A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors

Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms. We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice. The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers. The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

The Role of Machine Learning and Artificial Intelligence for making a Digital Classroom and its sustainable Impact on Education during Covid-19

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research.Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithmsFindings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated.Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015.

In recent years, the role of Artificial Intelligence (AI) and Machine Learning (ML) in structural engineering has gained significant attention, particularly in the context of Structural Health Monitoring (SHM) and Predictive Maintenance (PM). These technologies are poised to revolutionize the way we monitor and maintain critical civil infrastructure, such as bridges, buildings, and dams, by offering enhanced capabilities for early failure detection, performance prediction, and maintenance optimization. As the demand for infrastructure grows globally and the risks associated with aging structures increase, traditional methods of inspection and maintenance are proving inadequate. The integration of AI and ML algorithms into SHM systems presents an opportunity to detect potential issues long before they develop into catastrophic failures, reduce maintenance costs, and extend the lifespan of civil infrastructure. This paper explores the transformative impact of AI and ML on SHM and PM in civil engineering, discussing their application in real-time monitoring, anomaly detection, and the optimization of maintenance schedules. The advent of smart sensors, IoT technology, and big data has allowed for the continuous collection of structural health data, including vibration, strain, displacement, and temperature, which can be analyzed through AI and ML algorithms to provide actionable insights into the condition of infrastructure. By processing vast amounts of sensor data, AI and ML can identify hidden patterns and correlations, enabling the creation of models that predict the future performance of structures under various conditions. Predictive analytics is a key component of these technologies, as it allows engineers to forecast the remaining useful life (RUL) of components and determine the optimal timing for repairs or replacements. Machine learning models, including supervised learning, unsupervised learning, and reinforcement learning, are being applied to large datasets of historical inspection and operational data, enabling accurate predictions of when and where failures are likely to occur. These models use algorithms that learn from previous observations to make data-driven predictions, thereby providing a proactive rather than reactive approach to maintenance. For instance, in the context of bridges, AI-driven systems can process data from sensors embedded in the structure to monitor changes in load, strain, and stress. ML algorithms can detect anomalies such as cracks, corrosion, or material fatigue, and provide real-time alerts to engineers. By combining these findings with weather data, traffic patterns, and historical maintenance records, predictive models can optimize maintenance schedules, ensuring that repairs are carried out at the most cost-effective times, avoiding costly downtime or emergency repairs. Similarly, AI-based systems have been successfully implemented in dam monitoring, where sensor networks track the movements of the dam structure, water pressure, and other environmental factors. By using ML to analyses the data, engineers can predict potential structural failures such as seepage or deformation, long before they manifest as visible damage, thus minimizing risk and enhancing safety. Another promising application of AI and ML in structural engineering is smart cities and smart infrastructure, where interconnected systems allow for continuous monitoring and real-time decision-making. In these environments, AI-powered systems can be integrated with broader urban management frameworks, providing insights into the health of various infrastructure elements (e.g., roads, buildings, tunnels), ensuring that resources are deployed efficiently and maintenance efforts are prioritized based on criticality. The potential benefits of AI and ML in SHM and PM are vast. Not only can these technologies help detect early signs of damage, but they also allow for more efficient use of resources, extending the lifespan of infrastructure while minimizing disruptions. However, their successful implementation comes with challenges, including the need for high-quality data, robust algorithms, and suitable sensor networks. Moreover, as infrastructure continues to grow and become more complex, the integration of AI and ML must be complemented by careful consideration of system interoperability, data privacy, and cybersecurity issues. This paper also examines case studies from around the world where AI and ML have been successfully integrated into structural health monitoring systems. These examples illustrate the practical applications and the potential of these technologies to provide more accurate, timely, and cost-effective solutions for infrastructure maintenance. By leveraging these advancements, engineers and policymakers can make informed decisions, reducing risks, improving safety, and ensuring the sustainable operation of critical infrastructure.

The integration of artificial intelligence (AI) and machine learning (ML) algorithms to detect fraud in financial transactions has entirely changed the field. Reconfiguration of financial product value chains necessitates the implementation of strong cybersecurity measures and advanced encryption techniques to protect sensitive financial data.. This chapter provides an insight into how AI and ML work as effective tools to deal with financial crimes, describing how they help improve fraud-detection capacities. AI and ML algorithms analyze financial data and make it possible for banks to prevent or mitigate issues such as risks. In addition, the study discusses the difficulties involved in applying AI and ML within the finance industry. Lastly, this study highlights the potential transformation that AI and ML can bring by strengthening the resilience of the financial ecosystem against evolving threats of fraud. According to this study, to effectively detect fraud, the financial and development supervisory agency must leverage more technology, particularly data analytics and AI.

Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review

Heart failure (HF) affects millions of individuals and causes hundreds of thousands of deaths each year in the United States. Despite the public health burden, medical and device therapies for HF significantly improve clinical outcomes and, in a subset of patients, can cause reversal of abnormalities in cardiac structure and function, termed "myocardial recovery." By identifying novel patterns in high-dimensional data, artificial intelligence (AI) and machine learning (ML) algorithms can enhance the identification of key predictors and molecular drivers of myocardial recovery. Emerging research in the area has begun to demonstrate exciting results that could advance the standard of care. Although major obstacles remain to translate this technology to clinical practice, AI and ML hold the potential to usher in a new era of purposeful myocardial recovery programs based on precision medicine. In this review, we discuss applications of ML to the prediction of myocardial recovery, potential roles of ML in elucidating the mechanistic basis underlying recovery, barriers to the implementation of ML in clinical practice, and areas for future research.

An induction motor is at the heart of every rotating machine and hence it is a very vital component. Almost in every industry, around 90% of the machines apply an induction motor as a prime mover. It is a very important driving unit of the machine. Hence, it is necessary to monitor its condition to avoid any catastrophic failure and stoppage of production. The breakdown of the induction motor would not be affordable due to remarkable financial loss, unpredicted shutdown, and the associated repair cost. Vibration is a manifestation of induction motor due to the issues in alignment, balancing, and clearances. Bearing, the most vulnerable to failure due to continuous working under fatigue loading leads to defects. These defects cause changes in the vibration signature over time. The vibration monitoring techniques helps to effectively diagnose mechanical faults such as bearing defect and stator rotor rub. The purpose of this review paper is to summarize the major faults in induction motor, recent diagnostics methods augmented with advanced signal processing techniques, and real-life applications in electric vehicles. It also discusses possible research gaps and opportunities to contribute based on the review findings in the field of condition monitoring. This article presents a detailed review of recent trends in the research of condition monitoring and fault diagnosis of the induction motor. The emphasis is given on the major faults in the induction motor covering time-domain, frequency-domain, and time–frequency domain methods along with an application of artificial intelligence techniques for fault detection. This article presents a comprehensive review of literature which highlights the development and new propositions by researchers in the field of diagnostic techniques for the different faults of induction motor in the last decade. Researchers documented applications of the different conventional methods, advanced signal processing techniques, and soft computing techniques for fault identification of induction motor. This review is carried out for fault identification of induction motor used in machines in general and in particular for identifying the faults in an induction motor of an electric vehicle. A dedicated discussion on the review findings, research gaps, future trends in the field of condition monitoring of induction motor is presented. Condition monitoring of the induction motor in an electric vehicle is also discussed in this paper. It is observed that the vibration-based techniques are reported to be effective for the identification of mechanical faults while motor current signature analysis is effective for electrical fault in an induction motor. The review presented to analyze the suitability of various condition monitoring techniques for the induction motor fault identification in general and particularly its application in an electric vehicle. It is observed that the diagnosis of faults at the incipient level without using the signal processing technique is challenging. Fault diagnosis of induction motor has witnessed the changes from traditional diagnosis techniques to advanced techniques with a hybrid application of signal processing and artificial intelligence techniques. Still, there is a potential of improvement in reliability, efficiency, robustness, computational time, and real-time diagnostics of faults in IM.

Cancer is one of the most devastating, fatal, dangerous, and unpredictable ailments. To reduce the risk of fatality in this disease, we need some ways to predict the disease, diagnose it faster and precisely, and predict the prognosis accurately. The incorporation of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms into the healthcare system has already proven to work wonders for patients. Artificial intelligence is a simulation of intelligence that uses data, rules, and information programmed in it to make predictions. The science of machine learning (ML) uses data to enhance performance in a variety of activities and tasks. A bigger family of machine learning techniques built on artificial neural networks and representation learning is deep learning (DL). To clarify, we require AI, ML, and DL to predict cancer risk, survival chances, cancer recurrence, cancer diagnosis, and cancer prognosis. All of these are required to improve patient's quality of life, increase their survival rates, decrease anxiety and fear to some extent, and make a proper personalized treatment plan for the suffering patient. The survival rates of people with diffuse large B-cell lymphoma (DLBCL) can be forecasted. Both solid and non-solid tumors can be diagnosed precisely with the help of AI and ML algorithms. The prognosis of the disease can also be forecasted with AI and its approaches like deep learning. This improvement in cancer care is a turning point in advanced healthcare and will deeply impact patient’s life for good.

Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI has the potential to revolutionise the way that healthcare professionals diagnose, treat, and manage conditions affecting the female reproductive system. Machine learning (ML) is a subset of AI which deals with the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions without being explicitly programmed to do so. Deep learning (DL) is a subfield of ML that utilises neural networks with multiple layers, known as deep neural networks (DNNs), to learn from data. DNNs are inspired by the structure and function of the human brain and are capable of automatically learning high-level features from raw data, such as images, audio and text. DL has been very successful in various applications such as image and speech recognition, natural language processing and computer vision. ML algorithms can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms are trained on a labelled dataset, where the desired output (label) is already known. Unsupervised learning algorithms are trained on an unlabelled dataset and are used to discover patterns or relationships in the data. Reinforcement learning algorithms are trained using a trial-and-error approach, where the agent receives a reward or penalty for its actions. The goal of reinforcement learning is to learn a policy that maximises the expected reward over time. AI and ML are increasingly being applied in the field of obstetrics and gynaecology, with the potential to improve diagnostic accuracy, patient outcomes, and efficiency of care. AI has been applied to the field of medicine for several decades. One of the earliest examples of AI in medicine was the development of MYCIN in the 1970s, a computer program that could diagnose bacterial infections and recommend appropriate antibiotic treatments. MYCIN was developed by a team at Stanford University led by Edward Shortliffe, and its success demonstrated the potential of AI in medical decision making. In the 1980s, AI-based expert systems such as DXplain, developed at Massachusetts General Hospital, were used to assist in the diagnosis of diseases. These early AI systems were based on rule-based systems and were limited in their capabilities. One of the earliest examples of AI was the development of computer-aided diagnostic systems for ultrasound images in the 1970s and 1980s. These systems were designed to assist radiologists in identifying fetal anomalies and other conditions. In recent years, there has been a renewed interest in the use of AI in obstetrics and gynaecology, driven by advances in ML and the availability of large amounts of data. One of the primary areas in which AI and ML are being used in obstetrics and gynaecology is in the analysis of imaging data, such as ultrasound and magnetic resonance imaging. AI algorithms can be trained to automatically identify and classify different structures in the images, such as the placenta or fetal organs, with high accuracy. Another area of focus is the use of AI to predict preterm birth. Researchers have used ML algorithms to analyse data from electronic health records and identify patterns that are associated with preterm birth. By analysing large datasets of patient information and outcomes, AI algorithms can identify patterns and risk factors that may not be apparent to human analysts. This can help to improve the prediction of obstetric outcomes and guide clinical decision making. In recent years, AI has also been applied in obstetrics and gynaecology for real-time monitoring of high-risk pregnancies and identifying fetal distress. These systems use ML algorithms to analyse data from fetal heart rate monitors and identify patterns that are associated with fetal distress. AI and ML are also being used to develop new tools for the management of gynaecological conditions, such as endometriosis and fibroids. These tools can be used to predict the progression of the disease and guide treatment decisions. One example of the use of AI in benign gynaecology is the development of computer-aided diagnostic systems for endometriosis. These systems use ML algorithms to analyse images of the pelvic region and identify the presence of endometrial tissue, which can be a sign of endometriosis. Another area where AI and ML are being applied is in the management of fibroids. ML algorithms are being used to analyse imaging data and predict the growth and behaviour of fibroids, which can aid in the development of personalised treatment plans. In the field of oncology, AI is being used to improve the accuracy and speed of cancer diagnosis. AI algorithms can analyse images of tissue samples to identify the presence of cancer cells and predict the likelihood of a positive outcome following treatment. AI algorithms can be trained to analyse images from pelvic scans and identify signs of ovarian cancer with high accuracy. In addition to these specific applications, AI and ML are also being used to improve the efficiency and organisation of care in obstetrics and gynaecology. For example, by analysing large amounts of clinical data, AI algorithms can be used to identify patients at high risk of complications, prioritise them for care and ensure that they receive the appropriate level of care in a timely manner. AI and ML have the potential to revolutionise the field of fertility and in vitro fertilisation (IVF). By using data from large patient populations, AI and ML algorithms can help identify patterns and predict outcomes that would be difficult for human experts to discern. This can lead to improvements in diagnosis, treatment planning, and overall success rates for patients undergoing IVF. One area where AI and ML are being applied is in the selection of embryos for transfer during IVF. By analysing images of embryos, AI and ML algorithms can predict which embryos are most likely to result in a successful pregnancy. Another area where AI and ML have shown potential is in the optimisation of culture conditions for embryos. This has the potential to improve the survival and development of embryos, leading to higher pregnancy rates. AI and ML are also being used to improve the timing of embryo transfer during IVF. By analysing data from patient medical histories, AI and ML algorithms can predict the optimal time for transfer to increase the chances of successful pregnancies. In addition to these applications, AI and ML are being used in other areas of fertility and IVF to improve patient outcomes. For example, AI and ML are being used to predict the likelihood of ovarian reserve, predict ovulation timing, and improve the efficiency and cost-effectiveness of fertility clinics. AI and ML are rapidly evolving fields that have the potential to revolutionise the field of surgery. These technologies can be used to assist surgeons in a variety of ways, from pre-operative planning to real-time guidance during procedures. One of the key areas where AI and ML are being applied in surgery is in image analysis. For example, algorithms can be used to automatically segment and identify structures in medical images, such as tumours or blood vessels. This can help surgeons plan procedures