scholarly journals Machine Learning Techniques for Prediction of Lung Cancer

2020 ◽  
Vol 8 (6) ◽  
pp. 241-249
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Data Mining ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Machine Learning Techniques ◽  
Image Dataset

Lung cancer has been one of the deadliest diseases in today’s decades. It has become one of the causes of death in both man and woman. There are various reasons for which lung cancer occurs but classification of tumor and predicting it in the right stage is the most important part. This paper focused on the numerous approaches has been derived for lung cancer detection from different literature survey to advance the ability of detection of cancer. Digital image processing and data mining both are equally important because for prediction either image dataset or statistical dataset is used so for pre-processing the image dataset digital image processing is applied for statistical dataset data mining is applied. After pre-processing, segmentation and feature extraction we apply various machine learning algorithm for the prediction of lung cancer. So first we have provided a sketch of Machine learning and then various fields like in image data or statistical data where machine learning has been used for classification. Once the classification is done confusion matrix is generated for calculating accuracy, sensitivity, precision, these method is used to measure the rate of accuracy of the proposed model.

scholarly journals HEURISTIC ALGORITHM FOR PET IMAGES’ SEGMENTATION USING ARTIFICIAL INTELIGENCE TECHNIQUES

Anales AFA ◽  
2021 ◽  
Vol 31 (4) ◽  
pp. 165-171
Author(s):  
I. E. Scarinci ◽  
◽  
P. Pérez ◽  
M. Valente ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Nuclear Medicine ◽  
Digital Image Processing ◽  
Heuristic Algorithm ◽  
Digital Image ◽  
Data Science ◽  
Machine Learning Techniques ◽  
Patient Specific ◽  
Unsupervised Segmentation

The overall quantity of nuclear medicine procedures has increased remarkably in recent years, making them a daily tool capable of reaching wide sectors of the population. Regarding the nuclear medicine therapeutic applications, it is worth noting that there is an increasing demand of novel techniques and greater variety of radioisotopes requiring accurate patient-specific dosimetry aimed at evaluating lethal damage to the tumor while maintaining acceptable dose levels in healthy tissues. Image-guided internal dosimetry appears as particularly suitable for theranostics procedures, which allow the joint implementation of diagnose and treatment. In this case, the correct segmentation of the images is critical for the identification of different tissues and organs. On the other hand, modern tools based on data science and artificial intelligence have spread in several fields, particularly in the digital image processing. The use of machine learning models for digital image processing appears as a promising opportunity to complement clinical analysis by experts. This paper reports about an unsupervised segmentation heuristic algorithm using clustering and machine learning techniques together, based on the use of two algorithms: K-Means and HDBSCAN. The results obtained highlight the capacity of automatic segmentation by means of clustering algorithms, becoming a useful tool to assist clinician experts and shorten the segmentation times.

scholarly journals IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON

2021 ◽  
Vol 4 (1) ◽  
pp. 29-39
Author(s):  
I Gede Rusdy Mahayana Putra ◽  
Made Windu Antara Kesiman ◽  
Gede Aditra Pradnyana ◽  
I Made Dendi Maysanjaya
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Multilayer Perceptron ◽  
Learning Algorithm ◽  
Gabor Filter ◽  
Confusion Matrix ◽  
Identification Accuracy ◽  
Average Accuracy ◽  
Fold Cross Validation

Balinese ornament carving are a cultural heritage that is owned by especially the Balinese people. However, especially Balinese people only know the shape of the carving without knowing the name and characteristics of the Balinese traditional carving ornaments. Based on these problems, the researchers have a solution to research about Balinese Ornament Carving Identification by utilizing digital image processing technology. In this study uses Gabor Filter as a feature extraction from the carved image that used and Multilayer Perceptron as a classifier. There are 18 (eighteen) classes of Balinese carving ornaments use in this study with a total of dataset is 268 (two hundred and sixty eight). The purpose of this study was to determine the level of identification  accuracy  of Balinese ornament carving with Multilayer Perceptron method. In the implementation using digital image processing technic with Multilayer Perceptron method was based on backpropagation learning algorithm with 10560 neuron input layers, 50 neuron hidden layers, and 18 neuron output layers as classifier obtained the accuracy for testing is 43%. Classification testing based on k-fold cross validation with K=5 results in average accuracy of 41.14% with optimum accuracy of 56% and accuracy testing with Confusion Matrix obtained the accuracy 43.3%, sensitivity 42.68% and specificity 96.87%. 

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals A Review on Automatic Soil Classification in Digital Image Processing

2021 ◽  
Vol 9 (VIII) ◽  
pp. 449-455
Author(s):  
Shraddha Shivhare
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
False Alarm ◽  
Digital Image Processing ◽  
Digital Image ◽  
Processing Time ◽  
Soil Classification ◽  
Research Papers ◽  
Digital World ◽  
Precision Rate

Soil classification is an essential piece of geology. However, many examinations have assessed the precision and consistency of the soil classification using various techniques. This examination starts by evaluating the verifiable advancement of soil classification science. The verifiable audit contextualizes the wordings and the speculations of soil development factors, which supported soil classification frameworks. This paper is intended to review some research papers on soil classification and analyze the limitations of implemented techniques by their parameters. In the age of digital world, it is beneficial to obtain the information from image without any hassle. Machine learning is an approach through which we can obtain the better level of accuracy and minimize the false alarm rate. But machine learning requires so many samples through which we can observe the correct precision that also requires much storage that may takes much processing time that reduces the feasibility of the system. We have to train a system with limited number of samples with high iterations that produces higher precision rate with minimal errors.

scholarly journals Application of Machine Learning for Prediction of Lung Cancer using Omics Data

2020 ◽  
Vol 9 (6) ◽  
pp. 230-236
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Data Mining ◽  
Survival Analysis ◽  
Early Stage ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Omics Data

Cancer is one of the deadly diseases across many countries. However, cancer can be cured, if detected at an early stage. Researchers are working on healthcare for early detection and prevention of cancer. Medical data has reached its utmost potential by providing researchers with huge data sets collected from all over the globe. In the present scenario, Machine Learning has been widely used in the area of cancer diagnosis and prognosis. Survival analysis may help in the prediction of the early onset of disease, relapse, re-occurrence of diseases and biomarker identification. Applications of machine learning and data mining methods in medical field are currently the most widespread in cancer detection and survival analysis. In this survey, different ways to detect and predict lung cancer using latest Machine learning algorithms combined with data mining has been analyzed. Comparative study of various machine learning techniques and technologies has been done over different types of data such as clinical data, omics data, image data etc.

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