Role Of Machine Learning In Vlsi Ic Design

Введение.Определена актуальность внедрения технологий искусственного интеллекта в Российской Федерации (ИИ-технологий). Установлено, что в нашей стране строительство значительно уступает другим отраслям экономики по использованию ИИ-технологий. Цель исследования – определение приоритетных сфер применения технологий искусственного интеллекта в строительном комплексе РФ. Методы. В ходе исследования использовались генеративные технологии, методы машинного обучения, статистического анализа, экспертных оценок. Результаты. Изучен отечественный и зарубежный опыт внедрения ИИ-технологий в строительстве; выявлены основные причины недостаточного развития этих технологий в строительстве в нашей стране; определены основные направления использования искусственного интеллекта в строительном комплексе РФ; оценены техническая возможность и экономическая целесообразность реализации ИИ-технологий в практической сфере строительства. В качестве основных причин слабого использования искусственного интеллекта в строительном комплексе Российской Федерации следует назвать ограниченное использование данных, отсутствие квалифицированных специалистов, высокую стоимость внедрения, сложности с интеграцией; регулирование и стандарты. Определены наиболее значимые факторы эффективного применения технологий искусственного интеллекта в строительной отрасли: качество данных; интеграция с существующими системами; обучение и поддержка; выбор правильного инструмента; регулярное обновление и улучшение. На основе проведенного анализа выявлены приоритетные направления внедрения ИИ-технологий в строительном комплексе РФ: искусственный интеллект в автоматизированном проектировании, управлении строительством, контроле качества строительных работ, прогнозировании рисков в строительстве, анализе данных, оптимизации ресурсов, обеспечении безопасности и экологической устойчивости. Вывод. Для ускорения внедрения технологий искусственного интеллекта в строительную отрасль России необходимо развивать инфраструктуру данных, обучать специалистов, создавать благоприятные условия для инвестиций и разрабатывать соответствующие регулятивные нормы и стандарты. Introduction. Artificial intelligence has recently found extensive application in the Russian Federation. However, it has been determined that the application of AI technology in the construction industry in our country lags much behind other economic sectors. The aim of research is to determine priority areas for the application of artificial intelligence in the construction industry of the Russian Federation. Materials and methods. The research made use of generative technologies, statistical analysis, machine learning, and expert evaluation methods. Research outcomes. As a result of research, the authors examined the experiences of both national and international construction companies utilising artificial intelligence. They also determined the primary applications of AI in the Russian Federation's construction industry and evaluated the viability of both technological and financial implementation of AI in construction. The following should be listed as the primary causes of insufficient application of artificial intelligence in the Russian Federation's construction industry: restricted data use, a shortage of qualified personnel, high implementation costs, challenges related to integration, regulations and standards. The following are the key elements for effective application of AI in the construction sector: high-quality data; integration with current systems; assistance and training; selection of the appropriate tools; and regular updates and enhancements. Based on the analysis, priority areas for the implementation of AI technologies in the Russian construction industry were identified: artificial intelligence in automated design, construction management, quality control of construction work, risk forecasting in construction, data analysis, resource optimization, ensuring safety and environmental sustainability. Conclusions. The development of data infrastructure, the training of experts, the creation of investment-friendly environments, and the development of suitable regulatory norms and standards are all essential to accelerate the adoption of artificial intelligence technologies in the Russian construction sector.

Laser-based metal additive manufacturing (MAM), particularly laser powder bed fusion (LPBF) and laser directed energy deposition (L-DED), is a transformative technology enabling the production of complex, high-performance metal components with precision and minimal waste. However, its wider adoption is hindered by challenges in ensuring repeatability and consistency due to the complex, dynamic nature of the process. Closed-loop systems with in-situ monitoring and control have gained attention for quality assurance, where machine learning (ML) models are crucial for correlating sensor data with process state diagnosis and facilitating control actions. This review adopts an ML perspective to elucidate the role of ML in closed-loop control systems. It first outlines sensor data types and preprocessing methods used as ML datasets, then discusses roles and applications of ML models for in-situ monitoring, highlighting limitations and potential solutions. An ML-based closed-loop control framework is also presented, with emphasis on the integration of ML in the control component. Finally, future development directions of advanced ML approaches are envisioned. This review provides new insights into enhancing the role of ML models in laser-based MAM closed-loop control systems, serving as a crucial aid in the development of intelligent control systems.

