A Survey on Efficient Analysis of Soft Tissues Tumors using Machine Learning

Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

Machine learning (ML) is a type of artificial intelligence (AI) that empowers a framework to gain information instead of through express programming. The application of ML techniques is not limited to one sector, and it has been used in healthcare, fraud detection, automation etc. The bibliometric analytical approach has not yet been applied to the construct 'application of ML in banking and finance'. The study attempts to connect ML into banking and finance which is intrinsically a multidisciplinary study. The study identifies and put across a comprehensive analysis of the publications made in this construct so as to make significant contributions on the existing body of knowledge in this field with facts that are statistically backed using bibliometric analysis. This study will provide insights to faculty, researchers and industry experts about the research trends and the possible avenues for future research in this construct.

The kidney, an essential organ of the human body, can suffer pathological damage that can potentially have serious adverse consequences on the human body and even affect life. Furthermore, the majority of kidney-induced illnesses are frequently not readily identifiable in their early stages. Once they have progressed to a more advanced stage, they impact the individual's quality of life and burden the family and broader society. In recent years, to solve this challenge well, the application of machine learning techniques in renal medicine has received much attention from researchers, and many results have been achieved in disease diagnosis and prediction. Nevertheless, studies that have conducted a comprehensive bibliometric analysis of the field have yet to be identified. This study employs bibliometric and visualization analyses to assess the progress of the application of machine learning in the renal field and to explore research trends and hotspots in the field. A search was conducted using the Web of Science Core Collection database, which yielded articles and review articles published from the database's inception to May 12, 2024. The data extracted from these articles and review articles were then analyzed. A bibliometric and visualization analysis was conducted using the VOSviewer, CiteSpace, and Bibliometric (R-Tool of R-Studio) software. 2,358 papers were retrieved and analyzed for this topic. From 2013 to 2024, the number of publications and the frequency of citations in the relevant research areas have exhibited a consistent and notable increase annually. The data set comprises 3734 institutions in 91 countries and territories, with 799 journals publishing the results. The total number of authors contributing to the data set is 14,396. China and the United States have the highest number of published papers, with 721 and 525 papers, respectively. Harvard University and the University of California System exert the most significant influence at the institutional level. Regarding authors, Cheungpasitporn, Wisit, and Thongprayoon Charat of the Mayo Clinic organization were the most prolific researchers, with 23 publications each. It is noteworthy that researcher Breiman I had the highest co-citation frequency. The journal with the most published papers was "Scientific Reports," while "PLoS One" had the highest co-citation frequency. In this field of machine learning applied to renal medicine, the article "A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury" by Tomasev N et al., published in NATURE in 2019, emerged as the most influential article with the highest co-citation frequency. A keyword and reference co-occurrence analysis reveals that current research trends and frontiers in nephrology are the management of patients with renal disease, prediction and diagnosis of renal disease, imaging of renal disease, and development of personalized treatment plans for patients with renal disease. "Acute kidney injury," "chronic kidney disease," and "kidney tumors" are the most discussed diseases in medical research. The field of renal medicine is witnessing a surge in the application of machine learning. On one hand, this study offers a novel perspective on applying machine learning techniques to kidney-related diseases based on bibliometric analysis. This analysis provides a comprehensive overview of the current status and emerging research areas in the field, as well as future trends and frontiers. Conversely, this study furnishes data on collaboration and exchange between countries, regions, institutions, journals, authors, keywords, and reference co-citations. This information can facilitate the advancement of future research endeavors, which aim to enhance interdisciplinary collaboration, optimize data sharing and quality, and further advance the application of machine learning in the renal field.

Cultivated meat production, an innovative and sustainable alternative to conventional animal farming, has gained significant attention in recent years. As the demand for ethical and environmentally friendly protein sources continues to rise, the need for efficient and scalable production strategies becomes critical. Notably, the integration of advanced technology, such as machine learning (ML), can enhance the efficiency of the cultivated meat production process. The goal of this review paper is to highlight the advantages and limitations of various ML approaches and provide a balanced discussion on the integration of ML techniques for three-dimensional (3D)-bioprinted cultivated meat. This review paper explores the application of ML techniques in various facets of 3D-bioprinted cultivated meat and highlights the potential for ML to optimize various aspects of the process, from predicting printability and optimizing printing parameters to characterizing meat flavor and monitoring meat quality. ML plays a pivotal role in optimizing the material formulation to improve ink printability and identifying an optimal combination of printing parameters to achieve high printing resolution and accuracy. Furthermore, ML can aid in modeling sensory attributes, ensuring that the cultivated meat replicates the desired meat flavor. Finally, ML can be applied for meat quality control as it facilitates the automated detection of harmful pathogens, ensuring the safety and consistency of 3D-bioprinted cultivated meat.

