A new model for pattern recognition

Farhan, Hameed R.; Al-Muifraje, Mahmuod H.; Saeed, Thamir R.

doi:10.1016/j.compeleceng.2020.106602

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…Joint Probabilities (with P g ) Equation ( 17 [25] 98.60% 92.33% Enhanced ASM [16] 89.20% 69.20% CCA [23] 70.90% 29.06% CDMMA [24] 37.70% 12.26% MvDA [22] 44.40% 20.13% COLPP [17] 96.10% 88.66% LDA [21] 87.20% 66.00% Eigenfaces [20] 82.20% 52.00%…”

Section: Methods Fei Feretmentioning

confidence: 99%

“…The experimental results presented in Table 1 include our new methods, geodesic products using the score functions

, and joint probabilities using

, which use our geodesic distance approximations between landmarks on face images. Finally, we performed experiments with the method CM (Continuous Model) [ 25 ], summing dissimilarities from corresponding landmarks by using Mahalanobis distance.…”

Section: Face Recognition Experimentsmentioning

confidence: 99%

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Some Information Geometric Aspects of Cyber Security by Face Recognition

Dodson

Soldera

Scharcanski

2021

Entropy

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Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods.

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Section: Methods Fei Feretmentioning

confidence: 99%

“…The experimental results presented in Table 1 include our new methods, geodesic products using the score functions

, and joint probabilities using

Section: Face Recognition Experimentsmentioning

confidence: 99%

Some Information Geometric Aspects of Cyber Security by Face Recognition

Dodson

Soldera

Scharcanski

2021

Entropy

View full text Add to dashboard Cite

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“…They are also implemented utilising pattern recognition and an ANN [ 153 ]. Pattern recognition algorithms are also employed in face recognition systems, where patterns for diverse facial configurations are retrieved and examined for pattern matching in order to perform the facial recognition process [ 154 ]. An ANN system trained on clinical datasets for pattern identification and matching is capable of automatically distinguishing standardised functional data patterns from datasets containing long-term movement patterns [ 155 ].…”

Section: Human Activity Detection Using Deep Learning Techniquesmentioning

confidence: 99%

Review of Wearable Devices and Data Collection Considerations for Connected Health

Vijayan

Connolly

Condell³

et al. 2021

Sensors

209

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Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

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“…Computers are widely used in medical applications such as diagnosing diseases, which can be accomplished in two different methods according to the type of application. The first method utilizes the same pattern recognition procedure as [3], i.e., a dedicated algorithm trains certain images and different images are examined for decision-making. An example of this type of disease diagnosis is breast cancer detection, which depends on mammographic images [4].…”

Section: Introductionmentioning

confidence: 99%

Ovarian Cancer Detection Based on Elman Recurrent Neural Network

Farhan,

Kod,

Taqi

et al. 2024

Period. Polytech. Elec. Eng. Comp. Sci.

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The early detection of cancers increases the possibility of health recovery and prevents the disease from becoming a silent killer. This study introduces an effective method for identifying ovarian cancer (OC) using Elman Recurrent Neural Network (ERNN), which can recognize cancer via mass spectrometry data. The network has a topology of 100 input neurons for receiving data, five neurons for hidden and context layers, and two output nodes to indicate the status. The proposed method uses reduced-size features, including ion concentration levels at specific mass/charge values, which are trained using various learning algorithms to determine the suitable one that achieves the best results. The experimental results show that all the training algorithms achieve about 100% performance rate, with the Levenberg Marquardt (LM) being the most accurate and fastest algorithm, which converges after six epochs and achieves 0.0035, 0.0045 and 0.0045 mean square errors for training, validation, and test performances, respectively. Based on comparative results, the proposed LM-ERNN method outperforms other OC detection methods and holds promise for detecting other types of cancer.

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A new model for pattern recognition

Cited by 10 publications

References 8 publications

Some Information Geometric Aspects of Cyber Security by Face Recognition

Some Information Geometric Aspects of Cyber Security by Face Recognition

Review of Wearable Devices and Data Collection Considerations for Connected Health

Ovarian Cancer Detection Based on Elman Recurrent Neural Network

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