Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science

Sumathy, B.; Chakrabarty, Arindam; Gupta, Sandeep; Hishan, Sanil S.; Raj, Bhavana; Gulati, Kamal; Dhiman, Gaurav

doi:10.4018/ijrqeh.299959

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…Although the above models have achieved improvement in the accuracy of summary generation, the recurrent neural network and its variants are all time-step-based sequence structures, which seriously hinders the parallel training of the model [16][17][18], resulting in the inference process being limited by memory, resulting in reduced encoding and decoding speed of the summary generation model, and increased training overhead [19][20][21][22][23]. On the other hand, the above works optimize the model to maximize the ROUGE index or maximum likelihood without considering the coherence or fluency of the summary sentence [24][25][26] and relying on the ground-truth value of the annotated summary text in advance. With supervised training, the data cost involved in model training is high.…”

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

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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.

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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

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“…The First, the author provided an arrival-service model for FoT data traffic based on multilayer waiting for lines with finite-size intervals. The proposed policy was compared to first-in-first-out and multi-priority-discipline queue strategies with the help of a complete study of wait times and gaps in wait times [23][24][25]. Machine learning and Clustering based various methods are used for the text analysis [26], internet of things [27][28][29][30][31][32][33], and disease detection [34].…”

Section: Related Workmentioning

confidence: 99%

A Fog-Cluster Based Load-Balancing Technique

et al. 2022

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The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

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“…Invasive malignancies cannot heal quickly and might spread to other regions of the body. ese are deadly malignancies that can sprawl to other organs of the human body which results in metastatic cancer [6][7][8][9].…”

Section: Breast Cancermentioning

confidence: 99%

A Supervised Learning Identification System for Prognosis of Breast Cancer

Rawat

Gulati

Kaur

et al. 2022

Mathematical Problems in Engineering

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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.

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Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science

Cited by 12 publications

References 22 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

A Fog-Cluster Based Load-Balancing Technique

A Supervised Learning Identification System for Prognosis of Breast Cancer

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