scholarly journals CPIDM: A Clustering-Based Profound Iterating Deep Learning Model for HSI Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kriti Mahajan ◽  
Urvashi Garg ◽  
Mohammad Shabaz
Keyword(s):  
Deep Learning ◽  
Interplanetary Space ◽  
Direct Method ◽  
Three Dimensional ◽  
Learning Model ◽  
Unsupervised Segmentation ◽  
Learning Techniques ◽  
Neuro Fuzzy ◽  
Segmentation Approach ◽  
Deep Learning Model

The existing work on unsupervised segmentation frequently does not present any statistical extent to estimating and equating procedures, gratifying a qualitative calculation. Furthermore, regardless of the datum that enormous research is dedicated to the advancement of a novel segmentation approach and upgrading the deep learning techniques, there is an absence of research comprehending the assessment of eminent conventional segmentation methodologies for HSI. In this paper, to moderately fill this gap, we propose a direct method that diminishes the issues to some extent with the deep learning methods in the arena of a HSI space and evaluate the proposed segmentation techniques based on the method of the clustering-based profound iterating deep learning model for HSI segmentation termed as CPIDM. The proposed model is an unsupervised HSI clustering technique centered on the density of pixels in the spectral interplanetary space and the distance concerning the pixels. Furthermore, CPIDM is a fully convolutional neural network. In general, fully convolutional nets remain spatially invariant preventing them from modeling position-reliant outlines. The proposed network maneuvers this by encompassing an innovative position inclined convolutional stratum. The anticipated unique edifice of deep unsupervised segmentation deciphers the delinquency of oversegmentation and nonlinearity of data due to noise and outliers. The spectrum efficacy is erudite and incidental from united feedback via deep hierarchy with pooling and convolutional strata; as a consequence, it formulates an affiliation among class dissemination and spectra along with three-dimensional features. Moreover, the anticipated deep learning model has revealed that it is conceivable to expressively accelerate the segmentation process without substantive quality loss due to the existence of noise and outliers. The proposed CPIDM approach outperforms many state-of-the-art segmentation approaches that include watershed transform and neuro-fuzzy approach as validated by the experimental consequences.

scholarly journals Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Noha E. El-Attar ◽  
Mohamed K. Hassan ◽  
Othman A. Alghamdi ◽  
Wael A. Awad
Keyword(s):  
Deep Learning ◽  
Essential Oil ◽  
Biological Activities ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Chemical Compositions ◽  
Linear Processes ◽  
Learning Techniques ◽  
Important Trend ◽  
Deep Learning Model

AbstractReliance on deep learning techniques has become an important trend in several science domains including biological science, due to its proven efficiency in manipulating big data that are often characterized by their non-linear processes and complicated relationships. In this study, Convolutional Neural Networks (CNN) has been recruited, as one of the deep learning techniques, to be used in classifying and predicting the biological activities of the essential oil-producing plant/s through their chemical compositions. The model is established based on the available chemical composition’s information of a set of endemic Egyptian plants and their biological activities. Another type of machine learning algorithms, Multiclass Neural Network (MNN), has been applied on the same Essential Oils (EO) dataset. This aims to fairly evaluate the performance of the proposed CNN model. The recorded accuracy in the testing process for both CNN and MNN is 98.13% and 81.88%, respectively. Finally, the CNN technique has been adopted as a reliable model for classifying and predicting the bioactivities of the Egyptian EO-containing plants. The overall accuracy for the final prediction process is reported as approximately 97%. Hereby, the proposed deep learning model could be utilized as an efficient model in predicting the bioactivities of, at least Egyptian, EOs-producing plants.

scholarly journals Diabetes Prediction Using Enhanced SVM and Deep Neural Network Learning Techniques

2021 ◽  
Vol 16 (4) ◽  
pp. 1-20
Author(s):  
P. Nagaraj ◽  
P. Deepalakshmi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Model ◽  
Support Vector ◽  
Neural Network Learning ◽  
Learning Techniques ◽  
Diabetes Prediction ◽  
Deep Learning Model

