A Multiobjective, Lion Mating Optimization Inspired Routing Protocol for Wireless Body Area Sensor Network Based Healthcare Applications

Faheem, Muhammad; Butt, Rizwan Aslam; Raza, Basit; Alquhayz, Hani; Abbas, Muhammad; Ngadi, Md. Asri; Güngör, Vehbi Çağrı

doi:10.3390/s19235072

Cited by 21 publications

(9 citation statements)

References 42 publications

(46 reference statements)

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“…A detailed investigation on machine learning algorithms [33] have been experimented with the prominent methods for dimensionality reduction. For routing in BASNs, the optimal dynamic cluster-based solutions between input and output have been found by removing the challenges of local search given by Muhammad Faheem [34]. Table 1 presents a comparison of the related works presented in this section.…”

Section: Related Workmentioning

confidence: 99%

A novel SVM Kernel Classifier Technique using Support Vector Machine for Breast Cancer Classification

Ghantasala

Raparthi

B³

et al. 2023

Preprint

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Breast cancer prediction is an important topic in the field of healthcare. Breast cancer is one of the most common cancers in women and early detection is critical for successful treatment. There are several methods for predicting breast cancer, including imaging studies, genetic testing, and risk assessment models. Early detection can greatly improve the chances of successful treatment and long-term survival. One approach to detecting breast cancer is to use machine learning algorithms such as support vector machine (SVM) classifiers. SVMs are a popular type of supervised learning algorithm that can be used for classification or regression analysis. To use SVMs for breast cancer classification, you need to first prepare the data by dividing it into training and testing sets. The training set is used to train the SVM model, and the testing set is used to evaluate the performance of the model. The SVM model learns to classify the data by adjusting the parameters of the kernel function. In this paper, the performance of Linear, Polynomial, Gaussian and Sigmoid machine-learning kernels in the Support Vector Machine method was investigated to determine which kernel classifier is better at diagnosing breast cancer. In addition, this study made usage of the Wisconsin Breast Cancer (Diagnostic) dataset that contains 569 occurrences and 32 features for analysis. The major objective of this study is to compare a variety of kernel classifiers to identify the one that provides the best accuracy. Linear kernel support vector machine was shown to have the highest accuracy (97.90%) and lowest false discovery rates in this investigation. In contrast, other kernels and classification algorithms show low performance, which may not be more accurate in breast cancer prediction.

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Section: Related Workmentioning

confidence: 99%

A novel SVM Kernel Classifier Technique using Support Vector Machine for Breast Cancer Classification

Ghantasala

Raparthi

B³

et al. 2023

Preprint

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“…Wireless sensor networks also find applications in fields other than the underwater domain. The real‐time critical event monitoring application is presented in Faheem et al, 13 and Faheem et al 14 provide application in healthcare. Acoustic communication is generally feasible at lower frequencies, that is, tens of kHz, with limited transmission bandwidth. A detailed review of the techniques for the synchronization task in the underwater acoustic channel is presented in Sameer Babu et al 15 A few kHz of bandwidth is categorized as the wideband transmission, 16 resulting in the frequency selective channel 17 .…”

Section: Introductionmentioning

confidence: 99%

A prefix‐based approach for joint Doppler and channel estimation in underwater communication

Bhadouria,

Agrawal,

Kumar

2023

Int J Communication

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SummaryDeveloping a reliable and robust underwater acoustic communication system is a difficult task due to the complicated nature of the underwater channel, non‐stationary noise, and several other factors. Indeed, channel estimation or equalization presents numerous challenges in this non‐stationary, highly Doppler, multipath environment; as a result, traditional equalizers and PLL‐based methods have limited performance. Generally, communication over such time‐varying channels is accomplished via packets that contain a prefix/preamble signal for training, a payload containing the actual data, and a silent period for proper alignment. The prefix signal must be designed properly because it is used to estimate the channel and determine the start of the packet. In this paper, we propose a new prefix signal based on the hyperbolic chirp signal, which has Doppler invariance properties. These properties enable the extraction of the entire packet even under severe multipath and Doppler. Our new proposed prefix signal can accurately and efficiently characterize an underwater channel by estimating the multipath delay, amplitude, and Doppler scales. Our new method has been validated through extensive simulations using various channel models for its robustness and effectiveness under various conditions. It has also been tested on a real‐world channel.

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“…e traditional negative lm technology used in SEM laboratory has been gradually eliminated. At present, the new SEM instruments are digital photos, which saves analysts the tedious, time-consuming, and toxic darkroom work [2]. At present, the analysis results submitted to users are mostly realized by CD recording and U disk and other storage media copy.…”

Section: Introductionmentioning

confidence: 99%

Data Transmission Efficiency Optimization Algorithm of Laboratory Equipment Based on Frequency Control

Fang

2022

Mobile Information Systems

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In order to improve the efficiency and throughput of data transmission of laboratory instruments and equipment and reduce resource consumption, an optimization algorithm of data transmission efficiency of laboratory instruments and equipment based on frequency control is proposed. Firstly, the frequency control method is used to construct the real-time data transmission model of laboratory instruments and equipment, and the optimal equivalent load of instruments and equipment is obtained. Secondly, the filtering time constant is selected through the first-order low-pass filtering control model to obtain the value range of the filtering time constant. Finally, the filter time constant is optimized in real time according to the stabilized power prediction value sequence, the optimization objective function of data transmission efficiency is obtained, and the optimization of data transmission efficiency is realized by solving the objective function. The experimental results show that compared with the traditional algorithm, this algorithm can improve the data transmission efficiency of laboratory instruments and equipment and improve the data throughput and reduce the resource consumption. The data transmission efficiency of this algorithm is always maintained at more than 85%.

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A Multiobjective, Lion Mating Optimization Inspired Routing Protocol for Wireless Body Area Sensor Network Based Healthcare Applications

Cited by 21 publications

References 42 publications

A novel SVM Kernel Classifier Technique using Support Vector Machine for Breast Cancer Classification

A novel SVM Kernel Classifier Technique using Support Vector Machine for Breast Cancer Classification

A prefix‐based approach for joint Doppler and channel estimation in underwater communication

Data Transmission Efficiency Optimization Algorithm of Laboratory Equipment Based on Frequency Control

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