Effective Classification Model Research Articles

Potato Plant Leaf Diseases Identification Using Transfer Learning

Background/Objectives: Agriculture is a major food source for Ethiopian population. Plant diseases contribute a great production loss, which can be addressed with continuous monitoring. Early plant disease identification using computer vision and Artificial Intelligence (AI) helps the farmers to take preventive course of action to increase production quality. Manual plant disease identification is strenuous and error-prone. Methods: In this study, we present a convolutional neural network architecture inception-v3 model to detect potato leaf diseases using a deep learning-based transfer learning technique. We used separable convolution in the inception block that can minimize the number of parameters by an outsized margin and to utilize resource efficiently. The inception-V3 model have a higher training accuracy and needs less training time than the main CNN architecture, as the used parameters are fewer. Findings: In this study, there is an improvement on the little noisy on sample images which leads to misidentification of diseases. In our experiment, we have used an RGB color channel image dataset to train model, which yields an overall accuracy performance of 98.7% on the heldout test set. Novelty: In order to identify potato leave diseases, we conducted transfer learning for high performance classification with pixel-wise operation to enhance the number of leaf images. A model based on inception-v3 transfer learning approach is presented in this study for disease identification of potato leave images, thus provide an effective computer-aided recognition model for potato disease classification in the absence of large data. Keywords: Artificial intelligence; convolutional neural network; deep learning; leaf disease identification; Softmax

The Influencing Legal and Factors of Migrant Children's Educational Integration Based on Convolutional Neural Network.

This research aims to analyze the influencing factors of migrant children’s education integration based on the convolutional neural network (CNN) algorithm. The attention mechanism, LSTM, and GRU are introduced based on the CNN algorithm, to establish an ALGCNN model for text classification. Film and television review data set (MR), Stanford sentiment data set (SST), and news opinion data set (MPQA) are used to analyze the classification accuracy, loss value, Hamming loss (HL), precision (Pre), recall (Re), and micro-F1 (F1) of the ALGCNN model. Then, on the big data platform, data in the Comprehensive Management System of Floating Population and Rental Housing, Student Status Information Management System, and Student Information Management System of Beijing city are taken as samples. The ALGCNN model is used to classify and compare related data. It is found that in the MR, STT, and MPQA data sets, the classification accuracy and loss value of the ALGCNN model are better than other algorithms. HL is the lowest (15.2 ± 1.38%), the Pre is second only to the BERT algorithm, and the Re and F1 are both higher than other algorithms. From 2015 to 2019, the number of migrant children in different grades of elementary school shows a gradual increase. Among migrant children, the number of migrant children from other counties in this province is evidently higher than the number of migrant children from other provinces. Among children of migrant workers, the number of immigrants from other counties in this province is also notably higher than the number of immigrants from other provinces. With the gradual increase in the years, the proportion of township-level expenses shows a gradual decrease, whereas the proportion of district and county-level expenses shows a gradual increase. Moreover, the accuracy of the ALGCNN model in migrant children and local children data classification is 98.6 and 98.9%, respectively. The proportion of migrant children in the first and second grades of a primary school in Beijing city is obviously higher than that of local children (p < 0.05). The average final score of local children was greatly higher than that of migrant children (p < 0.05), whereas the scores of migrant children’s listening methods, learning skills, and learning environment adaptability are lower, which shows that an effective text classification model (ALGCNN) is established based on the CNN algorithm. In short, the children’s education costs, listening methods, learning skills, and learning environment adaptability are the main factors affecting migrant children’s educational integration, and this work provides a reference for the analysis of migrant children’s educational integration.

ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images

Breast cancer is considered an immense threat and one of the leading causes of mortality in females. It is curable only when detected at an early stage. A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images. Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images. Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection. Nowadays, computer-based automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the type of cancer. This study proposes an effective convolutional neural network-based (CNN-based) model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour, using histopathology images. Resnet50 architecture has been trained on new dataset for feature extraction, and fully connected layers have been fine-tuned for achieving highest training, validation and test accuracies. The result illustrated state-of-the-art performance of the proposed model with highest training, validation and test accuracies as 99.70%, 99.24% and 99.24%, respectively. Classification accuracy is increased by 0.66% and 0.2% when compared with similar recent studies on training and test data results. Average precision and F1 score have also improved, and receiver operating characteristic (RoC) area has been achieved to 99.1%. Thus, a reliable, accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.

