Classification Of Different Categories Research Articles

Analysis of Arrhythmia Features based on LightGBM and Kmeans Feature Extraction

The risk classification based on heart disease signals aims to classify patients into different risk levels, such as low risk, medium risk, and high risk. This classification can help doctors determine appropriate treatment plans, take timely measures, and improve patients' survival rate and quality of life. This article is based on the background of this problem, extracting electrocardiogram signals, and using LightGBM and Kmeans algorithms for arrhythmia classification and analysis. Extracted from the 2s features collected in this article are the data features of the electrocardiogram, including P-wave, P-R band, QRS complex, S-T band, and T-wave features. Construct a feature index pool for each electrocardiogram data, and use the feature index pool and the assigned risk level to construct a LightGBM classification regression model. The prediction accuracy is 93.5%, and the error fluctuation does not exceed ± 1. After that, use principal component analysis (PCA) to perform feature dimensionality reduction on the data. Specific classification of different categories of heart rates was carried out, and the specific performance of the algorithm and visualization of classification images were performed. Finally, this article conducted model validation on the above model, and constructed training and validation sets to validate the model. Through observation, it can be seen that the accuracy of the model is stable at over 90%. Therefore, the model constructed in this article is true, effective, reliable, and accurate.

Analysis of arrhythmia features based on LightGBM and kmeans feature extraction

The risk classification based on heart disease signals aims to classify patients into different risk levels, such as low risk, medium risk, and high risk. This classification can help doctors determine appropriate treatment plans, take timely measures, and improve patients' survival rate and quality of life. This article is based on the background of this problem, extracting electrocardiogram signals, and using LightGBM and Kmeans algorithms for arrhythmia classification and analysis. Extracted from the 2s features collected in this article are the data features of the electrocardiogram, including P-wave, P-R band, QRS complex, S-T band, and T-wave features. Construct a feature index pool for each electrocardiogram data, and use the feature index pool and the assigned risk level to construct a LightGBM classification regression model. The prediction accuracy is 93.5%, and the error fluctuation does not exceed ± 1. After that, use principal component analysis (PCA) to perform feature dimensionality reduction on the data. Specific classification of different categories of heart rates was carried out, and the specific performance of the algorithm and visualization of classification images were performed. Finally, this article conducted model validation on the above model, and constructed training and validation sets to validate the model. Through observation, it can be seen that the accuracy of the model is stable at over 90%. Therefore, the model constructed in this article is true, effective, reliable, and accurate.

A Pilot Study of Diabetes Mellitus Classification from rs-fMRI Data Using Convolutional Neural Networks

Background. As a chronic progressive disease, diabetes mellitus (DM) has a high incidence worldwide, and it impacts on cognitive and learning abilities in the lifetime even in the early stage, may degenerate memory in middle age, and perhaps increases the risk of Alzheimer’s disease. Method. In this work, we propose a convolutional neural network (CNN) based classification method to help classify diabetes by distinguishing the brains with abnormal functions from the normal ones on resting-state functional magnetic resonance imaging (rs-fMRI). The proposed classification model is based on the Inception-v4-Residual convolutional neural network architecture. In our workflow, the original rs-fMRI data are first mapped to generate amplitude of low-frequency fluctuation (ALFF) images and then fed into the CNN model to get the classification result to indicate the potential existence of DM. Result. We validate our method on a realistic clinical rs-fMRI dataset, and the achieved average accuracy is89.95% in fivefold cross-validation. Our model achieves a 0.8690 AUC with 77.50% and 77.51% sensitivity and specificity using our local dataset, respectively. Conclusion. It has the potential to become a novel clinical preliminary screening tool that provides help for the classification of different categories based on functional brain alteration caused by diabetes, benefiting from its accuracy and robustness, as well as efficiency and patient friendliness.

Classification of Erythemato-squamous diseases using Artificial Neural Network and Genetic algorithm

This paper introduces a hybrid model using artificial neural network (ANN) and genetic algorithm (GA) to develop an efficient classification technique for classification of different categories of Erythemato-squamous diseases. Neural network has been extensively used in many applications like classification, regression, web mining, system identification and pattern recognition. Weight optimization in neural network has been a matter of concern for researchers in the field of soft computing. In this paper the weights of ANN are optimized with GA. The proposed hybrid model is applied on the Erythemato-squamous dataset taken from UCI machine learning repository. The dataset contains six different categories: psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris of Erythemato-squamous diseases. The main aim of this paper is to determine the type of Eryhemato-Squamous disease using the hybrid model. The performance of the hybrid model is evaluated using statistical measures like accuracy, specificity and sensitivity. The accuracy of the proposed model is found to be 99.34% on test dataset. The experimental result shows the effectiveness of the hybrid model in classification of Erythematosquamous diseases.

