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A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques

computers, Materials & Continua 2021年第8期68卷 2727-2754页

作者： Saleem Ibraheem Saleem Adnan Mohsin Abdulazeez Zeynep Orman Department of Information Technology Technical Informatics College of AkreDuhok Polytechnic UniversityDuhok42004Kurdistan RegionIraq Department of Computer Engineering Faculty of EngineeringIstanbul University-CerrahpasaIstanbul34320Turkey

The writer identification(WI)of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist *** is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies,including old national and religious *** this study,we proposed a new affective segmentation model by modifying an artificial neural network model and making it suitable for the binarization stage based on *** modified method is combined with a new effective rotation model to achieve an accurate segmentation through the analysis of the histogram of binary ***,propose a new framework for correct text rotation that will help us to establish a segmentation method that can facilitate the extraction of text from its *** projections and the radon transform are used and improved using machine learning based on a co-occurrence matrix to produce binary *** training stage involves taking a number of images for model *** images are selected randomly with different angles to generate four classes(0–90,90–180,180–270,and 270–360).The proposed segmentation approach achieves a high accuracy of 98.18%.The study ultimately provides two major contributions that are ranked from top to bottom according to the degree of *** proposed method can be further developed as a new application and used in the recognition of handwritten Arabic text from small documents regardless of logical combinations and sentence construction.

关键词： Writer identification handwritten Arabic biometric systems artificial neural network segmentation skew detection model

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An Efficient Robust Color Watermarking Algorithm Based on DWT, DCT, BFO and Implementation

An Efficient Robust Color Watermarking Algorithm Based on DW...

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11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Hassan, Omer Mohammed Salih Abdulazeez, Adnan Mohsin Mohammed, Arman Ismael Salih, Sardar Omer Alih, Sarbast Husien Ahmed, Falah Y H Zeebaree, Diyar Qader Computer Engineering Duhok Polytechnic University Duhok Iraq Selangor Shah Alam Malaysia Duhok Polytechnic University Research Center Duhok Iraq

ISBN: (纸本)9781665437660

Digital watermarking is getting more research and industry attention. Digital multimedia data allows for robust and simple data editing and modification. However, the spread of digital media presents concerns for digital content owners. It is important to note that digital data can be copied without quality or content loss. This has a considerable impact on copyright holders' ability to safeguard their intellectual property rights. The method of transmitting information by imperceptibly embedding it into digital media is digital watermarking. There are various methods in literature, such as DWT and DCT, which take full energy, are seen and integrated. New strategies and procedures for optimization are required. The present study proposes a novel design and computation technique based on the discrete wavelet and discrete cosine transforms. Watermarking techniques have been progressing to shield media content such as text, audio, video, etc. From copyright. The proposed hybrid DWT-DCT Bacterial Foraging Optimization (BFO) technique improves the efficiency of watermarking digital images by 97%. Bacterial foraging optimization (BFO) is an innovative technique for intelligent optimization. It is a widely used optimization algorithm in a wide variety of applications. However, when compared to other optimizers, the BFO performs poorly in terms of convergence. This technique uses a high-frequency image region. A variety of techniques are compared with the (NCC) Normalized Cross Correlations, (PSNR) Peak Noise Signal Ratio and IF (Image Fidelity). The highest performance is seen in DWT-DCT-BFO watermarking. © 2021 IEEE.

关键词： Microorganisms Image color analysis Watermarking Media Systems engineering and theory Robustness Discrete wavelet transforms

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Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

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computers, Materials & Continua 2022年第11期73卷 4393-4406页

作者： Vasumathi Devi Majety N.Sharmili Chinmaya Ranjan Pattanaik ELaxmi Lydia Subhi R.M.Zeebaree Sarmad Nozad Mahmood Ali S.Abosinnee Ahmed Alkhayyat Department of Computer Science Engineering Vignan’s Nirula Institute of Technology and Science for WomenGuntur522005India Department of Computer Science Engineering Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnam530048India Department of Computer Science and Engineering Ajay Binay Institute of Technology CuttackOdisha750314India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityIraq Computer Technical Engineering Department College of Technical EngineeringThe Islamic UniversityNajaf54001Iraq College of Technical Engineering The Islamic UniversityNajafIraq

Histopathology is the investigation of tissues to identify the symptom of *** histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining *** investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s *** the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data *** this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)*** proposed EHCDLHIC technique initially performs Weiner filtering based noise removal *** the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are *** the classification process,the bidirectional gated recurrent unit(BGRU)model can be *** the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the *** validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is *** experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.

