Deep Learning Algorithm
Recently Published Documents


TOTAL DOCUMENTS

1112
(FIVE YEARS 1096)

H-INDEX

26
(FIVE YEARS 25)

Kerntechnik ◽  
2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Hong Xu ◽  
Tao Tang ◽  
Baorui Zhang ◽  
Yuechan Liu

Abstract Opinion mining and sentiment analysis based on social media has been developed these years, especially with the popularity of social media and the development of machine learning. But in the community of nuclear engineering and technology, sentiment analysis is seldom studied, let alone the automatic analysis by using machine learning algorithms. This work concentrates on the public sentiment mining of nuclear energy in German-speaking countries based on the public comments of nuclear news in social media by using the automatic methodology, since compared with the news itself, the comments are closer to the public real opinions. The results showed that majority comments kept in neutral sentiment. 23% of comments were in positive tones, which were approximate 4 times those in negative tones. The concerning issues of the public are the innovative technology development, safety, nuclear waste, accidents and the cost of nuclear power. Decision tree, random forest and long short-term memory networks (LSTM) are adopted for the automatic sentiment analysis. The results show that all of the proposed methods can be applied in practice to some extent. But as a deep learning algorithm, LSTM gets the highest accuracy approximately 85.6% with also the best robustness of all.


Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 536
Author(s):  
Pasquale Arpaia ◽  
Federica Crauso ◽  
Egidio De Benedetto ◽  
Luigi Duraccio ◽  
Giovanni Improta ◽  
...  

This work addresses the design, development and implementation of a 4.0-based wearable soft transducer for patient-centered vitals telemonitoring. In particular, first, the soft transducer measures hypertension-related vitals (heart rate, oxygen saturation and systolic/diastolic pressure) and sends the data to a remote database (which can be easily consulted both by the patient and the physician). In addition to this, a dedicated deep learning algorithm, based on a Long-Short-Term-Memory Autoencoder, was designed, implemented and tested for providing an alert when the patient’s vitals exceed certain thresholds, which are automatically personalized for the specific patient. Furthermore, a mobile application (EcO2u) was developed to manage the entire data flow and facilitate the data fruition; this application also implements an innovative face-detection algorithm that ensures the identity of the patient. The robustness of the proposed soft transducer was validated experimentally on five individuals, who used the system for 30 days. The experimental results demonstrated an accuracy in anomaly detection greater than 93%, with a true positive rate of more than 94%.


2022 ◽  
Author(s):  
Yan Ye ◽  
Xudong Luo ◽  
Qiong Nan ◽  
Yanhong Liu ◽  
Yinglei Miao ◽  
...  

Abstract The goal of treatment for ulcerative colitis is to achieve histological and endoscopic remission. Aiming at the problem that the observer will be affected by subjective factors in the endoscopic evaluation of ulcerative colitis and the cumbersome diagnosis process of histological images, this paper aims to develop a computer-assisted diagnosis system for real-time, objective diagnosis of endoscopic images and use the trained CNN model to predict histological images of patients with ulcerative colitis. Diagnosing endoscopic remission of ulcerative colitis, the accuracy of the CNN is 97.04% (95% CI,96.26%:97.62%). Diagnosing the severity of endoscopic inflammation in patients with ulcerative colitis, the accuracy of the CNN is 90.15% (95% CI, 89.49%:90.82%). The accuracy of predicting histological remission was 91.28%. The kappa coefficient between the CNN model and the biopsy results was 82.56%. The proposed computer-aided diagnosis system can effectively evaluate the inflammation of endoscopic images of patients with ulcerative colitis and predict the remission of histological images with high accuracy and consistency.


2022 ◽  
Author(s):  
Shaan Khurshid ◽  
Julieta Lazarte ◽  
James Pirruccello ◽  
Lu-Chen Weng ◽  
Seung Hoan Choi ◽  
...  

