Search

The purpose of this paper is to retrieve and study the highly cited papers as well as the correlation between the citation frequency and the download frequency of the 20 traditional Chinese medicine journals in China, in order to provide the guidance for improving the influence and academic quality of these journals. Bibliometric analyses were conducted on 1103 papers of 20 traditional Chinese medicine journals from 2011 to 2020 by retrieving for the China Academic Journal Network Publishing Database (CAJD) in China National Knowledge Infrastructure (CNKI). SPSS 17.0 software was used to analyze the correlation between the citation frequency and the download frequency via conducting regression fitting and establishing the mathematical models. The results showed that the total citations of the 1103 papers were 93051 times and the average citations were 84.36 times per paper. The total downloads of the 1103 papers were 2058442 times, and the average downloads were 1866.22 times per paper. China Journal of Chinese Materia Medica ranked first according to the number of papers, total citations and total downloads. The citations of Journal of Chinese Medicinal Materials ranked first based on the number of citations per paper. One of Li's paper had been cited the most (983 times). There were 629 (57.03%) papers whose first author was from universities. The scopes of the first authors were distributed in 29 regions and 2 special administrative regions (Macao, Hong Kong) in China. The authors from Beijing published 283 (25.66%) papers, ranking number one. The number of papers supported by funds was 882 (79.96%). The research results of correlation showed that the citation frequency and the download frequency of the highly cited papers had a highly positive correlation from both journal and paper level for whether the sample data of journals was normally distributed or nonnormally distributed. The correlation coefficients of the 20 journals at journal level and that at paper level were 0.9765 and 0.6677, respectively. The correlation was better at journal level than that at paper level, while the optimal regression fitting was all cubic polynomial. Among the 1103 papers, there were 684 (62.01%) research papers and 419 (37.99%) review papers. The main citation period of the top 15 papers was from the 2nd year to the 6th year after publication, accounting for 78.39%. Papers on clinical therapeutics research, papers on the pharmacological effects and its mechanism of traditional Chinese medicine, and papers on traditional Chinese medicine and natural medicine were the main source of the highly cited papers of the traditional Chinese medicine journals. Editors of the journals should focus on the above-mentioned research areas to select manuscripts for exploiting the excellent sources extensively, while paying attention to review papers, focusing on national major or key projects, paying attention to network spreading, stabilizing authors with quality services, in order to improve the influence and the academic quality of journals. [ABSTRACT FROM AUTHOR]

To address the issues in clothing pattern style migration, this paper proposes a digital simulation and re-editing method for clothing patterns based on deep learning and somatosensory interaction. First, the proposed method encodes the black-and-white line drawing image, generating random noise images through a diffusion process, introducing color information for synthesis, and using a decoder to reconstruct a colored image. Afterwards, an improved VGG19 model is used to reconstruct content features and perform linear color transformation on style images, enabling pattern style migration through the construction of a Gram matrix and resulting in colored clothing texture patterns. Finally, a KinectV2 is utilized for fabric simulation, overlaying colorful clothing texture patterns to achieve 3D virtual dressing. The experimental results show that the proposed method improves the structural similarity index measure (SSIM) by 9–11% and the peak signal-to-noise ratio (PSNR) by 3–8% when compared to existing algorithms. The experiments provide evidence that the proposed method effectively mitigates color overflow, delivers precise image coloring, and accomplishes realistic restoration of clothing texture. Furthermore, the method offers an improved garment fit to fulfill the user's interaction requirements. [ABSTRACT FROM AUTHOR]

The colorimetric method is usually used to test the concentration of substances. However, this method has a big error since different people have different sensitivities to colors. In this paper, in order to solve the identification problem of the color and the concentration of the test paper, firstly, we found out that the concentration of substance is correlated with the color reading by using the Pearson's Chi-squared test method. And by the concentration coefficient of Pearson correlation analysis, the concentration of substance and color reading is highly correlated. Secondly, according to the RGB value of the paper image, the color moments of the image are calculated as the characteristics of the image, and the Levenberg–Marquardt (LM) neural network is established to classify the concentration of the substance. The accuracy of the training set model is 94.5%, and the accuracy of the test set model is 87.5%. The model precision is high, and the model has stronger generalization ability. Therefore, according to the RGB value of the test paper image, it is effective to establish the LM neural network model to identify the substance concentration. [ABSTRACT FROM AUTHOR]

Video anomaly detection has always been a challenging task in computer vision due to data imbalance and susceptibility to scene variations such as lighting and occlusions. In response to this challenge, this paper proposes an unsupervised video anomaly detection method based on an attention-enhanced memory network. The method utilizes a dual-stream network structure of autoencoders, enhancing the model’s learning ability for important features in appearance and motion by introducing coordinate attention mechanisms and variance attention mechanisms, emphasizing significant characteristics of static objects and rapidly moving regions. By adding memory modules to both the appearance and motion branches, the network structure’s memory information is reinforced, enabling it to capture long-term spatiotemporal dependencies in videos and thereby improving the accuracy of anomaly detection. Furthermore, by optimizing the network structure’s activation functions to handle negative inputs, it enhances its nonlinear modeling capabilities, enabling better adaptation to complex environments, including variations in lighting and occlusions, further improving the effectiveness of anomaly detection. The paper conducts comparative experiments and ablation studies using three public available datasets and various models. The results demonstrate that compared to baseline models, the AUC performance is improved by 3.9%, 4.7%, and 1.7% on UCSD Ped2, CHUK Avenue, and ShanghaiTech datasets, respectively. When compared with the other models, the average AUC performance is improved by 4.3%, 5.4%, and 6.2%, with an average improvement of 8.75% in the ERR metric, validating the effectiveness and adaptability of the proposed method. The code can be obtained at the following URL: https://github.com/AcademicWhite/AEMNet. [ABSTRACT FROM AUTHOR]

With a rapid development of artificial intelligence technology, fine-grained image classification has gained widespread application. For mobile terminals, this paper introduces an image classification method built on MobileViT, and it can apply into fine-grained image classification. The original MobileViT model has been optimized in three ways. Initially, the h-swish activation function is used to enhance the network performance. Second, the cross-entropy loss function is used to further realize the parameter optimization and model accuracy improvement. Finally, a dropout layer is joined before the fully connected layer can effectively decrease the model recognition time and prevent over-fitting. Experimental data on public tomato disease datasets demonstrate that the improved fine-grained image classification method put forward in this paper exhibits higher classification accuracy, better stability and network generalization ability than other models. [ABSTRACT FROM AUTHOR]

