Pre-production movie rating prediction using machine learning. (c2017) by Gerges, Firas Abdallah

“…Results show that machine learning is useful in this domain and genetic algorithms can be used to build prediction models with relatively good performance. …”
Get full text
Get full text
Get full text

Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier by JAIN, SWETA

“…The hyper parameters are optimized with DM optimization algorithm which improves the accuracy of classifier. …”
Get full text
Get full text

A novel few shot learning derived architecture for long-term HbA1c prediction by Marwa Qaraqe (10135172)

“…The derived images are then fed into a convolutional neural network (CNN) adapted from a few-shot learning (FSL) model for feature extraction, and all the derived features are fused together. …”

Recent advances on artificial intelligence and learning techniques in cognitive radio networks by Abbas, Nadine

“…The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. …”
Get full text
Get full text
Get full text
Get full text

Design and implementation of a deep learning-empowered m-Health application by Akhan Akbulut (17380285)

“…The proposed model utilizes the Convolutional Neural Network and provides 84% accuracy and 72% precision. …”

Machine Learning-Based Approach for EV Charging Behavior by Shahriar, Sakib

Get full text

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review by Asma Alamgir (18288895)

“…Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). …”

Oversampling techniques for imbalanced data in regression by Samir Brahim Belhaouari (9427347)

“…For tabular data, we also present the Auto-Inflater neural network, utilizing an exponential loss function for Autoencoders. …”

Reinforcement Learning-Based School Energy Management System by 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. …”

Application of Metastructures for Targeted Low-Frequency Vibration Suppression in Plates by Ratiba F. Ghachi (14152455)

“…<h2>Purpose</h2> <p>We present an approach that combines finite element analysis and genetic algorithms to find the optimal configuration of local resonators created in the host structure to suppress their vibration in a target low-frequency range. …”

EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach by 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 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. …”
Get full text

Stacking-based ensemble learning for remaining useful life estimation by 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. …”

Identification of physically based models of residential air-conditioners for direct load control management by El-Ferik, S.

“…In this work, we address the problem of identifying the parameters of an aggregated elemental model representing a housing unit with an A/C system. …”
Get full text
Get full text

Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3 by 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. …”
Get full text
Get full text
Get full text
Get full text

Enhancing e-learning through AI: advanced techniques for optimizing student performance by 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 by 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 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). …”

Regression testing C# programs. (c2007) by Staitieh, Wael

Get full text
Get full text

Assessing Factors Influencing Customers’ Adoption of AI-Based Voice Assistants by Surbhi Choudhary (21633701)

“…<p dir="ltr">The study aims to assess the factors that engage and accentuate usage pertaining to consumers with artificial intelligence-based voice assistants. …”