Intelligent Pattern Recognition Research Articles

Automated Code Review Tool

As software development undergoes continual evolution, the assurance of code quality remains paramount in the development lifecycle. This research paper introduces an innovative Automated Code Review Tool engineered to augment and streamline the code review process. The tool capitalizes on advanced static code analysis, machine learning algorithms, and industry best practices to automate the detection of code quality issues, security vulnerabilities, and compliance with coding standards. The Automated Code Review Tool addresses the inherent challenges of manual code reviews, including human error, time constraints, and inconsistencies. Through automation, developers can expedite the identification and resolution of potential issues, thereby enhancing overall code quality and software reliability. Key features of the tool encompass intelligent pattern recognition, real-time feedback, and customizable rule sets, enabling development teams to tailor the tool to their project-specific requirements. Leveraging machine learning algorithms facilitates the tool's continuous learning from past reviews, thereby adapting to evolving coding practices and emerging security threats. This paper presents the architectural framework and design principles underpinning the Automated Code Review Tool, emphasizing its capacity to foster collaboration among development teams, reduce time-to-market, and ultimately contribute to the development of more robust and secure software. A comprehensive evaluation of the tool's efficacy across diverse development environments is conducted, offering insights into the practical implications of embracing automated solutions in real-world contexts. Keywords: Automated Code Review Tool, Code quality, Static code analysis, Machine learning algorithms, Coding standards, Manual code reviews, Human error, Time constraints, etc.

An intelligent pattern recognition model for assessment of terrorists’ activities in Nigeria

Terrorism and its brutal tendencies constitute a major setback to the development process of the Nigerian economy leading to severe loss of lives, destruction of properties, and a decline of interest in investment by both local and foreign investors. Many models for assessment of terrorist’s activities lack the ability of learning from previous patterns in order to guide pre-emptive actions against future occurrences, and there are no established regional pattern of weaponry, types of attack, as well as types of victims of terrorists’ operations. This study seeks to build a robust intelligent model for recognizing several terrorists’ patterns in each of the six geo-political zones of Nigeria. A data set of 5,503 instances of terrorists’ activities in Nigeria was obtained and a pattern recognition model was built using Artificial Neural Networks (ANN) with 70%, 15%, and 15% data splits for training, validation, and testing respectively. A 10-10-6 ANN architecture was designed and trained using the scaled conjugate gradient backpropagation algorithm. The training was carried out using Matlab’s neural network pattern recognition toolkit. In order to numerically represent categorical data, a sort-order scheme was developed by the authors and utilized. The results showed average percentage scores for accuracy, precision, recall and F1-score as 99.89, 99.96, 100 and 99.98 respectively. This showed acceptable performance. The developed model is therefore considered a robust one for recognition of terrorists’ patterns in Nigeria. This would assist security agencies to deal with terrorists’ incidences with high intelligent information and advanced preparation for prevention and control. The developed model is highly recommended for use by the security agencies in the country.

High-order OAM states unwrapping in multiplexed optical links

To accurately unwrap the high-order orbital angular momentum (OAM) for multiplexed vortex beams is a challenge. In this work, over ±160 order OAM topological charges have been unwrapped in multiplexed optical links. Optical imaging based discrepancy identification enables the multiplexed OAM modes separating in physics, and the intelligent pattern recognition further promotes its unwrapping in numerical domain. Particularly, the combination of annular phase grating and auxiliary beams features compound spiral stripes, which paves the way for optical intensity recognition with low-complexity and high-commonality. Moreover, the spiral direction characterizes the symbol of the OAM states, which dramatically broadens the amount of multiplexed links. Here, optical separating means assisted by intelligent pattern recognition opens up a new route to high-speed and large-capacity optical communication, which may shed new light on 6G application.