Channel identification is a useful function to support wireless telecommunication operations because the knowledge of the radio frequency propagation channel characteristics can improve communication efficiency and robustness. In recent times, the application of machine learning (ML) algorithms to the problem of channel identification has been proposed in the literature. In particular, Deep Learning (DL) has demonstrated superior performance to ’shallow’ machine learning algorithms for many wireless communication functions. Inspired by the success of DL in literature, the authors in this paper apply Convolutional Neural Networks (CNN) to the problem of channel identification, which is still an emerging research area. CNN is a deep learning algorithm that has demonstrated superior performance to ML algorithms, in particular for image processing tasks. Because the digitized RF signal is a one-dimensional time series, different algorithms are applied to convert the time series to images using various Time Frequency Transform (TFT) including the CWTs, spectrogram, and Wigner Ville distribution. The images are then provided as input to the CNN. The approach is applied to a data set based on weather radar pulse signals generated in the laboratory of the author’s facilities on which different fading models are applied. These models are inspired by the tap-delay-line 3GPP configurations defined in the standards, but they have been customized with Nakagami-m fading distribution (3GPP-like fading models). The results show the superior performance of time–frequency CNN in comparison to 1D CNN for different values of Signal to Noise Ratio (SNR) in dB. In particular, the study shows that the Continuous Wavelet Transform (CWT) has the optimal performance in this data set, but the choice of the mother wavelet remains a problem to be solved (this is a well-known problem in the research literature). Then, this study also proposes an adaptive technique for the choice of the optimal mother wavelet, which is evaluated on the mentioned data set. The results show that the adaptive proposed approach is able to obtain the optimal performance for most of the SNR conditions.

Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

With the popularization of machine learning (ML) techniques and the increased chipset’s performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

The Internet is a primary breeding ground for various network attacks, presenting a persistent challenge to cybersecurity. Machine learning has emerged as a powerful tool for automating the detection of cyber attacks and threats. Unlike traditional algorithms that rely on predefined signatures or rules, machine learning excels at uncovering complex patterns within vast volumes of Internet data. This capability enables the development of more intelligent and effective security solutions. The integration of machine learning into security systems has become a critical focus in both academia and industry. Nevertheless, the integration of machine learning and cybersecurity remains in its infancy. A significant gap exists between theory and practice: while machine learning may perform well in controlled or simulated environments, it may not work as expected in complex and dynamic real-world scenarios. Currently, machine learning security solutions are often evaluated using publicly available datasets in controlled laboratory settings. These lab-based evaluations introduce uncertainty about their practical effectiveness, which may hinder their adoption by security practitioners in real-world contexts. Proactive security measures aim to prevent threats before they cause harm, offering greater efficiency and effectiveness compared to reactive security, which relies on responsive strategies. Machine learning can enhance proactive security by automatically identifying potential vulnerabilities and predicting emerging threats, thereby strengthening the protection of Internet infrastructures. This dissertation aims to facilitate the application of machine learning in real-world cybersecurity settings, with a focus on addressing five core research questions: 1) What general challenges and limitations arise in the design, development, and deployment of practical ML-based security applications? 2) How can ML-based security solutions be effectively implemented in real-world scenarios? 3) What specific challenges and limitations are encountered when implementing such solutions in practical environments? 4) What current technologies, approaches, or solutions can be used to address these challenges and limitations, thereby facilitating the implementation of practical ML-based security systems? 5) What future research directions hold promise for advancing the practical application of machine learning in the cybersecurity field? We begin with a comprehensive literature review, identifying potential challenges and limitations in the practical application of machine learning (ML) within the field of cybersecurity. Building on these insights, we develop two practical tools designed to enhance existing proactive security mechanisms, directly addressing specific real-world challenges. Subsequently, we offer key recommendations regarding methodologies and solutions to mitigate obstacles in the development and deployment of realistic ML-based security systems. Finally, we propose future research directions, highlighting promising technologies and concepts with the potential to advance the field of practical ML applications in cybersecurity. This dissertation represents the first comprehensive investigation into the application of machine learning in realistic cybersecurity scenarios, with significant implications for the field of ML-based cybersecurity. It provides security researchers and practitioners with a deeper understanding of practical challenges and obstacles in this field. The development of two practical tools not only advances state-of-the-art proactive security mechanisms but also delivers actionable recommendations to assist researchers in addressing these challenges. Finally, the proposed future research directions highlight the potential of emerging technologies to enable more effective ML-driven security solutions in real-world environments. Overall, the insights presented in this dissertation help bridge the gap between theory and practice in ML-based cybersecurity, promoting the practical implementation of ML security systems that can replace or complement traditional approaches.

