Best Eco-Friendly College Competition

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As part of the "Best Environmentally Friendly College" competition Assiut University Vice President inspects the Faculty of Computers and Information The Faculty Evaluation Committee at Assiut University continues its field work as part of the "Best Environmentally Friendly College" competition, organized by the university to monitor colleges' compliance with sustainable environmental standards. The competition is held under the patronage of Dr. Ahmed El-Minshawy, President of Assiut University, under the supervision of Dr. Mahmoud Abdel-Alim, Vice President for Community Service and Environmental Development, and coordinated by Dr. Mohamed Mustafa Hamad, General Coordinator of the competition; Dr. Amr Saeed, Executive Coordinator; and Dr. Rehab El-Dakhili, Media Coordinator for the competition. The fourth round of the committee's work included a visit to the Faculty of Computers and Information, where Dr. Mahmoud Abdel-Alim conducted a field tour of the Faculty of Computers and Information to monitor the college's implementation of the competition's standards. For his part, Dr. Ahmed El-Minshawy, President of Assiut University, explained that the competition comes within the framework of the university's plan to promote the concepts of sustainability, spread environmental awareness among its members, and support the colleges' efforts to improve their educational environment. He emphasized the university's commitment to instilling a culture of sustainable practices that contribute to the protection of natural resources. Reducing waste and supporting engagement with the local community, reflecting the university's role as an environmentally and developmentally responsible institution. The visiting committee to the College of Computers and Information included Dr. Mohamed Mustafa Hamad, Dr. Mohamed Mahmoud El-Wakil, Dr. Sanaa Mohamed Zahran, Dr. Randa Youssef, Dr. Mahmoud El-Qadi, and Professor Osama El-Sayed. The committee was received by Dr. Tayseer Abdel Hamid, Dean of the College, Dr. Dalia Nashaat, Vice Dean for Community Service and Environmental Development, and the College Secretary, along with a team of faculty members and their assistants. Professor Nada Kamal documented the visit. During the visit, the College gave introductory presentations covering the most important environmental activities and practices implemented in the areas of infrastructure, risk management, energy conservation, and strengthening relationships with the community. The committee also reviewed efforts made in managing natural resources and water. The committee also conducted field tours of the College's various facilities, including classrooms, administrative offices, computer labs, cafeterias, green spaces, and parking lots. and bicycles, to assess compliance with sustainable environmental standards.

Tempered fractional Jacobi-Müntz basis for image reconstruction application and high-order pseudospectral tempered fractional differential matrices

Research Abstract

This paper develops two tempered fractional matrices that are computationally accurate, efficient, and stable to treat myriad tempered fractional differential problems. The suggested approaches are versatile in handling both spatial and temporal dimensions and treating integer- and fractional-order derivatives as well as non-tempered scenarios via utilizing pseudospectral techniques. We depend on Lagrange basis functions, which are derived from the tempered Jacobi-Müntz functions based on the left- and right-definitions of Erdélyi-Kober fractional derivatives. We aim to obtain the pseudospectral-tempered fractional differentiation matrices in two distinct ways. The study involves a numerical measurement of the condition number of tempered fractional differentiation matrices and the time spent to create the collocation matrices and find the numerical solutions. The suggested matrices' accuracy and efficiency are investigated from the point of view of the , -norms errors, the maximum absolute error matrix  of the two-dimensional problems, and the fast rate of spectral convergence. Finally, numerical experiments are carried out to show the exponential convergence, applicability, effectiveness, speed, and potential of the suggested matrices.

Research Authors
Sayed A Dahy, HM El-Hawary, Alaa Fahim, Amal A Farhat
Research Date
Research Department
Computer Science Department
Research Journal
Applied Mathematics and Computation
Research Member
Dr.Amal Abd-Elazim Mohamed
Research Pages
128954
Research Publisher
Elsevier
Research Rank
Q1
Research Vol
481
Research Website
https://www.sciencedirect.com/science/article/pii/S0096300324004156
Research Year
2024

Resnet50 and logistic Gaussian map-based zero-watermarking algorithm for medical color images

Research Abstract

Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it degrades the original images’ quality. Furthermore, medical-colored image watermarking options are constrained since most medical watermarking systems are built for gray-scale images. This paper proposes a zero-watermarking scheme for medical color image copyright protection based on a chaotic system and Resnet50, which is a convolutional neural network method. The network Resnet50 is used to extract features from the color medical image, and then a logistic Gaussian map is used to scramble these features and scramble the binary image. Finally, an exclusive OR operation is performed (scrambled binary image, scrambled features for the medical color image) to form a zero watermarking. The experimental result proves that our scheme is effective and robust to geometric and common image processing attacks. The BER values of the extracted watermarks are below 0.0039, and the NCC values are above 0.9942, while the average PSNR values of the attacked images are 29.0056 dB. Also, it is superior to other zero-watermark schemes for medical images in terms of robustness to conventional image processing and geometric attacks. Furthermore, the experimental results show that the Resnet50 model outperforms other models in terms of reducing the mean squared errors of the features between the attacked and original image.