RGTransformer: Region-Graph Transformer for Image Representation and Few-Shot Classification

The goal of few-shot image classification is to learn a classifier that can be well generalized to the unseen classes with a few available labeled samples. One major challenge for few-shot learning is how to conduct effective image representation for support and query images. Recently, local region-based image representation and metric learning approaches have been demonstrated effectively for few-shot classification problem. However, existing approaches generally conduct representations of image regions individually which thus lack of considering the rich spatial/structural relationships among image regions. In this paper, we propose to bridge the individual regions and exploit the structural contexts among regions via a novel Region-Graph Transformer (RGTransformer). In RGTransformer, each region aggregates the information from its neighboring regions and thus can obtain context-aware feature representations for regions. Using the proposed RGTransformer, we propose an effective metric learning model for few-shot image classification. We evaluate the proposed method on four benchmark datasets and experimental results demonstrate the effectiveness and advantages of the proposed RGTransformer.

Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results. Primarily, the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity. In addition, the information gain (IG) approach is applied for the optimal selection of feature subsets. Also, group search optimizer (GSO) with deep belief network (DBN) model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm. The choice of IG and GSO approaches results in promising medical data classification results. The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets. The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

Hybridization of Neutrosophic Logic with Quasi-Oppositional Chimp Optimization based Data Classification Model

Data classification is the procedure of investigating structured or unstructured data and forming it into distinct classes depending upon file types, size, etc. It assist the organizations to derive important solutions based on the data and helps decision making process. The computational intelligence techniques such as neural computing, fuzzy logic, machine learning, etc. can be used to design effective data classification models. This study offers a Hybridization of Neutrosophic Logic with Quasi-Oppositional Chimp Optimization based Data Classification (HNLQOCO) model. The presented HNLQOCO algorithm aims to integrate the concepts of NL and QOCO algorithm for improved data classification outcomes. Besides, the QOCO algorithm is designed by incorporating the concepts of quasi oppositional based learning (QOBL) with traditional chimp optimization algorithm (COA). Here, the NL is applied to represent various kinds of knowledge and the QOCO algorithm is applied to tune the produced NS rules. The experimental result analysis of the HNLQOCO model is tested using three benchmark medical dataset. The obtained results reported the significant performance of the HNLQOCO model over the other methods.

Machine Learning Methods for Predicting the Popularity of Movies

The movie industry has grown into a several billion-dollar enterprise, and there is now a ton of information online about it. Numerous machine learning techniques have been created by academics and can produce effective classification models. In this study, different machine learning classification techniques are applied to our own movie dataset for multiclass classification. This paper's main objective is to compare the effectiveness of various machine learning techniques. This study examined five methods: Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), Bagging (BAG), Naive Bayes (NBS) and K-Nearest Neighbor (KNN), while noise was removed using All K-Edited Nearest Neighbors (AENN). These techniques all utilize previous IMDb dataset to predict a movie's net profit value. The algorithms predict the profit at the box office for each of these five techniques. Based on the dataset used in this paper, which consists of 5043 rows and 14 columns of movies, this study evaluates the performance of all seven machine learning techniques. Bagging outperformed other machine learning techniques with a 99.56% accuracy rate.

Temporal and Spectral Feature Learning With Two-Stream Convolutional Neural Networks for Appliance Recognition in NILM

Non-intrusive load monitoring (NILM) can monitor the operating state and energy consumption of appliances without deploying sub-meters and is promising to be widely used in residential communities. With the rapid increase of electric loads in amount and type, constructing representative load signatures and designing effective classification models are becoming increasingly crucial for NILM. In this paper, temporal and spectral load signatures that preserve sufficient information are constructed from the monitored energy data. The fusion of these two types of load signatures can provide rich distinguishing features for improving the performance of appliance recognition in NILM. Benefiting from the development of deep learning, this study proposes the two-stream convolutional neural networks (TSCNN) to extract the features from the two types of load signatures and perform classification. Furthermore, this study introduces the affinity propagation clustering strategy to mitigate the negative impact of intra-class variety mainly caused by multi-state loads in appliance recognition. The experimental results on public NILM datasets demonstrate that the proposed method outperforms most of the existing methods based on the voltage-current trajectory or recurrence graph in the recognition accuracy of submetered and aggregated measurements.

