Reinforcement Learning-Based School Energy Management System حسب Yassine Chemingui (18891757)

"…In recent years, the Deep Reinforcement Learning algorithm, applying neural networks for function approximation, shows promising results in handling such complex problems. …"

EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach حسب Muhammad Adeel Asghar (6724982)

"…To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. …"

Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE حسب Ben-Romdhane, Haïfa

"…The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. …"
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Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning حسب 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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Stacking-based ensemble learning for remaining useful life estimation حسب Begum Ay Ture (17773170)

"…In this study, predictive models that estimate the remaining useful life of turbofan engines have been developed using deep learning algorithms on NASA’s turbofan engine degradation simulation dataset. …"

An exact and general model order reduction technique for the finite element solution of elastohydrodynamic lubrication problems حسب Habchi, W.

"…The technique is complemented with a splitting algorithm to alleviate the hurdle of solving an arising semidense matrix system. …"
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Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3 حسب Chakar, Joseph

"…In this study, we combine the state-of-the-art object-detection model YOLOv3 with depthwise separable convolutions and variational dropout in an attempt to bridge the gap between the superior accuracy of convolutional neural networks and the limited access to computational resources. …"
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Enhancing e-learning through AI: advanced techniques for optimizing student performance حسب Rund Mahafdah (21399854)

"…The practical results obtained by implementing machine learning and deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), show substantial enhancements in forecasting different performance metrics. …"

Single-channel speech denoising by masking the colored spectrograms حسب Sania Gul (18272227)

"…The results show that with masking-based targets, the colored spectrograms provide an improvement of 0.12 points in perceptual evaluation of speech quality (PESQ) score, 4 % in short time objective intelligibility (STOI), and a 163 times reduction in network learnable parameters, as compared to when they are processed by a mapping-based model using pix2pix generative adversarial network (GAN) followed by a feedforward regression neural network. …"

A comparative analysis to forecast carbon dioxide emissions حسب 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). …"

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

"…As a result of the models’ evaluation, a hybrid DL model is proposed that combines the best characteristics of the baseline models CNN, BLSTM and GRU for identifying cyberbullying. …"
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Predicting Android Malware Using Evolution Networks حسب Chahine, Joy

"…Experimental studies clearly show a higher accuracy of our proposed approach in comparison with existing machine learning models, namely random forest, artificial neural network, decision tree, and logistic regression.…"
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Small-Signal Stability Analysis and Parameters Optimization of Virtual Synchronous Generator for Low-Inertia Power System حسب Alaa Altawallbeh (22565837)

"…This paper presents a comprehensive small-signal modeling and stability analysis framework for grid-connected virtual synchronous generators (VSGs), integrating: an LCL-filter interfaced power converter, active/reactive power loop (APL/RPL) controllers, and dual-loop PI-based current and voltage control. Through systematic eigenvalue analysis and parameter sensitivity studies, complemented by time-domain verification in MATLAB/SIMULINK, we demonstrate the decisive influence of VSG control parameters on low-frequency oscillation (LFO) damping characteristics, transient frequency stability metrics, including the rate of change of frequency (ROCOF), maximum frequency deviation (<i>fnadir</i>), overshoot, and settling time. …"

A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education حسب Abu Zitar, Raed

"…As part of this work, two deep learning models are compared according to their performance. …"
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An efficient approach for textual data classification using deep learning حسب Abdullah Alqahtani (7128143)

"…Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. …"

Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks حسب Javed Butt , Usman

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Predicting and Interpreting Student Performance Using Machine Learning in Blended Learning Environments in a Jordanian School Context حسب 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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CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children حسب Jayakanth, Kunhoth

"…This work proposes various machine learning methods, including transfer learning via fine-tuning, transfer learning via feature extraction, ensembles of deep convolutional neural network (CNN) models, and fusion of CNN features, to develop a preliminary dysgraphia diagnosis system based on handwritten images. …"
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Automatic Recognition of Poets for Arabic Poetry using Deep Learning Techniques (LSTM and Bi-LSTM) حسب AL SHOUBAKI, HAMZA YOUNIS

"…We also explore a range of algorithms, including traditional classifiers and deep learning models, to determine and select the most suitable and accurate models of identifying poets' names from the verses. …"
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Developing an online hate classifier for multiple social media platforms حسب 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). …"