Pattern Recognition Processing Research Articles

Individual differences in visual pattern completion predict adaptation to degraded speech

Recognizing acoustically degraded speech relies on predictive processing whereby incomplete auditory cues are mapped to stored linguistic representations via pattern recognition processes. While listeners vary in their ability to recognize degraded speech, performance improves when a written transcription is presented, allowing completion of the partial sensory pattern to preexisting representations. Building on work characterizing predictive processing as pattern completion, we examined the relationship between domain-general pattern recognition and individual variation in degraded speech learning. Participants completed a visual pattern recognition task to measure individual-level tendency towards pattern completion. Participants were also trained to recognize noise-vocoded speech with written transcriptions and tested on speech recognition pre- and post-training using a retrieval-based transcription task. Listeners significantly improved in recognizing speech after training, and pattern completion on the visual task predicted improvement for novel items. The results implicate pattern completion as a domain-general learning mechanism that can facilitate speech adaptation in challenging contexts.

Pushing Boundaries: AI and Computer Science in the Era of Technological Revolution

The advent of Artificial Intelligence (AI) has heralded a transformative era in Computer Science, revolutionizing various facets of technology. This paper explores the profound impact of AI on the field of Computer Science, delving into its advancements, innovations, and the potential future trajectory of technology. The primary objective of this paper is to elucidate the significant role of AI in shaping the landscape of Computer Science. Through comprehensive research and analysis, it aims to provide insights into the evolution of AI, its applications, and the implications for future technological developments. This study employs a library research approach, gathering information from academic journals, conference proceedings, books, and reputable online sources. By synthesizing existing literature and scholarly works, it seeks to construct a comprehensive overview of the advancements and innovations in AI within the realm of Computer Science. The findings reveal a dynamic and rapidly evolving field driven by AI technologies. From machine learning algorithms to neural networks and deep learning models, AI has revolutionized data analysis, pattern recognition, and decision-making processes. Moreover, AI-driven applications such as natural language processing, computer vision, and robotics have reshaped various industries, including healthcare, finance, transportation, and manufacturing. However, alongside these advancements come ethical considerations, privacy concerns, and challenges related to algorithmic biases and societal implications. Looking ahead, the future of technology promises further integration of AI into various domains, leading to unprecedented opportunities and challenges in the realm of Computer Science

Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review

AbstractA cement‐based material that meets the general goals of mechanical properties, workability, and durability as well as the ever‐increasing demands of environmental sustainability is produced by varying the type and quantity of individual constituents in high‐performance concrete (HPC) and ultrahigh‐performance concrete (UHPC). Expensive and time‐consuming laboratory experiments can be used to estimate the properties of concrete mixtures and elements. As an alternative, these attributes can be approximated by means of predictive models created through the application of artificial intelligence (AI) methodologies. AI approaches are among the most effective ways to solve engineering problems due to their capacity for pattern recognition and knowledge processing. Machine learning (ML) and deep learning (DL) are a subfield of AI that is gaining popularity across many scientific domains as a result of its many benefits over statistical and experimental models. These include, but are not limited to, better accuracy, faster performance, greater responsiveness in complex environments, and lower economic costs. In order to assess the critical features of the literature, a comprehensive review of ML and DL applications for HPC and UHPC was conducted in this study. This paper offers a thorough explanation of the fundamental terms and ideas of ML and DL algorithms that are frequently used to predict mechanical properties of HPC and UHPC. Engineers and researchers working with construction materials will find this paper useful in helping them choose accurate and appropriate methods for their needs.

