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

“…An accuracy of up to 99.65% and 98.51% has been achieved on GREEND and UK-DALE data sets, respectively. While an accuracy of more than 96% has been attained on both WHITED and PLAID data sets. …”

Anonymizing multimedia documents by Haraty, Ramzi

“…A set of experiments are elaborated to demonstrate the efficiency of our technique.…”
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Structural similarity evaluation between XML documents and DTDs by Tekli, J.

“…It is of polynomial complexity, in comparison with existing exponential algorithms. Classification experiments, conducted on large sets of real and synthetic XML documents, underline our approach effectiveness, as well as its applicability to large XML repositories and databases. © Springer-Verlag Berlin Heidelberg 2007.…”
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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. …”

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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MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network by Sakib Mahmud (15302404)

“…<p dir="ltr">Electroencephalogram (EEG) signals suffer substantially from motion artifacts when recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. …”

Towards secure and trusted AI in healthcare: A systematic review of emerging innovations and ethical challenges by Muhammad Mohsin Khan (22303366)

“…Still, challenges in adversarial attacks, algorithmic bias, and variable regulatory frameworks remain strong. …”

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review by Alaa Abd-alrazaq (17058018)

“…The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine.…”

Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images by Rehan Raza (17019105)

“…The class imbalance issue was handled through multiple data augmentation methods to overcome the biases. …”

A Biologicaly Inspired Decision Model for Multivariable Genetic- Fuzzy-AHP System by Kouatli, Issam

“…This paper describes a hybridized intelligent algorithm as a tuning mechanism for one type of Genetic Fuzzy system termed the Genetic Fuzzimetric Technique (GFT). …”
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Inferential sensing techniques in industrial applications by Shakil,, Muhammad

“…Different types of dynamical neural networks are combined according to system operation and emission behavior. Real data from a boiler plant is used to develop the model. …”
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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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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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A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education by Abu Zitar, Raed

“…Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. …”
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Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care by Regina Padmanabhan (14231606)

“…Other highlights are (1) a novel set of easily available features for the prediction of the aforementioned clinical complications and (2) the use of data augmentation methods and model-scoring-based hyperparameter tuning to address the problem of class disproportionality, a common challenge in medical datasets and often the reason behind poor event prediction rate of various predictive models reported so far. …”

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

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Uplink Noma in UAV-Assisted IoT Networks by Mrad, Ali

“…This technology proves important in scenarios with time-sensitive services when data has to be collected before a set deadline, otherwise, it is rendered useless, as well as, in scenarios with limited resources and large number of users. …”
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Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method by Mohamed Massaoudi (16888710)

“…The proposed model is thoroughly assessed through an empirical study using a real data set from Australia. The simulation results confirm the performance superiority of the proposed model over the existing forecasting models with the lowest average root mean square error and mean absolute percentage error of 3.88kW and 2.30%, respectively.…”

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

“…The DT is trained offline using a set of experimental data and updated online to get the maximum possible accuracy. …”

Artificial Intelligence–Driven Serious Games in Health Care: Scoping Review by Alaa Abd-alrazaq (17058018)

“…The most common purposes of AI were the detection of disease and the evaluation of user performance. The size of the data set ranged from 36 to 795,600. The most common validation techniques used in the included studies were k-fold cross-validation and training-test split validation. …”