scholarly journals Faultless Decision Making for False Information in Online: A Systematic Approach

2021 ◽  
Vol 2 (4) ◽  
pp. 226-235
Author(s):  
Yasir Babiker Hamdan ◽  
Sathish
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Systematic Approach ◽  
Learning Algorithm ◽  
Research Work ◽  
High Accuracy ◽  
Fake News ◽  
False Information ◽  
Q Learning ◽  
Deep Learning Algorithm

An identifying the news are real or fake instantly with high accuracy is a challenging work. The deep learning algorithm is implementing here to acquire very accurate separation of real and fake news rather than other methods. This research work constructs naïve bayes and CNN classifiers with Q-learning decision making. The two different approaches detect fake news in online and it gives to decision making section which is designed at tail in our research. The deep decision making section compares the input and make the decision wisely and it provides the more accurate output rather than single classifiers in deep learning. This research work comprises compare between our proposed works with single classifiers.

scholarly journals Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning

2021 ◽  
Author(s):  
Evgeny Bobrov ◽  
Dmitry Kropotov ◽  
Hao Lu ◽  
Danila Zaev
Keyword(s):  
Deep Learning ◽  
Massive Mimo ◽  
Learning Algorithm ◽  
Adaptive Modulation ◽  
System Level ◽  
Adaptive Modulation And Coding ◽  
Q Learning ◽  
Deep Learning Algorithm ◽  
System Level Simulation ◽  
Fully Connected

The paper describes an online deep learning algorithm for the adaptive modulation and coding in 5G Massive MIMO. The algorithm is based on a fully connected neural network, which is initially trained on the output of the traditional algorithm and then is incrementally retrained by the service feedback of its output. We show the advantage of our solution over the state-of-the-art Q-Learning approach. We provide system-level simulation results to support this conclusion in various scenarios with different channel characteristics and different user speeds. Compared with traditional OLLA our algorithm shows 10% to 20% improvement of user throughput in full buffer case. <br>

scholarly journals Autism spectrum disorder classification on electroencephalogram signal using deep learning algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 91
Author(s):  
Nur Alisa Ali
Keyword(s):  
Autism Spectrum Disorder ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Research Work ◽  
Autism Spectrum ◽  
Performance Measure ◽  
Spectrum Disorder ◽  
Font Size ◽  
Deep Learning Algorithm

<span style="color: black; font-family: 'Times New Roman',serif; font-size: 9pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Autism Spectrum Disorder (ASD) is a neurodevelopmental that impact the social interaction and communication skills. Diagnosis of ASD is one of the difficult problems facing researchers. This research work aimed to reveal the different pattern between autistic and normal children via electroencephalogram (EEG) by using the deep learning algorithm. The brain signal database used pattern recognition where the extracted features will undergo the multilayer perceptron network for the classification process. The promising method to perform the classification is through a deep learning algorithm, which is currently a well-known and superior method in the pattern recognition field. The performance measure for the classification would be the accuracy. The higher percentage means the more effectiveness for the ASD diagnosis. </span><span style="color: black; font-family: 'Times New Roman',serif; font-size: 9pt; mso-fareast-font-family: 'Times New Roman+FPEF'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">This can be seen as the ground work for applying a new algorithm for further development diagnosis of autism to see how the treatment is working as well in future.</span>

scholarly journals Multimedia Teaching of College Musical Education Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Li
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Research Work ◽  
Auxiliary Function ◽  
Multimedia Technology ◽  
Application Process ◽  
Musical Education ◽  
Deep Learning Algorithm ◽  
Multimedia Teaching ◽  
College Music

In view of the current situation of musical education and the need for reform in China, we adopt two different methods, i.e., literature method and interview method in this research work. From these methods, we read a lot of musical education, multimedia technology, and modern teaching and reform. This research work is divided into two main phases. Firstly, the article mainly discusses the characteristics of college musical education compared with other cultural courses and the feasibility of multimedia technology and the auxiliary function of musical education that is applied in school’s musical education. Secondly, brain computing attempts to analyze things by simulating the structure and information processing of biological neural networks. The intelligent learning characteristic of a deep learning algorithm is proposed to monitor the process of musical education teaching and analyze the process quality. Finally, we introduced the design and production of network multimedia courseware which will help in theoretical guidance and reference to the application of multimedia technology in college musical education in China. Moreover, the outcome of the proposed model can play a role in solving and answering questions in the current multimedia application process and Chinese college music workers will apply multimedia technology more effectively and skillfully.

scholarly journals Comparison and Prediction of Hyperthyroidism Accuracy Rate Using Novel Deep Learning Technology and Vivo Monitoring

2021 ◽  
Vol 36 (1) ◽  
pp. 698-703
Author(s):  
Krushitha Reddy ◽  
D. Jenila Rani
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Research Work ◽  
Learning Technology ◽  
Accuracy Rate ◽  
Data Set ◽  
In Vivo Monitoring ◽  
Deep Learning Algorithm

Aim: The aim of this research work is to determine the presence of hyperthyroidism using modern algorithms, and comparing the accuracy rate between deep learning algorithms and vivo monitoring. Materials and methods: Data collection containing ultrasound images from kaggle's website was used in this research. Samples were considered as (N=23) for Deep learning algorithm and (N=23) for vivo monitoring in accordance to total sample size calculated using clinical.com. The accuracy was calculated by using DPLA with a standard data set. Results: Comparison of accuracy rate is done by independent sample test using SPSS software. There is a statistically indifference between Deep learning algorithm and in vivo monitoring. Deep learning algorithm (87.89%) showed better results in comparison to vivo monitoring (83.32%). Conclusion: Deep learning algorithms appear to give better accuracy than in vivo monitoring to predict hyperthyroidism.

