Application of data-driven machine learning in performance prediction and multi-objective optimization of green sustainable steam-cured concrete

Machine learning in predicting mechanical behavior of additively manufactured parts

Chapter 13 - Application of machine learning and artificial intelligence in design, optimization, and control of power electronics converters for renewable energy-based technologies

Machine Learning in nanoarchitectonics.

Maintaining optimal humidity in livestock buildings during winter is a major challenge in cold climate regions due to the conflict between moisture-removing ventilation and the need for heat preservation. To address this issue, a novel condensation dehumidification system is proposed that utilizes the natural low temperature of cold winters. An integrated energy consumption model, coupling moisture and thermal balances, was developed to evaluate room temperature drop, dehumidification rate (DR), and the internal circulation coefficient of performance (IC-COP). The model was calibrated and validated with experimental data comprising over 150 operational cycles under varied operation conditions, including initial temperature differences (ranging from −20 to −5 °C), air flow rates (0.6–1.5 m/s), refrigerant flow rates (3–7 L/min), and high-humidity conditions (>90% RH). Correlation analysis showed that higher indoor humidity improved both DR and IC-COP. Four machine learning models—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Multilayer Perceptron (MLP)—were developed and compared with a stacking ensemble learning model. Results demonstrated that the stacking model achieved superior prediction accuracy, with the best R2 reaching 0.908, significantly outperforming individual models. This work provides an energy-saving dehumidification solution for enclosed livestock housing and a case study on the application of machine learning for energy performance prediction and optimization in agricultural environmental control.

Chapter Twenty-Four - Application of machine learning in hydraulic fracture optimization

Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar.Graphical

This paper addresses the challenge of optimizing enzyme activity and production in various industries by leveraging machine learning models [9]. Traditionally, enzyme optimization has been resource-intensive and costly [10]. Our proposed solution involves collecting diverse enzymatic reaction data, generating synthetic data, and using cross-validation and ensemble methods for model selection. Challenges such as data availability and negative value generation in dummy data were addressed creatively. Experimentation revealed that ensemble methods like Random Forest and Decision Tree Regressor outperformed linear models, highlighting the potential of machine learning in enzyme optimization [11]. This research offers a data-driven approach that promises efficiency and resource conservation, with significant implications for biotechnologists, industrial manufacturers, and the scientific community [12]. The application of machine learning in enzyme optimization not only streamlines processes but also paves the way for sustainability and innovation in enzyme-related industries, making it a compelling solution for widespread adoption.

Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.

with the intensification of market competition and the complexity of consumer behavior, enterprises are faced with the challenge of how to accurately identify potential customers and improve user conversion rate. This paper aims to study the application of machine learning in consumer behavior prediction and precision marketing. Four models, namely support vector machine (SVM), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and backpropagation artificial neural network (BPANN), are mainly used to predict consumers' purchase intention, and the performance of these models in different scenarios is verified through experiments. The results show that CatBoost and XGBoost have the best prediction results when dealing with complex features and large-scale data, F1 scores are 0.93 and 0.92 respectively, and CatBoost's ROC AUC reaches the highest value of 0.985. while SVM has an advantage in accuracy rate, but slightly underperformance when dealing with large-scale data. Through feature importance analysis, we identify the significant impact of page views, residence time and other features on purchasing behavior. Based on the model prediction results, this paper proposes the specific application of optimization marketing strategies such as recommendation system, dynamic pricing and personalized advertising. Future research could improve the predictive power of the model by introducing more kinds of unstructured data, such as consumer reviews, images, videos, and social media data. In addition, the use of deep learning models, such as Transformers or Self-Attention Mechanisms, can better capture complex patterns in long time series data.

In the real world, it is challenging to calculate a trade-off alternative with traditional classical methods for complex non-linear systems, which always involve multiple conflicting objectives. Such complicated systems urgently desire advanced methods to conquer the multi-objective optimization problems (MOPs). As a promising AI method, the development and application of Machine Learning (ML) attract increasingly more attention from researchers. The natures of ML methods, such as parallel computation possibility, no need for any priori assumptions, etc., ensure the effectiveness and efficiency for solving MOPs. However, as we know, there is no literature related to the comprehensive review of ML in multi-objective optimization domain until now. This literature review aims to provide researchers a global view of mainstream ML methods for MOO in a general domain and a reference for applying ML methods to solve a specific type of MOPs. In this paper, the general ML mainstream methods are summarized, based on which the literature relating to ML on MOPs are retrieved in comprehensive domains. The relevant literature is categorized according to the emphasis of object types, purposes and methods, and the categorization results are finally analyzed and discussed.

