Pattern Recognition System Research Articles

A study of feature extraction for Arabic calligraphy characters recognition

Optical character recognition (OCR) is one of the widely used pattern recognition systems. However, the research on ancient Arabic writing recognition has suffered from a lack of interest for decades, despite the availability of thousands of historical documents. One of the reasons for this lack of interest is the absence of a standard dataset, which is fundamental for building and evaluating an OCR system. In 2022, we published a database of ancient Arabic words as the only public dataset of characters written in Al-Mojawhar Moroccan calligraphy. Therefore, such a database needs to be studied and evaluated. In this paper, we explored the proposed database and investigated the recognition of Al-Mojawhar Arabic characters. We studied feature extraction by using the most popular descriptors used in Arabic OCR. The studied descriptors were associated with different machine learning classifiers to build recognition models and verify their performance. In order to compare the learned and handcrafted features on the proposed dataset, we proposed a deep convolutional neural network for character recognition. Regarding the complexity of the character shapes, the results obtained were very promising, especially by using the convolutional neural network model, which gave the highest accuracy score.

Digital identification and pattern recognition capabilities using machine learning methods, navigation systems, and video surveillance

The objects of the study are unmanned vehicles and branches of the bridge of the city of Kyiv (Ukraine), which connects the Great Ring Road, Zhytomyr Highway and Peremogy Avenue. The built routes were analyzed using the technology of recognition of road signs, people and vehicles. The important problem of this research is to analyze the possibilities of detecting obstacles by an unmanned vehicle using pattern recognition, which combines the methods of machine communication, navigation and real-time video surveillance. Based on the study, the results of detecting and avoiding obstacles on the road, where a study was conducted to investigate the main reasons that can cause time delays (traffic jams, weather conditions, accidents). The results of planning and navigation are obtained to determine the appropriate road route, which allows detecting and eliminating obstacles on the road, as well as building a map plan of the route in advance using online map services (Google Maps). It is shown that recognition of road signs (based on the classification using a road sign map consisting of 7 categories), people and vehicles minimizes the occurrence of road accidents, traffic jams and time delays. To recognize the images of road signs, people and vehicles, we studied the road sections connecting to the branched bridge. Thus, the authors have reviewed and analyzed the digital capabilities of pattern identification and recognition using machine learning methods, navigation and video surveillance systems, where the safety of vehicles with detection of road signs and obstacles on the way is of great importance. The results obtained can complement the possibilities of using unmanned vehicles to avoid obstacles and road accidents based on a trained pattern recognition system. This system, using convolutional neural networks and video surveillance navigation systems, will be able to provide the driver and the people around it with safe driving conditions.

Implementation of Artificial Synapse Using IGZO-Based Resistive Switching Device.

In this study, we present the resistive switching characteristics and the emulation of a biological synapse using the ITO/IGZO/TaN device. The device demonstrates efficient energy consumption, featuring low current resistive switching with minimal set and reset voltages. Furthermore, we establish that the device exhibits typical bipolar resistive switching with the coexistence of non-volatile and volatile memory properties by controlling the compliance during resistive switching phenomena. Utilizing the IGZO-based RRAM device with an appropriate pulse scheme, we emulate a biological synapse based on its electrical properties. Our assessments include potentiation and depression, a pattern recognition system based on neural networks, paired-pulse facilitation, excitatory post-synaptic current, and spike-amplitude dependent plasticity. These assessments confirm the device's effective emulation of a biological synapse, incorporating both volatile and non-volatile functions. Furthermore, through spike-rate dependent plasticity and spike-timing dependent plasticity of the Hebbian learning rules, high-order synapse imitation was done.

Calligraphy and painting scraps of old and new Asian papers, their simulation, performance, sources, and characteristics

This article explores the importance of paper in traditional Chinese calligraphy and painting and investigates the performance differences between a new paper and an old paper. The characteristics of paper, such as texture, absorbency, color, and durability, significantly impact the artwork’s quality and expression. The development of new papers has been driven by the desire to improve characteristics and meet the evolving needs of artists. This research compares a new paper, made using alternative materials and technology, with an old paper, combining a simulation sample and authentic calligraphy and painting scraps. The evaluation considers parameters such as ink absorption, texture, color vibrancy, bleed-through, smudging, and durability. The new paper demonstrated superior ink absorption, a smooth texture suitable for detailed work, enhanced color vibrancy, minimal bleed-through and smudging, and improved durability. The old paper, while offering a unique rough texture, had slower ink drying times, slightly lower color vibrancy, and less durability. Artists seeking precision and vibrant colors may prefer the new paper, while those desiring a textured and traditional aesthetic may favor the old paper. The findings contribute to understanding the performance characteristics of different papers, aiding artists, collectors, and researchers in their selection and appreciation of paper in calligraphy and painting. Several challenges, such as the omission of specific paper types or brands, a limited sample size, and the oversight of long-term paper durability, warrant further exploration. In the context of modern tools, the article underscores the potential role of artificial intelligence (AI) in supporting artists, collectors, and researchers in the selection and appreciation of paper in calligraphy and painting. Integration of AI for tasks such as data analysis, pattern recognition, and recommendation systems could offer valuable insights, fostering objectivity and efficiency in the study and aiding in the exploration of a wider range of paper characteristics.