关键词： Histopathological image classification machine learning deep learning handcrafted features bacterial foraging optimization

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Design and Enhancement of a CNN Model to Augment the Face Recognition Accuracy 3

Design and Enhancement of a CNN Model to Augment the Face Re...

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3rd International Informatics and Software engineering Conference, IISEC 2022

作者： Kak, Shakir Fattah Mustafa, Firas Mahmood Varol, Asaf Duhok Polytechnic University Akre Technical Institutes Information Technology Department Duhok Iraq Duhok Polytechnic University Technical College of Engineering Chemical Engineering Dept. Iraq College of Engineering Nawroz University Department of Computer and Communication Duhok Iraq The University of Tennessee Dept. of Engineering Mgmt. & Tech. College of Engineering and Comp. Sci. ChattanoogaTN United States

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

In the last decade, smart home security applications have relied more on human biometrics in their functions, due to the reliability and the high-precision results these technologies provide. Face recognition is one of the popular biometrics in the field of image processing technologies. Human face recognition processing is a complicated operation that involves different factors and circumstances such as the illumination degree and the position of the face that affects the final recognition rate. In this research, the Convolution Neural Network (CNN) architecture is used in the extracting phase of significant features of the face shape, and the SoftMax classification layer was used to identify faces in the fully connected CNN layer. This paper provides an update of CNN architecture by applying a three-batch normalization layer to the CNN design. By applying this modification, the system network speed increased with a better recognition rate. The recognition rate also increased by applying two DWT levels with a bio5.5 filter to the training group of the database images and the tested image before applying the PCA dimensional reduction algorithm instead of using the PCA algorithm alone. The obtained face recognition rates have been improved to 99.75% by applying the proposed CNN scheme. While applying the proposed hybrid approach (using the PCA next to applying DWT-2 levels with bior5.5 filter) has registered a 99.25% recognition rate compared to a 96.75% recognition rate when obtained by applying the PCA method alone. The research has adopted using a set of 360 training images and 40 test images set of the standard ORL Database in its work. © 2022 IEEE.

关键词： Biometrics

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Automated Detection of Covid-19 from X-ray Using SVM 4

Automated Detection of Covid-19 from X-ray Using SVM

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4th International Conference on Advanced Science and engineering, ICOASE 2022

作者： Zebari, Dilovan Asaad Sulaiman, Dawlat Mustafa Sadiq, Shereen Saleem Zebari, Nechirvan Asaad Salih, Merdin Shamal College of Science Nawroz University Department of Computer Science Duhok Iraq Technical College of Administration Duhok Polytechnic University Department of It Management Iraq Harran University Department of Computer Engineering Şanlıurfa63300 Turkey

ISBN: (纸本)9781665472227

Earlier discovery of COVID-19 through precise diagnosis, particularly in instances with no evident symptoms, may reduce the mortality rate of patients. Chest X-ray images are the primary diagnostic tool for this condition. Patients exhibiting COVID-19 symptoms are causing hospitals to become overcrowded, which is becoming a big concern. The contribution that machine learning has made to big data medical research has been very helpful, opening up new ways to diagnose diseases. This study has developed a machine vision method to identify COVID-19 using X-ray images. The preprocessing stage has been applied to resize images and enhance the quality of X-ray images. The Gray-level co-occurrence matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM) are then used to extract features from the X-ray images, and these features are combined to develop the performance classification via training by Support Vector Machine (SVM). The testing phase evaluated the model's performance using generalized data. This developed feature combination utilizing the GLCM and GLRLM algorithms assured a satisfactory evaluation performance based on COVID-19 detection compared to the immediate, single feature with a testing accuracy of 96.65%, a specificity of 99.54%, and a sensitivity of 97.98%. © 2022 IEEE.

