AI-Driven Technologies for Sustainable Engineering

Nanomaterials, distinguished for their unique properties and diverse applications, are at the forefront of scientific and technological innovation. A transformative development in this field is integrating Artificial Intelligence (AI) into nanotechnology to discover, design, and apply materials at the nanoscale. By harnessing AI's ability to process complex data sets, researchers can predict material behaviors and develop methods to manufacture nanomaterials with tailored functionalities. AI is also revolutionizing nanomaterial characterization, delivering unparalleled imaging, spectroscopy, and structural analysis precision. Simultaneously, nanomaterials are advancing AI hardware, especially in neuromorphic computing and energy-efficient processes. While this chapter explores various applications such as healthcare innovations, environmental monitoring, sustainable energy solutions, and next-generation electronics, it highlights that these represent only a fraction of the potential where AI and nanomaterials converge. However, this integration raises challenges, including ethical considerations, environmental impacts, and safety regulations. The synergy between AI and nanotechnology holds immense promise for driving the discovery of next-generation nanomaterials across multiple industries, shaping a more sustainable and innovative future.

This work presents a comparative analysis of two Maximum Power Point Tracking (MPPT) methods used in solar energy systems. Artificial Neural Networks (ANN) and Perturb and Observe (P&O) are the methods considered in this work. P&O algorithm offers ease of implementation and performs well under uniform irradiance, but it exhibits oscillations around the steady-state operating point during rapid environmental changing conditions. Additionally, its efficacy in monitoring the MPP (Maximum Power Point) under shading conditions is not appreciable. To overcome these shortcomings and to enhance the system efficiency, an advanced MPPT technique, such as ANN based, has been used in this work. ANN-based MPPT technique can alleviate this burden by automatically learning these relationships through training data. However, the accuracy of MPP tracking is highly dependent upon the quality of the chosen data. By providing a comprehensive evaluation of these MPPT controllers, this chapter empowers the readers to select the most effective solution for maximizing solar power extraction.

AI has the potential to revolutionize curriculum design by creating more personalized, flexible, and efficient educational systems. Curriculum development has traditionally been a cumbersome and time-consuming approach of creating curricular content, and has often been unable to tailor quickly to the varied needs of individual learners. AI can revolutionize this process, as it can make use of data analytics and machine learning algorithms to develop dynamic, adaptive curricula that are suited to the individual students’ way of learning, their needs, and progress. This allows adjusting content and learning speed based on real-time interactions, which leads to a more student-centered teaching experience. In fact, AI-powered technologies could evaluate millions of pieces of educational information to find knowledge gaps, optimize learning paths, and recommend additional resources. This minimizes the blanket approach to teaching, which further supports differentiated instruction that can meet the needs of learners at different levels of proficiency.

All these together lead to administrative workloads in terms of content generation, assessment generation, and feedback analysis, which AI reduces in a significant way and enhances the curriculum design process. This enables educators to spend more time on mentoring and facilitating learning, as AI takes over repetitive tasks. AI also integrates into curriculum design in a way that can allow for predictive modeling, making it feasible for institutions to forecast trends in job markets and to adapt curricula accordingly. Despite such challenges, ethical issues including data privacy and the risk of excessive dependency on technology, AI's involvement in curriculum design is an exciting development that could transform the future of education. 

Artificial intelligence can be used in many walks of life; one such use is in designing and optimizing various antennas. The microstrip patch antenna uses AI for the same reason. In today’s techno world, the various aspects of micro-patch antenna design and optimization can be performed using Artificial Intelligence (AI). Micropatch antennas are compact, planar antennas and are mostly used in wireless communication systems. AI techniques can optimize the design of micro-patch antennas to enhance their performance. The designing of microstrip patch antennas, and specifically the antennas used in wireless communication and mobile phones, is challenging due to the presence of various physical limits. This chapter specializes in the usage of Artificial Neural Networks (ANN) and gadgets getting to know Machine Learning (ML) based on regression. Using ML and ANN, the challenges faced in the design of microstrip patch antennas are addressed, and solutions are provided for the analysis and synthesis of these antennas. The study is mainly focused on the calculation of resonant frequency, although the synthesis focuses on the mathematical geometrical dimensions of the antenna. These are two substantial processes that one will need for the precise designing of antennas, since these are mutually dependent. MATLAB and its graphical user interface have been used. Also, in order to minimize time duration and inaccuracy, the back-propagation technique has been used. In comparison with the standard simulation platforms and tools, this method provides a higher level of precision in calculating the geometric dimensions, along with minimizing the time required to calculate the dimensions. Usage of high-priced and prolonged software is not required along with the usage of the ANN method. ANN helps in achieving design attribute determination and parameter retrieval easily. The best performance of the Gaussian Process Regression model helps in determining the physical and electrical attributes. A prototype antenna is designed whose performance is close to the expected design, and with this prototype, the GPR model is verified.

