MSA Repository "MSAR"

MSAR University's Digital Repository is a documentation and digitization of all university outcomes that are of effective value in the scientific and academic community and reflects the university's image, work, and effective contribution to society Through MSAR Digital Repository, the university managed to collect, store, archive and publish digital content - including documents, audio files, images and data sets - all in a safe place. MSAR is one of the strongest University Digital Repositories in Egypt and documented in the DSPACE community with its latest versions.

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التحول القيمي في عصر ما بعد الإنسانية
(October University for Modern Sciences and Arts MSA , Faculty of Languages, 2025-11) محمد سعيد حسب النبى
تهدف هذه الدراسة إلى استكشاف ملامح التحول القيمي في عصر ما بعد الإنسانية، وتحديد أسبابه الرئيسة، والكشف عن القيم الإنسانية الأكثر عرضة للتحول، واستشراف السيناريوهات المستقبلية لمنظومة القيم. اعتمدت الدراسة على المنهج الوصفي التحليلي مدعومًا بالجانب الميداني، باستخدام أداة الاستبانة التي تم التحقق من صدقها وثباتها، وتطبيقها على عينة قوامها (42) خبيرًا من (30) تخصصًا علميًا وإنسانيًا في (14) دولة عربية وأجنبية. أظهرت النتائج أن التحول القيمي يختلف عن التغير القيمي بكونه عملية بنيوية عميقة تعيد صياغة النسق القيمي، وليس مجرد تبدل سطحي في الممارسات. كما تبين أن أبرز التحولات التكنولوجية التي تقود إلى نشوء عصر ما بعد الإنسانية تتمثل في: الذكاء الاصطناعي بنسبة (82%)، والهندسة الوراثية بنسبة (68%)، الواقع الافتراضي والميتافيرس بنسبة (57%)، والبيانات الضخمة بنسبة (63%)، مدعومة بتحولات فكرية أبرزها نقد النزعة الإنسانية وصعود ما بعد الحداثة. أما القيم الأكثر عرضة للتحول فهي: الترابط الاجتماعي بنسبة (76%)، الأمن الاجتماعي بنسبة (71%)، الخصوصية بنسبة (69%)، الكرامة الإنسانية بنسبة (64%)، الحرية بنسبة (61%)، المساواة بنسبة (58%)، والإيمان بنسبة (55%). كما أكد المشاركون أن التكنولوجيا هي العامل الجوهري للتحول القيمي بنسبة (65%)، تليها العوامل الاجتماعية والثقافية بنسبة (18%)، ثم الاقتصادية والسياسية بنسبة (10%)، وأخيرًا الفلسفية بنسبة (7%). وعلى صعيد المستقبل، برزت ثلاثة سيناريوهات محتملة: تفاؤلي بنسبة (26%) يرى أن التكنولوجيا قد تعزز العدالة والكرامة، وتشاؤمي بنسبة (39%) يتوقع انهيار القيم التقليدية وصعود قيم بديلة تؤدي إلى الاغتراب، ووسطي بنسبة (35%) يُرجّح إعادة تعريف القيم التقليدية بدل اندثارها. وتكمن مساهمة الدراسة في أنها لم تكتف بوصف التحولات، بل قامت بترتيب أولويات القيم المهددة كما يراها الخبراء، وربطت بين الأبعاد النظرية والفلسفية وما أظهرته الاستبانة الميدانية، مما يوفر أساسًا معرفيًا يُسهم في إثراء النقاش العلمي حول مستقبل القيم، ويدعم صياغة أطر تعليمية وتشريعية وأخلاقية للتعامل مع تحديات عصر ما بعد الإنسانية. This study aims to explore the value transformation in the posthuman era, by identifying its main features, analyzing its underlying causes, highlighting the human values most exposed to transformation, and anticipating possible future scenarios of the value system. The research employed a descriptive–analytical method supported by a field component, using a validated questionnaire applied to a sample of 42 experts representing 30 scientific and humanistic disciplines across 14 Arab and foreign countries. The findings reveal that value transformation differs from mere value change, as it represents a deep structural process that reshapes the entire value system, rather than a superficial shift in practices. The key technological drivers of this transformation were identified as artificial intelligence (82%), genetic engineering (68%), virtual reality and the metaverse (57%), and big data (63%), supported by intellectual transformations such as the critique of classical humanism and the rise of postmodernism. The most vulnerable values were found to be: social cohesion (76%), social security (71%), privacy (69%), human dignity (64%), freedom (61%), equality (58%), and faith (55%). Moreover, participants indicated that technology is the primary factor driving value transformation (65%), followed by socio-cultural factors (18%), economic and political factors (10%), and finally philosophical/existential causes (7%). Regarding the future, three scenarios emerged: an optimistic scenario (26%) in which technology enhances justice and dignity; a pessimistic scenario (39%) predicting a gradual collapse of traditional values and the rise of alternative technological ones, leading to alienation; and a moderate scenario (35%)—the most likely—which suggests that values will persist but be redefined, such as “digital privacy” instead of traditional privacy, and freedom constrained by algorithms instead of absolute freedom. The study contributes by not only describing these transformations but also by ranking the priorities of vulnerable values as identified by experts, and linking theoretical and philosophical perspectives with empirical field evidence. This provides a knowledge base that enriches the scientific debate on the future of values and supports the formulation of educational, legislative, and ethical frameworks to address the challenges of the posthuman era.
