scholarly journals Efficient Fruits Classification Using Convolutional Neural Network

2021 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
ADNAN ADNAN ABIDIN ◽  
Hamzah Hamzah ◽  
Marselina Endah
Keyword(s):  
Good Accuracy ◽  
Research Topic ◽  
Classification Model ◽  
Classification Methods ◽  
Proposed Model ◽  
Traditional Classification ◽  
Promising Solution ◽  
Learning In Computer Vision ◽  
Effective Result

Classification of fruits is a growing research topic in image processing. Various papers propose various techniques to deal with the classification of apples. However, some traditional classification methods remain drawbacks to producing an effective result with the big dataset. Inspired by deep learning in computer vision, we propose a novel learning method to construct a classification model, which can classify types of apples quickly and accurately. To conduct our experiment, we collect datasets, do preprocessing, train our model, tune parameter settings to get the highest accuracy results, then test the model using new data. Based on the experimental results, the classification model of green apples and red apples can obtain good accuracy with little loss. Therefore, the proposed model can be a promising solution to deal with apple classification.

Effective Classification of Melting Curve in Real-Time PCR based on Dynamic Filter-based Convolutional Neural Network

2021 ◽  
Vol 16 ◽  
Author(s):  
Di Gai ◽  
Xuanjing Shen ◽  
Haipeng Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Melting Curve ◽  
Classification Model ◽  
Proposed Model ◽  
Unbalanced Dataset ◽  
Manual Selection ◽  
Low Efficiency ◽  
Dynamic Filter

Background: The effective classification of the melting curve is conducive to measure the specificity of the amplified products and the influence of invalid data on subsequent experiments is excluded. Objective: In this paper, a convolutional neural network (CNN) classification model based on dynamic filter is proposed, which can categorize the number of peaks in the melting curve image and distinguish the pollution data represented by the noise peaks. Method: The main advantage of the proposed model is that it adopts the filter which changes with the input and uses the dynamic filter to capture more information in the image, making the network learning more accurate. In addition, the residual module is used to extract the characteristics of the melting curve, and the pooling operation is replaced with an atrous convolution to prevent the loss of context information. Result: In order to train the proposed model, a novel melting curve dataset is created, which includes a balanced dataset and an unbalanced dataset. The proposed method uses six classification-based assessment criteria to compare with seven representative methods based on deep learning. Experimental results show that proposed method is not only markedly outperforms the other state-of-the-art methods in accuracy, but also has much less running time. Conclusion: It evidently proves that the proposed method is suitable for judging the specificity of amplification products according to the melting curve. Simultaneously, it overcomes the difficulties of manual selection with low efficiency and artificial bias.

scholarly journals The Classificatied Prediction of Coronary Heart Disease Based on Patient Similarity Analysis

2021 ◽  
Author(s):  
Xinyao LI ◽  
Linlin ZHANG ◽  
Xuehua BI ◽  
Ying ZHANG ◽  
Guanglei YU ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Predictive Models ◽  
Distance Matrix ◽  
Clinical Decision ◽  
Medical Database ◽  
Traditional Classification ◽  
Promising Solution ◽  
Structured Representation

Abstract Objective:It is important for physicians' clinical decision support to classify the coronary heart disease (CHD).Customizing personalized predictive models for patients requires selecting a patient group from an existing medical database that most closely resembles the indexed patients. In this study,we introduce a new concept that using the patient similarity for the classification of patient with CHD.Materials and methods: We performed a structured representation of CHD patients. Obtain the multidimensional attribute distance matrix between patient pairs by calculating the multidimensional attribute distance of the patients. Predict similarity between patient pairs using machine learning (ML) models to predict clinical outcomes for indexed patients based on matched similar patients.Results:The new measure shows marked improvements over the traditional classification measures. LightGBM is the top-performing ML model. The best model achieved 88.52% accuracy.Conclusion:The medical applications of ML supported by similarity analytics represent a promising solution through which to reduce the physican workload to achieve the goal of “precision medicine”.

scholarly journals Research on Classification of Tibetan Medical Syndrome in Chronic Atrophic Gastritis

2019 ◽  
Vol 9 (8) ◽  
pp. 1664 ◽  
Author(s):  
Xiaolan Zhu ◽  
Lei Zhang ◽  
Yuan Zhang ◽  
Lu Wang ◽  
Shiying Wang ◽  
...  
Keyword(s):  
Atrophic Gastritis ◽  
Association Rules ◽  
Chronic Atrophic Gastritis ◽  
Classification Model ◽  
Apriori Algorithm ◽  
Time Cost ◽  
Medical Clinics ◽  
Traditional Classification ◽  
The Common