Cardiovascular diseases (CVD) are the foremost cause of death worldwide, leading to extensive research in prediction models for early prognosis and intervention. This study presents a comprehensive overview of Machine Learning (ML) approaches in predicting ischemic cardiovascular diseases, synthesizing insights from various studies. Key themes include dataset diversity, rigorous preprocessing, and the exploration of various ML algorithms like logistic regression, random forests, decision trees, and various deep learning models. Challenges such as data imbalance and model interpretability are addressed, with future directions emphasizing multimodal data integration and novel ML architectures. Overall, ML-based approaches offer significant potential to revolutionize heart disease prediction and management, driving improved risk assessment, treatment strategies, and patient outcomes in cardiovascular care.

Biomechanical Evaluation and health monitoring can be done by using gait analysis along with ML and AI. Using Artificial Intelligence (AI) and Machine Learning (ML) will enable us to analyze the anomalies and identify minute changes in the patterns, so that it can be helpful in real-world applications as well. It can identify minute variations and anomalies that standard techniques would overlook. The chapter examines potential artificial intelligence (AI)-powered algorithms that may diagnose diseases like Parkinson's disorder, evaluate flexibility, forecast damaging risks, along with offering prompt treatment techniques. It also helps to identify some diseases like Parkinson’s Disease, and Forecast Damage Risks, and also to provide early intervention techniques. Combining Artificial Intelligence and sensors improves healthcare outcomes through patient monitoring. Implementing these techniques can be a better approach and can provide well-insulated healthcare.

The availability of clean groundwater is critical for public health and economic development, especially in regions like the Indian subcontinent, where a significant portion of the population relies on this resource for daily use. However, increasing industrialization, agricultural practices, and urbanization have led to groundwater contamination, posing a severe threat to communities. Addressing this challenge requires innovative solutions for monitoring and predicting contamination levels to ensure safe water access. This study explores the use of machine learning models to predict groundwater contamination levels across the Indian subcontinent. Machine learning has emerged as a powerful tool in water resource management due to its ability to analyze large datasets, identify complex patterns, and make accurate predictions. By utilizing models like ARIMA (Auto-Regressive Integrated Moving Average), we aim to forecast the concentrations of key contaminants, such as fluoride and arsenic, prevalent in the region's groundwater. Our approach integrates historical data on groundwater quality and applies advanced predictive algorithms to estimate future contamination trends. These models provide reliable, data-driven insights that can inform policymakers and water management authorities, enabling them to take proactive measures to mitigate health risks and ensure sustainable water usage. By predicting future contamination levels, this research contributes to developing more efficient and informed strategies for groundwater management, ultimately improving water safety and public health outcomes in the Indian subcontinent. It has been shown that in the coming years, there might be a decrease in concentrations of contaminants (such as fluoride and arsenic).

Together with nanomanufacturing, Digital Manufacturing (DM) is going to set the cutting edge for Industry 4.0, the fourth industrial revolution, as it enables the processing of materials over many dimensions of length. This overview outlines the development of nanomaterials and nanomanufacturing in the digital era, including use cases in consumer electronics, robotics, medical, sensory technologies, and semiconductors. It has also discussed the prospects of possible applications of complex AI algorithms, including expandable artificial intelligence (XAI), reinforced learning, and big data analytics in materials synthesis, innovation in industrial processes, and nanosystem integration. This work provides a thorough assessment of an amalgamation of AI in nanomanufacturing processes, thereby addressing a substantial gap in the field. The challenges of applying machine learning and deep learning to produce reliable and accurate predictions are explained in detail. This chapter examines how AI continues to evolve nanomanufacturing in Industry 4.0 through a thorough assessment, case studies, and data analysis. A detailed literature review is conducted to accumulate existing knowledge on nanomanufacturing, DM, and AI technologies. It also focuses on how AI may improve quality control and process control, including material synthesis in the nanomanufacturing industry. Additionally, it shows how AI may boost market relevance and competitiveness by lowering costs, speeding up time to market, alongside improving product quality. As a result, it provides key knowledge for businesses looking to improve and grow their production capacity.

Rapid urbanization comes with an increasing demand on transportation systems, which are increasingly being subjected to great environmental concerns and changing mobility needs. This paper advances a human-centric approach for smart mobility by concentrating the solution on user experience, accessibility, and community well-being. This study takes an outlook on how people-first design can transform the available technology into a different paradigm for reshaping urban mobility in light of current urban transportation challenges, such as congestion, pollution, and inefficiencies. By using case studies and data analysis, it is brought out how the integration of intelligent transportation systems, shared mobility solutions, and multimodal networks can create sustainable and inclusive urban environments. Our findings suggest that human-centered design-based smart mobility solutions provide not only user satisfaction but also support resiliency and addictiveness in the transportation ecosystem. 

The Internet of Things (IoT), along with Artificial Intelligence (AI), plays a crucial role in developing rural areas with advanced approaches based on technology for the major problems occurring. AI can bring a drastic change in various sectors. One of the main sectors is agronomy, which also develops the farmers of the rural areas along with the Indian economy, so it is a better thing to implement it. It can also play a crucial role in education along with healthcare. The best is like telemedicine, which is useful for the farmers now and then for crop and yield prediction and water usage. The research also aims to show how advanced technologies can play a crucial role in distinct aspects like education, job prospects, etc. At last, AI and IOT prove good development in rural areas based on trending and upcoming technologies.