Adaptive inertia weight Bat algorithm with Sugeno-Function fuzzy search

Rauf, Hafiz Tayyab; Malik, Sumbal; Shoaib, Umar; Irfan, Muhammad Naeem; Lali, M. Ikramullah

doi:10.1016/j.asoc.2020.106159

Cited by 59 publications

(38 citation statements)

References 47 publications

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“…They support multiple inputs and outputs. These techniques are robust to noise, non-linear, multivariate inputs, and multi-step forecast [ 2 , 36 , 53 ].…”

Section: Methods and Modelsmentioning

confidence: 99%

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Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

Rauf

Lali

Khan

et al. 2021

Pers Ubiquit Comput

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“…They support multiple inputs and outputs. These techniques are robust to noise, non-linear, multivariate inputs, and multi-step forecast [ 2 , 36 , 53 ].…”

Section: Methods and Modelsmentioning

confidence: 99%

“…The uncertain circumstances of the epidemic forced the scientists and data analysts to proposed COVID-19 spreading models to predict its intensity shortly [1,3,18,32,33,38,52]. Similarly, some optimization techniques [36] can also help to optimize such models and leads to more optimal results.…”

mentioning

confidence: 99%

Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

Rauf

Lali

Khan

et al. 2021

Pers Ubiquit Comput

Self Cite

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“…In [41], a new variant of the Bat Algorithm (BA) [27] called ASF-BA was introduced. ASF-BA added adaptive inertia weight to speed up the velocity rate of bats.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Group-Based Cooperative Optimization Algorithm

et al. 2020

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Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.

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“…AI is now mature to be embedded with the state of the art machine learning, and deep learning algorithm in different domains such as medicine (Meraj et al, 2019), agriculture (Rauf et al, 2019), biometrics (Jin, Cruz & Goncalves, 2020), renewable energy (Gao, Wang & Shen, 2020b), and cloud computing (Gao, Wang & Shen, 2020c;Gao, Wang & Shen, 2020a). AI is gradually changing medicine as we know it, as more and more ways are being researched to automate medical practices and support the doctors and specialists (Brag, 2020;Rauf et al, 2020;Kulkarni et al, 2020;Briganti & Le Moine, 2020). Many benefits are attached to artificial intelligence in medicine (Jain et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

Albahli

Rauf

Algosaibi

et al. 2021

PeerJ Computer Science

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Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.

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Adaptive inertia weight Bat algorithm with Sugeno-Function fuzzy search

Cited by 59 publications

References 47 publications

Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

Dynamic Group-Based Cooperative Optimization Algorithm

AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

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