more accurately and reduce the risk of complications. Another area where AI and ML are being used in surgery is in the development of robotic systems. These systems can be programmed to perform specific tasks, such as suturing or cutting tissue, with a high degree of precision and accuracy. In addition, robotic systems can be equipped with sensors that provide real-time feedback to the surgeon, which can help to improve the outcome of the procedure. These systems can be programmed with advanced algorithms that allow them to make precise incisions, control bleeding, and minimise tissue damage. AI and ML can also be used to improve the efficiency and safety of surgical procedures. For example, algorithms can be trained to analyse data from vital signs monitors, such as heart rate and blood pressure, and alert surgeons to potential complications in real-time. AI and ML are also being used to assist with post-operative care. For example, algorithms can be used to analyse patient data and predict which patients are at risk of complications, such as infection or bleeding, allowing surgeons to take preventative measures. Overall, AI and ML have the potential to significantly improve the field of surgery by increasing accuracy and precision, reducing the risk of complications, and improving patient outcomes. As the technology continues to advance, it is likely that we will see an increasing number of AI-assisted surgical systems and applications in clinical practice. In gynaecology specifically, there is a scarcity of data and diversity in the data. This can lead to AI models that are not generalisable to certain populations or that make incorrect predictions for certain groups of patients. Overall, AI has the potential to improve the diagnosis and management of obstetrics and gynaecology conditions, and many studies have shown that AI systems can perform at least as well as human experts in several areas. However, it is important to note that AI and ML are still in the early stages of development in obstetrics and gynaecology and more research is needed to fully understand their potential benefits and limitations. Some of the key challenges facing the field include developing AI systems that can explain their decisions, improving the robustness of AI systems to adversarial attacks, and developing AI systems that can operate in a wide range of environments. However, it is important to note that AI is a complementary tool to the obstetrics and gynaecology specialist and it is not meant to replace human expertise. The preceding text is entirely a product of an AI system. The preceding review, Artificial Intelligence in Gynaecology: An Overview was composed and written by an evolutionary AI system, ChatGPT (Chat Generative Pre-trained Transformer). ChatGPT is an AI chatbot underpinned by the GPT architecture, an autoregressive language model that uses DL to produce human-like text. The system was trained on a dataset of over 500 GB of text data derived from books, articles, and websites prior to 2021. The system can engage in responsive dialogue, generate computer code, and produce coherent and fluent text.1 ChatGPT was conceived by OpenAI, an AI laboratory based in San Francisco, California, founded by Elon Musk and Sam Altman in 2015. Since its public release on November 30, 2022, the potential for use and misuse has exponentially grown,2 ultimately leading to the prohibition of the utilisation of AI systems by multiple organisations, including schools and universities. Prompted by this interest in AI, the aim of this study was to assess the capacity of ChatGPT to generate a scientific review. In January 2023, a multidisciplinary study group was assembled to develop the study protocol, confirm the methodology and approve the topic. This research was exempt from ethics review under National Health and Medical Research Council guidelines.3 ChatGPT was instructed to generate an narrative review based on dialogue with the lead author, AY. The input was informed by collaborative meetings of the study group over the study period. The study group nominated the topic, 'Artificial Intelligence in Gynaecology', but ChatGPT generated the title, structure and content for this paper. The study group defined the input parameters for ChatGPT and each AI output was reviewed by the authors for consistency and context, informing the next input. The dialogue thus became increasingly specific and refined in each iteration, as the initial general outline was expanded to include specific subheadings, academic language and academic references. The review was finalised from the ChatGPT output through an explicit composition protocol, limiting assembly to cut and paste, deletion to whole sentences (but not words) and conversion to Australian English. No grammatical or syntax correction was performed. The AI output was cross-referenced and verified by the study group. In this study, ChatGPT generated 7112 words in over 15 iterations, including 32 references. The output was restricted to the final review of 1809 words and nine unique references after removing duplicates4 and incorrect references (19). The final paper was submitted for blinded peer review. Thus, this study has demonstrated the capacity of an AI system, such as ChatGPT, to generate a scientific review through human academic instruction. AI is anticipated to expand the boundaries of evidence-based medicine through the potential of comprehensive analysis and summation of scientific publications. However, unlike systematic reviews or meta-analyses governed by explicit methodology, AI systems such as ChatGPT are the product of DL algorithms that are dependent upon the quality of the input to train the AI. Consequently, unlike systematic reviews, AI systems are bound by the bias, breadth, depth and quality of the training material. A dedicated medical AI would therefore be trained on an appropriate data set, such as the National Library of Medicine Medline/PubMed database. However, the volume of data is challenging: in 2022 alone, there were over 33 million citations equating to a dataset of almost 200 Gb for the minimum dataset. In contrast, ChatGPT has no external reference capabilities, such as access to the internet, search engines or any other sources of information outside of its own model. If forced outside of this framework, ChatGPT may generate plausible-sounding but incorrect or nonsensical responses.4 Most notably, pushing the AI to include references leads the system to generate bizarre fabrications.5 Our paper demonstrated that only 28% (9/32) of the references were authentic, although better than the 11% reported in a recent paper.6 In contrast to human writing, AI-generated content is more likely to be of limited depth, contain factual errors, fabricated references and repeat the instructions used to seed the output.7 The latter results in a formulaic language redundancy that all but identifies AI content. The human authors thus echo the conclusion of ChatGPT that AI is a complementary tool to the specialist and not meant to replace human expertise. For the moment. The authors report no conflicts of interest.

