scholarly journals Non Verbal Communication for Analyzing the Malpractice in Examinations– An Overview

2018 ◽  
Vol 7 (2.20) ◽  
pp. 227
Author(s):  
Kujani T ◽  
Sathya T ◽  
Bhuvanya R ◽  
Uma S
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Interpersonal Communication ◽  
Nonverbal Communication ◽  
Current Situation ◽  
Verbal Communication ◽  
Machine Learning Techniques ◽  
The People ◽  
Learning Techniques

Nonverbal communication can specify the psychosomatic behavior of people involved in interpersonal communication. Many researchers have specified the importance of gesture intimation. In the paper, we shall apply the previously used computer vision hardware and Machine Learning techniques for capturing the postures students undergoing examination in the classroom atmosphere. The main       intention is to classify the people who involved in misbehavior such as copying, prompting answers, sharing the answer scripts and any other such practices. Current situation prevailing is though a physical monitor, invigilator is available in the exam hall the students     attempt to misbehave in various ways mentioned above. We discuss about the techniques to be employed to get an analysis of the       behavior of each student involved in exam. 

Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques

2020 ◽  
Vol 22 (3) ◽  
pp. 27-29 ◽  
Author(s):  
Paula Ramos-Giraldo ◽  
Chris Reberg-Horton ◽  
Anna M. Locke ◽  
Steven Mirsky ◽  
Edgar Lobaton
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Drought Stress ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
Stress Detection ◽  
Vision Systems ◽  
Learning Techniques ◽  
Computer Vision Systems

scholarly journals Design and Implementation of V2V and V2I Communication Systems using ML based Li-Fi technology

2021 ◽  
Author(s):  
Manimegaai C T ◽  
kali muthu ◽  
sabitha gauni
Keyword(s):  
Machine Learning ◽  
Communication Systems ◽  
Machine Learning Techniques ◽  
Light Emitting ◽  
Design And Implementation ◽  
Vehicle To Vehicle ◽  
The People ◽  
Learning Techniques ◽  
Vehicle To Infrastructure ◽  
Vehicle To Infrastructure Communication

Abstract These days population are taking a risk in their drive and in no time dangers are happening, and loosing lives by doing tiny wrongs when on drive near restricted zones. To escape these accidents to make population risk free traffic department are introducing signboards. But then again with the ignorance of the people, dangers are happening again, so “Li-Fi technology” is being used here to decrease the count of accidents. The transmission takes place with the help of LEDs (Light Emitting Diodes).Text, audio and video can also be transmitted with the help of this li-fi. The transmission is done when the light turns on and off. When this is compared to Wi-Fi it has many advantages like this light is not harmful to human body. The Transmission takes place in the form of zeroes and ones. Therefore to avoid accidents we suggested an intelligent, adaptable, and efficient model that utilizes Machine Learning techniques. The proposed system helps in vehicle to vehicle and vehicle to Infrastructure communication systems.

scholarly journals Soil Classification and Harvest Proposal Implemented using Machine Learning Techniques

2020 ◽  
Vol 9 (12) ◽  
pp. 19-22
Keyword(s):  
Machine Learning ◽  
Soil Type ◽  
Soil Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
The People ◽  
Learning Techniques ◽  
Crop Type

The major source of living for the people of India is agriculture. It is considered as important economy for the country. India is one of the country that suffer from natural calamities like drought and flood that may destroy the crops which may lead to heavy loss for the people doing agriculture. Predicting the crop type can help them to cultivate the suitable crop that can be cultivated in that particular soil type. Soil is one major factor or agriculture. There are several types of soil available in our county. In order to classify the soil type we need to understand the characteristics of the soil. Data mining and machine learning is one of the emerging technology in the field of agriculture and horticulture. In order to classify the soil type and Provide suggestion of fertilizers that can improve the growth of the crop cultivated in that particular soil type plays major role in agriculture. For that here exploring Several machine learning algorithms such as Support vector machine(SVM),k-Nearest Neighbour(k-NN) and logistic regression are used to classify the soil type.

scholarly journals Suicide Prediction on Social Media by Implementing Sentiment Analysis along with Machine Learning

2019 ◽  
Vol 8 (2) ◽  
pp. 4833-4837
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Semantic Analysis ◽  
Machine Learning Techniques ◽  
Suicide Prediction ◽  
The People ◽  
Learning Techniques ◽  
Use Of Social Media ◽  
Day By Day

Technology is growing day by day and the influence of them on our day-to-day life is reaching new heights in the digitized world. Most of the people are prone to the use of social media and even minute details are getting posted every second. Some even go to the extent of posting even suicide related issues. This paper addresses the issue of suicide and is predicting the suicide issues on social media and their semantic analysis. With the help of Machine Learning techniques and semantic analysis of sentiments the prediction and classification of suicide is done. The model of approach is a four-tier approach, which is very beneficial as it uses the twitter4J data by using weka tool and implementing it on WordNet. The precision and accuracy aspects are verified as the parameters for the performance efficiency of the procedure. We also give a solution for the lack of resources regarding the terminological resources by providing a phase for the generation of records of vocabulary also.

scholarly journals Recognition of Soybean Diseases Using Machine Learning Techniques Based on Segmentation of Images Captured By UAVs

2020 ◽  
Author(s):  
Gercina Da Silva ◽  
Alessandro Ferreira ◽  
Denilson Guilherme ◽  
José Fernando Grigolli ◽  
Vanessa Weber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Computer Program ◽  
Random Forests ◽  
Machine Learning Techniques ◽  
Target Spot ◽  
Learning Techniques ◽  
Image Dataset ◽  
Segmentation Of Images ◽  
Soybean Diseases

Soybean is an important product for the Brazilian economy, however it has factors that can limit its productive income, like the diseases that are generally difficult to control. Thus, this article aims to use a computer program to recognize diseases in images obtained by a UAV in a soybean plantation. The program is based on computer vision and machine learning, using the SLIC algorithm to segment the images into superpixels. To achieve the objective, after the segmentation of the images, an image dataset was created with the following classes: mildew, target spot, Asian rust, soil, straw and healthy leaves, totaling 22,140 images. Diagrammatic scales were used to assess disease severity. The disease recognition computer program explored four supervised learning techniques: SVM, J48, Random Forest and KNN. The techniques that obtained the best performance were SVM and Random Forests, taking into account the results obtained with all the evaluation metrics used. It was found that the program is efficient to differentiate the classes of diseases treated in this article.

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