Abstract SY30-03: Composing each patient’s equation: Unraveling brain tumor complexity by integrating multi-regional image-localized biopsies

Abstract Glioblastoma is a uniquely challenging and aggressive cancer that is practically considered uniformly fatal. This aggressiveness is driven by heterogeneity between and within patients and the diffuse invasion of tumor cells deep into the normal appearing brain tissue surrounding the frank tumor abnormality. Mathematical neuro-oncology (MNO) is a burgeoning field that seeks to bring together individual patient data (like patient imaging, tissue biopsies etc) to tune mathematical models that accurately predict and quantify response to treatments for each patient. These mathematical models use both mechanistic “weather forecasting” methods and artificial intelligence pattern recognition methods. Ultimately, these models form the basis of modern "precision medicine" approaches to tailor therapy in a patient-specific manner. These models can be used to overcome the limited ability of imaging to accurately detect tumor cells, improve prognostic predictions, stratify patients, and assess treatment response in silico in the construction of effective clinical trials and treatment protocols, thus accelerating the pace of clinical research. This presentation will focus on the growing translation of MNO to clinical neuro-oncology highlighting burgeoning insights into sex differences in tumor incidence, outcomes, and response to therapy. Specifically, I will discuss 1) use of our 4,500+ patient cohorts of 100,000+ annotated serial MR imaging data to develop digital twins accurately predict response and outcomes in patients and 2) our unique collection of 1000+ image-localized biopsies across nearly 200 patients to detect sex-distinct patterns of mapping between regional imaging features and underlying regional tumor biology. We will demonstrate the value of these novel combined resources in predicting response to treatment for EGFR-targeted therapies in 3 clinical trials and differentiating response to novel dendritic cells vaccines and CAR T-cell therapies in 2 additional clinical trials. Taken together, these complimentary approaches provide a platform to incorporate inter- and intra- tumoral heterogeneity into accurate predictive models that advance precision oncology for glioblastoma patients. Citation Format: Kristin R. Swanson. Composing each patient’s equation: Unraveling brain tumor complexity by integrating multi-regional image-localized biopsies. [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 SY30-03.

Facial expression classification

At present, emotion classification has become a hot topic in artificial intelligence pattern recognition. The facial expression recognition (FER) is indispensable for computers to understand the emotional information conveyed by expressions. In the past, using traditional features to extract and classify facial expressions has not achieved satisfactory accuracy, so the classification of facial emotions is still a challenge. The model used in the paper is an existing one - MINI_XCEPTION, the dominant framework for CNNs that extracts features from images to identify and classify seven facial emotions. The model was trained on a dataset of people’s facial expressions (Kaggle). This model has a significant improvement over the previous model.

Data-efficient crop pest recognition based on KNN distance entropy

With the development of deep learning technique, the intelligent pattern recognition studies mainly focus on the optimization of models but ignore the impact of data quality for a long time. From our viewpoint, data quality is very important to the sustainability of intelligent applications, in terms of task performance, power consumption and training time. In this paper, we proposed a novel data quality assessment method, called k-nearest neighbors distance entropy (KNN-DE), to evaluate the crop pest images and screen high informative data to complete the data-efficient pest recognition task. Many comparative experiments were carried out, and the results show that the high informative data selected by the proposed KNN-DE method outperform other related works. In specific, under the data budget of 100 samples per category, the performance of the model trained with high informative data is 17 % higher than that trained with low informative data. In addition, by referring to the model accuracy trained with 100 low informative samples per class, the alternative selection of high informative data can realize the improvement of data efficiency and training time efficiency by 2.5 times. Thus, this paper provides a data-centric research perspective, lays a foundation for data quality assessment, and makes a positive attempt to explore the data-efficient sustainable learning.

ЗАСТОСУВАННЯ ДРОНІВ У ТРАНСПОРТНІЙ ІНФРАСТРУКТУРІ

Background. The article is devoted to the real possibilities and prospects of creating and using unmanned aerial vehicles in road and railway infrastructures. As part of the conducted experiment, the task of creating a drone was set using the simplest means of development in laboratory conditions. Methods. To assess the viability of the proposed solutions, the method of the natural experiment has been used. Results. The drone created as a result of the experiment is able to automatically stabilize its position, and a receiver board can be installed on it, which will enable remote control. In the future, additional modules can be installed on the quadro copter using the deep learning mechanism. And the use of an intelligent pattern detection and recognition system based on effective digital data processing algorithms will allow to significantly reduce the time for data processing, obtain more accurate results and ensure access to information in the shortest possible time, which will be another factor contributing to the active implementation of unmanned technologies. Сonclusions. Thanks to the ability to obtain various data, unmanned aerial vehicles will be able to significantly reduce the costs of solving various tasks in the near future and become indispensable assistants in the transport infrastructure sector.

