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Papers by Golmei Shaheamlung
2020 International Conference on Intelligent Engineering and Management (ICIEM), 2020
Suffering from liver disease has been rapidly increasing due to excessive drink of alcohol, inhal... more Suffering from liver disease has been rapidly increasing due to excessive drink of alcohol, inhale polluted gas, drugs, contamination food and packing food pickle, so the medical expert system will help a doctor to automatic prediction. With the repeated development in machine learning technology, early prediction of liver disease is possible so that people can easily diagnosis the deadly disease in the early stage. This will give more useful in the Healthcare department and also a medical expert system can be used in a remote area. The liver plays a very important role in life which supports the removal of toxins from the body. So early prediction is very important to diagnosis the disease and recovers. Different types of machine learning, Supervised, Unsupervised and Semi-Supervised, Reinforcement Learning for diagnosis of liver disease such as SVM, KNN, K-Mean clustering, neural network, Decision tree etc and give difference accuracy, precision, sensitivity. The motive of this pa...
Liver disease is also considered amongst one of the deadly disease for example, Liver cirrhosis (... more Liver disease is also considered amongst one of the deadly disease for example, Liver cirrhosis (LC), the end phase of numerous types of ceaseless hepatitis of various etiologies is a diffuse procedure portrayed by fibrosis and the change of ordinary liver design into fundamentally abnormal nodules surrounded by annular fibrosis As of now there is one of the predominant illnesses of 21st century is liver issue every year executing such a significant number of individuals' round the universes. The scope of treatments has been given by the analyst to assess results. Early determination is of extensive measure of hugeness in treating the malady. Despite all the standardization methods in medical diagnosis, a correct diagnosis is still considered to be an art much of this situation is for, that medical diagnosis needs proficiency as well as experience in dealing with uncertainty. In spite of the fact that, in our mechanized age, limits of restorative science have very extended, you can't conquer this vulnerability effectively. Offering a groundbreaking structure to develop the model of existing frameworks makes fuzzy hypothesis change to an important factor towards medicinal analysis improvement. Using various Artificial Intelligence methods for liver disorders diagnosis has recently become a wide-spreading area of research. These intelligent systems help physicians as diagnosis assistants. Presently, different Artificial Neural Network framework, Expert Systems, Fuzzy Neural Network and Classification, This book chapter gives an audit of various Artificial System and master framework strategy in determination and identifications of liver infection issue intensity is the key for results. Inspired by these, the work displayed in this paper is centered on contrasting these two incessant liver recognition systems and investigate the outcomes for future research. In the exhibited work, various patients’ datasets are considered are investigated for the location of Liver disease. Moreover, various performance parameters are evaluated like specificity, sensitivity, and accuracy for the overall assessment of the presented model.
In the past years, test case prioritization has been improved in the regression testing by the us... more In the past years, test case prioritization has been improved in the regression testing by the use of effective test cases. The continuous improvement and attention have increased in terms of prioritization algorithm, coverage criteria, measurement, application scenario and the practice concerned. It has focused mainly on Prioritizing and scheduling the test cases. The main purpose of this paper is to study the different prioritization techniques used by various authors in previous years. Regression testing, a type of testing in which is being used as a tool for checking, testing, and up-gradation of software. The test cases of all scheduling and prioritizing are set in the ordered and proper method and as result, this case shows the detection and a maximum number of faults in the software in which the technical faults are traced and detected as the detected faults. Through the fault detection of the test case, it reduces the test case and minimizes the execution cost. As a result, this method shows the running of the test case at a higher priority in order to reduce and minimize the cost, time and effort of software testing.
INFORMATION TECHNOLOGY IN INDUSTRY
In the 21st-century, the issue of liver disease has been increasing all over the world. As per th... more In the 21st-century, the issue of liver disease has been increasing all over the world. As per the latest survey report, liver disease death toll has been rise approximately 2 million per year worldwide. The overall percentage of death by liver disease is 3.5% worldwide. Chronic Liver disease is also considered to be one of the deadly diseases, so early detection and treatment can recover the disease easily. Due to rapid advancement in Artificial intelligence (AI), like various machine learning algorithms SVM, K-mean clustering, KNN, Random forest, Logistic regression, etc., This will improve the life span of a patient suffering from Chronic Liver Disease (CLD) in early stages. The data can be obtained in a large volume due to the broad exploitation of bar codes for supreme marketable products, the mechanization of various business and government dealings, and the development in the data collection tools. This research work is based on liver disease prediction using machine learning...
Suffering from liver disease has been rapidly increasing due to excessive drink of alcohol, inhal... more Suffering from liver disease has been rapidly increasing due to excessive drink of alcohol, inhale polluted gas, drugs, contamination food and packing food pickle, so the medical expert system will help a doctor to automatic prediction. With the repeated development in machine learning technology, early prediction of liver disease is possible so that people can easily diagnosis the deadly disease in the early stage. This will give more useful in the Healthcare department and also a medical expert system can be used in a remote area. The liver plays a very important role in life which supports the removal of toxins from the body. So early prediction is very important to diagnosis the disease and recovers. Different types of machine learning, Supervised, Unsupervised and Semi-Supervised, Reinforcement Learning for diagnosis of liver disease such as SVM, KNN, K-Mean clustering, neural network, Decision tree etc and give difference accuracy, precision, sensitivity. The motive of this paper is to give a survey and comparative analysis of the entire machine learning techniques for diagnosis and prediction of liver disease in the medical area, which has already been used for the prediction of liver disease by various authors and the analysis are based on Accuracy, Sensitivity, Precision, and Specificity.