Multiple regression-based glass composition prediction and statistics

The surface weathering of ancient glass can be distinguished from the type of ancient glass by chemical composition as well as color and ornamentation. This paper constructs mathematical models to analyze the relationship between surface weathering and glass type, color and ornamentation based on different chemical compositions and characteristic data of color and ornamentation in ancient glass, explores the statistical law of chemical composition content with and without weathering, and constructs an effective classification model to realize glass type classification. This paper solves the relationship between glass type, decoration and color and their weathering degree; analyzes the statistical law of chemical composition content with and without weathering on the surface of two types of glass; derives the prediction formula and predicts the chemical composition content of two types of glass before weathering.

A Multi-Strategy Whale Optimization Algorithm and Its Application

Whale Optimization Algorithm (WOA) is a key tool for solving complex engineering optimization problems, aiming at adjusting important parameters to satisfy constraints and optimal objectives. WOA has a simple structure, few parameters, high search capability, and easy implementation. However, it suffers from the same problems as other metaheuristic algorithms of being prone to local optima and slow convergence, for which the Multi-Strategy Whale Optimization Algorithm (MSWOA) is proposed. Four strategies are introduced in MSWOA. Firstly, a highly randomized chaotic logistic map is used to generate a high-quality initial population. Secondly, exploitation and exploration are enhanced by setting adaptive weights and dynamic convergence factors. Further, a Lévy flight mechanism is introduced to maintain the population diversity in each iteration. Finally, the Evolutionary Population Dynamics (EPD) mechanism is introduced to improve the efficiency of search agents in finding the optimum. Another problem lies in the Semi-Supervised Extreme Learning Machine (SSELM) based on manifold regularization is an effective classification and regression model, but the random generation of input weights and hidden layer thresholds and the grid selection of hyperparameters lead to unsatisfactory classification performance. To this end, we developed the MSWOA-SSELM model, optimally selected the parameters of SSLEM using MSWOA, and applied it to logging layer recognition, which effectively improved the accuracy of logging interpretation. By comparing the experiments with 14 swarm intelligence algorithms on 18 benchmark test functions, the CEC2017 benchmark suite, and an engineering application problem, the experimental results show that MSWOA is significantly superior and effective in solving global optimization problems. Finally, the proposed MSWOA-SSELM is applied in three wells and outperforms other classification models in terms of Accuracy (ACC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). It obtained the best results with 96.2567% ACC, MAE of 0.0749, and RMSE of 0.3870.

Predictive Model and Factors of Relapse after Allogeneic Stem Cell Transplantation in Adults with Ph-Positive Acute Lymphoblastic Leukemia