Diabetes, caused by the rise in level of glucose in blood, has many latest devices to identify from blood samples. Diabetes, when unnoticed, may bring many serious diseases like heart attack, kidney disease. In this way, there is a requirement for solid research and learning model’s enhancement in the field of gestational diabetes identification and analysis. SVM is one of the powerful classification models in machine learning, and similarly, Deep Neural Network is powerful under deep learning models. In this work, we applied Enhanced Support Vector Machine and Deep Learning model Deep Neural Network for diabetes prediction and screening. The proposed method uses Deep Neural Network obtaining its input from the output of Enhanced Support Vector Machine, thus having a combined efficacy. The dataset we considered includes 768 patients’ data with eight major features and a target column with result “Positive” or “Negative”. Experiment is done with Python and the outcome of our demonstration shows that the deep Learning model gives more efficiency for diabetes prediction.

scholarly journals Using deep learning for trajectory classification in imbalanced dataset

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Nicksson Ckayo Arrais de Freitas ◽  
Ticiana L. Coelho Da Silva ◽  
José Antônio Fernandes De Macêdo ◽  
Leopoldo Melo Júnioer
Keyword(s):  
Deep Learning ◽  
Classification Problem ◽  
Learning Model ◽  
Learning Approaches ◽  
Imbalanced Dataset ◽  
Imbalanced Datasets ◽  
Learning Techniques ◽  
Trajectory Classification ◽  
Low Performance ◽  
Deep Learning Model

Deep learning has gained much popularity in the past years due to GPU advancements, cloud computing improvements, and its supremacy, considering the accuracy results when trained on massive datasets. As with machine learning, deep learning models may experience low performance when handled with imbalanced datasets. In this paper, we focus on the trajectory classification problem, and we examine deep learning techniques for coping with imbalanced class data. We extend a deep learning model, called DeepeST (Deep Learning for Sub-Trajectory classification), to predict the class or label for sub-trajectories from imbalanced datasets. DeepeST is the first deep learning model for trajectory classification that provides approaches for coping with imbalanced dataset problems from the authors' knowledge. In this paper, we perform the experiments with three real datasets from LBSN (Location-Based Social Network) trajectories to identify who is the user of a sub-trajectory (similar to the Trajectory-User Linking problem). We show that DeepeST outperforms other deep learning approaches from state-of-the-art concerning the accuracy, precision, recall, and F1-score.

scholarly journals Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification

2021 ◽  
Vol 11 (24) ◽  
pp. 11659
Author(s):  
Sheng-Chieh Hung ◽  
Hui-Ching Wu ◽  
Ming-Hseng Tseng
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Quality ◽  
Learning Model ◽  
Image Features ◽  
Quality Enhancement ◽  
Scene Classification ◽  
Image Quality Enhancement ◽  
Learning Techniques ◽  
Deep Learning Model

Through the continued development of technology, applying deep learning to remote sensing scene classification tasks is quite mature. The keys to effective deep learning model training are model architecture, training strategies, and image quality. From previous studies of the author using explainable artificial intelligence (XAI), image cases that have been incorrectly classified can be improved when the model has adequate capacity to correct the classification after manual image quality correction; however, the manual image quality correction process takes a significant amount of time. Therefore, this research integrates technologies such as noise reduction, sharpening, partial color area equalization, and color channel adjustment to evaluate a set of automated strategies for enhancing image quality. These methods can enhance details, light and shadow, color, and other image features, which are beneficial for extracting image features from the deep learning model to further improve the classification efficiency. In this study, we demonstrate that the proposed image quality enhancement strategy and deep learning techniques can effectively improve the scene classification performance of remote sensing images and outperform previous state-of-the-art approaches.

scholarly journals Object or Background: An Interpretable Deep Learning Model for Covid-19 Detection from CT-Scan Images