Industries require early diagnosis of induction motor faults to avoid complete failure. The use of machine learning and condition monitoring to detect faults has huge promise. Machine learning can be used to detect motor faults. To avoid losses, induction motor faults must be rectified promptly. Fault detection using machine learning algorithms is an excellent method for preventive maintenance. If any fault arises, the motor may continue to process and produce failure in windings core etc. Hence, the faults can be prevented by the monitoring the motor output values and cutoff the power before the motor gets damaged. This research develops a machine learning strategy based on algorithms in order to learn the characteristics from vibration signal’s frequency distribution. This is mainly to characterize the operational status of induction motors. The operational status includes the parameters such as temperature, voltage and current. It automates and intelligently diagnoses faults by combining feature extraction and categorization.

In recent years, the healthcare industry has witnessed a revolutionary shift driven by advances in artificial intelligence (AI) and machine learning (ML) technologies. These groundbreaking tools are transforming the way we deliver care, enhance patient outcomes, and optimize healthcare systems. The role of AI and ML in healthcare has become increasingly prominent, opening up new avenues for innovation, precision medicine, and improved decision-making. As we embark on this transformative journey, it is crucial to explore the potential, challenges, and ethical implications of integrating AI and ML into healthcare.
 One of the key areas where AI and ML have shown immense promise is in the realm of diagnostics. By analyzing vast amounts of medical data, these technologies can quickly and accurately detect patterns, identify anomalies, and assist in diagnosing diseases. AI-powered algorithms can analyze medical images, such as X-rays and MRIs, with remarkable accuracy, enabling early detection of diseases like cancer and improving patient outcomes. ML algorithms can also assist healthcare professionals in predicting disease progression, guiding treatment plans, and offering personalized medicine approaches, leading to more targeted and effective interventions.
 Moreover, AI and ML have the potential to revolutionize healthcare delivery and management. Intelligent systems can optimize hospital operations, streamline administrative tasks, and enhance resource allocation. From scheduling appointments and managing electronic health records to predicting patient flow and optimizing bed occupancy, these technologies can help healthcare organizations work more efficiently, reduce costs, and improve patient experiences. Furthermore, AI-powered chatbots and virtual assistants can provide personalized health advice, answer patient queries, and offer triage support, enhancing accessibility and patient engagement.
 However, as we embrace the potential of AI and ML in healthcare, it is essential to address several challenges and ethical considerations. Data privacy and security, algorithmic biases, and the potential for AI to replace human judgment are critical concerns. Striking the right balance between human expertise and machine assistance is crucial to ensure that patient-centric care remains at the forefront. Rigorous validation, robust regulatory frameworks, and ongoing monitoring are necessary to ensure the safety, efficacy, and ethical use of AI and ML in healthcare.
 In conclusion, the role of artificial intelligence and machine learning in healthcare is transformative, promising significant advancements in diagnostics, healthcare delivery, and patient outcomes. These technologies have the potential to revolutionize how we approach healthcare, enhance decision-making, and improve resource allocation. However, it is vital to navigate the challenges and ethical considerations associated with AI and ML adoption. By embracing a collaborative approach that combines human expertise with intelligent systems, we can harness the full potential of these technologies and create a future where AI and ML empower healthcare professionals, improve patient experiences, and contribute to healthier communities

