Designing Offline Arabic Handwritten Isolated Character Recognition System using Artificial Neural Network Approach

Abdalkafor, Ahmed Subhi

doi:10.14716/ijtech.v8i3.6723

Cited by 14 publications

(16 citation statements)

References 2 publications

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“…Recognition is important, especially in terms of security, through the identification amongst images, such as car pictures and images of languages, databases, signatures and others [18][19][20]. Therefore, integrating image recognition techniques with the application of the image sensor node of the wireless network is possible in forming an integrated system that contributes to the integration of new wireless sensor networks, as well as identifying the images distributed within the network and distinguishes them from the rest of the images [21][22][23][24][25]. Transportation application of WSN as shown in Figure 5.…”

Section: Transportation Applicationsmentioning

confidence: 99%

An overview of wireless sensor network and its applications

Al-Ani

Abdalkafor

Nassar

2020

IJEECS

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<p>Small devices that can sense and collect data from the surrounding environment have been produced with the rapid development of wireless technology and digital electronics. As a result, a wireless sensor network (WSN) was constructed from these sensor devices to send collected data to the base station for monitoring and analysis. This paper provides an overview of WSN, as well as its architecture and applications.</p>

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Section: Transportation Applicationsmentioning

confidence: 99%

An overview of wireless sensor network and its applications

Al-Ani

Abdalkafor

Nassar

2020

IJEECS

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“…can fit functions better with less parameters than a nondeep network.in addition to the number of hidden layers are larger than the first type (Abdalkafor & AlHamouz , 2016;Abdalkafor, 2017;Chong, Han & Park, 2017).…”

Section: Deep Neural Network (Feedback Network)mentioning

confidence: 99%

Artificial Neural Networks Technical Transaction for Bitcoin(BTC )

Taher¹,

Subhi²

2018

مؤتمرات الآداب والعلوم الانسانية والطبيعية

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Bitcoin it is digital currency used for any Electronic Financial Transactions, created in 2008 by Satoshi Nakamoto as Peer-to-Peer Electronic System cash. The precise prediction of bitcoin exchange rate with respect to the US dollar is an essential matter because it effected on the world economic constancy. Meanwhile the technology that used in Bitcoin has flickered a revolution in world. This paper aim to a survy on the use artificial neural networks to predict the exchange rate of a Bitcoin(BTC) currency.

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“…These often resulted in low efficiency and poor performance evident in many current approaches (Kaur & Bajaj, 2016;Birabadar & Raikar, 2017). In recent times, learning systems based on ANN and KCDM for classification tasks in high-dimensional problem space, including intrinsic plagiarism detection, image and speech recognitions, identity and non-parametric testing, risk assessment, cellular automata classification, spam filtering, malicious URL detection, text and music classifications, DNA analysis, radar signal classification, EEG classification, e-commerce product classification, etc, are becoming more evident (Revolle et al, 2016;Oyewole & Olugbara, 2017;Abdalkafor, 2017;Haris et al, 2018). Actually, ANN has previously been identified as a good approach for dealing with large text classification problems (Lai et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

Fagbola¹,

Thakur²,

Olugbara³

2019

IJTech

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News article classification is a recently growing area of interest in text classification because of its associated multiple matching categories. However, the weak reliability indices and ambiguities associated with state-of-the-art classifiers often employed make success in this domain very limited. Also, the high sensitivity and large disparity in performance results of classifiers to the varying nature of real-world datasets make the need for comparative evaluation inevitable. In this paper, the accuracy and computational time efficiency of the Kolmogorov Complexity Distance Measure (KCDM) and Artificial Neural Network (ANN) were experimentally evaluated for a prototype large dimensional news article classification problem. 2000 News articles from a dataset of 2225 British Broadcasting Corporation (BBC) news documents (including examples from sport, politics, entertainment, education and technology, and business) were used for categorical testing purposes. Porter's algorithm was used for word stemming after tokenization and stop-words removal, and a Normalized Term Frequency-Inverse Document Frequency (NTF-IDF) technique was adopted for feature extraction. Experimental results revealed that ANN performs better in terms of accuracy while the KCDM produced better results than ANN in terms of computational time efficiency.

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Designing Offline Arabic Handwritten Isolated Character Recognition System using Artificial Neural Network Approach

Cited by 14 publications

References 2 publications

An overview of wireless sensor network and its applications

An overview of wireless sensor network and its applications

Artificial Neural Networks Technical Transaction for Bitcoin(BTC )

News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

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