关键词： COVID-19

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Reconstruction of the Mosul Old City by Procedural Modelling

SSRN

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SSRN 2024年

作者： Hussein, Khalid Ahmed Ismaeel, Emad Hani Ihsan, Rasheed Rebar Sadeeq, Haval Tariq Architecture Engineering Department College of Engineering Nawroz University KRD Duhok Iraq Dept. of Architecture Coll. of Engineering University of Mosul Iraq Computer and Communication Department College of Engineering Nawroz University KRD Duhok Iraq Information Technology Department Technical College of Duhok Duhok Polytechnic University KRD Region Iraq

The old city of Mosul possesses a rich built heritage, featuring distinct architectural styles evolved, incorporating indigenous knowledge and expertise to address functional, climatic, economic, and construction requirements. Tragically, during the military operations in 2016-2017, Mosul Old City experienced extensive devastation, resulting in the loss of numerous landmarks and the irreplaceable built heritage of this city. However, digitization has emerged as a potent heritage preservation and restoration tool. It serves as a vital initial step in reviving deteriorated heritage by providing invaluable information to specialists. In light of this, the present study aims to regenerate the urban facades of Mosul's old city automatically, ensuring they retain their original characteristics. This objective will be accomplished by utilizing procedural modelling techniques and algorithms incorporating specific characteristics as constraints within the regeneration process. The research makes a notable contribution to digital architectural design by employing Shape Grammar analysis to derive rules. Subsequently, these rules are implemented through Python to facilitate the automatic generation of 750 heritage facade designs. Ultimately, the study demonstrates the feasibility of using procedural modelling to recreate the decimated urban fabric, ensuring the restored structures exhibit the same formal and structural characteristics as their predecessors. © 2024, The Authors. All rights reserved.

关键词： Python

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Hybrid Client/Server Peer to Peer Multitier Video Streaming

Hybrid Client/Server Peer to Peer Multitier Video Streaming

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2021 International Conference on Advanced computer Applications, ACA 2021

作者： Ibrahim, Ibrahim Mahmood Zeebaree, Subhi R. M. Yasin, Hajar Maseeh Sadeeq, Mohammed A. M. Shukur, Hanan M. Alkhayyat, Ahmed Duhok Polytechnic University Information Technology Dept. Duhok Iraq Duhok Polytechnic University Culture Center Duhok Iraq Duhok Polytechnic University Quality Assurance Duhok Iraq Al-Kitab University Computer Engineering Dept. Kirkuk Iraq Islamic University Technical Engineering Dept. Najaf Iraq

ISBN: (纸本)9781665435031

Real-Time Video Distribution (RTVD) is now the focus of many applications, including video conferencing, surveillance, video broadcasting, etc. This paper introduces a multi-IP camera-based method for distributing video signals to several levels using a hybrid client/server with peer-to-peer. There are four primary functions in the proposed structure: firstly, all linked camera transmissions will be received by the central server, and video signals will be shown to all attached clients and servers in Level two. The second function means that the clients/servers in level two start viewing received video signals and rebroadcasts them to the next level. The third function is clients/servers in level three who receive the video signals from the upper level and rebroadcast them to the fourth level. The fourth level consists of many clients that start viewing received video signals from level three. Therefore, the planned architecture and mechanism of the proposed system can provide the admin the capability to concentrate on the frames obtained from IP cameras to/from the central server. Furthermore, this mechanism can register and store the stream of frames and encode these frames during transmission. The proposed system depended on VC# programming language as a dependent tool that relied on various architectures and algorithms. © 2021 IEEE.

关键词： Video streaming

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Clustering Document based Semantic Similarity System using TFIDF and K-Mean

Clustering Document based Semantic Similarity System using T...

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2021 International Conference on Advanced computer Applications, ACA 2021

作者： Ibrahim, Rowaida Khalil Zeebaree, Subhi R. M. Jacksi, Karwan Sadeeq, Mohammed A. M. Shukur, Hanan M. Alkhayyat, Ahmed University of Zakho Computer Science Dept. Duhok Iraq Duhok Polytechnic University Culture Center Duhok Iraq Duhok Polytechnic University Quality Assurance Duhok Iraq Al-Kitab University Computer Engineering Dept. Kirkuk Iraq Islamic University Technical Engineering Dept. Najaf Iraq

