EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach حسب Muhammad Adeel Asghar (6724982)

"…This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. …"

A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems حسب Devinder Kaur (264278)

Improving INS/GPS Integration for Mobile Robotics Applications حسب AbuHashim, Tariq Salman

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Robustness evaluation of real-Time Protocols حسب Tarhini, Abbas

"…In this paper, we suggest a methodology for measuring the degree of robustness for Real-Time Component-Based Systems (RTCBS). Each component of the RTCBS system is modeled as a Timed Labeled Transition System (TLTS). …"
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Robustness testing of composed real-time systems حسب Fouchal, Hacène

"…In this paper, we suggest a methodology for testing robustness of Real-Time Component-Based Systems (RTCBS). A RTCBS system is described as a collection of components where each component is modeled as a Timed Input-Output Automaton (TIOA). …"
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Edge intelligence for network intrusion prevention in IoT ecosystem حسب Mansura, Habiba

"…A system architecture is designed for a cloud-based IoT framework to implement the proposed algorithm efficiently. …"
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Face Recognition in Uncontrolled Indoor Environment حسب Abuqaaud, Kamal Adel

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Edge intelligence for network intrusion prevention in IoT ecosystem حسب Mansura Habiba (17808302)

"…A system architecture is designed for a cloud-based IoT framework to implement the proposed algorithm efficiently. …"

Regression testing C# programs. (c2007) حسب Staitieh, Wael

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Soft Sensor for NOx Emission using Dynamical Neural Network حسب Shakil, M.

"…Lag space for the model is found by using genetic algorithm to find the best time delayed model. Lag space obtained from the linear model is then used for constriction of the dynamical neural network. …"
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Semi-supervised Clustering of Facial Expressions حسب Jalal, Ahsan

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Collision-Free Autonomous Navigation Solution for Mobile Wheeled حسب Elsergany, Ahmed Moataz Mahmoud

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Inferential sensing techniques in industrial applications حسب Shakil,, Muhammad

"…Inferential sensing technique is a method to estimate certain quantities based on a set of conventional measurements. The core of inferential sensing is based on modeling and estimation techniques. …"
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Mining airline data for CRM strategies. (c2006) حسب Maalouf, Lena

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StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features حسب Muhammad Arif (769250)

"…Next, principal component analysis (PCA) is used to select the best subset of attributes. …"

Assessment of static pile design methods and non-linear analysis of pile driving حسب Abou-Jaoude, Grace G.

"…The outcome of this research is an algorithm that can be used to predict pile displacement and driving stresses. …"
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A Robust Approach for Enhanced Autonomous Robot Navigation حسب Ismail, Sherif Khaled Mohamed Issa

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Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems حسب Ahmad K. Sleiti (14778229)

"…Then, the requirements and rules for the power plant DT are established and the major DT components are determined. These components include the physics-based formulations; the statistical analysis of data from the sensor network; the real-time data; the pre-performed localized in-depth simulations to predict activities of the corresponding physical twin; and the system Genome with a digital thread that connects all these components together. …"

Modeling and Guidance of an Underactuated Autonomous Underwater Vehicle حسب Wadi, Ali H M

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Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting حسب Abdelkader Baggag (16864140)

"…The learned factors, with a graph-based temporal dependency, are then used in an autoregressive algorithm to predict the future state of the road network with a large horizon. …"