An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion

Jose, Jais; Gautam, Neha; Tiwari, Mohit; Tiwari, Tripti; Suresh, Anupama; Sundararaj, Vinu; Mr, Rejeesh

doi:10.1016/j.bspc.2021.102480

Cited by 131 publications

(45 citation statements)

References 35 publications

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“…The interpretation of results necessitates significant resources and time. Various researchers have used different ensemble‐based classifiers such as ensemble classifier based on differential evolution algorithm, 30 Ensemble classifier, 25 hybrid ensemble pruning approach, 26 word embeddings and clustering, 27 classifier and feature set ensembles, 28 and ensemble scheme 27,29,31–34 . Multi‐classifier weighted ensemble 27–29,31–34 may effectively accentuate exceptional classifiers' decision‐making abilities while decreasing the influence of high‐deviation classifiers' incorrect information on classification.…”

Section: Review Of Related Workmentioning

confidence: 99%

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ESVM‐SWRF: Ensemble SVM‐based sample weighted random forests for liver disease classification

Padmakala

Subasini

Karuppiah

et al. 2021

Numer Methods Biomed Eng

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Recently, a significant way to diagnose the disease is using the model of medical data mining. The most challenging task in the healthcare field is to face a large amount of data during disease analyzes and prediction. Once the data are transformed into valuable data by means of data mining models then the actual prediction and decision making is easier. The existing studies met few shortcomings because of higher execution time, more computational complexities, less scalability, slow convergence, and lack of providing the solution. In this article, we have proposed an ensemble SVM‐based sample weighted random forests (eSVM‐swRF) with novel improved colliding body optimization (NICBO) algorithm to predict liver diseases. The extraction, loading, transformation, and analysis (ELTA) are used to pre‐process the patient data. The significant feature with a suitable model is generated depending upon the filter‐based method. Based on eSVM‐swRF, the parameter values such as penalty parameter (P), threshold (T), and mTry are optimized via a novel improved colliding boding optimization (NICBO) algorithm. The UCI dataset provides liver disease data for this study. The implementation platform of RapidMiner Studio version 7.6 with different evaluation measures is used to validate the performance of eSVM‐swRF with the NICBO method. Anyway, the proposed method yields outstanding performance than other existing methods such as Particle Swarm Optimization‐based Support Vector Machine (PSO‐SVM), fuzzy adaptive, and neighbor weighted k‐NN (FuzzyANWKNN), Naïve Bayes‐based Support Vector Machine (NB‐SVM), and Neural network.

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Section: Review Of Related Workmentioning

confidence: 99%

“…We have collected few issues from the existing studies due to higher execution time, more computational complexities, less scalability, slow convergence, and lack of providing the solution 24–29 . To overcome these issues, we introduced eSVM‐swRF with the NICBO algorithm for liver disease prediction.…”

Section: Introductionmentioning

confidence: 99%

ESVM‐SWRF: Ensemble SVM‐based sample weighted random forests for liver disease classification

Padmakala

Subasini

Karuppiah

et al. 2021

Numer Methods Biomed Eng

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“…But this approach failed to apply in various machine learning algorithms. In recent years, many soft computing approaches has been acheived better performance in engineering applications 27–38.…”

Section: Review Of Related Workmentioning

confidence: 99%

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Alqahtani

Pandiaraj²,

Murali³

et al. 2021

Concurrency and Computation

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Presently, large recordings like audio and video are uploaded each day on the public networks particularly Facebook, YouTube, etc. Such postings generate unlimited data via the Internet. Over the next few decades adapting and dealing with data mining to obtain relevant details from social media is considered as a difficult task. To tackle these challenges, a novel text-audio-video consistency-driven multimodal sentiment analysis method is proposed in this paper. This proposed system examines the correlation among the text, audio and video followed by multimodal sentiment analysis.A different set of features are extracted and then the extracted features are chosen optimally by employing a new hybrid grass bee optimization algorithm (HGBEE) thus obtaining a feature set containing an optimal value for better precision and low computational time. Then an utterance level multimodular fusion regarding text, audio, and video features is developed. Finally, the proposed multikernal extreme learning classifier (MKELM) is employed for sentiment classification. Then the proposed system is evaluated by testing with three multimodal datasets in terms of precision, accuracy, recall, and F-measure, respectively. From the simulation, it is clear that the proposed system accomplishes the maximum classification accuracy value of 98.3% with minimum computation time. The implementation of our proposed approach is done under the MATLAB platform. K E Y W O R D Saccuracy and computation time, feature level fusion, grass bee, kernal extreme learning classifier, multimodal sentiment analysis Recently, the public network facilitates gathering information for posting and sharing various types of multimodal features in social media without knowing much detail regarding the network topology and architecture. 1 Individuals are now widely utilizing the internet-based life condition, for example, YouTube, Facebook, Blog, and Microblog so as to express their opinions. 2 Here, sentiment analysis acts as a key for unlocking huge data since

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“…The cyber threat to ITS systems causes traffic jams, a drastic traffic accident that leads to a domino effect which causes an economic burden over an extensive traffic network area as well as entire members and operators in the transportation system (Deichmann et al, 2019). In recent years, soft computing methods has been attracted in engineering applications (Jose et al, 2021;Rejeesh, 2019;Sundararaj, 2019;Sundararaj et al, 2018Sundararaj et al, , 2020Vinu, 2016Vinu, , 2019. This paper proposes convolutional neural network-oppositionalbased Henry gas solubility optimization (CNN-OHGS) for an AV control system to enhance the robustness of an AV.…”

Section: Introductionmentioning

confidence: 99%

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

Ravikumar

Kavitha

2021

Journal of Field Robotics

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Numerous developments in technology toward autonomous vehicle systems (AVSs) have been performed for so many years all over the world. As our day-to-day life is becoming progressively dependent on automation vehicle system and control devices, the craze on automation advancements is expected to move closer through scientific technologies like artificial intelligence and robotics. From another point of view, the cyber threat to the AVS causes drastic accidents and traffic congestion by varying the speed differences among the vehicles. To overcome such shortcomings, this paper presented a convolutional neural network-oppositional-based Henry gas solubility optimization (CNN-OHGS) algorithm for an autonomous vehicle control system to enhance the robustness of the vehicle. At the same time, the attackers attempt to embed the faulty or defective data into the sensor readings of the autonomous vehicle to interrupt the optimal distances among the automated vehicles. Therefore to minimize such issues, our proposed framework employs the CNN-OHGS algorithm to reduce the distance variations among the vehicles thus ensuring the safety and optimal distance variation. Finally, the experimental analysis is conducted and the performance evaluation for various attacks, FID evaluation, and remorse function, and distance deviation for all sensor signal attacks are evaluated.The comparative analysis is made and we can clearly state that the proposed work has outperformed other existing approaches.

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An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion

Cited by 131 publications

References 35 publications

ESVM‐SWRF: Ensemble SVM‐based sample weighted random forests for liver disease classification

ESVM‐SWRF: Ensemble SVM‐based sample weighted random forests for liver disease classification

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

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