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1732
Author(s):  
Gurmail Singh ◽  
Kin-Choong Yow
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Learning Model ◽  
Chest Ct ◽  
High Stake ◽  
Reasoning Process ◽  
Economic Problems ◽  
Learning Techniques ◽  
Deep Learning Model ◽  
Chest Ct Scan

The new strains of the pandemic Covid-19 are still looming. It is important to develop multiple approaches for timely and accurate detection of Covid-19 and its variants. Deep learning techniques are well proved for their efficiency in providing solutions to many social and economic problems. However, the transparency of the reasoning process of a deep learning model related to a high stake decision is a necessity. In this work, we propose an interpretable deep learning model Ps-ProtoPNet to detect Covid-19 from the medical images. Ps-ProtoPNet classifies the images by recognizing the objects rather than their background in the images. We demonstrate our model on the dataset of the chest CT-scan images. The highest accuracy that our model achieves is 99.29%

scholarly journals Arabic Handwriting Classification using Deep Transfer Learning Techniques

2022 ◽  
Vol 30 (1) ◽  
pp. 641-654
Author(s):  
Ali Abd Almisreb ◽  
Nooritawati Md Tahir ◽  
Sherzod Turaev ◽  
Mohammed A. Saleh ◽  
Syed Abdul Mutalib Al Junid
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Learning Transfer ◽  
Learning Model ◽  
Text Recognition ◽  
Suitable Model ◽  
Learning Models ◽  
Recognition Algorithms ◽  
Learning Techniques ◽  
Deep Learning Model

Arabic handwriting is slightly different from the handwriting of other languages; hence it is possible to distinguish the handwriting written by the native or non-native writer based on their handwriting. However, classifying Arabic handwriting is challenging using traditional text recognition algorithms. Thus, this study evaluated and validated the utilisation of deep transfer learning models to overcome such issues. Hence, seven types of deep learning transfer models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were used to determine the most suitable model for classifying the handwritten images written by the native or non-native. Two datasets comprised of Arabic handwriting images were used to evaluate and validate the newly developed deep learning models used to classify each model’s output as either native or foreign (non-native) writers. The training and validation sets were conducted using both original and augmented datasets. Results showed that the highest accuracy is using the GoogleNet deep learning model for both normal and augmented datasets, with the highest accuracy attained as 93.2% using normal data and 95.5% using augmented data in classifying the native handwriting.

scholarly journals A Deep Learning Model for Predicting Tumor Suppressor Genes and Oncogenes from PDB Structure

2017 ◽  
Author(s):  
Amirhossein Tavanaei ◽  
Nishanth Anandanadarajah ◽  
Anthony Maida ◽  
Rasiah Loganantharaj
Keyword(s):  
Deep Learning ◽  
Tumor Suppression ◽  
Protein Structures ◽  
Three Dimensional ◽  
Learning Model ◽  
Dimensional Structure ◽  
Regulatory Function ◽  
Cancer Genes ◽  
Feature Map ◽  
Deep Learning Model

AbstractWhile cancer is a heterogeneous complex of distinct diseases, the common underlying mechanism for uncontrolled tumor growth is due to mutations in proto-oncogenes and the loss of the regulatory function of tumor suppression genes. In this paper we propose a novel deep learning model for predicting tumor suppression genes (TSGs) and proto-oncogenes (OGs) from their Protein Data Bank (PDB) three dimensional structures. Specifically, we develop a convolutional neural network (CNN) to classify the feature map sets extracted from the tertiary protein structures. Each feature map set represents particular biological features associated with the atomic coordinates appearing on the outer surface of protein’s three dimensional structure. The experimental results on the collected dataset for classifying TSGs and OGs demonstrate promising performance with 82.57% accuracy and 0.89 area under ROC curve. The initial success of the proposed model warrants further study to develop a comprehensive model to identify the cancer driver genes or events using the principle cancer genes (TSG and OG).

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