Classification for Dermoscopy Images Using Convolutional Neural Networks Based on the Ensemble of Individual Advantage and Group Decision

Skin cancer is a common and deadly cancer. Dermoscopy is an effective tool for the observation of abnormal skin pigmentation. However, dermoscopy images are extremely complex and present great challenges for diagnosis. Therefore, we proposed a classification method based on the ensemble of individual advantage and group decision in dermoscopy images, including the ensemble strategy of group decision, the ensemble strategy of maximizing individual advantage, and the ensemble strategy of block-integrated voting. We used generative adversarial networks (GANs) to create a balanced sample space to better train convolutional neural networks (CNNs). Through transfer learning, the pre-training CNNs were used for fine-tuning, then the effects of different CNNs on the classification of different categories of dermoscopy images were compared, and the CNNs with better classification effect were selected for the ensemble of different strategies. This study is based on the ISIC 2018 dataset and ISIC 2019 dataset. Compared with the different individual CNNs and the frameworks, the proposed ensemble strategies achieve a better improvement in the evaluation criteria.

Methodological Principles of Poly-Artistic Education of a Person in the Integrated Education System

The article outlines the methodological principles of the artistic education of an individual in the system of integrated artistic education. It is proved that the methodological basis of the integrative processes in education is the philosophical, psychological and cultural conditions and concepts. The determinants of artistic and pedagogical integration necessary for the construction of the integration theory in the field of artistic education and poly-artistic upbringing of the personality are revealed. It is determined that the manifestation of the integration specificity in the field of artistic education is due to the action of psychophysiological mechanisms, and human-centeredness is the fundamental feature of didactic integration. The classification of different categories of integration in pedagogy and didactics is given. The influence of integrative processes on the consciousness and sensory-sensual sphere of a person is considered. It is determined that the psychological preconditions of integrated learning are the peculiarities of human thinking, which organically combines sensual, imaginative-figurative, rational-logical, and creative-intuitive aspects of cognition. The necessity for developing the content of integrated courses and their corresponding technological support is substantiated.

Adaptive metabolic pattern biomarker for disease monitoring and staging of lung cancer with liquid biopsy

In this manuscript, we demonstrate the applicability of a metabolic liquid biopsy for the monitoring and staging of patients with lung cancer. This method provides an unbiased detection strategy to establish a more precise correlation between CTC quantification and the actual burden of disease, therefore improving the accuracy of staging based on current imaging techniques. Also, by applying statistical analysis techniques and probabilistic models to the metabolic status and distribution of peripheral blood mononuclear cell (PBMC) populations “perturbed” by the presence of CTCs, a new category of adaptive metabolic pattern biomarker (AMPB) is described and unambiguously correlated to the different clinical stages of the patients. In fact, this strategy allows for classification of different categories of disease within a single stage (stage IV) before computed tomography (CT) and positron emission tomography (PET) scans and with lower uncertainty.

A Classification Model of Identifying Weeds Based on Chaotic Neural Network and the Empirical Mode Decomposition

This paper puts forward a classification method of imitating the human eyes to recognize image as a whole which combined chaotic neural network and the Empirical Mode Decomposition (EMD). The method takes the individual of weeds plant as the research object and utilizes the chaotic neural network, the EMD, Teager energy operator and cluster analysis technology comprehensively. Firstly take the two-dimensional gray image matrix as the chaotic neural network weight matrix directly. Use the chaotic neural network with n neurons expression to iterate and get the one-dimensional output curve. Secondly, make the EMD decomposition for the curve and get the corresponding intrinsic mode function (IMF) curves. Then make the Teager energy transformation for each IMF component and get the average Teager energy. Finally use the fuzzy clustering algorithm to cluster analyze and get the clustering results. It can realize the classification of different categories of weeds through analysis and contrast the results with the original images. The experiment proves the effectiveness of the proposed algorithm for classifying weeds, and it is a universal new method of weeds classification.

Video-based Vehicle Detection and Classification in Heterogeneous Traffic Conditions using a Novel Kernel Classifier

Vehicle classification in a traffic video is considered a difficult task due to similarity in appearances among different vehicles. This paper presents a real-time algorithm for detection and classification of different categories of vehicles in a heterogeneous traffic video. The processing of the video is done in four steps starting with camera calibration, vehicle detection, speed estimation, and classification. Vehicle detection is achieved by using background subtraction and blob tracking method. Speed of the detected vehicle is estimated by utilizing virtual start and stop line markers and calibration parameters. The vehicle classification is done by extracting multiple features of the detected vehicles which serve as input to a support vector machine based classifier. A histogram-based nonlinear kernel is used in the classifier. A combination of interest point detectors and low-level shape detectors as features was found to produce accurate and consistent results.

Learning and memory

Under various circumstances and in different species the outward expression of learning varies considerably, and this has led to the classification of different categories of learning. Just as there is no generally agreed on definition of learning, there is no one system of classification. Types of learning commonly recognized are: Habituation, sensitization, classical conditioning, operant conditioning, trial and error, taste aversion, latent learning, cultural learning, imprinting, insight learning, learning-set learning and instinct. The term memory must include at least two separate processes. It must involve, on the one hand, that of learning something and on the other, at some later date, recalling that thing. What lies between the learning and (he remembering must be some permanent record — a memory trace — within the brain. Memory exists in at least two forms: memory for very recent events (short-term) which is relatively labile and easily disruptable; and long-term memory, which is much more stable. Not everything that gets into short-term memory becomes fixed in the long-term store; a filtering mechanism selects things that might be important and discards the rest.