ARX-Naïve Bayes based structural damage identification

ABSTRACT The nature of damage identification is close to a pattern recognition process that classifies different damage patterns. The naïve Bayes classifier (NBC) can effectively handle multiple-classification problems by choosing patterns with high probabilities. Therefore, by absorbing the autoregressive model with exogenous inputs (the ARX model), an ARX-Naïve Bayes damage identification strategy has been proposed in which the autoregressive coefficients of the ARX model are taken as the sensitive damage feature. The classification training and test sample datasets are then built on these coefficients corresponding to various damage scenarios. The model order of an AR model is first determined for the subsequent order selection of the ARX model, whose autoregressive coefficients are further used to construct the NBC. This procedure can enhance the pattern recognition robustness to uncertainties such as measurement noises. Different damage patterns are determined by calculating the sum of logarithmic likelihoods of testing samples. The effectiveness of the proposed method has been verified against a bridge benchmark model having different damage scenarios under the noise pollution. In addition, an experimental five-story shear frame structure was adopted for validation, showing that compared with the SVM algorithm suitable for handling binary classification problems, the proposed method excels in multi-classification of damage patterns.

Organic Synaptic Transistors with Environmentally Friendly Core/Shell Quantum Dots for Wavelength-Selective Memory and Neuromorphic Functions.

Organic transistors based on organic semiconductors together with quantum dots (QDs) are attracting more and more interest because both materials have excellent optoelectronic properties and solution processability. Electronics based on nontoxic QDs are highly desired considering the potential health risks but are limited by elevated surface defects, inadequate stability, and diminished luminescent efficiency. Herein, organic synaptic transistors based on environmentally friendly ZnSe/ZnS core/shell QDs with passivating surface defects are developed, exhibiting optically programmable and electrically erasable characteristics. The synaptic transistors feature linear multibit storage capability and wavelength-selective memory function with a retention time above 6000 s. Various neuromorphic applications, including memory enhancement, optical communication, and memory consolidation behaviors, are simulated. Utilizing an established neuromorphic model, accuracies of 92% and 91% are achieved in pattern recognition and complicated electrocardiogram signal processing, respectively. This research highlights the potential of environmentally friendly QDs in neuromorphic applications and health monitoring.

Zero-One Matrix on Tudung Saji Pattern via Image Processing

Pattern recognition is a method for classifying or describing the quantitative measurement of features of any object, data, or source. It involves any input that depends on the context of the study, such as biometric data, sensor data, signals, images, and so on. Prior research had been conducted on tudung saji, with a particular emphasis placed on ethnographic research that was related to group theory. In order to advance the study of the relationships between tudung saji and group theory, additional analysis is undertaken to examine the pattern of tudung saji within certain finite matrix rings. This analysis involves studying the zero-one matrix that is obtained through image processing. It is important to understand the process of pattern recognition to produce the output as a classification result from the feature extraction of the input. The study focuses on the stages of image preprocessing, which include scaling, grayscale conversion, and thresholding. The binarization image is classified through feature extraction using the image of tudung saji patterns. The binarization image produces a binary value. The threshold value is calculated by using the Otsu’s Method. This method evaluates the binary value by comparing the threshold value with the grayscale value. The result assists researchers in the subsequent classification of tudung saji patterns into group theory by means of analysing the finite zero-one matrix. Hence, the classification of tudung saji patterns can be achieved by utilising finite zero-one matrices, which allows for a more comprehensive analysis of these patterns from the standpoint of group theory. In light of these considerations, a mathematical analysis is conducted on the patterns of tudung saji under the field of pattern recognition. In this analysis, the image of the tudung saji pattern serves as the input for data processing, resulting in the production of a finite zero-one matrix.

Pen Stroke Digit Recognition Using CNN

Hand-written character and digit recognition have been one of the most exigent and engrossing field of pattern recognition and image processing. The main aim of this paper is to demonstrate and represent the work which is related to hand-written digit recognition. The hand-written digit recognition is a very exigent task. In this recognition task, the numbers are not accurately written or scripted as they differ in shape or size; due to which the feature extraction and segmentation of hand-written numerical script is arduous. The vertical and horizontal projections methods are used for the purpose of segmentation in the proposed work. KNN is applied for recognition and classification. The digit recognition is a very exigent task. In this recognition task, the numbers are not accurately written or scripted as they differ in shape or size; due to which the feature extraction and segmentation of numerical script is arduous. The vertical and horizontal projections methods are used for the purpose of segmentation in the proposed work. CNN is applied for recognition, classification and we are also getting nice accuracy while doing prediction.

