Technology readiness levels for machine learning systems

In order to identify areas that need further research, this study will analyze how machine learning (ML) and artificial intelligence (AI) technologies are applied in various customer services. This is done by using a systematic review of literature procedure to examine publications on the application of AI and ML in customer service that have been posted on various academic websites. Numerous AI and ML approaches may be applied by businesses to improve customer support and assistance. The use of self-service technologies, integrated product-service offers, service excellence, and word-of-mouth marketing are some of these techniques. The study’s conclusion addresses the customer service knowledge gaps that it identified when reviewing the literature. The information provided by the current study on how Artificial Intelligence (AI) and Machine Learning (ML) technologies may be applied or properly used to improve customer service or may be fruitful to businesses. The findings of the study will provide theoretical and practical implication of AI and ML in customer support system to develop more effective and efficient strategies to the users and policy makers. Ultimately the findings of the study will help increase the degree of satisfaction of customer. Longitudinal studies should assess AI and ML’s long-term impact on customer support systems, balancing human-AI collaboration, developing transparent models, considering cross-cultural factors, and adopting a human-centric design approach.

Machine Tools’ Running State Monitoring in the Learning Factory—Based on Machine Learning Method and Vibration Data

Machine Learning (ML) technologies have become essential tools, transforming industries and unlocking incredible potential in various fields. ML is now widely used for data-driven decision-making and predictive analytics across fields like healthcare, finance, transportation, and more. However, building and implementing ML models can be complex and time-consuming, often requiring programming proficiency and data science skills. Despite significant progress in ML, non-experts often struggle with selecting algorithms, optimizing models, and deploying ML solutions. This paper conducts a systematic literature review to explore challenges in the area of machine learning based on multiple categories involving features engineering and data extraction, learning model structure and activities, learning-based analysis and visualization, analysis algorithms in data-based systems, machine learning algorithms and systems development, and declarative ML-based prediction. Addressing these challenges underlines the importance of following AutoML and Declarative ML strategies in simplifying the ML process.

The purpose of this research is to investigate the nature of the relationship between artificial intelligence (AI) and machine learning (ML) technologies in service-based organizations on the one hand and organizational resilience on the other, as well as the potential impact of external market shocks on this relationship. A quantitative survey of 171 service-based groups was used to perform the research. The study's findings indicate that using AI and ML technologies in service delivery has a beneficial and substantial effect on organizational resilience. Furthermore, the research discovered that the degree to which external market shocks influence the efficacy of these technologies in improving an organization's resilience moderates the connection between the use of AI and ML technologies and organizational resilience. According to the results, service-based companies can improve their resilience by incorporating AI and ML technologies into their service delivery processes. They must, however, evaluate the possible influence of external market shocks on the efficacy of these technologies in increasing resilience. The research has ramifications for service-based organizations and adds to the body of knowledge on organizational resilience and the application of AI and ML technologies in service delivery.

In honor of this issue’s theme of “Artificial Intelligence (AI) Engineering—Realizing the Potential of AI,” this edition of the “Practitioners’ Digest” brings you recent papers on AI and machine learning (ML) engineering, which we believe will be of interest to practitioners. These papers were published in the First International Conference on AI Engineering (CAIN 2022), the 26th International Conference on Evaluation and Assessment in Software Engineering 2022 (EASE 2022), and the 2022 IEEE/Association for Computing Machinery First International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI).

The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation monitoring. The advent of high-throughput sequencing technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the fields of cfDNA genetics, epigenetics, transcriptomics, and fragmentomics, providing a plethora of biomarkers. Machine learning (ML) and/or artificial intelligence (AI) technologies that are known for their ability to integrate high-dimensional features have recently been applied to the field of liquid biopsy. In this review, we highlight various AI and ML approaches in cfDNA-based diagnostics. We first introduce the biology of cell-free DNA and basic concepts of ML and AI technologies. We then discuss selected examples of ML- or AI-based applications in noninvasive prenatal testing and cancer liquid biopsy. These applications include the deduction of fetal DNA fraction, plasma DNA tissue mapping, and cancer detection and localization. Finally, we offer perspectives on the future direction of using ML and AI technologies to leverage cfDNA fragmentation patterns in terms of methylomic and transcriptional investigations.

