Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer

Senthilkumar, G.; Ramakrishnan, Jothilakshmi; Frnda, Jaroslav; Ramachandran, Manikandan; Gupta, Deepak; Tiwari, Prayag; Mohammed, Mazin Abed

doi:10.1109/access.2021.3087022

Cited by 22 publications

(7 citation statements)

References 45 publications

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“…This section evaluated the performance of our proposed O-LAR mechanism against the existing LAR, D-LAR, and LEPR schemes using simulated experiments. The simulations of the proposed and existing routing protocols were conducted for different scenarios with varying number of UAVs and their speed through the NS-2.35 simulator [38][39][40][41][42][43][44][45].At the beginning of FANET scenario, all UAVs (05-25) are randomly distributed in the area of 1×1km 2 with a transmission range of each UAV is maximum as 250m and used IEEE 802.11g as a mac layer wireless standard. The speed of each UAV is varying from 20 to 100m/sec.…”

Section: Simulation Setup and Results Discussionmentioning

confidence: 99%

3D Location Oriented Routing in Flying Ad-Hoc Networks for Information Dissemination

et al. 2021

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A Flying Ad-hoc Networks (FANETs) is an autonomous technology that creates a selforganized wireless network via Unmanned Arial Vehicles (UAVs). In this network, all UAVs can communicate within a restricted range of wireless communication in the absence of fixed infrastructure. As a result of high mobility, the limited energy, and the communication range of UAVs, network forming, and deformation between them are very frequent that causes packet delivery failure. Therefore, a stable route is always needed to ensure effective data dissemination between source and destination in FANETs. Since it has drastically changing network topology, therefore, to maintain the stable route during packet transmission, there is a need for a suitable routing protocol. This paper proposes an Optimized Location-Aided Routing (O-LAR) protocol which is the modified version of Location-Aided Routing (LAR) protocol. Our protocol's novelty comes from the fact that it established an optimal route between UAVs for information dissemination towards their respective destination UAV by considering weight function. A weighted function is used to decide the best next-hop node selection based on the parameters like residual energy, distance, and UAV movement direction. The performance of the O-LAR is evaluated mathematically and simulated through the NS-2 simulator. The empirical results attest that O-LAR improves the link duration, network lifetime, packet delivery ratio, and average throughput compared with the state-of-the-art protocols: LEPR, D-LAR, and LAR. Further, the proposed scheme reduces the number of next-hops, routing overhead and end-to-end delay compared to the state-of-the-art protocols.

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Section: Simulation Setup and Results Discussionmentioning

confidence: 99%

3D Location Oriented Routing in Flying Ad-Hoc Networks for Information Dissemination

et al. 2021

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“…developed a deep learning auxiliary diagnosis based on cloud and 5 G technology to address breast cancer diagnosis in source-limited regions [ 30 ]. To evaluate the effectiveness of treatment and to predict the death risk in cervical cancer patients, analysis systems based on the multi-task logistic regression algorithm [ 31 , 32 ], the artificial fish swarm algorithm [ 33 ], and the K-means clustering and support vector machines algorithm [ 34 ] have been proposed. Compared with traditional machine learning algorithms, deep learning methods are more intelligent and suitable for complex and large data analysis.…”

Section: Introductionmentioning

confidence: 99%

Siamese deep learning video flow cytometry for automatic and label-free clinical cervical cancer cell analysis

Liu,

Yuan,

Liu

et al. 2024

Biomed. Opt. Express

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Automatic and label-free screening methods may help to reduce cervical cancer mortality rates, especially in developing regions. The latest advances of deep learning in the biomedical optics field provide a more automatic approach to solving clinical dilemmas. However, existing deep learning methods face challenges, such as the requirement of manually annotated training sets for clinical sample analysis. Here, we develop Siamese deep learning video flow cytometry for the analysis of clinical cervical cancer cell samples in a smear-free manner. High-content light scattering images of label-free single cells are obtained via the video flow cytometer. Siamese deep learning, a self-supervised method, is built to introduce cell lineage cells into an analysis of clinical cells, which utilizes generated similarity metrics as label annotations for clinical cells. Compared with other deep learning methods, Siamese deep learning achieves a higher accuracy of up to 87.11%, with about 5.62% improvement for label-free clinical cervical cancer cell classification. The Siamese deep learning video flow cytometry demonstrated here is promising for automatic, label-free analysis of many types of cells from clinical samples without cell smears.

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“…They proposed a novel wrapper-based feature selection model for the predictive analysis of early onset in diabetic patients. Senthilkumar et al (2021) proposed a recursive prediction model based on AI techniques for the prediction of cervical cancer incidence, named the ENSemble classification framework (ENSCF).…”

Section: Introductionmentioning

confidence: 99%

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

Zhao²,

Liu³

et al. 2022

Front. Neuroinform.

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Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.

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Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer

Cited by 22 publications

References 45 publications

3D Location Oriented Routing in Flying Ad-Hoc Networks for Information Dissemination

3D Location Oriented Routing in Flying Ad-Hoc Networks for Information Dissemination

Siamese deep learning video flow cytometry for automatic and label-free clinical cervical cancer cell analysis

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

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