Design Of Machine Learning Algorithms Research Articles

Design of Machine Learning Algorithms and Internal Validation of a Kidney Risk Prediction Model for Type 2 Diabetes Mellitus.

This study aimed to explore specific biochemical indicators and construct a risk prediction model for diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D). This study included 234 T2D patients, of whom 166 had DKD, at the First Hospital of Jilin University from January 2021 to July 2022. Clinical characteristics, such as age, gender, and typical hematological parameters, were collected and used for modeling. Five machine learning algorithms [Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF)] were used to identify critical clinical and pathological features and to build a risk prediction model for DKD. Additionally, clinical data from 70 patients (nT2D = 20, nDKD = 50) were collected for external validation from the Third Hospital of Jilin University. The RF algorithm demonstrated the best performance in predicting progression to DKD, identifying five major indicators: estimated glomerular filtration rate (eGFR), glycated albumin (GA), Uric acid, HbA1c, and Zinc (Zn). The prediction model showed sufficient predictive accuracy with area under the curve (AUC) values of 0.960 (95% CI: 0.936-0.984) and 0.9326 (95% CI: 0.8747-0.9885) in the internal validation set and external validation set, respectively. The diagnostic efficacy of the RF model (AUC = 0.960) was significantly higher than each of the five features screened with the highest feature importance in the RF model. The online DKD risk prediction model constructed using the RF algorithm was selected based on its strong performance in the internal validation.

Robust design of a machine learning-based GNSS NLOS detector with multi-frequency features.

The robust detection of GNSS non-line-of-sight (NLOS) signals is of vital importance for land- and close-to-land-based safe navigation applications. The usage of GNSS measurements affected by NLOS can lead to large unbounded positioning errors and loss of safety. Due to the complex signal conditions in urban environments, the use of machine learning or artificial intelligence techniques and algorithms has recently been identified as potential tools to classify GNSS LOS/NLOS signals. The design of machine learning algorithms with GNSS features is an emerging field of research that must, however, be tackled carefully to avoid biased estimation results and to guarantee algorithms that can be generalized for different scenarios, receivers, antennas, and their specific installations and configurations. This work first provides new options to guarantee a proper generalization of trained algorithms by means of a pre-normalization of features with models extracted in open-sky (nominal) scenarios. The second main contribution focuses on designing a branched (or parallel) machine learning process to handle the intermittent presence of GNSS features in certain frequencies. This allows to exploit measurements in all available frequencies as compared to current approaches in the literature based on only the single frequency. The detection by means of logistic regression not only provides a binary LOS/NLOS decision but also an associated probability which can be used in the future as a means to weight-specific measurements. The detection with the proposed branched logistic regression with pre-normalized multi-frequency features has shown better results than the state-of-the-art algorithms, reaching 90% detection accuracy in the validation scenarios evaluated.

From the Editor and Chair of the Award Committee: 2022 Clemen–KleinmuntzDecision AnalysisBest Paper Award

From the Editor and Chair of the Award Committee: 2022 Clemen–Kleinmuntz<i>Decision Analysis</i>Best Paper Award

Extension procedures for lattice Lipschitz operators on Euclidean spaces

We present a new class of Lipschitz operators on Euclidean lattices that we call lattice Lipschitz maps, and we prove that the associated McShane and Whitney formulas provide the same extension result that holds for the real valued case. Essentially, these maps satisfy a (vector-valued) Lipschitz inequality involving the order of the lattice, with the peculiarity that the usual Lipschitz constant becomes a positive real function. Our main result shows that, in the case of Euclidean space, being lattice Lipschitz is equivalent to having a diagonal representation, in which the coordinate coefficients are real-valued Lipschitz functions. We also show that in the linear case the extension of a diagonalizable operator from the values in their eigenvectors coincide with the operator obtained both from the McShane and the Whitney formulae. Our work on such extension/representation formulas is intended to follow current research on the design of machine learning algorithms based on the extension of Lipschitz functions.

Progress towards machine learning reaction rate constants.

Quantum and classical reaction rate constant calculations come at the cost of exploring potential energy surfaces. Due to the "curse of dimensionality", their evaluation quickly becomes unfeasible as the system size grows. Machine learning algorithms can accelerate the calculation of reaction rate constants by predicting them using low cost input features. In this perspective, we briefly introduce supervised machine learning algorithms in the context of reaction rate constant prediction. We discuss existing and recently created kinetic datasets and input feature representations as well as the use and design of machine learning algorithms to predict reaction rate constants or quantities required for their computation. Amongst these, we first describe the use of machine learning to predict activation, reaction, solvation and dissociation energies. We then look at the use of machine learning to predict reactive force field parameters, reaction rate constants as well as to help accelerate the search for minimum energy paths. Lastly, we provide an outlook on areas which have yet to be explored so as to improve and evaluate the use of machine learning algorithms for chemical reaction rate constants.