Industry 4.0 brought with it by the next-gen Industry 5.0 and Society 5.0 paradigms, catalysed by Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies. These advances have the benefit of encouraging sustainability, improving output, and updating manufacturing. By enabling self-decision, continuous monitoring, and predictive maintenance with the processing of large data, AI is dramatically reducing downtime and associated costs of system downtime. As a result, ML algorithms, in light of their applicability for continuous learning and adaptation, have contributed to enriching product quality, streamlining supply networks and okaying personalized customer experiences. Neural networks are also being leveraged to improve computer vision and speech capabilities, for applications such as smart automation and human-robot cooperation in challenging industrial contexts. Industry 5.0 truly puts humans back at the centre of innovation. It is aimed to create an evolved society in which AI, ML, and DL are fused with the digital and physical world Society 5.0. The integration aims to address a plethora of societal challenges: environmental sustainability, health and ageing population, among others. It is a convergence of these said technologies that lead to a paradigm shift towards more resilient, adaptive, and sustainable industrial ecosystems. This paper aims to address these questions in a systematic way to offer a comprehensive view of what the future industrial landscape could look like leveraging the promise of Industry 4.0 and Industry 5.0 thus, and more opportunities to embrace intelligent and sustainable industries of tomorrow.

The automotive industry stands at the crossroads of a profound transformation—one driven by the convergence of sustainability goals, digital innovation, and evolving consumer expectations. Integrated Innovations in Automotive Manufacturing, R&D, Marketing, Financial Services, and Connected Mobility explores how Artificial Intelligence (AI), Machine Learning (ML), and Cloud Technologies are reshaping every facet of the automotive value chain to deliver smarter, greener, and more customer-centric solutions. This book offers a holistic perspective on how integrated technologies are no longer optional enhancements but essential components for competitiveness in the modern automotive landscape. In manufacturing, AI-powered robotics, predictive maintenance, and digital twins are streamlining operations and minimizing waste. In R&D, advanced simulation and generative design are accelerating innovation cycles. Marketing and customer engagement have become increasingly data-driven, delivering personalized experiences powered by real-time insights. Financial services are also evolving—leveraging cloud-based platforms and AI to offer flexible financing models, dynamic insurance products, and risk analysis tailored to consumer behavior. Meanwhile, connected mobility, encompassing smart vehicles, shared transportation, and autonomous systems, is redefining how people interact with and move through the world. By presenting real-world case studies, emerging trends, and actionable strategies, this book serves as a roadmap for automakers, technology leaders, policymakers, and innovators seeking to drive sustainable growth through digital integration. It also addresses critical challenges such as data privacy, cybersecurity, ethical AI, and the need for cross-sector collaboration. At its core, this book is about transformation—not just of vehicles or systems, but of the broader purpose of the automotive industry. The road ahead is not only electric and autonomous but also intelligent and interconnected. Integrated Innovations invites readers to explore how the synergy of AI, ML, and cloud computing is accelerating progress toward a more sustainable and resilient mobility future.

The technologies of artificial intelligence and machine learning have made a fundamental leap in their capabilities in the last five years. The growth of processing power and the emergence of more and more effective methods of machine learning allows AI to not just solve the most typical tasks associated with the field, such as statistical analysis and optimization of mathematical processes, but also to find new applications in related fields of research, as well as practical applications, including those on the free market, available to the mass consumer. Image generation, audio, animation, self-learning models of control of robotic platforms and virtual mechanical models – these and many more novel applications of the recent years have led to a media-boom around AI and a growing interest from developers and authors from various fields and industries. That being said, the methods for developing, research, testing, and integration of AI have largely remained unchanged and still require the knowledge of programming languages, machine learning libraries, as well as a deep understanding and experience specifically in the narrow field of AI. This barrier of specialization not only demands inclusion of machine learning specialists in the development process of otherwise trivial computer applications, typical for the field of AI, but also prevents small teams and independent developers from using the latest advances in these technologies without significant monetary and time investments into studying the subject. I offer a novel solution to this issue in the form of a prototype graphical interface that allows the user without technical education and without the need for knowledge of programming languages to develop and tune various architectures of neural nets and other machine learning methods, methods of unsupervised machine learning, and to test these methods on a wide range of experimental tasks – from mathematical equations to controlling virtual mechanical models in a simulated physical environment. In this article, I give a brief description of its structure and organisation, its fundamental principles of operation, and the capabilities of this GUI.

Strategic Environmental Assessment (SEA) Process towards the Sustainable Design and Construction of Computer, Communication, Network Engineering, Machine Learning and Artificial Intelligence Systems