Cross-Language Approach for Quranic QA
(Springer International Publishing AG, 2025-10-01) Islam Oshallah; Mohamed Basem; Ali Hamdi; Ammar Mohammed
Question answering systems face critical limitations in languages with limited resources and scarce data, making the development of robust models especially challenging. The Quranic QA system holds significant importance as it facilitates a deeper understanding of the Quran, a Holy text for over a billion people worldwide. However, these systems face unique challenges, including the linguistic disparity between questions written in Modern Standard Arabic and answers found in Quranic verses written in Classical Arabic, and the small size of existing datasets, which further restricts model performance. To address these challenges, we adopt a cross-language approach by (1) Dataset Augmentation: expanding and enriching the dataset through machine translation to convert Arabic questions into English, paraphrasing questions to create linguistic diversity, and retrieving answers from an English translation of the Quran to align with multilingual training requirements; and (2) Language Model Fine-Tuning: utilizing pre-trained models such as BERT-Medium, RoBERTa-Base, DeBERTa-v3-Base, ELECTRA-Large, Flan-T5, Bloom, and Falcon to address the specific requirements of Quranic QA. Experimental results demonstrate that this cross-language approach significantly improves model performance, with RoBERTa-Base achieving the highest MAP@10 (0.34) and MRR (0.52), while DeBERTa-v3-Base excels in Recall@10 (0.50) and Precision@10 (0.24). These findings underscore the effectiveness of cross-language strategies in overcoming linguistic barriers and advancing Quranic QA systems.
A Multi-Layered Large Language Model Framework for Disease Prediction
(Springer International Publishing AG, 2025-10-01) Malak Mohamed; Rokaia Emad; Ali Hamdi
Social telehealth has made a breakthrough in healthcare by allowing patients to share their symptoms and have medical consultations remotely. Users frequently post symptoms on social media and online health platforms, creating a huge repository of medical data that can be leveraged for disease classification and symptom severity assessment. Large language models (LLMs) like LLAMA3, GPT-3.5 Turbo, and BERT process complex medical data, enhancing disease classification. This study explores three Arabic medical text preprocessing techniques: text summarization, text refinement, and Named Entity Recognition (NER). Evaluating CAMeL-BERT, AraBERT, and Asafaya-BERT with LoRA, the best performance was achieved using CAMeL-BERT with NER-augmented text (83% Type classification, 69% Severity assessment). Non-fine-tuned models performed poorly (13–20% Type classification, 40–49% Severity assessment). Embedding LLMs in social telehealth enhances diagnostic accuracy and treatment outcomes.
Few-Shot Optimized Framework for Hallucination Detection in Resource-Limited NLP Systems
(Springer International Publishing AG, 2025-10-01) Baraa Hikal; Ahmed Nasreldin; Ali Hamdi; Ammar Mohammed
Hallucination detection in text generation remains an ongoing struggle for natural language processing (NLP) systems, frequently resulting in unreliable outputs in applications such as machine translation and definition modeling. Existing methods struggle with data scarcity and the limitations of unlabeled datasets, as highlighted by the SHROOM shared task at SemEval-2024. In this work, we propose a novel framework to address these challenges, introducing DeepSeek Few-shot Optimization to enhance weak label generation through iterative prompt engineering. We achieved high-quality annotations that considerably enhanced the performance of downstream models by restructuring data to align with instruct generative models. We further fine-tuned the Mistral-7B-Instruct-v0.3 model on these optimized annotations, enabling it to accurately detect hallucinations in resource-limited settings. Combining this fine-tuned model with ensemble learning strategies, our approach achieved 85.5% accuracy on the test set, setting a new benchmark for the SHROOM task. This study demonstrates the effectiveness of data restructuring, few-shot optimization, and fine-tuning in building scalable and robust hallucination detection frameworks for resource-constrained NLP systems.
Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning
(Springer International Publishing AG, 2025-10-01) Youssef Maklad; Fares Wael; Wael Elsersy; Ali Hamdi
This paper presents a novel approach to evaluate the efficiency of a RAG-based agentic Large Language Model (LLM) architecture in network packet seed generation for network protocol fuzzing. Enhanced by chain-of-thought (COT) prompting techniques, the proposed approach focuses on the improvement of the seeds’ structural quality in order to guide protocol fuzzing frameworks through a wide exploration of the protocol state space. Our method leverages RAG and text embeddings in two stages. In the first stage, the agent dynamically refers to the Request For Comments (RFC) documents knowledge base for answering queries regarding the protocol Finite State Machine (FSM), then it iteratively reasons through the retrieved knowledge, for output refinement and proper seed placement. In the second stage, we evaluate the response structure quality of the agent’s output, based on metrics as BLEU, ROUGE, and Word Error Rate (WER) by comparing the generated packets against the ground truth packets. Our experiments demonstrate significant improvements of up to 18.19%, 14.81%, and 23.45% in BLEU, ROUGE, and WER, respectively, over baseline models. These results confirm the potential of such approach, improving LLM-based protocol fuzzing frameworks for the identification of hidden vulnerabilities.