The rapid growth of contents on the Web in different languages increases the demand of Cross-Lingual Information Retrieval (CLIR). The accuracy of result suffers due to many problems such as ambiguity and drift issue in query. Query Expansion (QE) offers reliable solution for obtaining suitable documents for user queries. In this paper, we proposed an architecture for Hindi–English CLIR system using QE for improving the relevancy of retrieved results. In this architecture, for the addition of term(s) at appropriate position(s), we proposed a location-based algorithm to resolve the drift query issue in QE. User queries in Hindi language have been translated into document language (i.e. English) and the accuracy of translation is improved using Back-Translation. Google search has been performed and the retrieved documents are ranked using Okapi BM25 to arrange the documents in the order of decreasing relevancy to select the most suitable terms for QE. We used term selection value (TSV) for QE and for retrieving the terms, we created three test collections namely the (i) description and narration of the Forum for Information Retrieval Evaluation (FIRE) dataset, (ii) Snippets of retrieved documents against each query and (iii) Nearest-Neighborhood (NN) words against each query word among the ranked documents. To evaluate the system, 50 queries of Hindi language are selected from the FIRE-2012 dataset. In this paper, we performed two experiments: (i) impact of the proposed location-based algorithm on the proposed architecture of CLIR; and (ii) analysis of QE using three datasets, i.e. FIRE, NN and Snippets. In the first case, result shows that the relevancy of Hindi–English CLIR is improved by performing QE using the location-based algorithm and a 12% of improvement is achieved as compared to the results of QE obtained without applying the location-based algorithm. In the second case, the location-based algorithm is applied on three datasets. The Mean Average Precision (MAP) values of retrieved documents after QE are 0.5379 (NN), 0.6018 (FIRE) and 0.6406 (Snippets) for the three test collections, whereas the MAP before QE is 0.37102. This clearly shows the significant improvement of retrieved results for all three test collections. Among the three test collections, QE has been found most effective along with Snippets as indicated by the results with the improvements of 6.48% and 19.12% over FIRE and NN test collections, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes an image compression algorithm based on Swin Transformer and residual network (STRN), aiming to reduce blurring and distortions in traditionally compressed images. The algorithm utilizes a dual-channel mechanism to remove artifacts from the image, which takes advantage of the complementary features of the transform and residual networks. The Swin Transformer networks address the issue of long-range dependency, leading to an enhanced and improved reconstructed image quality. The residual network is an effective network that mitigates gradient loss and recovers image details during the image compression process. The paper demonstrates that image compression can be achieved by training a convolutional network based on a transformer and residuals network, which significantly reduces artifacts and provides better reconstructed image quality compared to previously used and current mainstream methods based on traditional convolutional neural networks. The proposed approach can remove blocking artifacts by subtracting estimated artifacts from the input image, while still preserving most of the original details. Therefore, our proposed method is highly effective in improving image quality and reducing visual artifacts caused by traditional compression methods. Moreover, this method is useful for enhancing image transmission and storage efficiency in various computer vision systems that employ digital visual codecs. [ABSTRACT FROM AUTHOR]

Deep learning classification is commonly used to assess biomedical data. Fetal heart rate (FHR) signal data monitor the health of both the mother and the fetus and prevent mobility and death in fetuses at risk of hypoxia. But, the data imbalance is a major problem and happens frequently. To overcome this problem, a Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm (HBA) is proposed in this paper for Fetal Arrhythmia Detection and Classification from ECG signals (DSDACNN-HBA-FAC). The input ECG signals are collected from the fetal ECG dataset. Then, the ECG data are pre-processed using coherence shock filtering (CSF), which eliminates artifacts, like baseline drift, contact noise, muscle artifacts, electrode motion, electromyography artifacts. Using the improved non-subs sampled Shearlet transform (INSST) method, the pre-processed ECG data recover the mean, standard deviation, root mean square, skewness, kurtosis, zero-crossing, autoregressive coefficients, estimated entropy, and Higuchi’s fractal dimension. The DSDACNN classifier utilizes the features extracted to categorize the output as normal and arrhythmia. In general, DSDACNN does not agree on any optimization techniques to define the optimum parameters and to ensure accurate classification. Therefore, HBA is employed to optimize the DSDACNN weight parameters. The proposed method is implemented in MATLAB. The performance metrics, like sensitivity, precision, recall, f-measure, specificity, accuracy, computation time, RoC, and error rate are evaluated. The performance of DSDACNN-HBA-FAC achieves 3.101%, 7.12%, 7.73% high accuracy, 51.136%, 59.04%, 44.51% lower computation Time and 2.292%, 5.365%, 1.551% greater AUC compared with the existing techniques, like Fetal Arrhythmia Detection with Classification from ECG Signals: A NonInvasive Method (ANN-FAC), Semi-supervised active transfer learning for fetal ECG arrhythmia identification (DNN-FAC), fetal arrhythmia detection using adaptive single channel electrocardiogram extraction (CNN-FAC), respectively. [ABSTRACT FROM AUTHOR]

With the increasing scale of mobile cellular network applications, 5G mobile network infrastructure provides customizable services to users in the form of network slices. How to effectively allocate existing resources in real dynamic networks with time-varying network utility is a key issue that previous work did not consider. This paper first initializes the multi-resource allocation problem of network slicing in an online manner, where the utility function is set to change over time. Therefore, we propose Metis, an online network slicing resource allocation framework that combines the time-varying nature of the network utility function given bandwidth and processing power constraints with the requirement of virtual network function isolation. The goal is to maximize the cumulative network utility in the long term and specify multiple resource allocation problems by utilizing concave optimization methods. In addition, a distributed algorithm based on the online alternating direction method of multipliers with regret optimization has been developed to achieve optimal resource allocation. Our mathematical analysis proves that Metis can provably converge to the optimal solution and the result of experiments demonstrates a steady state behavior of Metis, which converges in dynamic network settings. [ABSTRACT FROM AUTHOR]

Multi-interest sequential recommendations leverage users’ historical behavior to provide recommendations that match multiple interests. Most of these methods have not fully extracted higher-order information hidden in users’ interactions and have overlooked the multiple features of items. To this end, this paper proposes a multi-interest model called “multi-interest sequential recommendation with simplified graph convolution and item multi-features (SGCMF)”. Firstly, a simplified graph convolution module is designed based on bipartite graphs, which utilizes mean pooling to aggregate neighboring information and employs a feedforward neural network (FNN) for nonlinear transformations and combinations. This method reduces redundant information and captures higher-order relationships, thereby simplifying the complexity of modeling high-order interactions and improving prediction accuracy. Secondly, an item multi-feature extraction module is proposed, which represents item features with multiple vectors, and analyzes each feature from multiple perspectives while preserving important relationships between features. The model correlates multiple features of the item with user interests, thereby achieving a fine-grained analysis of user interests. Extensive experiments are conducted on five real-world scenarios, and the results are compared with state-of-the-art methods. The experimental results show that SGCMF outperforms other baselines. [ABSTRACT FROM AUTHOR]