Semantic and Context Understanding for Sentiment Analysis in Hindi Handwritten Character Recognition Using a Multiresolution Technique

The rapid growth of Web 2.0, which enables people to generate, communicate, and share information, has resulted in an increase in the total number of users. In developing countries, online users’ sentiment influences decision-making, social views, individual consumption decisions, and entity quality monitoring. As a result, more accurate sentiment analysis, particularly in their native language such as Hindi, is preferred over crude binary categorization. This is because of the abundance of web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. Analyzing this data and recovering valuable and relevant information from handwritten text has become extremely important. Despite years of research and development, no optical writing recognition (OCR) system has ever been certified as completely reliable. The first step in any pattern recognition system is feature selection. In many fields, feature selection is studied as a combinatorial optimization problem. The primary goal of feature selection is to reduce the number of redundant and ineffective traits in the recognition system. This feature selection is used to maintain or improve the performance of the classifier used by the recognition system: A support vector machine (SVM) technique could be used to solve this character recognition problem. The Hindi character recognition system recognizes Hindi characters by employing morphological operations, edge detection, HOG feature extraction, and an SVM-based classifier. The proposed model outperformed the current state-of-the-art method, achieving an accuracy of 96.77%.

A Review of Pattern Recognition and Machine Learning

This article aims to provide a concise overview of diverse methodologies employed at different stages of a pattern recognition system, highlighting contemporary research challenges and applications in this dynamic field. The integration of machine learning techniques has played a pivotal role in converging pattern recognition frameworks in academic literature. The process relies heavily on supervised or unsupervised categorization methods to achieve its objectives, with a notable focus on statistical approaches. More recently, there is a growing emphasis on incorporating neural network methodologies and insights from statistical learning theory. Designing an effective recognition system necessitates careful consideration of various factors, including pattern representation, pattern class definition, feature extraction, sensing environment, feature selection, classifier learning and design, cluster analysis, test and training sample selection, and performance assessment.

Reconfigurable all-optical pattern recognition for PSK and QAM signals in high-speed optoelectronic firewalls.

In the fifth generation fixed networks (F5G) era, full-fiber-connected optical networks support emerging bandwidth-hungry services. However, optical networks are vulnerable to attack by tapping or other methods, which has been paid more and more attention in modern optical infrastructure. Therefore, high-speed optoelectronic firewalls appear as one of the promising technologies to guarantee security. The most significant and challenging component of a high-speed optoelectronic firewall is all-optical pattern recognition, especially for more advanced high-order modulation formats such as phase shift keying (PSK) or quadrature amplitude modulation (QAM) to satisfy efficient enhanced fixed broadband in F5G. In this paper, what we believe to be a novel reconfigurable all-optical pattern recognition system for PSK and QAM signals is proposed with two implementation architectures. The proposed system mainly consists of a generalized XNOR (GXNOR) and a recirculating loop. The two implementation architectures are precisely two realization methods of the GXNOR part. One employs two cascaded IQ Mach-Zehnder modulators and the other is implemented by the four-wave mixing. The numerical simulation results demonstrate that the two implementation architectures can both achieve all-optical pattern recognition for the reconfigurable high-order modulation formats of QPSK, 8PSK, and 16QAM with the recorded baud rate of 260GBaud.

Lower Limb Activity Recognition Based on sEMG Using Stacked Weighted Random Forest.

