Feature Selection Effects on Gradient Descent Logistic Regression for Medical Data Classification

Kumar, P.Sunil; Gera, Pradeepini; Kamakshi, P.

doi:10.22266/ijies2019.1031.28

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…In generating a random data CUDA programming is very fast, i.e, the time taken for generating one lakh of data has took 0.05 second.In the second phase we have to fix the number of attributes, type of the attributes and label of the attributes. The third phase of the implementation the attributes are represented in the form of a decision tree [21,22]. Here decision tree is a binary tree, because we classify only completed data sets.In the fourth phase sorting the generated values and determining the split point for each numerical attribute is done.The fifth phase of implementation includes in modeling the decision tree-based classification rules which are used in GPU programming.…”

Section: Implementation and Resultsmentioning

confidence: 99%

Feature-Based Sentimental Analysis On Product Review System Using CUDA-BB Algorithm

2020

IJETER

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Sentiment analysis methods are classified as feature extraction method, i.e., view's, review's or sentence to predict the emotion of a sentence or a text using natural languages processing(NLP). The analysis of sentiment involves classification of the text into three phases "positive," "negative" or "The Neutral." It Analyzes the data and labels and classified as either "good" "better" "best" or "bad", "worse" based on the emotions or feeling given by customer and finally classified as positive and negative or neutral respectively. So, in regard for the past few years, The World Wide Web (WWW) has become a vast source in providing raw data which is is in the form of opinions or emotions or reviews given by the user or customer about a particular product. E -commerce, has grabbed the attention of the business people to improve the quality of their product by taking the review's from different Social media websites like Facebook , Twitter, Amazon, Flipkart, etc Sentimental analysis or Opinion mining is one of the major challenges of NLP (natural language processing).Business Analytics plays a very important role in the current scenario. In particular, these people rely on the feedback of their products given by the customer's to withstand the competition and knowledge mining that can give them an outstanding view of what to expect in the future..Classification and rule induction describes the main topics in the field of decision making and knowledge discovery. In this paper, we propose CUDABB (CUDA Bag-Boost) algorithm is used to find the overall star rating of Smartphone's using GPU parallel computing. This strategy not only reduces variance and bias but also this approach allows to produce better predictive performance compared to a single model. And the results obtained is compared with SLIQ and MMDBM using pycuda and GPU with computed acceleration rate(speedup) time using Amazon mobile review dataset. The aim of GPU mining technique is to enhance the execution speed with less handling time. Finally, we conclude that the proposed method achieves better accuracy.

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Section: Implementation and Resultsmentioning

confidence: 99%

Feature-Based Sentimental Analysis On Product Review System Using CUDA-BB Algorithm

2020

IJETER

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“…Let us use a simple example to make it easier to understand. Suppose we have a model which has to predict whether a review given by a person is positive or negative [ 30 ]. About COVID-19 (completely fictitious), based on the review, we calculate the polarity of that review by considering the coefficients of b0 = −0.05 and b1 = 0.05.…”

Section: Proposed Methodsmentioning

confidence: 99%

Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model

Pathuri

Anbazhagan

Joshi

et al. 2021

Sensors

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The COVID-19 pandemic has spread to almost all countries of the World and affected people both mentally and economically. The primary motivation of this research is to construct a model that takes reviews or evaluations from several people who are affected with COVID-19. As the number of cases has accelerated day by day, people are becoming panicked and concerned about their health. A good model may be helpful to provide accurate statistics in interpreting the actual records about the pandemic. In the proposed work, for sentimental analysis, a unique classifier named the Sentimental DataBase Miner algorithm (SADBM) is used to categorize the opinions and parallel processing, and is applied on the data collected from various online social media websites like Twitter, Facebook, and Linkedin. The accuracy of the proposed model is validated with trained data and compared with basic classifiers, such as logistic regression and decision tree . The proposed algorithm is executed on CPU as well as GPU and calculated the acceleration ratio of the model. The results show that the proposed model provides the best accuracy compared with the other two models, i.e., 96% (GPU).

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“…The third and important approach is the usage of GAN models, an unsupervised technique that can generate new images by learning from the patterns that exist in between the input image. GAN's consists of two components: generator to create new images by training the model and discriminator for classification purpose [12,13]. In GAN's, the generator takes input as a fixed-length vector, which is known as "Noise Vector" for producing the salt and pepper noise images because most of the plants have smoked layer above them.…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation Techniques on Chilly Plants to Classify Healthy and Bacterial Blight Disease Leaves

Govathoti¹,

Reddy²,

Kamidi³

et al. 2022

IJACSA

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Designing an automation system for the agriculture sector is difficult using machine learning approach. So many researchers proposed deep learning system which requires huge amount of data for training the system. The proposed system suggests that geometric transformations on the original dataset help the system to generate more images that can replicate the physical circumstances. This process is known as "Image Augmentation". This enhancement of data helps the system to produce more accurate systems in terms of all metrics. In olden days when researchers work with machine learning techniques they used to implement traditional approaches which are a time consuming and expensive process. In deep learning, most of the operations are automatically taken care by the system. So, the proposed system applies neural style and to classify the images it uses the concept of transfer learning. The system utilizes the images available in the open source repository known as "Kaggle", this majorly consists of images related to chilly, tomato and potato. But this system majorly focuses on chilly plants because it is most productive plant in the South Indian regions. Image augmentation creates new images in different scenarios using the existing images and by applying popular deep learning techniques. The model has chosen ResNet-50, which is a pretrained model for transfer learning. The advantage of using pretrained model lies in not to develop the model from scratch. This pre-trained model gives more accuracy with less number of epochs. The model has achieved an accuracy of "100%".

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Feature Selection Effects on Gradient Descent Logistic Regression for Medical Data Classification

Cited by 7 publications

References 20 publications

Feature-Based Sentimental Analysis On Product Review System Using CUDA-BB Algorithm

Feature-Based Sentimental Analysis On Product Review System Using CUDA-BB Algorithm

Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model

Data Augmentation Techniques on Chilly Plants to Classify Healthy and Bacterial Blight Disease Leaves

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