models, enhancing user-friendly interfaces, and promoting policy frameworks that support ethical and inclusive AI adoption. By critically evaluating current methodologies and outlining actionable strategies, this review contributes to advancing sustainable, efficient, and welfare-oriented poultry farming systems through smart technologies.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

With the rapid increase of interest in independent living along with the continual growth of the global aging population, there is a significant need for developing technological solutions that are trustworthy, functional, and scalable. Towards this vision, we developed a novel framework that provides an organic, evolving, and immersive assistive interaction between human, robot, and their environment. Our contribution is in the system design and the computing aspect of the proposed solution. At the system level, we are utilizing the concept of the Internet of Robotic Things (IoRT) to integrate smart personal devices and smart space automation technologies with social companion robot(s) and mixed/augmented reality smart glasses. At the computing level, we propose utilizing advancements in the field of Artificial Intelligence (AI) and Machine Learning (ML) towards providing task assistance in daily activities, smart space control, and improving emotion and mood through Affective Computing. We provide details of our IoRT system integration and quantitative experimental results on Facial Emotion Recognition (FER) which is one of the core components for Affective Computing. We improved FER utilizing Deep Learning on the edge and then deployed it on our Social Companion Robot (SCR) called Miro. We integrated the SCR with a Mixed Reality smart glass (Microsoft Hololens 2) and smart home automaton hub (Google). While the smart automation controller is directly in charge of controlling the environment (e.g., lighting, temperature), the robot can increase the control level with a more context-aware decision providing a socially embodied control.KeywordsHuman-robot-interactionInternet of Robotic Things (IoRT)Social companion robotsSmart home automationSocial embodimentAffective computingMixed/augmented realityIndependent living

This book is a platform for anyone who wishes to explore Artificial Intelligence in the field of agriculture from scratch or broaden their understanding and its uses. This book offers a practical, hands-on exploration of Artificial Intelligence, machine learning, deep Learning, computer vision and Expert system with proper examples to understand. This book also covers the basics of python with example so that any anyone can easily understand and utilize artificial intelligence in agriculture field. This book is divided into two parts wherein first part talks about the artificial intelligence and its impact in the agriculture with all its branches and their basics. The second part of the book is purely implementation of algorithms and use of different libraries of machine learning, deep learning and computer vision to build useful and sightful projects in real time which can be very useful for you to have better understanding of artificial intelligence. After reading this book, the reader will an understanding of what Artificial Intelligence is, where it is applicable, and what are its different branches, which can be useful in different scenarios. The reader will be familiar with the standard workflow for approaching and solving machine-learning problems, and how to address commonly encountered issues. The reader will be able to use Artificial Intelligence to tackle real-world problems ranging from crop health prediction to field surveillance analytics, classification to recognition of species of plants etc.

The presented article discusses the social impact of ‎innovations in the field of ICT and artificial ‎intelligence on society. The article examines the ‎possibilities of artificial intelligence's impact on the ‎social character of society, analyzes artificial ‎intelligence from its positive and negative aspects, and ‎notes the possibilities of pragmatic use of artificial ‎intelligence against the background of new changes, as ‎well as its real and probable consequences. At the same ‎time, it discusses global and regional challenges related ‎to artificial intelligence and analyzes the main goals of ‎national strategies related to artificial intelligence. ‎Preventive security measures and ethical aspects during ‎the use of artificial intelligence against the background ‎of changes in the field of artificial intelligence in ‎Azerbaijan are also among the main research topics.

The global healthcare ecosystem is being changed thanks to substantial advancements in the fields of machine learning (ML) and artificial intelligence (AI). The potential of ML and AI to improve patient care, increase diagnostic accuracy, optimize treatment regimens, and reduce administrative procedures is covered in this chapter as we investigate the many methods and uses of ML and AI within the healthcare industry. ML and AI have the potential to change healthcare delivery, resource allocation, and disease prevention through the use of large-scale data analysis, predictive modeling, and intelligent decision-making systems. This chapter presents a thorough review of the existing ML and AI methods used in healthcare, emphasizing their advantages, difficulties, and potential future applications. The global healthcare ecosystem might be changed by the introduction of ML and AI, resulting in improved patient outcomes. ML and AI can help expand access to healthcare by enabling remote diagnosis and telemedicine, especially in underserved areas. This aligns with the goal of ensuring healthy lives and promoting well-being for all ages.

