Time Series Analysis Using Stacked LSTM Model for Indian Stock Market

Goswami, Shruti; Sagar, Anil Kumar; Nand, Parma; Khalaf, Osamah Ibrahim

doi:10.1109/globconet53749.2022.9872386

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…The higher magnitude in epileptic conditions indicates a more pronounced skewness. Results are analyzed depending on performance parameters like specificity, accuracy, sensitivity, in which developed method accuracy seems [103], [104] to be improved by integrating the extended run and flexible elements which instructs the algorithm and states that the EEG signals consist many information about the [105] brain waves. This approach aligns with the research objectives by providing a holistic and innovative solution to the challenges of epilepsy identification from EEG signals.…”

Section: Resultsmentioning

confidence: 99%

Epilepsy Identification using Hybrid CoPrO-DCNN Classifier

Basawaraj Birajadar,

Osman Mulani,

Ibrahim Khalaf

et al. 2024

IJCDS

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The Electroencephalogram (EEG) stands as a burgeoning frontier in the study of neuronal activity, offering a rich tapestry of information crucial for identifying abnormalities and addressing cognitive disorders and irregularities. This paper delves into the examination of EEG from subjects exhibiting abnormalities, contrasting them with those from normal subjects. Various topographical features such as Mean, Entropy, and Wavelet bands are meticulously evaluated and compared.Inspired by the adaptive hunting strategies observed in coyotes, this study introduces a novel hybrid computational model that integrates deep learning architectures, aiming to amplify diagnostic accuracy. The methodology hinges upon the development of a unique computational algorithm inspired by the intricate hunting behaviors of coyotes, seamlessly fused with the potent data-driven capabilities of deep neural networks. This hybrid model is meticulously applied to scrutinize EEG data for the detection of brain disorders, capitalizing on both the biologically-inspired algorithm and the data-centric strengths of deep learning. The results obtained from this innovative approach are highly promising. The proposed scheme exhibits a remarkable diagnostic accuracy, achieving an impressive rate of 98.65 per for training (True Positive -TP) and 98.82 per utilizing k-fold validation. These preliminary findings underscore the potential efficacy of the hybrid methodology in accurately discerning brain disorders from EEG signals. However, it is essential to acknowledge that these results represent an initial success and form just a fragment of the extensive evaluation process.This study marks a significant stride towards leveraging interdisciplinary insights, blending principles from ethology with advanced computational techniques to tackle complex neurological challenges. By harnessing the sophisticated strategies observed in nature alongside cutting-edge technological advancements, this research endeavors to carve a path towards more nuanced and precise diagnostic tools for understanding and addressing brain disorders. Further exploration and refinement of this hybrid model hold promise for revolutionizing the landscape of neurodiagnostics, offering hope for more effective interventions and treatments in the realm of cognitive health.

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Section: Resultsmentioning

confidence: 99%

Epilepsy Identification using Hybrid CoPrO-DCNN Classifier

Basawaraj Birajadar,

Osman Mulani,

Ibrahim Khalaf

et al. 2024

IJCDS

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“…When lowering the PAPR with the help of the CBBO technique [ 35 ], each symbol is treated independently from the others, and the interaction between them is not taken into consideration. However, when the PAPR-reduced symbols that are used to generate the GFDM transmission signal are added together, this results in a considerable increase in the amount of PAPR that is present in the entire signal.…”

Section: Literature Surveymentioning

confidence: 99%