Technological Applications of AI in the Development of Sustainable Future

 Considering the reduction in labor costs and associated benefits, investment in automated fabric defect detection proves to be highly cost-effective. A robust and efficient algorithm is essential for the development of a fully automated web inspection system. The examination of genuine fabric flaws is particularly difficult because of the multitude of defect categories, which are defined by their ambiguity and indistinctness. This work aims to classify and describe several strategies developed for detecting fabric faults. This work also provides the inaugural survey on methodologies for fabric defect detection, referencing around 160 sources. The categorization of fabric defect detection methodologies is beneficial for assessing the characteristics of the identified features. The characterization of authentic fabric surfaces through their structure and primitive set has not yet demonstrated success. Consequently, the characteristics derived from fabric surfaces have led to the classification of the proposed methodologies into three categories: statistical, spectral, and model-based. To evaluate the state-of-the-art, the constraints of several prospective techniques have been identified, and their performance has been appraised based on demonstrated findings and proposed applications. The results of this work indicate that integrating certain statistical, spectral, and model-based methodologies may produce superior outcomes compared to any individual strategy, warranting additional investigation into this matter. 

Artificial intelligence is revolutionizing education by enhancing learning results, accessibility, and engagement. AI is changing education by fostering diversity, streamlining administrative tasks, and enabling individualized learning. AI-powered tools that simplify complex subjects and provide individualized learning include virtual reality, adaptive learning platforms, and intelligent tutoring systems. This chapter explores the applications, benefits, challenges, and ethical quandaries of artificial intelligence as it relates to education. It investigates how teaching strategies in educational institutions are impacted by care ethics. These methods have been found to improve accuracy, efficiency, and resource allocation. Teachers may spend more time discussing concepts and communicating with students because administrative automation reduces administrative burden. Additionally, with the help of AI-supported simulation, students can practice technologies, evaluate ideas, and work through issues in a dynamic yet controlled setting. It imagines a future where artificial intelligence enhances human creativity and collaboration by looking at these opportunities and challenges, making education an inclusive, learner-centered, and lifelong experience. 

Heart disease remains one of the most prevalent causes of death worldwide, highlighting the critical need for early diagnosis and preventive measures. This chapter focuses on developing a heart stroke risk prediction system using machine learningbased models. A publicly accessible heart disease dataset that includes important health markers like age, sex, type of chest pain, resting blood pressure, cholesterol, fasting blood sugar, electrocardiogram readings, maximum heart rate, and other pertinent variables is used to train the algorithms. Several machine learning algorithms, including Random Forest, Logistic Regression, and SVM, were used to analyze the dataset that contained all of the patients' health-related data in order to determine which one best fit the data and could most effectively forecast risk. The study's main conclusion is that the Random Forest algorithm performs the best, as shown by important metrics including accuracy, precision, recall, and f1-score.

The agriculture industry is severe suffering due to climate variation, high investment, low returns, and manpower shortage. Furthermore, agricultural land is contracting due to rapid industrialization and urbanization. These challenges have affected the sustainable agricultural growth and raised a serious concern about food security for growing populations. Therefore, investing in innovative tools and techniques in agricultural practices has become a necessity to reduce farming costs, minimize labour dependency, optimize water needs in irrigation, and enhance crop yield. The learning capabilities of AI algorithms can significantly analyze historical patterns of environmental data to predict weather patterns and anticipate climate change impacts. The integration of IOT with AI technology provides valuable insights into soil quality, seed selection, irrigation scheduling, weed management, and pesticide use. AI-enabled farming robots and drones are improving the detection of weeds and crop diseases, and enabling precise pesticide application, thereby reducing farming costs and enhancing crop yields. Research has shown that AI-enabled robots and drones can enhance crop productivity by up to 30%. In this context, this chapter highlights the transformative role of AI in crop monitoring, irrigation management, yield prediction, disease and pest detection, weed control, and weather forecasting. 

