Sensor-based authentication in smartphone: A systematic review

Shuwandy, Moceheb Lazam; Jouda, A.S.; Ahmed, M.A.; Salih, Mahmood M.; Al-qaysi, Z.T.; Alamoodi, A.H.; Garfan, Salem; Albahri, O.S.; Zaidan, B.B.; Albahri, A.S.

doi:10.1016/j.jer.2024.02.003

Cited by 6 publications

(1 citation statement)

References 63 publications

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“…Currently, designing powerful pattern recognition methods with strong generalizability is one of the vital issues in the field of BCI-based applications [81,82]. The literature has established several different time courses as optimal, including two seconds in the frequency domain [46] and seven seconds in the time-frequency domain [83].…”

Section: Introductionmentioning

confidence: 99%

Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control

Al-Qaysi,

Suzani,

Abdul Rashid

et al. 2024

Mesopotamian Journal of Big Data

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Brain-computer interface (BCI-MI)-based wheelchair control is, in principle, an appropriate method for completely paralyzed people with a healthy brain. In a BCI-based wheelchair control system, pattern recognition in terms of preprocessing, feature extraction, and classification plays a significant role in avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe condition. Therefore, this research's goal is to create a time-domain generic pattern recognition model (GPRM) of two-class EEG-MI signals for use in a wheelchair control system.This GPRM has the advantage of having a model that is applicable to unknown subjects, not just one. This GPRM has been developed, evaluated, and validated by utilizing two datasets, namely, the BCI Competition IV and the Emotive EPOC datasets. Initially, fifteen-time windows were investigated with seven machine learning methods to determine the optimal time window as well as the best classification method with strong generalizability. Evidently, the experimental results of this study revealed that the duration of the EEG-MI signal in the range of 4–6 seconds (4–6 s) has a high impact on the classification accuracy while extracting the signal features using five statistical methods. Additionally, the results demonstrate a one-second latency after each command cue when using the eight-second EEG-MI signal that the Graz protocol used in this study. This one-second latency is inevitable because it is practically impossible for the subjects to imagine their MI hand movement instantly. Therefore, at least one second is required for subjects to prepare to initiate their motor imagery hand movement. Practically, the five statistical methods are efficient and viable for decoding the EEG-MI signal in the time domain. Evidently, the GPRM model based on the LR classifier showed its ability to achieve an impressive classification accuracy of 90%, which was validated on the Emotive EPOC dataset. The GPRM developed in this study is highly adaptable and recommended for deployment in real-time EEG-MI-based wheelchair control systems.

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Section: Introductionmentioning

confidence: 99%

Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control

Al-Qaysi,

Suzani,

Abdul Rashid

et al. 2024

Mesopotamian Journal of Big Data

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Auth4App: Streamlining authentication for integrated cyber–physical environments

Quincozes,

Mansilha,

Kreutz

et al. 2024

Journal of Information Security and Applications

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Semantic Image Retrieval Analysis Based on Deep Learning and Singular Value Decomposition

Hadid,

Al-Qaysi,

Hussein

et al. 2024

Applied Data Science and Analysis

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The exponential growth in the total quantity of digital images has necessitated the development of systems that are capable of retrieving these images. Content-based image retrieval is a technique used to get images from a database. The user provides a query image, and the system retrieves those photos from the database that are most similar to the query image. The image retrieval problem pertains to the task of locating digital photographs inside extensive datasets. Image retrieval researchers are transitioning from the use of keywords to the utilization of low-level characteristics and semantic features. The push for semantic features arises from the issue of subjective and time-consuming keywords, as well as the limitation of low-level characteristics in capturing high-level concepts that users have in mind. The main goal of this study is to examine how convolutional neural networks can be used to acquire advanced visual features. These high-level feature descriptors have the potential to be the most effective compared to the handcrafted feature descriptors in terms of image representation, which would result in improved image retrieval performance. The (CBIR-VGGSVD) model is an ideal solution for content-based image retrieval that is based on the VGG-16 algorithm and uses the Singular Value Decomposition (SVD) technique. The suggested model incorporates the VGG-16 model for the purpose of extracting features from both the query images and the images kept in the database. Afterwards, the dimensionality of the features retrieved from the VGG-16 model is reduced using SVD. Then, we compare the query photographs to the dataset images using the cosine metric to see how similar they are. When all is said and done, images that share a high degree of similarity will be successfully extracted from the dataset. A validation of the retrieval performance of the CBIR-VGGSVD model is performed using the Corel-1K dataset. When the VGG-16 standard model is the sole one used, the implementation will produce an average precision of 0.864. On the other hand, when the CBIR-VGGSVD model is utilized, this average precision is revealed to be (0.948). The findings of the retrieval ensured that the CBIR-VGGSVD model provided an improvement in performance on the test pictures that were utilized, surpassing the performance of the most recent approaches.

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Sensor-based authentication in smartphone: A systematic review

Cited by 6 publications

References 63 publications

Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control

Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control

Auth4App: Streamlining authentication for integrated cyber–physical environments

Semantic Image Retrieval Analysis Based on Deep Learning and Singular Value Decomposition

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