Print ISSN: 2204-1990

Online ISSN: 1323-6903

Keywords : Deep learning

Critical Review of Deep Learning Algorithms for Plant Diseases by Leaf Recognition

M. Jaithoon Bibi, Dr. S.Karpagavalli, A. Kalaivani

Journal of Contemporary Issues in Business and Government, 2021, Volume 27, Issue 5, Pages 720-729
DOI: 10.47750/cibg.2021.27.05.044

The identification and classification of the crop leaf diseases plays an essential role in the
cultivation. Plants are the livelihood. Peoples depend entirely on crops for the breathing of
their daily lives. Thus, suitable crop caring should take place. Most research suggests that
the quality of agricultural commodities can be restricted depending on different factors. Crop
diseases include microorganisms and pathogens. The leaf diseases not only reduce crop
growth, the cultivation is also destroyed. Several researchers have been identified crop
leaf diseases using image processing algorithms but it take more time for detection.
Therefore, advanced algorithms are required to identify and classify the crop leaf diseases
automatically with higher accuracy. There are different deep learning algorithms using crop
leaf images developed for automatically detecting the crop leaf diseases in an efficient
manner. In this article, a survey on different deep learning algorithms using image processing
for detecting and classifying the crop or plant leaf diseases is presented. Also, the merits and
demerits of the surveyed algorithms for crop leaves diseases identification are addressed in a
tabular form. Finally, a comprehensive analysis is concluded and future directions are
suggested to increase the accuracy of leaf diseases classification.

Psychotic Motivation for Improving Student Performance Based On Pattern Learner Features Using Deep Neural Classifier for Bipolar Disorder Students


Journal of Contemporary Issues in Business and Government, 2021, Volume 27, Issue 3, Pages 504-514
DOI: 10.47750/cibg.2021.27.03.069

Bipolar disorder is a depressive fact that makes manic illness pressures in young ages due to the non-intensive nature of brain functions, energy levels, mood-outs, and health disorders. These abnormalities may affect student performance under the learning strategies of students. Improvement of bipolar disorder affected student performance needs more data analysis forums   that lead to high dimensional nature of features. The problem is that non-relation feature analysis depends on the nature of student fitness that creates low prediction during classifications for students' motivation. To resolve this problem, a Psychotic motivation is proposed for improving student performance based on Pattern Learner Features (PLF) using Intra Segment Recurrent Deep Neural Network (ISRDNN) for bipolar disorder students. The proposed system makes student academic data's with physical fitness data collection progressive approach to predict important features to classify the result. Bipolar Disorder Influence Rate (BDIR) is usedto spill the progressive student defectives and the learning capabilities for classification result. With Intra Segment Activation Function (ISAF), the recurrent neural network is optimized to classify the result. This classifier improved the student's academic performance based on psychological motivation recommendations. Results prove that the accuracy of the proposed system produces high results compared to the previous system.

Mosquito Detection using Deep Learning based on Acoustics

Ankur Singh Bist; Mohd Mursleen; Lalit Mohan; Himanshu Pant; Purushottam Das

Journal of Contemporary Issues in Business and Government, 2021, Volume 27, Issue 1, Pages 1036-1041

Deep learning based techniques are becoming popular because of its stability.
Success of voice analytics can be seen because in various applications like alexa, siri etc.
Behind the scenes main concept is to generate and analyze features so that it can be
applicable in real world. In this paper we are proposing a deep learning based pipeline for
mosquito detection. Hardware integration with software techniques will create device that
can meet need of end user. Further AI Nano Jetson and flying machine are used to
complete the end goal.