Public computer education systems provide students essential opportunities to enhance computer literacy and information skills. However, the widespread adoption of online education technology exposes the field to several critical security risks. Threats, such as malware infections, data breaches, and other network intrusions, are all challenging the security of education systems, posing potential hazards to students’ personal information and even the entire teaching environment. To spur further work into specialized anomaly detection techniques for computer education, this paper presents an anomaly detection framework tailored for network services in computer education environments to safeguard these systems. Specifically, the proposed approach learns from large-scale online educational traffic data to classify the security state into five alert levels, enabling more granular anomaly detection and analysis. To assess their detection performance, deep learning and traditional machine learning algorithms are implemented and compared for multi-class intrusion classification. The results show that the proposed framework provides an effective security solution to bolster the integrity and stability of computer education systems against evolving network threats, enhancing threat intelligence to inform proactive security by detecting and characterizing anomalies through multilevel classification. [ABSTRACT FROM AUTHOR]

In the last years, endowing the machine with emotional and behavioral intelligence has been one of the most challenging issues in the human–computer interaction area. In this context, this paper investigates the detection of simultaneous fear emotion and deception behavior in speech. To do so, a set of 72 pitch-based features has been investigated first to recognize fear and deception separately. Then, different feature selection techniques have been used in order to select the most relevant ones that best discriminate between fear/nonfear and deception/nondeception classes. Next, a decision-level fusion approach based on the belief theory has been proposed to infer whether fear and deception are detected simultaneously. Simulation results carried on databases dealing with fear/nonfear emotions and deception/truth behaviors have shown classification results reaching 83.33% and 72.45% as accuracy rates for fear and deception classifiers, respectively. The proposed fusion approach has revealed a correspondence between fear emotion and deception behavior in speech modality. [ABSTRACT FROM AUTHOR]

Cleaning control based on changes in cleaning loss rate and impurity rate has emerged as a hot topic in the research on intelligent control for rice and wheat combined harvesters. However, numerous operation parameters can lead to deviations beyond the normal range of cleaning loss rate and impurity rate. The impact of cleaning control parameters in rice and wheat combined harvesters on cleaning loss rate and impurity rate often tends to be contradictory. How to combine different contradictory cleaning control strategies to get a more widely used and better cleaning control strategy is a hot issue in the current cleaning control research. In this paper, the granularity of quotient space is introduced into the cleaning control based on operation parameters, and a cleaning control model based on granularity synthesis theory is proposed. This method first constructs a knowledge base for intelligent control of cleaning tailored to the cleaning loss rate and impurity rate using production rule representation, and considers that these control strategies constitute different quotient spaces, and then organizes these quotient spaces according to granularity synthesis theory to get the cleaning control strategy. The experimental results verify that the validity of the cleaning control based on granular computing is better than fuzzy control. [ABSTRACT FROM AUTHOR]

Recent deep learning-based watermarking methods have achieved impressive results. However, they struggle with unknown distortions and often suffer from poor generalization, slow convergence, unstable training, and degraded visual quality in watermarked images. To address the above problems, this paper proposes a two-stage separable adversarial distortion (TSAD)-based robust watermarking algorithm for diffusion tensor imaging (DTI). The algorithm uses a noise-free end-to-end network in the first stage for learning and training DTI images. In the second stage, it fixes the watermark embedding network trained in the first stage, interacts the noise distortion network with the watermark extraction network to perform adversarial training for improving robustness. Experimental results show that our method achieves comparable or better robustness to seen distortions and better robustness to unseen distortions, along with enhanced stability, faster convergence, and improved visual quality in watermarked DTI images. [ABSTRACT FROM AUTHOR]

Collaborative robots face challenges in achieving high accuracy using existing parameter identification methods, especially in applications like dragging, teaching, and collision detection. To address the issue of robot dynamics identification accuracy in scenarios with unknown parameters, this paper introduces a method for robot joint parameter identification based on the Cuckoo Search-backpropagation neural network (CS-BPNN). Initially, a kinetic model incorporating Coulomb viscous friction is presented. Subsequently, a BPNN is employed to approximate and fit the nonlinear function, while the CS algorithm is integrated into the BPNN model to optimize its parameters. Collected data undergo third-order Butterworth filtering, and a loss function compares predicted values with actual values to ascertain the accuracy of the proposed method. Experimental results demonstrate that the CS-BPNN approach proposed herein can converge the mean square error (MSE) to 9.79×10−4 N⋅m when robot dynamics parameters are unknown. This method boasts of a lower MSE and superior discrimination accuracy compared to traditional methods like least squares (LS), genetic algorithm (GA), and BPNN. Consequently, the method presented in this study not only enhances the accuracy of parameter identification but also offers a fresh perspective for the parameter identification of robot joint dynamics models. [ABSTRACT FROM AUTHOR]

Nanodrones are insect-sized drones that could fly in complex environments and confined spaces, and act as an emerging tool for covert surveillance and intelligence attacks, which would become a potential threat to national security. Radar has the advantage of wide range, all-day, and all-weather detection ability, making it a means of detecting such threat. First, this paper introduces a pertinent multiple-input multiple-output (MIMO) millimeter-wave (MMW) radar system, with the advantages of low cost and high accuracy. It is utilized to detect three targets: nanodrone, small helicopter, and mechanical bird, through which more detailed features can be obtained. Then, the echo data of the three targets are processed and analyzed, and their distinct micro-Doppler characteristics were obtained. Finally, the Radar Transformer target classification network is used to classify and identify the targets. It has been confirmed that desired results could be achieved through the above process. [ABSTRACT FROM AUTHOR]

To design a dialogue model and standard artificial programming interface (API), this paper designs an intelligent dialogue support for medical service systems and improving hospital intelligent serviceability, which combines patient pre-diagnosis, diagnosis, and post-diagnosis services with artificial intelligence depth. This is done via an intelligent man–machine dialogue support platform (MMDSP) suitable for medical services based on a multi-dimensional disease model and outpatient knowledge base with artificial intelligence. As a result of the intelligent service capability of the hospital service systems and platforms, patients’ medical experiences have significantly improved. The platform standardizes the multi-channel service process, improves patient service efficiency, and reduces the cost of human resources and business knowledge learning. In addition, the integration of intelligent multi-system service information provides data support for hospitals to serve patients accurately and has good application and promotion value. [ABSTRACT FROM AUTHOR]

