A Systematic Review of Attendance Systems based on Face Recognition

In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the most implemented biometrics across various different fields. Classroom attendance check is a contributing factor to student participation and the final success in the courses. Every institute follows their own way for taking attendance. Some are taking attendance manually using papers or a register file or different biometric techniques. Taking attendance by calling out names or passing around an attendance sheet is time-consuming, and the latter is open to easy fraud. In this paper, the comparative analysis of various existing approaches on attendance management system based on facial recognition that are used to monitor attendance in various institutions using Fingerprint, GPS, RFID etc. is discussed with their limitations.

Legitimidade de fontes e opinião sobre coronavírus em O Grande Debate / Legitimacy of sources and opinion about coronavirus in O Grande Debate / Legitimidad de fuentes y opinión sobre coronavirus en O Grande Debate

A partir do tensionamento de conceitos como objetividade e opinião no jornalismo, polarização espetacularizada e abordagem da saúde pelo jornalismo, o artigo reflete sobre a legitimidade das fontes às quais recorrem os participantes de O Grande Debate, da CNN Brasil. Foram observadas 14 edições do quadro, com o registro das estratégias empregadas pelos comentaristas, a fim de identificar os padrões de legitimidade e de legitimação das fontes. Percebemos que foram acionadas fontes oficiais ou documentais e também fontes falsas ou pseudofontes, contribuindo para a equiparação entre opinião, fato e inverdade nos debates sobre o novo coronavírus.

Based on the tension between concepts such as objectivity and opinion in journalism, spectacular polarization and the approach to health by journalism, this article reflects on the legitimacy of the sources to which participants in O Grande Debate, transmitted by CNN Brazil, resort. Fourteen editions of this panel were observed, recording the strategies employed by commentators in order to identify patterns of legitimacy and legitimation of the sources. We could detect that official or documentary sources were exploited, as well as false or pseudo sources, contributing to the equivalence between opinion, fact and untruth in the debates about the new coronavirus.

A partir de la tensión de conceptos como la objetividad y la opinión en el periodismo, la polarización espectacular y la manera de plantear la salud por parte del periodismo, este artículo reflexiona sobre la legitimidad de las fuentes a que recurren los participantes de O Grande Debate, de la CNN Brasil. Se observaron catorce ediciones del programa y se registraron las estrategias empleadas por los comentaristas para identificar las configuraciones de legitimidad y de legitimación de las fuentes. Notamos que fueron empleadas fuentes oficiales o documentales, así como fuentes falsas o pseudo-fuentes, contribuyendo a la equiparación entre opinión, hecho y falsedad en los debates sobre el nuevo coronavirus.

Design of Wearable Wireless Health Monitoring System and Status Recognition Algorithm / 中国医疗器械杂志

A wearable wireless health monitoring system for drug addicts in compulsory rehabilitation centers was proposed. The system can continuously monitor multiple physiological parameters of drug addicts in real time, and issue early warning information when abnormal physiological parameters occur, so as to play the role of timely medical practice. In addition, this study proposes a convolutional neural network (CNN)model, which can evaluate the health status of drug addicts based on multiple physiological parameters. Experiments show that the model can be applied to the task of body state recognition in the open physiological parameter data set, and the recognition accuracy can reach up to 100% in a single physiological parameter data set; when the whole physiological data set is used, the recognition accuracy can reach 99.1%. The recognition accuracy exceeds the performance of the traditional pattern recognition method on this task, which verifies the superiority of the model.

Detection of white blood cells in microscopic leucorrhea images based on deep active learning / 生物医学工程学杂志

The number of white blood cells in the leucorrhea microscopic image can indicate the severity of vaginal inflammation. At present, the detection of white blood cells in leucorrhea mainly relies on manual microscopy by medical experts, which is time-consuming, expensive and error-prone. In recent years, some studies have proposed to implement intelligent detection of leucorrhea white blood cells based on deep learning technology. However, such methods usually require manual labeling of a large number of samples as training sets, and the labeling cost is high. Therefore, this study proposes the use of deep active learning algorithms to achieve intelligent detection of white blood cells in leucorrhea microscopic images. In the active learning framework, a small number of labeled samples were firstly used as the basic training set, and a faster region convolutional neural network (Faster R-CNN) training detection model was performed. Then the most valuable samples were automatically selected for manual annotation, and the training set and the corresponding detection model were iteratively updated, which made the performance of the model continue to increase. The experimental results show that the deep active learning technology can obtain higher detection accuracy under less manual labeling samples, and the average precision of white blood cell detection could reach 90.6%, which meets the requirements of clinical routine examination.

