Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey

Swain, Shasank S.; Bhushan, Bharat; Viriyasitavat, Wattana

doi:10.1007/s11831-022-09733-8

Cited by 23 publications

(16 citation statements)

References 128 publications

(98 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is worth noting that the existing works all use manual evaluation methods when evaluating the coherence of summary sentences. ere is a lack of a mechanism or process for automatic measurement of sentence coherence within the summary generation model [32][33][34]. To sum up, the current generative text summarization methods should meet or solve the following problems: first, it can generate coherent and highly readable summary texts based on the given original text; the processing mechanism for automatic coherence measurement of the generated summary sentences; thirdly, the labeling dependence of the summary ground-truth data in the model training process should be minimized to reduce the model training cost [35][36][37].…”

Section: Related Workmentioning

confidence: 99%

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Abdeljaber

Ahmad

Alharbi

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

The purpose of automatic text summarising technology is to condense a given text while properly portraying the main information in the original text in a summary. To present generative text summarising approaches, on the other hand, restructure the original language and introduce new words when constructing summary sentences, which can easily lead to incoherence and poor readability. This research proposes a XAI (explainable artificial intelligence)-based Reinforcement Learning-based Text Summarization of Social IoT-Based Content using Reinforcement Learning. Furthermore, standard supervised training based on labelled data to improve the coherence of summary sentences has substantial data costs, which restricts practical applications. In order to do this, a ground-truth-dependent text summarization (generation) model (XAI-RL) is presented for coherence augmentation. On the one hand, based on the encoding result of the original text, a sentence extraction identifier is generated, and the screening process of the vital information of the original text is described. Following the establishment of the overall benefits of the two types of abstract writings, the self-judgment approach gradient assists the model in learning crucial sentence selection and decoding the selected key phrases, resulting in a summary text with high sentence coherence and good content quality. Experiments show that the proposed model's summary content index surpasses text summarising ways overall, even when there is no pre-annotated summary ground-truth; information redundancy, lexical originality, and abstract perplexity also outperform the current methods.

show abstract

Section: Related Workmentioning

confidence: 99%

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Abdeljaber

Ahmad

Alharbi

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…e cryptographic keys can guard against active attacks from the outside, but they are unreliable in the case of passive attacks that compromise service quality as well as reliability. Optimization techniques based on the behaviour of lions during territorial defence and takeover [29,30], such as the Lion algorithm, were devised to minimise passive attacks and to optimise path selection. e nonlinear system identification solution is implemented.…”

Section: Related Workmentioning

confidence: 99%

Vampire Attack Mitigation and Network Performance Improvement Using Probabilistic Fuzzy Chain Set with Authentication Routing Protocol and Hybrid Clustering-Based Optimization in Wireless Sensor Network

Alkwai

Aledaily

Almansour

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The most effective threat for wireless sensor networks (WSN) is Vampire attacks on sensor nodes as they can stretch the network connectivity among them and influence the network’s energy, which can drain the network. Vampire attack has particular malicious nature of sensor nodes in which they can widely exploit features of combined routing protocol. Fuzzy rules and fuzzy sets are highly optimal techniques in mitigating the vampire attacks of the network, which can quantify the uncertain behaviour of sensor nodes. This study aims to propose a novel technique using a probabilistic fuzzy chain set with authentication-based routing protocol and hybrid clustering technique for data optimization of the network. The suggested approach here employs a fuzzy-based chain rule set to combat growing types of vampire assaults using probability formulas. The authentication routing protocol has increased network routing security. The proposed technique (PFCS-ARP_HC) has optimized the energy consumption of network. Simulation for this technique has been carried out using NS2 and experimental results show the performance of the proposed model in terms of throughput of 98%, packet delivery ratio of 89%, energy consumption of 67%, latency of 46% control overhead of 53%, and attack detection ratio of 87.9%.

show abstract

“…ere are 2 categories of breast cancer: intrusive and non-intrusive. Ductal carcinoma in situ is a non-intrusive variety, whereas the rest are intrusive [10][11][12][13].…”

Section: Categories Of Breast Cancermentioning

confidence: 99%

A Supervised Learning Identification System for Prognosis of Breast Cancer

Rawat

Gulati

Kaur

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Breast cancer is one of the most dangerous cancers, accounting for a large number of fatalities each year. It is the leading cause of mortality among women globally. It is getting a lot of interest in the scientific community because of its possible life-threatening danger. As a consequence, many machine learning methods (MLMs) have been modified to provide the best results for early diagnosis of this malignancy. Machine learning methods (MLMs) offer several beneficial implications in breast cancer, including early prognosis, detection, and diagnosis. Compared to traditional statistical analysis, machine learning methods (MLMs) have the capacity to improve the analysis of various health data, such as unstructured, complicated, and noisy data. With the demanding prevalence of breast cancer and the arrival of “data reformation,” it is thus imperative to mention the ethical consequences of machine learning (ML) on society and cancer care. It offers conclusively strong tools, smart methods, and efficient algorithms that can help in the prognosis of breast cancer. The focus of this review is on supervised techniques such as classification and regression that may be implemented and used for breast cancer data analysis. Some supervised learning methods like Naive Bayes, AdaBoost, and support vector machine are presented in this work in the early identification of breast cancer. These algorithms have been analyzed for their accuracy and efficiency using various assessment metrics and methods.

show abstract

Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey

Cited by 23 publications

References 128 publications

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Vampire Attack Mitigation and Network Performance Improvement Using Probabilistic Fuzzy Chain Set with Authentication Routing Protocol and Hybrid Clustering-Based Optimization in Wireless Sensor Network

A Supervised Learning Identification System for Prognosis of Breast Cancer

Contact Info

Product

Resources

About