Thanks to the rapid development of deep learning in recent years, image inpainting has made significant progress. As a fundamental task in the field of computer vision, many researchers are committed to exploring more efficient methods, and state-of-the-art research results prove that generative adversarial networks (GAN) have superior performance. However, due to the inherent ill-posedness of image inpainting tasks, these approaches suffer from lack of detailed information, local structural fractures or boundary artifacts. In this paper, we leverage the properties of GAN architecture to process images in more detail and more comprehensively. A novel dual U-Net GAN is designed to inpaint images, which is composed of a U-Net based generator and a U-Net-based discriminator. The former captures semantic information of different scales layer by layer and decodes it back to the original size to repair damaged images, while the latter optimizes the network by combining reconstruction loss, adversarial loss, perceptual loss and style loss. In particular, the U-Net-based discriminator allows per-pixel detail and global feedback to be provided to the generator, guaranteeing the global consistency of the inpainted image and the realism of local shapes and textures. Extensive experiments demonstrate that for different proportions of damage, the images inpainted by our proposed model have reasonable texture structure and contextual semantic information. Furthermore, the proposed model outperforms state-of-the-art models in both qualitative and quantitative comparisons. The code will be available at https://github.com/yjjswu. [ABSTRACT FROM AUTHOR]

Intelligent control has become an important research direction of a combine harvester. However, the impact of cleaning control parameters in rice–wheat combine harvesters on cleaning loss rate and impurity rate often tends to be contradictory. In this paper, an intelligent controlling model based on multi-objective optimization particle swarm (MOPSO) was constructed to solve this problem. The control model can real-time monitor the cleaning performance such as the cleaning loss rate and impurity rate and regulate the cleaning operation conditions such as the angle of the air distributor plate, the opening of the upper sieve and the fan speed. The field operation experiment of 10kg feeding rice–wheat combine harvester proves that the control model based on MOPSO is more effective than the model based on fuzzy control. [ABSTRACT FROM AUTHOR]

To further improve the performance of the kernel adaptive filtering algorithm in a non-Gaussian environment, a robust kernel least mean square algorithm is proposed, and the effectiveness of the root cost function and the convergence of the algorithm is theoretically analyzed. An improved online vector quantization criterion is then applied to the proposed algorithm to suppress the linearly growing network size. Finally, the different performances of the algorithm of this paper and other kernel adaptive filtering algorithms as well as this paper's algorithm before and after quantization are compared in Mackey Glass chaotic time series as well as in system identification, confirming the superiority of the algorithm of this paper and the improved online vector quantization criterion. [ABSTRACT FROM AUTHOR]

The cleaning loss rate is a crucial performance metric for rice-wheat combine harvesters. Most studies on the relationship between the cleaning operation parameters of the combined harvester and the cleaning loss rate lack research on the dynamic correlation between harvest losses and operational parameters, specifically focusing on the dynamic correlation between harvest losses and multiple operational parameters such as the cleaning loss rate. In this paper, we formulate a dynamic deductive model for the scenario of harvesting loss regulation involving multiple operational parameters, delving into the dynamic associations between cleaning loss and the regulation of multiple operational parameters and constructing dynamic Bayesian network prediction model. Through experimental analysis of the dynamic Bayesian network prediction model for cleaning loss rates, the comparison of results among the other three algorithms demonstrated that it was efficient for our DBN to predict the cleaning loss rate. [ABSTRACT FROM AUTHOR]

In the medical field, analyzing various bone structures is crucial due to the rigid nature of bones. X-ray imaging plays an essential role in medical procedures, including bone age evaluation, fracture detection, and implant creation. However, operator involvement can introduce biases and increase processing time. Automating the process could reduce processing time and enhance diagnostic accuracy by minimizing biases and operator involvement. This paper introduces the Refined DeepLab model, a lightweight encoder–decoder-based approach for multiclass segmentation of hand bones. The primary objective is to assist physicians in tasks such as bone age analysis, fracture detection, hand movement analysis, and implant design. The research objectives are organized into three phases, with this work focusing on the first phase of our objectives, which is delineating bones from tissues, studying the bone structure, and multiclass segmentation of hand bones. The model utilizes DenseNet121 as its feature extractor and Sigmoid-weighted Linear Unit (SiLU) as its activation function. Experimental findings demonstrate promising performance in hand bone multiclass segmentation, with a Mean Intersection over Union (mIoU) of 85.02% and a Dice score of 92.2%. The comprehensive analysis of the results confirms that the proposed model excels, particularly in achieving the right balance between computational efficiency and precise segmentation. [ABSTRACT FROM AUTHOR]

Precisely forecasting the levels of PM 2. 5 is crucial for environmental conservation and human health. Thus, it serves as an essential indicator of atmospheric purity. In this paper, a PM 2. 5 concentration prediction model based on random forest and SHAP is proposed using air pollutants and meteorological conditions as the characterizing factors. Initially, pertinent information is gathered and subsequently manipulated, educated, and forecasted through the application of the random forest technique. Then, SHAP is used to explain the degree of influence of each feature in the model and the prediction results. Results of the experiment demonstrate that the random forest-based PM 2. 5 concentration prediction model for the three cities surpass the comparison model in the RMSE, MAE, and R 2 indicators. Examining SHAP values, the essential elements influencing the PM 2. 5 concentration are pinpointed. [ABSTRACT FROM AUTHOR]

Modern face detection algorithms fail to provide optimal results when they have to deal with larger amounts of data per frame while processing higher quality videos. This paper tackles that problem and offers a solution to deploy commercially used state-of-the-art face detection algorithms to process only the regions of interest in a frame, and discard the rest to decrease the data to be processed. The model maintains the accuracy of the base algorithm while decreasing the processing time per frame, thereby increasing the overall efficiency. The selection of region of interest is dependent on the detection of facial window in the previous frame. Therefore, the choice of base algorithm plays an important role in determining the speed of the framework. The model achieves increased processing speeds of about 69–76% more than the standalone usage of the detection algorithms for analyzed frame rates. [ABSTRACT FROM AUTHOR]