The existing surface electromyography-based pattern recognition system (sEMG-PRS) exhibits limited generalizability in practical applications. In this paper, we propose a stacked weighted random forest (SWRF) algorithm to enhance the long-term usability and user adaptability of sEMG-PRS. First, the weighted random forest (WRF) is proposed to address the issue of imbalanced performance in standard random forests (RF) caused by randomness in sampling and feature selection. Then, the stacking is employed to further enhance the generalizability of WRF. Specifically, RF is utilized as the base learner, while WRF serves as the meta-leaning layer algorithm. The SWRF is evaluated against classical classification algorithms in both online experiments and offline datasets. The offline experiments indicate that the SWRF achieves an average classification accuracy of 89.06%, outperforming RF, WRF, long short-term memory (LSTM), and support vector machine (SVM). The online experiments indicate that SWRF outperforms the aforementioned algorithms regarding long-term usability and user adaptability. We believe that our method has significant potential for practical application in sEMG-PRS.

Expression of Toll-like receptors in the cerebellum during pathogenesis of prion disease.

Prion diseases, such as scrapie, entail the accumulation of disease-specific prion protein (PrPSc) within the brain. Toll-like receptors (TLRs) are crucial components of the pattern recognition system. They recognize pathogen-associated molecular patterns (PAMPs) and play a central role in orchestrating host innate immune responses. The expression levels of Toll-like receptors (TLRs) in the central nervous system (CNS) were not well-defined. To establish a model of prion diseases in BALB/C mice, the 22L strain was employed. The features of the 22L strain were analyzed, and the cerebellum exhibited severe pathological changes. TLR1-13 levels in the cerebellum were measured using quantitative polymerase chain reaction (qPCR) at time points of 60, 90, 120, and the final end point (145 days post-infection). During the pathogenesis, the expression levels of Toll-like receptors (TLRs) 1, 2, 7, 8, and 9 increased in a time-dependent manner. This trend mirrored the expression patterns of PrPSc (the pathological isoform of the prion protein) and glial fibrillary acidic protein. Notably, at the end point, TLR1-13 levels were significantly elevated. Protein level of TLR7 and TLR9 showed increasing at the end point of the 22L-infected mice. A deeper understanding of the increased Toll-like receptors (TLRs) in prion diseases could shed light on their role in initiating immune responses at various stages during pathogenesis. This insight is particularly relevant when considering TLRs as potential therapeutic targets for prion diseases.

Random Permutation Set Reasoning.

In artificial intelligence, it is crucial for pattern recognition systems to process data with uncertain information, necessitating uncertainty reasoning approaches such as evidence theory. As an orderable extension of evidence theory, random permutation set (RPS) theory has received increasing attention. However, RPS theory lacks a suitable generation method for the element order of permutation mass function (PMF) and an efficient determination method for the fusion order of permutation orthogonal sum (POS). To solve these two issues, this paper proposes a reasoning model for RPS theory, called random permutation set reasoning (RPSR). RPSR consists of three techniques, including RPS generation method (RPSGM), RPSR rule of combination, and ordered probability transformation (OPT). Specifically, RPSGM can construct RPS based on Gaussian discriminant model and weight analysis; RPSR rule incorporates POS with reliability vector, which can combine RPS sources with reliability in fusion order; OPT is used to convert RPS into a probability distribution for the final decision. Besides, numerical examples are provided to illustrate the proposed RPSR. Moreover, the proposed RPSR is applied to classification problems. An RPSR-based classification algorithm (RPSRCA) and its hyperparameter tuning method are presented. The results demonstrate the efficiency and stability of RPSRCA compared to existing classifiers.

Pattern recognition system for rapid detection of gases using microfluidic olfaction detector: A case study using methane and ethane

Two significant shortcomings of metal oxide semiconductor (MOS) sensors are their low selectivity and prolonged recovery time after exposure to target gases. Microfluidic olfaction detectors (MFOD), having an embedded MOS at the distal end of a microchannel, improve MOS selectivity but further prolong the recovery time due to the delay in the gas movement as the result of diffusion through the microchannel. This paper aims to reduce the sampling time of a single MFOD. Such reduction is achieved here by developing a pattern recognition system involving: (i) an ensemble of four heterogeneous base machine learning models, (ii) a Long-Short Term Memory Neural Network (LSTM) to construct a portion of the transient response, (iii) features from an additional fast response sensor, and (iv) detachment of MOS from the microchannel for accelerated recovery. Using data collected from methane and ethane as a case study, the results of classification and regression demonstrate the need for only 17.5% of the previously used response time without any loss in accuracy. This reduces the total testing cycle of an MFOD by 60%, further enhancing the ability of selective MFODs by enabling faster detection of gases.

