Pattern Recognition Identification Research Articles

A battery-free, wireless, flexible bandlike e-nose based on MEMS gas sensors for precisely volatile organic compounds detection

Real-time detection of VOCs in the car cabin is crucial for protecting driver’s health. MOS gas sensors, with the advantages of low cost, simplicity and fast response, are widely used for gas monitoring. However, they typically exhibit broad-spectrum responsiveness to multiple gases. Despite numerous improvement strategies proposed from the perspective of MOS material modification, achieving ideal gas selectivity remains challenging. An e-nose based on gas sensor array is a promising approach for gas identification. However, the hardware circuit system and battery supply module in traditional e-nose result in large size, complex structure, and inconvenience in use. In this work, we synthesized MOS materials with different morphologies to form a microarray containing six MEMS gas sensors, and developed a novel belt-shaped wireless and battery-free flexible e-nose, where a mobile phone serves as its wireless power supply and data communication system. This design provides a flexible e-nose with excellent wearability and safety, allowing users to monitor surrounding gas conditions at any time conveniently. Subsequently, we design pattern recognition models using algorithms such as MLP, RF, XGBoost, and LGBM, in combination with ensemble learning strategies, achieving high accuracy in VOCs classification and low-error concentration prediction. This flexible e-nose holds significant application value for gas detection in car cabins and indoor environments.

Identifying Concurrent Behaviours in Erlang Legacy Systems

In Erlang, behaviours are special forms of design patterns. There are many benefits to using behaviours. For example, behaviours can help abstract away the most common parts when solving similar problems. Design pattern recognition may help understand the source code of the software. It can provide structured information about the purpose of specific parts and the design decisions behind the implementation. For object-oriented languages, several tools exist that use different approaches and methods to identify design patterns. We present a method for identifying source code fragments in legacy Erlang systems amenable to transforming into client-server Erlang design patterns. In our analysis, we identify the base set of server candidates using concurrent process analysis and narrow down the result using further static analysis knowledge using the RefactorErl framework.

Rule-Based Architectural Design Pattern Recognition with GPT Models

Architectural design patterns are essential in software development because they offer proven solutions to large-scale structural problems in software systems and enable developers to create software that is more maintainable, scalable, and comprehensible. Model-View-Whatever (MVW) design patterns are prevalent in many areas of software development, but their use in Web development is on the rise. There are numerous subtypes of MVW design patterns applicable to Web systems, but there is no exhaustive listing of them. Additionally, it is unclear how these subtypes can be utilized in contemporary Web development, as their usage is typically unconscious. Here, we discuss and define the most prevalent MVW design patterns used in Web development, as well as provide Angular framework examples and guidance on when to employ a particular design pattern. On the premise of the primary characteristics of design patterns, we created a rule system that large language models (LLMs) can comprehend without doubt. Here, we demonstrate how effectively Generative Pre-trained Transformer (GPT) models can identify various design patterns based on our principles and verify the quality of our recommendations. Together, our solution and GPT models constitute an effective natural language processing (NLP) solution capable of detecting MVW design patterns in Angular projects with an average accuracy of 90%.

Abstract 3140: Computational modeling of stromal-immune cell interactions in triple-negative breast cancer