Research Authors
Amal A. Farhat, Mohamed M. Darwish & T. M. El-Gindy
Research Date
Research Department
Computer Science Department
Research Journal
Neural Computing and Applications
Research Member
Dr.Amal Abd-Elazim Mohamed
Mohamed Mostafa Mohamed Darwish
Research Pages
19707–19727
Research Publisher
Springer London
Research Rank
Q2
Research Vol
36
Research Website
https://link.springer.com/article/10.1007/s00521-024-10121-5
Research Year
2024

Deep Learning for Table Detection and Structure Recognition: A Survey

Research Abstract

Tables are everywhere, from scientific journals, articles, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of table detection has substantially increased thanks to the rapid development of deep learning networks. The goals of this survey are to provide a profound comprehension of the major developments in the field of table detection, offer insight into the different methodologies, and provide a systematic taxonomy of the different approaches. Furthermore, we provide an analysis of both classic and new applications in the field. Lastly, the datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature. Finally, we go over the architecture of utilizing various object detection and table structure recognition methods to create an effective and efficient system, as well as a set of development trends to keep up with state-of-the-art algorithms and future research. We have also set up a public GitHub repository where we will be updating the most recent publications, open data, and source code. The GitHub repository is available at https://github.com/abdoelsayed2016/tabledetection-structure-recognition.

Research Authors
Mahmoud SalahEldin Kasem, Abdelrahman Abdallah, Alexander Berendeyev, Ebrahem Elkady, Mohamed Mahmoud, Mahmoud Abdalla, Mohamed Hamada, Sebastiano Vascon, Daniyar Nurseitov, and Islam TajEddin
Research Date
Research Department
Information Technology Department
Research File
Deep Learning for Table Detection and Structure-5.pdf (1.49 MB)
Research Journal
ACM Computing Surveys
Research Member
Islam Ali Taj-Eddin Fathalla Taj-Eddin
Mahmoud Salah Eldin Mohammed
abdoelsayed2016
Ebrahem Mohamed Mustafa Elkady
Mohamed Hamdy Hamdan Mahmoud
Research Pages
41 pages
Research Publisher
ACM
Research Vol
56
Research Website
https://doi.org/10.1145/3657281
Research Year
2024

Customer profiling, segmentation, and sales prediction using AI in direct marketing

Research Abstract

In the current business environment, where the customer is the primary focus, effective communication between marketing and senior management is vital for success. Effective customer profiling is a cornerstone of strategic decision-making for digital start-ups seeking sustainable growth and customer satisfaction. This research investigates the clustering of customers based on recency, frequency, and monetary (RFM) analysis and employs validation metrics to derive optimal clusters. The K-means clustering algorithm, coupled with the Elbow method, Silhouette coefficient, and Gap Statistics
method, facilitates the identification of distinct customer segments. The study unveils three primary clusters with unique characteristics: new customers (Cluster A), best customers (Cluster B), and intermittent customers (Cluster C). For platform-based Edutech start-ups, Cluster A underscores the importance of tailored learning content and support, Cluster B emphasizes personalized incentives, and Cluster C suggests re-engagement strategies. By understanding and addressing the diverse needs of these clusters, digital start-ups can forge enduring connections, optimize customer engagement, and fuel
sustainable business growth.

