Identification of Influential Parameter for Early Detection of Dengue Using Machine Learning Approach

2019 ◽  
Author(s):  
Rajeev Kapoor ◽  
Varinder Kadyan ◽  
Sachin Ahuja
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

2020 ◽  
Vol 20 (1) ◽  
pp. 841-857
Author(s):  
Malena Manzi ◽  
Martín Palazzo ◽  
María Elena Knott ◽  
Pierre Beauseroy ◽  
Patricio Yankilevich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Renal Cell Carcinoma ◽  
Early Detection ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Learning Approach ◽  
Cell Renal Cell Carcinoma ◽  
Machine Learning Approach

A novel machine learning approach for early detection of hepatocellular carcinoma patients

2019 ◽  
Vol 54 ◽  
pp. 116-127 ◽  
Author(s):  
Wojciech Książek ◽  
Moloud Abdar ◽  
U. Rajendra Acharya ◽  
Paweł Pławiak
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Early Detection ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Role of Machine Learning Approach for Detection and Classification of Diseases in Cotton Plant

2021 ◽  
Vol 12 (5) ◽  
pp. 810-817
Author(s):  
Sandhya N. dhage, Dr. Vijay Kumar Garg
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Cotton Plant ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Early Detection Of Disease ◽  
Cotton Crop ◽  
Machine Learning Approach ◽  
Crop Diseases

Qualitative and quantitative agricultural production leads to economic benefits which can be achieved by periodic monitoring of crop, detection and prevention of crop diseases and insects. Quality of crop production is reduced by pest infection and crop diseases. Existing measures involves manual detection of cotton diseases by farmers and experts which requires  regular monitoring and detection manifest at middle to later stage of infection which causes many disadvantages such as becoming  too late for diseases to be cured.  Lack of early detection of diseases causes the diseases to be spread in nearby crops in the field and also spraying of pesticides is done on entire field for minimizing the infection of disease. The main goal of proposed research topic is to find the solution to the agriculture problem which involves detecting disease in cotton plant at early stage and classify the disease based on symptoms. Early detection of disease at an early stage prevent it from spreading to another area and preventive measures can be taken by farmers by spraying pesticides to control its growth which helps to increase the cotton yield production. Automatic identification of the different diseases affecting cotton crop will give many benefits to the farmers so that time, money will be saved and also gives healthy life to the crop. The contribution of this paper is to present the machine learning approach used for cotton crop disease diagnosis and classification.

Early Detection of Alcohol Use Disorder Based on a Novel Machine Learning Approach Using EEG Data

2021 ◽  
Author(s):  
Dennis Flathau ◽  
Johannes Breitenbach ◽  
Hermann Baumgartl ◽  
Ricardo Buettner
Keyword(s):  
Machine Learning ◽  
Alcohol Use ◽  
Early Detection ◽  
Alcohol Use Disorder ◽  
Learning Approach ◽  
Eeg Data ◽  
Machine Learning Approach

Early Detection of Preeclampsia based on a Machine Learning Approach

2019 ◽  
Author(s):  
Iuliana Marin ◽  
Bujorel-Ionel Pavaloiu ◽  
Constantin-Viorel Marian ◽  
Vlad Racovita ◽  
Nicolae Goga
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Learning Approach ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document