Pattern Recognition Technology Research Articles

Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System

Facial Recognition (FR) has been an active area of research and has diverse applicable environment, it continues to be a challenging research topic. With the development of image processing and pattern recognition technology, there are many challenges in machine learning to select the appropriate classification algorithms, most especially in the area of classification of extracted features to have low classification time, high sensitivity and accuracy of the classification algorithms, so it is very important to explore the performance of different algorithms in image classification. The three selected supervised learning classification algorithms: Learning Vector Quantization (LVQ), Relevance Vector Machine (RVM), and Support Vector Machine (SVM) performance were evaluated so as to know the most effective out of the selected algorithms for facial images classification. The development of the system has four stages, the first stage is image acquisition and 180 images were taken by digital camera under same illumination and light colour background. The second stage is pre-processing to improve the images data by suppressing unwilling distortion; grayscale and normalization were used for image pre-processing. The third stage is feature extraction; Discrete Cosine Transform (DCT) is adopted for this purpose. While the fourth stage is face recognition classification, Receiver Operating Characteristics (ROC) was used to test the performance of each the three algorithms. However the Learning Vector Quantization algorithm, Relevance Vector Machine and Support Vector Machine performance have not been compared together to the most effective out of the three algorithms in term of False Positive Rate, Sensitivity, Specificity, Precision, Accuracy and Computation Time. Hence, this work evaluated the performance of the Learning Vector Quantization; Relevance Vector Machine and Support Vector Machine classification algorithms in facial recognition system and Support Vector Machine outwit the other two algorithms in facial recognition in term of specificity, recognition time and recognition accuracy at different threshold.

Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model’s efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model’s potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PDF

Application of sensor recognition based on artificial intelligence image algorithms in sports and human health

With the rapid development of artificial intelligence technology, the application of image algorithm in multimedia vision technology has made remarkable progress. In the field of sports, human health is an important concern, so it is of great significance to apply artificial intelligence image algorithm to the research of human health in sports. This paper uses computer vision and image processing technology, combined with artificial intelligence algorithm, to analyze and process the multimedia image of the movement. Through the acquisition and preprocessing of moving images, the key information and features of the movement are extracted. Then, deep learning algorithms and pattern recognition technology are used to analyze and evaluate the posture, movement and body state of the exercisers. Finally, according to the analysis results, personalized health advice and guidance are provided. The results show that the method can accurately identify and analyze the posture, movement and body state of the athletes, provide personalized health advice and guidance, help the athletes to improve the training effect, avoid sports injuries, and improve the level and quality of sports.

CATNet: Cross-modal fusion for audio–visual speech recognition

Automatic speech recognition (ASR) is a typical pattern recognition technology that converts human speeches into texts. With the aid of advanced deep learning models, the performance of speech recognition is significantly improved. Especially, the emerging Audio–Visual Speech Recognition (AVSR) methods achieve satisfactory performance by combining audio-modal and visual-modal information. However, various complex environments, especially noises, limit the effectiveness of existing methods. In response to the noisy problem, in this paper, we propose a novel cross-modal audio–visual speech recognition model, named CATNet. First, we devise a cross-modal bidirectional fusion model to analyze the close relationship between audio and visual modalities. Second, we propose an audio–visual dual-modal network to preprocess audio and visual information, extract significant features and filter redundant noises. The experimental results demonstrate the effectiveness of CATNet, which achieves excellent WER, CER and converges speeds, outperforms other benchmark models and overcomes the challenge posed by noisy environments.

A fast and efficient numerical algorithm for image segmentation and denoising

<abstract><p>Image segmentation is the process of partitioning an image into homogenous regions, and represents one of the most fundamental and important procedures in image processing. Image denoising is a process to remove unwanted noise from a digital image, enhancing its visual quality. Various algorithms, like non-local means and deep learning-based approaches, have been developed to remove noise while preserving important image details. Currently, the prevalent application of pattern recognition technology is achieved through the implementation of image segmentation algorithms. In this study, we present a new, highly efficient, and fast computational scheme specifically developed for a phase-field mathematical model of image segmentation. The numerical methodology is based on an operator splitting method (OSM). The split operators are solved by using closed-form analytic solutions and a finite difference method (FDM) with an alternating direction explicit (ADE) method. To show the notable efficiency and rapid computational performance of the proposed computational algorithm, we conduct a series of numerical experiments. Through these computational tests, we confirm a significant contribution to the advancement of methodologies employed in the critical domain of image processing.</p></abstract>