Abstract Breast cancer is the most common cancer type in women worldwide, and it represents the second most common cause of cancer deaths. Compared with other breast cancer subtypes, triple-negative breast cancer (TNBC) has the worst prognosis. The tumor microenvironment (TME) of TNBC is a complex ecosystem, characterized by the dynamic interactions between stromal, immune, extracellular matrices (ECM), and cancer cells. Understanding the fibroblasts as a heterogeneous population with pleiotropic actions on TME, mediated by ECM formation and interactions with immune cells, is necessary to develop approaches to interfere with the loss of ECM homeostasis, promote immunity, and improve the outcome of concomitant therapeutic approaches in TNBC and other cancer types. Our recent studies suggested distinct fibroblast sub cell types of varied biosynthesis of ECM components in the TME of TNBC, which have significant associations with the infiltration and activity of immune cells. In this study, we developed advanced computational methods to characterize the role of fibroblast sub cell types, their functional variations, and stromal-immune cell interactions in TNBC, by integrative analysis of tissue, single-cell, and spatial multi-omics data. We hypothesized that the genes involved in sub cell types and functional variations of fibroblast cells may form distinct data structures in omics data, which are identifiable by carefully designed pattern recognition methods. We also hypothesized that different subtypes and functional activities of fibroblast cells lead to varied ECM formations, metabolic shifts, and affect the infiltration and functions of immune cells, which could be identified as the spatial regions with distinct expression changes of stromal and immune marker genes.We first reconstructed TNBC TME context-specific gene sets of fibroblast sub cell types and functions. We assemble a series of computational methods including tissue data deconvolution, functional module detection on scRNA-seq data, and metabolic flux analysis into an integrative data analysis framework to characterize fibroblast cells and their functions in the TME of TNBC and reconstruct context-specific pathways of fibroblast sub cell types and functions. We further detected spatially dependent ECM formation and stromal-immune cell interactions in TNBC by using spatial resolved transcriptomics data. We observed spatially dependent variations of (1) sub cell type and cell type-specific functions, and (2) stromal-immune cell interactions, in the TME of TNBC. Citation Format: Yuhui Wei, Haiqi Zhu, Xiao Wang, Xinyu Zhou, Jia Wang, Tingbo Guo, Pengtao Dang, Sha Cao, Chi Zhang. Computational modeling of stromal-immune cell interactions in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3140.

Review of ways to apply machine learning methods in software engineering

This article reviews the integration of machine learning (ML) techniques into Software Engineering (SE) across various phases of the software development life cycle (SDLC). The purpose is to investigate the applications of ML in SE, analyze its methodologies, present findings, and draw conclusions regarding its impact. The study categorized ML applications in SE and assessed the performance of various ML algorithms. Authors identified ML applications in SDLC phases, including requirements analysis, design, implementation, testing, and maintenance. ML algorithms, such as supervised and unsupervised learning, are employed for tasks like software requirement identification, design pattern recognition, code generation, and automated testing. In summary, we find that ML-based techniques are experiencing a substantial surge in adoption within the field of software engineering. Nevertheless, it is evident that substantial endeavors are needed to establish thorough comparisons and synergies among these approaches, perform meaningful evaluations grounded in detailed real-world implementations that are applicable to industrial software development. Therefore, our key takeaway is the necessity for a shift in focus towards reproducible research, prioritizing this over isolated novel concepts. Failure to do so may result in the limited practical implementation of these promising applications.

Synchronization of Belousov-Zhabotinsky oscillators with electrochemical coupling in a spontaneous process.

A passive electrochemical coupling approach is proposed to induce spontaneous synchronization between chemical oscillators. The coupling exploits the potential difference between a catalyst redox couple in the Belousov-Zhabotinsky (BZ) reaction, without external feedback, to induce surface reactions that impact the kinetics of the bulk system. The effect of coupling in BZ oscillators under batch condition is characterized using phase synchronization measures. Although the frequency of the oscillators decreases nonlinearly over time, by a factor of 2 or more within 100 cycles, the coupling is strong enough to maintain synchronization. In such a highly drifting system, the Gibbs-Shannon entropy of the cyclic phase difference distribution can be used to quantify the coupling effect. We extend the Oregonator BZ model to account for the drifting natural frequencies in batch condition and for electrochemical coupling, and numerical simulations of the effect of acid concentration on synchronization patterns are in agreement with the experiments. Because of the passive nature of coupling, the proposed coupling scheme can open avenues for designing pattern recognition and neuromorphic computation systems using chemical reactions in a spontaneous process.

Low-Power Low-Area architecture of Probability Density Function Estimation for Histogram Equalization

Estimation of Probability Density Functions (PDFs) in view of accessible information is critical issue emerging in various fields, for example, broadcast communications, machine learning, information mining, design pattern recognition and Personal Computer (PC) vision. In this paper, the Look-Up Table–Carry Select Adder-PDF (LUT-CSLA-PDF) mehod is implemented to increase system performance. The LUT is one of the fast way to recognize a complex function in the digital logic circuit. In this work, The FPGA (field programmable gate array) analysis, LUT, slices, flip flops, frequency are improved as well as ASIC (application specified integrated chip) implementation analysis an area, power, delay, Area Power Product (APP), Area Delay Product (ADP) are enhanced in LUT-CSLA-PDF technique compared to conventional methods.

