Recognition Algorithm Research Articles

Evaluation on Network Social Media Named Entity Recognition Model Based on Active Learning

The medical security privacy and named entity recognition (NER) technology under the blockchain technology has been a hot topic in all walks of life. As a typical representative of medical security risks and NER, the NER model of online social media based on active learning has attracted worldwide attention. NER is an important part of natural language processing. Traditional recognition technology usually requires a lot of external information, and through the manual identification of its features, which costs a lot of time and energy. In order to solve the shortcomings of traditional recognition algorithms and the lack of feature extraction in network media NER, a new active learning model was introduced in this paper. In the information age, people are increasingly demanding a large amount of text information, and NER technology came into being. Its main function is to accurately identify important information from text and provide useful information for high-level work. The initial design of NER system is mainly based on the recognition of rules, so as to realize the recognition of named entities. However, in a complex network environment, it takes a lot of time and energy to establish rules without conflicts, and it has poor mobility. In recent years, with the continuous development of computer technology, the use of machine learning to actively learn the unknown information in the target area reduces the workload of manual annotation, thus realizing the active learning of large amounts of data. The research showed that the recognition accuracy under the traditional NER was low, and the information processing speed was slow; the accuracy rate of NER based on active learning was as high as 97%, and the speed of information processing had also been greatly improved, which had solved many problems under the traditional mode. User satisfaction could be as high as 95%, which showed that the latter had broad prospects. The progress of the new era cannot be separated from the support of new technologies. The research of this article has important guiding significance for medical security privacy and the application of NER under blockchain technology.

Exploration on the change process of human glucocorticoid during incremental exercise load training

Glucocorticoid is a kind of hormone synthesized from glycogen, which has many physiological functions such as improving the body’s anti fatigue, anti stress, and promoting growth. Oxidation medicine is a new medical science with dual characteristics of theory and technology, which is based on biological processes. Glucocorticoids can treat many diseases, but there are certain limitations in this process, especially in diseases that play a vital role. The purpose of this paper is to explore the change process of human glucocorticoid in the process of incremental exercise load training. This paper put forward the algorithm of incremental load training and action recognition in sports training, and analyzed the changes of human glucocorticoid (GC) in the process of incremental load training based on this research. The experimental design includes the selection of research subjects, the determination of sample size, and the setting of experimental and control groups. The load was set to start at 10 a.m., with cycling training from full effort to exhaustion. The initial load was 100 W, and it increased by 20 W every 10 min. The experimental results in this paper showed that for the changes of testosterone (T) during exercise, the salivary T secretion rate immediately after exercise was significantly different from that in the resting state at the recovery period of 30 min. For the change of GC during exercise, the secretory rate of saliva GC was 2.84 mmol/min in the resting state; the secretion rate of saliva GC was 2.46 mmol/min after 20 min of exercise and 2.15 mmol/min after 40 min of exercise; the secretion rate was 1.71 mmol/min immediately after exercise and 2.55 mmol/min after 30 min rest; there was a significant difference in the secretion rate of saliva GC for 5 times. In a word, there was no significant change in saliva T/GC value during exercise and rehabilitation under static state, which was related to strong adaptability of the body and no significant stress response.

A review of research on micro-expression recognition algorithms based on deep learning

A review of research on micro-expression recognition algorithms based on deep learning

A lightweight weld defect recognition algorithm based on convolutional neural networks

A lightweight weld defect recognition algorithm based on convolutional neural networks

Novel identification algorithm for plasma boundary gap based on visible endoscope diagnostic on EAST tokamak

The precise plasma boundary gap identification at the midplane is a prerequisite for achieving controlled plasma positioning and holds a significant importance for the stable operation of tokamak devices. This study proposes a plasma boundary gap at the midplane recognition algorithm based on visual endoscopy diagnostic. The model is an end-to-end one that uses a convolutional neural network that does not require manual data labeling. The model performance is improved by experimentally comparing different convolutional layers and input image sizes. The model is validated using a testing dataset comprising 400 plasma discharge moments. The model has average errors of 3.7 and 4 mm for gap-in and -out, respectively, when compared to those obtained by equilibrium fitting. The proposed approach offers a convenient and effective means of obtaining the boundary gap value and is particularly suited for future fusion experimental devices, such as BEST and ITER tokamak.

Modernizing the NCI60 Cell Line Screen for Phenotypic Drug Discovery in the 21st Century.