ISBN: (纸本)9781665435031

The steady success of the Internet has led to an enormous rise in the volume of electronic text records. Sensitive tasks are increasingly being used to organize these materials in meaningful bundles. The standard clustering approach of documents was focused on statistical characteristics and clustering using the syntactic rather than semantic notion. This paper provides a new way to group documents based on textual similarities. Text synopses are found, identified, and stopped using the NLTK dictionary from Wikipedia and IMDB datasets. The next step is to build a vector space with TFIDF and cluster it using an algorithm K-mean. The results were obtained based on three proposed scenarios: 1) no treatment. 2) preprocessing without derivation, and 3) Derivative processing. The results showed that good similarity ratios were obtained for the internal evaluation when using (txt-sentoken data set) for all K values. In contrast, the best ratio was obtained with K = 20. In addition, as an external evaluation, purity measures were obtained and presented V measure of (txt). -sentoken) and the accuracy scale of (nltk-Reuter) gave the best results in three scenarios for K = 20 as subjective evaluation, the maximum time consumed with the first scenario (no preprocessing), and the minimum time recorded with the second scenario (excluding derivation). © 2021 IEEE.

关键词： Vector spaces

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Design a Clustering Document based Semantic Similarity System using TFIDF and K-Mean 4

Design a Clustering Document based Semantic Similarity Syste...

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4th International Iraqi Conference on engineering Technology and Their Applications, IICETA 2021

作者： Ibrahim, Rowaida Khalil Zeebaree, Subhi R.M. Jacksi, Karwan Sadeeq, Mohammed A.M. Shukur, Hanan M. Alkhayyat, Ahmed Computer Science Dept University of Zakho Duhok Iraq Culture Center Duhok Polytechnic University Duhok Iraq Computer Science Dept. University of Zakho Duhok Iraq Quality Assurance Duhok Polytechnic University Duhok Iraq Dept. Computer Engineering Al-Kitab University Kirkuk Iraq Dept. Technical Engineering Islamic University Najaf Iraq

ISBN: (纸本)9781665494618

The continuing success of the Internet has led to an enormous rise in the volume of electronic text records. The strategies for grouping these records into coherent groups are increasingly important. Traditional text clustering methods are focused on statistical characteristics, with a syntactic rather than semantical concept used to do clustering. A new approach for collecting documentation based on textual similarities is presented in this paper. The method is accomplished by defining, tokenizing, and stopping text synopses from Wikipedia and IMDB datasets using the NLTK dictionary. Then, a vector space is created using TFIDF with the K-mean algorithm to carry out clustering. The results were shown as an interactive website. © 2021 IEEE.

关键词： K-means clustering

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Machine Learning-based Diabetic Retinopathy Early Detection and Classification Systems - A Survey 1

Machine Learning-based Diabetic Retinopathy Early Detection ...

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1st Babylon International Conference on Information Technology and Science, BICITS 2021

作者： Hasan, Dathar A. Zeebaree, Subhi R.M. Sadeeq, Mohammed A.M. Shukur, Hanan M. Zebari, Rizgar R. Alkhayyat, Ahmed H. Dept. Information Technology Duhok Polytechnic University Duhok Iraq Culture Center Duhok Polytechnic University Duhok Iraq Quality Assurance Duhok Polytechnic University Duhok Iraq Dept. Computer Engineering Al-Kitab University Kirkuk Iraq Research Center Newroz University Nawroz University Duhok Iraq Dept. Technical Engineering Islamic University Najaf Iraq

ISBN: (纸本)9781665415736

Diabetes Mellitus is a chronic disease that spreads quickly worldwide. It results from increasing the blood glucose level and causes complications in the heart, kidney, and eyes. Diabetic Retinopathy (DR) is an eye disease that refers to the bursting of blood vessels in the retina as Diabetes exacerbates. It is considered the main reason for blindness because it appears without showing any symptoms in the primitive stages. Earlier detection and classification of DR cases is a crucial step toward providing the necessary medical treatment. Recently, machine learning plays an efficient role in medical applications and computer-aided diagnosis due to the accelerated development in its algorithms. In this paper, we aim to study the performance of various machine learning algorithms-based DR detection and classification systems. These systems are trained and tested using massive amounts of retina fundus and thermal images from various publicly available datasets. These systems proved their success in tracking down the warning signs and identifying the DR severity level. The reviewed systems' results indicate that ResNet50 deep convolutional neural network was the most effective algorithm for performance metrics. The Resnet50 contains a set of feature extraction kernels that can analyze retina images to extract wealth information. We conclude that machine learning algorithms can support the physician in adopting appropriate diagnoses and treating DR cases. © 2021 IEEE

关键词： computer aided diagnosis

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