A four-way decision-making system for the Indian summer monsoon rainfall

Singh, Pritpal; Rabadiya, Kinjal

doi:10.1142/s0217984918503049

Cited by 41 publications

(5 citation statements)

References 33 publications

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“…The various techniques for predicting the traffic collisions in machine learning are sampling, regressions, correlations [35], clustering algorithms [36,37], k-nearest neighbor (kNN) algorithm [38], and artificial neural network (ANN) [39] are clobbered by the deep learning (DL) models in terms of accuracy in predicting the collision. CNN [40], transpose CNN [41], and long short-term Memory (LSTM) [42] are some of the deep learning techniques [43][44][45][46][47][48] used for predicting the collision [41]. The systematic random sampling ameliorates in getting the automobilist samples, samples of the commuter, and samples of arid for reducing the hazards of bias.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

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Section: Introductionmentioning

confidence: 99%

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

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“…Finally, the splitting task is to divide the processed data into a training set and testing set according to a certain ratio determined by pre-processor (Singh et al , 2018b).…”

Section: Proposed Frameworkmentioning

confidence: 99%

Implementation of the QoS framework using fog computing to predict COVID-19 disease at early stage

Singh

Kaur

2021

WJE

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Purpose The purpose of this paper is to provide more accurate structure that allows the estimation of coronavirus (COVID-19) at a very early stage with ultra-low latency. The machine learning algorithms are used to evaluate the past medical details of the patients and forecast COVID-19 positive cases, which can aid in lowering costs and distinctively enhance the standard of treatment at hospitals. Design/methodology/approach In this paper, artificial intelligence (AI) and cloud/fog computing are integrated to strengthen COVID-19 patient prediction. A delay-sensitive efficient framework for the prediction of COVID-19 at an early stage is proposed. A novel similarity measure-based random forest classifier is proposed to increase the efficiency of the framework. Findings The performance of the framework is checked with various quality of service parameters such as delay, network usage, RAM usages and energy consumption, whereas classification accuracy, recall, precision, kappa static and root mean square error is used for the proposed classifier. Results show the effectiveness of the proposed framework. Originality/value AI and cloud/fog computing are integrated to strengthen COVID-19 patient prediction. A novel similarity measure-based random forest classifier with more than 80% accuracy is proposed to increase the efficiency of the framework.

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“…According to this theorem, the performance of one optimization algorithm for a specific set of problems does not guarantee solving other optimization problems because of their different nature. The NFL theorem allows researchers to propose some novel optimization algorithms for solving the problems in various fields [34][35][36].…”

Section: Algorithms Abbreviationmentioning

confidence: 99%

Optimizing the Design of Airfoil and Optical Buffer Problems Using Spotted Hyena Optimizer

Dhiman

Kaur

2018

Designs

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This paper presents the contemporary metaheuristic optimization algorithm named the Spotted Hyena Optimizer (SHO). The proposed technique is based on the law of gravitation and simulates the social behavior of spotted hyenas. The three basic steps of SHO, namely, searching for prey, encircling, and attacking prey, are mathematically modelled and discussed. The main concept of this work is to apply the recently developed SHO algorithm to two real-life design problems, namely optical buffer design and airfoil design. Experimental results reveal the supremacy of the SHO algorithm for solving the engineering design problems as compared to other competitor algorithms.

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A four-way decision-making system for the Indian summer monsoon rainfall

Cited by 41 publications

References 33 publications

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Implementation of the QoS framework using fog computing to predict COVID-19 disease at early stage

Optimizing the Design of Airfoil and Optical Buffer Problems Using Spotted Hyena Optimizer

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