Exploring Pattern Recognition for Bearing Fault Diagnosis

Traditional bearing sensory diagnostic include touching and hearing rely on personal experience, and for more complex system are unable to meet the needs of equipment fault diagnosis. The research on bearing fault diagnosis is developing significantly. Bearings are used in rotating machinery and most machinery failures are caused by bearing failures. The fault diagnosis of bearings is an important research area. The core of bearing fault diagnosis is the pattern recognition of fault features. The key of pattern recognition is to develop a reasonable classifier. Intelligent pattern recognition has been developed such as principal components, support vector machine, neural network. In this study, a bearing fault diagnosis based on exploring pattern recognition is proposed. The key to pattern recognition is to design a significant classifier. A number of features from bearing vibration of normal and fault bearing are extracted and processed using principal components of correlation matrix. Plot of principal components shows the visualization of normal and fault bearing and the classifier is chosen subjectively. The principal components exploration will be confirmed using least squares support vector machine. The parameter of support vector machine estimated using heuristic optimization particle swarm optimization. The proposed method can be applied in the detection of faults of bearing

Applications of Electronic Nose Coupled with Statistical and Intelligent Pattern Recognition Techniques for Monitoring Tea Quality: A Review

Tea is the most widely consumed non-alcoholic beverage worldwide. In the tea sector, the high demand for tea has led to an increase in the adulteration of superior tea grades. The procedure of evaluating tea quality is difficult to assure the highest degree of tea safety in the context of consumer preferences. In recent years, the advancement in sensor technology has replaced the human olfaction system with an artificial olfaction system, i.e., electronic noses (E-noses) for quality control of teas to differentiate the distinct aromas. Therefore, in this review, the potential applications of E-nose as a monitoring device for different teas have been investigated. The instrumentation, working principles, and different gas sensor types employed for E-nose applications have been introduced. The widely used statistical and intelligent pattern recognition methods, namely, PCA, LDA, PLS-DA, KNN, ANN, CNN, SVM, etc., have been discussed in detail. The challenges and the future trends for E-nose devices have also been highlighted. Overall, this review provides the insight that E-nose combined with an appropriate pattern recognition method is a powerful non-destructive tool for monitoring tea quality. In future, E-noses will undoubtedly reduce their shortcomings with improved detection accuracy and consistency by employing food quality testing.

Accurate Recognition of Motion Patterns Based on Artificial Visual Neural Network

In order to improve the accuracy of motion pattern recognition, this paper combines the artificial visual neural network to construct a motion pattern recognition system. Moreover, this paper discusses the psychological perception properties of human eyes to color stimuli and gives a description of the observation field of view where the color stimuli are located. At the same time, this paper analyzes the phenomenon of color adaptation and provides a method of color appearance matching through modeling to achieve color appearance matching under variable observation conditions. Based on the chromatic adaptation transformation, a chromatic appearance model is given, which can predict the corresponding color and also predict the chromatic appearance properties of color stimuli under given observation conditions. In addition, this paper constructs an intelligent motion pattern recognition system combined with artificial visual neural network. The experimental results show that the motion pattern recognition system based on artificial visual neural network can accurately identify the motion pattern category.

In-situ manifestation of lapping mechanisms by rapid intelligent pattern recognition analysis (RIPRA) of acoustic emission via a point density fuzzy C-means (PD-FCM) method

Rapid pattern-recognition capabilities are very challenging tasks to fulfill while essential steps for the ultimate mission of achieving in-time security response, hazardous disaster prevention, structural failure prediction, and intelligent manufacturing applications. Mechanistic manifestation of a machining process through pattern recognition is even more daunting since one has to gain full understanding at the very fundamental level of the manufacturing processes, but it would be the most beneficial should it be accomplished. We designed and built an in-situ Acoustic Emission (AE) monitoring system, and successfully applied this newly developed AE system to manifest the lapping removal mechanisms of a novel fixed abrasive pad embedded with agglomerated diamond (AD) abrasives. To successfully extract valuable mechanistic information, the time-frequency spectrum of collected AE signals in a sapphire lapping process was decomposed by Wavelet Packet Transform (WPT) into three characteristically patterned bands, each representing rubbing, ploughing and cutting action in the lapping process correspondingly. We creatively applied Point Density Fuzzy C-Means (PD-FCM) algorithm for self-identifying clustering centers to classify contributions from rubbing, ploughing and cutting actions in the lapping process. By rapid intelligent pattern recognition analysis (RIPRA) of AE signals, the lapping removal mechanisms using fixed abrasive pads were clearly identified and quantitatively monitored in-situ throughout the whole lapping processes. Such a capable approach implicates a great potential in a wide range of ultra-precision manufacturing applications.

