Higher-order statistics (HOS)-based deconvolution for ultrasonic nondestructive evaluation (NDE) of materials by Ghouti, Lahouari

“…The proposed techniques are: i) a batch-type deconvolution method using the complex bicepstrum algorithm, and ii) automatic ultrasonic defect classification system using a modular learning strategy. …”
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A multi-pretraining U-Net architecture for semantic segmentation by Cagla Copurkaya (22502042)

“…In this research, we propose and evaluate a modified version of a deep learning algorithm called U-Net architecture for partitioning histopathological images. …”

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

“…In all likelihood, while features from several modalities may enhance the classification performance, they might exhibit high dimensionality and make the learning process complex for the most used machine learning algorithms. …”
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Estimating Bridge Deterioration Age Using Artificial Neural Networks by AL HUSSEIN, ASEEL

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Investigation of Forming a Framework to shortlist contractors in the tendering phase by DABASH, MOHANNAD SALAH

“…After obtaining the weights of the decision factors, a model using Machine Learning algorithm on Google Colab was written using the Python language. …”
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Intelligent route to design efficient CO₂ reduction electrocatalysts using ANFIS optimized by GA and PSO by Majedeh Gheytanzadeh (17541927)

“…Accordingly, a dataset containg 258 data points was extracted from the DFT method to use in machine learning method. …”

Predicting COVID-19 cases using bidirectional LSTM on multivariate time series by Ahmed Ben Said (14158926)

“…Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. …”

Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier by Yassine Himeur (14158821)

“…Specifically, short local histograms are drawn to represent individual appliance consumption signatures and robustly extract appliance-level data from the aggregated power signal. Furthermore, an improved k-nearest neighbors (IKNN) algorithm is presented to reduce the learning computation time and improve the classification performance. …”

Generic metadata representation framework for social-based event detection, description, and linkage by Abebe, Minale A.

“…SEDDaL consists of four main modules for: i) describing social media objects in a generic Metadata Representation Space Model (MRSM) consisting of three composite dimensions: temporal, spatial, and semantic, ii) evaluating the similarity between social media objects’ descriptions following MRSM, iii) detecting events from similar social media objects using an adapted unsupervised learning algorithm, where events are represented as clusters of objects in MRSM, and iv) identifying directional, metric, and topological relationships between events following MRSM’s dimensions. …”
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Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test by Hasan T. Abbas (8115014)

“…Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. …”

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers by Yousef, Hibba

“…In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. …”
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Predicting insulin dosage for diabetic patients to reach optimal glucose levels. (c2012) by Bitar, Mandy

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Digital-Twin-Based Diagnosis and Tolerant Control of T-Type Three-Level Rectifiers by Ali Sharida (17947847)

“…To develop the DT, a dense deep neural network (DNN) machine learning approach is used. The DT is trained offline using a set of experimental data and updated online to get the maximum possible accuracy. …”

Performance Analysis of Artificial Neural Networks in Forecasting Financial Time Series by Lasfer, Assia

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Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying by Tekli, Joe

“…Dedicated weighting functions and various search algorithms have been developed for that purpose and will be presented here. …”
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Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques by Fares, Samar

“…The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. …”
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Design and analysis of entropy-constrained reflected residual vector quantization by Mousa, W.A.H.

“…Jointly optimized RVQ (JORVQ) is an effective design algorithm for minimizing the overall quantization error. …”
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Design and Analysis of Lightweight Authentication Protocol for Securing IoD by Saeed Ullah Jan (9079260)

“…We proposed a hash message authentication code/secure hash algorithmic (HMACSHA1) based robust, improved and lightweight authentication protocol for securing IoD. …”

Clustering Tweets to Discover Trending Topics about دبي (Dubai) by ALYALYALI, SALAMA KHAMIS SALEM KHAMIS

“…One of these social networks is Twitter - a microblogging hub and rich environment of data. Scanning tweets online is a hard task and searching effortlessly to find intended topic from huge amount of data is also time consuming. …”
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Role of authentication factors in Fin-tech mobile transaction security by Habib Ullah Khan (12024579)

“…In addition, this also ensures the user is legitimate using advanced technologies and algorithms to predict and discover transaction risks and discourage fraudsters from trying.…”