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.

scholarly journals AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

2021 ◽  
Author(s):  
Naser Zaeri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Ct Scan ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Related Data ◽  
Recent Developments ◽  
Intelligence Modeling ◽  
Health Organization ◽  
Imaging Algorithms

The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease.

scholarly journals Reviewing artificial intelligence methods for predictive modeling applications

2021 ◽  
Author(s):  
Andreas Sepp
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Modeling ◽  
Significant Contribution ◽  
Predictive Analytics ◽  
State Of The Art ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Artificial Intelligence Methods

Artificial intelligence and machine learning methods had significant contribution to the advancement and progress of predictive analytics. This article presents a state of the art of methods and applications of artificial intelligence and machine learning.

scholarly journals Flight Delay Prediction Using Machine Learning

2021 ◽  
pp. 473-476
Author(s):  
Prajwal Dhone ◽  
Uday Kirange ◽  
Rushabh Satarkar ◽  
Prof. Shashant Jaykar
Keyword(s):  
Machine Learning ◽  
Negative Impact ◽  
Weather Conditions ◽  
Current Data ◽  
Environmental Harm ◽  
Flight Delays ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Flight Delay ◽  
Delay Prediction

In this fast growing world as airplanes continue flying, flight delays are the part of the experience. According to the Bureau Of Statistics(BOS), about 20% of all flights are delayed by 15 minutes or more. Flight delays causes a negative impact, mainly economical for airport authorities, commuters and airline industries as well. Furthermore, in the domain of sustainability, it can even cause environmental harm by the rise in fuel consumption and gas emissions and also some of the important factors including adverse weather conditions, preparing the aircraft, fixing of mechanical issue, getting security clearance, etc. Hence, these are the factors which indicates the necessity it has become to predict the delays of airline problems. To carry out the predictive analysis, which includes a range of statistical techniques from machine learning, this studies historical and current data to make predictions about the future delays, taking help of Regression Analysis using regularization technique used in Python.

scholarly journals A Supervised Learning Algorithm to Forecast Weather Conditions for Playing Cricket

2019 ◽  
Vol 9 (1) ◽  
pp. 1560-1565
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Weather Conditions ◽  
Redundant Data ◽  
Classification Technique

Now days, Machine learning is considered as the key technique in the field of technologies, such as, Internet of things (IOT), Cloud computing, Big data and Artificial Intelligence etc. As technology enhances, lots of incorrect and redundant data are collected from these fields. To make use of these data for a meaningful purpose, we have to apply mining or classification technique in the real world. In this paper, we have proposed two nobel approaches towards data classification by using supervised learning algorithm

scholarly journals Automated predictive analytics tool for rainfall forecasting

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maulin Raval ◽  
Pavithra Sivashanmugam ◽  
Vu Pham ◽  
Hardik Gohel ◽  
Ajeet Kaushik ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Weather Conditions ◽  
Machine Learning Algorithms ◽  
Rainfall Data ◽  
Machine Learning Techniques ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Prediction Techniques

AbstractAustralia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.

scholarly journals CUSTOMER PREDICTIVE ANALYTICS USING ARTIFICIAL INTELLIGENCE

2020 ◽  
pp. 1-12
Author(s):  
SITI ZULAIKHA ◽  
HAZIK MOHAMED ◽  
MASMIRA KURNIAWATI ◽  
SULISTYA RUSGIANTO ◽  
SYLVA ALIF RUSMITA
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Historical Analysis ◽  
Review Of The Literature ◽  
Use Of Technology ◽  
Customer Targeting ◽  
Conceptual Paper ◽  
Customer Lifecycle ◽  
Applications Of Ai

This conceptual paper exclusively focused on how artificial intelligence (AI) serves as a means to identify a target audience. Focusing on the marketing context, a structured discussion of how AI can identify the target customers precisely despite their different behaviors was presented in this paper. The applications of AI in customer targeting and the projected effectiveness throughout the different phases of customer lifecycle were also discussed. Through the historical analysis, behavioral insights of individual customers can be retrieved in a more reliable and efficient way. The review of the literature confirmed the use of technology-driven AI in revolutionizing marketing, where data can be processed at scale via supervised or unsupervised (machine) learning.

scholarly journals Artificial Intelligence — An Enabler of Naval Tactical Decision Superiority

AI Magazine ◽  
2019 ◽  
Vol 40 (1) ◽  
pp. 63-78
Author(s):  
Bonnie Johnson ◽  
William A. Treadway
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Multidimensional Complex ◽  
Tactical Decision ◽  
Advanced Computing

Artificial intelligence, as a capability enhancer, offers significant improvements to our tactical warfighting advantage. AI provides methods for fusing and analyzing data to enhance our knowledge of the tactical environment; it provides methods for generating and assessing decision options from multidimensional, complex situations; and it provides predictive analytics to identify and examine the effects of tactical courses of action. Machine learning can improve these processes in an evolutionary manner. Advanced computing techniques can handle highly heterogeneous and vast datasets and can synchronize knowledge across distributed warfare assets. This article presents concepts for applying AI to various aspects of tactical battle management and discusses their potential improvements to future warfare.

Artificial intelligence: predictive analytics of perinatal risks

2020 ◽  
Vol 19 (6) ◽  
pp. 133-144
Author(s):  
A.A. Ivshin ◽  
◽  
A.V. Gusev ◽  
R.E. Novitskiy ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Congenital Abnormalities ◽  
Predictive Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Fetal Hypoxia ◽  
Perinatal Medicine ◽  
Processing Power

Artificial intelligence (AI) has recently become an object of interest for specialists from various fields of science and technology, including healthcare professionals. Significantly increased funding for the development of AI models confirms this fact. Advances in machine learning (ML), availability of large data sets, and increasing processing power of computers promote the implementation of AI in many areas of human activity. Being a type of AI, machine learning allows automatic development of mathematical models using large data sets. These models can be used to address multiple problems, such as prediction of various events in obstetrics and neonatology. Further integration of artificial intelligence in perinatology will facilitate the development of this important area in the future. This review covers the main aspects of artificial intelligence and machine learning, their possible application in healthcare, potential limitations and problems, as well as outlooks in the context of AI integration into perinatal medicine. Key words: artificial intelligence, cardiotocography, neonatal asphyxia, fetal congenital abnormalities, fetal hypoxia, machine learning, neural networks, prediction, prognosis, perinatal risk, prenatal diagnosis

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