Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

Onyema, Edeh Michael; Shukla, Piyush Kumar; Dalal, Surjeet; Mathur, Mayuri Neeraj; Zakariah, Mohammed; Tiwari, Basant

doi:10.1155/2021/5196000

Cited by 63 publications

(31 citation statements)

References 46 publications

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“…e test results for all variables are summarized in Table 7. It should be noted that the optimal delay (Lag) was established using the information criteria of Akaike and Schwarz [27][28][29][30][31][32].…”

Section: Resultsmentioning

confidence: 99%

[Retracted] Empirical Analysis of Apnea Syndrome Using an Artificial Intelligence‐Based Granger Panel Model Approach

Onyema

Ahanger

Ghouali

et al. 2022

Computational Intelligence and Neuroscience

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Sleep apnea is a serious sleep disorder that occurs when a person's breathing is interrupted during sleep. People with untreated sleep apnea stop breathing repeatedly during their sleep. This study provides an empirical analysis of apnea syndrome using the AI-based Granger panel model approach. Data were collected from the MIT-BIH polysomnographic database (SLPDB). The panel is composed of eighteen patients, while the implementation was done using MATLAB software. The results show that, for the eighteen patients with sleep apnea, there was a significant relationship between ECG-blood pressure (BP), ECG-EEG, and EEG-blood pressure (BP). The study concludes that the long-term interaction between physiological signals can help the physician to understand the risks associated with these interactions. The study would assist physicians to understand the mechanisms underlying obstructive sleep apnea early and also to select the right treatment for the patients by leveraging the potential of artificial intelligence. The researchers were motivated by the need to reduce the morbidity and mortality arising from sleep apnea using AI-enabled technology.

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Section: Resultsmentioning

confidence: 99%

[Retracted] Empirical Analysis of Apnea Syndrome Using an Artificial Intelligence‐Based Granger Panel Model Approach

Onyema

Ahanger

Ghouali

et al. 2022

Computational Intelligence and Neuroscience

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“…It is a part of the dimensionality reduction process in which an initial set of images were divided into many manageable groups. Determination of humans based on skeletal parts available is the most challenging task for forensic experts when only fragmented parts of the body are recovered [ 26 ]. In this situation, forensic dentistry will help in gender identification and age estimation based on the dental remaining and skull part.…”

Section: Feature Extractionmentioning

confidence: 99%

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

Santosh¹,

Pradeep²,

Goel³

et al. 2022

Journal of Healthcare Engineering

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The use of digital medical images is increasing with advanced computational power that has immensely contributed to developing more sophisticated machine learning techniques. Determination of age and gender of individuals was manually performed by forensic experts by their professional skills, which may take a few days to generate results. A fully automated system was developed that identifies the gender of humans and age based on digital images of teeth. Since teeth are a strong and unique part of the human body that exhibits least subject to risk in natural structure and remains unchanged for a longer duration, the process of identification of gender- and age-related information from human beings is systematically carried out by analyzing OPG (orthopantomogram) images. A total of 1142 digital X-ray images of teeth were obtained from dental colleges from the population of the middle-east part of Karnataka state in India. 80% of the digital images were considered for training purposes, and the remaining 20% of teeth images were for the testing cases. The proposed gender and age determination system finds its application widely in the forensic field to predict results quickly and accurately. The prediction system was carried out using Multiclass SVM (MSVM) classifier algorithm for age estimation and LIBSVM classifier for gender prediction, and 96% of accuracy was achieved from the system.

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“…When PHRs are moved to the cloud, several security issues occur. Thus, accessing sensitive patient health information must be secured [ 5 , 6 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The information about these constant attributes is present in the metadata repository. For example, each patient can have one type of chest pain at a time, which is denoted as 1 – typical angina, enough to search for the rules having only one type of chest pain [ 6 ]. This property is called rule reduction.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In addition to compromising privacy, the existing approach is unable to withstand collusion and replay assaults. In order to address these concerns, this study presents security techniques for accessing personal health records (PHR) in the cloud [ 6 ]. Patient health records (PHR) are first established as a basic storage service but are eventually upgraded to a cloud-based healthcare system that can be controlled by patients themselves.…”

Section: Introductionmentioning

confidence: 99%

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Novel Method for Safeguarding Personal Health Record in Cloud Connection Using Deep Learning Models

Kumar¹,

Wajeed²,

Kunabeva

et al. 2022

Computational Intelligence and Neuroscience

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It is a new online service paradigm that allows consumers to exchange their health data. Health information management software allows individuals to control and share their health data with other users and healthcare experts. Patient health records (PHR) may be intelligently examined to predict patient criticality in healthcare systems. Unauthorized access, privacy, security, key management, and increased keyword query search time all occur when personal health records (PHR) are moved to a third-party semitrusted server. This paper presents security measures for cloud-based personal health records (PHR). The cost of keeping health records on a hospital server grows. This is particularly true in healthcare. As a consequence, keeping PHRs in the cloud helps healthcare institutions save money on infrastructure. The proposed security solutions include an optimized rule-based fuzzy inference system (ORFIS) to determine the patient’s criticality. Patients are classified into three groups (sometimes known as protective rings) based on their severity: very critical, less critical, and normal. In trials using the UCI machine learning archive, the new ORFIS outperformed existing fuzzy inference approaches in detecting the criticality of PHR. Using a graph-based access policy and anonymous authentication with a NoSQL database in a private cloud environment improves data storage and retrieval efficiency, granularity of data access, and response time.

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Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

Cited by 63 publications

References 46 publications

[Retracted] Empirical Analysis of Apnea Syndrome Using an Artificial Intelligence‐Based Granger Panel Model Approach

[Retracted] Empirical Analysis of Apnea Syndrome Using an Artificial Intelligence‐Based Granger Panel Model Approach

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

Novel Method for Safeguarding Personal Health Record in Cloud Connection Using Deep Learning Models

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