Native house designing pattern recognition in Mazandaran province with climatic approach

In Forming residential house in Khazar’s shore, climatic condition plays a basic role because in them had used of natural energy and environmental condition for heating, cooling and air conditioning.With studying, recognition and analyses of principles and patterns of native architecture can find out the compatibility of them with the climatic condition. With appearing modern architecture and destroying construction identity, patternology is one of the important issues that wants to find the practical methods for accessing to identity maker elements and patterns of past and using of them in now a day modern architecture. Traditional residential texture as the fundamental core of cities and so elements and architecture construction in them undoubtedly are worthy heritage for studying and analyzing and presenting proper patterns of native architecture. In this study first with an analyzing- descriptive approach have an overview and then the skeletal factors affecting texture and traditional residential constructions of Mazandaran province patternology are identified. Study method in this method is analyzing-descriptive and used of fieldwork for fundamental recognition and identifying for sample structures that are under studying. Finally, due to the obtained results, principles of native architecture and comparison of available criteria, native house designing patterns will present.

APPROACH TO THE SYNTHESIS OF NEURAL NETWORK STRUCTURE DURING CLASSIFICATION

Evaluating the number of hidden neurons necessary for solving of pattern recognition and classification tasks is one of the key problems in artificial neural networks. Multilayer perceptron is the most useful artificial neural network to estimate the functional structure in classification. In this paper, we show that artificial neural network with a two hidden layer feed forward neural network with d inputs, d neurons in the first hidden layer, 2d+2 neurons in the second hidden layer, k outputs and with a sigmoidal infinitely differentiable function can solve classification and pattern problems with arbitrary accuracy. This result can be applied to design pattern recognition and classification models with optimal structure in the number of hidden neurons and hidden layers. The experimental results over well-known benchmark datasets show that the convergence and the accuracy of the proposed model of artificial neural network are acceptable. Findings in this paper are experimentally analyzed on four different datasets from machine learning repository.

SWCNTs-based MEMS gas sensor array and its pattern recognition based on deep belief networks of gases detection in oil-immersed transformers

MEMS gas sensor arrays and specially designed pattern recognition systems are the main research directions in the field of modern sensing technology in the engineering, especially in the smart sensing and monitoring of faults in large power equipment such as oil-immersed transformers. In this paper, the MEMS sensor array composed by eight SWCNTs-based (pure, OH functionalized, COOH functionalized, NH2 functionalized by ethylenediamine, NH2 functionalized by aniline, Ni-coated, Pd-doped, ZnO-doped) sensing units was palced in the fault characteristic gases (H2, CO, and C2H2) of oil-immersed transformers, and their gas-sensing characteristics were tested in single and mixed gas atmosphere. Combined with the DBN-DNN pattern recognition method, the qualitative identification and quantitative analysis of the sensor array in a mixed gas atmosphere was realized, and the accuracy and reliability of the results are higher than the traditional BPNN model.

Applying learning-based methods for recognizing design patterns

Recognizing design patterns in source code helps in improving the aspect of reusability and maintainability that play an essential role during analysis and design phases of software development process. Software patterns provide design-level documents, which are applied for the recurring design issues. Analysis of design patterns is often carried out by using forward engineering as well as reverse engineering. In this study, a reverse engineering approach has been applied for recognizing design patterns. The study is comprised of two phases such as preparation of requisite dataset based on object-oriented software metrics and recognition of design patterns. The first phase, i.e., dataset preparation, is carried out by various object-oriented metrics. Design pattern recognition is performed by using learning-based algorithms such as artificial neural network and logistic regression. The presented method is validated by using three case studies such as JRefactory, JUnit and Quaqua.

