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2024 International Conference on computer and Applications, ICCA 2024

作者： Eltaki, Nada Nagaty, Khaled Elzanfaly, Doaa Maghawry, Noura The British University in Egypt Faculty of Informatics and Computer Science Department of Information System Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt Helwan University Faculty of Informatics and Computer Science Department of Information System Cairo Egypt

ISBN: (纸本)9798350367560

The entire world encounters a significant problem which is food availability. 'Zero-hunger' is the second Sustainable Development Goal (SDG) of the goals established by the United Nations in 2015. With the increasing number of people and various factors affecting food availability such as climate and country-based food production rate, food forecasting models have become increasingly essential. However, traditional forecasting techniques are limited in dealing with the data and identifying the relationships between them within a specific timestamp. This paper proposes a framework for an intelligent food availability forecasting model built using deep learning techniques and based on a temporal knowledge graph where different timestamps are taken into consideration. The proposed framework helps in enhancing and overcoming the challenges and limitations of the recent forecasting techniques leading to a dynamic, scalable, intelligent forecasting model. © 2024 IEEE.

关键词： Prediction models

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AdaptVR: An Adaptive Learning VR System for Dental Students Training

AdaptVR: An Adaptive Learning VR System for Dental Students ...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Sherif, Meriam Barakat, Nahla Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Artificial Intelligence Cairo Egypt

ISBN: (纸本)9798350367560

The paper proposes 'AdaptVR' a virtual reality (VR) system designed to enhance dental training through realistic real tile simulations and adaptive learning environment, to overcome traditional training challenges in dental education. The system aims to provide personalized learning experiences, facilitating skill development and comprehensive assessments for students. The proposed system was tested by 17 dentistry students and 5 experts. AdaptVR framework is done for operative dentistry which is removing decay especially Class I and Class II of classification of caries. Results from the experiment revealed that 77.3% of participants exceed their expectations, 59.1% saw a positive effect on their dental skills, 90.9% reported higher levels of engagement, and 65% indicated that vibration and force feedback provided students with a genuine sensation. AdpatVR's experimental findings show an average accuracy of 50.85% for Class I, 51.7% for Class II, and 82.09% for the Class II box technique. The error percentage averages 49% for Class I, 48% for Class II, and 18% for Class II box technique. © 2024 IEEE.

关键词： Virtual environments

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The Impact of Trendy Topics on Information Diffusion 12

The Impact of Trendy Topics on Information Diffusion

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12th International Conference on Software and Information Engineering, ICSIE 2023

作者： Fahmy, Sara G Khalil, Rana Moukhtar, Hebat Allah Adel Elzanfaly, Doaa S Abdelgaber, Khaled M Department of Information Systems Faculty of Informatics and Computer Science The British University in Egypt Egypt Faculty of Informatics and Computer Science The British University in Egypt Egypt

ISBN: (纸本)9798400708107

关键词： Information Diffusion SIR model SI_{t_w}R model Social Networks Structure of Network Textual Content Topic Identification Trend Extraction Trendy Topics

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Deep mining of open source software bug repositories

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International Journal of computers and Applications 2022年第7期44卷 614-622页

作者： Hamdy, Abeer Ezzat, Gloria Faculty of Informatics and Computer Science British University in Egypt Cairo Egypt

Large scale software projects adopt bug tracking systems such as Bugzilla and Jira to manage the bugs’ fixes and store their information. Mining bug repositories is essential to automate some maintenance phase activities like bug triage. Recently, embedding and deep learning models have emerged as a breakthrough for text classification applications. This paper proposes utilizing word embedding and deep learning models for mining bug repositories, in order to identify severity classes of newly reported bugs. Embedding models were utilized to represent bug reports, due to their ability to acquire semantic relations among words and sentences. Five effective deep learning models (CNN, LSTM, GRU, hybrid CNN-LSTM/GRU) were trained to classify the bug reports to their severity classes. We attempted these models as each of them has different capabilities;CNN can extract key features of an input and reduce the feature space size. While, LSTM and GRU can represent sequential words (sentences) effectively. Experimental results on two open large-scale bug repositories, Eclipse and Mozilla, demonstrated that CNN model is the superior. However, it was found that the micro-average classification performance of the CNN could be achieved by some traditional classifiers, e.g. SVM and KNN, trained using the embedding vectors instead of the bag-of-words-model. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Embeddings

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Semi-Automated Video Archiving Using Object Detection, Tracking and Ontological Annotation 4

Semi-Automated Video Archiving Using Object Detection, Track...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350348248

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries. © 2023 IEEE.

关键词： computer vision

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Tiny Machine Learning for Underwater Image Enhancement: Pruning and Quantization Approach 5

Tiny Machine Learning for Underwater Image Enhancement: Prun...