The article examines the impact of artificial intelligence (AI) on the transformation of the educational and scientific spheres. It is determined that today, AI is evolving from a simple technology into a powerful tool for transforming education and science. The use of AI offers several advantages, including personalization of the learning process through adaptive systems, automation of routine tasks, and the utilisation of data analytics tools to integrate research and data analysis into scientific and educational cycles, such as those facilitated by intelligent laboratories and digital twins. At the same time, AI can pose risks in areas such as academic integrity, research ethics, publications, and peer review; traditional assessment and teaching methods become irrelevant and ineffective. The use of AI in educational and scientific activities not only improves the teaching process, but also brings universities closer to achieving sustainable development goals, ensuring a higher level of educational services through the use of digital tools, adaptive technologies, learning stimulators, increasing the inclusiveness of the educational environment, increasing the energy efficiency of higher education institutions, training specialists in the development and use of AI for Industry x.0. At the same time, this process negatively affects SDG 7 and 13 due to the increase in electricity consumption by data centres and the carbon footprint of supercomputers. Analysis of international practices reveals that leading universities prioritise innovation in the academic environment by leveraging AI tools. A university's innovative AI-oriented ecosystem is being formed, which combines educational and research initiatives through ethical principles, transparency, a focus on interdisciplinary approaches, and the development of cooperation. Focusing on the market, universities must also adapt their educational products, taking into account requirements such as skill transformation, automation of routine roles, and the demand for AI specialists. It is shown that the educational services market itself is transforming in response to the demands of the time, including the formation of data analytics, machine learning, and programming competencies, the development of skills in using AI tools in the workplace, and a deepening of knowledge about advanced developments in the field of AI. The following areas of educational services are highlighted: university educational programs, courses (for beginners or specialised, such as those on studying Power BI), comprehensive boot camps, intensive training, and self-study. The price range, depending on the profile and volume of such educational services in Ukraine, was analysed, which ranges from 11,000 UAH for short-term courses to 332,800 UAH per year for bachelor's programs in the speciality «Systems Analysis and Data Science». It is demonstrated that enhancing the level of AI competencies is a promising approach to increasing a specialist's competitiveness in the labour market. For the education and science system, it is crucial to select training forms and methods that most effectively meet the current requirements.

Within the past few decades, the increasing use of advanced technology in the manufacturing industry has drawn extensive study attention from all around the world. Smart technology is technology that gives previously thought-to-be-inanimate objects cognitive awareness through the application of artificial intelligence, machine learning and big data analysis. Smart Technology (ST) involves many emerging technologies like the internet of things, Industrial Internet of Things (IoT), cyber-physical production system artificial intelligence, blockchain and systematic data analysis. We provide an overview of different smart technologies to control the intensive features of computer and sensor technologies to diminish the gap between humans and machines. This research paper shows the complete literature review of ST to increase the production and maintenance of machinery equipment and future threats faced by ST. This study also includes the survey report of the rate of adoption today as compared to the past five years, to obtain real-time results, cost-effective techniques and the use of different sensors to reduce human workload in the manufacturing sector.

From the Editor's Desk: Systems Science 2010-2020, and Post-COVID-19.

The cross-fusion research of artificial intelligence technology and spinal surgery represented by machine learning and neural network model is a new research direction and hot issue in the field of artificial intelligence in recent years. The anatomy and disease symptoms of the spine are complex, and the diagnosis and treatment of spinal surgery require rich clinical experience. However, the distribution of medical resources in China is seriously uneven. How to improve the ability of primary medical services so that the most extensive patient groups can benefitis still an urgent problem to be solved. Artificial intelligence is a technical science that researches and develops theories, methods, technologies, and application systems for simulating, extending and expanding human intelligence. With the advent of the era of big data medical technology, artificial intelligence technology may solve this problem by transforming experts sinking into tech sinking . At present, technologies such as confrontation learning, weakly supervised learning, intensive learning and graph neural networks have become research hotspots in the field of artificial intelligence, and have also played an important role in many fields of clinical medicine. Based on the advantages of deep learning and neural network in disease learning, many spine surgeons combine it with the diagnosis and treatment of cervical spondylosis, low back pain, lumbar degenerative diseases, spinal deformity, spinal tumors, and other spine-related diseases. The rapid location and accurate diagnosis of the disease not only makes it an effective tool for the comprehensive diagnosis of spinal diseases but also provides the basis for the most reasonable treatment options for spinal diseases. In the domestic application of artificial intelligence in the diagnosis and treatment of spinal surgery, it can also solve the problems of difficult diagnosis and complicated treatment of spinal diseases faced by primary doctors, reduce the rate of misdiagnosis and missed diagnosis, and effectively reduce the economic and social burden of spinal diseases. This paper reviews the research progress of artificial intelligence represented by deep learning in the field of diagnosis and treatment of spinal surgery at home and abroad, and the advantages and application prospects of artificial intelligence in the diagnosis and treatment of spinal surgery.