Utilizing domain-based features to improve classification accuracy of biomedical text having bacterial associations

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

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Characterization of Ischemic Stroke in CT Images using Image Processing

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i9.405 ◽

2017 ◽

Vol 7 (9) ◽

pp. 18

Author(s):

Amal Alzain ◽

Suhaib Alameen ◽

Rani Elmaki ◽

Mohamed E. M. Gar-Elnabi

Keyword(s):

Ischemic Stroke ◽

White Matter ◽

Classification Accuracy ◽

Ct Images ◽

Gray Level ◽

Level Variation ◽

Interactive Data ◽

The Brain ◽

Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

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Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i7.88 ◽

2017 ◽

Vol 7 (7) ◽

pp. 8

Author(s):

Mona E. Elbashier ◽

Suhaib Alameen ◽

Caroline Edward Ayad ◽

Mohamed E. M. Gar-Elnabi

Keyword(s):

Size Distribution ◽

Texture Analysis ◽

Classification Accuracy ◽

Ct Images ◽

Diabetic Patients ◽

Gray Level ◽

Grey Level ◽

Run Length ◽

Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

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Enhancement of Data Classification Accuracy using Bagging Technique in Random Forest

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i8.185188 ◽

2019 ◽

Vol 7 (8) ◽

pp. 185-188

Author(s):

Vikas S. ◽

Thimmaraju S.N.

Keyword(s):

Random Forest ◽

Classification Accuracy ◽

Data Classification

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Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2213111607666190215120017 ◽

2020 ◽

Vol 13 (2) ◽

pp. 153-164

Author(s):

Narina Thakur ◽

Deepti Mehrotra ◽

Abhay Bansal ◽

Manju Bala

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Time Window ◽

Optimal Solution ◽

Similarity Score ◽

Learning Objects ◽

Retrieval Algorithm ◽

Support Vector ◽

Computationally Efficient ◽

User Query

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm

Procedia Computer Science ◽

10.1016/j.procs.2018.11.111 ◽

2018 ◽

Vol 145 ◽

pp. 488-494 ◽

Cited By ~ 3

Author(s):

Aleksandr Sboev ◽

Alexey Serenko ◽

Roman Rybka ◽

Danila Vlasov ◽

Andrey Filchenkov

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Classification Accuracy ◽

Spiking Neural Network ◽

Network Parameters

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An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666181220124756 ◽

2019 ◽

Vol 21 (9) ◽

pp. 631-645 ◽

Cited By ~ 5

Author(s):

Saeed Ahmed ◽

Muhammad Kabir ◽

Zakir Ali ◽

Muhammad Arif ◽

Farman Ali ◽

...

Keyword(s):

Gene Expression ◽

Feature Selection ◽

Gene Expression Data ◽

Classification Accuracy ◽

Early Stage ◽

Small Sample Size ◽

Feature Selection Method ◽

Small Sample ◽

Expression Data ◽

Base Function

Aim and Objective: Cancer is a dangerous disease worldwide, caused by somatic mutations in the genome. Diagnosis of this deadly disease at an early stage is exceptionally new clinical application of microarray data. In DNA microarray technology, gene expression data have a high dimension with small sample size. Therefore, the development of efficient and robust feature selection methods is indispensable that identify a small set of genes to achieve better classification performance. Materials and Methods: In this study, we developed a hybrid feature selection method that integrates correlation-based feature selection (CFS) and Multi-Objective Evolutionary Algorithm (MOEA) approaches which select the highly informative genes. The hybrid model with Redial base function neural network (RBFNN) classifier has been evaluated on 11 benchmark gene expression datasets by employing a 10-fold cross-validation test. Results: The experimental results are compared with seven conventional-based feature selection and other methods in the literature, which shows that our approach owned the obvious merits in the aspect of classification accuracy ratio and some genes selected by extensive comparing with other methods. Conclusion: Our proposed CFS-MOEA algorithm attained up to 100% classification accuracy for six out of eleven datasets with a minimal sized predictive gene subset.

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