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5th International Conference on computer and Applications, ICCA 2023

作者： Rejal, Ayah Abo El Pester, Andreas Nagaty, Khaled The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350303254

Many people have expressed an interest in underwater image processing in a variety of fields, including underwater vehicle control, archaeology, marine biological studies, etc. Underwater exploration is becoming an increasingly important element of our lives, with applications ranging from underwater marine and creature research to pipeline and communication logistics, touristic and entertainment use. Underwater images suffer from poor visibility, distortion, and poor quality for a variety of causes, including light propagation. The major issue arises when these images must be captured at depths greater than 500 feet and artificial lighting needs to be provided. Efficient algorithms and models were proposed to enhance the images taken underwater. However, these networks challenge efficient real-time deployment and needs significant computational resources and energy costs. In this paper these challenges will be tackled by using different network compression techniques such as pruning and quantization. These techniques will be applied on a model named WGH-net and the resulting in a tiny machine learning model is named tiny WGH-net model. Moreover, we compare the results of both models along with other models proposed by other researchers. Sustainable Development Goals (SDG) will also be discussed along with our contribution to some of the goals. © 2023 IEEE.

关键词： Machine learning

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Fine-Tuning GPT-4o-Mini for Programming Questions Generation

Fine-Tuning GPT-4o-Mini for Programming Questions Generation

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2024 International Conference on computer and Applications, ICCA 2024

作者： Hesham, Alaa Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt

ISBN: (纸本)9798350367560

Creating programming questions that are both meaningful and educationally relevant is a critical task in computer science education. This paper introduces a fine-tuned GPT4o-mini model (C2Q). It is designed to generate meaningful questions by leveraging semantic feature extraction and well- crafted prompts. The approach addresses the limitations of traditional generative models, offering a deeper understanding of programming code and producing questions that are precise, diverse, and relevant to a given code snippets. The proposed framework incorporates essential code elements, such as control structures and method attributes, to generate questions that align with programming concepts. Evaluation metrics used were BLEU, ROUGE-1, and ROUGE-L to evaluate the model's performance. The findings reveal that the model achieves better structural coherence and conceptual relevance while focusing on contextual understanding over exact term matching. This work highlights the potential of the proposed approach to advance teaching and assessment methods in computer science. © 2024 IEEE.

关键词： Program processors

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The Influence of ChatGPT on Student Learning and Academic Performance 5

The Influence of ChatGPT on Student Learning and Academic Pe...

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5th International Conference on computer and Applications, ICCA 2023

作者： Ayman, Shehab Eldeen El-Seoud, Samir A. Nagaty, Khaled Karam, Omar H. The British University In Egypt Faculty of Informatics and Computer Science Cairo Egypt

ISBN: (纸本)9798350303254

This study delves into the integration of ChatGPT, an artificial intelligence-driven language model, within undergraduate education. The research scrutinizes the potential advantages, obstacles, and ethical dimensions linked to including ChatGPT in educational practices. It assesses how ChatGPT may impact student engagement, critical thinking, problem-solving abilities, writing proficiency, and the delivery of personalized learning experiences. A survey administered to university faculty members captures their perspectives on ChatGPT usage, encompassing its influence on learning outcomes and academic performance. An internal inquiry at the british University in Egypt found high plagiarism rates in various faculties, with mass media having the highest at 66%, while political science had the lowest at 28%. A survey has been conducted on teaching staff at the university. 75% of teaching staff believes that ChatGPT should be integrated into teaching. However, they are still undecided about whether ChatGPT has affected the teaching and learning process. The study underscores the significance of responsible implementation, faculty training, and continuous assessment to optimize ChatGPT's benefits while adhering to ethical standards in education. In conclusion, this paper illuminates ChatGPT's potential as a valuable tool in undergraduate education, underscoring the imperative of preserving critical thinking skills and human interaction in the learning journey. © 2023 IEEE.

关键词： Students

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Comparison of Transformer-based Architectures for Product Categorization 5

Comparison of Transformer-based Architectures for Product Ca...

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5th International Conference on computer and Applications, ICCA 2023

作者： Mohamed, Cherine Hamdy, Abeer British University in Egypt Faculty of Informatics & Computer Science Cairo Egypt

ISBN: (纸本)9798350303254

The fast & continuous evolution of e-commerce search engines globally has encouraged researchers & industry specialists alike to improve product categorization models to elevate user experience. Also, the revolutionary emergence of large language models has given us the opportunity to capture the complex relationships and dependencies found in large datasets that we were not able to discover with traditional techniques, and not to mention the multitude of other natural language understanding tasks it performs. In this paper, we aim to evaluate different transformer-based architectures on the Amazon Shopping Queries dataset to determine which architecture is well-suited for the task of product categorization within an e-commerce platform. © 2023 IEEE.

关键词： Large datasets

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Artificial Bee Colony for Automated Black-Box Testing of RESTful API 1

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Ahmed, Seif Hamdy, Abeer Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

ISBN: (数字)9789819967063

ISBN: (纸本)9789819967056

Recently, RESTful APIs are widely utilized in a variety of web applications;developers utilize RESTful APIs as a blacked-out component in micro-services. Black-box testing for RESTful APIs is essential as neither of the API’s source code nor its compiled binary is always available for public access. A handful number of research studies were conducted for the automatic generation of test suite for RESTful APIs based on black-box testing. However, to our knowledge, none of them considered the test coverage criteria or the test suite optimization. This paper proposed adapting the Artificial Bee Colony (ABC) swarm intelligence algorithm for the automatic generation of test suites for RESTful APIs based on OpenAPI Specification (OAS), while also maximizing the API test coverage (path, operation, parameter, input value, and status code). Experiments were conducted on six APIs that differ from each other in the number of routes, operation types, input values, and how well the API is documented. The experiments showed that the ABC algorithm can generate test suites that achieve high coverage criteria. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Web services

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