TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection by Zina Chkirbene (16869987)

Subjects:

Enhancing Healthcare Systems With Deep Reinforcement Learning: Insights Into D2D Communications and Remote Monitoring by Zina Chkirbene (16869987)

“…By formulating the video resource allocation challenge as a multi-objective optimization problem, the framework aims to minimize network delays while respecting node capacity limitations. The core of DRLLVT is its novel algorithm that leverages Deep Reinforcement Learning (DRL) to dynamically adapt to changing environmental conditions, facilitating real-time decisions that consider node capacities, latency, and the overall network dynamics. …”

Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation by Maymouna Ez Eddin (21633650)

Recent advances on artificial intelligence and learning techniques in cognitive radio networks by Abbas, Nadine

“…The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. …”
Get full text
Get full text
Get full text
Get full text

Reinforcement R-learning model for time scheduling of on-demand fog placement by Farhat, Peter

“…On the fly deployment of fog nodes near users provides the flexibility of pushing services anywhere and whenever needed. …”
Get full text
Get full text
Get full text
Get full text

Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark by Ameema Zainab (16864263)

“…<p>Machine learning algorithms have been intensively applied to perform load forecasting to obtain better accuracies as compared to traditional statistical methods. …”

Isolating Physical Replacement of Identical IoT Devices Using Machine and Deep Learning Approaches by Mohammad, Areej Osama

Get full text

Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting by 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. …”

Cooperative Caching Policy in Fog Computing for Connected Vehicles by Ghazleh, Ali

“…In this thesis, we implemented cooperation between a Deep Reinforcement Learning (DRL) model and Federated Learning to improve caching in Connected Vehicles connected to fog nodes. …”
Get full text
Get full text
Get full text

Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces by Uzair Sajjad (19646296)

“…In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. …”

Predicting Android Malware Using Evolution Networks by Chahine, Joy

“…Android malware propagation is thus transformed into a directed network in which nodes represent IP addresses and edges represent aggregated multiple packet transmissions weighted by communication frequency. …”
Get full text
Get full text
Get full text

FoGMatch by Arisdakessian, Sarhad

“…Simulation results reveal that our solution outperforms the two common scheduling algorithms (i.e., Min-Min and Max-Min) in terms of IoT services execution makespan, fog nodes resource utilization efficiency and execution time.…”
Get full text
Get full text
Get full text

Energy-Efficient VoI-Aware UAV-Assisted Data Collection in Wireless Sensor Networks by Kalash, Saeddin

“…However, UAV-assisted data collection in SNs faces several challenges, primarily due to energy constraints at both UAV and SN nodes and the inefficiencies caused by collecting redundant data. …”
Get full text
Get full text
Get full text

SDODV. (c2018) by Assi, Maram

“…Mobile ad hoc networks (MANETS) are a set of nodes connected to each other in peer to peer fashion. …”
Get full text
Get full text
Get full text

CEAP by Abdel Wahab, Omar

“…To reduce the overhead of the proposed detection model and make it feasible for the resource-constrained nodes, we reduce the size of the training dataset by (1) restricting the data collection, storage, and analysis to concern only a set of specialized nodes (i.e., Multi-Point Relays) that are responsible for forwarding packets on behalf of their clusters; and (2) migrating only few tuples (i.e., support vectors) from one detection iteration to another. …”
Get full text
Get full text
Get full text
Get full text