2020 International Conference on Intelligent Engineering and Management (ICIEM), 2020
Suffering from liver disease has been rapidly increasing due to excessive drink of alcohol, inhal... more Suffering from liver disease has been rapidly increasing due to excessive drink of alcohol, inhale polluted gas, drugs, contamination food and packing food pickle, so the medical expert system will help a doctor to automatic prediction. With the repeated development in machine learning technology, early prediction of liver disease is possible so that people can easily diagnosis the deadly disease in the early stage. This will give more useful in the Healthcare department and also a medical expert system can be used in a remote area. The liver plays a very important role in life which supports the removal of toxins from the body. So early prediction is very important to diagnosis the disease and recovers. Different types of machine learning, Supervised, Unsupervised and Semi-Supervised, Reinforcement Learning for diagnosis of liver disease such as SVM, KNN, K-Mean clustering, neural network, Decision tree etc and give difference accuracy, precision, sensitivity. The motive of this pa...
Liver disease is also considered amongst one of the deadly disease for example, Liver cirrhosis (... more Liver disease is also considered amongst one of the deadly disease for example, Liver cirrhosis (LC), the end phase of numerous types of ceaseless hepatitis of various etiologies is a diffuse procedure portrayed by fibrosis and the change of ordinary liver design into fundamentally abnormal nodules surrounded by annular fibrosis As of now there is one of the predominant illnesses of 21st century is liver issue every year executing such a significant number of individuals' round the universes. The scope of treatments has been given by the analyst to assess results. Early determination is of extensive measure of hugeness in treating the malady. Despite all the standardization methods in medical diagnosis, a correct diagnosis is still considered to be an art much of this situation is for, that medical diagnosis needs proficiency as well as experience in dealing with uncertainty. In spite of the fact that, in our mechanized age, limits of restorative science have very extended, you can't conquer this vulnerability effectively. Offering a groundbreaking structure to develop the model of existing frameworks makes fuzzy hypothesis change to an important factor towards medicinal analysis improvement. Using various Artificial Intelligence methods for liver disorders diagnosis has recently become a wide-spreading area of research. These intelligent systems help physicians as diagnosis assistants. Presently, different Artificial Neural Network framework, Expert Systems, Fuzzy Neural Network and Classification, This book chapter gives an audit of various Artificial System and master framework strategy in determination and identifications of liver infection issue intensity is the key for results. Inspired by these, the work displayed in this paper is centered on contrasting these two incessant liver recognition systems and investigate the outcomes for future research. In the exhibited work, various patients’ datasets are considered are investigated for the location of Liver disease. Moreover, various performance parameters are evaluated like specificity, sensitivity, and accuracy for the overall assessment of the presented model.
In the past years, test case prioritization has been improved in the regression testing by the us... more In the past years, test case prioritization has been improved in the regression testing by the use of effective test cases. The continuous improvement and attention have increased in terms of prioritization algorithm, coverage criteria, measurement, application scenario and the practice concerned. It has focused mainly on Prioritizing and scheduling the test cases. The main purpose of this paper is to study the different prioritization techniques used by various authors in previous years. Regression testing, a type of testing in which is being used as a tool for checking, testing, and up-gradation of software. The test cases of all scheduling and prioritizing are set in the ordered and proper method and as result, this case shows the detection and a maximum number of faults in the software in which the technical faults are traced and detected as the detected faults. Through the fault detection of the test case, it reduces the test case and minimizes the execution cost. As a result, this method shows the running of the test case at a higher priority in order to reduce and minimize the cost, time and effort of software testing.
INFORMATION TECHNOLOGY IN INDUSTRY
In the 21st-century, the issue of liver disease has been increasing all over the world. As per th... more In the 21st-century, the issue of liver disease has been increasing all over the world. As per the latest survey report, liver disease death toll has been rise approximately 2 million per year worldwide. The overall percentage of death by liver disease is 3.5% worldwide. Chronic Liver disease is also considered to be one of the deadly diseases, so early detection and treatment can recover the disease easily. Due to rapid advancement in Artificial intelligence (AI), like various machine learning algorithms SVM, K-mean clustering, KNN, Random forest, Logistic regression, etc., This will improve the life span of a patient suffering from Chronic Liver Disease (CLD) in early stages. The data can be obtained in a large volume due to the broad exploitation of bar codes for supreme marketable products, the mechanization of various business and government dealings, and the development in the data collection tools. This research work is based on liver disease prediction using machine learning...
Suffering from liver disease has been rapidly increasing due to excessive drink of alcohol, inhal... more Suffering from liver disease has been rapidly increasing due to excessive drink of alcohol, inhale polluted gas, drugs, contamination food and packing food pickle, so the medical expert system will help a doctor to automatic prediction. With the repeated development in machine learning technology, early prediction of liver disease is possible so that people can easily diagnosis the deadly disease in the early stage. This will give more useful in the Healthcare department and also a medical expert system can be used in a remote area. The liver plays a very important role in life which supports the removal of toxins from the body. So early prediction is very important to diagnosis the disease and recovers. Different types of machine learning, Supervised, Unsupervised and Semi-Supervised, Reinforcement Learning for diagnosis of liver disease such as SVM, KNN, K-Mean clustering, neural network, Decision tree etc and give difference accuracy, precision, sensitivity. The motive of this paper is to give a survey and comparative analysis of the entire machine learning techniques for diagnosis and prediction of liver disease in the medical area, which has already been used for the prediction of liver disease by various authors and the analysis are based on Accuracy, Sensitivity, Precision, and Specificity.