Mathematical morphology and convolution operators are two different methods to extract the characteristics and structures of images. Over the past decades, Deep Convolutional Neural Networks (DCNN) have been proven to be more powerful than traditional image-processing approaches. In this paper, we propose a novel structure called Deep Hybrid Neural Network (DHNN) by taking advantage of the convolution and morphological neural layers. Its practical application to polyp detection in medical images is illustrated. For experimental completeness, we adopt nine polyp image datasets, including publicly available data and our own collected data. For performance comparisons, we select three backbone models. Experimental results show that our DHNN achieves the best performance in comparisons in terms of computational complexity and accurate performance. [ABSTRACT FROM AUTHOR]

This paper focuses on the intelligent recognition problem of radar detection targets. Aiming at the low accuracy and slow speed of millimeter-wave radar clustering point cloud information, a feature algorithm of millimeter-wave radar suitable for detecting targets is proposed. In the detection of targets by millimeter-wave radar, distance is the biggest factor affecting the number and degree of sparsity. A method that combines the feature information of the point cloud with the KD tree proximity search algorithm and the DBSCAN clustering algorithm is proposed, which can adapt to the problems of uneven target point cloud, small amount of data and slow clustering speed. The improved algorithm can use the KD tree to quickly find adjacent points and calculate the distance between adjacent points. The corresponding number of thresholds is set according to the distance where the target is located, and the radius of the target area reflected by the millimeter-wave radar plus the distance of the last threshold point is used as the neighborhood radius of the improved algorithm. Therefore, fast and adaptive parameter adjustment of the millimeter-wave radar can be realized. Simulation tests show that the improved clustering algorithm has better parameters. The accuracy of the improved algorithm is increased by 4.2%, and it also greatly improves the clustering speed. [ABSTRACT FROM AUTHOR]

To improve the recognition ability of clinical named entity recognition (CNER) in a limited number of Chinese electronic medical records, it provides meaningful support for clinical advanced knowledge extraction. In this paper, using CCKS2019 Chinese electronic medical record as an experimental data source, a fusion model enhanced by knowledge graph (KG) is proposed, and the model is applied to specific Chinese CNER tasks. This study consists of three main parts: single-mode model construction and comparison experiment, KG enhancement experiment, and model fusion experiment. The model has achieved good performance in CNER from the results. The accuracy rate, recall rate, and F1 value are 83.825%, 84.705%, and 84.263%, respectively, which is the global optimal, which proves the effectiveness of the model. This provides a good help for further research of medical information. [ABSTRACT FROM AUTHOR]

In this paper, the method of embedding piezoelectric ceramic sensors is used to test the damage of the materials and models of the urban comprehensive pipe gallery. The monitoring signal is processed by HHT method, and the frequency and energy changes of the piezoelectric signal before and after the explosion were analyzed, thus the damage characteristics of the urban comprehensive pipe gallery under the internal gas explosion are determined. The results show that in the gas explosion test inside the pipe gallery model, the waveform and peak value of the piezoelectric signal before and after the explosion did not change significantly, indicating that there was no obvious damage to the side wall of the integrated pipe gallery. However, after further time-frequency analysis of the piezoelectric signal by HHT, it is found that the dominant frequency of the piezoelectric signal after explosion has a downward trend, so it is judged that there is a small damage in the pipe gallery after explosion. [ABSTRACT FROM AUTHOR]

Aiming at problems such as tracking failure caused by illumination changes often encountered during unmanned aerial vehicle (UAV) tracking, a target tracking algorithm with illumination adaptive and future-aware correlation filters is proposed based on the background-aware correlation filters (BACF) algorithm, which realizes reliable UAV tracking tasks at night. First, the dark scene is recognized, and an efficient image enhancement module is used to enhance the brightness of low illumination images. Then a future-aware module is constructed to train the tracking model using the contextual information of the target in the next frame for better robustness. Finally, the model updating stage involves adaptive filter updating and adaptive learning rate updating to enhance target tracking precision. The results of the comparison experiments with the state-of-the-art algorithms on the UAVDark135, UAVDT, and DTB70 datasets show that the algorithm in this paper outperforms the state-of-the-art tracking methods and has better tracking performance under light changes and fast motion (FM) scenarios. The tracking speed on a single Central processing unit (CPU) reaches 49 FPS, which satisfies the real-time requirement of UAV tracking. [ABSTRACT FROM AUTHOR]

The recognition of steel microstructure images plays a crucial role in the metallographic analysis process. Although some progress has been made through the application of artificial intelligence algorithms, several challenges remain. First, existing algorithms exhibit weak nonlinear feature extraction capabilities and noticeable limitations. Second, they overlook the intrinsic noise and redundant interference present in microscopic images. To address these issues, this paper investigates the automatic recognition of metallographic tissues by leveraging residual structures in deep neural networks. An enhanced residual network model based on transfer learning is proposed, which utilizes the pre-trained weights from the ImageNet dataset to facilitate learning with small sample data. This network offers higher classification accuracy and higher F1 scores. In addition, a deep residual shrinkage network model based on an attention mechanism is proposed. This model incorporates an attention sub-network into the original residual module and employs a soft threshold function to eliminate redundant features, including noise. The proposed algorithms are evaluated against various convolutional neural networks using 20 types of metallographic test sets. The experimental results showed that both methods have high accuracy rates of 95% and 94.44%, respectively, and F1 scores of 0.9464 and 0.9419. While maintaining the complexity of the model, there has been a significant improvement in accuracy, and the models exhibit strong generalization capabilities. Our research contributes to enhancing production efficiency, strengthening quality control, and improving material performance through computer vision technology. [ABSTRACT FROM AUTHOR]

Handwritten documents possess immense significance in domains such as law, history, and administration. However, they are vulnerable to forgery, which can undermine their credibility and reliability. This paper aims to establish a dependable technique for identifying altered text in handwritten document images, even in scenarios with high levels of noise and blur. Our study investigates 10 distinct categories of handwritten text that have been altered through various forgery operations. The suggested approach employs the deep neural architectures VGG16 and Resnet50 as feature extractors. The architecture comprises three parts: Feature extraction using individual models, a feature fusion layer, and a classification layer. Initially, we optimize the training process and feature extraction using VGG16 and ResNet50. The feature vectors obtained from both models are then fused together in the feature fusion layer and input into the classification layer for the classification task. Experiments are conducted on a custom-created dataset as well as benchmark datasets including ICPR FDC, IMEI Forged Number, and Kundu to demonstrate that the proposed method is superior to existing approaches. [ABSTRACT FROM AUTHOR]