Introduction to the special section on intelligent systems and pattern recognition (SS:ISPR20)

Introduction to the special section on intelligent systems and pattern recognition (SS:ISPR20)

On crack tip localisation in quasi-statically loaded, adhesively bonded double cantilever beam specimens by acoustic emission

The feasibility of acoustic emission structural health monitoring to detect, localise and monitor crack propagation during quasi-static mode I loading of adhesively bonded joints was studied. Unsupervised artificial intelligence pattern recognition methods (Self-Organised maps and K-means) were used to classify acoustic emission raw data as either background noise or relevant information. After that, three different time-of-arrival picking algorithms were considered and implemented to determine the acoustic emission source’s location, and their accuracy was discussed. Localised acoustic emission events were divided into well-defined groups with different energy levels and compared to Digital Image Correlation and visual evaluation results. It was possible to conclude that the highest energetic group allows the assessment of the onset of plasticisation ahead of the crack-tip within the studied adhesive, bringing novel standpoints to the use of acoustic emission as a structural health monitoring method for adhesively bonded joints.

Study of Gene-Gene Interaction Networks

Gene-Gene Interactions (GGI) Networks-Genomics essentially finds the driver node to understand the functional mechanism of Gene-Gene Interaction or GGI. This process can significantly improvise while examining molecular processes perturbed by genetics in human diseases. With Artificial Intelligence (AI) developments, pattern recognition and machine learning advancements can be exploited to automate from more superficial to handle any task in the medical field. In the past, deep learning-based methods have provided encouraging results in the medical field, such as breast cancer detection, skin cancer classification, brain disease classification, arrhythmia detection, pneumonia detection from X-Ray images, and lung segmentation. Interestingly, employing machine learning in the medical field has caught my great attention and motivated us to utilize machine learning-based algorithms to detect drive nodes. Several recent types of research have focused on identifying the bare minimum of driver nodes required to control underlying gene-gene interaction networks. This study is about analyzing gene interaction networks statistically. One or more abnormalities cause a genetic condition in the DNA. Disease-related genes play essential biological roles in the cell. Multiple genes often work together to cause complex genetic illnesses. So, the central concept is to create a system that can assess networks in various elements that influence them.

Intelligent water treatment management system for swimming pools

The article presents the assessment methodology for anthropogenic water pollution in swimming pools, based on intelligent video monitoring of people physical activity, as well as a model for correlation data analysis and a regression model for the main parameters of chemical water contamination and doses of introduced reagents. The ultimate goal of the project is to create an intelligent water treatment management system for swimming pools. It is planned to use YOLOv4 neural networks to implement an intelligent pattern recognition module (swimmers in the pool) in the data stream coming from video cameras in real time. This innovative system will make it possible to automatically make managerial and technical decisions to determine the required amount of introduced reagents at various time intervals depending on the actual amount of organic contaminants, as well as other factors determined by the results of the correlation-regression analysis of the data. A review of information resources has shown that such a methodology does not exist both in Russia and abroad.

Smart data driven defect detection method for surface quality control in manufacturing

The use of machine vision systems for quality control of reflective metal surfaces has increased over the years with new systems that combine higher resolution cameras and better illumination of inspected objects. With the advances in artificial intelligence pattern recognition of images, the integration of machine vision systems in a manufacturing line for accurate automatic classification of defects would work towards the application of a smart manufacturing concept. To investigate the feasibility of such integration, a vision system that combines a 4K camera and chromatic confocal technology was employed to analyze surfaces of copper parts after the laser machining process. By the application of three machine-learning algorithms (decision trees, random forest and multi-layer perceptron) on features extracted from the Sobel edge detector, segmentation of defects has been performed using the Weka segmentation plugin. A simple convolutional neural network (CNN) was also applied for the classification of defects. Later on, using smart data rather than big data, transfer learning (TL) has been successfully performed with retraining the mobilenet-v1 model, which is based on CNN. This lean learning process can be implemented in devices that are limited by their computation resources. The maximum average of validation accuracy achieved using TL trained over 500 epochs was 90.5%. Whereas for the simple CNN classification models, the best validation accuracy was achieved by a model with a batch size equal to ten and with 40% of validation data with an average equal to 98.7% over 500 epochs.

Generating Enriched Synthetic German Hospital Claims Data - A Use Case Driven Approach.