Background: Relapses after allo-HSCT remain an unsolved problem in Ph-positive acute lymphoblastic leukemia (ALL) patients, especially in patients with detectable BCR/ABL levels prior to allogeneic stem cell transplantation (allo-HSCT). Majority of centers make efforts to manage with it by preemptive or prophylactic administration of TKIs after allo-HSCT. However, the risk factors in this setting are yet to be determined. Moreover, persistence of minimal residual disease (MRD) after induction plays a critical role in relapse probability, but its fluctuation after transplant in the context of TKIs application is still a question. The aim of this study is to apply modern machine-learning approaches for building relapse predicting models and testing variable importance.Patients and methods: This study analyses the data in retrospective cohort of 74 Ph-positive ALL patients with posttransplant BCR/ABL expression levels available at different time intervals with median age of 30,5 years (range, 18-55), in whom allo-HSCT were performed between 2008 and 2021. Patient characteristics and features of the disease are presented in Table 1. For the analysis, all TKIs were divided into 2 groups TKIs1 - imatinib, TKIs2 - other TKIs, regardless of generation. Machine learning models were developed using R programming language and Caret package. The dependent variable was relapse after prediction moment, the following independent variable features were used: time intervals between allo-HSCT and prediction moment, BCR/ABL expression level at prediction moment, therapy after allo-HSCT (TKIs1 or TKIs2), the highest BCR/ABL expression level before prediction moment, chronic «graft-versus-host» disease (GvHD) before prediction for the patients, who reached D+100 after allo-HSCT.Results: At the time of analysis median follow-up was 26 months (range, 1-116). 5-year OS and EFS were 67,1% (95% CI 54,2 - 80) and 55,1% (95% CI 42,5 - 68,3), respectively, whereas 5-year cumulative incidence of relapse was 46,1% (95% CI 26,2 - 66) for MRD-positive prior to allo-HSCT patients, compared to 24,1% (95% CI 6,9-41,3) for MRD-negative patients (р=0,04).The resulting ROC-curve for 3 most effective classification models is given in figure 1A. As one can see Gradient Boosting Method (GBM) provided maximal AUC score (0.88). For this a decision-making threshold may be adjusted for obtaining Specificity = 0.75, Sensitivity = 0.88. Variable importance plot (figure 1B) showed that the highest BCR/ABL level, prediction moment, chronic GvHD and current BCR/ABL level have the strongest importance, while preceding therapy turned out to be less significant factor. In fact, exclusion of TKIs type almost did not affect the ROC curves. In GBM model AUC still demonstrated appropriate level of 0.87. When analyzing the model accuracy, false-negative rate (FNR) and false-positive rate (FPR) errors were estimated for the three ranges of prediction moments (figure 1C). It was shown that after D+100 both error rates don't exceed 22%, while before D+100 the model fails to make accurate prediction based on the independent variables used.Conclusions: Using independent factors, we built the model for both bone marrow and extramedullary relapses prediction after allo-HSCT with high sensitivity and reasonable specificity based on the relatively small group of patients. According to the predicting model, we confirm, that a high level of BCR/ABL at any time point after allo-HSCT is the most significant predictor of relapse, which may indicate the presence of subclones of cells that cause resistance to chemotherapy or TKIs. The BCR/ABL MRD levels before D+100 have low predictive ability for early relapses, which may develop rapidly without MRD phase. At the same time, BCR/ABL levels relatively accurate predict relapses after D+100 with ongoing TKI prophylaxis. The absence of chronic GvHD is an important independent factor influencing the risk of relapse. This means that for high-risk patients, approaches to induce a «graft-versus-leukemia» effect should be considered. In addition, prophylactic use of monoclonal antibodies in combination with TKIs may be considered to prevent relapse in the absence of chronic GvHD in high-risk patients. In summary, we believe that verification of this model on a multicenter group of patients is required to facilitate its clinical application. [Display omitted] DisclosuresKulagin: Pfizer: Speakers Bureau; Johnson & Johnson: Speakers Bureau; Alexion: Research Funding; Roche: Speakers Bureau; Novartis: Speakers Bureau; Generium: Speakers Bureau; Sanofi: Speakers Bureau; Apellis: Research Funding; Biocad: Research Funding; X4 Pharmaceuticals: Research Funding.

Automatic 12-Leading Electrocardiogram Classification Network with Deformable Convolution.

Electrocardiogram (ECG) is an electrical signal that helps monitor the physiology of the heart. A complete ECG record includes 12 leads, each reflecting features from a different angle of the heart. In recent years, various deep learning algorithms, especially convolutional neural networks (CNN), have been applied to detect ECG features. However, the conventional CNN can only extract the local features and cannot extract the data correlation across the leads of ECG. Based on deformable convolution networks (DCN), this article proposes a new neural network structure (DCNet) to detect ECG features. The network architecture consists of four DCN blocks and a classification layer. For the ECG classification task, in a DCN block, the combination of normal convolution and deformable convolution with better effect was testified by the experiments. Based on the feature learning capability of DCN, the architecture can better extract the characteristics between leads. Using the public 12-leading ECG data in CPSC-2018, the diagnostic accuracy of this architecture is the highest, reaching 86.3%, which is superior to other common network architectures with good results in ECG signal classification.Clinical relevance-In this paper, we proposed an effective automatic ECG classification model that can reduce medical staff workload.

Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure.

