منصة إيفاد العلمية

نور نافع ثامر

إرسال رسالة

التخصص: ماجستير علوم الحاسوب

الجامعة: جامعة بغداد

النقاط:

22.5

معامل الإنتاج البحثي

الخبرات العلمية

شهادة ICDL رسمية منذ عام 2013
شهادة في امن البيانات في عام 2018
شهادة في الحوكمة الالكترونية عام 2014
شهادة في البوربوينت 2020

الأبحاث المنشورة

Early esophageal cancer detection using deep learning techniques (review article)

المجلة: Journal of Physics: Conference Series

سنة النشر: 2021

تاريخ النشر: 2021-09-16

Esophageal cancer is one of the deadliest diseases for humans, since it is discovered in very advanced stages. As result, pathologists are increasingly relying in image recognition and artificial intelligence tools to aid in the early identification and evaluation of this lesion. We examined a number of papers that dealt with this issue during the time span in order to shed light on the studies that were performed in this area (2017 and 2020). We have looked at experiments that used Convolutional Neural Network (CNN) technologies in the study of endoscopic images to help with early detection or diagnosis of esophageal cancer and its various forms. More research on esophageal malignant growth is required, as well as improving the disease's indicative existence and employing more proven techniques for feature selection/extraction of endoscopic images. The aim of this review is to highlight the research conducted on endoscopic images of the esophagus using deep learning algorithms, including CNN, Support Vector Machine (SVM), Random Forests (RF) and other techniques that were used to design the Computer-Aided Detection (CAD) system. In this review we covered some but not all articles that was of great contact with our master's thesis research in this regard.

Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy

المجلة: Comput Biol Med.

سنة النشر: 2021

تاريخ النشر: 2021-10-16

As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Logo of pheelsevier Comput Biol Med. 2021 Dec; 139: 104957. Published online 2021 Oct 16. doi: 10.1016/j.compbiomed.2021.104957 PMCID: PMC8520445PMID: 34735945 Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy A.H. Alamoodi,a,∗ B.B. Zaidan,b Maimonah Al-Masawa,c Sahar M. Taresh,d Sarah Noman,e Ibraheem Y.Y. Ahmaro,f Salem Garfan,a Juliana Chen,g,i,j M.A. Ahmed,k A.A. Zaidan,a O.S. Albahri,a Uwe Aickelin,h Noor N. Thamir,l Julanar Ahmed Fadhil,m and Asmaa Salahaldinn Author information Article notes Copyright and License information PMC Disclaimer Associated Data Data Availability Statement Go to: Abstract A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline (i.e., social, medical, and technological). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: (1) social, (2) medical, public health, and (3) technology sciences. Therefore, this research aimed to review and analyze articles related to different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science, Scopus, PubMed, IEEEXplore, ScienceDirect, and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon.

Performance Evaluation of ISR-Assisted Wireless Communication

المجلة: IJAS

سنة النشر: 2021

تاريخ النشر: 2024-01-02

sciencedirect.com Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy Abdullah Hussein Alamoodi, BB Zaidan, Maimonah Al-Masawa, Sahar M Taresh, Sarah Noman, Ibraheem YY Ahmaro, Salem Garfan, Juliana Chen, Mohamed Aktham Ahmed, AA Zaidan, Osamah Shihab Albahri, Uwe Aickelin, Noor N Thamir, Julanar Ahmed Fadhil, Asmaa Salahaldin Computers in Biology and Medicine 139, 104957, 2021 A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline (i.e., social, medical, and technological). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: (1) social, (2) medical, public health, and (3) technology sciences. Therefore, this research aimed to review and analyze articles related to different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science, Scopus, PubMed, IEEEXplore, ScienceDirect, and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon. View at sciencedirect.com [HTML] nih.gov Cited by 56 Related articles All 11 versions iasj.net Performance Evaluation of ISR-Assisted Wireless Communication Noor N Thamir This research presents a comprehensive MATLAB-based implementation and analysis of a Wireless Sensor Network-Intelligent Reflecting Surface (WSN-IRS) system, focusing on the evaluation of Signal-to-Noise Ratios (SNRs) for both direct signal propagation and signal paths involving IRS reflection. The methodology includes parameter initialization, random phase shift generation for IRS elements, modeling of network layouts with random sensor and access point positions, distance computation, gain calculation, SNR determination, and data visualization. The research discusses challenges associated with IRS technology, including design complexity, channel estimation, real-time adaptability, synchronization, hardware constraints, interference management, security, scalability, regulatory compliance, environmental factors, and hardware imperfections. The core of the proposed methodology lies in the fine-grained control of phase shifts by IRS elements, offering valuable insights into signal quality improvement. The code's adaptability is demonstrated through default parameter settings, random phase shift generation, and modelling of sensor, access point, and IRS locations in a confined space. SNR calculations for various sensor-access point pairs and data visualization provide a clear representation of network performance.

