On applying machine learning techniques for design pattern detection

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.

Present day software engineering concept gives emphasis on the use of design patterns for developing software which are recurring in nature. Detection of design pattern is one of the emerging field of Software Reverse Engineering. In this work, an attempt has been made to present an approach for design pattern detection with the help of Graph Isomorphism and Normalized Cross Correlation techniques. In this study, system and design pattern are presented in the form of graphs. The Graph Isomorphism technique finds the pattern subgraph in the system graph and Normalized Cross Correlation provides a way to formulate the percentage existence of the design pattern in the system. An Eclipse Plug-in i.e., ObjectAid is used to extract Unified Modeling Language (UML) class diagrams as well as eXtensible Markup Language (XML) files from the software system and design patterns. An algorithm is proposed to identify relevant information from the XML files. Then Graph Isomorphism and Normalized Cross Correlation techniques are used to find the pattern subgraph and its percentage existence in the system. This approach has been applied on four open source software tools for the evaluation of five design patterns, such as Composite, Facade, Flyweight, State, and Template Method.

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.

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.

Many structural analyses of software systems are naturally formalized as relational queries, for example, the detection of design patterns, patterns of problematic design, code clones, dead code, and differences between the as-built and the as-designed architecture. This paper describes CrocoPat, an application-independent tool for relational programming. Through its efficiency and its expressive language, CrocoPat enables practically important analyses of real-world software systems that are not possible with other graph analysis tools, in particular analyses that involve transitive closures and the detection of patterns in graphs. The language is easy to use, because it is based on the well-known first-order predicate logic. The tool is easy to integrate into other software systems, because it is a small command-line tool that uses a simple text format for input and output of relations.

ABSTRACTThis paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.

Design pattern detection is a popular re-engineering job in software development. It provides essential information to the developers to recognize, sustain, and recycle the software. This article proposes a graph matching algorithm using a correlation-based feature selection technique to discover the design pattern (DP) instances from the software or system design. Usually, the feature selection method is used to choose the best instances from the dataset, which mostly matches the target feature. However, this paper has applied a correlation feature selection method to match the system design to the design pattern. We have taken three DPs comparing examples for this intention:•the first example for an entire match•the second example for a partial match•the third example of a not matchIn this experiment, we took the Abstract factory DP as a model graph, and the remaining twenty-two DPs are system or DPs. Software instances groups lots of classes and objects we converted into a graph. We observed that three of the 22 DPs are completely matched to the model DP, one pattern is not matched, and the remaining 18 patterns are only partially matched.

Design patterns in software development have shown great promise in improving software quality. Traditionally, software developers utilize a set of design patterns to foster reusability and better software design. Recently, mobile applications (apps) have become a mainstay of modern computing, as well as, a challenging domain for software engineers. This is because mobile apps now target more critical domains such as health, banking, m-payments, and even military to mention just a few. Android is a very popular mobile platform, and has managed to take over the majority of mobile market. It is true that there are comprehensive studies in the area of design patterns detection in several object-oriented languages such as Java, C# and C++. However, little studies target design patterns in Android apps. As a step toward helping to measure and explore the application of design patterns in Android apps, we introduce PatRoid, an automated framework for detecting design patterns. PatRoid is a model-based approach that is able to detect design patterns laying inside Android apps source code. The model is based on a graph isomorphism approach, where design patterns are divided into sub-patterns that can be aggregated to formulate design patterns. We have conducted a preliminary evaluation and the results show that PatRoid can detect all of the 23 GoF design patterns.

Design pattern detection, or rather the detection of structures that match design patterns, is useful for reverse engineering, program comprehension and for design recovery as well as for re-documenting object-oriented systems. Finding design patterns inside the code gives hints to software engineers about the methodologies adopted and the problems found during its design phases, and helps the engineers to evolve and maintain the system. In this paper, we present the results provided by four dierent design pattern detection tools on the analysis of JHotDraw 6.0b1, a well-known Java GUI framework. We show that the tools generally provide dierent results, even while evaluating the same system. From this observation, we introduce an approach based on micro structures detection that aims to discard the false positives from the detected results, hence improving the precision of the analyzed tools results. For this purpose we exploit a set of micro structures called design pattern clues, which give useful hints for the detection of design patterns.

