Nagwa Badr’s research while affiliated with Ain Shams University and other places

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Publications (86)

Proposed sarcasm detection approach workflow.
Sample utterance with its context in Mustard dataset.
Conversation summarization with Bart-large.
A contextual-based approach for sarcasm detection
  • Article
  • Full-text available

July 2024

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77 Reads

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3 Citations

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Nagwa L. Badr

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Sarcasm is a perplexing form of human expression that presents distinct challenges in understanding. The problem of sarcasm detection has centered around analyzing individual utterances in isolation which may not provide a comprehensive understanding of the speaker’s sarcastic intent. Our work addresses this problem by exploring and understanding the specific contextual cues that contribute to sarcasm. In this paper, we propose an enhanced approach for sarcasm detection using contextual features. Our methodology involves employing pre-trained transformer models, RoBERTa and DistilBERT, and fine-tuning them on two datasets: the News Headlines and the Mustard datasets. Incorporating contextual information, the proposed approach yielded the best performance, achieving an impressive F1 score of 99% on News Headlines dataset and 90% on Mustard dataset. Moreover, we experimented summarizing the context into a concise short sentence. This enhancement reduced training time by 35.5% of the original time. We further validated the model trained on the News headlines dataset against the Reddit dataset, which resulted in 49% F1 score without context data. However, with the inclusion of context data, the F1 score surged to 75%. Proposed approach enhances the understanding of sarcasm in different contextual settings, enabling more accurate sentiment analysis and better decision-making in various applications.

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Deep Learning for Extracting Human Movement Patterns from Spatio-Temporal Data

November 2023

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26 Reads

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Nagwa Badr

Human mobility data play an important role across various domains and applications by providing valuable insights into the movement of individuals, groups, and populations. Mining these data improves the ability to extract simple and complex human mobility patterns. These patterns are crucial for understanding the human behavior and predicting next-location of human movements that are essential for many applications, such as traffic forecasting, epidemic spreading, urban planning, and commuting flows. Great research efforts are spent to under- stand complex characteristics reflected in human mobility, and challenges related to trajectory data, such as time sensitivity, privacy risk assessment, sparsity, and higher-order dependencies. In this paper, we review recent approaches for extracting human movement patterns using deep learning techniques. We discuss these techniques and show their relevance to the different dimensions of human mobility data, challenges of trajectory data, and applications they can serve. Through this study, we reveal the limitations of traditional machine learning models, and how deep learning models can overcome these limitations. Additionally, we discuss the important factors of privacy risk assessment that should be considered in human mobility. Finally, our review helps researchers to understand challenges of human mobility, and demonstrate the future directions for leveraging deep learning to extract human movement patterns using the power of graph- based methods.

Citations (54)

... The widespread use of sarcastic expressions in social media and user-generated content poses significant challenges for sentiment analysis tasks in Natural Language Processing (NLP). Sarcasm conveys negative emotions through superficially positive or exaggerated language, creating difficulties for existing sentiment analysis models, which struggle to accurately detect the true sentiment behind sarcasm [1]. However, the complexity of sarcastic language is often not sufficiently modeled in traditional NLP models, making sarcasm detection a key area of research. ...

Reference:

An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework
A contextual-based approach for sarcasm detection

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Nagwa L. Badr

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... It effectively captures the subtle genetic variations and patterns that are key to distinguishing between different viruses. The other model is the Convolutional Neural Network with Extreme Learning Machines (CNN-ELM) [11]. It harnesses the power of Convolutional Neural Networks (CNNs) for extracting key features from complex viral sequences. ...

Reference:

Integration of Deep Learning Models for Enhanced Classification of Viral DNA Sequences Across Specific Viruses and Viral Families
A Combined ELM and CNN Model Architecture for Accurate Viral Family DNA Classification
  • Citing Conference Paper

  • November 2023

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Nagwa Badr

... In regression testing, models, frameworks and automated tools are designed and proposed to establish multi-objective test case selection, prioritization and reduction techniques [9]. Models, algorithms, and frameworks are predicated on the relationship between Time and efficiency (fault detection [10], redundancy [11], coverage [2]) measures for Test Case Selection (TCS). Therefore, test case selection techniques have the ability for coverage-based, fault detection and redundancy as compared to TCM and TCP [12]. ...

