scholarly journals Cognitive Computing: Brain Tumour Detection

2019 ◽  
Vol 9 (1S) ◽  
pp. 57-62
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Brain Tumour ◽  
Cognitive Computing ◽  
Tumour Detection ◽  
Complete Cure ◽  
Level Of Abstraction

The generation of the computers and the growth in this field is found to be more and more valuable to the level of abstraction of the details to the artificial intelligence and machine learning etc.., For a human the most dreadful and non-curable diseases is brain tumour. Some of the treatments and methods have been initiated, but there is no complete cure of this brain tumour. But the recent development over the computer field has brought up an idea to cure Brain Tumour.

scholarly journals Brain Tumour Detection Using Machine Learning Algorithm

2021 ◽  
Vol 1937 (1) ◽  
pp. 012008
Author(s):  
A. Keerthana ◽  
B. Kavin Kumar ◽  
K.S Akshaya ◽  
S. Kamalraj
Keyword(s):  
Machine Learning ◽  
Brain Tumour ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumour Detection

Cognitive Computing For Sustainable Agriculture

2019 ◽  
Vol 8 (3) ◽  
pp. 32-34
Author(s):  
T. Manjula ◽  
T. Sudha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Sustainable Agriculture ◽  
Food Supply ◽  
Green Revolution ◽  
Modern Society ◽  
Cognitive Computing ◽  
Cognitive Computation ◽  
Ancient Times ◽  
Modern Technologies

Cognitive computing in agriculture is going to be a big revolution like the green revolution. Agriculture is a big step that accompanied the humanity to evolve from the ancient times to the modern days and has fulfilled the basic need for food supply. Today still remains it’s at most importance. Cognitive computing uses cognitive technologies in agriculture that help to understand, learn from experiences and environment, reason, interact and thus increase the efficiency. Civilization has led to more urbanization. There are more people than available food. There is a great necessity to increase the per meter yield, So many techniques have been for seen in agriculture in terms of usage of pesticides and fertilizers, use of hybridization and green revolution to increase the production in agriculture. Now the use of modern technologies such as artificial intelligence and cognitive computation is going to bring a new big revolution for sustainable agriculture. The present paper focuses on the problems faced by the modern society in agriculture and how the cognitive computation provides an ultimate solution to the problems. We also discuss some illustrations for the usage of cognitive technologies and machine learning in the field of agriculture.

The emergence of artificial intelligence in cardiology: current and future applications

2021 ◽  
Vol 17 ◽  
Author(s):  
Prashanth Kulkarni ◽  
Manjappa Mahadevappa ◽  
Srikar Chilakamarri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Patient Care ◽  
Current Evidence ◽  
Cognitive Computing ◽  
Cardiovascular Medicine ◽  
Artificial Intelligence Technology ◽  
Application Prospects

: Artificial intelligence technology is emerging as a promising entity in cardiovascular medicine, potentially improving diagnosis and patient care. In this article, we review the literature on artificial intelligence and its utility in cardiology. We provide a detailed description of concepts of artificial intelligence tools like machine learning, deep learning, and cognitive computing. This review discusses the current evidence, application, prospects, and limitations of artificial intelligence in cardiology.

scholarly journals Artificial intelligence - The promise for an agricultural revolution in new era

2020 ◽  
Vol 15 (4) ◽  
pp. 435-437
Author(s):  
Reshma J. Murugan ◽  
B. N. Bindhya ◽  
G. S. Sreedaya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Weather Conditions ◽  
Disruptive Technology ◽  
Cognitive Computing ◽  
Related Data ◽  
Agricultural Revolution ◽  
Adverse Weather ◽  
Optimal Mix

Agriculture is slowly becoming digital. The adoption of Artificial Intelligence (AI) and Machine Learning (ML) both in terms of agricultural products and in-field farming techniques are increasing. Artificial Intelligence in agriculture is emerging in three major areas, namely agricultural robotics, soil and crop monitoring and predictive analytics. The use of sensors and soil sampling techniques are increasing day by day which helps in gathering of data. In turn, this data is stored in farm management system which is better processed and analysed. Thus, the data available along with other related data paves a way to successfully deploy AI in agriculture. AI in agriculture is emergingin cognitive computing and it has all the scope to become the most disruptive technology in agriculture services as it is able to understand, learn and respond to different situations (based on learning) to increase efficiency. The areas where the use of cognitive solutions can benefit agriculture are growth driven by IOT, image-based insight generation, identification of optimal mix for agronomic products, health monitoring of crops and automation techniques in irrigation and enabling farmers. In addition, the drone based solutions have significant impact in terms of productivity gains, coping with adverse weather conditions, yield management and precision farming.The emergence of new age technologies like Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are creating an ecosystem for smart farming. Fusion of all this technology is enabling farmers achieve higher average yield and better price control.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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