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- Herausgeber: Bentham Science Publishers
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing industries, reshaping the way we interact with technology, and driving innovation across multiple disciplines. Advancements in Artificial Intelligence and Machine Learning is a comprehensive exploration of the latest developments, applications, and challenges in AI and ML, offering insights into cutting-edge research and real-world implementations. This book is a collection of twelve chapters, each exploring a distinct application of Artificial Intelligence (AI) and Machine Learning (ML). It begins with an overview of AI’s transformative role in Next-Gen Mechatronics, followed by a comprehensive review of key advancements and trends in the field. The book then examines AI’s impact across diverse sectors, including energy, digital communication, and security, with topics such as AI-based aging analysis of power transformer oil, AI in social media management, and AI-driven human detection systems. Further chapters address sentiment analysis, visual analysis for image processing, and the integration of AI in smart grid networks. The volume also covers AI applications in hardware security for wireless sensor networks, drone robotics, and crime prevention systems. The final set of chapters highlight AI’s role in healthcare and automation, including an AI-assisted system for women’s safety in India and the use of EfficientNet B0 CNN architecture for brain tumor detection and classification. Together, these chapters showcase the versatility and growing influence of AI and ML across critical modern industries. Key features A multidisciplinary approach covering AI applications in robotics, cybersecurity, healthcare, and digital transformation in 12 organized chapters. A focus on contemporary challenges and solutions in AI and ML across industries. Research-driven insights from experts and practitioners in the field. Practical discussions on AI-driven automation, security, and intelligent decision-making systems.
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Veröffentlichungsjahr: 2025
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PREFACE
Artificial Intelligence (AI) and Machine learning (ML) are big fields and their algorithms have been employed in various domains for the last decade to solve complex problems. John McCarthy defined AI in 1956 as "AI involves machines that can perform tasks that are characteristics of human intelligence". In this book, the authors cover the basics of AI, and ML and the applicability of these fields to many real-life applications. Arthur Samuel defined Machine Learning (ML) in 1959 as a "Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed".
The presented book will consist of twelve full chapters which cover the use of AI and ML tools in a number of practical applications such as the analysis of power transformer oil, awareness and prevention of crimes against women, next-gen mechatronics, social media, digital forensics, cyber security, sentiment analysis, image processing, pattern recognition, medical device network system, business sectors, tumor detection, classification, cloud services, automation in drone robotics and human detection systems.
The landscape has shifted significantly since those early days, with the emergence of advanced AI and ML tools and the exponential increase in computing power. These advancements have enabled the analysis of vast quantities of data on a monumental scale. AI now relies heavily on Big Data and Machine Learning to expand its capabilities. Machine learning involves the training of algorithms, enabling them to learn from extensive datasets and enhance their performance over time. Deep Learning, a subset of Machine Learning, draws inspiration from the intricate workings of complex datasets and functionality.
This book gives a brief overview of Machine Learning and lists various ML techniques such as decision tree learning, Hidden Markov Models, reinforcement learning, and Bayesian networks, as well as covering some aspects of Deep Learning and how this relates to AI. It will help you achieve an understanding of some of the advances in the field of AI and Machine Learning, and at the same time, giving you an idea of the specific skills so that you can apply advanced techniques if you wish to work as a Machine Learning expert.
The authors stand behind the assurance that this book will serve as a valuable asset and a wellspring of inspiration for all those captivated by the advancements in AI and ML. As you delve into its pages, you are invited to embark on a journey into the enthralling realm of intelligent solutions. Let us together envision the limitless possibilities that await us with these transformative technologies, and enthusiastically embrace the opportunity to shape the future.
List of Contributors
Next-Gen Mechatronics: The Role of Artificial Intelligence
Nafees Akhter Farooqui1,Zulfikar Ali Ansari1,*,Rafeeq Ahmed2,Ahmad Neyaz Khan1,Shadab Siddiqui3,Mohammad Ishrat1,Mohd Haleem4,Sarosh Patel5Abstract
The incorporation of artificial intelligence (AI) into healthcare systems has demonstrated significant potential to transform patient care, diagnosis, and treatment. Nevertheless, the implementation of artificial intelligence (AI) in the healthcare sector presents difficulties concerning transparency, interpretability, and trust, especially when there are new possibilities for automated decision-making and enhanced efficiency in many different areas, thanks to the combination of artificial intelligence and mechatronics. Automation and robotics are improving as mechatronics integrates AI. Grand View Research expects the global mechatronics and robotics course market to reach $3.21 billion by 2028, expanding 13.7% from 2021 to 2028. This chapter aims to give a general outline of mechatronics-related artificial intelligence (AI), including its applications, advantages, and challenges. The field focuses on developing intelligent machines with the ability to learn, understand data, and react accordingly. Machine learning and deep learning are two forms of artificial intelligence that have enabled robots and autonomous vehicles to detect their environment, traverse complicated scenarios, and make smart decisions using the data they collect. Artificial intelligence (AI) improves mechatronic systems by expanding their capabilities, which boosts their performance, output, and reliability. Nevertheless, ethical considerations and implementation challenges need to be resolved before the full potential of AI in mechatronics can be realized.