Advancements towards the development artificial intelligence for sensory analysis: Integrating pattern recognition and signal processing in ATR-FTIR analysis of spirits

Spirits have been gaining acceptance by international consumers due to its distinct organoleptic profile, which provide each beverage with a characteristic sensation of the product (taste, smell, and color). However, it is important to establish differences by an analytical technique supported by artificial intelligence to demonstrate with mathematical solutions rather of being subjective such as human senses. To reduce the uncertainty associated to the sensory analysis, the present study shows as strategy the recognition of patterns of ATR-FTIR spectra of a variety of distilled beverages: Tequila 100% agave (n = 20), Mezcal (n = 20), Raicilla (n = 20), Aguardiente (n = 20), and Vodka (n = 20). All ATR-FTIR spectra present seven characteristic bands associated with methanol, ethanol, isoamyl alcohol, butanol, ethyl acetate, acetaldehyde, and furfural in beverages. Based on the analysis of the peak signals obtained from the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectra, the implementation of an Artificial Neural Network was proposed with the objective to classify each alcoholic beverage in its corresponding groups. Results showed that the artificial intelligence algorithm proposed permits to correct classify 100% of the samples being a great alternative strategy being innovative, fast, and of low-cost strengthening food quality analyses by machine learning decision-making.

Urban Farming Growth Monitoring System Using Artificial Neural Network (ANN) and Internet of Things (IOT)

As an introduction to this project, the growth-related traits, such as above-ground biomass and leaf area, are critical indicators to characterize the growth of indoor lettuce plants. Currently, non-destructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. It is also one of the problem statements in this project. Based on this project the next problem is manual control of nutrients may cause quality issues to the lettuce plant. If the nutrient supply is too much or less, it will disturb the growth of the lettuce plant either the lettuce plant is dead or stunted. This project is about urban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IoT). In this project, a method for monitoring the growth of indoor lettuce plants was proposed by using digital images and an ANN using Deep Learning Architecture. DLA is mostly developed by the software of MATLAB or Python to insert and run the coding. DLA is mostly used for image detection, pattern recognition, and natural language processing through the graph for Neural Network. Next, the Internet of Things (IoT) is a medium to store images of indoor lettuce plant growth into the Cloud (Google Drive). Furthermore, it takes indoor lettuce plant images as the input, an ANN was trained to learn the relationship between images and the corresponding growth- related traits with other fixed parameters. The pH level parameters were controlled by other fixed parameters to take the images of indoor lettuce plant growth. The parameters used in this project are temperature and humidity. This helps to compare the results of Artificial Neural Network (ANN), widely adopted methods were also used. Concisely, this project is expected to develop the Deep Learning Architecture using an Artificial Neural Network (ANN) with digital images as a robust tool for the monitoring of the growth of indoor lettuce plants every 30 minutes per day. Generally, focused on an urban farming growth monitoring system using Artificial Neural Network (ANN) and the Internet of Things (IoT).

Public interest trends for COVID-19 and pandemic trajectory: A time-series analysis of US state-level data.

Google Trends provides spatiotemporal data for user-specific terms scaled from less than 1 (lowest relative popularity) to 100 (highest relative popularity) as a proxy for the public interest. Here we use US state-level data for COVID-19 to examine popularity trends during the pandemic evolution. We used "coronavirus" and "covid" search terms and set the period up from January 1st, 2020, to November 12, 2022. We measured the agreement on web rankings between states using the nonparametric Kendall's W (0 for no concordance to 1 for perfect agreement). We compiled state-level weekly data on COVID-19 incidence and mortality and scaled state curves from 0 to 100 through a min-max normalization process. We used a dynamic time-warping algorithm to calculate similarities between the popularity, mortality, and incidence of COVID-19. The methodology is a pattern recognition process between time series by distance optimization. The similarity was mapped from 0 to 1, with 1 indicating perfect similarity and 0 indicating no similarity. The peak in popularity was in March 2020, succeeded by a decline and a prolonged period of fluctuation around 20%. Public interest rose briefly at the end of 2021, to fall to a low activity of around 10%. This pattern was remarkably consistent across states (Kendal's W 0.94, p < 0.001). Web search trends were an impression of contagion growth: Overall, popularity-mortality trajectories yielded higher similarity indices (median 0.78; interquartile range 0.75-0.82) compared to popularity-incidence trajectories (median 0.74; interquartile range 0.72-0.76, Wilcoxon's exact p<0.001). The popularity-mortality trajectories had a very strong similarity (>0.80) in 19/51 (37%) regions, as opposed to only 4/51 (8%) for popularity-incidence trajectories. State-level data show a fading public concern about COVID-19, and web-search popularity patterns may reflect the COVID-19 trajectory in terms of cases and mortality.