An improved human activity recognition technique based on convolutional neural network

A convolutional neural network (CNN) is an important and widely utilized part of the artificial neural network (ANN) for computer vision, mostly used in the pattern recognition system. The most important applications of CNN are medical image analysis, image classification, object recognition from videos, recommender systems, financial time series analysis, natural language processing, and human–computer interfaces. However, after the technological advancement in the power of computing ability and the emergence of huge quantities of labeled data provided through enhanced algorithms, nowadays, CNN is widely used in almost every area of study. One of the main uses of wearable technology and CNN within medical surveillance is human activity recognition (HAR), which must require constant tracking of everyday activities. This paper provides a comprehensive study of the application of CNNs in the classification of HAR tasks. We describe their enhancement, from their antecedents up to the current state-of-the-art systems of deep learning (DL). We have provided a comprehensive working principle of CNN for HAR tasks, and a CNN-based model is presented to perform the classification of human activities. The proposed technique interprets data from sensor sequences of inputs by using a multi-layered CNN that gathers temporal and spatial data related to human activities. The publicly available WISDM dataset for HAR has been used to perform this study. This proposed study uses the two-dimensional CNN approach to make a model for the classification of different human activities. A recent version of Python software has been used to perform the study. The rate of accuracy for HAR through the proposed model in this experiment is 97.20%, which is better than the previously estimated state-of-the-art technique. The findings of the study imply that using DL methods for activity recognition might greatly increase accuracy and increase the range of applications where HAR can be used successfully. We have also described the future research trends in the field of HAR in this article.

Analysis of Pattern Recognition of Disorders in Children Using Artificial Neural Network (ANN) and Decision Tree Methods: A Case Study of Disorders in Children with Disabilities

In this era of globalization, technological developments in the field of digital information have greatly affected services in the health sector in healing and meeting public satisfaction. Digital technology-based services are needed by children with disabilities so that they can be given superior and effective treatment. Digital technology services are used as a clustering and pattern recognition system using Artificial Neural Network (ANN) and decision tree. The data collection process is done by taking a video of a child walking on a track in the form of manila cardboard whose feet have been colored with food coloring so that the footprints can be seen clearly. After that, Artificial Neural Network (ANN) and decision tree have two stages, namely the training stage and the testing stage. The training stage is carried out using 20 respondent data from children who walk. The testing stage is carried out using three new data that are different from the training stage. From these two methods, a high accuracy rate of 99.91% is obtained for ANN and close to 100% for decision trees.

The St. Petersburg Scientific School of Pattern Recognition, Signal Analysis, and Biotechnical Systems

The St. Petersburg Scientific School of Pattern Recognition, Signal Analysis, and Biotechnical Systems

Short-term hand gestures recognition based on electromyography signals

<p>Electromyography pattern recognition to predict limb movements can<br />significantly enhance the control of the prosthesis. However, this technique<br />has not yet been widely used in clinical practice. Improvements in the<br />myoelectric pattern recognition (MPR) system can improve the functionality<br />of the prosthesis. This study proposes new sets of time domain features to<br />enhance the MPR control system. Three groups of features are evaluated, time<br />domain with auto regression (TD-AR), frequency domain (FD), and timefrequency domain (TFD). The electromyography signals (EMG) are obtained from the Ninapro database-5 (DB5), a publicly available dataset for hand prosthetics. The long-term signals of DB5 are divided into short-term signals to perform short-term signals recognition. The three feature sets are extracted from the short-term signals. The results showed that the performance of the proposed TD-AR features outperformed that of the FD and TFD feature sets. The TD-AR-based discrimination performance of 40 gestures achieved a precision of 88.8% and a sensitivity of 82.6%. The integration of short-term identification with reliable features can improve classification accuracy even for a large number of gestures. A comparison with the latest works shows the reliability of the proposed work.</p>

Deep neural network based acoustic pattern recognition system for fault localization application

Abstract This paper explores the process of traditional voiceprint recognition, analyzes the traditional GMM recognition algorithm, and proposes a GE2E-based voiceprint recognition algorithm by combining it with the deep neural network. It firstly uses the Bi-GRU network to replace the LSTM network to prevent the lack of semantic information, then adds the SGD algorithm to optimize the speech features, and finally improves the stability and accuracy of recognition by the GE2E loss function. On this basis, a voiceprint recognition system based on GE2E is designed, and the overall performance of the system is tested. Additionally, a voiceprint recognition system is being explored for fault localization. The results show that the recognition accuracy of male voiceprints in the test is at [0.89,0.95], and the recognition accuracy of female voiceprints is at [0.88,0.96], and there is not much difference in the voiceprint recognition accuracy of the voiceprint recognition system for both male and female students, and the overall recognition accuracy is greater than 0.9. When applied in fault location, the error between the measured distance and the actual fault distance is within 0.1 meters, enabling fault location.