Automatic Identification and Classification Diagnosis of Atrial Ventricular Hypertrophy Electrocardiogram Based on Convolutional Neural Network / 中国医疗器械杂志

OBJECTIVE@#Identifying Atrial Ventricular Hypertrophy Electrocardiogram (AVH ECG)and diagnosing the classification of theirs automatically.@*METHODS@#The ECG data used in this experiment was collected from the First Affiliated Hospital of China Medical University. CNN are combined with conventional methods and a 10 layers of one dimensional CNN are created in this experiment to extract the features of ECG signals automatically and achieve the function of classifying. ROC, sensitivity and F1-score are used here to evaluate the effects of the model.@*RESULTS@#In the experiment of identifying AVH ECG, the AUC of test dataset is 0.991, while in the experiment of classifying AVH ECG, the maximal F1-score can reach 0.992.@*CONCLUSIONS@#The CNN model created in this experiment can achieve the auxiliary diagnosis of AVH ECG.

Chlorophyll content for millet leaf using hyperspectral imaging and an attention-convolutional neural network / Estimativa do teor de clorofila para folhas de milheto usando imagens hiperespectrais e uma rede neural atenção-convolucional

ABSTRACT: Chlorophyll is a major factor affecting photosynthesis; and consequently, crop growth and yield. In this study, we devised a chlorophyll-content detection model for millet leaves in different stages of growth based on hyperspectral data. The hyperspectral images of millet leaves were obtained under a wavelength range of 380-1000 nm using a hyperspectral imager. Threshold segmentation was performed with near-infrared (NIR) reflectance and normalized difference vegetation index (NDVI) to intelligently acquire the regions of interest (ROI). Furthermore, raw spectral data were preprocessed using multivariate scatter correction (MSC). A correlation coefficient-successive projections algorithm (CC-SPA) was used to extract the characteristic wavelengths, and the characteristic parameters were extracted based on the spectral and image information. A partial least squares regression (PLSR) prediction model was established based on the single characteristic parameter and multi-characteristic parameter fusion. The determination coefficient (Rv 2) and the root-mean-square error (RMSEv) of the validation set for the multi-characteristic parameter fusion model were reported to be 0.813 and 1.766, respectively, which are higher than those obtained by the single characteristic parameter model. Based on the multi-characteristic parameter fusion, an attention-convolutional neural network (attention-CNN) (Rv 2 = 0.839, RMSEv = 1.451, RPD = 2.355) was established, which is more effective than the PLSR (Rv 2 = 0.813, RMSEv = 1.766, RPD = 2.167) and least squares support vector machine (LS-SVM) models (Rv 2 = 0.806, RMSEv = 1.576, RPD = 2.061). These results indicated that the combination of hyperspectral imaging and attention-CNN is beneficial to the application of nutrient element monitoring of crops.

RESUMO: A clorofila é um fator importante que afeta a fotossíntese e, consequentemente, o crescimento e o rendimento das culturas. Neste estudo, um modelo de detecção de conteúdo de clorofila é construído para folhas de milheto em diferentes estágios de crescimento, com base em dados hiperespectrais. As imagens hiperespectrais dos diferentes estágios de crescimento das folhas de milheto foram obtidas para 380-1000 nm, utilizando um gerador de imagens hiperespectrais. Uma segmentação de limiar foi realizada com refletância no infravermelho próximo (NIR) e índice de vegetação com diferença normalizada (NDVI) para adquirir de forma inteligente as regiões de interesse (ROI). Além disso, os dados espectrais brutos foram pré-processados usando o método de correção de dispersão multivariada (MSC). Um algoritmo de projeção de coeficiente de correlação sucessivo (CC-SPA) foi utilizado para extrair os comprimentos de onda característicos, e os parâmetros característicos foram extraídos com base nas informações espectrais e de imagem. O modelo de previsão de regressão parcial dos mínimos quadrados (PLSR) foi estabelecido com base nos parâmetros de característica única e na fusão de parâmetros de característica múltipla. O coeficiente de determinação (Rv2) e o erro quadrático médio da raiz (RMSEv) do conjunto de validação para o modelo de fusão de parâmetros com várias características foram obtidos como 0,813 e 1,766, sendo melhores do que os do modelo de parâmetro de característica única. Com base na fusão de parâmetros com várias características, foi estabelecida uma rede neural atenção-convolucional (atenção-CNN) (Rv2 = 0,839, RMSEv = 1,451, RPD = 2,355) mais eficaz que o PLSR (Rv2 = 0,813, RMSEv = 1,766, RPD = 2,167) e mínimos quadrados que suportam modelos de máquina de vetores (LS-SVM) (Rv2 = 0,806, RMSEv = 1,576, RPD = 2,061). Estes resultados indicam que o modelo atenção-CNN atinge uma previsão efetiva do teor de clorofila nas folhas de milheto usando os dados hiperespectrais. Além disso, esta pesquisa demonstra que a combinação de imagens hiperespectrais e a atenção-CNN se mostra benéfica para a aplicação do monitoramento dos elementos nutricionais das culturas.

