A quantum method for dynamic nonlinear programming technique using Schrödinger equation and Monte Carlo approach

Kaur, Amandeep; Kaur, Satnam; Dhiman, Gaurav

doi:10.1142/s0217984918503748

Cited by 47 publications

(15 citation statements)

References 34 publications

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“…As a result, the indirect method is more commonly used. Not only is the blood pressure monitored without any trauma to the human body but the monitored values are also more accurate [6][7][8][9][10][11][12][13][14][15]. The system diagram is shown in Figure 7.…”

Section: Body Temperature Acquisition Modulementioning

confidence: 99%

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[Retracted] A Detailed Research on Human Health Monitoring System Based on Internet of Things

Zhang

Duan

et al. 2021

Wireless Communications and Mobile Computing

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The technological advent in smart sensing devices and the Internet has provided practical solutions in various sectors of networking, public and private sector industries, and government organizations worldwide. This study intends to combine the Internet of Things (IoT) technology with health monitoring to make it personalized and timely through allowing the interconnection between the devices. This work is aimed at exploring various wearable health monitoring modules that people wear to monitor heart rate, blood pressure, pulse, body temperature, and physiological information. The information is acquired using the wireless sensor to create a health monitoring system. The data is integrated using the Internet of Things for processing, connecting, and computing to achieve real-time monitoring. The temperature of three people measured by the temperature thermometer is 36.4, 36.7, and 36.5 (°C), respectively, and the average acquired by the monitoring system of the three people is 36.5, 36.4, and 36.5 (°C), respectively, indicating that the system demonstrated relatively accurate and stable testability. The user’s ECG is displayed clearly and conveniently using the ECG acquisition system. The pulse rate of the three people tested by the system is 78, 78, and 79 (times/min), respectively, similar to the medical pulse meter results. The physiological information acquired using the semantic recognition, matching system, and character matching system is relatively accurate. It concludes that the human health monitoring system based on the Internet of Things can provide people with daily health management, instrumental in heightening health service quality and level.

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Section: Body Temperature Acquisition Modulementioning

confidence: 99%

“…The various applications [11][12][13][14][15] in which IOT can comprise security and surveillance, automation of agriculture, healthcare, traffic management, the emergence of smart cities, etc. [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] A Detailed Research on Human Health Monitoring System Based on Internet of Things

Zhang

Duan

et al. 2021

Wireless Communications and Mobile Computing

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“…In the SHO, it is assumed that the elite search agent knows about the prey location and other individuals try to update their positions by making a cluster-reliable group of friends-towards the elite agent. Further information about the ruling equations of the SHO can be found in previous studies [44][45][46].…”

Section: Spotted Hyena Optimizermentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.

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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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A quantum method for dynamic nonlinear programming technique using Schrödinger equation and Monte Carlo approach

Cited by 47 publications

References 34 publications

[Retracted] A Detailed Research on Human Health Monitoring System Based on Internet of Things

[Retracted] A Detailed Research on Human Health Monitoring System Based on Internet of Things

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

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

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