Malaria Disease Prediction Based on Convolutional Neural Networks

المجلة: Journal of Applied Engineering and Technological Science

سنة النشر: 2024

تاريخ النشر: 2024-04-01

This study delves into the investigation of the efficacy of Convolutional Neural Networks (CNNs) in identifying malaria through the examination of cell images. The dataset employed encompasses a total of 27,558 images, harvested from the renowned Malaria Cell Images Dataset on Kaggle, encompassing cells of diverse nature. The architectonics of the CNN model is meticulously devised, comprising of six blocks and three interconnected blocks, thereby rendering an efficient extraction of features and subsequent classification of the cells. Creative paraphrasing: Various strategies such as dropout, batch normalization, and global average pooling are artfully utilized to refine and fortify the model, ensuring its robustness and adaptability. In order to confront the challenge of diminishing gradient and facilitate the attainment of convergence, the activation function known as Rectified Linear Unit (ReLU) is ingeniously employed. Assessing the efficacy of the model via a perplexity grid produces outcomes. Exhibiting a precision rate of 99.59%, a specificity measure of 99.69%, an Sensitivity of 99.40%, F1 Measurement of 99.44%, and a Precision of 99.48, it showcases its capacity to effectively distinguish betwixt malaria-afflicted cells and unafflicted cells. The focal point of this research highlights the substantial potential of CNNs in facilitating the automated identification of malaria using image analysis. By harnessing their unique architecture and regularization techniques, CNNs have the capability to enhance the results and potentially bring about better outcomes in areas with prevalent cases of malaria. Keywords: Convolutional Neural Network, Malaria disease, Computer Aides Detection, Deep Learning

Esophageal Cancer Detection Using Feed-Forward Neural Network

المجلة: webology

سنة النشر: 2022

تاريخ النشر: 2024-07-21

Background and Objectives:- Esophageal malignant growth is probably the deadliest kind of disease in people. It positions seventh as far as disease and 6th as far as passings worldwide, as indicated by the insights of the World Health Organization for the year 2020. That is why we aim to develop a computer system that works on the early detection of esophageal cancer using modern image processing techniques and algorithms. To reduce the death rate by helping specialized doctors to detect it in its early condition. In our research, we relied on the use the Fuzzy C-Means (FCM) algorithm at the stage of clustering and segmentation. In addition, it could use the Convolutional Neural Network (CNN) algorithm in the detection stage. After applying the proposed system to 100 color esophagogastroduodenoscopy images that we downloaded from the Kaggle website, we obtained an accuracy of up to 95%. Note that we did not find researchers who used this data set in their systems to the best of our knowledge. It has been noticed that using the FCM algorithm with the CNN algorithm added a good character in detecting esophageal cancer, although the FCM algorithm needs a lot of development to get the results.

Breast cancer prediction: a CNN approach

المجلة: Multidisciplinary Science Journal

سنة النشر: 2024

تاريخ النشر: 2024-05-18

Detecting breast cancer promptly holds utmost importance in ensuring effective treatment, as it is a matter of great concern in the global health context. The primary objective of this research endeavor is to enhance the process of breast cancer identification through the utilization of a Convolutional Neural Network (CNN). The purpose of this study is to mitigate potential errors in human interpretation of mammograms by comparing this approach to conventional machine-learning techniques. In our present investigation on breast imaging, we have leveraged the well-established mammographic dataset, CBIS-DDSM. This dataset effectively categorizes the images into three distinct classes: normal, benign, or malignant. This compilation encompasses a grand sum of 10,239 images. A myriad of approaches were employed to arrange the content, including the manipulation of the image dimensions to a size of 256x256 pixels. A CNN architecture that was specifically crafted was educated through the fusion of backpropagation and angle plunge techniques. Numerous measures, such as sensitivity, specificity, F1 score, and accuracy, were deftly utilized to thoroughly assess the model's effectiveness. It is truly awe-inspiring to witness the outstanding exhibition of performance showcased by the CNN model, as evidenced by the extraordinary values attained for sensitivity, specificity, F1 score, total precision, and accuracy, all of which are undeniably remarkable. These evaluations undeniably serve as irrefutable evidence that the model possesses exceptional diagnostic capabilities, surpassing even the most advanced techniques currently in use. In truth …

NAVIGATING THE CONTENT DELIVERY NETWORK LANDSCAPE: CHALLENGES, OPPORTUNITIES, AND FUTURE DIRECTIONS

المجلة: وقائع المؤتمر الطلمي الدادس تحت ذطار )جودة مخرجات التطليم... أداس الإصلاح التربوي والأكاديمي( وبطنوان )المتطلبات المدتقبليظ 2)ذباط( / 2024 م. / لل

