Design of QoS on data collection in wireless sensor network for automation process

Hassan, Jalel; Abdulsaheb, Ghaida Muttasher; Khalaf, Osamah Ibrahim

doi:10.1504/ijcat.2022.124955

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Band power represents the distribution of power in different [91], [92] frequency bands. There is a substantial increase in band power for epileptic conditions across all detectors, reinforcing the notion of altered frequency characteristics [93] during seizures. Entropy measures the disorder or unpredictability in [94] the signals.…”

Section: Resultsmentioning

confidence: 58%

Epilepsy Identification using Hybrid CoPrO-DCNN Classifier

Basawaraj Birajadar,

Osman Mulani,

Ibrahim Khalaf

et al. 2024

IJCDS

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 58%

Epilepsy Identification using Hybrid CoPrO-DCNN Classifier

Basawaraj Birajadar,

Osman Mulani,

Ibrahim Khalaf

et al. 2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…Like most biometric devices, they are vulnerable to spoofing attacks. In the early 1970's, the subject of facial recognition was introduced, but in the 1990s, new image processing and machine learning technologies developed rapidly (Hassan et al, 2022). The face recognition system is software that automatically recognizes the face and confirms an individual's identity.…”

Section: Introductionmentioning

confidence: 99%

An Attendance System Using the Face Extraction and Recognition Technique Using PCA Algorithm

Mohammed

Alothman

2022

International Journal of Information Technology and Web Engineering

View full text Add to dashboard Cite

Face detection is the most critical and first step in the attendance management system. The human face is non-rigid and has many differences in visual situations, scale, clarity, poses, and rotation. The precise and reliable identification was a challenge for the researcher. A variety of methods and techniques are suggested, but due to many variations, no one technique is very effective for all sorts of faces and pictures. Many techniques show good results under certain conditions, and others are successful for various types of images. Image-discriminating methods are commonly used for pattern and image analysis. Specific forms of prejudice are discussed.

show abstract