Aiming at problems such as tracking failure caused by illumination changes often encountered during unmanned aerial vehicle (UAV) tracking, a target tracking algorithm with illumination adaptive and future-aware correlation filters is proposed based on the background-aware correlation filters (BACF) algorithm, which realizes reliable UAV tracking tasks at night. First, the dark scene is recognized, and an efficient image enhancement module is used to enhance the brightness of low illumination images. Then a future-aware module is constructed to train the tracking model using the contextual information of the target in the next frame for better robustness. Finally, the model updating stage involves adaptive filter updating and adaptive learning rate updating to enhance target tracking precision. The results of the comparison experiments with the state-of-the-art algorithms on the UAVDark135, UAVDT, and DTB70 datasets show that the algorithm in this paper outperforms the state-of-the-art tracking methods and has better tracking performance under light changes and fast motion (FM) scenarios. The tracking speed on a single Central processing unit (CPU) reaches 49 FPS, which satisfies the real-time requirement of UAV tracking. [ABSTRACT FROM AUTHOR]

The recognition of steel microstructure images plays a crucial role in the metallographic analysis process. Although some progress has been made through the application of artificial intelligence algorithms, several challenges remain. First, existing algorithms exhibit weak nonlinear feature extraction capabilities and noticeable limitations. Second, they overlook the intrinsic noise and redundant interference present in microscopic images. To address these issues, this paper investigates the automatic recognition of metallographic tissues by leveraging residual structures in deep neural networks. An enhanced residual network model based on transfer learning is proposed, which utilizes the pre-trained weights from the ImageNet dataset to facilitate learning with small sample data. This network offers higher classification accuracy and higher F1 scores. In addition, a deep residual shrinkage network model based on an attention mechanism is proposed. This model incorporates an attention sub-network into the original residual module and employs a soft threshold function to eliminate redundant features, including noise. The proposed algorithms are evaluated against various convolutional neural networks using 20 types of metallographic test sets. The experimental results showed that both methods have high accuracy rates of 95% and 94.44%, respectively, and F1 scores of 0.9464 and 0.9419. While maintaining the complexity of the model, there has been a significant improvement in accuracy, and the models exhibit strong generalization capabilities. Our research contributes to enhancing production efficiency, strengthening quality control, and improving material performance through computer vision technology. [ABSTRACT FROM AUTHOR]

Handwritten documents possess immense significance in domains such as law, history, and administration. However, they are vulnerable to forgery, which can undermine their credibility and reliability. This paper aims to establish a dependable technique for identifying altered text in handwritten document images, even in scenarios with high levels of noise and blur. Our study investigates 10 distinct categories of handwritten text that have been altered through various forgery operations. The suggested approach employs the deep neural architectures VGG16 and Resnet50 as feature extractors. The architecture comprises three parts: Feature extraction using individual models, a feature fusion layer, and a classification layer. Initially, we optimize the training process and feature extraction using VGG16 and ResNet50. The feature vectors obtained from both models are then fused together in the feature fusion layer and input into the classification layer for the classification task. Experiments are conducted on a custom-created dataset as well as benchmark datasets including ICPR FDC, IMEI Forged Number, and Kundu to demonstrate that the proposed method is superior to existing approaches. [ABSTRACT FROM AUTHOR]

Head Pose Estimation (HPE) has a wide range of applications in computer vision, but still faces challenges: (1) Existing studies commonly use Euler angles or quaternions as pose labels, which may lead to discontinuity problems. (2) HPE does not effectively address regression via rotated matrices. (3) There is a low recognition rate in complex scenes, high computational requirements, etc. This paper presents an improved unconstrained HPE model to address these challenges. First, a rotation matrix form is introduced to solve the problem of unclear rotation labels. Second, a continuous 6D rotation matrix representation is used for efficient and robust direct regression. The RepVGG-A2 lightweight framework is used for feature extraction, and by adding a multi-level feature fusion module and a coordinate attention mechanism with residual connection, to improve the network's ability to perceive contextual information and pay attention to features. The model's accuracy was further improved by replacing the network activation function and improving the loss function. Experiments on the BIWI dataset 7:3 dividing the training and test sets show that the average absolute error of HPE for the proposed network model is 2.41. Trained on the dataset 300W_LP and tested on the AFLW2000 and BIWI datasets, the average absolute errors of HPE of the proposed network model are 4.34 and 3.93. The experimental results demonstrate that the improved network has better HPE performance. [ABSTRACT FROM AUTHOR]

Versatile video coding (VVC) aims to achieve high compression but also issues like varying content/network conditions. Existing rate control (RC) methods struggle to achieve optimal quality under these complex scenarios. This paper proposes a novel RC scheme for VVC based on a linear model. The Lagrange minimization multiplier is introduced under bit budget constraints, allowing optimized bit allocation. RC optimization is formulated as a convex solution, and is derived into the optimal quantization parameter (QP) for RC. Experimental analysis demonstrates the proposed linear model-based RC algorithm performances are better compared to other state-of-the-art methods due to their use of a linear model and optimal QP determination. [ABSTRACT FROM AUTHOR]

With the development of research on multi-modal data fusion and its combination with online data management, the application of multi-modal big data fusion in information management systems is more and more extensive. How to integrate multi-modal big data effectively is the key technology to building an efficient information management system. In this paper, based on the combination of a multi-support vector machine and convolution neural network, the feature-level data fusion of multi-source heterogeneous big data is implemented, and it is applied to the real data set to test the relevant model. Experimental results show that this method can not only realize heterogeneous integration of big data, but also has high accuracy and reliability. [ABSTRACT FROM AUTHOR]

Counting small pixel-sized vehicles and crowds in unmanned aerial vehicles (UAV) images is crucial across diverse fields, including geographic information collection, traffic monitoring, item delivery, communication network relay stations, as well as target segmentation, detection, and tracking. This task poses significant challenges due to factors such as varying view angles, non-fixed drone cameras, small object sizes, changing illumination, object occlusion, and image jitter. In this paper, we introduce a novel multi-data-augmentation and multi-deep-learning framework designed for counting small vehicles and crowds in UAV images. The framework harnesses the strengths of specific deep-learning detection models, coupled with the convolutional block attention module and data augmentation techniques. Additionally, we present a new method for detecting cars, motorcycles, and persons with small pixel sizes. Our proposed method undergoes evaluation on the test dataset v2 of the 2022 AI Cup competition, where we secured the first place on the private leaderboard by achieving the highest harmonic mean. Subsequent experimental results demonstrate that our framework outperforms the existing YOLOv7-E6E model. We also conducted comparative experiments using the publicly available VisDrone datasets, and the results show that our model outperforms the other models with the highest AP50 score of 52%. [ABSTRACT FROM AUTHOR]

