scholarly journals Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1573
Author(s):  
Amin Alqudah ◽  
Ali Mohammad Alqudah ◽  
Hiam Alquran ◽  
Hussein R. Al-Zoubi ◽  
Mohammed Al-Qodah ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Recognition Rate ◽  
Second Stage ◽  
The Past ◽  
The World ◽  
Very High ◽  
Handwritten Arabic

Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%.

scholarly journals A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Data Augmentation ◽  
Ct Scans ◽  
Automated Classification ◽  
Imaging Data ◽  
Use Of Data

Abstract The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult and can take a long time to be recognized by radiologists. Artificial intelligence methods for automated classification of COVID-19 on CT scans have been found to be very promising. However, current investigation of pretrained convolutional neural networks (CNNs) for COVID-19 diagnosis using CT data is limited. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Among the 16 CNNs, DenseNet-201, which is the deepest net, is the best in terms of accuracy, balance between sensitivity and specificity, $$F_1$$ F 1 score, and area under curve. Furthermore, the implementation of transfer learning with the direct input of whole image slices and without the use of data augmentation provided better classification rates than the use of data augmentation. Such a finding alleviates the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models for COVID-19 classification.

scholarly journals Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
P. V. V. Kishore ◽  
K. V. V. Kumar ◽  
E. Kiran Kumar ◽  
A. S. C. S. Sastry ◽  
M. Teja Kiran ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Action Identification ◽  
Classical Dance ◽  
Human Movements ◽  
Indian Classical Dance

Extracting and recognizing complex human movements from unconstrained online/offline video sequence is a challenging task in computer vision. This paper proposes the classification of Indian classical dance actions using a powerful artificial intelligence tool: convolutional neural networks (CNN). In this work, human action recognition on Indian classical dance videos is performed on recordings from both offline (controlled recording) and online (live performances, YouTube) data. The offline data is created with ten different subjects performing 200 familiar dance mudras/poses from different Indian classical dance forms under various background environments. The online dance data is collected from YouTube for ten different subjects. Each dance pose is occupied for 60 frames or images in a video in both the cases. CNN training is performed with 8 different sample sizes, each consisting of multiple sets of subjects. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our data to obtain a better accuracy in recognition. We achieved a 93.33% recognition rate compared to other classifier models reported on the same dataset.

scholarly journals An efficient classification of flower images with convolutional neural networks

2017 ◽  
Vol 7 (1.1) ◽  
pp. 384 ◽  
Author(s):  
M V.D. Prasad ◽  
B JwalaLakshmamma ◽  
A Hari Chandana ◽  
K Komali ◽  
M V.N. Manoja ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Recognition Rate ◽  
Image Data ◽  
Image Database

Machine learning is penetrating most of the classification and recognition tasks performed by a computer. This paper proposes the classification of flower images using a powerful artificial intelligence tool, convolutional neural networks (CNN). A flower image database with 9500 images is considered for the experimentation. The entire database is sub categorized into 4. The CNN training is initiated in five batches and the testing is carried out on all the for datasets. Different CNN architectures were designed and tested with our flower image data to obtain better accuracy in recognition. Various pooling schemes were implemented to improve the classification rates. We achieved 97.78% recognition rate compared to other classifier models reported on the same dataset.

scholarly journals Tape Automated Bonding for High Density Packaging

1981 ◽  
Vol 8 (1-2) ◽  
pp. 15-19 ◽  
Author(s):  
Karel Kurzweil
Keyword(s):  
High Performance ◽  
Cost Effective ◽  
High Density ◽  
Electronic Systems ◽  
The Past ◽  
The World ◽  
Assembly Technology ◽  
Processing Steps ◽  
Very High ◽  
High Density Packaging