Distance-Based Fractional Dimension of Certain Wheel Networks

Distance-Based Fractional Dimension of Certain Wheel Networks

Synaptic Characteristics of Fully Depleted Silicon‐on‐Insulator Metal‐Oxide‐Semiconductor Field‐Effect Transistors and Synapse‐Neuron Arrayed Neuromorphic Hardware System

A fully depleted silicon‐on‐insulator (FDSOI) metal‐oxide‐semiconductor field‐effect transistors (MOSFETs) device is investigated as for an electronic synapse emulating the synaptic functions of the human brain with stable characteristics. Gate‐last processed FDSOI MOSFET with a high‐k/metal gate stack features a memory window of 103. Synaptic conductance is stably regulated by utilizing the FDSOI MOSFET, which offers the advantage of mitigating leakage current when compared to bulk Si MOSFET. Short‐ and long‐term plasticity are investigated by applying engineered pulse, verifying the long‐term synaptic properties of pattern recognition processes. With controllable synaptic conductance, the trade‐off between conductance change and linearity regarding the recognition rate is evaluated, attaining a recognition rate of 0.83. To verify the pre‐ and post‐synaptic weights within a real hardware‐based neuromorphic system, 5 × 6 FDSOI field‐effect transistor (FET) synapse array is interconnected to 10 × 10 leaky integrate‐and‐fire (LIF) neuron array. The synaptic plasticity of FDSOI MOSFET in post‐neurons following neuron firing in the neuron device is successfully demonstrated. These results indicate that FDSOI MOSFET devices could be applicable as synapse devices due to controllability and capability to realize signal transmissions and self‐learning processes simultaneously and used to mimic a synapse neuron network system by configuring a hardware system interconnected with the LIF neuron.

Review on Smart Evaluation of Descriptive Answer Sheets

This descriptive abstract summarizes a thorough examination into the use of smart technology for answer sheet evaluation. The study explores how to automate the grading process using Artificial Intelligence, Machine Learning and other algorithms to improve efficiency and objectivity while evaluating student responses. Examined are several smart assessment systems, stressing attributes such as adaptive learning processes, pattern recognition and natural language processing. The abstract delves into the possible advantages, obstacles and ramifications linked to the implementation of intelligent response sheet assessment techniques in educational environments. The abstract offers insights into the changing landscape of assessment methodologies through a synthesis of recent research findings, illuminating the revolutionary potential of intelligent systems in reshaping education in the future

PbS/CsPbBr3 Heterojunction for Broadband Neuromorphic Vision Sensing.

Neuromorphic light sensors with analogue-domain image processing capability hold promise for overcoming the energy efficiency limitations and latency of von Neumann architecture-based vision chips. Recently, metal halide perovskites, with strong light-matter interaction, long carrier diffusion length, and exceptional photoelectric conversion efficiencies, exhibit reconfigurable photoresponsivity due to their intrinsic ion migration effect, which is expected to advance the development of visual sensors. However, suffering from a large bandgap, it is challenging to achieve highly tunable responsivity simultaneously with a wide-spectrum response in perovskites, which will significantly enhance the image recognition accuracy through the machine learning algorithm. Herein, we demonstrate a broadband neuromorphic visual sensor from visible (Vis) to near-infrared (NIR) by coupling all-inorganic metal halide perovskites (CsPbBr3) with narrow-bandgap lead sulfide (PbS). The PbS/CsPbBr3 heterostructure is composed of high-quality single crystals of PbS and CsPbBr3. Interestingly, the ion migration of CsPbBr3 with the implementation of an electric field induces the energy band dynamic bending at the interface of the PbS/CsPbBr3 heterojunction, leading to reversible, multilevel, and linearly tunable photoresponsivity. Furthermore, the reconfigurable and broadband photoresponse in the PbS/CsPbBr3 heterojunction allows convolutional neuronal network processing for pattern recognition and edge enhancements from the Vis to the NIR waveband, suggesting the great potential of the PbS/CsPbBr3 heterostructure in artificial intelligent vision sensing.