Drones powered by hydrogen fuel cells, which have longer flight durations and emit less carbon emissions, are emerging as a sustainable alternative for intercity cargo transportation. However, there are still issues related to reliability and fuel efficiency, as well as achieving optimal fuel efficiency. This study examines the development and application of hydrogen-powered drones for cargo delivery between cities. Because of their high energy density and zero emissions, hydrogen fuel cells provide an attractive alternative to traditional modes of transportation, which have decreased their sustainability benefits due to worries regarding carbon emissions. Because of the development, construction, and operation of hydrogen-powered drones, this endeavour aims to reduce dependency on conventional air and road transportation, and these drones will be able to transport supplies between cities. The study begins with a presentation of current drone technology, emphasizing the limitations of batterypowered drones in terms of range and cargo capacity. After that, a thorough analysis of hydrogen fuel cell technology is presented, with a focus on how it might improve drone endurance and operation. The challenges and useful benefits of implementation are illustrated through case studies and real-world applications. The findings of this study support the broader goal of developing effective and environmentally friendly transportation networks and set the stage for future developments in drone technology. 

With a secure distributed ledger system, blockchain technology helps to shape the healthcare sector in terms of transparency and accountability. This research paper highlights the transformational impact that blockchain technology plays in the healthcare sector. It focuses on a variety of technologies, including patient data security, real-time monitoring enabled Internet of Things (IoTs). This paper also addresses the revolutionary impact that current technology, such as cryptography and Zero Knowledge Proofs (ZKPs), has played in shaping modern technology and improving privacy and security in the health care system. This study contrasts existing blockchain technologies with emergent developments that aid in the creation of a decentralized, safe ecosystem that promotes accountability. Blockchain provides a decentralized, immutable, and transparent ledger. It enhances security, privacy, and trust in data transactions without relying on centralized servers.

The advent of Artificial Intelligence (AI) is revolutionizing healthcare and reshaping diagnostics, treatment, and patient care. This chapter explores AI's transformative role in modern medicine, emphasizing its ability to enhance precision, streamline processes, and make healthcare more accessible. Through algorithms that analyze vast amounts of data, AI enables early detection of diseases, accurate diagnosis, and personalized treatment plans tailored to individual genetic profiles and health histories. It optimizes workflows, reducing the administrative burden on healthcare professionals and allowing them to focus more on patient care. AI-driven tools, such as predictive analytics, assist in identifying at-risk populations, aiding in preventative care and resource allocation. Robotics and AI-powered surgical systems enhance the accuracy and outcomes of complex procedures, reducing recovery times and complications.

Additionally, AI-driven telemedicine platforms increase access to healthcare in remote and underserved areas, breaking geographical barriers. However, the rapid integration of AI presents ethical and regulatory challenges, including concerns about data privacy, algorithmic biases, and the need for human oversight in critical decision-making. This chapter delves into these issues, discussing the evolving role of AI in clinical settings and research and the collaborative efforts required to ensure AI is used responsibly and effectively in healthcare. By examining case studies and current applications, the chapter aims to comprehensively understand how AI shapes a future where healthcare is more innovative, faster, and patient-centered.

This chapter provides an overview of histogram-based steganography detection techniques, one of the earliest methods in contemporary steganalysis. Despite its early development, this technique did not receive the recognition it deserved, with more focus on methods like LSB, DWT, and DCT. To address this, the histogram model is revisited to enhance its performance and efficiency. Using small datasets, we demonstrated that this approach is versatile, faster, and capable of detecting hidden information with minimal data. Additionally, the histogram technique offers potential for further technical improvements, such as integration into widely used platforms like Google Gmail, Microsoft Outlook, and Google Messages. This would enhance security in media-sharing applications by preventing the transmission of harmful hidden information. Our research confirms the efficiency of the histogram technique, particularly in comparison to other steganography detection methods, making it a clear and easily implementable solution for current digital security challenges.