A deep learning-based algorithm for crop Disease identification positioning using computer vision

Food security is fundamental to a country. As the main risk factors, pests and diseases seriously restrict the normal growth of crops and the quality and safety of agricultural products. With the intensification of climate change and the continuous adjustment of farming methods, crop diseases and pests have become more frequent in recent years. Therefore, the agricultural production mode has gradually moved from family production to large-scale agricultural planting, and the production equipment has become more automated and intelligent. Agricultural intelligent robots can reduce labor costs in the process of agricultural production and improve the standardization of agricultural production. The application of computer vision in agriculture is rapidly becoming an important aspect of modern agricultural technology, especially in crop positioning and management. Through the use of advanced image processing algorithms and pattern recognition technology, computer vision systems are able to accurately identify and locate various crops in the field, enabling automated and precise management. This technology shows great potential for crop health monitoring, pest identification, and maturity assessment. For example, by analyzing images of plants, computer vision systems can spot signs of lesions or nutrient deficiencies in time and guide farmers to treat them accordingly. In addition, this technology can also be used to guide automated agricultural machinery, such as driverless tractors and harvesters, to improve the efficiency of crop harvesting and reduce labor costs. In general, the combination of computer vision and crops provides new technical means for the development of modern precision agriculture, which helps to improve the efficiency and sustainability of agricultural production.

Few-shot sheep counting based on density map regression

With the continuous expansion of breeding scale, the automatic counting of sheep has attracted a lot of research attention in recent years, which aims to count the number of sheep in real time to rationally use grassland resources and design appropriate agricultural production policies. The traditional method of counting sheep mainly relies on manual work, which is time-consuming and labor-intensive. Thanks to the rapid development of image acquisition equipment and pattern recognition technology, the recognition and counting of sheep based on deep learning has become the current mainstream counting framework. However, for dense flocks of sheep, such as occlusions between sheep, the accuracy of existing methods cannot meet the needs of practical applications. In this paper, we suggest FamNet, a few-shot sheep counting method based on UAV images and density map regression. Specifically, we first collected 190 sheep images with different resolutions, aspect ratios, backgrounds, and sheep numbers; secondly, by selecting count samples to extract objects in the images, and using density map regression to predict the number of sheep, this enables sheep counting in small numbers of samples. A large number of experimental results have verified the effectiveness of the method in this paper, which can achieve an average counting accuracy of 91.02%. In addition, we discuss the impact of sample selection, backbone selection, and feature scaling on model accuracy. All the results show that the FamNet flock counting method proposed in this paper can be applied to flock counting, has good adaptability to flock counting under different background conditions and can count the number of flocks in a timely and effective manner.

Portable LWNIR and SWNIR spectroscopy with pattern recognition technology for accurate and nondestructive detection of hidden mold infection in citrus

An accurate and nondestructive detection method of hidden mold infection in citrus was established based on portable near infrared diffuse reflectance spectroscopy (NIRDRS) and chemometric methods. Penetrability of NIRDRS light on the peel of Chunjian hybrid citrus was studied. The results show that NIRDRS light can penetrate the peel to a certain extent, while the penetrability of short-wave near infrared (SWNIR) was better than that of long-wave near infrared (LWNIR). The identification models of hidden mold infection were established with five pattern recognition methods combined with different wavelength bands. The results show that the identification models of LWNIR were much better than those of SWNIR. 100% identification accuracies of LWNIR were obtained with soft independent pattern classification (SIMCA), support vector machine (SVM), partial least squares discriminant analysis (PLS-DA) and the optimized pretreatment methods. In addition, the developed models were further validated by the external validation set collected one month later.