There are unanswered questions with regards to acute respiratory outcomes, particularly asthma, due to environmental exposures. In contribution to asthma research, the current study explored a computational intelligence paradigm of artificial neural networks (ANNs) called self-organizing maps (SOM). To train the SOM, air quality data (nitrogen dioxide, sulphur dioxide and particulate matter), interpolated to geocoded addresses of asthmatics, were used with clinical data to classify asthma outcomes. Socio-demographic data such as age, gender and race were also used to perform the classification by the SOM. All pollutants and demographic traits appeared to be important for the correct classification of asthma outcomes. Age was more important: older patients were more likely to have asthma. The resultant SOM model had low quantization error. The study concluded that Kohonen self-organizing maps provide effective classification models to study asthma outcomes, particularly when using multidimensional data. SO2 was concluded to be an important pollutant that requires strict regulation, particularly where frail subpopulations such as the elderly may be at risk.

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model.

COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

Classification utility aware data stream anonymization

Data streams are continuous, infinite and ordered sequences of data. In comparison to static dataset anonymization, data stream anonymization confront with a number of constraints and difficulties due to the dynamic nature of data flow. The literature already addressed the k-anonymization of data streams which contain quasi-identifier attributes. However, today most data streams contain sensitive and classification target attributes as well. This work’s main motivation is to develop a k-anonymization method for data streams which additionally protects the sensitivity and enables effective classification models. The k-anonymization, as a result, is formulated as a weighted multi-objective optimization problem. There are three objectives with respective weights as user parameters. A clustering based k-anonymization algorithm is developed as the solution. An extensive experimental evaluation on three real datasets shows the effectiveness of our proposal in various configurations. Moreover, the experimental results also confirm that our proposal attains better classification accuracies in comparison to popular data stream anonymization techniques.

Efficient-PrototypicalNet with self knowledge distillation for few-shot learning

The focus of recent few-shot learning research has been on the development of learning methods that can quickly adapt to unseen tasks with small amounts of data and low computational cost. In order to achieve higher performance in few-shot learning tasks, the generalizability of the method is essential to enable it generalize well from seen tasks to unseen tasks with limited number of samples. In this work, we investigate a new metric-based few-shot learning framework which transfers the knowledge from another effective classification model to produce well generalized embedding and improve the effectiveness in handling unseen tasks. The idea of our proposed Efficient-PrototypicalNet involves transfer learning, knowledge distillation, and few-shot learning. We employed a pre-trained model as a feature extractor to obtain useful features from tasks and decrease the task complexity. These features reduce the training difficulty in few-shot learning and increase the performance. Besides that, we further apply knowledge distillation to our framework and achieve extra performance improvement. The proposed Efficient-PrototypicalNet was evaluated on five benchmark datasets, i.e., Omniglot, miniImageNet, tieredImageNet, CIFAR-FS, and FC100. The proposed Efficient-PrototypicalNet achieved the state-of-the-art performance on most datasets in the 5-way K-shot image classification task, especially on the miniImageNet dataset.

Effective multilayer hybrid classification approach for automatic bridge health assessment on large-scale uncertain data

The health level of the bridge is critical to the safety and maintainability of the bridge. However, with the rapid increase of bridge data with complex information, manual evaluation of bridge health requires high labor and time costs and a lot of related knowledge. Meanwhile, due to the error of sensors and subjective judgment of experts, as well as the influence of the external environment of the bridge, there is great uncertainty in the evaluation of the bridge data. Thence, how to use a large amount of bridge data with large-scale uncertain labels to build a robust classification model for efficient and high-quality bridge health assessment has become an urgent task. In order to better assess the health of the bridge, we adopt a multi-layer hybrid method to iteratively determine the uncertain labels of the target dataset, evaluate the confidence of the large-scale uncertain labels, add high-confidence data to the training set, and correct the low-confidence data. Finally, we get an effective classification model with the optimized training dataset. This paper studies the learning problem of classification model on labeled data with large-scale uncertain labels, and proposes an effective hybrid classification model (HCM), which can establish a supervised classifier under the condition of detecting uncertain labels and realize error label correction. In order to measure the HCM label assignment problem, we introduce a new penalty function, which can evaluate the label consistency problem of two basic classifiers. Meanwhile, we apply the model to bridge data with uncertain labels for bridge health evaluation. Experiments conducted on synthetic data, benchmark data and real bridge datasets show that the proposed method is superior to other methods and provides an effective and convenient solution for bridge health assessment. At the same time, this method can also be used in other research fields where there are large-scale uncertain labels.