Clinical data and above all individual patient data are highly sensitive. All the more it is important to protect these critical information while analyzing and exploring their specifics for further research. However, in order to enable students and other researchers to develop decision support systems and to use modern data analysis methods such as intelligent pattern recognition, the provision of clinical data is essential. In order to allow this while completely protecting the privacy of a patient, we present a mixed approach to generate semantically and clinically realistic data: (1) We use available synthetic data, extract information on patient visits and diagnoses and adapt them to the encoding systems of German claims data; (2) based on a statistical analysis of real German hospital data, we identify distributions of procedures, laboratory data and other measurements and transfer them to the synthetic patient's visits and diagnoses in a semi-automated way. This enabled us to provide students a data set that is as semantically and clinically realistic as possible to apply patient-level prediction algorithms within the development of clinical decision support systems without putting patient data at any risk.

DCRL: Approach for Pattern Recognition in Price Time Series using Directional Change and Reinforcement Learning

Developing an intelligent pattern recognition model for electronic markets has been a vital research direction in the field. Ongoing research continues for intelligent learning algorithms capable of recognizing and classifying price patterns and hence providing investors and market analysts with better insights into price time-series. In this paper, an adaptive intelligent Directional Change (DC) pattern recognition model with Reinforcement Learning (RL) is proposed, so called DCRL model. Compared with traditional analytical approaches that uses fixed time interval and specified features of the market, the DCRL is an alternative intelligent approach that samples price time-series using an event-based time interval and RL. In this model, the environment’s behavior is incorporated into the RL process to automate the identification of directional price changes. The DCRL learns the price time-series representation by adaptively selecting different price features depending on the current state. DCRL is evaluated using Saudi stock market data with different price trends. A series of analyses demonstrate the effective analytical performance in detecting price changes and the extensive applicability of the DCRL model.

Research on Ultra-Wideband Radar Target Recognition Method

UWB (Ultra wide-brand) radar, as a new system radar, has attracted more and more attention in military and civil fields such as target detection, imaging and recognition with its high range resolution, strong penetration, low interception rate and strong anti-interference. This paper focuses on the feature extraction methods based on pole distribution and scattering center combined with the characteristics and application directions of UWB radar. On this basis, combined with intelligent pattern recognition methods such as neural networks, fuzzy patterns, and information fusion, the problems that still need to be solved in the process of UWB radar target recognition are proposed.

A New Method Based on CEEMD Combined With Iterative Feature Reduction for Aided Diagnosis of Epileptic EEG.

In the clinical diagnosis of epileptic diseases, the intelligent diagnosis of epileptic electroencephalogram (EEG) signals has become a research focus in the field of brain diseases. In order to solve the problem of time-consuming and easily influenced by human subjective factors, artificial intelligence pattern recognition algorithm has been applied to EEG signals recognition. However, at present, the common empirical mode decomposition (EMD) signal decomposition algorithm does not consider the problem of mode aliasing. The EEG features obtained by feature extraction may be mixed with some unimportant features that affect the classification accuracy. In this paper, we proposed a new method based on complementary ensemble empirical mode decomposition (CEEMD) combined with iterative feature reduction for aided diagnosis of epileptic EEG. First of all, the evaluation indexes of decomposing and reconstructing signals by several methods were compared. The CEEMD was selected as the decomposition method of the signals. Then, the support vector machine recursive elimination (SVM-RFE) was used to reduce 9 features extracted from EEG data. The support vector classification of the gray wolf optimizer (GWO-SVC) recognition model was established for different feature subsets. By comparing the classification accuracy of training set and test set of different feature subsets, and considering the complexity of the model reflected by the number of features selected by SVM-RFE, the analysis showed that the 6 feature subsets with fewer features and higher classification accuracy could reflect the key information of epileptic EEG. The accuracy of the training set classification was 99.38% and the test set was as high as 100%. The recognition time was only 1.6551 s. Finally, in order to verify the reliability of the algorithm proposed in this paper, the proposed algorithm compared with the classification model established by the raw EEG signals and the optimization model established by other intelligent optimization algorithms. It is found that the algorithm used in this paper has higher classification accuracy and faster recognition time than other processing methods. The experimental results show that CEEMD combined with SVM-RFE is feasible for rapid and accurate recognition of EEG signals, which provides a theoretical basis for the aided diagnosis of epilepsy.