Over the past three decades, high-throughput phenotypic cancer cell line screens have revealed unanticipated small-molecule activities and illuminated connections between tumor genotypes and anticancer efficacy. Founded in 1984, the National Cancer Institute's "NCI60" screen laid the conceptual groundwork for the contemporary landscape of phenotypic drug discovery. NCI60 first operated as a primary bioactivity screen, but molecular characterization of the NCI60 cell line panel and development of a small-molecule sensitivity pattern recognition algorithm (called "COMPARE") have enabled subsequent studies into drug mechanisms of action and biomarker identification. In this issue of Cancer Research, Kunkel and colleagues report an updated version of the NCI60 screen, dubbed "HTS384" NCI60, that better aligns with current cell proliferation assay standards and has higher throughput. Changes include the use of a 384-well plate format, automated laboratory equipment, 3 days of compound exposure, and a CellTiter-Glo luminescent endpoint. To confirm that data from the HTS384 and classic NCI60 screen are comparable, the authors tested a library of 1,003 anticancer agents using both protocols and applied COMPARE to analyze patterns of cell line sensitivities. More than three dozen groups of targeted therapies showed high comparability between screens. Modernization of NCI60, and closer integration with other large-scale pharmacogenomic screens and molecular feature sets, will help this public screening service remain pertinent for cancer drug discovery efforts for years to come. See related article by Kunkel et al., p. 2403.

Improving Kui digit recognition through machine learning and data augmentation techniques

Speech digit recognition research is growing decisively, and a bulk of digit recognition algorithms are used in European and a few Asian languages. Kui is a low-resourced tribal language locally used in several states of India. Despite its significance, there is not much research on Kui's speech. This research aims to present an in-depth analysis of novel Kui digit recognition using predefined machine learning (ML) techniques. For this purpose, we first gathered spoken numbers i.e. from 0 to 9 of eight different speakers containing a total of 200 words. Secondly, we choose the numbers: ଶୂନ (zero), ଏକ (one), ଦୁଇ (two), ତିନି(three), ସାରି(four), ପାସ (five), ସଅ (six), ସାତ (seven), ଆଟ (eight), ନଅ (nine). Meanwhile, we build nine different ML models to recognize Kui digits that take the Mel-frequency cepstral coefficients (MFCCs) method to extract the relevant features for model predictions. Finally, we compared the performance of ML models for both augmented and non-augmented Kui data. The result shows that the SVM+Augmentation method for Kui digit recognition combined obtained the highest accuracy of 83% than other methods. Moreover, the difficulties and potential prospects for Kui digit recognition are also highlighted in this work.

Colorectal cancer image recognition algorithm based on improved transformer

Aiming at the problems of the complex background of colorectal cancer tissue cell images and the difficulty of detection caused by the low differentiation of cancer cell regions, a deep learning method is used to detect the cancer cell regions. By integrating the skip feedback connection structure into U-Net and combining it with the Swin Transformer for feature extraction, we improve the multi-level feature extraction capabilities of the model. This algorithm enables end-to-end recognition of colorectal adenocarcinoma tissue images and achieves an accuracy of 95.8% on the NCT-CRC-HE-100K dataset, demonstrating its potential to significantly support colorectal cancer detection and treatment.

Association between tooth loss-related speech and psychosocial impairment with cognitive function: A pilot study in Hong Kong's older population.

Tooth loss has been associated with cognitive decline, but the underlying mechanisms involving speech and psychosocial impairment remain unclear. To investigate the impact of tooth loss-related speech and psychosocial impairment on cognitive function in Hong Kong's older population. Seventy-six Cantonese-speaking participants between the ages of 51-92 were classified into three groups: patients with complete dentures (CD), partially edentulous patients with less than 10 occluding tooth pairs (OU <10), and at least 10 occluding tooth pairs (OU ≥10). Cognitive function was assessed using the Montreal Cognitive Assessment Hong Kong Version, One-minute Verbal Fluency Task and Hayling Sentence Completion Test. Objective and subjective speech assessments were carried out using artificial intelligence speech recognition algorithm and a self-designed speech questionnaire. The impact of tooth loss on psychosocial condition was evaluated by the Reading the Mind in the Eyes Test and a self-designed questionnaire. Statistical analyses (one-way ANOVA, ANCOVA, Kruskal-Wallis test, Spearman correlation test) were performed. Tooth loss was significantly associated with lower cognitive function (p = .008), speech accuracy (p = .018) and verbal fluency (p = .001). Correlations were found between cognitive function and speech accuracy (p < .0001). No significant difference in tooth loss-related psychosocial impact was found between the three groups. While warranting larger sample sizes, this pilot study highlights the need for further research on the role of speech in the association between tooth loss and cognitive function. The potential cognitive impact of tooth retention, together with its known biological and proprioceptive benefits, supports the preservation of the natural dentition.