Research Authors
Mahmoud SalahEldin Kasem, Mohamed Hamada, Islam Taj-Eddin
Research Date
Research Department
Information Technology Department
Research File
s00521-023-09339-6.pdf (650.69 KB)
Research Journal
Neural Computing and Applications
Research Member
Islam Ali Taj-Eddin Fathalla Taj-Eddin
Mahmoud Salah Eldin Mohammed
Research Pages
4995-5005
Research Publisher
Springer Nature
Research Vol
36
Research Website
https://doi.org/10.1007/s00521-023-09339-6
Research Year
2024

A Proposed Technique Using Machine Learning for the Prediction of Diabetes Disease through a Mobile App

Research Abstract

With the increasing prevalence of diabetes in Saudi Arabia, there is a critical need for early detection and prediction of the disease to prevent long-term health complications. This study addresses this need by using machine learning (ML) techniques applied to the Pima Indians dataset and private diabetes datasets through the implementation of a computerized system for predicting diabetes. In contrast to prior research, this study employs a semisupervised model combined with strong gradient boosting, effectively predicting diabetes-related features of the dataset. Additionally, the researchers employ the SMOTE technique to deal with the problem of imbalanced classes. Ten ML classification techniques, including logistic regression, random forest, KNN, decision tree, bagging, AdaBoost, XGBoost, voting, SVM, and Naive Bayes, are evaluated to determine the algorithm that produces the most accurate diabetes prediction. The proposed approach has achieved impressive performance. For the private dataset, the XGBoost algorithm with SMOTE achieved an accuracy of 97.4%, an F1 coefficient of 0.95, and an AUC of 0.87. For the combined datasets, it achieved an accuracy of 83.1%, an F1 coefficient of 0.76, and an AUC of 0.85. To understand how the model predicts the final results, an explainable AI technique using SHAP methods is implemented. Furthermore, the study demonstrates the adaptability of the proposed system by applying a domain adaptation method. To further enhance accessibility, a mobile app has been developed for instant diabetes prediction based on user-entered features. *is study contributes novel insights and techniques to the field of ML-based diabetic prediction, potentially aiding in the early detection and management of diabetes in Egypt and Saudi Arabia.
 

Research Authors
Hosam El-Sofany , Samir A. El-Seoud , Omar H. Karam , Yasser M. Abd El-Latif , and Islam A. T. F. Taj-Eddin
Research Date
Research Department
Information Technology Department
Research File
International Journal of Intelligent Systems - 2024 - El-Sofany - A Proposed Technique Using Machine Learning for the.pdf (1011.32 KB)
Research Journal
International Journal of Intelligent Systems
Research Member
Islam Ali Taj-Eddin Fathalla Taj-Eddin
Research Pages
13 pages
Research Publisher
Wiley/Hindawi
Research Vol
2024
Research Website
https://doi.org/10.1155/2024/6688934
Research Year
2024

A real-time predicting online tool for detection of people’s emotions from Arabic tweets based on big data platforms

Research Abstract

Emotion prediction is a subset of sentiment analysis that aims to extract emotions from text, speech, or images. The researchers posit that emotions determine human behavior, making the development of a method to recognize emotions automatically crucial for use during global crises, such as the COVID-19 pandemic. In this paper, a real-time system is developed that identifes and predicts emotions conveyed by users in Arabic tweets regarding COVID-19 into standard six emotions based on the big data platform, Apache Spark. The system consists of two main stages: (1) Developing an ofine model and (2) Online emotion prediction pipeline. For the frst stage, two diferent approaches: The deep Learning (DL) approach and the Transfer Learning-based (TL) approach to fnd the optimal classifer for identifying and predicting emotion. For DL, three classifers are applied: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). For TL, fve models are applied: AraBERT, ArabicBERT, ARBERT, MARBERT, and QARiB. For the second stage, create a Transmission Control Protocol (TCP) socket between Twitter’s API and Spark used to receive streaming tweets and Apache Spark to predict the label of tweets in real-time. The experimental results show that the QARiB model achieved the highest Jaccard accuracy (65.73%), multi-accuracy (78.71%), precision-micro (78.71%), recall-micro (78.71%), f-micro (78.71%), and f-macro (78.55%). The system is available as a web-based application that aims to provide a real-time visualization of people’s emotions during a crisis.

Research Authors
Naglaa Abdelhady, Ibrahim E. Elsemman and Taysir Hassan A. Soliman
Research Date
Research Department
Information Systems Department
Research Journal
Journal of Big Data
Research Member
Taiseer Hassan Abdel-Hamid Sulaiman
Ibrahim E. Elsemman
Naglaa Abdel-Hade Sleem Darder
Research Pages
171
Research Publisher
Springer International Publishing
Research Rank
international
Research Vol
11 (1)
Research Year
2024

Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweets

Research Abstract

Social networks are popular for advertising, idea sharing, and opinion formation. Due to COVID-19, coronavirus information disseminated on social media affects people’s lives directly. Individuals sometimes managed it well, but it often hampered daily activities. As a result, analyzing people’s feelings is important. Sentiment analysis identifies opinions or sentiments from text. In this paper, we present an effective model that leverages the benefits of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to categorize Arabic tweets using a stacked ensemble learning model. First, the tweets are represented as vectors using a word embedding model, then the text feature is extracted by CNN, and finally the context information of the text is acquired by BiLSTM. Aravec, FastText, and ArWordVec are employed separately to assess the impact of the word embedding on the our model …

