scholarly journals Grain Yield Estimation in Cultivated Land Using Machine Learning Techniques

2021 ◽  
Vol 36 (2) ◽  
pp. 70-75
Author(s):  
Dr.K. Venkata Nagendra ◽  
Dr.B. Prasad ◽  
K.T.P.S. Kumar ◽  
K.S. Raghuram ◽  
Dr.K. Somasundaram
Keyword(s):  
Machine Learning ◽  
Market Price ◽  
Specific Area ◽  
Continuous Variable ◽  
Machine Learning Techniques ◽  
Accurate Information ◽  
Agricultural Crop ◽  
Agricultural Field ◽  
Learning Techniques ◽  
Selection Of

Agriculture contributes approximately 28 percent of India's GDP, and agriculture employs approximately 65 percent of the country's labor force. India is the world's second-largest agricultural crop producer. Agriculture is not only an important part of the expanding economy, but it is also necessary for our survival. The technological contribution could assist the farmer in increasing his yield. The selection of each crop is critical in the planning of agricultural production. The selection of crops will be influenced by a variety of factors, including market price, production rate, and the policies of the various government departments. Numerous changes are required in the agricultural field in order to improve the overall performance of our Indian economy. By using machine learning techniques that are easily applied to the farming sector we can improve agriculture. Along with all of the advancements in farming machinery and technology, the availability of useful and accurate information about a variety of topics plays an important role in the success of the industry. It is a difficult task to predict agricultural output since it depends on a number of variables, such as irrigation, ultraviolet (UV), insect killers, stimulants & the quantity of land enclosed in that specific area. It is proposed in this article that two distinct Machine Learning (ML) methods be used to evaluate the yields of the crops. The two algorithms, SVR and Linear Regression, have been well suited to validate the variable parameters of the continuous variable estimate with 185 acquired data points.

scholarly journals Secure Continuous-Variable Quantum Key Distribution with Machine Learning

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 511
Author(s):  
Duan Huang ◽  
Susu Liu ◽  
Ling Zhang
Keyword(s):  
Machine Learning ◽  
Quantum Key Distribution ◽  
Key Distribution ◽  
Continuous Variable ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Common ◽  
Practical Security

Quantum key distribution (QKD) offers information-theoretical security, while real systems are thought not to promise practical security effectively. In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities for eavesdroppers and stressors for two parties. However, the common quantum hacking strategies and countermeasures inevitably increase the complexity of practical CV systems. Machine-learning techniques are utilized to explore how to perceive practical imperfections. Here, we review recent works on secure CVQKD systems with machine learning, where the methods for detections and attacks were studied.

scholarly journals Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 38 ◽  
Author(s):  
Fabrizio Balducci ◽  
Donato Impedovo ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Agricultural Field ◽  
Heterogeneous Information ◽  
Learning Techniques ◽  
Farm Model ◽  
Machine Learning Applications ◽  
Crop Harvest ◽  
The Right ◽  
The Internet Of Things

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

scholarly journals Current Developments in Machine Learning Techniques in Biological Data Mining

2017 ◽  
Vol 11 ◽  
pp. 117793221668754 ◽  
Author(s):  
Gerard G Dumancas ◽  
Indra Adrianto ◽  
Ghalib Bello ◽  
Mikhail Dozmorov
Keyword(s):  
Machine Learning ◽  
Biomarker Discovery ◽  
Wide Spectrum ◽  
Specific Area ◽  
Biological Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Indispensable Tool ◽  
Underlying Mechanisms ◽  
Applications Of Machine Learning

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

scholarly journals Efficiently Coupling the I-DLV Grounder with ASP Solvers

2018 ◽  
Vol 20 (2) ◽  
pp. 205-224 ◽  
Author(s):  
FRANCESCO CALIMERI ◽  
CARMINE DODARO ◽  
DAVIDE FUSCÀ ◽  
SIMONA PERRI ◽  
JESSICA ZANGARI
Keyword(s):  
Machine Learning ◽  
Answer Set Programming ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Selection Of ◽  
Answer Set

We present ${{{{$\mathscr{I}$}-}\textsc{dlv}}+{{$\mathscr{MS}$}}}$, a new answer set programming (ASP) system that integrates an efficient grounder, namely ${{{$\mathscr{I}$}-}\textsc{dlv}}$, with an automatic selector that inductively chooses a solver: depending on some inherent features of the instantiation produced by ${{{$\mathscr{I}$}-}\textsc{dlv}}$, machine learning techniques guide the selection of the most appropriate solver. The system participated in the latest (7th) ASP competition, winning the regular track, category SP (i.e., one processor allowed).

scholarly journals Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning

2017 ◽  
Vol 58 ◽  
pp. 829-858 ◽  
Author(s):  
Michael Abseher ◽  
Nysret Musliu ◽  
Stefan Woltran
Keyword(s):  
Machine Learning ◽  
Dynamic Programming ◽  
Tree Decomposition ◽  
Machine Learning Techniques ◽  
Problem Instance ◽  
Actual Problem ◽  
Learning Techniques ◽  
Tree Decompositions ◽  
Selection Of ◽  
General Method

Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems - that are in general NP-hard - efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; and (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact, given an instance of the problem at hand, even decompositions of the same width might yield extremely diverging runtimes. We thus propose here a novel and general method that is based on selection of the best decomposition from an available pool of heuristically generated ones. For this purpose, we require machine learning techniques that provide automated selection based on features of the decomposition rather than on the actual problem instance. Thus, one main contribution of this work is to propose novel features for tree decompositions. Moreover, we report on extensive experiments in different problem domains which show a significant speedup when choosing the tree decomposition according to this concept over simply using an arbitrary one of the same width.

Machine Learning as an Enabler of Continuous and Adaptive Authentication in Multimedia Mobile Devices

2020 ◽  
pp. 21-47
Author(s):  
José María Jorquera Valero ◽  
Pedro Miguel Sánchez Sánchez ◽  
Alberto Huertas Celdran ◽  
Gregorio Martínez Pérez
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Machine Learning Techniques ◽  
Security Level ◽  
Design And Implementation ◽  
Continuous Authentication ◽  
Learning Techniques ◽  
Authentication System ◽  
And Performance ◽  
Selection Of

Continuous authentication systems allow users not to possess or remember something to authenticate themselves. These systems perform a permanent authentication that improves the security level of traditional mechanisms, which just authenticate from time to time. Despite the benefits of continuous authentication, the selection of dimensions and characteristics modelling of user's behaviour, and the creation and management of precise models based on Machine learning, are two important open challenges. This chapter proposes a continuous and adaptive authentication system that uses Machine Learning techniques based on the detection of anomalies. Applications usage and the location of the mobile device are considered to detect abnormal behaviours of users when interacting with the device. The proposed system provides adaptability to behavioural changes through the insertion and elimination of patterns. Finally, a proof of concept and several experiments justify the decisions made during the design and implementation of this work, as well as demonstrates its suitability and performance.

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