This study presents internet of things (IOT) and artificial intelligence technologies that are critical in reducing the harmful effects of this illness and assisting its recovery. It explores COVID-19’s economic impacts before learning about new technologies and potential solutions. The research objective was to propose a solution for self-diagnosis, self-monitoring, and self-management of COVID-19 with personal mobiles and personal data using cloud solutions and mobile applications with the help of an intelligent IoT system, artificial intelligence, machine learning, and 5G technologies. The proposed solution based on self-diagnosis without any security risk for users’ data with low cost of cloud-based data analytics by using handsets only is an innovative approach. Since the COVID-19 outbreak, the global social, economic, religious, and cultural frameworks and schedules have been affected adversely. The fear and panic associated with the new disease, which the world barely knew anything about, amplified the situation. Scientists and epidemiologists have traced the first outbreak of COVID-19 at Wuhan, China. A close examination of the genetic makeup of the virus showed that the virus is zoonotic, meaning that the virus changed hosts from animals to humans. The uncertainty associated with the above features and characteristics of the virus, as well as the high mortality rates witnessed in many parts of the globe, significantly contributed to the widespread global panic that brought the world to a standstill. Different authorities and agencies associated with securing the public have implemented different means and methods to try and mitigate the transmission of the infection as scientists and medical practitioners work on remedies to curb the spread of COVID-19. Owing to different demographics, different parts of the globe have attempted to effectively implement locally available resources to efficiently fight and mitigate the adverse effects of the COVID-19 pandemic. The general framework provided by the World Health Organization (WHO) has been implemented or enhanced in different parts of the globe by locally available resources and expertise to effectively mitigate the impact of COVID-19. There is currently no effective vaccine for COVID-19, but new technology can be available within weeks to reduce the spread of the disease; current approaches such as contact tracing and testing are not secure, and the cost of testing is high for end users. The proposed solution based on self-diagnosis without any security risk for users’ data with low cost of cloud-based data analytics functions by using an intelligent internet of things (IOT) system for collecting sensors data and processing them with artificial intelligence to improve efficiency and reduce the spread of COVID-19.

Routing protocols that support energy efficiency and Quality of Service (QoS) in wireless sensor networks (WSNs) are continuously evolving. In particular, many studies are being conducted on protocols that apply reinforcement learning algorithms in which agents learn the optimal path according to network conditions to maximize energy consumption and network lifetime, intelligent clustering protocols that automatically cluster sensor nodes and select cluster heads (CHs) to minimize energy consumption, and dynamically adjust routing paths based on real-time monitoring of traffic load, energy balance, and network failures. In addition, studies are being conducted to support multiple QoS requirements, such as delay, bandwidth, and reliability, by integrating AI technology with IoT communication protocols. In this paper, the current trends and future directions of routing protocols that support energy efficiency and QoS by utilizing artificial intelligence (AI), machine learning (ML), and other hybrid AI technologies in WSNs are described.

Introduction: The digital technology in healthcare has revolutionized during the last decade with the increased implementation of Electronic Health Record (EHR) systems. The growth of EHR has been from the simple database system to incorporating the applications of artificial intelligence and machine learning systems in the system. One of the most important aspects of digital system proves to be electronic health record system because of the intense data produced geographically which can be used for epidemiological studies and research. The EHRs also have a wide range of benefits such as effective communication between the clinicians, clinicians and laboratory, decreased human errors, effective tool for diagnosis and treatment planning, increased patient satisfaction, and quality care. Materials and Methods: The quantitative study included opinions and perspectives of dental clinicians toward EHR system. The data collection of the participants was from April to December 2024. The quantitative part involved a descriptive survey. After obtaining written informed consent from each participant, a structured survey e-questionnaire was given to dental clinicians to fill it through online mode by dental practitioners who had independent clinics. The sample size calculation done by www.openepi.com software quantitative study which included two hundred and eighty five (285) dentists participated. Results: The findings indicated a favorable disposition among dentists regarding the adoption of EHR; however, dental practitioners also emphasized the problems encountered during EHR implementation. Conclusion: EHRs are an effective way inside health information systems; however, they necessitate ongoing training, regular upgrades, and the integration of artificial intelligence and machine learning technologies in the future. The threat to data security continues to be a concern for dental practitioners due to the anticipated rise in cybercrimes, as patient-sensitive information is highly confidential.

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications.

Artificial Intelligence (AI) is an area of ​​research driven by innovation and development that culminates in computers, machines with human-like intelligence characterized by cognitive ability, learnability, adaptability and decision-making ability. The study found that AI is widely adopted and used in education, especially by educational institutions, in various forms. This article reviewed articles by various scientists from different countries. The paper discusses the prospects for the application of artificial intelligence and machine learning technologies in education and in everyday life. The history of the development of artificial intelligence is described, technologies of machine learning and neural networks are analyzed. An overview of already implemented projects for the use of artificial intelligence is given, a forecast of the most promising, according to the authors, directions for the development of artificial intelligence technologies for the next period is given. This article provides an analysis of how educational research is being transformed into an experimental science. AI is combined with the study of science into new ‘digital laboratories’, in which ownership of data, as well as power and authority in the production of educational knowledge, are redistributed between research complexes of computers and scientific knowledge.