IMPLEMENTATION OF IMAGE PROCESSING IN THE RECOGNITION OF OFFICIAL VEHICLE LICENSE PLATES

Vehicle license plates are identifiers used to uniquely identify vehicles. However, to identify vehicle license plates there are several problems encountered, namely the different formats of vehicle license plates that make license plate recognition more complicated, vehicle license plates often contain visually similar combinations of letters and numbers (for example the letter "O" and the number "0" or the letter "I" and the number "1"), . in poor lighting conditions license plates may not be clearly visible. To solve this problem, image recognition, image processing, and pattern recognition technologies can be used. The three technologies can be used to recognize characters on vehicle license plates, but cannot yet be used to recognize the colors contained on vehicle license plates. The purpose of this research is to identify and record vehicle license plate numbers quickly and accurately, monitor the presence of vehicles in a supervised area, assist in managing parking, reduce the need for human interaction in the vehicle identification process, The methods used to recognize motor vehicle plates are edge detection and character segmentation which involves image processing to detect the edges of the vehicle plate, followed by segmentation of individual characters in the plate. Another method used is optical character recognition which involves using an optical sensor to take an image of a vehicle plate, then using character recognition techniques to identify the numbers and letters on the plate. The result of this research is that the motor vehicle number recognition system can work in various lighting conditions and poor weather conditions and can monitor and control vehicles in the parking area. The finding obtained from this research is that no method has been used for color recognition on motor vehicle plates.

Studies Advanced in Artificial intelligence based Game

Thanks to the rapid development of high-performance computing equipment and pattern recognition technology, artificial intelligence technology has gradually matured in recent years and is widely used in many fields. Due to its potentially huge economic value, AI-based game production has attracted a lot of research attention from academia and industry in recent years. AI has progressed from using simple rules to playing a variety of games, and it has proven to be a formidable opponent, defeating human champions in some instances. This paper provides an overview of the algorithms used in different games and highlights the power of AI demonstrated in chess games. However, despite its successes, there are still some problems that exist. This paper concludes by discussing possible solutions to these issues.

The Optimization of Face Detection Technology Based on Neural Network and Deep Learning

Face detection is a biometric technology that automatically contains facial feature information. It integrates digital image processing, pattern recognition, and other technologies and collects images or video streams containing human faces by cameras or cameras for automatic detection and tracking. Starting from the idea of local features and deep learning, aiming at the problem that traditional convolutional neural network (CNN) only extracts features from the whole image and ignores practical local details, this article proposes a deep CNN model based on the fusion of global and local features. It explores the face detection algorithm with better performance under the interference of illumination, expression, and other internal or external factors. This method designs a suitable network structure according to the size of the training data set, and the core technology is the debugging of super parameters. The simulation results show that compared with SVM, the improved CNN has obvious advantages in the later stage of operation, and the error is reduced by 36.85%. Compared with the traditional face detection method, it can automatically extract image features and also automatically learn its model and get a higher recognition rate.

Volatile Organic Compound Assessment as a Screening Tool for Early Detection of Gastrointestinal Diseases.

Gastrointestinal (GI) diseases have a high prevalence throughout the United States. Screening and diagnostic modalities are often expensive and invasive, and therefore, people do not utilize them effectively. Lack of proper screening and diagnostic assessment may lead to delays in diagnosis, more advanced disease at the time of diagnosis, and higher morbidity and mortality rates. Research on the intestinal microbiome has demonstrated that dysbiosis, or unfavorable alteration of organismal composition, precedes the onset of clinical symptoms for various GI diseases. GI disease diagnostic research has led to a shift towards non-invasive methods for GI screening, including chemical-detection tests that measure changes in volatile organic compounds (VOCs), which are the byproducts of bacterial metabolism that result in the distinct smell of stool. Many of these tools are expensive, immobile benchtop instruments that require highly trained individuals to interpret the results. These attributes make them difficult to implement in clinical settings. Alternatively, electronic noses (E-noses) are relatively cheaper, handheld devices that utilize multi-sensor arrays and pattern recognition technology to analyze VOCs. The purpose of this review is to (1) highlight how dysbiosis impacts intestinal diseases and how VOC metabolites can be utilized to detect alterations in the microbiome, (2) summarize the available VOC analytical platforms that can be used to detect aberrancies in intestinal health, (3) define the current technological advancements and limitations of E-nose technology, and finally, (4) review the literature surrounding several intestinal diseases in which headspace VOCs can be used to detect or predict disease.

