Artificial intelligence-based methods for fusion of electronic health records and imaging data by Farida Mohsen (16994682)

“…In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. …”

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

“…<p>Speech denoising (SD) covers the algorithms that remove the background noise from the target speech and thus improve its quality and intelligibility. …”

Automated skills assessment in open surgery: A scoping review by Hawa Hamza (17707224)

“…Most of the studies acquired data by capturing surgeon's hands (50 %, <i>n </i>= 20). About 35 % utilized deep learning algorithms, specifically convolutional neural networks (CNN) (<i>n </i>= 14). …”

Defense against adversarial attacks: robust and efficient compressed optimized neural networks by Insaf Kraidia (19198012)

Extreme Early Image Recognition Using Event-Based Vision by Abubakar Abubakar (18278998)

“…<p dir="ltr">While deep learning algorithms have advanced to a great extent, they are all designed for frame-based imagers that capture images at a high frame rate, which leads to a high storage requirement, heavy computations, and very high power consumption. …”

Integration of Artificial Intelligence in E-Procurement of the Hospitality Industry: A Case Study in the UAE by Mathew, Elezabeth

“…Demand forecasting is done using deep learning techniques. Long short-term memory (LSTM) is used to find the demand forecasting of spend and quantity using time lags. …”
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Personalization in Real-Time Physical Activity Coaching Using Mobile Applications: A Scoping Review by Francisco Monteiro-Guerra (16864122)

A Multi-Channel Convolutional Neural Network approach to automate the citation screening process by Raymon van Dinter (10521952)

“…This study aims to automate the citation screening process using Deep Learning algorithms. With this, it is aimed to reduce the time and costs of the citation screening process and increase the precision and recall of the relevant primary studies. …”

Competitive learning/reflected residual vector quantization for coding angiogram images by Mourn, W.A.H.

“…Medical images need to be compressed for the purpose of storage/transmission of a large volume of medical data. Reflected residual vector quantization (RRVQ) has emerged recently as one of the computationally cheap compression algorithms. …”
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Leveraging UAVs for Coverage in Cell-Free Vehicular Networks by Samir, Moataz

“…To address these challenges, we formulate the trajectories decisions making as a Markov decision process where the system state space considers the vehicular network dynamics. Then, we leverage deep reinforcement learning to propose an approach for learning the optimal trajectories of the deployed UAVs to efficiently maximize the coverage, where we adopt Actor-Critic algorithm to learn the vehicular environment and its dynamics to handle the complex continuous action space. …”
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Design and analysis of entropy-constrained reflected residual vector quantization by Mousa, W.A.H.

“…Residual vector quantization (RVQ) is a vector quantization (VQ) paradigm which imposes structural constraints on the encoder in order to reduce the encoding search burden and memory storage requirements of an unconstrained VQ. …”
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Corona power loss computation in bundled bipolar conductors by Al-Hamouz, Z.M.

“…In this paper, a finite element (FE) based algorithm devoted for the computation of the corona current and hence the corona power loss associated with bundled bipolar high voltage direct current (HVDC) conductors is presented. …”
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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. …”

Newton-Raphson based adaptive inverse control scheme for tracking of nonlinear dynamic plants by Shafiq, M.

“…The U-model is utilized to design an adaptive inverse controller by using a simple root-solving algorithm of Newton-Raphson. The synergy of U-model with AIC structure has provided an effective and straight forward method for adaptive tracking of nonlinear plants. …”
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Simulations of the penetration of 6061-T6511 aluminum targets by spherical-nosed VAR 4340 steel projectiles by Warren, Thomas

“…In the context of an analysis code, this approximation eliminates the need for discretizing the target as well as the need for a contact algorithm. Thus, this method substantially reduces the computer time and memory requirements. …”
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A Fully Optical Laser Based System for Damage Detection and Localization in Rail Tracks Using Ultrasonic Rayleigh Waves: A Numerical and Experimental Study by Masurkar, Faeez

“…Further, As the quality of received signals differs at different sensing points as a result of the surface conditions of the specimen, the Self Adaptive Smart Algorithm (SASA) method was adopted to filter out the noise and accurately pinpoint the defect reflected wave packet which ultimately aids in better detection and localization. …”
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Systematic reviews in sentiment analysis: a tertiary study by Alexander Ligthart (14150871)

“…According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis.…”

The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review by Uzair Shah (15740699)

“…We categorized the studies based on AI techniques, such as machine learning and deep learning. The most prominent ML algorithm was a support vector machine, and the DL algorithm was a convolutional neural network. …”

Vibration suppression in a cantilever beam using a string-type vibration absorber by Issa, Jimmy S.

“…The string is rigidly connected to the fixed end of the beam and through a spring and damper to a second point on the beam. The finite element method is used to model the system and a reduced order model is obtained through modal reduction performed on both the string and the beam. …”
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Predicting COVID-19 cases using bidirectional LSTM on multivariate time series by Ahmed Ben Said (14158926)

“…This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. …”