Cache Memory Design for Single Bit Architecture with Different Sense燗mplifiers

Agrawal, Reeya; Kumar, Anjan; AlQahtani, Salman A.; Maashi, Mashael; Khalaf, Osamah Ibrahim; Aldhyani, Theyazn H. H.

doi:10.32604/cmc.2022.029019

Cited by 3 publications

(4 citation statements)

References 36 publications

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“…Entropy measures the disorder or unpredictability in [94] the signals. There is a notable increase in negative entropy values for epileptic conditions, indicating a higher degree of disorder [95] and complexity during seizures. Kurtosis describes the shape of [96] the distribution.…”

Section: Resultsmentioning

confidence: 97%

“…A high positive skewness in theta/alpha ratio indicates an asymmetric distribution [118], [119] with a longer tail towards higher values, suggesting dominance of theta activity over alpha activity. An increase in alpha/theta ApEntropy suggests [120] greater irregularity or unpredictability in the relative balance between alpha and theta activity. A decrease in beta/(alpha + theta) variance suggests reduced [121], [122] variability in the balance between beta activity and the combined alpha and theta activity, [123], [124] which reflects altered cortical excitability observed in epilepsy.…”

Section: Ratio Indicesmentioning

confidence: 99%

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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: 97%

Section: Ratio Indicesmentioning

confidence: 99%

Epilepsy Identification using Hybrid CoPrO-DCNN Classifier

Basawaraj Birajadar,

Osman Mulani,

Ibrahim Khalaf

et al. 2024

IJCDS

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“…Hossain et al [25] puts forth a methodology employing fuzzy clustering and a neuro-fuzzy classifier to identify BC. This employs an adaptive neuro-fuzzy inference system (ANFIS) in benign and malignant BC's classification.…”

Section: Survey Upon Optimal Classifier For Hismentioning

confidence: 99%

“…Osteosarcoma HyI Database was employed in this study. This database could be downloaded out of the Cancer Imaging Archive (TCIA) webpage [25]. This database has been gathered out of 4 sick persons…”

Section: Database Descriptionmentioning

confidence: 99%

Optimized Swarm Enabled Deep Learning Technique for Bone Tumor Detection using Histopathological Image

Anand,

Khalaf,

Hajjej

et al. 2023

Sinergi

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Cancer subjugates a community that lacks proper care. It remains apparent that research studies enhance novel benchmarks in developing a computer-assisted tool for prognosis in radiology yet an indication of illness detection should be recognized by the pathologist. In bone cancer (BC), Identification of malignancy out of the BC’s histopathological image (HI) remains difficult because of the intricate structure of the bone tissue (BTe) specimen. This study proffers a new approach to diagnosing BC by feature extraction alongside classification employing deep learning frameworks. In this, the input is processed and segmented by Tsallis Entropy for noise elimination, image rescaling, and smoothening. The features are excerpted employing Efficient Net-based Convolutional Neural Network (CNN) Feature Extraction. ROI extraction will be employed to enhance the precise detection of atypical portions surrounding the affected area. Next, for classifying the accurate spotting and for grading the BTe as typical and a typical employing augmented XGBoost alongside Whale optimization (WOA). HIs gathering out of prevailing scales patients is acquired alongside texture characteristics of such images remaining employed for training and testing the Neural Network (NN). These classification outcomes exhibit that NN possesses a hit ratio of 99.48 percent while this occurs in BT classification.

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