Artificial Intelligence (AI) is revolutionizing the renewable energy sector by providing innovative solutions to challenges related to energy production, storage, and distribution. Due to increasing demand and the need for an uninterrupted supply to consumers, AI applications have become essential for optimizing Renewable Energy Systems (RES). Renewable energy sources like solar energy, wind power, hydro, geothermal, and biomass generate clean energy without emitting greenhouse gases, hence reducing global warming. AI technologies, including Machine Learning (ML), Deep Learning (DL), and robotics, enable better forecasting of weather conditions, energy production, and distribution while optimizing energy storage systems to address intermittency issues. AI algorithms enhance grid management by dynamically balancing supply and demand, ensuring efficient energy distribution, and reducing environmental impact. In addition to this, AI plays a crucial role in predictive fault diagnosis and maintenance, maximizing the lifespan of RES and minimizing repair and operational costs. Despite these benefits, challenges, such as qualitative data availability, the high cost of AI implementation, and the difficulty of replacing existing energy infrastructure remain. However, by improving decision-making through advanced AI algorithms such as reinforcement learning, generative AI, and edge computing, efficient energy generation, grid stability, and energy distribution can be achieved, and environmental harassment can be avoided. In this context, this chapter provides a comprehensive roadmap to incorporate AI benefits in renewable energy systems for efficient and sustained green energy production and its distribution.

The Internet of Things (IoT) is not a new thing today. The networks used in offices, homes, shopping complex, railways, etc., can be made smart by using IoT technologies. Once a smart network is designed, the network admin and the owner may access the smart network from a far location. For example, the tech admin on a business tour or the owner at their home. The proposed network is designed to deal with these security issues of the network. The proposed network is about the secure remote monitoring of an IoT-enabled smart office using VPN (Virtual Private Network) technology. The network makes use of the Wire Guard protocol framework to secure the data of the IoT network on the internet. 

Artificial Intelligence (AI) is transforming the way communication occurs, altering how individuals, businesses, and societies interact. This chapter aims to examine the technology’s impact regarding its applications, challenges, and potential in communication, based on existing research. AI enhances human contact and business communication through NLP-driven chatbots, automated content generation, deep learning, and machine learning-based translation tools. Despite having advantages, these advancements raise ethical issues such as privacy concerns linked to large-scale data collection, discrimination in automated decision-making, and the spread of AI misinformation, like deepfakes. This chapter provides a thorough overview of AI integration into communication systems by reviewing the relevant literature, highlighting research gaps, and emerging trends. Key topics include AI in customer service, personalised media, data security, and the challenge of reduced human engagement. The review also considers potential future developments such as braincomputer interfaces and AI’s role in addressing misinformation. By offering a comprehensive overview, this chapter aims to inform researchers, practitioners, and policymakers about AI’s influence on global communication, emphasising the importance of responsible development and regulation. This chapter demonstrates that AI’s growing role as a facilitator of communication simultaneously introduces socioeconomic challenges, making it crucial to understand how to manage this evolving landscape.

 Wind energy potential regions such as Tamil Nadu, India, need precise longterm wind forecasts to enhance wind farm energy production and efficiency. The Muppandal Wind Farm case study showcases how advanced forecasting techniques can accurately predict local wind patterns across extended periods. Meteorological variables and previous wind data can be analyzed through Machine Learning (ML) and statistical methods to enhance a precise forecast of wind speed and its direction. Energy produced can be increased with improved precision, which results in reduced operational downtime. Temperature data at two meters above ground, wind speed data above five meters above ground have been incorporated for wind forecasting, which were collected between 2018 and 2024. According to the long-term forecast data, the chosen area maintains reliable wind energy so as to provide seamless wind energy integration with the electrical grid. By this analysis, the usefulness of predictive modeling in wind farm management and shed light on how sophisticated computational tools has been shown which can raise wind energy systems' dependability and profitability.