Automatic classification of nerve discharge rhythms based on sparse auto-encoder and time series feature

BackgroundNerve discharge is the carrier of information transmission, which can reveal the basic rules of various nerve activities. Recognition of the nerve discharge rhythm is the key to correctly understand the dynamic behavior of the nervous system. The previous methods for the nerve discharge recognition almost depended on the traditional statistical features, and the nonlinear dynamical features of the discharge activity. The artificial extraction and the empirical judgment of the features were required for the recognition. Thus, these methods suffered from subjective factors and were not conducive to the identification of a large number of discharge rhythms.ResultsThe ability of automatic feature extraction along with the development of the neural network has been greatly improved. In this paper, an effective discharge rhythm classification model based on sparse auto-encoder was proposed. The sparse auto-encoder was used to construct the feature learning network. The simulated discharge data from the Chay model and its variants were taken as the input of the network, and the fused features, including the network learning features, covariance and approximate entropy of nerve discharge, were classified by Softmax. The results showed that the accuracy of the classification on the testing data was 87.5%, which could provide more accurate classification results. Compared with other methods for the identification of nerve discharge types, this method could extract the characteristics of nerve discharge rhythm automatically without artificial design, and show a higher accuracy.ConclusionsThe sparse auto-encoder, even neural network has not been used to classify the basic nerve discharge from neither biological experiment data nor model simulation data. The automatic classification method of nerve discharge rhythm based on the sparse auto-encoder in this paper reduced the subjectivity and misjudgment of the artificial feature extraction, saved the time for the comparison with the traditional method, and improved the intelligence of the classification of discharge types. It could further help us to recognize and identify the nerve discharge activities in a new way.

Fuzzy C-Means (FCM) Clustering with Probabilistic Neural Network (PNN) Model for Detection and Classification of Rice Plant Diseases in Internet of Things-Cloud Centric Precision Agriculture

Presently, the field of Internet of Things (loT) has been employed in diverse applications like Smart Grid, Surveillance, Smart homes, and so on. Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop health. Recognition of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. This paper introduces an effective rice plant disease identification and classification model to identify the type of disease from infected rice plants. The proposed method aims to detect three rice plant diseases such as Bacterial leaf blight, Brown spot, and Leaf smut. The proposed method involves a set of different processes namely image acquisition, preprocessing, segmentation, feature extraction and classification. At the earlier stage, IoT devices will be used to capture the image and stores it with a cloud server, which executes the classification process. In the cloud, the rice plant images under preprocessing to improvise the quality of the image. Then, fuzzy c-means (FCM) clustering method is utilized for the segmentation of disease portion from a leaf image. Afterwards, feature extraction takes place under three kinds namely color, shape, and texture. Finally, probabilistic neural network (PNN) is applied for multi-class classification. A detailed experimental analysis ensured the effective classification performance of the proposed method under all the test images applied.

Hybrid manifold-deep convolutional neural network for sleep staging.

Analysis of electroencephalogram (EEG) is a crucial diagnostic criterion for many sleep disorders, of which sleep staging is an important component. Manual stage classification is a labor-intensive process and usually suffered from many subjective factors. Recently, more and more computer-aided techniques have been applied to this task, among which deep convolutional neural network has been performing well as an effective automatic classification model. Despite some comprehensive models have been developed to improve classification results, the accuracy for clinical applications has not been reached due to the lack of sufficient labeled data and the limitation of extracting latent discriminative EEG features. Therefore, we propose a novel hybrid manifold-deep convolutional neural network with hyperbolic attention. To overcome the shortage of labeled data, we update the semi-supervised training scheme as an optimal solution. In order to extract the latent feature representation, we introduce the manifold learning module and the hyperbolic module to extract more discriminative information. Eight subjects from the public dataset are utilized to evaluate our pipeline, and the model achieved 89% accuracy, 70% precision, 80% sensitivity, 72% f1-score and kappa coefficient of 78%, respectively. The proposed model demonstrates powerful ability in extracting feature representation and achieves promising results by using semi-supervised training scheme. Therefore, our approach shows strong potential for future clinical development.