Pattern-Recognition Processes of First-Grade Students: An Explorative Eye-Tracking Study

AbstractRecognizing patterns is an essential skill in early mathematics education. However, first graders often have difficulties with tasks such as extending patterns of the form ABCABC. Studies show that this pattern-recognition ability is a good predictor of later pre-algebraic skills and mathematical achievement in general, or the development of mathematical difficulties on the other hand. To be able to foster children’s pattern-recognition ability, it is crucial to investigate and understand their pattern-recognition processes early on. However, only a few studies have investigated the processes used to recognize patterns and how these processes are adapted to different patterns. These studies used external observations or relied on children’s self-reports, yet young students often lack the ability to properly report their strategies. This paper presents the results of an empirical study using eye-tracking technology to investigate the pattern-recognition processes of 22 first-grade students. In particular, we investigated students with and without the risk of developing mathematical difficulties. The analyses of the students’ eye movements reveal that the students used four different processes to recognize patterns—a finding that refines knowledge about pattern-recognition processes from previous research. In addition, we found that for patterns with different units of repeat (i.e. ABABAB versus ABCABCABC), the pattern-recognition processes used differed significantly for students at risk of developing mathematical difficulties but not for students without such risk. Our study contributes to a better understanding of the pattern-recognition processes of first-grade students, laying the foundation for enhanced, targeted support, especially for students at risk of developing mathematical difficulties.

Systematising clustering techniques through cross-disciplinary research, leading to the development of new methods

Clustering algorithms can help scientists gain valuable insights from data. Thereâ–™s a variety of clustering methods in use, which means there are gaps between the methods used in different fields. Associate Professor Kohei Inoue, Department of Media Design, Kyushu University, Japan, wants to bridge these gaps by investigating the relationships among various clustering methods developed in different fields, in order to systematise the world of clustering. He is bringing two decades of research activities in pattern recognition and image processing to this work. In order to clarify the relationships between different clustering methods, Inoue and the team are conducting an interdisciplinary survey. First, the researchers are working to clarify the relationship between the technologies used across different fields. So far, they have successfully clarified the relationship between the rolling guidance filter and the local mode filter. In a previous study, Inoue and his collaborators proposed a robust K-means clustering al-algorithm. The researchers demonstrated the effectiveness of their technique utilising a BBC dataset originating from BBC News. In their work, the team is collaborating with a laboratory at a university in Japan that is studying non-photorealistic rendering. They have so far published several co-authored papers, as well as having obtained results from their joint research. Ultimately, by systemising clustering technology, Inoue believes that the characteristics of each method, as well as the interrelationships between each method, can be explained and clustering technology enhanced, as well as new clustering techniques developed.

Knowledge-based Dual External Attention Network for peptide detectability prediction

Accurate prediction of peptide detectability plays a crucial role in various proteomics data analyses such as peptide identification and protein quantification. To improve the precision of peptide detectability prediction, we propose a novel approach called the Knowledge-based Dual External Attention Network (KDEAN). KDEAN introduces several innovative elements to enhance its representation and prediction capabilities. Firstly, it extracts valuable knowledge-based features from peptide sequences to facilitate the pattern recognition process. Secondly, KDEAN adopted dual networks to separately train peptide sequences from both the forward and backward directions, capturing comprehensive information. Thirdly, an external attention mechanism is utilized to identify and understand the connections between different peptide samples. The structure of KDEAN enables long-term dependency learning from both directions of the peptide sequences. Extensive evaluations on four testing datasets demonstrate that KDEAN outperforms existing methods, achieving a higher average performance in peptide detectability prediction. Additionally, comprehensive ablation studies confirm the effectiveness and advantages of key components in KDEAN, including knowledge-based feature representation, dual network architecture, and external attention.