INTRODUCTION
The primary objective of mechatronics is to build intelligent systems through the integration of several disciplines, including electronics, control engineering, computer science, mechanical engineering, and mechanical engineering. It is a young and expanding area that has already made a big splash in many sectors, including robotics, manufacturing, aerospace, healthcare, and automobiles. In the development of cutting-edge technology and novel approaches to difficult challenges, mechatronics is an indispensable tool. The Japanese invented the word “mechatronics” in the late 1960s, fusing the mechanical “mecha” with the electrical “tronics” [1].
It arose in reaction to the growing need for systems and products to incorporate both mechanical and electronic parts. Intelligent machines that are precise, efficient, and adaptable in their work are the goal of mechatronics.
The remarkable adaptability and versatility of mechatronic systems are attributed to their capacity to perceive and react to their surroundings. To accomplish complicated tasks independently or with little to no human involvement, these systems are programmed to communicate with one another, with other machines, and with the real environment. They can detect, analyze, and respond to data because of the sensors, actuators, microcontrollers, and algorithms built into their software.
Everything from basic home appliances and cell phones to advanced industrial robots and driverless cars falls under the umbrella of mechatronics. When it comes to making sure these systems work, are reliable, and are safe to use, mechatronic engineers are the ones to call. The capacity of mechatronics to unite several branches of engineering is one of its main strengths. More efficient, dependable, and cost-effective systems can be created by mechatronics engineers by integrating mechanical, electrical, and computer engineering principles [2]. By bringing together experts from different fields, we can improve performance and functionality by integrating hardware and software components seamlessly.
Innovation and technological progress are propelled by mechatronics. It makes possible the creation of state-of-the-art technology including smart systems, automation, robotics, and artificial intelligence. In addition to enhancing productivity, security, and quality of life, these technologies may cause a revolution in several different industries [3].
Hence, mechatronics is an interdisciplinary discipline that integrates electrical engineering, control engineering, computer science, and mechanical engineering to develop intelligent systems. Because it facilitates the creation of cutting-edge technology and novel solutions, it has grown into an important field in many different sectors. When it comes to developing flexible and versatile systems, mechatronics experts are crucial in combining software and hardware components [4]. I am confident that mechatronics will revolutionize engineering and our daily lives thanks to its capacity to spur innovation and technical progress.
OVERVIEW OF ARTIFICIAL INTELLIGENCE
The field of Artificial Intelligence (AI) is ever-evolving as scientists work tirelessly to develop increasingly intelligent and powerful machines. Over the past few years, advancements in artificial intelligence (AI) have completely altered our daily lives and the way we accomplish collective goals. An extensive review of AI, including its background, current uses, difficulties, and possible future advancements, will be presented in this essay [5]. Artificial intelligence has been around for a long time; in fact, machines that look like humans first appeared in ancient tales and folklore. In contrast, computer scientists began investigating the possibility of developing computers with intelligence comparable to that of humans in the 1950s, marking the beginning of the contemporary era of AI development. The inaugural use of the term “artificial intelligence” was during the 1956 Dartmouth Symposium, when researchers deliberated on developing intelligent robots [6]. Fig. (1) shows just an overview of Artificial Intelligence.
Creating expert systems and rule-based systems that could simulate human decision-making was the primary goal of early artificial intelligence research. Unfortunately, data shortages and insufficient computer capacity caused progress to be slow. A lot of data was available and machine learning techniques came out in the 1990s, but AI didn't take off until then [7]. The term “artificial intelligence” describes computers that can learn, reason, and make judgments just like a person. Two main schools of thought exist within the field of artificial intelligence: narrow AI and general AI. Narrow AI is purpose-built to excel in a small subset of general AI activities. However, the goal of general AI is to make machines as smart as humans are in a variety of contexts. The widespread use of AI is revolutionizing many different industries and bringing about significant gains in productivity. The healthcare industry is seeing a surge in the use of artificial intelligence. Medical data can be analysed by machine learning algorithms to aid in drug discovery, forecast patient outcomes, and identify disorders. The use of AI-powered robots in surgery has also been found to increase accuracy and decrease the likelihood of human mistakes [8].
Fig. (1)) Overview of artificial intelligence.Autonomous vehicles are being reshaped by artificial intelligence in the transportation industry. To assess their surroundings, make decisions, and makeovers safely, self-driving cars employ artificial intelligence algorithms. Better and more environmentally friendly transportation may be possible with the help of this technology if it can lessen traffic jams, accidents, and carbon emissions. The banking sector is another area where AI is creating a splash. Financial fraud can be detected, market trends can be predicted, and individualized financial advice can be provided by algorithms that analyse massive volumes of data. Artificial intelligence (AI) chatbots are revolutionizing customer care by offering instant and efficient assistance.
