Search Results for author: Mohamed Medhat Gaber

Found 14 papers, 6 papers with code

DAAI at CASE 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection

no code implementations ACL (CASE) 2021 Hansi Hettiarachchi, Mariam Adedoyin-Olowe, Jagdev Bhogal, Mohamed Medhat Gaber

Automatic socio-political and crisis event detection has been a challenge for natural language processing as well as social and political science communities, due to the diversity and nuance in such events and high accuracy requirements.

Diversity Event Detection +1

ABCO: Adaptive Bacterial Colony Optimisation

1 code implementation2 May 2025 Barisi Kogam, Yevgeniya Kovalchuk, Mohamed Medhat Gaber

The performance of the proposed ABCO algorithm is compared to that of established optimisation algorithms--particle swarm optimisation (PSO) and ant colony optimisation (ACO)--on a set of benchmark functions.

PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary Algorithms

1 code implementation5 Mar 2025 Shady Ali, Mahmoud Ashraf, Seif Hegazy, Fatty Salem, Hoda Mokhtar, Mohamed Medhat Gaber, Mohamed Taher Alrefaie

Evolutionary Algorithms (EAs) employ random or simplistic selection methods, limiting their exploration of solution spaces and convergence to optimal solutions.

Diversity Evolutionary Algorithms +4

The dynamics of meaning through time: Assessment of Large Language Models

no code implementations9 Jan 2025 Mohamed Taher Alrefaie, Fatty Salem, Nour Eldin Morsy, Nada Samir, Mohamed Medhat Gaber

Understanding how large language models (LLMs) grasp the historical context of concepts and their semantic evolution is essential in advancing artificial intelligence and linguistic studies.

MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

1 code implementation24 Aug 2021 Zakaria Senousy, Mohammed M. Abdelsamea, Mohamed Medhat Gaber, Moloud Abdar, U Rajendra Acharya, Abbas Khosravi, Saeid Nahavandi

It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUamodelhas achieved a high accuracy of 98. 11% on a breast cancer histology image dataset.

Breast Cancer Histology Image Classification Classification +3

Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification

no code implementations14 Apr 2020 Zahraa S. Abdallah, Mohamed Medhat Gaber

The developed technique similarly uses different lenses and representations to look at the time series, and then combines them for broader visibility.

Diversity General Classification +3

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

1 code implementation26 Mar 2020 Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Medhat Gaber

Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNNs) for image recognition and classification.

General Classification Medical Diagnosis +3

A Heuristically Modified FP-Tree for Ontology Learning with Applications in Education

no code implementations29 Oct 2019 Safwan Shatnawi, Mohamed Medhat Gaber, Mihaela Cocea

Subject experts rated the quality of the system's answers on a subset of questions and their ratings were used to identify the most appropriate automatic semantic text similarity metric to use as a validation metric for all answers.

Question Answering text similarity

EnSyth: A Pruning Approach to Synthesis of Deep Learning Ensembles

no code implementations22 Jul 2019 Besher Alhalabi, Mohamed Medhat Gaber, Shadi Basurra

In this paper, we describe EnSyth, a deep learning ensemble approach to enhance the predictability of compact neural network's models.

Deep Learning Ensemble Learning +1

AnyThreat: An Opportunistic Knowledge Discovery Approach to Insider Threat Detection

no code implementations1 Dec 2018 Diana Haidar, Mohamed Medhat Gaber, Yevgeniya Kovalchuk

To address this shortcoming, we propose an opportunistic knowledge discovery system, namely AnyThreat, with the aim to detect any anomalous behaviour in all malicious insider threats.

Feature Engineering General Classification

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