Application of SSD network algorithm in panoramic video image vehicle detection system

Abstract Due to the popularity of high-performance cameras and the development of computer video pattern recognition technology, intelligent video monitoring technology is widely used in all aspects of social life. It mainly includes the following: industrial control system uses video monitoring technology for remote monitoring and comprehensive monitoring; in addition, intelligent video monitoring technology is also widely used in the agricultural field, for example, farm administrators can view the activities of animals in real time through smart phones, and agricultural experts can predict future weather changes according to the growth of crops. In the implementation of intelligent monitoring system, automatic detection of vehicles in images is an important topic. The construction of China’s Intelligent Transportation System started late, especially in video traffic detection. Although there are many related studies on video traffic detection algorithms, these algorithms usually only analyze and process information from a single sensor. This article describes the application of the single-shot detector (SSD) network algorithm in a panoramic video image vehicle detection system. The purpose of this article is to investigate the effectiveness of the SSD network algorithm in a panoramic video image vehicle detection system. The experimental results show that the detection accuracy of a single convolutional neural network (CNN) algorithm is only 0.7554, the recall rate is 0.9052, and the comprehensive detection accuracy is 0.8235. The detection accuracy of SSD network algorithm is 0.8720, recall rate is 0.9397, and the comprehensive detection accuracy is 0.9046, which is higher than that of single CNN algorithm. Thus, the proposed SSD network algorithm is compared with a single convolution network algorithm. It is more suitable for vehicle detection, and it plays an important role in panoramic video image vehicle detection.

Opportunities and Challenges in Biometric Technology

Biometric advancements have the potential to provide more robust security and authentication approaches compared to conventional password-based security measures. When compared to knowledge-based security, biometrics relies on what makes you unique rather than what you know. Biometrics offers promising opportunities to enhance security but also comes with its own set of challenges. Successful implementation requires attention to these possible flaws and a rise in user approval. This article explores the potential benefits of biometric security in an effort to ascertain how broadly adopted it is. It analyses the correlation between user approval and their opinion of the feature's usefulness and accessibility. It also examines crucial acceptability elements like social, organizational, and cost considerations. Biometric systems are a type of pattern recognition technology that may identify a person by vetting their claimed physiological and/or behavioural traits. The following concept of a biometric system can be very broad, which is problematic when thinking about the specifics of contemporary biometric applications. The use of biometrics has the potential to greatly improve upon the security of current methods of authentication and identification. The broad use of biometrics solutions, in fact, has significant ramifications for the way we think about the individual's place in society. However, certain factors and limits are taken into account in the context of the application area, as is the case with any technology. Further progress will be made as scientists investigate multiple, interconnected topics. This paper addresses technical and engineering concerns while highlighting potential applications of contemporary biometric technologies

RDNF Oriented Analytics to Random Boolean Functions

Dominant areas of computer science and computation systems are intensively linked to the hypercube-related studies and interpretations. This article presents some transformations and analytics for some example algorithms and Boolean domain problems. Our focus is on the methodology of complexity evaluation and integration of several types of postulations concerning special hypercube structures. Our primary goal is to demonstrate the usual formulas and analytics in this area, giving the necessary set of common formulas often used for complexity estimations and approximations. The basic example under considered is the Boolean minimization problem, in terms of the average complexity of the so-called reduced disjunctive normal form (also referred to as complete, prime irredundant, or Blake canonical form). In fact, combinatorial counterparts of the disjunctive normal form complexities are investigated in terms of sets of their maximal intervals. The results obtained compose the basis of logical separation classification algorithmic technology of pattern recognition. In fact, these considerations are not only general tools of minimization investigations of Boolean functions, but they also prove useful structures, models, and analytics for constraint logic programming, machine learning, decision policy optimization and other domains of computer science.

A novel portable smart phone sensing platform based on a supramolecular fluorescence probe for quick visual quantitative detection of picric acid.

Picric acid (PA) is a lethal explosive substance that is easily soluble in water and harmful to the environment. Here, a supramolecular polymer material BTPY@Q[8] with aggregation induced emission (AIE) was prepared by supramolecular self-assembly of cucurbit uril (Q[8]) and 1,3,5-tris[4-(pyridin-4-yl) phenyl] benzene derivative (BTPY), which exhibited aggregation-induced fluorescence enhancement. To this supramolecular self-assembly, the addition of a number of nitrophenols was found to have no obvious effect on the fluorescence, however on addition of PA, the fluorescence intensity underwent a dramatic quench. For PA, BTPY@Q[8] had sensitive specificity and effective selectivity. Based on this, a quick and simple on-site visual PA fluorescence quantitative detection platform was developed using smart phones, and the platform was used to monitor temperature. Machine learning (ML) is a popular pattern recognition technology, which can accurately predict the results from data. Therefore, ML has much more potential for analyzing and improving sensing data than the widely used statistical pattern recognition method. In the field of analytical science, the sensing platform offers a reliable method for the quantitative detection of PA that can be applied to other analytes or micropollutant screening.