Emotions indicate the feelings of the individual which are linked with personal experiences, moods, and affective states. Detection of emotion can be helpful in many fields like maintaining a patient's psychological well-being, surveillance, driver monitoring, etc. In this paper, an effective machine learning approach has been put forth for emotion detection where an ensemble of three out of five best-performing classifiers has been formed to enhance the classification accuracy. Two deep learning models (AlexNet and ResNet) have been optimally combined with k -nearest neighbor (KNN). The optimal weights for ensemble weighted averaging of classifiers have been computed with aid of particle swarm optimization (PSO) and genetic algorithm (GA) optimization. The developed framework has been tested on two publicly available datasets. An overall accuracy of above 95% has been achieved on the testing set for both datasets. The best performance was obtained by training the classifiers with segmented images and combining them by using the weights obtained through PSO. The results depicted the efficiency of the optimized ensemble machine learning approach for all performance measures used in this study in comparison to the performance of individual classifiers and majority voting fusion. [ABSTRACT FROM AUTHOR]

In recent years, deep learning-based networks have been able to achieve state-of-the-art performance in medical image segmentation. U-Net, one of the currently available networks, has proven to be effective when applied to the segmentation of medical images. A Convolutional Neural Network's (CNN) performance is heavily dependent on the network's architecture and associated parameters. There are many layers and parameters that need to be set up in order to manually create a CNN, making it a complex procedure. Designing a network is made more difficult by using a variety of connections to increase the network's complexity. Evolutionary computation can be used to set the parameters of CNN and/or organize the CNN layers as an optimization strategy. This paper proposes an automatic evolutionary method for detecting an optimal network topology and its parameters for the segmentation of clinical image using Grey Wolf Optimization algorithm. Also, Bi-Directional LSTM integrated in the skip connection to extract dense feature characteristics of image by combining feature maps extracted from encoded and previous decoded path in nonlinear way (MIS-GW-U-Net-BiDCLSTM) is proposed. The experimental results demonstrate that the proposed method attains 98.49% accuracy with minimal parameters, which is much better than that of the other methods. [ABSTRACT FROM AUTHOR]

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV has been widely used in agricultural plant protection, electric power inspection, security patrols, and other fields. However, the control system of the UAV is a complex human–computer interaction system, which requires higher requirements in practical applications. Due to differences in hardware design, software development, and other aspects among different manufacturers, these UAV control systems require high hardware requirements, resulting in a long development cycle. At the same time, in practical applications, due to various reasons, there are equipment failures that are difficult to detect and eliminate in a timely manner. This paper used the UAV graphical control strategy identification method based on cloud server technology, and used the support vector machine (SVM) algorithm to analyze its identification accuracy. The research results showed that when other conditions were the same, the number of researchers and experts who were satisfied with the drone trial effect of the cloud server was 42 and 10, respectively, accounting for 84% and 100%. It indicates that they believe that the cloud server can effectively improve the effectiveness of the drone graphical control strategy recognition method, indicating a positive relationship between the two. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient method for rectifying distorted document images via deep learning, ultimately improving the legibility of graphics and text in documents. The framework comprises two interconnected UNets, working in tandem to predict a 3D coordinate map and a forward map for the input distorted document image, respectively. At the beginning of the process, a page mask is predicted and used as input to both U-Nets to help improve the performance of their tasks. In the last step, the predicted forward map is transformed into a corresponding backward map, which is utilized to rectify the distorted image. The experimental results not only reveal that the predicted page masks and 3D coordinate maps significantly enhance the accuracy of predicting forward maps for subsequent rectification but also demonstrate satisfactory results both globally and locally. [ABSTRACT FROM AUTHOR]

Aerial gaze target recognition is an important step in aerial eye control interaction. In order to achieve accurate aerial gaze target recognition, the VT4LM recognition algorithm was constructed in this paper. By this algorithm, the facial images containing the eyes of the flight operators were first inputted into Vision Transformer (ViT) to extract the local features, and the head posture of the flight operators was inputted into four LSTMs to extract the global features. Then, the local and global features of the flight operators were inputted into three fully connected layers, two dropout layers and one softmax classifier. Finally, the recognition result of aerial gaze target was obtained. In this paper the effectiveness of the VT4LM recognition algorithm was verified through the identification of four aerial line-of-sight gaze targets named Head-up display, Accelerator push rod, Control lever and Rudder by four flight operators during simulated flight. The experimental results showed that the accuracy of the VT4LM algorithm for aerial gaze target recognition reached 89.29% and the Cross Entropy Loss was 1.45. Compared to the other three recognition methods, the VT4LM algorithm had the highest recognition accuracy and minimum loss. When using the VT4LM algorithm to detect four simulated flight operators staring at four aerial gaze targets, the recognition accuracy was all higher than 85.00%. It could be seen that the VT4LM algorithm had a good performance in aerial gaze target recognition. [ABSTRACT FROM AUTHOR]

This paper proposes LHFNet, an improved object detection method based on CenterNet, which achieves a better speed-accuracy trade-off. The main contributions of this paper are as follows: We design a lightweight Hourglass network to reduce the number of parameters and computations of CenterNet, thus improving the detection speed. We introduce an intermediate feature map fusion module to enhance the feature extraction capability of Hourglass, which compensates for the possible performance degradation caused by the network simplification. We apply an attention module to the final heat map to improve the image feature representation. We design and optimize a series of loss functions to train LHFNet effectively. Through the research in this paper, we demonstrate that the feature map fusion and the attention module greatly improve the performance of LHFNet. We evaluate our method on the COCO dataset and achieve a high accuracy at a fast speed (Tesla V100), which outperforms other object detection methods in terms of both accuracy and efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature review on optimizing feature extraction for palm and wrist multimodal biometrics. Identifying informative features across different modalities can be computationally expensive and time-consuming in such complex systems. Optimization techniques can streamline this process, making it more efficient thereby improving accuracy and reliability. The paper frames four research questions on input traits, approaches for feature extraction, classification approaches, and performance metrics of image data. The search query is generated based on the research questions that help retrieve the information on the above parameters. The focus of this paper is to provide the comprehensive and exhaustive gestalt of the appropriate input traits for image data from the information retrieved as well as optimal feature extraction and selection. However, the paper also intends to highlight the various classification approaches taken as well as the performance indicators against those classifiers. Further, the paper aims to analyze the effectiveness of various filtering techniques in eliminating image noise and improving overall system performance using MATLAB 2018. The paper concludes that a combination of palm and wrist biometrics could be a good input-trait combination. This work is novel as it covers multi-faceted processing, addressing various aspects of optimizing feature extraction and selection for palm and wrist multimodal biometrics. [ABSTRACT FROM AUTHOR]

