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2022 ◽  
Vol 11 (2) ◽  
pp. 387
Author(s):  
Hiroteru Kamimura ◽  
Hirofumi Nonaka ◽  
Masaya Mori ◽  
Taichi Kobayashi ◽  
Toru Setsu ◽  
...  

Deep learning is a subset of machine learning that can be employed to accurately predict biological transitions. Eliminating hepatitis B surface antigens (HBsAgs) is the final therapeutic endpoint for chronic hepatitis B. Reliable predictors of the disappearance or reduction in HBsAg levels have not been established. Accurate predictions are vital to successful treatment, and corresponding efforts are ongoing worldwide. Therefore, this study aimed to identify an optimal deep learning model to predict the changes in HBsAg levels in daily clinical practice for inactive carrier patients. We identified patients whose HBsAg levels were evaluated over 10 years. The results of routine liver biochemical function tests, including serum HBsAg levels for 1, 2, 5, and 10 years, and biometric information were obtained. Data of 90 patients were included for adaptive training. The predictive models were built based on algorithms set up by SONY Neural Network Console, and their accuracy was compared using statistical analysis. Multiple regression analysis revealed a mean absolute percentage error of 58%, and deep learning revealed a mean absolute percentage error of 15%; thus, deep learning is an accurate predictive discriminant tool. This study demonstrated the potential of deep learning algorithms to predict clinical outcomes.


2021 ◽  
Author(s):  
YuRang Park ◽  
JooHyun Lee ◽  
TaeSeon Lee ◽  
SeungWoo Lee ◽  
JiHye Jang ◽  
...  

BACKGROUND Autism spectrum disorder (ASD) is characterized by abnormalities in social communication and limited and repetitive patterns of behavior. Children with autism spectrum disorder, who lack social communication skills, will eventually not interact with others and lack peer relationships compared to ordinary people. Thus, it is necessary to develop a program to improve social communication ability using digital technology for people with ASD. OBJECTIVE To develop and application a metaverse based child social skill training program. The program is to improve the social interaction ability of child with ASD in aged 7 to 12 years. To compare and analyze the biometric information collected through wearable device when applying the Metaverse based social skills training program to check the emotional changes of ASD children in stressful situations. METHODS This parallel randomized controlled study will be conducted on children aged 7 to 12 years who have been diagnosed with ASD. A metaverse-based social skills training program using digital technology will be administer to children, who voluntarily wished to participate in research and who obtained consent from their legal guardians. Treatment group will participate in the metaverse-based social skills training program developed by this research team once a week for 60 minutes per session for 4weeks. Control group will not intervene for the duration of the experiment. Treatment group will use wearable devices during the experiment to enable the collection of biometric information in real time. RESULTS The study is expected to recruit and enroll participants in November 2021. After registering the participants, the study will be conducted from January 2022 to April 2022. This research will be conducted jointly by Yonsei University and Dobrain Co., Ltd. Children participating in the program will use the online platform. CONCLUSIONS The metaverse-based PEERS program will also be effective in improving the social skills of children with ASD, similar to the offline PEERS program. The metaverse-based PEERS program offers excellent accessibility and low cost because it can be administered at home, and thus it is expected to be effective for many children with ASD. If a method can be applied to detect children's emotional changes early on by using biometric information collected through wearable devices, then the emotional changes like anxiety and anger can be alleviated in advance, reducing issues in children with ASD. CLINICALTRIAL Clinical Research Information Service KCT0006859; https://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E&search_page=M&pageSize=10&page=undefined&seq=20942&status=5&seq_group=20942


Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 25
Author(s):  
Jaehun Park ◽  
Kwangsu Kim

Face recognition, including emotion classification and face attribute classification, has seen tremendous progress during the last decade owing to the use of deep learning. Large-scale data collected from numerous users have been the driving force in this growth. However, face images containing the identities of the owner can potentially cause severe privacy leakage if linked to other sensitive biometric information. The novel discrete cosine transform (DCT) coefficient cutting method (DCC) proposed in this study combines DCT and pixelization to protect the privacy of the image. However, privacy is subjective, and it is not guaranteed that the transformed image will preserve privacy. To overcome this, a user study was conducted on whether DCC really preserves privacy. To this end, convolutional neural networks were trained for face recognition and face attribute classification tasks. Our survey and experiments demonstrate that a face recognition deep learning model can be trained with images that most people think preserve privacy at a manageable cost in classification accuracy.


2021 ◽  
Vol 9 (1) ◽  
pp. 29-40
Author(s):  
Sharon Chan Suet Yan ◽  
Alice Tang Su Wei ◽  
Jie Hui Bong ◽  
Quor Ling Teh ◽  
Shanmugapiriya Sivalingam ◽  
...  