One of the major challenges that has been growing and has evolved in the last few decades is the production of waste. The types of waste produced has changed over the years and in the present day and age finding a constructive system for efficient and effective waste disposal has become a centre of concern. Application of AI, Machine Learning and other smart technologies have become focal points that play an extremely vital role in the management of different types of waste. This approach has optimized the process of handling waste. The methodology used in this work is the systematic literature review (SLR) to identify, evaluate, and synthesize studies on the application of artificial intelligence in waste management. The two major key contributing methods in this approach being-Neural Networks and Advanced Machine Learning Algorithms. The use of neural networks plays a crucial role in predicting the amount of waste produced. Whereas, the use of advanced machine learning algorithm will attain optimization in waste collection. This process of integration has reduced the waste quantity produced by 90%, landfill by 40% and transport costs by 15%. Implementation of AI and smart technologies in waste management process has reduced industrial waste by 30-50%, food waste by 40%, medical waste by 30% and has enhanced the recycling of plastics by 50%. AI has no doubt revolutionized the waste management process in multiple ways like reduction in waste, recycling of waste, smarter resource utilization and so forth. However, this reliance has to be limited to a considerable extent and major control of the whole process has to be taken care of with human aid. This study will play a crucial role in understanding the present trends and innovation, the effectiveness of this integration process and will help address environmental impact in a more efficacious approach.

Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

Cyberbullying constitutes a threat to adolescents’ psychosocial wellbeing that developed alongside technological progress. Detecting online bullying cases is still an issue because most of victims and bystanders do not timely report cyberbullying episodes to adults. Therefore, automatized technologies may play a critical role in detecting cyberbullying through the use of Machine Learning (ML). ML covers a broad range of techniques that enables systems to quickly access and learn from data, and to make decisions about complex problems. This contribution aims at deepening the role of ML in cyberbullying detection and prevention. Specifically, the following issues are addressed: i. identifying the features most frequently considered to develop ML models predicting cyberbullying; ii. identifying the most used ML algorithms and their evaluation methods; iii. understanding the implication of ML for prevention; iv. highlighting the main theoretical and methodological issues of ML algorithms in predicting cyberbullying. To answer these research questions, a systematic review of literature reviews, from a total of n=186 records from online databanks, has been conducted. Ten literature reviews have been elected to analyze and discuss evidence about ML preventative potential against cyberbullying. Most of the models used content-based features to predict cyberbullying. The majority of these features includes words written in social network posts, whereas Support Vector Machine, Naïve Bayes, and Convolutional Neural Networks are the most used alghorithms. Methodological and technical issues have been critically discussed. ML represents an innovative preventative strategy that may optimize and integrate educational programs for adolescents and be the starting point of the development of technology-based automatized detection strategies. Future research is challenged to develop algorithms capable of detecting cyberbullying from several multimedia sources.

Fog computing is a fundamental facilitating technology in the future of networks. It broadens cloud computing services to its network edge, so that fog computing is able to bear the load of different emerging applications like IoT, blockchain, and big data without incurring high latency or cost of bandwidth consumption. In order to reach the optimal capacity of fog computing, designing an incentive mechanism for its service provider is essential. Over the recent years, we have noticed steady growth in the adoption of Machine Learning not only for the enhancement of fog computing applications but also for providing fog services, which include better resource management, improved security, reduced energy consumption, latency and traffic modelling. There hasn’t been a study yet, which investigates the role of Machine Learning in fog computing paradigm and this is what our current research aims to shed light on. Machine Learning application used for fog computing needs high layers services profound analytics, a strong end-user as well as smart responses for its designated tasks. Here, in this paper we work on presenting a comprehensive study that underlines the current advancements in Machine Learning techniques associated with the management of three important aspects of fog computing: accuracy, resource, security, and, as well as highlighting the role of Machine Learning in edge computing.

Despite extensive existing literature on the role of machine learning (ML) in pharmacokinetics and drug development, there remains a gap in understanding its real-world implementation challenges, especially across diverse populations. This review aims to bridge this gap by focusing on specific case studies that illustrate the practical impact of ML on addressing the limitations of traditional pharmacokinetic (PK) methods. By leveraging large datasets and sophisticated algorithms, ML techniques provide improved predictions of absorption, distribution, metabolism, and excretion (ADME) processes, offering an individualized approach to patient care. Unlike traditional PK modeling, ML allows for the handling of large-scale, multidimensional data, improving the prediction accuracy for diverse patient populations. This review delves into recent advancements in ML applications for PK, emphasizing their impact on early-stage drug discovery, dose optimization, and tailoring personalized treatment plans. Specific case studies illustrate the advantages of ML over conventional approaches, particularly in addressing the variability in drug responses among patients. The challenges and opportunities of using ML in PK modeling are discussed, highlighting the potential of these techniques to revolutionize pharmaceutical sciences. Słowa kluczowe: absorption, distribution, metabolism, excretion (ADME), machine learning (ML), pharmacokinetics (PK)

Diabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating these conditions have faced limitations, the emergence of Machine Learning (ML) technologies heralds a new era, offering the promise of revolutionizing diabetic foot care through enhanced precision and tailored treatment strategies. This review aims to explore the transformative impact of ML on managing diabetic foot complications, highlighting its potential to advance diagnostic accuracy and therapeutic approaches by leveraging developments in medical imaging, biomarker detection, and clinical biomechanics. A meticulous literature search was executed across PubMed, Scopus, and Google Scholar databases to identify pertinent articles published up to March 2024. The search strategy was carefully crafted, employing a combination of keywords such as "Machine Learning," "Diabetic Foot," "Diabetic Foot Ulcers," "Diabetic Foot Care," "Artificial Intelligence," and "Predictive Modeling." This review offers an in-depth analysis of the foundational principles and algorithms that constitute ML, placing a special emphasis on their relevance to the medical sciences, particularly within the specialized domain of diabetic foot pathology. Through the incorporation of illustrative case studies and schematic diagrams, the review endeavors to elucidate the intricate computational methodologies involved. ML has proven to be invaluable in deriving critical insights from complex datasets, enhancing both the diagnostic precision and therapeutic planning for diabetic foot management. This review highlights the efficacy of ML in clinical decision-making, underscored by comparative analyses of ML algorithms in prognostic assessments and diagnostic applications within diabetic foot care. The review culminates in a prospective assessment of the trajectory of ML applications in the realm of diabetic foot care. We believe that despite challenges such as computational limitations and ethical considerations, ML remains at the forefront of revolutionizing treatment paradigms for the management of diabetic foot complications that are globally applicable and precision-oriented. This technological evolution heralds unprecedented possibilities for treatment and opportunities for enhancing patient care.

Traditional healthcare systems have grappled with meeting the diverse needs of millions of patients, resulting in inefficiencies and suboptimal outcomes. However, the emergence of machine learning (ML) has ushered in a transformative paradigm shift towards value-based treatment, empowering healthcare providers to deliver personalized and highly effective care. Modern healthcare equipment and devices now integrate internal applications that collect and store comprehensive patient data, providing a rich resource for ML-driven predictive models. In this research article, we explore the profound impact of ML on contemporary healthcare, emphasizing its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, leveraging extensive datasets encompassing diverse patient populations. We compared several ML algorithms, including Logistic Regression (accuracy: 0.796875), K-Nearest Neighbors (accuracy: 0.7864583333333334), XG Boost (accuracy: 0.78125), and PyTorch (accuracy: 0.7337662337662337), to identify the best-performing model. The achieved accuracies underscore the effectiveness of these ML techniques in disease prediction and underscore the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and the integration of ML for various healthcare stakeholders. By emphasizing the numerous benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML in healthcare lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs. This article significantly contributes to the field by providing comprehensive insights into the experimental stages, showcasing the achieved results, and highlighting the key conclusions derived from our study. By addressing the limitations of the previous abstract, we ensure a more informative and substantial overview of our research, offering valuable knowledge for researchers, practitioners, and decision-makers striving to leverage the power of ML in healthcare innovation.

As the digital workplace becomes more prevalent, organizations are faced with the challenge of balancing security and information management. On one hand, there is a need to protect sensitive data and prevent cyberattacks, while on the other hand, organizations must enable employees to collaborate and share information effectively. Machine learning (ML) is a promising technology that can help organizations address this challenge. By analyzing data patterns and identifying potential security threats, ML algorithms can enhance security measures and mitigate risks. At the same time, ML can also facilitate information management by automating routine tasks and improving the accuracy of data analysis. In this paper, we explore the role of ML in balancing security and information management in the digital workplace. We propose a hybrid ML model that integrates autoencoder and convolutional subnetworks in unified architecture to help capturing and security threats in the digital workplace, without compromising the information management tasks. We also present a case study of a real-world implementation of ML in a digital workplace setting, highlighting the benefits and limitations of this approach. Our findings suggest that ML can be a valuable tool for achieving a balance between security and information management in the digital workplace, but its successful implementation requires careful consideration of organizational context and stakeholder needs.