Because of the limitations of convolution kernel, the traditional image segmentation network is not sufficient to obtain the context information, but the image segmentation task is very dependent on the context information. Transformer's linear input can just get enough context information. In this paper, we propose a transformer segmentation network hyperfusion transformer based on a pyramid structure. First, the model divides the single-scale coding form into several-different-scale coding forms, and then fuses the decoding results. Second, in order to ensure the specificity of the output characteristics of each branch, we orthogonalize the results of a variety of different scales. By orthogonalizing in pairs, we can ensure that the results obtained by different branches are not completely similar to a certain extent, and reduce the redundancy of branch information. On the two datasets, the method in this paper surpasses a variety of classical models under multiple evaluation indexes, confirming that it is an effective segmentation method. [ABSTRACT FROM AUTHOR]

At present, some achievements have been made in the research of plant leaf classification such as the introduction of artificial intelligence algorithm. But there are still some problems. First, the existing achievements do not consider the subjective perception mechanism and role of human visual system in leaf classification data labels. Second, the implementation of the deep learning algorithm completely depends on the computing power level of the high-cost machine hardware and the large-scale image database. Finally, these research results rarely consider the noise pollution of leaf image samples. In order to solve the above problems, the paper fully considered the subjective perception principle and characteristics of human vision system (HVS), and proposed a lightweight classification method of noisy plant leaves (LCM-NPLs) based on visual domain perception. First, the most suitable HVS front-end perception characteristics were applied to the physical visual processing of leaves. Then the plant leaves were denoised through the information processing mechanism of HVS back-end. The visual effect of regular and orderly plant leaves is obtained. Finally, the classification is realized by principal component analysis (PCA) and third-order nearest neighbor algorithm. The results of ablation contrast experiments show that the classification accuracy of the method in this paper is 82.50% for plant leaves in the presence of serious noise interference with PSNR of 10.2421, more than 90% for plant leaves with general noise pollution transmission with PSNR of more than 15.3759, and 98.33% for plant leaves of light pollution with PSNR of 20.5659. The proposed method has achieved very good results. The proposed method can not only accurately classify plant leaves in different growth periods, but also maintain a high classification accuracy rate in the presence of serious noise interference. [ABSTRACT FROM AUTHOR]

Coastal grounding electrodes are currently an important means to alleviate land grounding electrode land constraints. In order to better invert the terrestrial geodesic resistivity in the coastal region, this paper proposes a complete set of inversion technology schemes. First, this paper proposes a layered land model for the coastal region, and a composite geodetic model is modeled by the fold junction of the land model and the ocean. Based on this, an adaptive subdivision boundary element method is proposed for solving the composite soil grounding calculation problem, and the accuracy and advantages of the method are demonstrated by examples. Finally, the paper uses the differential evolutionary algorithm to invert the exploration data of the four-point method in the coastal area, and obtains the parameters of the terrestrial layered geodetic model that meet the engineering requirements. The comparison with the grounding software CDEGS illustrates the effectiveness of the method. This paper carries out the research on the modeling and inversion methods of composite layered soil model, combining advanced numerical calculation methods and artificial intelligence algorithms to provide the support of computational tools for coastal resistivity inversion. [ABSTRACT FROM AUTHOR]

To improve the accuracy of cross-domain object detection, the existing unsupervised domain adaptation (UDA) object detection methods mostly use Feature Pyramid Network (FPN), multiple Region Proposal Network (RPN), and multiple domain classifier, but these methods lead to complex network structures, slow model convergence, and low detection efficiency. To solve the above problems, this paper proposes an Efficient and Accurate Cross-domain Object Detection Method Using One-level Feature and Domain Adaptation (OFDA). This method realizes one-level feature object detection through feature fusion and divide-and-conquer technology; realizes overfitting feature suppression and unsupervised domain adaptation through domain-specific suppression and domain feature alignment technology; realizes background feature suppression through the Objectness branch, which replaces the time-consuming Region Proposal Network (RPN) structure and improves the efficiency of unsupervised adaptive detection. The paper verifies the feasibility and superiority of the proposed method by the comparative experiments and ablation experiments of multiple datasets. The proposed OFDA method not only improves the efficiency of object detection, but also ensures the accuracy of cross-domain detection. [ABSTRACT FROM AUTHOR]

In response to the aging trend in society and to Human Augmentation beings for home-based activities, this paper proposes an Abnormal Movement Detection system, using the common at-home movements of standing up and hand tremors while picking up items for abnormal movement verification. This can be easily applied in ordinary homes or long-term care institutions; for those living alone with limited resources, there is no longer any need to purchase expensive monitoring equipment to achieve improved quality of life. Therefore, this research collected and built the own dataset as the first important step of the study. The proposed Abnormal Movement Detection system is implemented by designing a deep learning network. Several issues, including the network architecture, the novel method of data augmentation and the scoring method of expanding the intervals between abnormality levels, are studied. For achieving the home-based real-time detection, there are four main contributions of this paper. The first is that a training dataset was collected and established: From this, the pathognomonic movement categories are easy to observe in home activities and geometric data augmentation can be used to improve the related home activity video collection. The second is the abnormal behavior detection architecture: This architecture has several important function blocks including detecting object, detecting action, inspecting abnormal movement and reminding event, using Convolutional Neural Network combined with Long Short-Term Memory (CNN + LSTM) as the core network for abnormal motion detection. With movement abnormality evaluation based on different levels, it can judge abnormal behaviors and conduct model training, performance evaluation and architecture optimization with both public domain datasets and the movement dataset collected in this research project. The third is the proliferation of new attributes in the videos: New attributes are added to the original videos through a Generative Adversarial Network (GAN), producing new training videos; the effectiveness of two different generation methods is evaluated. Finally, the algorithms developed in this paper are deployed on resource-constrained On-device Artificial Intelligence (AI). Activity videos from a total of 20 people were collected; in all, 53 videos of StandUp and 60 videos of PickUpItems were obtained to establish the training dataset. When CNN and LSTM network were added to Batch Normalization (BN), and Global Average Pooling (GAP) replaced Fully Connected (FC) layers, the accuracy rate reached 98.4%. In terms of data augmentation, geometric transformations and GAN were used to estimate the performance. The experimental results showed that the geometric transformation using brightness adjustment had the highest accuracy rate of 98.6%. Finally, the Softmax layer using Phi-Softmax–tan(⋅) function was shown to be the best method to expand the intervals between abnormality levels. [ABSTRACT FROM AUTHOR]