Over the past years there has been a surge of interest on the application of machine learning, and more broadly, artificial intelligence, to scientific applications. This interest has been spurred by an advancement of our computational capabilities coupled with the development of a large set of methods and machine learning frameworks to carry out complex analysis of large datasets in ways that were not possible in the past. In this presentation, I will explore the application of machine learning techniques to the area of atomic layer deposition, and in particular to process optimization. While most ALD processes are highly reproducible, the optimization of both new and existing ALD processes in novel reactors is often time consuming and labor intensive. This motivated us to search for ways of accelerating this task using both ex-situ and in-situ experimental data that is readily available in many laboratories.First, I will focus on the development of surrogate models that can predict the saturation dose times based on partially saturated growth profiles, a type of data that is part of the routine characterization of ALD processes in many labs. In order to develop this surrogate model, we first created a dataset using simulated data providing growth profiles across one of our ALD reactors for a wide range of possible experimental conditions, including precursor pressure, surface reactivity, growth per cycle, flow conditions, and dose times. We then used this dataset to train deep neural networks to predict the dose time that would lead to complete saturation. By tailoring the complexity of the model, the number of growth profiles per condition, and the structure/depth of the neural network we can explore the minimum number experiments and data points required to accurately predict saturation for an unknown ALD process. Our results show that for the optimization of a single precursor, neural networks can accurately predict saturation times using just a single growth profile and its corresponding dose time as an input.Then, I will focus on the use of in-situ techniques for the automatic optimization of ALD processes without a human in the loop. The advantage of using in-situ techniques is that they can provide a much faster response, reducing optimization times by more than two orders of magnitude. Using a realistic model of a single quartz crystal microbalance, we have developed both expert systems and Bayesian optimization-based models that are capable of exploring the process conditions and converge on optimal conditions leading to saturation. We then implemented the expert system model in one of our experimental reactors, experimentally validating this approach. This methodology can be expanded to other in-situ techniques, such as spectroscopic ellipsometry, or more sophisticated arrangement including more than one probe to obtain spatially dependent data.Finally, I will briefly discuss other potential applications of machine learning, for instance for process optimization using spatial ALD configurations, or to predict the self-limited nature of novel processes.This material is based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.

This survey presents an in-depth analysis of machine learning techniques applied to lidar observations for the detection of aerosol and cloud optical, geometrical, and microphysical properties. Lidar technology, with its ability to probe the atmosphere at very high spatial and temporal resolution and measure backscattered signals, has become an invaluable tool for studying these atmospheric components. However, the complexity and diversity of lidar technology requires advanced data processing and analysis methods, where machine learning has emerged as a powerful approach. This survey focuses on the application of various machine learning techniques, including supervised and unsupervised learning algorithms and deep learning models, to extract meaningful information from lidar observations. These techniques enable the detection, classification, and characterization of aerosols and clouds by leveraging the rich features contained in lidar signals. In this article, an overview of the different machine learning architectures and algorithms employed in the field is provided, highlighting their strengths, limitations, and potential applications. Additionally, this survey examines the impact of machine learning techniques on improving the accuracy, efficiency, and robustness of aerosol and cloud real-time detection from lidar observations. By synthesizing the existing literature and providing critical insights, this survey serves as a valuable resource for researchers, practitioners, and students interested in the application of machine learning techniques to lidar technology. It not only summarizes current state-of-the-art methods but also identifies emerging trends, open challenges, and future research directions, with the aim of fostering advancements in this rapidly evolving field.