Increased left ventricular (LV) mass (LVM) and LV hypertrophy (LVH) are risk markers for adverse cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance (CMR) is the gold standard for LVM estimation, but is challenging to obtain at scale, which has limited the power of prior genetic analyses. In the current study, we performed a genome-wide association study (GWAS) of CMR-derived LVM indexed to body surface area (LVMI) estimated using a deep learning algorithm within nearly 50,000 participants from the UK Biobank. We identified 12 independent associations (1 known at TTN and 11 novel) meeting genome-wide significance, implicating several candidate genes previously associated with cardiac contractility and cardiomyopathy. Greater CMR-derived LVMI was associated with higher risk of incident dilated (hazard ratio [HR] 2.58 per 1-SD increase, 95% CI 2.10-3.17) and hypertrophic (HR 2.62, 95% CI 2.09-3.30) cardiomyopathies. A polygenic risk score (PRS) for LVMI was also associated with incident hypertrophic cardiomyopathy within a separate set of UK Biobank participants (HR] 1.12, 95% CI 1.01-1.12) and among individuals in an external Mass General Brigham dataset (HR 1.18, 95% CI 1.01-1.37). In summary, using CMR-derived LVM available at scale, we have identified 12 common variants associated with LVMI (11 novel) and demonstrated that both CMR-derived and genetically determined LVMI are associated with risk of incident cardiomyopathy.


2022 ◽  
Vol 12 (2) ◽  
pp. 639
Author(s):  
Yin-Chun Hung ◽  
Yu-Xiang Zhao ◽  
Wei-Chen Hung

Kinmen Island was in a state of combat readiness during the 1950s–1980s. It opened for tourism in 1992, when all troops withdrew from the island. Most military installations, such as bunkers, anti airborne piles, and underground tunnels, became deserted and disordered. The entries to numerous underground bunkers are closed or covered with weeds, creating dangerous spaces on the island. This study evaluates the feasibility of using Electrical Resistivity Tomography (ERT) to detect and discuss the location, size, and depth of underground tunnels. In order to discuss the reliability of the 2D-ERT result, this study built a numerical model to validate the correctness of in situ measured data. In addition, this study employed the artificial intelligence deep learning technique for reprocessing and predicting the ERT image and discussed using an artificial intelligence deep learning algorithm to enhance the image resolution and interpretation. A total of three 2D-ERT survey lines were implemented in this study. The results indicate that the three survey lines clearly show the tunnel location and shape. The numerical simulation results also indicate that using 2D-ERT to survey underground tunnels is highly feasible. Moreover, according to a series of studies in Multilayer Perceptron of deep learning, using deep learning can clearly show the tunnel location and path and effectively enhance the interpretation ability and resolution for 2D-ERT measurement results.


Author(s):  
Yichuan Liu ◽  
Hui-Qi Qu ◽  
Frank D. Mentch ◽  
Jingchun Qu ◽  
Xiao Chang ◽  
...  

AbstractMental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patients who have more than one type of mental disorder, especially for young toddlers who are not able to complete questionnaires or standardized rating scales for diagnosis. In the past decade, multiple genomic association signals have been reported for mental disorders, some of which present attractive drug targets. Concurrently, machine learning algorithms, especially deep learning algorithms, have been successful in the diagnosis and/or labeling of complex diseases, such as attention deficit hyperactivity disorder (ADHD) or cancer. In this study, we focused on eight common mental disorders, including ADHD, depression, anxiety, autism, intellectual disabilities, speech/language disorder, delays in developments, and oppositional defiant disorder in the ethnic minority of African Americans. Blood-derived whole genome sequencing data from 4179 individuals were generated, including 1384 patients with the diagnosis of at least one mental disorder. The burden of genomic variants in coding/non-coding regions was applied as feature vectors in the deep learning algorithm. Our model showed ~65% accuracy in differentiating patients from controls. Ability to label patients with multiple disorders was similarly successful, with a hamming loss score less than 0.3, while exact diagnostic matches are around 10%. Genes in genomic regions with the highest weights showed enrichment of biological pathways involved in immune responses, antigen/nucleic acid binding, chemokine signaling pathway, and G-protein receptor activities. A noticeable fact is that variants in non-coding regions (e.g., ncRNA, intronic, and intergenic) performed equally well as variants in coding regions; however, unlike coding region variants, variants in non-coding regions do not express genomic hotspots whereas they carry much more narrow standard deviations, indicating they probably serve as alternative markers.


