Traffic scheduling for Green city through energy efficient Wireless sensor Networks

Vijayan, V.

doi:10.30534/ijatcse/2019/81842019

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…E. The sensor details are accessed from various [14] sensors which is then stored in these cloud platforms for further accessing and processing. The farmers will be able to provide with appropriate and accurate actions for the farm with these information and analysis C, C++, Java, Android apps etc.…”

Section: Figure5 Fish Feedermentioning

confidence: 99%

Cost Effective IoT based Automated Fish Farming System with Flood Prediction

Prasad¹

2020

IJATCSE

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The Internet of things (IoT) is a system of interconnected computing devices which can be employed for the automation of assorted systems that are usually handled manually. For the past decades, fish farming has been handled manually using techniques which are not efficient enough in terms of monetary benefits, time consumption and labour. In conventional fish farming, the management of the farm is impossible without the physical presence of the farmer and the chemical changes in the environment cannot be efficiently detected by farmers. In order to overcome the drawbacks of conventional fish farming, we propose an embedded system for automatic control of fish farms. The proposed system supports real-time monitoring and controlling of the fish farm by employing Internet of Things (IoT) and a user-friendly Android application. The proposed system also incorporates an automatic feeding system and an efficient real time water quality management system that monitors the quality of thewater by sensing the physical parameters like pH, temperature, light and water level. Sensors are employed to reduce the human involvement and thereby to reduce the labour cost. The proposed system ensures uniform maintenance of temperature, oxygen, and pH with minimal human involvement by automating recirculation of water. The proposed system also includes a camera module which is deployed in the farm for the live surveillance of the farm via an Android application and a module to notify the farmer of a possible flooding of the farm. The proposed system is aimed at improving the harvest with minimal human involvement reaping greater economic benefits for the farmer.

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Section: Figure5 Fish Feedermentioning

confidence: 99%

Cost Effective IoT based Automated Fish Farming System with Flood Prediction

Prasad¹

2020

IJATCSE

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“…Finally the ensemble model is trained by subsets predict the testing set and then the obtained result are compiled as the final prediction result. The model will be applied to different datasets then the result shows that the proposed will be superior in performance [7,10].…”

Section: Literature Surveymentioning

confidence: 99%

Optimized algorithm for Credit Scoring

Chacko¹

2020

IJATCSE

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The rapid expansion of credit scoring technologies is increased today. Credit scoring will be considered as the significant element in the financial industries. It plays an important role in modern affairs such as credit customer selection, risk measurement, post-loan and after-loan supervision, comprehensive performance evaluation etc. Credit scoring has been recognized as a binary classification technique distinguishing applicants into two classes: good credit and bad credit, based on characteristics such as gender, age, occupation, and salary. These determine the applicability of loans for applicants. There are two main stream classification techniques, statistical techniques and machine learning techniques. Linear discriminant analysis and logistic regression are the two most commonly used statistical techniques in credit scoring. Machine learning techniques include K-nearest neighbor, support vector machine, decision tree and neural network. Use different best algorithms for classify the credit scoring data sets. Here uses four algorithms for the classification of credit scoring data sets and then the accuracy of different algorithms on the data sets will be obtained.

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“…According to [4], the image obtained is advanced through a RetinaNet based hand detector to extract the hand regions and then they are detected, extracted and passed through a light -weight Convolutional Neural Network(CNN) for recognizing the hand gestures.. In [5,14] [8,15], during the feature extraction localized contour sequences and block based features are extracted which helps in a much better characterisation of static hand gesture.…”

Section: Related Workmentioning

confidence: 99%

A Software based ASL Translator and ASL to Voice Emulation System

Joel¹

2020

IJITI

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Sign languages use the visual modality to convey meaning. People who are hearing and speech impaired are left behind video consultations as there is no easier way for non vocal people to communicate with vocal people and express themselves to the hearing world. Since they can't sign themselves they are forced to use a basic text chat to hold their consults. To overcome this issue, we have developed a real time software based ASL Translator and voice emulation system where the image captured is converted into predicted text. We use Deep Neural Networks like Convolutional Neural Network for performing feature extraction and the SVM classifier for classification. The system returns top and most probable predictions. We obtained an accuracy rate of 93.75%.

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Traffic scheduling for Green city through energy efficient Wireless sensor Networks

Cited by 5 publications

References 7 publications

Cost Effective IoT based Automated Fish Farming System with Flood Prediction

Cost Effective IoT based Automated Fish Farming System with Flood Prediction

Optimized algorithm for Credit Scoring

A Software based ASL Translator and ASL to Voice Emulation System

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