Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework by Tayyabah Hasan (18427887)

“…Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the prioritized contents are stored in QMM using a Two-Level Spin Quantum Phenomenon (TLSQP). …”

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%). …”

A novel approach for real time flows scheduling by Fawaz, W.

“…A plethora of packet-scheduling algorithms have been proposed in the literature in order to meet the stringent time constraints of real time flows at an IP router level. …”
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Low Cost Autopilot Design Using Fuzzy Supervisory Control by Hadi, Mahmoud

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Resource allocation scheme for eMBB and uRLLC coexistence in 6G networks by Muhammed Al-Ali (16810677)

“…The proposed solution was compared with three puncturing baseline reference algorithms and the performance was evaluated in terms of eMBB Sum throughput and Fairness level. …”

A comparative study of regression testing methods. (c1996) by Baradhi, Ghinwa S.

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AUTOMATIC WEINBERGER ARRAY SYNTHESIS FROM UAHPL DESCRIPTION by Sait, Sadiq M.

“…The system also minimizes the area required by the WA by performing row compaction. An algorithm similar to that used for channel routing is employed for compaction (Hashimoto and Stevens 1971). …”
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Mechanical perturbation control of cardiac alternans by Hazim, Azzam

“…We analyze the effects of mechanical perturbation on the dynamics of amap model that couples themembrane voltage and active tension systems at the cellular level. Therefore, a two-dimensional iterative map of the heart beat-to-beat dynamics is introduced, and a stability analysis of the system of coupled maps is performed in the presence of a mechanical perturbation algorithm. …”
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Learning-Based Space-Time Adaptive Processing by El Khatib, Alaa

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A simple approach for testing web service based applications by Tarhini, Abbas

“…Test cases are executed automatically using a test execution algorithm and a test framework is also presented. …”
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Isolating Physical Replacement of Identical IoT Devices Using Machine and Deep Learning Approaches by Mohammad, Areej Osama

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A feature‐based approach for guiding the selection of Internet of Things cybersecurity standards using text mining by Koen Schaaf (14778139)

“…Third, a text mining algorithm has been implemented. Fourth, the systematic approach has been modeled using business process modeling notation. …”

Learning-Based Spectrum Sensing and Access for Cognitive Radio Systems by Shehabeldin, Menatalla Diaaeldin

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

“…This basically follows either a feature-level or decision-level strategy. 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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Blood Glucose Regulation Modelling and Intelligent Control by Nasar, Nabeel

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Comparative Study of Control Strategies for Under Actuated Manipulator by Abed, Joudeh Yasin

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An automated temporal partitioning tool for a class of DSP applications by Ouaiss, Iyad

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

“…For image datasets, we employ Multi-Level Autoencoders, consisting of Convolutional and Fully Connected Autoencoders. …”

Analyzing the Influence of Climate and Anthropogenic Development on Vegetation Cover in the Coastal Ecosystems of GCC by Abhilash Dutta Roy (22466830)

“…We used Landsat satellite imagery and a Random Forest classification algorithm to map various land cover classes along the GCC coastline. …”

Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast d... by Tarik Elhadd (5480393)

“…The optimal XGBoost model prioritized age, gender, BMI and HbA1c followed by glucose levels and physical activity. Interestingly, the blood glucose level prediction by our model was influenced by use of SGLT2i.…”