A Novel Technique for Multi-Class Ordinal Regression-APDC

Gladence, L. Mary; Karthi, M.; Ravi, T.

doi:10.17485/ijst/2016/v9i10/88890

Cited by 9 publications

(4 citation statements)

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“…once evaluating these techniques, Neural Networks classifier was found to grant the best leads to term of classification accuracy. [18,19] planned predicting aspects of upper education students. during this paper, they analyze that one amongst the most important challenges that educational activity faces nowadays is predicting the behavior of scholars.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Model for Predicting Student Dropout using Data Mining and Machine Learning Techniques

2019

IJITEE

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Education could be a important resource that has to lean to all or any kids. one in all the largest assets of the longer term generation cloud is alleged because the education that's given to the youngsters. Most of the youngsters aren't ready to continue their education because of many reasons. The prediction of student dropout plays a very important role in characteristic the scholars World Health Organization are on the sting of being a dropout from their education. whereas predicting this, we will simply try and solve their issues and create them continue their education. during this paper, we've planned a model for predicting the scholars can get born out or not mistreatment many machine learning techniques. we have a tendency to create use of decision trees that make a call mistreatment many factors. the choice of the prediction involves crucial wherever many knowledge attributes are used for prediction like correlations, similarity measures, frequent patterns, and associations rule mining. The planned work is evaluated mistreatment numerous parameters and is well-tried to figure expeditiously in predicting the dropout students compared with alternative.

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Section: Related Workmentioning

confidence: 99%

An Efficient Model for Predicting Student Dropout using Data Mining and Machine Learning Techniques

2019

IJITEE

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“…let consider that the video object has been extracted using the method. After perform the experimentation on diffident numbers of levels, it stated that use of two levels provide the best resolution [17].…”

Section: Hiding the Encrypted Biometric Signalmentioning

confidence: 99%

Usage of Bioinformatic Data for Remote Authentication in Wireless Networks

Priscilla¹,

Karthi²

2018

IJIVP

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Authentication is the step to approve the correctness of an attribute of a individual or entity group. Sensitive information might help in making the authentication. Regularly this encrypted information is processed via wireless network and which need remote authentication for information access process. In the proposed work, a robust authentication technique is performed, which is based on segmentation, symmetric encryption and data hiding. If a user wants to be remotely authenticated, initially user has to select a video. The user's biometric signal is encrypted using a symmetric encryption method. After encrypted information is vectorized the information hiding process is accomplish using Qualified Significant Wavelet Trees (QSWTs). QSWT is effectively achieve the invisibility and resistance during attacks and stability in data hidden process. Also, the Inverse Discrete Wavelet Transform (IDWT) is applied to extract the hiding data from the stego-object subsequently an appropriate decryption process to recover the biometric image. Experimental results are stated that the proposed method would turnout security virtue and robustness. Triple DES technique is used in the proposed work. This is the technique that is used to encrypt the biometric data into a scrambled format which is difficult to understand by the attackers. It is a very useful and efficient method of encryption because of its tendency to use less data for performing its services.

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“…At present, an important development direction for various countries in the world is related to the research and development of robots. China has clearly pointed out in its future development plan that robots, especially robots with autonomous mobility, will be included in the field of advanced manufacturing technology [5,6]. Germany pointed out in 2013 that the future development of frontier industries should give priority to the combination of intelligent robots and man-machines.…”

Section: Introductionmentioning

confidence: 99%

The Direction Analysis on Trajectory of Fast Neural Network Learning Robot

2021

IEEE Access

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This study is to efficiently apply artificial neural network (ANN) to the robotics, so as to provide experimental basis for mobile robots to learn the optimal trajectory planning strategy. An algorithm model is innovatively proposed based on back propagation neural network (BPNN) and reinforcement learning (Q-Learning) by combining the motion space, selective strategy, and reward function design. The simulation experiment environment is set and the ROS mobile robot is adopted for simulation experiments. The algorithm proposed in this study is compared with other neural network algorithms from the perspectives of accuracy, precision, recall, and F1. It can be found that the accuracy of algorithm proposed was at least 5.47% higher than that of the model algorithm proposed by other scholars, and the values of precision, recall, and F1 were at least 5.5% higher. The results show that the mobile robot could find the shortest trajectory and the best trajectory in a discrete obstacle environment, no matter the more or less the discrete obstacles or the large or small the space. Therefore, compared to the advanced model algorithms proposed by other scholars in related fields, the robot trajectory planning based on the improved BPNN combined with Q-Learning constructed in this study could realize better results, and can be used in practical applications with robot trajectory planning, providing practical value for the field of machine vision.

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A Novel Technique for Multi-Class Ordinal Regression-APDC

Cited by 9 publications

References 0 publications

An Efficient Model for Predicting Student Dropout using Data Mining and Machine Learning Techniques

An Efficient Model for Predicting Student Dropout using Data Mining and Machine Learning Techniques

Usage of Bioinformatic Data for Remote Authentication in Wireless Networks

The Direction Analysis on Trajectory of Fast Neural Network Learning Robot

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