The Robust and Energy Efficient Authentication Protocol works for Industrial Internet of Things. The Internet of Things (IoT) is an arising innovation and expected to give answers for different modern fields. The IoT enable connection of physical devices all around the world to the internet by collecting and sharing critical and real-time data among each other. The increment of devices increases the computational cost during data transmission between devices and towards the internet. In this paper we proposed a solution that is a multi-factor authentication protocol to enhance the protocol proposed by Li et al. For Industrial IoT by adding One Time Password (OTP) after the biometric information of the user is checked by the Gateway Node (GWN) to be able to tackle additional network attack aside from those that are overcome by Li et al. scheme. Our contribution for this project is, we proposed the solution that a multi-factor authentication protocol to enhance the protocol proposed. For Industrial IoT by adding One Time Password (OTP) after the biometric information of the user is checked by the Gateway Node (GWN) to be able to tackle additional network attack aside from those that are overcome. The idea of adding OTP is inspired by where they scheme correlates to biometric of user as well. Our proposal is lower cost than the three protocols regarding authentication overhead and computational cost perspectives. Challenges and future directions of this paper examined the security shortcomings of a client confirmation convention for WSN, which is as proposed by Chang and Le. To address the normal security shortcomings of past protocols, we proposed a strong and energy effective three-factor authentication protocol for WSN.


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7921
Author(s):  
Toshiya Arakawa

Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness. Driver monitoring systems usually detect three types of information: biometric information, vehicle behavior, and driver’s graphic information. This review summarizes the research and development trends of drowsiness detection systems based on various methods. Drowsiness detection methods based on the three types of information are discussed. A prospect for arousal level detection and estimation technology for autonomous driving is also presented. In the case of autonomous driving levels 4 and 5, where the driver is not the primary driving agent, the technology will not be used to detect and estimate wakefulness for accident prevention; rather, it can be used to ensure that the driver has enough sleep to arrive comfortably at the destination.


Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2976
Author(s):  
Qi Han ◽  
Heng Yang ◽  
Tengfei Weng ◽  
Guorong Chen ◽  
Jinyuan Liu ◽  
...  

Multimodal identification, which exploits biometric information from more than one biometric modality, is more secure and reliable than unimodal identification. Face recognition and fingerprint recognition have received a lot of attention in recent years for their unique advantages. However, how to integrate these two modalities and develop an effective multimodal identification system are still challenging problems. Hetero-associative memory (HAM) models store some patterns that can be reliably retrieved from other patterns in a robust way. Therefore, in this paper, face and fingerprint biometric features are integrated by the use of a hetero-associative memory method for multimodal identification. The proposed multimodal identification system can integrate face and fingerprint biometric features at feature level when the system converges to the state of asymptotic stability. In experiment 1, the predicted fingerprint by inputting an authorized user’s face is compared with the real fingerprint, and the matching rate of each group is higher than the given threshold. In experiment 2 and experiment 3, the predicted fingerprint by inputting the face of an unauthorized user and the stealing authorized user’s face is compared with its real fingerprint input, respectively, and the matching rate of each group is lower than the given threshold. The experimental results prove the feasibility of the proposed multimodal identification system.


2021 ◽  
Vol 12 (5) ◽  
Author(s):  
Johnny Marcos S. Soares ◽  
Luciano Barbosa ◽  
Paulo Antonio Leal Rego ◽  
Regis Pires Magalhães ◽  
Jose Antônio F. de Macêdo

Fingerprints are the most used biometric information for identifying people. With the increase in fingerprint data, indexing techniques are essential to perform an efficient search. In this work, we devise a solution that applies traditional inverted index, widely used in textual information retrieval, for fingerprint search. For that, it first converts fingerprints to text documents using techniques, such as Minutia Cylinder-Code and Locality-Sensitive Hashing, and then indexes them in inverted files. In the experimental evaluation, our approach obtained 0.42% of error rate with 10% of penetration rate in the FVC2002 DB1a data set, surpassing some established methods.


Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 154
Author(s):  
Alfonso Ortega ◽  
Julian Fierrez ◽  
Aythami Morales ◽  
Zilong Wang ◽  
Marina de la Cruz ◽  
...  

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the processing of data. Learning from interpretation transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicity.


Author(s):  
Peeraya Sripian ◽  
Muhammad Nur Adilin Mohd Anuardi ◽  
Teppei Ito ◽  
Yoshito Tobe ◽  
Midori Sugaya

An important part of nursing care is the physiotherapist’s physical exercise recovery training (for instance, walking), which is aimed at restoring athletic ability, known as rehabilitation (rehab). In rehab, the big problem is that it is difficult to maintain motivation. Therapies using robots have been proposed, such as animalistic robots that have positive psychological, physiological, and social effects on the patient. These also have an important effect in reducing the on-site human workload. However, the problem with these robots is that they do not actually understand what emotions the user is currently feeling. Some studies have been successful in estimating a person’s emotions. As for non-cognitive approaches, there is an emotional estimation of non-verbal information. In this study, we focus on the characteristics of real-time sensing of emotion through heart rates – unconsciously evaluating what a person experiences – and applying it to select the appropriate turn of phrase by a voice-casting robot. We developed a robot to achieve this purpose. As a result, we were able to confirm the effectiveness of a real-time emotion-sensitive voice-casting robot that performs supportive actions significantly different from non-voice casting robots.


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