Study on the relationship between meteorological factors and the number of hypertension outpatients based on CNN-LSTM / 中华疾病控制杂志

Objective To study the effect of meteorological factors on the number of hypertension outpatients in four areas of Gansu Province, then predict and analyze the trend of the number of hypertension outpatients, so as to provide reference for the prevention and control of hypertension diseases. Methods On the basis of controlling the confounding factors such as long-term trends, date effects, meteorological information and contaminant influence, a mixed model of convolutional neural network (CNN) and long-short term memory (LSTM) was constructed for the number of hypertension outpatients in the four regions of Baiyin, Chengxian, Qingcheng and Liangzhou by Python programming language. Results The root mean square errors of the CNN-LSTM model for the number of hypertensive outpatients in the four regions was 6.330 9, 6.814 2, 6.393 6 and 6.867 6. The mean absolute percentage error was 74.082 2, 78.508 2, 56.618 3 and 50.235 4. And the average absolute errors was 4.875 7, 5.431 1, 4.542 0 and 6.460 8. All the results was superior to those of support vector machine (SVM), autoregressive integrated moving average model (ARIMA), random forest (RF), CNN and LSTM. Conclusion The CNN-LSTM model can accurately predict the number of hypertension outpatients in Gansu. The hospital can rationally allocate medical resources according to the needs of hypertension for medical treatment at different times.

CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network

OBJECTIVE: The aim of our study was to develop and validate a convolutional neural network (CNN) architecture to convert CT images reconstructed with one kernel to images with different reconstruction kernels without using a sinogram. MATERIALS AND METHODS: This retrospective study was approved by the Institutional Review Board. Ten chest CT scans were performed and reconstructed with the B10f, B30f, B50f, and B70f kernels. The dataset was divided into six, two, and two examinations for training, validation, and testing, respectively. We constructed a CNN architecture consisting of six convolutional layers, each with a 3 × 3 kernel with 64 filter banks. Quantitative performance was evaluated using root mean square error (RMSE) values. To validate clinical use, image conversion was conducted on 30 additional chest CT scans reconstructed with the B30f and B50f kernels. The influence of image conversion on emphysema quantification was assessed with Bland–Altman plots. RESULTS: Our scheme rapidly generated conversion results at the rate of 0.065 s/slice. Substantial reduction in RMSE was observed in the converted images in comparison with the original images with different kernels (mean reduction, 65.7%; range, 29.5–82.2%). The mean emphysema indices for B30f, B50f, converted B30f, and converted B50f were 5.4 ± 7.2%, 15.3 ± 7.2%, 5.9 ± 7.3%, and 16.8 ± 7.5%, respectively. The 95% limits of agreement between B30f and other kernels (B50f and converted B30f) ranged from −14.1% to −2.6% (mean, −8.3%) and −2.3% to 0.7% (mean, −0.8%), respectively. CONCLUSION: CNN-based CT kernel conversion shows adequate performance with high accuracy and speed, indicating its potential clinical use.