Assistance Method for the Application-Driven Design of Machine Learning Algorithms

Machine learning (ML) offers a lot of potential for applications in Industry 4.0. By applying ML many processes can be improved. Possible benefits in production are a higher accuracy, an early detection of failures, a better resource efficiency or improvements in quantity control. The use of ML in industrial production systems is currently not widespread. There are several reasons for this, among others the different expertise of data scientists and automation engineers. There are no specific tools to apply ML to industrial facilities neither guidelines for setting up, tuning and validating ML implementations. In this paper we present a taxonomy structure and according method which assist the design of ML architectures and the tuning of involved parameters. As this is a very huge and complex field, we concentrate on a ML algorithm for time series forecast, as this can be used in many industrial applications. There are multiple possibilities to approach this problem ranging from basic feed-forward neural networks to recurrent networks and (temporal) convolutional networks. These different approaches will be discussed and basic guidelines regarding the model selection will be presented. The introduced assistance method will be validated on a industrial dataset.

Breakthrough technologies as a factor of formation of information economy in the conditions of digitalization

The relevance of the study of breakthrough technologies objectively determines the need for radical digital changes in society, aimed at the development of robotics, artificial intelligence, the Internet of Things, nanotechnology, synthetic biology, quantum computers, "smart cities". The purpose of the study is to conceptualize breakthrough technologies as a factor in the formation of the information economy in the context of digitalization and the conditions for deepening these processes. Objectives of the study &nbsp;1) study of the phenomenon of "smart city" as an information system represented by a complicated social organism, which is an intellectual environment associated with a set of sensors that significantly improve people's lives; 2) identifying the world of cloud technologies and the Internet of Things, which represent a variety of digital services that have a profound impact on the service sector - from transport to medicine and education; 3) clarification of the place, role and functions of robotics in a digitalized society; 4) analysis of neurotechnologies, based on new forms of cognitive computing and improving the design of machine learning algorithms. Research methodology - the use of methods of analysis and synthesis, process modeling, synergetic methodology as a methodology of nonlinearity, the method of systems analysis and synthesis, Agile-methodology as a methodology of complex social systems. The result of the study. 1. The concept of "smart cities" associated with digital services, which bring together a meaningful whole of information about the work of hospitals, schools and public transport networks. 2. The world of cloud technologies and the Internet of Things is revealed, which is based on the analysis of a huge amount of data generated by the Internet of Things and the search for patterns to provide services to the population to improve their lives. 3. The era of prosperity of robots and their economic potential in a digitalized society, which play an important role in both industrial production and human household, is revealed. 4. The analysis of neurotechnologies based on new forms of cognitive computations and improvement of designing of machine learning algorithms is presented. It is concluded that in the process of unfolding the Fourth Industrial Revolution, new breakthrough technologies will create new advantages in various industries and lead to important social consequences.

Big Data architecture for intelligent maintenance: a focus on query processing and machine learning algorithms

Exploiting available condition monitoring data of industrial machines for intelligent maintenance purposes has been attracting attention in various application fields. Machine learning algorithms for fault detection, diagnosis and prognosis are popular and easily accessible. However, our experience in working at the intersection of academia and industry showed that the major challenges of building an end-to-end system in a real-world industrial setting go beyond the design of machine learning algorithms. One of the major challenges is the design of an end-to-end data management solution that is able to efficiently store and process large amounts of heterogeneous data streams resulting from a variety of physical machines. In this paper we present the design of an end-to-end Big Data architecture that enables intelligent maintenance in a real-world industrial setting. In particular, we will discuss various physical design choices for optimizing high-dimensional queries, such as partitioning and Z-ordering, that serve as the basis for health analytics. Finally, we describe a concrete fault detection use case with two different health monitoring algorithms based on machine learning and classical statistics and discuss their advantages and disadvantages. The paper covers some of the most important aspects of the practical implementation of such an end-to-end solution and demonstrates the challenges and their mitigation for the specific application of laser cutting machines.

Survey on Clustering High-Dimensional data using Hubness

Most data of interest today in data-mining applications is complex and is usually represented by many different features. Such high-dimensional data is by its very nature often quite difficult to handle by conventional machine-learning algorithms. This is considered to be an aspect of the well known curse of dimensionality. Consequently, high-dimensional data needs to be processed with care, which is why the design of machine-learning algorithms needs to take these factors into account. Furthermore, it was observed that some of the arising high-dimensional properties could in fact be exploited in improving overall algorithm design. One such phenomenon, related to nearest-neighbor learning methods, is known as hubness and refers to the emergence of very influential nodes (hubs) in k-nearest neighbor graphs. A crisp weighted voting scheme for the k-nearest neighbor classifier has recently been proposed which exploits this notion.