A lightweight and accurate recognition algorithm of pointer meter based on improved Deeplabv3+ for inspection robots

Pointer meter is widely utilized in the fields of modern industry. Nowadays, intelligent inspection robots are gradually employed in place of labors for inspection task. Pointer meter recognition is one of the most important tasks of inspection robots. This article presents a lightweight pointer meter recognition algorithm, which is suitable for deployment on inspection robots. First, pointer meter is identified by pruned YOLOv5. Afterwards, the dial and the pointer are segmented through improved Deeplabv3+ in which the JPU and depthwise separable convolutions are utilized in lieu of dilated convolutions. After perspective transform and central-line extraction of pointer, the reading of the pointer meter can be determined using the angle method. Experimental results verify the FPS of pruned YOLOv5 improves 13.18% compared to original YOLOv5 and the FPS of improved Deeplabv3+ improves 45.74% compared to original Deeplabv3+. Additionally, the reading accuracy of the algorithm is 98.40% and average fiducial error is 0.32%, which indicate good accuracy. The relative standard deviation of reading is less than 1.4%, which indicates good stability of proposed algorithm. This study proposes a lightweight and accurate pointer meter recognition algorithm based on improved Deeplabv3+, the algorithm is ported to NVIDIA Jetson TX2 NX to verify its stability and accuracy.

Software Key Node Recognition Algorithm for Defect Detection based on Node Expansion Degree and Improved K-shell Position

Software Key Node Recognition Algorithm for Defect Detection based on Node Expansion Degree and Improved K-shell Position

An Intelligent Parameter Identification Algorithm of Linear Fuzzy Fractional Differential Equation

Abstract The traditional linear generation fuzzy fractional differential equation parameter identification algorithm lacks the update of parameter identification process, has large amount of calculation, slow convergence speed of parameter identification and strong dependence on initial values. In this paper, a new intelligent recognition algorithm for linearly generated fuzzy fractional differential equations is proposed. The parameters of the equation are re expressed by the constant level set. The piecewise constant level set algorithm based on equation parameter intelligent identification is used to solve the steady-state solution of fractional differential equation, and the non convergence problem caused by too much calculation is solved. A new algorithm scheme for linearly generating fuzzy fractional differential equations is established, the constraints of the level set of the differential equations are calculated, and the updated algorithm for parameter identification of the equation is obtained. The evolutionary algorithm is used to solve the updating algorithm to realize the intelligent identification algorithm of linear fractional fuzzy differential equations. Experimental results show that the algorithm has the advantages of fast convergence speed, high calculation accuracy and low initial value.

Overview of Pest Detection and Recognition Algorithms

Detecting and recognizing pests are paramount for ensuring the healthy growth of crops, maintaining ecological balance, and enhancing food production. With the advancement of artificial intelligence technologies, traditional pest detection and recognition algorithms based on manually selected pest features have gradually been substituted by deep learning-based algorithms. In this review paper, we first introduce the primary neural network architectures and evaluation metrics in the field of pest detection and pest recognition. Subsequently, we summarize widely used public datasets for pest detection and recognition. Following this, we present various pest detection and recognition algorithms proposed in recent years, providing detailed descriptions of each algorithm and their respective performance metrics. Finally, we outline the challenges that current deep learning-based pest detection and recognition algorithms encounter and propose future research directions for related algorithms.

Digit and Mark Recognition Using Convolutional Neural Network for Voting Digitization in Indonesia

Digitization of voting results in Indonesia is essential to ensure the accuracy and integrity of the election process. This research introduces an innovative approach that uses Convolutional Neural Networks (CNN) for handwritten number recognition and tally mark recognition. This research uses a dataset obtained from Kaggle. The research process is conducted in several stages, namely Data Collection, Preprocessing, Data Sharing, Modelling, and Evaluation. The results showed that the proposed model achieved an accuracy of 98%. This research shows that using the CNN algorithm for handwritten number recognition and tally mark recognition can improve accuracy and efficiency in digitizing voting results. It is expected that this research can make a significant contribution to the development of a more reliable digital voting system. Future research is recommended to use a larger dataset to validate the strength of the model, which has been built on more varied data.