Although AI has tremendous promise, it also raises serious concerns about ethics and presents a number of obstacles. Concerns about job loss are significant. Some worry that AI may make people unemployed since it takes over jobs that people have been doing for a long time. Experts, however, contend that AI will open up new employment options, necessitating that people acquire new skills and adjust to a different way of working. The issue of AI systems' impartiality and prejudice is another obstacle. If the data used to train machine learning algorithms is biased, then those biases will likely be amplified and perpetuated by the algorithms themselves [9]. Questions of equity and prejudice in hiring and loan approval procedures arise from this. The goal of current AI research is to create systems that are open, and comprehensible.
An enormous amount of potential lies in AI's future. Artificial intelligence is being expanded by recent developments in robotics, deep learning, and natural language processing. In the fight against climate change, for more affordable healthcare, and for the end of poverty, artificial intelligence is anticipated to be an indispensable tool. Machines that can do a broad variety of tasks at a human level are the goal of continuing artificial general intelligence (AGI) research. Although artificial general intelligence is still a way off, progress toward it might cause us to reevaluate our understanding of consciousness and the ethics of machines.
Machine Learning (ML)
The goal of machine learning, a branch of artificial intelligence, is to create algorithms that let computers analyse, interpret, and forecast data in order to make judgments. It encompasses methods such as deep learning, reinforcement learning, unsupervised learning, and supervised learning [10].
Deep Learning (DL)
A branch of machine learning, deep learning mimics the way the human brain's neural networks are organised and operate. It entails feeding massive volumes of data into artificial neural networks in order to teach them to spot patterns and make judgements automatically [11].
Natural Language Processing (NLP)
A subfield of Artificial Intelligence, natural language processing (NLP) focuses on how computers and humans communicate using everyday language. Applications like language translation, sentiment analysis, chatbots, and more are made possible by machines' ability to comprehend, interpret, and produce human language [12].
Computer Vision
The area of artificial intelligence known as computer vision focuses on teaching computers to recognize and understand visual data found in the physical world, including photos and films. Image generating, object tracking, object classification, and object identification are all part of it [13].
Robotics
Robotics is the integration of artificial intelligence and engineering to create, construct, and control robots. Artificial intelligence empowers robots to observe their surroundings, make choices, and carry out activities independently or with a certain level of autonomy. Applications encompass a wide spectrum, including industrial automation, household robotics, and autonomous vehicles [14].
Expert Systems
Expert systems are AI programs that are made to make decisions like a person expert in a certain field. These systems look at data, make choices, and offer suggestions or answers by using rules and knowledge bases [15].
Data Science
The goal of data science is to discover new insights and information by integrating several disciplines, such as statistics, machine learning, data visualisation, and domain knowledge. It includes a variety of approaches that try to explain complicated events, forecast their future occurrence, and guide people in making decisions [16].
Explainable AI
The term “explainable artificial intelligence” (XAI) refers to the development of artificial intelligence (AI) systems and algorithms that can provide meaningful explanations for their decisions or outputs. This is especially important in high-stakes or critical applications where transparency and interpretability are necessary [17].
So, to sum up, AI has come a long way from its humble beginnings, and its impact on society is still growing. As a result of AI, many sectors are undergoing radical changes and becoming more efficient, including healthcare, transportation, and finance [18]. To make sure that an AI-powered future is fair and inclusive, though, problems like bias and job loss must be solved. Future AI research and development bode well for this technology, which could alter our daily lives and the way we do business.
APPLICATIONS OF AI IN MECHATRONICS
AI has found extensive applications in mechatronics, enabling the development of intelligent systems that can perform complex tasks with high precision and efficiency as shown in Fig. (2). Some of the key applications of AI in mechatronics include:
Robotics
AI-driven robots can execute a diverse array of activities, spanning from industrial automation to healthcare support. They possess the ability to traverse intricate surroundings, identify items, and engage with individuals, rendering them highly advantageous assets across several sectors as shown in (Fig. 3). The swift progress in AI technology has facilitated the development of robotics powered by AI. Advancements in machine learning algorithms, deep learning networks, and natural language processing techniques have led to increased sophistication in robots' ability to learn, adapt, and engage with their surroundings [19].
Fig. (2)) AI mechatronics (https://www.themechatronicsblog.com).These technological developments have enabled robots to carry out jobs that were previously considered unattainable. Artificial intelligence (AI) driven robots have been utilized in numerous sectors. Within the industrial industry, robots that are equipped with artificial intelligence algorithms have the capability to carry out repetitive activities with a high level of accuracy and efficiency. This results in a decrease in human errors and an increase in overall productivity [20].
Fig. (3)) AI in robotics (https://medium.com/vsinghbisen/ai-in-robotics).