High density packaging of semiconductor devices is necessary for high performance in compact electronic systems. But the assembly technology must also remain cost attractive.Through the development efforts conducted during the past years in the world, the Tape Automated Bonding – TAB – has become the assembly technology allowing a very high density packaging. In combination with substrate technology it has grown into a complete, cost effective, micropackaging concept.The paper describes the main technical characteristics of this packaging concept. Specific equipments for TAB were designed and built by CII-Honeywell Bull for installation in the factory. These equipments are not only those, directly related to the TAB technology processing steps but include also other equipments like high precision thick film printer.The main features of the new micropackaging facility are also presented. Some examples of high density packages built with tape automated bonding are described and some of the main quality and reliability aspects are discussed.

scholarly journals Evrişimsel Sinir Ağları Kullanılarak Yumurta Kabuğu Kusurlarının Tespiti

2021 ◽  
Vol 9 (3) ◽  
pp. 559-567
Author(s):  
Muammer Türkoğlu
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Experimental Studies ◽  
Original Data ◽  
Accuracy Score ◽  
Data Set ◽  
Complex Processes

In commercial egg farming industries, the automatic sorting of defective eggs is economically and healthily important. Nowadays, detect of defective eggs is performed manually. This situation involves time consuming, tiring and complex processes. For all these reasons, automatic classification of defects that may occur on the egg surface has become a very important issue. For this purpose, in this study, classification of egg defects was performed using AlexNet, VGG16, VGG19, SqueezeNet, GoogleNet, Inceptionv3, ResNet18, and Xception architectures, which were developed based on Convolutional Neural Networks (CNN), which provide high performance in object recognition and classification. To test the performance of these architectures, an original data set containing dirty, bloody, cracked, and intact eggs were built. As a result of experimental studies, the highest accuracy score was obtained with VGG19 architecture as 96.25%. In these results, it was observed that ESA methods achieved high success in classifying defective eggs.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

scholarly journals Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2284
Author(s):  
Asma Maqsood ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Red Blood Cells ◽  
Convolutional Neural Networks ◽  
Blood Cells ◽  
Superior Performance ◽  
Learning Models ◽  
Infected Female ◽  
The World ◽  
Augmentation Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

scholarly journals Morphological Characteristics, Classifications and Difficulties in the Use of Diagnostic Criteria for Serrated Lesions of the Large Intestine

2021 ◽  
Author(s):  
Cesar de Souza Bastos Junior ◽  
Vera Lucia Nunes Pannain ◽  
Adriana Caroli-Bottino
Keyword(s):  
Diagnostic Criteria ◽  
Morphological Characteristics ◽  
World Health ◽  
Diagnostic Tools ◽  
Serrated Adenoma ◽  
The Past ◽  
The World ◽  
Serrated Lesions ◽  
Health Organization

Abstract Introduction Colorectal carcinoma (CRC) is the most common gastrointestinal neoplasm in the world, accounting for 15% of cancer-related deaths. This condition is related to different molecular pathways, among them the recently described serrated pathway, whose characteristic entities, serrated lesions, have undergone important changes in their names and diagnostic criteria in the past thirty years. The multiplicity of denominations and criteria over the last years may be responsible for the low interobserver concordance (IOC) described in the literature. Objectives The present study aims to describe the evolution in classification of serrated lesions, based on the last three publications of the World Health Organization (WHO) and the reproducibility of these criteria by pathologists, based on the evaluation of the IOC. Methods A search was conducted in the PubMed, ResearchGate and Portal Capes databases, with the following terms: sessile serrated lesion; serrated lesions; serrated adenoma; interobserver concordance; and reproducibility. Articles published since 1990 were researched. Results and Discussion The classification of serrated lesions in the past thirty years showed different denominations and diagnostic criteria. The reproducibility and IOC of these criteria in the literature, based on the kappa coefficient, varied in most studies, from very poor to moderate. Conclusions Interobserver concordance and the reproducibility of microscopic criteria may represent a limitation for the diagnosis and appropriate management of these lesions. It is necessary to investigate diagnostic tools to improve the performance of the pathologist's evaluation, for better concordance, and, consequently, adequate diagnosis and treatment.

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