Artificial intelligence-based methods for fusion of electronic health records and imaging data حسب 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 حسب 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 حسب 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 حسب Insaf Kraidia (19198012)

Extreme Early Image Recognition Using Event-Based Vision حسب 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. …"

Personalization in Real-Time Physical Activity Coaching Using Mobile Applications: A Scoping Review حسب Francisco Monteiro-Guerra (16864122)

Integration of Artificial Intelligence in E-Procurement of the Hospitality Industry: A Case Study in the UAE حسب 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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A Multi-Channel Convolutional Neural Network approach to automate the citation screening process حسب 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 حسب 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 حسب 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 حسب 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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Communications in electronic textile systems حسب Nakad, Z.

"…Abstract- Electronic textiles (e-textiles) are emerging as a novel method for constructing electronic systems in wearable and large area applications. …"
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A multi-pretraining U-Net architecture for semantic segmentation حسب 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. …"

Systematic reviews in sentiment analysis: a tertiary study حسب 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 حسب 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. …"

Predicting COVID-19 cases using bidirectional LSTM on multivariate time series حسب 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. …"

An Artificial Intelligence Approach for Predictive Maintenance in Electronic Toll Collection System حسب 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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Efficient Seismic Volume Compression using the Lifting Scheme حسب Khene, M. F.

"…In addition, the lifting scheme offers: 1) a dramatic reduction of the required auxiliary memory, 2) an efficient combination with parallel rendering algorithms to perform arbitrary surface and volume rendering for interactive visualization, and 3) an easy integration in the parallel I/O seismic data loading routines. …"
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Digital-Twin-Based Diagnosis and Tolerant Control of T-Type Three-Level Rectifiers حسب Ali Sharida (17947847)

"…To develop the DT, a dense deep neural network (DNN) machine learning approach is used. …"

Applications of artificial intelligence in ultrasound imaging for carpal-tunnel syndrome diagnosis: a scoping review حسب Yosra Magdi Mekki (21673721)

"…The majority of studies utilized deep learning approaches, particularly CNNs (<i>n</i> = 15), and some focused on radiomics features (<i>n</i> = 5) and traditional machine learning techniques.…"