The Role of the Innate Immune Response in Oral Mucositis Pathogenesis.

Oral mucositis (OM) is a significant complication of cancer therapy with limited management strategies. Whilst inflammation is a central feature of destructive and ultimately ulcerative pathology, to date, attempts to mitigate damage via this mechanism have proven limited. A relatively underexamined aspect of OM development is the contribution of elements of the innate immune system. In particular, the role played by barriers, pattern recognition systems, and microbial composition in early damage signaling requires further investigation. As such, this review highlights the innate immune response as a potential focus for research to better understand OM pathogenesis and development of interventions for patients treated with radiotherapy and chemotherapy. Future areas of evaluation include manipulation of microbial-mucosal interactions to alter cytotoxic sensitivity, use of germ-free models, and translation of innate immune-targeted agents interrogated for mucosal injury in other regions of the alimentary canal into OM-based clinical trials.

JoyPose: Jointly learning evolutionary data augmentation and anatomy-aware global–local representation for 3D human pose estimation

Video-based 3D human pose estimation is an important yet challenging task for many human-involved pattern recognition systems. Existing deep learning-based 3D human pose estimation methods are faced with the problems of lacking large-scale training data and lacking effective solutions to represent the complicated human body structure. To this end, this paper proposes a jointly learning framework entitled JoyPose that simultaneously leverages both human pose data augmentation and human pose estimation. In particular, JoyPose consists of an evolutionary data augmentation module and an anatomy-aware global–local pose feature representation module for 3D human pose estimation. The evolution for data augmentation is guided by a reinforcement learning strategy in a probabilistic way according to pose estimation loss. The anatomy-aware global–local pose feature representation module separately captures global features and local features according to anatomical and kinematic patterns observed from pose estimation errors across different human joints. The performance of the final human pose estimation is leveraged by both data augmentation and anatomy-aware global–local feature representation. Extensive experiments on three real-world datasets demonstrate the superiority and robustness against state-of-the-art methods.

Applying deep learning in brain computer interface to classify motor imagery

Deep-learning (DL) is a new paradigm in the artificial intelligence field associated with learning structures able to connect directly numeric data with high-level patterns or categories. DL seems to be a suitable technique to deal with computationally challenging Brain Computer Interface (BCI) problems. Following DL strategy, a new modular and self-organized architecture to solve BCI problems is proposed. A pattern recognition system to translate the measured signals in order to establish categories representing thoughts, without previous pre-processing, is developed. To achieve an easy interpretability of the system internal functioning, a neuro-fuzzy module and a learning methodology are carried out. The whole learning process is based on machine learning. The architecture and the learning method are tested on a representative BCI application to detect and classify motor imagery thoughts. Data is gathered with a low-cost device. Results prove the efficiency and adaptability of the proposed DL architecture where the used classification module (S-dFasArt) exhibits a better behaviour compared with the usual classifiers. Additionally, it employs neuro-fuzzy modules which allow to offer results in a rules format. This improves the interpretability with respect to the black-box description. A DL architecture, going from the raw data to the labels, is proposed. The proposed architecture, based on Adaptive Resonance Theory (ART) and Fuzzy ART modules, performs data processing in a self-organized way. It follows the DL paradigm, but at the same time, it allows an interpretation of the operation stages. Therefore this approach could be called Transparent Deep Learning.

Improved Resistive and Synaptic Characteristics in Neuromorphic Systems Achieved Using the Double-Forming Process.

In this study, we investigate the electrical properties of ITO/ZrOx/TaN RRAM devices for neuromorphic computing applications. The thickness and material composition of the device are analyzed using transmission electron microscopy. Additionally, the existence of TaON interface layers was confirmed using dispersive X-ray spectroscopy and X-ray photoelectron analysis. The forming process of the ZrOx-based device can be divided into two categories, namely single- and double forming, based on the initial lattice oxygen vacancies. The resistive switching behaviors of the two forming methods are compared in terms of the uniformity properties of endurance and retention. The rationale behind each I-V forming process was determined as follows: in the double-forming method case, an energy band diagram was constructed using F-N tunneling; conversely, in the single-forming method case, the ratio of oxygen vacancies was extracted based on XPS analysis to identify the conditions for filament formation. Subsequently, synaptic simulations for the applications of neuromorphic systems were conducted using a pulse scheme to achieve potentiation and depression with a deep neural network-based pattern recognition system to display the achieved recognition accuracy. Finally, high-order synaptic plasticity (spike-timing-dependent plasticity (STDP)) is emulated based on the Hebbian rule.