An autoencoder-based deep learning model for solving the sparsity issues of Multi-Criteria Recommender System

In recent times, recommender systems have acquired significant popularity as a solution to the issue of information overload. These systems offer personalised recommendations to users. The superiority of multi-criteria recommender systems over their single-criterion counterparts has been demonstrated, as the former are able to provide more precise recommendations by taking into account multiple dimensions of user preferences when rating items. The prevalent recommendation technique, matrix factorization of collaborative filtering, is hindered by the data sparsity problem of the user-item matrix. On the other hand, it is noteworthy that deep learning techniques have demonstrated significant potential in various research domains, including but not limited to image processing, pattern recognition, computer vision, and natural language processing. In recent times, there has been a surge in the utilisation of deep learning techniques in recommender systems, yielding promising outcomes. This study presents a novel approach to multi-criteria recommender systems through the utilisation of deep learning algorithms to mitigate the data sparsity issue. Specifically, deep autoencoders are utilised to uncover complex, non-linear, and latent relationships between users' multi-criteria preferences followed by matrix factorization technique, ultimately leading to more precise recommendations. The proposed model is evaluated by conducting the experiments on the multi-criteria dataset of Yahoo! Movies. According to the outcomes, the proposed approach outperforms the state of the art recommendation methods by generating more accurate and personalized recommendations. Also, it reduces the data sparsity up to 11% from the multi-criteria dataset.

Deep Learning Model Compression With Rank Reduction in Tensor Decomposition.

Large neural network models are hard to deploy on lightweight edge devices demanding large network bandwidth. In this article, we propose a novel deep learning (DL) model compression method. Specifically, we present a dual-model training strategy with an iterative and adaptive rank reduction (RR) in tensor decomposition. Our method regularizes the DL models while preserving model accuracy. With adaptive RR, the hyperparameter search space is significantly reduced. We provide a theoretical analysis of the convergence and complexity of the proposed method. Testing our method for the LeNet, VGG, ResNet, EfficientNet, and RevCol over MNIST, CIFAR-10/100, and ImageNet datasets, our method outperforms the baseline compression methods in both model compression and accuracy preservation. The experimental results validate our theoretical findings. For the VGG-16 on CIFAR-10 dataset, our compressed model has shown a 0.88% accuracy gain with 10.41 times storage reduction and 6.29 times speedup. For the ResNet-50 on ImageNet dataset, our compressed model results in 2.36 times storage reduction and 2.17 times speedup. In federated learning (FL) applications, our scheme reduces 13.96 times the communication overhead. In summary, our compressed DL method can improve the image understanding and pattern recognition processes significantly.

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

Electromyography (EMG) is about studying electrical signals from muscles and can provide a wealth of information on the function, contraction, and activity of your muscles. In the field of EMG pattern recognition, these signals are used to identify and categorize patterns linked to muscle activity. Various machine learning (ML) methods are used for this purpose. Successful detection of these patterns depends on using effective signal-processing techniques. It is crucial to reduce noise in EMG for accurate and meaningful information about muscle activity, improving signal quality for precise assessments. ML tools such as SVMs, neural networks, KNNs, and decision trees play a crucial role in sorting out complex EMG signals for different pattern recognition tasks. Clustering algorithms also help analyze and interpret muscle activity. EMG and ML find diverse uses in rehabilitation, prosthetics, and human-computer interfaces, though real-time applications come with challenges. They bring significant changes to prosthetic control, human-computer interfaces, and rehabilitation, playing a vital role in pattern recognition. They make prosthetic control more intuitive by understanding user intent from muscle signals, enhance human-computer interaction with responsive interfaces, and support personalized rehabilitation for those with motor impairments. The combination of EMG and ML opens doors for further research into understanding muscle behavior, improving feature extraction, and advancing classification algorithms.