Research Authors
Naglaa Abdelhady, Taysir Hassan A. Soliman, Mohammed F. Farghally
Research Date
Research Department
Information Systems Department
Research Journal
Journal of Cloud Computing
Research Member
Taiseer Hassan Abdel-Hamid Sulaiman
Naglaa Abdel-Hade Sleem Darder
Research Vol
13
Research Year
2024

the activities of the employment and self-employment forum

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The president of Assiut University opens the exhibition of graduation projects of students of the Faculty of computers and information and ITI institute within the activities of the employment and self-employment forum

- Dr. El-Minshawy praises the innovations of computer students and"ITI" in artificial intelligence and modern technological applications

- President of Assiut University: graduation projects reflect the skills of our students in employing٠ technology to serve various scientific and knowledge fields

Dr. Ahmed El-Minshawy, president of Assiut University, opened on Sunday, May 4, the exhibition of graduation projects for final-year students at the Faculty of computers and Information Technology, and students of the Information Technology Institute in Assiut ITI, which was organized by the Faculty of computers and information as part of the activities of the "employment and self-employment forum" in its third edition, in cooperation with the branch of the Information Technology Institute in Assiut.

The president of the university was accompanied on his tour during the exhibition by: Dr. Jamal Badr, vice president for graduate studies and research, Dr. Tayseer Hassan Abdul Hamid, dean of the Faculty of computers and Information, Dr. Khaled Fathy, vice dean for education and Student Affairs, Engineer Ayman Ayad, director of the Institute branch in Assiut, and Mr. Ihab Abdul Hamid, director of the medium, small and Micro Enterprise Development Authority, with the participation of a number of faculty members, students, and a number of developers and technicians from various technological companies.

During the opening ceremony, Dr. El-Minshawy expressed his appreciation and admiration for the creative and innovative scientific production provided by the students through their projects, which reflected the level of development and outstanding training enjoyed by the University's students and graduates, calling on them to continue to develop their ideas, seek to apply them and compete regionally and globally.

Dr. El-Minshawy stressed that Assiut University is constantly keen on qualifying promising university youth to keep up with the requirements of the labor market and the latest technology, praising the unlimited creativity of its students and the ability to keep up with the latest technological and technical education in various fields of science.

During his tour of the exhibition, the president of the University praised the extent to which students were able to explore and apply artificial intelligence technologies in their ideas and projects, which keeps pace with the strengthening of the University's management of all modern applied and technological fields in various sectors, wishing them a bright future and continuous success in all fields of technology and its applications.

The projects of the students of the Faculty of computers and information included a lot of creative ideas, which numbered (16) a project, during which the students relied on employing the latest artificial intelligence applications and technologies in various scientific fields and knowledge, including; applications and websites for educational games, Smart networks, electronic exam monitoring systems, a website to facilitate learning, a surveillance camera equipped with the latest technologies, as well as a mobile application to find lost items, and another application for recipes and diets.

The projects also included the "Rahala" platform, which is an integrated tourism platform on the web and mobile, the "Nabta" application in the agricultural field, as well as a desktop application with a computer to measure glycines and diagnose diseases, the "business" website for data analysis, a mobile application to identify skin diseases, burns and necessary first aid, a digital website for managing the fleet of Assiut University cars, in addition to a medical mobile website and application to identify the nearest pharmacy and doctors ' consultant, as well as a smart watch that determines the health status of the child according to his health history.

As for the projects of ITI students, they were participated by 10 students, submitted about 400 an idea and an innovative project that they worked on and implemented during their years of study in the fields of; Free Lance freelance jobs, website building and design, open source applications, digital marketing, and website development.

the "employment and self-employment forum

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The Faculty of Computers and Information at Assiut University launches the activities of the third edition of the

"employment and self-employment" forum with the wide participation of major technology companies

Assiut University witnessed, on Sunday, May 4, 2025, the launch of the activities of the third session of the "employment and self-employment forum", which was organized by the Faculty of Computers and Information in cooperation with the branch of the Institute of Information Technology in Assiut, affiliated with the Ministry of Communications and Information Technology, under the supervision of Dr. Ahmed El-Minshawy, university president, and the supervision of Dr. Ahmed Abdul-Mawla, vice president of the University for Education and Student Affairs, Dr. Heba Saleh, president of the Institute of Information Technology, and Dr. Tayseer Hassan Abdul Hamid, dean of the College of computers and information.

