Evolving Fusion-Based Visibility Restoration Model for Hazy Remote Sensing Images Using Dynamic Differential Evolution

Singh, Dilbag; Kaur, Manjit; Jabarulla, Mohamed Yaseen; Kumar, Vijay; Lee, Heung-No

doi:10.1109/tgrs.2022.3155765

Cited by 39 publications

(30 citation statements)

References 62 publications

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“…Singh et al [4] in their study proposed nonlinear estimations based on sparse KLMSs (Kernel Least Mean Squares).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Singh et al [ 4 ] in their study proposed nonlinear estimations based on sparse KLMSs (Kernel Least Mean Squares). Their scheme used adaptive kernel width optimizations for reducing computational complexities and easier implementations [ 17 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Radars detect and locate objects by using electromagnetic radiation bursts. Radars can be classi ed in a variety of ways, but categorized into eleven groups based on their functionality and primary characteristics [4]. Generic pulse radars play a prominent role in ECG where they emit a series of short-duration rectangular pulses in repeated patterns.…”

Section: Introductionmentioning

confidence: 99%

“…Radars detect and locate objects by using electromagnetic radiation bursts. Radars can be classified in a variety of ways, but categorized into eleven groups based on their functionality and primary characteristics [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services

Jain

Alsanie

Gago

et al. 2022

Computational Intelligence and Neuroscience

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ECG (electrocardiogram) identifies and traces targets and is commonly employed in cardiac disease detection. It is necessary for monitoring precise target trajectories. Estimations of ECG are nonlinear as the parameters TDEs (time delays) and Doppler shifts are computed on receipt of echoes where EKFs (extended Kalman filters) and electrocardiogram have not been examined for computations. ECG, certain times, results in poor accuracies and low SNRs (signal-to-noise ratios), especially while encountering complicated environments. This work proposes to track online filter performances while using optimization techniques to enhance outcomes with the removal of noise in the signal. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using IMCEHOs (Improved Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system nonlinearity where multi-iterative function (Optimized Iterative UKFs) predicts a target’s unknown parameters. To obtain optimal solutions theoretically, multi-iterative function takes less iteration, resulting in shorter execution times. The proposed multi-iterative function provides numerical approximations, which are derivative-free implementations. Signals are updated in the cloud environment; the updates are received by the patients from home. The simulation evaluation results with estimators show better performances in terms of reduced NMSEs (normalized mean square errors), RMSEs (root mean squared errors), SNRs, variances, and better accuracies than current approaches. Machine learning algorithms have been used to predict the stages of heart disease, which is updated to the patient in the cloud environment. The proposed work has a 91.0% accuracy rate with an error rate of 0.05% by reducing noise levels.

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“…Singh et al [4] in their study proposed nonlinear estimations based on sparse KLMSs (Kernel Least Mean Squares).…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services

Jain

Alsanie

Gago

et al. 2022

Computational Intelligence and Neuroscience

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“…is necessitates the use of automated detection to predict the severity of infections on rice leaves during the early stages of the disease cycle [16]. A consequence of this is that computer vision technologies are becoming more relevant in the field of sickness prediction.…”

Section: Detailed Analysis Of Existing Studymentioning

confidence: 99%

Instinctive Recognition of Pathogens in Rice Using Reformed Fractional Differential Segmentation and Innovative Fuzzy Logic-Based Probabilistic Neural Network

Preetham

Ahmad

Wattar

et al. 2022

Journal of Food Quality

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Rice is an essential primary food crop in the world, and it plays a significant part in the country’s economy. It is the most often eaten stable food and is in great demand in the market as the world’s population continues to expand. Rice output should be boosted to fulfil the growing demand. As a result, the yield of plant crops diminishes, creating an environment conducive to the spread of infectious illnesses. To boost the production of agricultural fields, it is necessary to remove plant diseases from the environment. This study presents ways for recognising three types of rice plant diseases, as well as a healthy leaf, in rice plants. This includes image capture, image preprocessing, segmentation, feature extraction, and classification of three rice plant illnesses, as well as classification of a healthy leaf, among other techniques. Following the K-means segmentation, the features are extracted utilising three criteria, which are colour, shape, and texture, to generate a final product. Colour, shape, and texture are the parameters used in the extraction of the features. It is proposed that a novel intensity-based technique is used to retrieve colour features from the infected section, whereas the form parameters of the infected section, such as the area and diameter, and the texture characteristics of the infected section are extracted using a grey-level co-occurrence matrix. The colour features are retrieved depending on the characteristics of the features. All three previous techniques were surpassed by the proposed fuzzy logic-based probabilistic neural network on a range of performance metrics, with the new network obtaining greater accuracy. Finally, the result is validated using the fivefold cross-validation method, with the final accuracy for the diseases such as bacterial leaf blight, brown spot, healthy leaf, and rice blast being 95.20 percent, 97.60 percent, 99.20 percent, and 98.40 percent, respectively, and 95.40 percent for the disease brown spot.

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Fusion of multi-modality biomedical images using deep neural networks

Gupta¹,

Kumar

Gupta³

et al. 2022

Soft Comput

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With the recent advancement in the medical diagnostic tools, multi-modality medical images are extensively utilized as a lifesaving tool. An efficient fusion of medical images can improve the performance of various medical diagnostic tools. But, gathering of all modalities for a given patient is defined as an ill-posed problem as medical images suffer from poor visibility and frequent patient dropout. Therefore, in this paper, a novel image fusion model is designed to fuse multi-modality medical images. The proposed model model suffers from hyper-parameters tuning issue, therefore, a multi-objective differential evolution (MDE) is used to optimize the initial parameters of the proposed model. The fusion factor and edge strength metrics are utilized to form a multi-objective fitness function. The performance of the proposed model is validated by comparing the proposed model models with nine competitive models over fifteen benchmark images. Performance analysis reveal that the proposed model outperforms the competitive fusion models.

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Evolving Fusion-Based Visibility Restoration Model for Hazy Remote Sensing Images Using Dynamic Differential Evolution

Cited by 39 publications

References 62 publications

A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services

A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services

Instinctive Recognition of Pathogens in Rice Using Reformed Fractional Differential Segmentation and Innovative Fuzzy Logic-Based Probabilistic Neural Network

Fusion of multi-modality biomedical images using deep neural networks

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