The Amalgamative Sharp Wireless Sensor Networks Routing and with Enhanced Machine Learning

Ramisetty, Sowjanya; Kavita, .; Varma, Sahil

doi:10.1166/jctn.2019.8247

Cited by 35 publications

(3 citation statements)

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“…When a certificate for a server is issued, the CA signs the certificate using its private key. The client can then check that the server has a certificate from a CA that the platform recognizes [64]. Beyond this, there is one more factor, i.e., response time, that directly affects our developed application's performance, as a delay can hamper the performance of the system.…”

Section: Practicalmentioning

confidence: 99%

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

Kaur

Singh

Mansoor

et al. 2022

Wireless Communications and Mobile Computing

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The brain tumor is the 22nd most common cancer worldwide, with 1.8% of new cancers. It is likely the most severe ailment that necessitates early discovery and treatment, and it requires the competence of neurosubject-matter experts and radiologists. Because of their enormous increases in data search and extraction speed and accuracy, as well as individualized treatment suggestions, machine and deep learning techniques are being increasingly commonly applied throughout healthcare industries. The current study depicts the methodologies and procedures used to detect a tumor inside the brain utilizing machine and deep learning techniques. Initially, data were preprocessed using contrast limited adaptive histogram equalization. Then, features were extracted using principal component analysis and independent component analysis (ICA). Next, the image was smoothed using multiple optimization techniques such as firefly and cuckoo search, lion, and bat optimization. Finally, Naïve Bayes and recurrent neural networks were utilized to classify the improved results. According to the findings, the ICA with cuckoo search and Naïve Bayes has the best mean square error rate of 1.02. With 64.81% peak signal-to-noise and 98.61% accuracy, ICA with hybrid optimization and a recurrent neural network (RNN) proved to better than the other algorithms. Furthermore, a Smartphone application is designed to perform quick and decisive actions. It helps neurologists and patients identify the tumor from a brain image in the early stages.

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Section: Practicalmentioning

confidence: 99%

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

Kaur

Singh

Mansoor

et al. 2022

Wireless Communications and Mobile Computing

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“…The approach for predicting the traffic 38 in two extreme conditions like peak hour traffic and post‐accident traffic was discussed 39 using the model of deep LSTM network. In Cheng et al, 40 a forecasting model was proposed, which combines the deep architecture of CNN and RNN for extracting the features from the traffic flow information.…”

Section: Related Workmentioning

confidence: 99%

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Vijayalakshmi¹,

Ramar²,

Jhanjhi

et al. 2020

Int J Communication

101

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Summary In the development of smart cities, the intelligent transportation system (ITS) plays a major role. The dynamic and chaotic nature of the traffic information makes the accurate forecasting of traffic flow as a challengeable one in ITS. The volume of traffic data increases dramatically. We enter the epoch of big data. Hence, a 1deep architecture is necessary to process, analyze, and inference such a large volume of data. To develop a better traffic flow forecasting model, we proposed an attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model. The proposed scheme uses the spatial and time‐based details of the traffic data, which are extracted using CNN and LSTM networks to improve the model accuracy. The attention‐based model helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow. The results show that our attention‐based CNN‐LSTM prediction model provides better accuracy in terms of prediction during weekdays and weekend days in the case of peak and nonpeak hours also. We used data from the largest traffic data set the California Department of Transportation (Caltrans) for our prediction work.

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“…Recent research breakthroughs have made significant contributions to the advancement of machine learning algorithms by using the benefits of quantum computing. However, tremendous work has been done to design and implement quantum versions [4] of ANN. Besides this, they are also based on more natural aspects that are yet to be accomplished [5].…”

Section: Introductionmentioning

confidence: 99%

Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Kumar

Koul

Sisodia

et al. 2021

Wireless Communications and Mobile Computing

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Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure.

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The Amalgamative Sharp Wireless Sensor Networks Routing and with Enhanced Machine Learning

Cited by 35 publications

References 0 publications

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

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