The events were attended by Dr. Mahmoud Abdul Alim, vice president of the University for Community Service and Environmental Development, Dr. Jamal Badr, vice president of the University for Graduate Studies and Research, along with several faculty members, a selection of experts, developers, and specialists in the fields of technology, and representatives of 22 local and international companies operating in the sector, which provided more than 250 job opportunities and training for university graduates.

The forum was held under the supervision of Professor Dr. Khaled Fathi Abdul Aziz, vice dean of the Faculty of Computers and Information for Education and Student Affairs, and engineer Ayman Ayad, director of the branch of the Institute of Information Technology in Assiut.

 Dr. Ahmed El-Minshawy stressed that the forum is an important interactive platform that enables students and graduates to communicate directly with employment institutions and learn about the realistic requirements of the labor market in various fields of modern technology, including artificial intelligence, cybersecurity, data analysis, and digitization. He explained that the University seeks to support the culture of self-employment, promote entrepreneurship thought in line with national trends of digital transformation, and empower young people in the fields of advanced technology.

For his side, Prof. Dr. Mahmoud Abdel Alim explained that the forum embodies the University's vision of preparing qualified cadres capable of competing in the labor market, and reflects the efforts of the Ministry of Higher Education and scientific research in strengthening the link between academic institutions and the labor market and providing real opportunities for Egyptian youth.

Dr. Jamal Badr toured the exhibition of companies participating in the forum, which included 22 technological companies, that provided more than 250 job opportunities and training for university graduates, praising the level of fruitful cooperation between the University and those companies, reflecting the business community's confidence in the quality of Education outputs at the University.

 Dr. Tayseer Hassan Abdul Hamid expressed her pride in the students of the Faculty of Computers and Information, stressing that they represent the most prominent beneficiaries of this type of partnership, because of their efficiency and excellence, which enabled them to prove themselves in major international institutions, wishing them further success and progress.

Dr. Khalid Fathi Abdul Aziz pointed out that the forum is a golden opportunity to develop students ' technical and professional skills and enhance their ability to actively engage in the labor market through interaction with leading technology companies.

In his speech, engineer Ayman Ayad conveyed the greetings of Dr. Heba Saleh, president of the Institute of Information Technology, to the administration of Assiut University, praising the partnership that began since 2007 when the University hosted the headquarters of the Institute, which started with only 100 trainees, and today reached more than 5,000 training grants annually, stressing that the institute is not limited in its support on the technological side, but is keen to develop comprehensive skills qualified for the labor market.

Engineer Hossam Megahed, chairman of the board of Directors of the association "Elites NGO", stressed that the ICT sector in Egypt is facing a challenge represented by the lack of qualified cadres, in exchange for the availability of a large number of young people looking for job opportunities, stressing that the best solution is to develop technological skills for young people, especially in Upper Egypt governorates, through effective partnerships such as cooperation between Assiut University and the Institute of Information Technology.

The opening of the forum witnessed the presentation of a documentary film highlighting the aspects of cooperation between the Institute of Information Technology and the College of Computers and Information, a review of the most important activities of the Institute and its services provided to students, as well as a video of a number of graduates of the college and the Institute who joined the labor market in major companies.

The forum included a series of lectures and dialogue sessions, presented by several representatives of local and international companies, including: "From Education to Employment: Building Bridges of Success" by engineer Hossam Megahed, "digital generations of Egypt: empowering youth to shape the future" by Dr. Firouz Mahmoud, Director of training at the Ministry of Communications and Information Technology," success story" by Dr. Hussein Ibrahim, Microsoft," programming at the speed of thinking" by Dr. Mohamed Beltagi, Optomotica, and"beyond coding" by engineer Nada Ehab, from gabi, and"remote work ethics" by Engineer Ahmed Ayman, from turn digital, and"data technologies and products" by engineer Mohamed Abdo, from spaceman, and"post-graduation" by engineer Hani Abdul Wahab, and"financial technology" by engineer Andrew saber, from Petra, and"the story of the establishment of clinical platform for booking medical services" by engineer Haitham Essam

This forum comes within the framework of Assiut University's efforts to enhance employment opportunities and digital entrepreneurship and support the capabilities of its students and graduates to qualify them for strong competition in the local and international labor markets.

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