An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Rashid, Junaid; Batool, Saba; Kim, Jung-Eun; Nisar, Muhammad Wasif; Hussain, Amir; Juneja, Sapna; Kushwaha, Riti

doi:10.3389/fpubh.2022.860396

Cited by 46 publications

(17 citation statements)

References 73 publications

(85 reference statements)

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“…As PSO is an optimization technique, it has the ability to optimize the ELM parameters like weight and bias as well as the hidden neurons. From those hidden neurons, the above technique can find the good accuracy [ 41 ]. Here, the automatic categorization of encephalon tumor accomplished through our suggested PSO-ELM model [ 42 ].…”

Section: Methodsmentioning

confidence: 99%

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

Mohapatra

Sahu

Almotiri

et al. 2022

Computational Intelligence and Neuroscience

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Nowadays, so many people are living in world. If so many people are living, then the diseases are also increasing day by day due to adulterated and chemical content food. The people may suffer either from a small disease such as cold and cough or from a big disease such as cancer. In this work, we have discussed on the encephalon tumor or cancer which is a big problem nowadays. If we will consider about the whole world, then there are deficiency of clinical experts or doctors as compared to the encephalon tumor affected person. So, here, we have used an automatic classification of tumor by the help of particle swarm optimization (PSO)-based extreme learning machine (ELM) technique with the segmentation process by the help of improved fast and robust fuzzy C mean (IFRFCM) algorithm and most commonly feature reduction method used gray level co-occurrence matrix (GLCM) that may helpful to the clinical experts. Here, we have used the BraTs (“Multimodal Brain Tumor Segmentation Challenge 2020”) dataset for both the training and testing purpose. It has been monitored that our system has given better classification accuracy as an approximation of 99.47% which can be observed as a good outcome.

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Section: Methodsmentioning

confidence: 99%

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

Mohapatra

Sahu

Almotiri

et al. 2022

Computational Intelligence and Neuroscience

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“…One of two strategies is commonly used to categorize medical images using CNN [ 30 ]. The first is learning from the ground up, and the second is transfer learning.…”

Section: Background On Cnnmentioning

confidence: 99%

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Tiwari

Pant

Elarabawy

et al. 2022

Computational Intelligence and Neuroscience

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Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.

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“…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]. Under any given IoT workload, the mathematical model estimates the minimum number of fog nodes required to meet QoS requirements.…”

Section: Related Workmentioning

confidence: 99%

“…The controller in the Fog cluster-based approach needs to have such a mechanism for better performance. Since Quality of Service (QoS) is very important in fog computing, load balancing plays a crucial role in it [32][33][34].…”

Section: User Subsystemmentioning

confidence: 99%

A Fog-Cluster Based Load-Balancing Technique

et al. 2022

Self Cite

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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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An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Cited by 46 publications

References 73 publications

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

A Fog-Cluster Based Load-Balancing Technique

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