Classification association rules that integrate association rules with classification are playing an important role in data mining. However, the time cost on constructing the classification model, and predicting new instances, will be long, due to the large number of rules generated during the mining of association rules, which also will result in the large system consumption. Therefore, this paper proposed a classification model based on atomic classification association rules, and applied it to construct the classification model of a Tibetan medical syndrome for the common plateau disease called Chronic Atrophic Gastritis. Firstly, introduce the idea of “relative support”, and use the constraint-based Apriori algorithm to mine the strong atomic classification association rules between symptoms and syndrome, and the knowledge base of Tibetan medical clinics will be constructed. Secondly, build the classification model of the Tibetan medical syndrome after pruning and prioritizing rules, and the idea of “partial classification” and “first easy to post difficult” strategy are introduced to realize the prediction of this Tibetan medical syndrome. Finally, validate the effectiveness of the classification model, and compare with the CBA algorithm and four traditional classification algorithms. The experimental results showed that the proposed method can realize the construction and classification of the classification model of the Tibetan medical syndrome in a shorter time, with fewer but more understandable rules, while ensuring a higher accuracy with 92.8%.

scholarly journals Reliable Classification of FAQs with Spelling Errors Using an Encoder-Decoder Neural Network in Korean

2019 ◽  
Vol 9 (22) ◽  
pp. 4758 ◽  
Author(s):  
Youngjin Jang ◽  
Harksoo Kim
Keyword(s):  
Neural Network ◽  
Domain Knowledge ◽  
Classification Model ◽  
Word Embeddings ◽  
Spelling Errors ◽  
Proposed Model ◽  
Sentence Classification ◽  
Improved Performance ◽  
Reliable Classification

To resolve lexical disagreement problems between queries and frequently asked questions (FAQs), we propose a reliable sentence classification model based on an encoder-decoder neural network. The proposed model uses three types of word embeddings; fixed word embeddings for representing domain-independent meanings of words, fined-tuned word embeddings for representing domain-specific meanings of words, and character-level word embeddings for bridging lexical gaps caused by spelling errors. It also uses class embeddings to represent domain knowledge associated with each category. In the experiments with an FAQ dataset about online banking, the proposed embedding methods contributed to an improved performance of the sentence classification. In addition, the proposed model showed better performance (with an accuracy of 0.810 in the classification of 411 categories) than that of the comparison model.

Classification of breast cancer types based on deep learning approach

2021 ◽  
Vol 10 (1) ◽  
pp. 122-132
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Classification Model ◽  
Microscopic Image ◽  
Cancer Disease ◽  
Microscopic Images ◽  
Cad System ◽  
Proposed Model ◽  
Cancer Types

Breast cancer is one of the most serious diseases that affect women, so it must be discovered in the early stages to avoid complications such as redness of the skin, pain in the armpits or breast, and discharge from a nipple, possibly containing blood. Recently, the CAD system that is based on the classification of microscopic image play a vital rule to limit cancer disease and reduce cases. Microscopic image is the currently recommended image system used to detect cancer. A computer-aided diagnosis system will help radiologists to accurately detection of cancerous cells and achieve the best result. This paper proposes a deep learning technique that exploits CAD system features and microscopic images to fight breast cancer. The proposed technique builds a classification model based on the DenseNet-161 deep learning method. The proposed model classifies the microscopic images of breast cancer into benign with four types and malignant with four types. Our proposed technique is experimentally tested and the result confirmed that a proposed technique outperforms baseline techniques.

scholarly journals Extraction and Classification of Mouth Shape Features in Oral English Teaching Based on Image Processing

2021 ◽  
Vol 38 (4) ◽  
pp. 1013-1021
Author(s):  
Qian Zhang ◽  
Liyan Xiao ◽  
Yanfang Shi
Keyword(s):  
Image Processing ◽  
English Learners ◽  
Classification Model ◽  
Shape Features ◽  
Shape Identification ◽  
English Teaching ◽  
Video Frames ◽  
Proposed Model ◽  
English Speaking

Mouth shape identification helps oral English learners discover the features of their lip movements in English speaking, and correct their pronunciation more smoothly. So far, few scholars have applied image processing to identify mouth shape features of oral English learners. Most studies consider little about environmental factors, and ignore the changing mouth shape in pronunciation. Therefore, this paper explores the extraction and classification of mouth shape features in oral English teaching based on image processing. Firstly, an extraction and classification model were established for mouth shape features in oral English teaching. Then, the mouth shape images of oral English teaching were preprocessed. After that, the authors segmented the lips in oral English video frames based on neural network, extracted the lip boundaries from the said frames, and fitted them into curves. The proposed model was proved effective through experiments.

scholarly journals An Efficient Model for TV broadcast Audio Classification through InceptionV3 and ResNet50