Chapter 17 - Bone cancer detection using machine learning techniques

This study uses machine learning (ML) techniques to predict maximum flank wear on cutting tools during the turning of Ti6Al4V, a titanium alloy known for its challenging machinability. We used three models (a) support vector machines (SVM), (b) random forests (RF), and (c) neural networks (NN) to predict maximum flank wear. The machining parameters are taken as input data sets, which compromise cutting speed, feed, and machining time. All the experiments are done at a constant depth of cut to predict maximum flank wear as an output variable. The forecasted maximum flank wear was validated using experimental data to verify their precision. The SVM model outperformed the other ML models in a wider range of cutting parameters concerning tool flank wear. The SVR model is the most accurate model with an average forecasting error of 3.24%. In forecasting maximum flank wear, the RF and NN models followed, with average forecasting errors of 3.88% and 3.71%, respectively. Additionally, the SVM model's robustness was further validated by its ability to maintain stability at varying cutting speeds and feed rates. The research enhances the economics of the machining process by predicting tool flank wear and fostering sustainability in the manufacturing industry, thereby demonstrating the applicability of ML techniques.

Machine Learning (ML) based condition monitoring and fault detection of industrial equipment is the current scenario for maintenance in the era of Industry-4.0. The application of ML techniques for automatic fault detection minimizes the unexpected breakdown of the system. However, these techniques heavily rely on the historical data of equipment for its training which limits its widespread application in industry. As the historical data is not available for each industrial machine and generating the data experimentally for each fault condition is not viable. Therefore, this challenge is addressed for gear application with tooth defect. In this paper, ML algorithms are trained using simulated vibration data of the gearbox and tested with the experimental data. Simulated data is generated for the gearbox with different operating and fault conditions. A gearbox dynamic model is utilized to generate simulated vibration data for normal and faulty gear condition. A pink noise is added to simulated data to improve the exactness to the actual field data. Further, these simulated-data are processed using Empirical Mode Decomposition and Discrete Wavelet Transform, and features are extracted. These features are then fed to the training of different well-established ML techniques such as Support Vector Machine, Random Forest and Multi-Layer Perceptron. To validate this approach, trained ML algorithms are tested using experimental data. The results show more than 87% accuracy with all three algorithms. The performance of the trained model is evaluated using precision, recall and ROC curve. These metric show the affirmative results for the applicability of this approach in gear fault detection.

This chapter discusses the application of machine learning techniques in the healthcare sector for the prediction of epidemic disease outbreaks. Prediction is an important element in the decision-making processes for responding to and controlling any epidemic disease outbreak. Recently, a large number of countries across the globe has experienced coronavirus infectious disease outbreaks. Machine learning techniques can be very helpful for predicting and managing such epidemic outbreaks. In the new era of artificial intelligence, there exist huge opportunities for technology to be involved to assist in the monitoring and controlling these types of epidemics. With the growth of big data in the healthcare and biomedical sectors, it is now viable to exploit machine learning techniques and accurate disease prediction models to markedly improve epidemic prediction, which will ultimately help in the prevention and control capabilities. This chapter examines the variety of machine learning models that have been developed to predict epidemic diseases and shows how machine learning techniques can be of importance to the public health practitioners for predicting and detecting disease spread, improving epidemic management, and reducing the impact of outbreaks. It also highlights how machine learning techniques can be used for containing the current outbreak of COVID-19, which spread across the globe within a short period of three to four months.

With a significant improvement in performance and efficiency recently, machine learning techniques have been widely applied in the areas of computer vision, natural language processing, and pattern recognition. While machine learning techniques have shown their superior ability in solving many complex problems, their applications to the networking area is still at an early stage. This chapter reviews state-of-the-art machine learning applications in the networking area, with the purpose of providing some insights on existing solutions and future opportunities. An overview of machine learning begins the chapter. This is followed by discussions of the applications of machine learning techniques to traffic classification, traffic routing, and resource management, respectively.

This study presents a comprehensive exploration of the collection and analysis of diverse geological and geophysical datasets from the eastern sector of the Russian Arctic. By leveraging advanced machine learning (ML) techniques, including convolutional neural networks, decision trees, and classical regression models, we provide insights into both data acquisition—encompassing geological, gravimetric, magnetic, and other parameters—and the subsequent analysis and interpretation of these data.The research is structured around three primary objectives:Data Collection and Structuring: A systematic approach to the acquisition and organization of information on the geological and geophysical conditions in the eastern Russian Arctic. Application of Machine Learning Techniques: Employing cutting-edge ML methods to analyze and interpret the collected datasets. Findings and Practical Implications: Highlighting key results and conclusions, with an emphasis on their practical applications in Arctic geological and geophysical research. This work aims to introduce conference participants to innovative ML methodologies in geophysical data analysis and emphasizes the significance of employing diverse approaches to enhance understanding and application. The study also underscores the broader potential of these methods for application in other regions and global-scale research.