Focal point computation and homogeneous geometrical transformation for linear curves

Summary In computer graphics, computer aided design, solid modelling and robotics and pattern recognition evaluation of focal point for a collection of line segments is the bottleneck problem. In the computer era the significant importance is given to this problem in present research world, however resolving its complexity has persisted imperceptible. This paper presents an efficient and optimal algorithm to evaluate intersection point in Euclidean space by evolving the mathematical analysis. Analysis of mathematics of the algorithm is rather seems to be intricate but implementation of algorithm is simple and straightforward.

The dissimilarity representation for finding universals from particulars by an anti-essentialist approach

The dissimilarity representation for designing pattern recognition systems is analyzed for its ability to build new knowledge from examples using an anti-essentialist approach. It is argued that it may find universals (pattern classifiers) from particulars (training set of examples) but that the resulting knowledge can just be applied but not accessed. Consequently, its use for a conscious human decision maker is limited.

A Customizable Approach to Design Patterns Recognition Based on Feature Types

Accurate recognition of design patterns from source code supports development-related tasks such as program comprehension, maintenance, reverse engineering, and re-engineering. Researchers focused on this problem for many years, and a variety of recognition approaches have been proposed. Though, much progress has been made, we still identify a lack of flexibility and accuracy in the pattern recognition process. This paper evaluates different design pattern recovery approaches and examines the detection accuracy of these approaches. We found that the major impedance in the accurate recovery of design patterns is the large number of variations for implementing the same pattern. Furthermore, we realized that the combination of multiple searching techniques is required to improve accuracy of pattern detection. Based on these observations, we propose variable pattern definitions, which can be customized and improved towards a pattern catalog that detects patterns in all their variations. The customizable pattern definitions are created from reusable feature types. Each feature type can use one or more searching techniques for efficient detection. The proposed approach supports detection of patterns from multiple programming languages. A prototype implementation of the approach was tested on seven different open-source software projects. For each software project, a baseline was determined and the trustworthiness of each pattern–project combination was rated. The extracted results have been compared with established baselines and with the results of previous techniques.

PATTERN RECOGNITION SYSTEMS UNDER ATTACK: DESIGN ISSUES AND RESEARCH CHALLENGES

We analyze the problem of designing pattern recognition systems in adversarial settings, under an engineering viewpoint, motivated by their increasing exploitation in security-sensitive applications like spam and malware detection, despite their vulnerability to potential attacks has not yet been deeply understood. We first review previous work and report examples of how a complex system may be evaded either by leveraging on trivial vulnerabilities of its untrained components, e.g. parsing errors in the pre-processing steps, or by exploiting more subtle vulnerabilities of learning algorithms. We then discuss the need of exploiting both reactive and proactive security paradigms complementarily to improve the security by design. Our ultimate goal is to provide some useful guidelines for improving the security of pattern recognition in adversarial settings, and to suggest related open issues to foster research in this area.

Skew-sensitive boolean combination for adaptive ensembles – An application to face recognition in video surveillance

Several ensemble-based techniques have been proposed to design pattern recognition systems when data has imbalanced class distributions, although class proportions may change over time according to the operational environment. For instance, in video surveillance applications, face recognition (FR) is employed to detect the presence of target individuals of interest in potentially complex and changing environments. Systems for FR in video surveillance are typically designed a priori with a limited amount of reference target data and prior knowledge of underlying class distributions. However, the relatively proportion of target and non-target faces captured during operations varies over time. Estimating the actual proportion of data from the input data stream could allow to dynamically adapt ensembles to reflect operational conditions. In this paper, the selection and fusion of ensembles produced through Boolean Combination (BC) of classifiers is periodically adapted based on the class proportions estimated from input streams. BC techniques have been shown to efficiently integrate the responses of multiple diversified classifiers in the ROC space, yet the impact on performance of imbalanced data distributions is difficult to observe from ROC curves. Given a diversified pool of classifiers and a desired false positive rate (fpr), the new Skew-Sensitive Boolean Combination (SSBC) technique exploits the Precision-Recall Operating Characteristic (PROC) space, leading to a higher level of performance. A set of BCs of base classifiers is initially produced with imbalanced reference data in the PROC space, where each BC curve corresponds to different level of imbalance (a growing number of non-target samples versus a fixed number of target ones). Then, during operations, the closest adjacent levels of class imbalance are periodically estimated using the Hellinger distance between the data distribution of inputs and that of imbalance levels, and used to approximate the most accurate BC of classifiers from operational points of these curves. Simulation results on Faces In Action video surveillance data indicate that ensemble-based FR systems using the SSBC technique outperform the same systems using traditional BC techniques with Random Under-Sampling and One-Sided Selection. It allows to dynamically select BCs that provide a higher level of precision (and F1 value) for target individuals, and a significantly smaller difference between desired and actual fpr. Performance of this adaptive approach is also comparable to the costly full recalculation of BCs (as required by a BC technique to accommodate a specific level of imbalance), but for a computational complexity that is considerably lower. Finally, SSBC is shown to achieve a high level of discrimination between target and non-target individuals when face tracking is exploited to accumulate ensemble predictions for facial captures that correspond to a same person in the video scene.