2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Yisheng Xu ◽  
Jianghua Lou ◽  
Zhiqin Gao ◽  
Ming Zhan

The research is aimed at investigating computed tomography (CT) image based on deep learning algorithm and the application value of ceramide glycosylation in diagnosing bladder cancer. The images of ordinary CT detection were improved. In this study, 60 bladder cancer patients were selected and performed with ordinary CT detection, and the detection results were processed by CT based on deep learning algorithms and compared with pathological diagnosis. In addition, Western Blot technology was used to detect the expression of glucose ceramide synthase (GCS) in the cell membrane of tumor tissues and normal tissues of bladder. The comparison results found that, in simple CT clinical staging, the coincidence rates of T1 stage, T2a stage, T2b stage, T3 stage, and T4 stage were 28.56%, 62.51%, 78.94%, 84.61%, and 74.99%, respectively; and the total coincidence rate of CT clinical staging was 63.32%, which was greatly different from the clinical staging of pathological diagnosis ( P < 0.05 ). In the clinical staging of algorithm-based CT test results, the coincidence rates of T1 stage and T2a stage were 50.01% and 91.65%, respectively; and those of T2b stage, T3 stage, and T4 stage were 100.00%; and the total coincidence rate was 96.69%, which was not obviously different from the clinical staging of pathological diagnosis ( P > 0.05 ). Therefore, it could be concluded that the algorithm-based CT detection results were more accurate, and the use of CT scans based on deep learning algorithms in the preoperative staging and clinical treatment of bladder cancer showed reliable guiding significance and clinical value. In addition, it was found that the expression level of GCS in normal bladder tissues was much lower than that in bladder cancer tissues. This indicated that the changes in GCS were closely related to the development and prognosis of bladder cancer. Therefore, it was believed that GCS may be an effective target for the treatment of bladder cancer in the future, and further research was needed for specific conditions.


2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Qiang Wang ◽  
Dong Liu ◽  
Guangheng Liu

This study is aimed at discussing the value of ultrasonic image features in diagnosis of perinatal outcomes of severe preeclampsia on account of deep learning algorithm. 140 pregnant women singleton with severe preeclampsia were selected as the observation group. At the same time, 140 normal singleton pregnant women were selected as the control group. The hemodynamic indexes were detected by color Doppler ultrasound. The CNN algorithm was used to classify ultrasound images of two groups of pregnant women. The differential scanning calorimetry (DSC), mean pixel accuracy (MPA), and mean intersection of union (MIOU) values of CNN algorithm were 0.9410, 0.9228, and 0.8968, respectively. Accuracy, precision, recall, and F 1 -score were 93.44%, 95.13%, 95.09%, and 94.87%, respectively. The differences were statistically significant ( P < 0.05 ). Compared with the normal control group, the umbilical artery (UA), uterine artery-systolic/diastolic (UTA-S/D), uterine artery (UTA), and digital video (DV) of pregnant women in the observation group were remarkably increased; the minimum alveolar effective concentration (MCA) of the observation group was obviously lower than the MCA of the control group, and the differences between groups were statistically valid ( P < 0.05 ). Logistic regression analysis showed that UA-S/D, UA-resistance index (UA-RI), UTA-S/D, UTA-pulsatility index (UTA-PI), DV-peak velocity index for veins (DV-PVIV), and MCA-S/D were independent risk factors for the outcome of perinatal children with severe preeclampsia. In the perinatal management of severe epilepsy, the combination of the above blood flow indexes to select the appropriate delivery time had positive significance to improve the pregnancy outcome and reduce the perinatal mortality.


Sign in / Sign up

Export Citation Format

Share Document