This paper explores the applications of machine learning in the prediction of anemia, highlighting its potential to revolutionize clinical diagnosis and management. Anemia, a prevalent condition affecting millions globally, is often underdiagnosed due to traditional diagnostic methods that rely on clinical judgment and standard laboratory tests. Machine learning techniques provide innovative solutions by analyzing complex datasets that incorporate questionnaire, clinical features, demographic information, and laboratory results, thereby enhancing the accuracy of anemia predictions. This paper examines decision trees, random forests, support x'ector machines, and neural networks. emphasizing their efficacy in identifying patterns and risk factors associated with anemia. Obstacles such as data quality, feature selection, and model interpretability continue to hinder clinical adoption. The review identifies future research directions aimed at improving model generalizability and interpretability, ensuring that these technologies can be effectively integrated into healthcare practice. This paper advocates for the systematic adoption of machine learning methodologies in anemia management, positing that such innovations are crucial for advancing public health and optimizing resource allocation in clinical settings.

The purpose of this research is to offer a technique for assessing user experience in mobile applications utilizing AIAM technology. Due to ineffective and time-consuming nature of conventional data gathering techniques (such as user interviews and user inference), AIAM concentrates on using Artificial Intelligence (AI) to assess and enhance user experience. Logs from a mobile application may be used to gather information about user activity. Only a few parameters of data are utilized in the process of surfing and running mobile applications to ensure the privacy of users. The method's objective is to create the deep neural network prototype as close as feasible to a user's experience when using a mobile app. For particular objectives, we create and employ application interfaces to train computational models. The click data from all users participating in a certain task is shown on these projected pages. User activity may therefore be mapped in connected and hidden layers of the system. Finally, the social communications application is used to test the efficacy of the suggested method by implementing the improved design.

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system’s properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted.

Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Over half of Indians find gainful employment in agriculture, and this industry also makes a sizable contribution to the country's GDP. However, Indian farmers face several challenges that hinder their productivity and profitability, including low productivity and yield, dependence on monsoon rains, soil degradation, and nutrient depletion. Additionally, farmers in India face infrastructural and logistical challenges, market volatility, and limited access to credit and technology.
 In recent years, computer science has emerged as a crucial field that can help address some of the challenges facing Indian agriculture. This paper aims to explore the role of computer science in Indian agriculture, with a focus on precision farming and IoT-based solutions, the use of AI and machine learning in crop prediction and yield optimization, and the applications of data analytics in crop monitoring and disease detection.
 The paper begins with an overview of Indian agriculture and its challenges, providing a background and context for the problem. It then outlines the research questions and objectives, the study's objectives, as well as its scope and limits.
 The agricultural difficulties experienced by Indian farmers are the subject of the paper's second section. The section highlights low productivity and yield, dependence on monsoon rains, soil degradation and nutrient depletion, lack of access to credit and technology, market volatility, and infrastructural and logistical challenges. The section discusses the extent of these challenges and their impact on Indian agriculture.
 The following section of the paper focuses on the role of computer science in agriculture. It provides an overview of computer science applications in agriculture, including precision farming and IoT-based solutions, the use of AI and machine learning in crop prediction and yield optimization, and the applications of data analytics in crop monitoring and disease detection. The section highlights the potential benefits of these applications in Indian agriculture and discusses the opportunities for innovation and collaboration in this area.
 The paper then discusses the existing solutions and their limitations. The section presents an overview of existing solutions and interventions, including success stories and case studies. It also discusses the limitations and challenges of existing solutions, as well as policy and regulatory challenges in technology adoption in agriculture.
 The following section focuses on emerging technologies and their potential for Indian agriculture. It provides an overview of emerging technologies in agriculture, including case studies of successful implementation in India. This section covers the role of the government and the private sector in encouraging the use of emerging technology in Indian agriculture, highlighting the potential benefits and problems of doing so.
 The report then moves on to discuss data-driven strategies for Indian agriculture. This section explores the ways in which data analytics have been put to use in Indian agriculture, the opportunities for innovation and collaboration that have arisen as a result, and the limitations of data-driven techniques.
 The paper then discusses the role of digital platforms in connecting farmers to markets. It provides an overview of digital platforms in agribusiness, including case studies of successful digital platforms in India. The section discusses the potential benefits and challenges of digital platforms for Indian farmers and highlights the role of government and the private sector in promoting the adoption of digital platforms in agriculture.
 Finally, the paper concludes with a summary of the key findings and implications of the study. The section emphasizes the potential of computer science in addressing the challenges facing Indian agriculture and the need for policymakers, researchers, and practitioners to collaborate to promote its adoption. The paper concludes with recommendations for future research in this area.