Multi-disease big data analysis using beetle swarm optimization and an adaptive neuro-fuzzy inference system

Singh, Parminder; Kaur, Avinash; Batth, Ranbir Singh; Kaur, Sukhpreet; Gianini, G.

doi:10.1007/s00521-021-05798-x

Cited by 23 publications

(14 citation statements)

References 59 publications

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“…In Figure 12 , we can say that BSO-ANFIS better than others; it shows the accuracy and precision near about 96.08 and 98.63, respectively, which is higher as compared to the others. With the help of these algorithms, the multidiseases can be detected by the advancement in this field [ 15 ].…”

Section: Fuzzy Allocation-based Model In Health Care Data Managementmentioning

confidence: 99%

“…For the accurate diagnosis and treatment, many researchers are working on the deep learning of neurological diseases with the help of artificial intelligence [ 13 , 14 ]. The computing system plays an important role in the diagnosis and treatments; the most common and popular artificial intelligence (AI) technology is the neuro fuzzy system that is applied for the effective classification and detection of diseases [ 9 , 15 ]. This system decreases the staff workload.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks

Vyas

Gupta

Bhargava

et al. 2022

Journal of Healthcare Engineering

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The development of wireless sensors and wearable devices has led health care services to the new paramount. The extensive use of sensors, nodes, and devices in health care services generate an enormous amount of health data which is generally unstructured and heterogeneous. Many generous methods and frameworks have been developed for efficient data exchange frameworks, security protocols for data security and privacy. However, very less emphasis has been devoted to structuring and interpreting health data by fuzzy logic systems. The wireless sensors and device performances are affected by the remaining battery/energy, which induces uncertainties, noise, and errors. The classification, noise removal, and accurate interoperation of health data are critical for taking accurate diagnosis and decision making. Fuzzy logic system and algorithms were found to be effective and energy efficient in handling the challenges of raw medical data uncertainties and data management. The integration of fuzzy logic is based on artificial intelligence, neural network, and optimization techniques. The present work entails the review of various works which integrate fuzzy logic systems and algorithms for enhancing the performance of healthcare-related apps and framework in terms of accuracy, precision, training, and testing data capabilities. Future research should concentrate on expanding the adaptability of the reasoning component by incorporating other features into the present cloud architecture and experimenting with various machine learning methodologies.

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Section: Fuzzy Allocation-based Model In Health Care Data Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks

Vyas

Gupta

Bhargava

et al. 2022

Journal of Healthcare Engineering

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“…In this context, authors have proposed an improved skin lesion segmentation and classification technique taking advantage of swarm intelligence (SI) and neural network architecture. Based on this, the beetle swarm optimization and adaptive neuro-fuzzy inference system (BSO-ANFIS) model is efficient for the disease diagnosis used for skin lesion classification [ 16 ]. It is very clear that skin cancer is one of the most serious types of cancer.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Thapar

Rakhra

Cazzato

et al. 2022

Journal of Healthcare Engineering

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Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals’ visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques’ results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.

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“…Therefore, fog computing helps latencysensitive applications achieve their quality of service. Moreover, with the advent of various machine-learning algorithms [8,9] and soft-computing technologies [10], it has become viable to offer automated remote healthcare services. In this study, we mainly focus on leveraging various emerging technologies of Industry 4.0 to provide new solutions to the growing problem of viral encephalitis.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Healthcare Cyber Physical Framework for Encephalitis Diagnosis Based on Information Fusion and Soft-Computing Techniques

Gupta

Singh

2022

New Gener. Comput.

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Viral encephalitis is a contagious disease that causes life insecurity and is considered one of the major health concerns worldwide. It causes inflammation of the brain and, if left untreated, can have persistent effects on the central nervous system. Conspicuously, this paper proposes an intelligent cyber-physical healthcare framework based on the IoT-fog-cloud collaborative network, employing soft-computing technology and information fusion. The proposed framework uses IoT-based sensors, electronic medical records, and user devices for data acquisition. The fog layer, composed of numerous nodes, processes the most specific encephalitis symptomrelated data to classify possible encephalitis cases in real time to issue an alarm when a significant health emergency occurs. Furthermore, the cloud layer involves a multi-step data processing scheme for in-depth data analysis. First, data obtained across multiple data generation sources are fused to obtain a more consistent, accurate, and reliable feature set. Data preprocessing and feature selection techniques are applied to the fused data for dimensionality reduction over the cloud computing platform. An adaptive neuro-fuzzy inference system is applied in the cloud to determine the risk of a disease and classify the results into one of four categories: no risk, probable risk, low risk, and acute risk. Moreover, the alerts are generated and sent to the stakeholders based on the risk factor. Finally, the computed results are stored in the cloud database for future use. For validation purposes, various experiments are performed using real-time datasets. The analysis results performed on the fog and cloud layers show higher performance than the existing models. Future research will focus on the resource allocation in the cloud layer while considering various security aspects to improve the utility of the proposed work.

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Multi-disease big data analysis using beetle swarm optimization and an adaptive neuro-fuzzy inference system

Cited by 23 publications

References 59 publications

Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks

Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

An Intelligent Healthcare Cyber Physical Framework for Encephalitis Diagnosis Based on Information Fusion and Soft-Computing Techniques

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