Browsing by Author "Gadallah, Mahmoud"

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Autonomous Checkers Robot Using Enhanced Massive Parallel Game Tree Search
(IEEE, 2014) Elnaggar, Ahmed A.; Gadallah, Mahmoud; Aziem, Mostafa Abdel; Aldeeb, Hisham
The dream of building intelligent robotic systems to interact and communicate with people and help them in their lives is a very old and ongoing study. In this research, the massive parallel autonomous checkers agent "MPACA" can autonomously play checkers with a human upto Grandmaster level without requiring a special checkers board for detecting human movements. The main aim and contribution of this research is proposing enhanced algorithms for a game tree search using two different approaches. The first was a task-based approach on CPU with a parallel database, while the second was a threads-based approach on the GPU with no divergence and dynamic parallelism. The two approaches were compared with previous studies using various approaches, including threads on CPU for up to 6x speedup for an 8-core processor and threads on GPU using iterative dependence and fixed grid and block size of up to 40x speedup at 14 depth. Furthermore, the approaches were tested with different depths on the CPU and the GPU. The result shows speed up for parallel CPU tasks up to 7x for an 8-core processor and parallel GPU of up to 80x at 14 depth.
A Comparative Study of Game Tree Searching Methods
(SCIENCE & INFORMATION SAI ORGANIZATION LTD, 2014-05) El-Deeb, Hesham; Gadallah, Mahmoud; Aziem, Mostafa Abdel; Elnaggar, Ahmed A
In this paper, a comprehensive survey on gaming tree searching methods that can use to find the best move in two players zero-sum computer games was introduced. The purpose of this paper is to discuss, compares and analyzes various sequential and parallel algorithms of gaming tree, including some enhancement for them. Furthermore, a number of open research areas and suggestions of future work in this field are mentioned.
Deep Learning Algorithms for Detecting Fake News in Online Text
(IEEE, 2018) Girgis, Sherry; Amer, Eslam; Gadallah, Mahmoud
Spreading of fake news is a social phenomenon that is pervasive at the social level between individuals, and also through social media such as Facebook and Twitter. Fake news that we are interested in is one of many kinds of deception in social media, but it's more important one as it is created with dishonest intention to mislead people. We are concerned about this issue because we have noticed that this phenomenon has recently caused through the means of social communication to change the course of society and peoples and also their views, for example, during revolutions in some Arab countries have emerged some false news that led to the absence of truth and stirs up public opinion and also fake of news is one of the factors Trump successes in the presidential election. So we decided to face and reduce this phenomenon, which is still the main factor to choose most of our decisions. Techniques of fake news detection varied, ingenious, and often exciting. In this paper our objective is to build a classifier that can predict whether a piece of news is fake or not based only its content, thereby approaching the problem from a purely deep learning perspective by RNN technique models (vanilla, GRU) and LSTMs. We will show the difference and analysis of results by applying them to the dataset that we used called LAIR. We found that the results are close, but the GRU is the best of our results that reached (0.217) followed by LSTM (0.2166) and finally comes vanilla (0.215). Due to these results, we will seek to increase accuracy by applying a hybrid model between the GRU and CNN techniques on the same data set.
English-Arabic Hybrid Machine Translation System using EBMT and Translation Memory
(SCIENCE & INFORMATION SAI ORGANIZATION LTD, 2019-01) Amer, Eslam; Gadallah, Mahmoud; Ehab, Rana
The availability of a machine translation to translate from English-to-Arabic with high accuracy is not available because of the difficult morphology of the Arabic Language. A hybrid machine translation system between Example Based machine translation technique and Translation memory was introduced in this paper. Two datasets have been used in the experiments that were constructed by using internal medicine publications and Worldwide Arabic Medical Translation Guide Common Medical Terms sorted by Arabic. To examine the accuracy of the system constructed four experiments were made using Example Based Machine Translation system in the first, Google Translate in the second and Example Based with Google translate in the third and the fourth is the system proposed using Example Based with Translation memory. The system constructed achieved 77.17 score for the first dataset and 63.85 score
Enhanced Parallel NegaMax Tree Search Algorithm on GPU
(IEEE, 2014) Elnaggar, Ahmed A.; Gadallah, Mahmoud; Aziem, Mostafa Abdel; El-Deeb, Hesham
Parallel performance for GPUs today surpasses the traditional multi-core CPUs. Currently, many researchers started to test several AI algorithms on GPUs instead of CPUs, especially after the release of libraries such as CUDA and OpenCL that allows the implementation of general algorithms on the GPU. One of the most famous game tree search algorithms is Negamax, which tries to find the optimal next move for zero sum games. In this research, an implementation of an enhanced parallel NegaMax algorithm is presented, that runs on GPU using CUDA library. The enhanced algorithms use techniques such as no divergence, dynamic parallelism and shared GPU table. The approach was tested in checkers and chess games. It was compared with previous studies, including threads on CPU for up to 6x speedup for an 8 core processor and threads on GPU using iterative dependence and fixed grid and block size for up to 40x speedup at 14 depth. Furthermore, the approach was tested with different depths on the CPU and the GPU. The result shows speed up for parallel GPU up to 80x at 14 depth for checkers game and 90x at 14 depth for chess game, which doubled the previous research results.