scholarly journals A Short-Patterning of the Texts Attributed to Al Ghazali: A “Twitter Look” at the Problem

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1937
Author(s):  
Zeev Volkovich
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Convolutional Neural Network ◽  
High Reliability ◽  
Authorship Attribution ◽  
Formal Argument ◽  
Novel Approach ◽  
Significant Difference ◽  
Computational Perspective ◽  
New Look

This article presents an novel approach inspired by the modern exploration of short texts’ patterning to creations prescribed to the outstanding Islamic jurist, theologian, and mystical thinker Abu Hamid Al Ghazali. We treat the task with the general authorship attribution problematics and employ a Convolutional Neural Network (CNN), intended in combination with a balancing procedure to recognize short, concise templates in manuscripts. The proposed system suggests new attitudes make it possible to investigate medieval Arabic documents from a novel computational perspective. An evaluation of the results on a previously tagged collection of books ascribed to Al Ghazali demonstrates the method’s high reliability in recognizing the source authorship. Evaluations of two famous manuscripts, Mishakat al-Anwa and Tahafut al-Falasifa, questioningly attributed to Al Ghazali or co-authored by him, exhibit a significant difference in their overall stylistic style with one inherently assigned to Al Ghazali. This fact can serve as a substantial formal argument in the long-standing dispute about these manuscripts’ authorship. The proposed methodology suggests a new look on the perusal of medieval documents’ inner structures and possible authorship from the short-patterning and signal processing perspectives.

A Novel Approach to Fault Diagnosis of High Voltage Transmission line - A Self Attentive Convolutional Neural Network Model

2020 ◽  
Author(s):  
Shahriar Rahman Fahim ◽  
Md. Rabiul Islam Sarker ◽  
Md. Arifuzzaman ◽  
Md. Sakhawat Hosen ◽  
Subrata K Sarker ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transmission Line ◽  
Convolutional Neural Network ◽  
Network Model ◽  
High Voltage ◽  
Neural Network Model ◽  
High Voltage Transmission Line ◽  
Novel Approach ◽  
High Voltage Transmission

A Novel Approach on Epileptic Seizures Detection Using Convolutional Neural Network

2020 ◽  
Author(s):  
Arpana Mahajan ◽  
Sheshang Degadwala ◽  
Prama Talukder ◽  
Baron Meetei ◽  
M. Rameshkumar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Epileptic Seizures ◽  
Novel Approach

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

scholarly journals A novel approach to reconstruction based saliency detection via convolutional neural network stacked with auto-encoder

2019 ◽  
Vol 349 ◽  
pp. 145-155 ◽  
Author(s):  
Xinchen Lin ◽  
Yang Tang ◽  
Huaglory Tianfield ◽  
Feng Qian ◽  
Weimin Zhong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Saliency Detection ◽  
Novel Approach

Applying cascaded convolutional neural network design further enhances automatic scoring of arthritis disease activity on ultrasound images from rheumatoid arthritis patients

2020 ◽  
Vol 79 (9) ◽  
pp. 1189-1193
Author(s):  
Anders Bossel Holst Christensen ◽  
Søren Andreas Just ◽  
Jakob Kristian Holm Andersen ◽  
Thiusius Rajeeth Savarimuthu
Keyword(s):  
Neural Network ◽  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Convolutional Neural Network ◽  
Objective Assessment ◽  
Rank Test ◽  
Ultrasound Images ◽  
Data Set ◽  
Unseen Data ◽  
Significant Difference

ObjectivesWe have previously shown that neural network technology can be used for scoring arthritis disease activity in ultrasound images from rheumatoid arthritis (RA) patients, giving scores according to the EULAR-OMERACT grading system. We have now further developed the architecture of this neural network and can here present a new idea applying cascaded convolutional neural network (CNN) design with even better results. We evaluate the generalisability of this method on unseen data, comparing the CNN with an expert rheumatologist.MethodsThe images were graded by an expert rheumatologist according to the EULAR-OMERACT synovitis scoring system. CNNs were systematically trained to find the best configuration. The algorithms were evaluated on a separate test data set and compared with the gradings of an expert rheumatologist on a per-joint basis using a Kappa statistic, and on a per-patient basis using a Wilcoxon signed-rank test.ResultsWith 1678 images available for training and 322 images for testing the model, it achieved an overall four-class accuracy of 83.9%. On a per-patient level, there was no significant difference between the classifications of the model and of a human expert (p=0.85). Our original CNN had a four-class accuracy of 75.0%.ConclusionsUsing a new network architecture we have further enhanced the algorithm and have shown strong agreement with an expert rheumatologist on a per-joint basis and on a per-patient basis. This emphasises the potential of using CNNs with this architecture as a strong assistive tool for the objective assessment of disease activity of RA patients.

scholarly journals SBC-Based Cataract Detection System using Deep Convolutional Neural Network with Transfer Learning Algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 4605-4613
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
System Development ◽  
Deep Convolutional Neural Network ◽  
Raspberry Pi ◽  
Significant Difference ◽  
Cataract Patients

This Raspberry Pi Single Board Computer-Based Cataract Detection System using Deep Convolutional Neural Network through GoogLeNet Transfer Learning and MATLAB digital image processing paradigm based on Lens Opacities Classification System III with Python application, which would capture the image of the eyes of cataract patients to detect the type of cataract without using dilating drops. Additionally, the system could also determine the severity, grade, color or area, and hardness of cataract. It would also display, save, search and print the partial diagnosis that can be done to the patients. Descriptive quantitative research, Waterfall System Development Life Cycle and Evolutionary Prototyping Models was used as the methodologies of this study. Cataract patients and ophthalmologists of one of the eye clinics in City of Biñan, Laguna, as well as engineers and information technology professionals tested the system and also served as respondents to the conducted survey. Obtained results indicated that the detection of cataract and its characteristics using the system were accurate and reliable, which has a significant difference from the current eye examination for cataract. Generally, this would be a modern cataract detection system for all Cataract patients

Sign in / Sign up

Export Citation Format

Share Document