Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information by M. Ghoniem, Rania

“…For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. …”
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Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning by ALFALASI, FARIS Jr

“…To categorize electronic text in these two cases, deep learning models such as convolutional neural networks and recurrent neural networks and a combination of CNN-RNN were trained on this data. …”
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Sentiment Analysis of Dialectal Speech: Unveiling Emotions through Deep Learning Models by EZZELDIN, KHALED MOHAMED KHALED

“…Dialect Speech Sentiment Analysis is an evolutional field where machine learning algorithms are utilized to detect emotions in spoken language. …”
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Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3 by Chakar, Joseph

“…We also explore variational dropout: a technique that finds individual and unbounded dropout rates for each neural network weight. …”
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An Artificial Intelligence Approach for Predictive Maintenance in Electronic Toll Collection System by Alkhatib, Osama

“…Historical data of Dubai Toll Collection System is utilized to investigate multiple machine learning algorithms. Experiment is performed using Azure Machine Learning (ML) platform to test and assess the most efficient model that would predict the failure of system elements and predict the abnormality of the operation. …”
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Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah (21399854)

“…This research highlights the ability of AI to develop adaptable, effective, and successful e-learning environments, promoting enhanced academic achievement and customized learning experiences. The findings demonstrate that CNN outperformed other deep learning and machine learning algorithms in terms of accuracy during the prediction phase, showcasing the advanced capabilities of AI in educational contexts. …”

A comparative analysis to forecast carbon dioxide emissions by Md. Omer Faruque (17545671)

“…Based on multivariate time series prediction, four deep learning algorithms are analyzed in this work, those are convolution neural network (CNN), CNN long short-term memory (CNN–LSTM), long short-term memory (LSTM), and dense neural network (DNN). …”

Localizing-ground Transmitters Using Airborne Antenna Array by Daffalla, Mirghani Moutaman

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Cyberbullying Detection in Arabic Text using Deep Learning by ALBAYARI, REEM RAMADAN SA’ID

“…In this study, I conduct a performance evaluation and comparison for various DL algorithms (LSTM, GRU, LSTM-ATT, CNN-BLSTM, CNN-LSTM, CNN-BILSTM-LSTM, and LSTM-TCN) on different datasets of Arabic cyberbullying to obtain more precise and dependable findings. …”
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A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education by Abu Zitar, Raed

“…Transfer learning for a pre-trained deep neural network is used as well to increase the accuracy of the emotion classification stage. …”
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Face Flow: Constrained Optical Flow Framework for Faces by Alkaddour, Muhannad

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An Evolutionary Meta-Heuristic for State Justification in Sequential Automatic Test Pattern Generation by El-Maleh, Aiman H.

“…In this work, we propose a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification. A new method based on Genetic algorithms is proposed, in which we engineer state justification sequences vector by vector. …”
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An evolutionary meta-heuristic for state justification insequential automatic test pattern generation by El-Maleh, A.H.

“…In this work, we propose a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification. A new method based on Genetic Algorithms is proposed, in which we engineer state justification sequences vector by vector. …”
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Predicting and Interpreting Student Performance Using Machine Learning in Blended Learning Environments in a Jordanian School Context by SALIM, MAHA JAWDAT

“…A dataset generated by a digital learning platform used by a private school in Jordan is utilised. Various ML algorithms, such as Support Vector Machines, Logistic Regression, K-Nearest Neighbors, Naïve Bayes, Decision Trees, Random Forest, AdaBoost, Bagging, and Artificial Neural Networks are applied to predict student performance. …”
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Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta by Vaisali Chandrasekar (16904526)

“…<p dir="ltr">Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. …”

Developing an online hate classifier for multiple social media platforms by Joni Salminen (7434770)

“…We then experiment with several classification algorithms (Logistic Regression, Naïve Bayes, Support Vector Machines, XGBoost, and Neural Networks) and feature representations (Bag-of-Words, TF-IDF, Word2Vec, BERT, and their combination). …”

LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications by Aldamani, Raghad

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Automatic Recognition of Poets for Arabic Poetry using Deep Learning Techniques (LSTM and Bi-LSTM) by AL SHOUBAKI, HAMZA YOUNIS

“…The reason behind selecting such models is observing a concentration on RNN (Recurrent Neural Network) and its variants when it comes to text classification. …”
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Nonlinear Friction Identification of A Linear Voice Coil DC Motor by Takrouri, Mohannad Hashem

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A Framework for Predictive Modeling in Sustainable Projects by Labban, Tarek

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