Where the Sun never Shines

In this paper I elaborate on models of »post-enlightened cognition« that fall squarely in place with current research paradigms in cognitive neuroscience, psychology and artificial intelligence. I show how the design of machine learning algorithms is entangled with research on creativity and pathology in cognitive neuroscience and psychology through and interest in »episodic memory« and various forms of »spontaneous thought.« The most prominent instances of spontaneous thought - mind wandering and day dreaming - appear when the demands of the environment abate and have for a long time been stigmatized as signs of distraction or conceived to be of pathological character. Recent research in cognitive neuroscience, however, conceptualizes spontaneous thought as serving the purpose of, e.g., creative problem solving and invokes older discussions around the links between creativity and pathology. I discuss how attendant attempts at differentiating creative cognition from its pathological forms in contemporary psychology, cognitive neuroscience, and AI confronts traditional understandings of rational decision making with its post-enlightened Other.

A Survey of Adversarial Machine Learning in Cyber Warfare

&lt;div class="page" title="Page 1"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;The changing nature of warfare has seen a paradigm shift from the conventional to asymmetric, contactless warfare such as information and cyber warfare. Excessive dependence on information and communication technologies, cloud infrastructures, big data analytics, data-mining and automation in decision making poses grave threats to business and economy in adversarial environments. Adversarial machine learning is a fast growing area of research which studies the design of Machine Learning algorithms that are robust in adversarial environments. This paper presents a comprehensive survey of this emerging area and the various techniques of adversary modelling. We explore the threat models for Machine Learning systems and describe the various techniques to attack and defend them. We present privacy issues in these models and describe a cyber-warfare test-bed to test the effectiveness of the various attack-defence strategies and conclude with some open problems in this area of research.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;

Machine learning for tackling microbiota data and infection complications in immunocompromised patients with cancer.

Machine learning for tackling microbiota data and infection complications in immunocompromised patients with cancer.

Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets

Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure.

Foreword to the Special Issue on Machine Learning for Remote Sensing Data Processing

The twenty-seven articles in this special issue is a follow-up to special sessions organized at WHISPERS conferences. Such sessions drew unexpectedly large attendance, signaling the interest and need for a focused platform to exchange knowledge at the intersection of machine learning and remote sensing. The collection of papers in this special issue presents a comprehensive sample of the latest trends in the design of machine learning algorithms for geospatial data. It covers a wide spectrum of remote sensing applications and presents new solutions to answer to the call of the new generation of sensors, covering the electromagnetic range from optical to microwave data.

Fixed-rank matrix factorizations and Riemannian low-rank optimization

Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix.

Hubness-based fuzzy measures for high-dimensional k-nearest neighbor classification

Most data of interest today in data-mining applications is complex and is usually represented by many different features. Such high-dimensional data is by its very nature often quite difficult to handle by conventional machine-learning algorithms. This is considered to be an aspect of the well known curse of dimensionality. Consequently, high-dimensional data needs to be processed with care, which is why the design of machine-learning algorithms needs to take these factors into account. Furthermore, it was observed that some of the arising high-dimensional properties could in fact be exploited in improving overall algorithm design. One such phenomenon, related to nearest-neighbor learning methods, is known as hubness and refers to the emergence of very influential nodes (hubs) in k-nearest neighbor graphs. A crisp weighted voting scheme for the k-nearest neighbor classifier has recently been proposed which exploits this notion. In this paper we go a step further by embracing the soft approach, and propose several fuzzy measures for k-nearest neighbor classification, all based on hubness, which express fuzziness of elements appearing in k-neighborhoods of other points. Experimental evaluation on real data from the UCI repository and the image domain suggests that the fuzzy approach provides a useful measure of confidence in the predicted labels, resulting in improvement over the crisp weighted method, as well as the standard kNN classifier.

Anomaly Detection Using Temporal Data Mining in a Smart Home Environment

To many people, home is a sanctuary. With the maturing of smart home technologies, many people with cognitive and physical disabilities can lead independent lives in their own homes for extended periods of time. In this paper, we investigate the design of machine learning algorithms that support this goal. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a smart home, and that the results can be used to perform automated health monitoring and to detect anomalies. Specifically, our algorithms draw upon the temporal nature of sensor data collected in a smart home to build a model of expected activities and to detect unexpected, and possibly health-critical, events in the home. We validate our algorithms using synthetic data and real activity data collected from volunteers in an automated smart environment. The results from our experiments support our hypothesis that a model can be learned from observed smart home data and used to report anomalies, as they occur, in a smart home.