Reference:

A Detection-Based Multi-Objective Test Case Selection Algorithm to Improve Time and Efficiency in Regression Testing
ENHANCED REGRESSION TESTING EXECUTION PROCESS USING TEST SUITE REDUCTION TECHNIQUES AND PARALLEL EXECUTION

International Journal of Intelligent Computing and Information Sciences

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Nagwa Badr

... The text presents the current issues and prospective research directions that pave the way for further study. 62 According to Hamed et al., 63 colon cancer is one of the primary causes of death and illness in humans. Histopathological diagnosis is a crucial factor in defining cancer type. ...

Reference:

Advancements and Challenges in the Image-Based Diagnosis of Lung and Colon Cancer: A Comprehensive Review
Large-scale Histopathological Colon Cancer Annotation Model Using Machine Learning Techniques

International Journal of Intelligent Computing and Information Sciences

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Nagwa Badr

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... Even though virtual histopathology has matured into a very potent technique that has the potential to disrupt the field of medical imaging and diagnostics, many issues need to be resolved [3,4]. Solving these problems will be Open Access ...

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Virtual histopathology methods in medical imaging - a systematic review
A Deep Learning-Based Classification Framework for Annotated Histopathology Lung Cancer Images

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Nagwa L. Badr

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... The sphere of AT encompasses a diverse array of solutions, ranging from rudimentary to sophisticated. For individuals with physical impairments, AT offers opportunities to enhance their mobility and foster independence by employing a multitude of aids, including but not limited to crutches, electric wheelchairs, specialized vehicles, and supplementary devices specifically designed to provide bodily support [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. ...

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Building Python Application for Webmail Interfaces Navigation using Voice Recognition Technology
Using Character-Level Sequence-to-Sequence Model for Word Level Text Generation to Enhance Arabic Speech Recognition

IEEE Access

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Nagwa L. Badr

... The Internet of Things (IoT) is a vast network of interconnected exchange data and information in both wired and wireless modes to accomplish certain tasks or run specific applications. IoT devices are used in various domains such as smart cities, education, fleet management, and industrial plants [1,2]. They are even used as personal cardiac monitoring wristlet [3]. ...

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STUDYING THE IMPACT OF DATASET BALANCING ON MACHINE LEARNING-BASED INTRUSION DETECTION SYSTEMS FOR IOT
DATA FUSION FOR DATA PREDICTION: AN IoT-BASED DATA PREDICTION APPROACH FOR SMART CITIES

International Journal of Intelligent Computing and Information Sciences

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Nagwa Badr

... Analysis of enrichment procedures in each journal regarding the ontological life cycle (development, integration, and implementation) [17] refers to the system development life cycle. The literature review is shown in Table 7, where EN1 is preprocessing [18]- [21], EN2 is relation extraction [20], [22], EN3 is the enrichment process [5], [10], [18]- [21], [23]- [25] . Some papers use various terms to refer to the enrichment process, so we classify it as another term for enrichment. ...

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Evaluation of Enrichment in Ontology-based Knowledge Management System
An Ontology Development Methodology Based on Ontology-Driven Conceptual Modeling and Natural Language Processing: Tourism Case Study

Big Data and Cognitive Computing

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Rasha M. Ismail

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Nagwa Badr

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... Suggested methods have been examined using actual web error information that was taken from HTTP logs (access and error logs) gathered from the ISM web server. ElGhondakly et al. [26] proposed deep learning-based defect localization and prediction for testing service compositions based on service-oriented models. They proposed a deep learning techniques that was employed in a multilateral model-driven defect prediction and localization strategy to evaluate composition of web services rather than individual web services. ...

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Software Fault Prediction in Service-Oriented Based Systems
Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniques
  • Citing Article

  • May 2023

Applied Soft Computing

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Nagwa Badr

... Analysis of enrichment procedures in each journal regarding the ontological life cycle (development, integration, and implementation) [17] refers to the system development life cycle. The literature review is shown in Table 7, where EN1 is preprocessing [18]- [21], EN2 is relation extraction [20], [22], EN3 is the enrichment process [5], [10], [18]- [21], [23]- [25] . Some papers use various terms to refer to the enrichment process, so we classify it as another term for enrichment. ...

Reference:

Evaluation of Enrichment in Ontology-based Knowledge Management System
The Combination of Ontology-Driven Conceptual Modeling and Ontology Matching for Building Domain Ontologies: E-Government Case Study

International Journal of Computers and Their Applications

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Rasha Ismail

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Nagwa Badr

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