Automatic Identifcation of Heart Block Precise Location Based on Sparse Connection Residual Network / 中国医疗器械杂志

OBJECTIVE@#To classify Right Bundle Branch Block (RBBB),Left Bundle Branch Block (LBBB) and normal ECG signals automatically.@*METHODS@#The MIT-BIH database was used as experimental data sources.The training set and test set were extracted for training and testing network models.Based on convolutional neural network,this paper proposed the core algorithm:sparse connection residual network.Compared the sparse connected residual network with classic network models,then evaluated the recognition effect of the model.@*RESULTS@#The accuracy of the test set the MIT-BIH database was 95.2%,the result is better than classic network models.@*CONCLUSIONS@#The algorithm proposed in this paper can assist doctors in the diagnosis of heart block related disease and place a high value on clinical application.

Gastrointestinal polyp detection in endoscopic images using an improved feature extraction method

Gastrointestinal polyps are treated as the precursors of cancer development. So, possibility of cancers can be reduced at a great extent by early detection and removal of polyps. The most used diagnostic modality for gastrointestinal polyps is video endoscopy. But, as an operator dependant procedure, several human factors can lead to miss detection of polyps. In this peper, an improved computer aided polyp detection method has been proposed. Proposed improved method can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention. Color wavelet features and convolutional neural network features are extracted from endoscopic images, which are used for training a support vector machine. Then a target endoscopic image will be given to the classifier as input in order to find whether it contains any polyp or not. If polyp is found, it will be marked automatically. Experiment shows that, color wavelet features and convolutional neural network features together construct a highly representative of endoscopic polyp images. Evaluations on standard public databases show that, proposed system outperforms state-of-the-art methods, gaining accuracy of 98.34%, sensitivity of 98.67% and specificity of 98.23%. In this paper, the strength of color wavelet features and power of convolutional neural network features are combined. Fusion of these two methodology and use of support vector machine results in an improved method for gastrointestinal polyp detection. An analysis of ROC reveals that, proposed method can be used for polyp detection purposes with greater accuracy than state-of-the-art methods.

Obstructive sleep apnoea detection using convolutional neural network based deep learning framework

This letter presents an automated obstructive sleep apnoea (OSA) detection method with high accuracy, based on a deep learning framework employing convolutional neural network. The proposed work develops a system that takes single lead electrocardiography signals from patients for analysis and detects the OSA condition of the patient. The results show that the proposed method has some advantages in solving such problems and it outperforms the existing methods significantly. The present scheme eliminates the requirement of separate feature extraction and classification algorithms for the detection of OSA. The proposed network performs both feature learning and classifies the features in a supervised manner. The scheme is computation-intensive, but can achieve very high degree of accuracy—on an average a margin of more than 9% compared to other published literature till date. The method also has a good immunity to the contamination of the signals by noise. Even with pessimistic signal to noise ratio values considered here, the methods already reported are not able to outshine the present method. The software for the algorithm reported here can be a good contender to constitute a module that can be integrated with a portable medical diagnostic system.

Construction and identification of lentivirus-mediated vectors targeting CNN3 gene in the rat hippocampus / 中国比较医学杂志

Objective To establish a method focusing on regulation of CNN3 gene in the rat hippocampus and help to explore the role of CNN3 gene played in the brain physiology and pathology.Methods One cDNA sequence and three shRNAs targeting CNN3 gene were designed and synthesized.The recombinant lentivirus-mediated expressing and three short hairpin RNA ( shRNA) vectors targeting CNN3 gene in the rats were constructed with engineering technology.All recombinant vectors were intravenously injected into rats hippocampi guided by stereotaxic apparatus.Western blot was performed to explore the best shRNA and to study the changes of CNN3 gene in the rat hippocampus after transfection with the silence and over-expressed vectors.Results The lentivirus-mediated vector expressing CNN3-OE and three shRNA vectors targeting CNN3 gene were successfully constructed.Within eight weeks after transfection, the vectors of CNN3-OE and three CNN3-shRNAs changed the expression of CNN3 gene in the rat hippocampus, in particular, all the protein levels of calponin-3 encoded by CNN3 gene were significantly down-regulated along with the time, with the highest inhibitory rate of 73.6%in the CNN3-shRNA2 group.Significant up-regulation of calponin-3 protein level by 93.88%, was found only on the 14th day after transfection.Conclusions Lentivirus-mediated vectors of CNN3-OE and CNN3-shRNAs may regulate in vivo the CNN3 gene level in the local brain region of rats via stereotactic injection.The study lays a foundation for disease prevention and treatment in the future.