A Note on Generalization Loss When Evolving Adaptive Pattern Recognition Systems

Evolutionary computing provides powerful methods for designing pattern recognition systems. This design process is typically based on finite sample data and therefore bears the risk of overfitting. This paper aims at raising the awareness of various types of overfitting and at providing guidelines for how to deal with them. We restrict our considerations to the predominant scenario in which fitness computations are based on point estimates. Three different sources of losing generalization performance when evolving learning machines, namely overfitting to training, test, and final selection data, are identified, discussed, and experimentally demonstrated. The importance of a pristine hold-out data set for the selection of the final result from the evolved candidates is highlighted. It is shown that it may be beneficial to restrict this last selection process to a subset of the evolved candidates.

Divergence-based feature selection for separate classes

Feature selection is one of the core issues in designing pattern recognition systems and has attracted considerable attention in the literature. Most of the feature selection methods in the literature only handle relevance and redundancy analysis from the point of view of the whole class, which neglects the relation of features and the separate classes. In this paper, we propose a novel feature selection framework to explicitly handle the relevance and redundancy analysis for each class label. Then we propose two simple and effective feature selection algorithms based on this framework and Kullback–Leibler divergence. An empirical study is conducted to evaluate the efficiency and effectiveness of our algorithms comparing with five representative feature selection algorithms. Empirical results show that our proposed algorithms are efficient and outperform the selected algorithms in most cases, and show the superiority of our proposed feature selection framework.

Feature subset selection with cumulate conditional mutual information minimization

Feature selection is one of the core issues in designing pattern recognition and machine learning systems, and has attracted considerable attention in the literature. In this paper, a new feature subset selection algorithm with conditional mutual information is proposed, which firstly guarantees to find a subset of which the mutual information with the class is the same as that of the original set of features, and then eliminates potential redundant features from the view of minimal information loss based on the cumulate conditional mutual information minimization criterion. From the reliability point of view, this criterion can also abate the disturbance caused by sample insufficiency in conditional mutual information estimation. In addition, a fast implementation of conditional mutual information estimation is proposed and used to tackle the computationally intractable problem. Empirical results verify that our algorithm is efficient and achieves better accuracy than several representative feature selection algorithms for three typical classifiers on various datasets.

An Ada design pattern recognition tool for AADL performance analysis

This article deals with performance verification of architecture models of real-time embedded systems. Although real-time scheduling theory provides numerous analytical methods called feasibility tests for scheduling analysis, their use is a complicated task. In order to assist an architecture model designer in early verification, we provide an approach, based on real-time specific design patterns, enabling an automatic schedulability analysis. This analysis is based on existing feasibility tests, whose selection is deduced from the compliance of the system to a design pattern and other system's properties. Those conformity verifications are integrated into a schedulability tool called Cheddar. We show how to model the relationships between design patterns and feasibility tests and design patterns themselves. Based on these models, we apply a model-based engineering process to generate, in Ada, a feasibility test selection tool. The tool is able to detect from an architecture model which are the feasibility tests that the designer can apply. We explain a method for a designer willing to use this approach. We also describe the design patterns defined and the selection algorithm.