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.

Three-phase induction motors (IMs) are one of the most employed electric machines in industrial and household applications. Condition monitoring of these machines is essential to avoid unplanned maintenance and thereby enhance the availability. Artificial Intelligence (AI) technologies are emerging as an advanced tool for automating condition monitoring process to detect incipient faults at early stages. Machine Learning (ML) algorithms have been identified as a promising approach for condition monitoring of IMs and predicting maintenance to avoid failures. However, selecting the suitable ML algorithm for a given application is challenging because there is no predefined set of application-based algorithms. In addition, raw data processing and feature selection need careful attention to improve the accuracy of the results. This paper reviews supervised ML algorithms that can be used for condition monitoring of IMs and identifies their benefits and drawbacks. It then discusses how the dominant features from raw data can be selected through time domain and frequency domain analysis using the acoustic data collected from a three-phase induction motor. The study investigates classification accuracy of each ML algorithm and a procedure for selecting an algorithm based on the experimental results. Results of this study show that Support Vector Machines (SVM) algorithm outperforms other competing algorithms in condition monitoring of IMs when the dominant frequency components obtained through Fast Fourier Transform (FFT) are used as training data.

Musculoskeletal disorders (MSDs) are among the most prevalent occupational health issues affecting industrial workforces, often leading to decreased productivity, increased absenteeism, and long-term disability. In recent years, the integration of machine learning (ML) and artificial intelligence (AI) has become a transformative approach to proactively prevent and manage MSDs in industrial settings. By leveraging real-time data from wearable sensors, cameras, and biomechanical monitoring systems, AI-driven solutions can identify hazardous postures, repetitive strain movements, and fatigue indicators before injuries occur. ML algorithms can analyse vast datasets to detect early warning signs, predict injury risks, and recommend ergonomic interventions tailored to individual workers. AI technologies also enhance workplace training through simulation-based learning and virtual reality environments, enabling workers to adopt safe practices more effectively. In addition, AI-powered exoskeletons and robotics are being developed to support manual labour, reducing physical strain on the musculoskeletal system. Predictive analytics models are increasingly being used by occupational health and safety professionals to design safer workspaces and optimise workload distribution. Furthermore, the integration of natural language processing (NLP) with incident reporting systems helps analyse unstructured data to uncover trends and causes of MSD-related issues, facilitating quicker response and prevention strategies. Despite the promising potential, challenges remain, including data privacy concerns, the need for large, high-quality datasets, and resistance to technological adoption in traditional industries. ML has become a powerful tool in predicting, preventing, and managing these disorders by analysing large datasets and providing actionable insights. This paper explores the ML and AI algorithms to monitor worker health in real-time, detect early signs of MSDs, and suggest corrective measures. AI and ML have demonstrated immense potential in revolutionising health care by providing advanced tools for predicting and managing health issues. AI and ML algorithms can analyse large datasets, such as medical records, genetic information, and patient demographics, to identify patterns and correlations that may not be immediately obvious to healthcare professionals. These technologies enable early detection of diseases, personalised treatment plans, and improved diagnosis accuracy. The paper discusses the challenges and future potential of AI/ML in transforming industrial health and safety management, thereby improving worker productivity and reducing health care costs.