Coastal aquifers are vital for sustaining ecosystems and providing freshwater for agriculture and domestic use. However, rising groundwater salinity in these regions demands innovative approaches for accurate modeling and prediction. In recent decades, machine learning (ML) and deep learning (DL) techniques have been extensively used across various water resource management fields, often yielding promising outcomes. This review examines the growing usage of ML and DL techniques in modeling groundwater salinity in coastal aquifers. The analysis is based on 104 peer-reviewed journal articles including the review articles indexed in PubMed, ScienceDirect, Google Scholar, Scopus, and SpringerLink using keywords like "saltwater intrusion", "machine learning", “deep learning”, and "groundwater salinity". The review discusses recent advancements, challenges, and future directions for improving ML model accuracy and applicability. Studies reveal an increasing reliance on ML techniques to predict groundwater salinity and manage seawater intrusion across various coastal geological settings. Researchers have employed a range of ML models, from traditional methods like Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems to advanced DL-based models like Convolutional Neural Networks and Long Short-Term Memory Networks, often using ensemble methods to enhance accuracy. Key findings include the top-tier output of ensemble and optimized models, including Random Forest and Grasshopper Optimization Algorithm-XGBoost. These studies highlight the importance of model selection, input variable ranking, and computational efficiency, demonstrating ML's global relevance in environmental management. The review highlights the prospects of ML, DL, and ensemble models to redefine groundwater management in coastal aquifers, offering more effective and sustainable solutions to the challenges posed by groundwater salinity and saltwater intrusion. Future advancements will focus on further integrating DL, explainable artificial intelligence, and ensemble approaches, along with other innovative techniques, to explore sparsely studied variables, model new or unique study areas, and apply ML methods for managing groundwater salinity.

Application of machine learning in carbon capture and storage: An in-depth insight from the perspective of geoscience

With the evolution of technology come along risks. With complex prototypes and methods, not only the decision-making propensity of the machines increases but also the risk assessment reduces and frauds increase. This calls for a better and improved version of risk analysis in the industry. Machine learning is considered an appropriate solution for the management of risks as it can produce the desired solution with less human effort. Risk is the probability of occurrence of failure of the system, which is unwanted in any project as it reduces the profit as well productivity of the system. So, to minimize the possibility of risks, certain methods are adopted, which operate through machine learning. Risk analysis mainly includes hazard identification, risk communication, risk assessment, and risk management. The chapter provides an insight into various applications of machine learning techniques in the field of risk analysis, which includes liquidity risk, operational risk, market risk, and RegTech. The application of machine learning in this sector has led to the conclusion that these methods or techniques can be used to analyze huge amounts of data with efficient predictive analysis. Various use cases have also been discussed such as supervision of conduct and market abuse in trading, fraud detection, and credit risk. Moreover, the future of machine learning in risk analysis and management is presented as well bringing out a positive picture. The chapter concludes with some limitations, which need to be fixed for better risk management.

The application of machine learning (ML) in dentistry has expanded significantly, promising enhanced diagnostic accuracy and efficiency. This study reviews ML techniques employed in detecting dental caries and periodontal diseases using intraoral images [1]. Traditional diagnostic methods often involve subjective assessment, potentially leading to diagnostic inconsistencies. Advanced ML algorithms, particularly convolutional neural networks (CNNs), offer objective, rapid, and accurate analysis of dental imagery. This review summarizes current methodologies, examines dataset characteristics, and evaluates algorithm performances reported in recent literature. Findings indicate that CNN-based systems achieve high accuracy, sensitivity, and specificity, significantly outperforming traditional radiographic interpretation. Challenges including dataset limitations, image quality variability, and model generalization are addressed. The review highlights the importance of integrating diverse and high-quality image datasets to enhance the robustness and generalizability of ML models. Additionally, the potential of ML techniques to streamline clinical workflows, reduce diagnostic time, and improve early intervention strategies is discussed. Ethical considerations, such as transparency in algorithm decision-making proces ses and ensuring patient data privacy, are also emphasized. Conclusively, ML applications in intraoral diagnostics represent a transformative advancement in dental care, underscoring the necessity for standardized image acquisition protocols, comprehensive clinical validation studies, and extensive, diverse datasets to ensure practical implementation and widespread clinical adoption.