Bat Algorithm for Congestion Alleviation in Power System Network

Paul, Kaushik; Kumar, Niranjan; Dalapati, Poulami

doi:10.1007/s40866-020-00100-9

Cited by 22 publications

(2 citation statements)

References 41 publications

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“…The frequency is adjusted to generate a new solution, and the position and velocity of each bat is updated in each iteration. The solutions proposed by all the bats in each iteration are compared to obtain the global solution [41,47]. Each bat also generates a local solution based on the current global solution by random flying.…”

Section: Bat Algorithmmentioning

confidence: 99%

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Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

et al. 2022

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Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow.

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Section: Bat Algorithmmentioning

confidence: 99%

“…The pulse emission rate and the loudness are updated in every iteration, and as each bat is nearing its target, it will reduce its loudness and increase the rate of emitting pulses. Once the bat is successful in detecting its prey, the loudness is reduced to zero [41,47].…”

Section: Bat Algorithmmentioning

confidence: 99%