The semiconductor industry plays a vital role in driving technological advancements, and the incorporation of AI (Artificial Intelligence) can greatly enhance its efficiency and productivity. Through optimizing material usage and reducing defects, AI can significantly reduce costs and enhance production efficiency and product quality. However, despite the increasing interest in AI applications in the semiconductor industry, comprehensive reviews are lacking to systematically analyze existing research and identify the challenges and opportunities in this field. This review aims to bridge this gap by providing a thorough overview of AI-driven techniques in optimizing semiconductor manufacturing and offering valuable insights for future research directions. The integration of Artificial Intelligence (AI) into chip design marks a transformative phase for the semiconductor industry. Traditional design methodologies, often labor-intensive and time-consuming, are increasingly constrained by human expertise and iterative processes. Generative AI, utilizing advanced machine learning models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), offers innovative approaches to automate and optimize various stages of chip design. This paper examines how generative AI can revolutionize chip design by automating complex tasks, including architecture exploration, circuit optimization, and layout generation. Through case studies, we demonstrate significant improvements in design efficiency, performance optimization, and reduced time-to-market. Additionally, we address challenges such as data availability, model interpretability, and the integration of AI-generated designs into existing verification workflows. The findings highlight the potential of generative AI to enhance design capabilities, reduce development costs, and accelerate innovation in semiconductor technology.

Abstract: This article examines the transformative impact of machine learning (ML) on data analytics and reporting practices, highlighting its potential to revolutionize decision-making processes across various industries. We comprehensively analyze current literature and industry practices and explore how ML techniques enhance data processing, predictive analytics, and visualization capabilities. The article delves into advanced ML applications, including automated data cleaning, feature engineering, and real-time analytics, demonstrating their efficacy in extracting actionable insights from complex datasets. Furthermore, we investigate the role of ML in generating intelligent reports, creating interactive dashboards, and providing contextual recommendations. The article also addresses critical challenges such as data security, regulatory compliance, and ethical considerations in ML-driven analytics. Our findings suggest that the integration of ML in data analytics and reporting not only improves accuracy and efficiency but also enables more sophisticated, data-driven decision-making strategies. This article contributes to the growing body of knowledge on ML applications in business intelligence and provides a foundation for future research in this rapidly evolving field.

PurposeInjuries are common in sports and can have significant physical, psychological and financial consequences. Machine learning (ML) methods could be used to improve injury prediction and allow proper approaches to injury prevention. The aim of our study was therefore to perform a systematic review of ML methods in sport injury prediction and prevention.MethodsA search of the PubMed database was performed on March 24th 2020. Eligible articles included original studies investigating the role of ML for sport injury prediction and prevention. Two independent reviewers screened articles, assessed eligibility, risk of bias and extracted data. Methodological quality and risk of bias were determined by the Newcastle–Ottawa Scale. Study quality was evaluated using the GRADE working group methodology.ResultsEleven out of 249 studies met inclusion/exclusion criteria. Different ML methods were used (tree-based ensemble methods (n = 9), Support Vector Machines (n = 4), Artificial Neural Networks (n = 2)). The classification methods were facilitated by preprocessing steps (n = 5) and optimized using over- and undersampling methods (n = 6), hyperparameter tuning (n = 4), feature selection (n = 3) and dimensionality reduction (n = 1). Injury predictive performance ranged from poor (Accuracy = 52%, AUC = 0.52) to strong (AUC = 0.87, f1-score = 85%). ConclusionsCurrent ML methods can be used to identify athletes at high injury risk and be helpful to detect the most important injury risk factors. Methodological quality of the analyses was sufficient in general, but could be further improved. More effort should be put in the interpretation of the ML models.

Machine learning (ML) is becoming ubiquitous, yet it is still heavily underutilised in the logistics industry. This paper showcases the role of ML in modernising decision making in freight forwarding. After introducing the concept of ML at its simplest application in freight forwarding, a few examples from a time-critical tech-enabled logistics company, Airspace, are showcased to support the idea. The benefits and costs of ML are highlighted with a focus on what business metrics are improved by implementing ML. In the current world, any logistics company not investing in ML development is abdicating strategic advantage to their competition and losing the ability to compete with the more technologically forward-facing companies.

A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification. The objective of this comprehensive review is to outline the application of ML approaches in cardio-oncology, including deep learning, artificial neural networks, random forest and summarize the cardiotoxicity identified by ML. The current literature shows that ML has been applied for the prediction, diagnosis and treatment of cardiotoxicity in cancer patients. In addition, role of ML in gender and racial disparities for cardiac outcomes and potential future directions of cardio-oncology are discussed. It is essential to establish dedicated multidisciplinary teams in the hospital and educate medical professionals to become familiar and proficient in ML in the future.