2020 ◽  
Vol 9 (5) ◽  
pp. 2234-2238
Keyword(s):  
Classification Model ◽  
Audio Classification ◽  
Development Environment ◽  
Data Set ◽  
Novel Approach ◽  
Proposed Model ◽  
Audio Data ◽  
Data Surveillance ◽  
Voice Data

In the recent advancements of applications, one of the challenging task in many gadgets are incorporated, which is based on audio classification and recognition. A set of emotion detection after post-surgical issues, classification of various voice sequence, classification of random voice data, surveillance and speaker detection audio data act as a crucial input. Most of the audio data is inherent with the environmental noise or instrumental noise. Extracting the unique features from the audio data is very important to determine the speaker effectively. Such kind of a novel idea is evaluated here. The research focus is based on classification of TV broadcast audios in which the type of audio is being class separated through a novel approach. The design evaluates, the five different categories of audio data such as advertisement, news, songs, cartoon and sports from the data collected using the TV tuner card. The proposed design associated with python as a Development environment. The audio samples are converted to images using Spectrogram and then transfer learning is applied on the pretrained models ResNet50 and Inceptionv3 to extract the deep features and to classify the audio data. Inception V3 is compared here with the ResNet50 to get greater accuracy in classification. The pre-trained models are models that was trained on the ImageNet data set for a certain task and are used here to quick train the audio classification model on training set with high accuracy. The proposed model produces accuracy of 94% for Inceptionv3 which gives greater accuracy when compared with the ResNet50 which gives 93%. accuracy.

scholarly journals A Deep Learning-Based Text Classification of Adverse Nursing Events

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenjing Lu ◽  
Wei Jiang ◽  
Na Zhang ◽  
Feng Xue
Keyword(s):  
Deep Learning ◽  
Health System ◽  
Medical Care ◽  
Text Classification ◽  
Rapid Development ◽  
Classification Model ◽  
Diagnosis And Treatment ◽  
Event Analysis ◽  
Classification Methods

Adverse nursing events occur suddenly, unpredictably, or unexpectedly during course of clinical diagnosis and treatment processes in the hospitals. These events adversely affect the patient’s diagnosis and treatment results and even increase the patient’s pain and burden. Additionally, It is high likely to cause accidents and disputes and affect normal medical work and personnel safety and is not conducive to the development of the health system. Due to the rapid development of modern medicine, health and safety of patients have become the most concerned issue in society and patient safety is an important part of medical care management. Research and events have shown that classified management of adverse nursing events, event analysis, and improvement measures are beneficial, specifically to the health system, to continuously improve the quality of medical care and reduce the occurrence of adverse nursing events. In the management of adverse nursing events, it is very important to categorize the text reports of adverse nursing events and divide these into different categories and levels. Traditional reports of adverse nursing events are mostly unstructured and simple data, often relying on manual classification, which is difficult to analyze. Furthermore, data is relatively inaccurate and practical reference significance is not obvious. In this paper, we have extensively evaluated various deep learning-based classification methods which are specifically designed for the healthcare systems. It becomes possible with the development of science and technology; text classification methods based on deep learning are gradually entering people’s field of vision. Additionally, we have proposed a text classification model for adverse nursing events in the health system. Experiments and data comparison test of both the proposed deep learning-based method and existing methods in the text classification of nursing adverse events effect are performed. These results show the exceptional performance of the proposed mechanism in terms of various evaluation metrics.

scholarly journals A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhiyu Tao ◽  
Yanjuan Li ◽  
Zhixia Teng ◽  
Yuming Zhao
Keyword(s):  
Computer Technology ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Subject Area ◽  
Transport Proteins ◽  
Research Topic ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Classification Methods ◽  
Classification Result

With the development of computer technology, many machine learning algorithms have been applied to the field of biology, forming the discipline of bioinformatics. Protein function prediction is a classic research topic in this subject area. Though many scholars have made achievements in identifying protein by different algorithms, they often extract a large number of feature types and use very complex classification methods to obtain little improvement in the classification effect, and this process is very time-consuming. In this research, we attempt to utilize as few features as possible to classify vesicular transportation proteins and to simultaneously obtain a comparative satisfactory classification result. We adopt CTDC which is a submethod of the method of composition, transition, and distribution (CTD) to extract only 39 features from each sequence, and LibSVM is used as the classification method. We use the SMOTE method to deal with the problem of dataset imbalance. There are 11619 protein sequences in our dataset. We selected 4428 sequences to train our classification model and selected other 1832 sequences from our dataset to test the classification effect and finally achieved an accuracy of 71.77%. After dimension reduction by MRMD, the accuracy is 72.16%.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

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