DEVELOPMENT OF AN ELECTRONIC EXAMINATION PLATFORM USING FACE RECOGNITION METHODS

Online systems face a major challenge in efficiently monitoring participants and students throughout lectures, particularly during exams. Establishing robust approaches and technologies is essential for identifying unjust, unethical, and illegal conduct in educational environments, particularly during academic courses and examinations. This study introduces a novel online proctoring system that employs deep learning to continuously supervise physical locations without requiring a human proctor. The system utilizes face detection and recognition algorithms to implement biometric procedures, including facial recognition. This work presents a novel approach for face recognition training, which involves an incremental training procedure. This eliminates the requirement for an additional step, resulting in reduced computation cost and time. In order to achieve high accuracy, the suggested model evaluated three distinct face detectors: HOG, MTCNN, and Yoloface. The assessment of the suggested model demonstrates that the HOG approach has surpassed the others. The proposed approach yields a significantly high accuracy rate.

Application of speech recognition algorithm based on interactive artificial intelligence system in English video teaching system

Social robots are intelligent programs that can autonomously run, automatically publish information, and interact on social media platforms. This article focuses on the speech recognition algorithm of interactive artificial intelligence systems, combined with the Hanning window function and the Vitit algorithm, and explores its application value in English video teaching systems through detailed experiments. Adopting speech feature extraction technology provides strong support for the subsequent recognition process. The application of Hanning window function has a positive impact on speech signal preprocessing. The most indispensable algorithm in speech recognition models is the Vitit algorithm, which successfully solves the problem of time variation and continuity of speech signals. This study successfully applied speech recognition algorithms based on interactive artificial intelligence systems to English video teaching systems. By combining speech feature extraction, Hanning window function, and Vitit algorithm, a system with strong recognition ability and interactivity was constructed. The English video teaching system aims to improve students’ learning process. By combining interactive artificial intelligence speech recognition technology with English video teaching, it can improve students’ experience in learning English and also meet their personalized learning habits.

Retraction Note: Performance of deer hunting optimization based deep learning algorithm for speech emotion recognition

Retraction Note: Performance of deer hunting optimization based deep learning algorithm for speech emotion recognition

A wall climbing robot based on machine vision for automatic welding seam inspection

With the ongoing progress of industrial technology such as shipbuilding, the importance of weld quality in industrial production is becoming increasingly prominent. Intelligent and automated welding seam inspection robots are more efficient than traditional manual inspection and can avoid dangerous accidents. This article describes the design of a welding seam inspection robot suitable for high-altitude ship operation. The robot uses machine vision and object segmentation models to automatically detect the position of welding seams, and uses a cubic polynomial to fit the welding seam path. The upper and lower computers of the robot communicate through WIFI transmission and TCP protocol, which can realize remote real-time detection of weld surface defects. In addition, this article designs a permanent magnet adsorption structure for robot high-altitude wall climbing, which has been verified through simulation and experimental verification. To verify the intelligence of the robot, this paper conducted performance analysis experiments on weld line recognition and tracking models and surface defect models. The experimental results showed that the average detection accuracy of the weld line recognition and tracking algorithm was 96.8%, and the average detection accuracy of surface defects in the three types of welds was 94.2%. The method proposed in this article combines two algorithms and a special robot structure, providing a new approach for the automated inspection of welds in large industrial products such as ships.

Emotional recognition and feedback of students in English e-learning based on computer vision and face recognition algorithms

In existing English teaching, teachers often find it difficult to accurately understand students’ emotional changes, thus unable to adjust teaching strategies in a timely manner. Therefore, the introduction of computer vision and facial recognition algorithms will help improve the effectiveness of English teaching. This article is based on computer vision technology and facial recognition algorithms. Based on the sample data provided during the learning process, a model is learned and established to recognize facial expressions under different emotions and identify students’ emotional states. Use computer vision technology to capture and analyze students’ facial expressions in real-time. Then, facial recognition algorithms are used to recognize and classify the captured facial features to determine the current emotional state of the students. Finally, based on the students’ emotional state, the system will provide corresponding feedback. This technology based on computer vision and facial recognition algorithms can help teachers better understand students’ emotional changes, adjust teaching strategies in a timely manner, provide personalized learning feedback, and thereby improve students’ learning effectiveness and the quality of English teaching. This technology can also provide students with a better learning experience, enhancing their learning motivation and interest.

Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification

Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people’s security and promote their well-being.