سنة النشر: 2024

تاريخ النشر: 2024-02-02

A Content Delivery Network (CDN) refers to a collection of servers strategically positioned across multiple geographical locations, with the primary purpose of caching content close to end users. A Content Delivery Network (CDN) facilitates the expeditious transmission of essential components for rendering digital content on the Internet, encompassing HTML pages, JavaScript files, style sheets, images, and videos. The utilization of Content Delivery Network (CDN) services is witnessing an upsurge in popularity, presently accounting for a significant proportion of web traffic, even encompassing major platforms like Facebook, Netflix, and Amazon. Due to the importance of this site, this paper will discuss the concept of CDN, and the important topics related to it related to the network, what are its types and divisions, and its importance in the world of information technology. The research analyzes the most important studies presented by researchers from 2021 to 2023.

Crowd Scene Analysis for Zeyarat AlArabaeen: A Comprehensive Literature Survey

المجلة: Semi-Annual Scientific Journal

سنة النشر: 2024

تاريخ النشر: 2024-02-13

Understanding how people behave in crowded places is an important endeavor with several uses, like controlling the spread of COVID-19 and boosting security. In-depth study of crowd scene analysis methods, including both crowd counting and crowd activity detection, is included in this survey article. This article fills the gap by exhaustively examining the spectrum up to contemporary deep learning techniques, whereas current studies frequently focus primarily on certain aspects or traditional approaches. Paper proposes the innovative idea of Crowd Divergence (CD) evaluation as a matrix for evaluating crowd scene analysis approaches, which was motivated by information theory. Contrary to conventional measurements, CD quantifies the agreement between expected and observed crowd count distributions. This paper makes three key contributions: an examination of readily available crowd scene datasets, the use of CD for thorough technique evaluation, and a thorough examination of crowd scene methodologies. The investigation starts with conventional computer vision methods, closely examining density estimates, detection, and regression strategies. Convolutional neural networks (CNNs) become effective tools as deep learning progresses, as seen by new models like ADCrowdNet and PDANet, which make use of attention mechanisms and structured feature representation. To evaluate algorithmic effectiveness, a variety of benchmark datasets including ShanghaiTech, UCF CC 50, and UCSD are carefully examined. Computer vision's exciting and challenging topic of "crowd scene analysis" has numerous 58 Crowd Scene Analysis for Zeyarat Al-Arabaeen... c-karbala.com Semi-Annual Scientific Journal An issue related to the research of the Seventh International Scientific Conference of Ziyarat Al-Arba'een applications, from crowd control to security surveillance. This survey article offers a comprehensive viewpoint on crowd scene analysis, bringing several approaches under a single heading and presenting the CD measure to guarantee reliable assessment. This article provides a complete resource for researchers and practitioners alike through an elaborate investigation of methods, datasets, and cutting-edge evaluation approaches, paving the way for improved crowd scene analysis techniques across a variety of fields.

A Comprehensive Literature Survey for Crowd Scene Analysis techniques

المجلة: Online Available at: www.multiarticlesjournal.com

سنة النشر: 2023

تاريخ النشر: 2023-06-07

Understanding how people behave in crowded places is an important endeavor with several uses, like controlling the spread of COVID-19 or other diseases that spread through contact. An in-depth study of crowd scene analysis methods, including both crowd counting and crowd activity detection, is included in this survey article. This article fills the gap by exhaustively examining the spectrum up to contemporary deep learning techniques, whereas current studies frequently focus primarily on certain aspects or traditional approaches. The paper proposes the innovative idea of Crowd Divergence (CD) evaluation as a matrix for evaluating crowd scene analysis approaches, which was motivated by information theory. Contrary to conventional measurements, CD quantifies the agreement between expected and observed crowd count distributions. This paper makes three key contributions: an examination of readily available crowd scene datasets, the use of CD for thorough technique evaluation, and a thorough examination of crowd scene methodologies. The investigation starts with conventional computer vision methods, closely examining density estimates, detection, and regression strategies. Convolutional neural networks (CNNs) become effective tools as deep learning progresses, as seen by new models like ADCrowdNet and PDANet, which make use of attention mechanisms and structured feature representation. To evaluate algorithmic effectiveness, a variety of benchmark datasets, including ShanghaiTech, UCF CC 50, and UCSD, are carefully examined. Computer vision's exciting and challenging topic of "crowd scene analysis" has numerous applications, from crowd control to security surveillance. This survey article offers a comprehensive viewpoint on crowd scene analysis, bringing several approaches under a single heading and presenting the CD measure to guarantee reliable assessment. This article provides a complete resource for researchers and practitioners alike through an elaborate investigation of methods, datasets, and cutting-edge evaluation approaches, p