Information-analytical technology for monitoring the flow of visitors to the university

In the 21st century, one of the most widespread problems in developed countries is the unraveling of complex tasks related to the security of citizens. An example is the need to conduct a security check at universities, when at one checkpoint there may be a need to let a thousand people pass within 5 minutes. Inspection of each (even a formal presentation of the document) will lead to the disruption of 4 classes; automated turnstiles will not ensure quality inspection + queues will be created (or will require many turnstiles that will actually be used for a short time). The Covid'19 pandemic only transfers the problem to another plane - a distance of one and a half meters + the risk of infecting the guard, who will turn into a source of infection. Military and, especially, terrorist events (when civil infrastructure objects with a large concentration of civilians become the targets of attacks) in Ukraine show the need to simultaneously ensure high throughput and for people and the safety of the object itself. The paper considers the concept of impersonal monitoring of the number of visitors. A safe approach is considered, when a recognition system based on the use of artificial neural networks allows checking and accompanying a large number of people impersonally at the same time. The system is implemented as a pattern recognition technology with statistical analysis. The system (visualization in the figures in the text) was tested on the video streams of the security cameras of the main building of the Lviv Polytechnic. The purpose of the work is the first phase of testing the hypothesis of the possibility of impersonal verification by using several impersonal classifiers. In the work, people are recognized not by their faces, but by a large set of parameters that allow classifying a person, but not identifying them.

Recent Progress in Sensor Arrays: From Construction Principles of Sensing Elements to Applications.

The traditional sensors are designed based on the "lock-and-key" strategy with high selectivity and specificity for detecting specific analytes, which however are not suitable for detecting multiple analytes simultaneously. With the help of pattern recognition technologies, the sensor arrays excel in distinguishing subtle changes caused by multitarget analytes with similar structures in a complex system. To construct a sensor array, the multiple sensing elements are undoubtedly indispensable units that will selectively interact with targets to generate the unique "fingerprints" based on the distinct responses, enabling the identification among various analytes through pattern recognition methods. This comprehensive review mainly focuses on the construction strategies and principles of sensing elements, as well as the applications of sensor array for identification and detection of target analytes in a wide range of fields. Furthermore, the present challenges and further perspectives of sensor arrays are discussed in detail.

Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and Their Role in Engineering Research

The purpose of this study is to summarize the pattern recognition (PR) and deep learning (DL) artificial intelligence methods developed for the management of data in the last six years. The methodology used for the study of documents is a content analysis. For this study, 186 references are considered, from which 120 are selected for the literature review. First, a general introduction to artificial intelligence is presented, in which PR/DL methods are studied and their relevance to data management evaluated. Next, a literature review is provided of the most recent applications of PR/DL, and the capacity of these methods to process large volumes of data is evaluated. The analysis of the literature also reveals the main applications, challenges, approaches, advantages, and disadvantages of using these methods. Moreover, we discuss the main measurement instruments; the methodological contributions by study areas and research domain; and major databases, journals, and countries that contribute to the field of study. Finally, we identify emerging research trends, their limitations, and possible future research paths.

Investigation into the Development of Intelligent Financial Management Systems Based on Artificial Intelligence

Artificial intelligence technology is primarily used to create and investigate methods of simulating human brain thinking. It is a new science and technology that extends current science and technology. In a theoretical sense, AI technology is a branch of computer science. We can create an intelligent machine similar to how humans think about problems by researching AI technology. The innovative financial management system has been gradually integrated into artificial intelligence technology. Artificial intelligence has endowed the intelligent financial management system with multi-agent systems, pattern recognition, expert techniques, and other vital technologies. Based on the influence of AI on financial information technology and financial management functions, this paper explores a new financial management model in the era of AI. It constructs an intelligent financial management system based on AI. It will assist businesses in utilizing intelligent technological resources to undertake forward-looking assessments, prevent financial risks, enhance the efficiency of financial management, and achieve tremendous company success.