scholarly journals The Study of Noise Effect on CNN-Based Deep Learning from Medical Images

2021 ◽  
Vol 11 (3) ◽  
pp. 202-207
Author(s):  
Kittipat Sriwong ◽  
◽  
Kittisak Kerdprasop ◽  
Nittaya Kerdprasop
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Medical Images ◽  
Research Work ◽  
Health Science ◽  
Learning Performance ◽  
Machine Learning Techniques ◽  
Disease Symptoms ◽  
Deep Learning Algorithm ◽  
Learning Techniques

Currently, computational modeling methods based on machine learning techniques in medical imaging are gaining more and more interests from health science researchers and practitioners. The high interest is due to efficiency of modern algorithms such as convolutional neural networks (CNN) and other types of deep learning. CNN is the most popular deep learning algorithm because of its prominent capability on learning key features from images that help capturing the correct class of images. Moreover, several sophisticated CNN architectures with many learning layers are available in the cloud computing environment. In this study, we are interested in performing empirical research work to compare performance of CNNs when they are dealing with noisy medical images. We design a comparative study to observe performance of the AlexNet CNN model on classifying diseases from medical images of two types: images with noise and images without noise. For the case of noisy images, the data had been further separated into two groups: a group of images that noises harmoniously cover the area of the disease symptoms (NIH) and a group of images that noises do not harmoniously cover the area of the disease symptoms (NNIH). The experimental results reveal that NNIH has insignificant effect toward the performance of CNN. For the group of NIH, we notice some effect of noise on CNN learning performance. In NIH group of images, the data preparation process before learning can improve the efficiency of CNN.

scholarly journals 297 – Deep Learning Algorithm for Characterization of Barrett's Neoplasia Demonstrates High Accuracy on Nbi-Zoom Images

2019 ◽  
Vol 156 (6) ◽  
pp. S-58 ◽  
Author(s):  
Maarten R. Struyvenberg ◽  
Jeroen de Groof ◽  
Joost van der Putten ◽  
Fons van der Sommen ◽  
Francisco Baldaque-Silva ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
High Accuracy ◽  
Deep Learning Algorithm

scholarly journals Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1668
Author(s):  
Mohammad Moghaddam ◽  
Paul A Ferre ◽  
Mohammad Reza Ehsani ◽  
Jeffrey Klakovich ◽  
Hoshin Vijay Gupta
Keyword(s):  
Deep Learning ◽  
Energy Dissipation ◽  
Hydraulic Conductivity ◽  
Learning Algorithm ◽  
High Accuracy ◽  
Effective Hydraulic Conductivity ◽  
Deep Learning Algorithm ◽  
Localized Structures ◽  
Hydrologic Systems ◽  
Controlled Flow

We confirm that energy dissipation weighting provides the most accurate approach to determining the effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained to infer the energy dissipation weighting pattern from an image of the K distribution, although it was less accurate for cases with highly localized structures that controlled flow. Furthermore, the UNET architecture learned to infer the energy dissipation weighting even if it was not trained directly on this information. However, the weights were represented within the UNET in a way that was not immediately interpretable by a human user. This reiterates the idea that even if ML/DL algorithms are trained to make some hydrologic predictions accurately, they must be designed and trained to provide each user-required output if their results are to be used to improve our understanding of hydrologic systems.

scholarly journals Automatic Car Damage detection by Hybrid Deep Learning Multi Label Classification

2021 ◽  
Vol 3 (4) ◽  
pp. 341-352
Author(s):  
P. Ebby Darney
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Learning Algorithm ◽  
Research Work ◽  
Computation Time ◽  
Damage Classification ◽  
Neural Network Approach ◽  
Convolutional Networks ◽  
Deep Learning Algorithm ◽  
Car Insurance

Automating image-based automobile insurance claims processing is a significant opportunity. In this research work, car damage categorization that is aided by the hybrid convolutional neural network approach is addressed and hence the deep learning-based strategies are applied. Insurance firms may leverage this paper's design and implementation of an automobile damage classification/detection pipeline to streamline car insurance claim policy. Using deep convolutional networks to detect car damage is now possible because of recent improvements in the artificial intelligence sector, mainly due to less computation time and higher accuracy with a hybrid transformation deep learning algorithm. In this paper, multiclass classification proposed to categorize the car damage parts such as broken headlight/taillight, glass fragments, damaged bonnet etc. are compiled into the proposed dataset. This model has been pre-trained on a wide-ranging and benchmark dataset due to the dataset's limited size to minimize overfitting and to understand more common properties of the dataset. To increase the overall proposed model’s performance, the CNN feature extraction model is trained with Resnet architecture with the coco car damage detection datasets and reaches a higher accuracy of 90.82%, which is much better than the previous findings on the comparable test sets.

scholarly journals Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning

2021 ◽  
Author(s):  
Evgeny Bobrov ◽  
Dmitry Kropotov ◽  
Hao Lu ◽  
Danila Zaev
Keyword(s):  
Deep Learning ◽  
Massive Mimo ◽  
Learning Algorithm ◽  
Adaptive Modulation ◽  
System Level ◽  
Adaptive Modulation And Coding ◽  
Q Learning ◽  
Deep Learning Algorithm ◽  
System Level Simulation ◽  
Fully Connected

The paper describes an online deep learning algorithm for the adaptive modulation and coding in 5G Massive MIMO. The algorithm is based on a fully connected neural network, which is initially trained on the output of the traditional algorithm and then is incrementally retrained by the service feedback of its output. We show the advantage of our solution over the state-of-the-art Q-Learning approach. We provide system-level simulation results to support this conclusion in various scenarios with different channel characteristics and different user speeds. Compared with traditional OLLA our algorithm shows 10% to 20% improvement of user throughput in full buffer case. <br>

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