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. …”

Proactive machine learning-based solution for advanced manageability of multi-persona mobile computing by Tout, Hanine

“…A dynamic programming algorithm is advocated to find the adequate strategies. …”
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Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects by Mohamed Massaoudi (16888710)

“…Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. …”

A Fusion-Based Approach for Skin Cancer Detection Combining Clinical Images, Dermoscopic Images, and Metadata by Siddiqui, Ansah Juned

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Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas by Marwan Dhuheir (19170898)

“…Due to the complexity of the problem, the combinatorial nature of the formulated problem, and the difficulty of obtaining the optimal solution using conventional optimization problems, we propose a lightweight meta-RL solution capable of solving the problem by learning the system dynamics. We conducted extensive simulations and compared our approach with two state-of-the-art models using traditional RL algorithms represented by a deep Q-network algorithm, a Particle Swarm Optimization (PSO) algorithm, and one greedy solution. …”

Privacy-preserving energy optimization via multi-stage federated learning for micro-moment recommendations by Md Mosarrof Hossen (21399056)

“…A comparative evaluation of three FL algorithms (FedAvg, FedProx, Mime-lite) identifies the most suitable aggregation strategy. …”

Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique by Iqbal Hassan (22155274)

“…The primary objective of this study is to estimate the CL index through an innovative approach that employs a hybrid, cluster-based, unsupervised learning technique seamlessly integrated with a 1D Convolutional Neural Network (CNN) architecture tailored for automated feature extraction, rather than conventional supervised algorithms, which facilitated in the acquisition of latent complex patterns without the need for manual categorization. …”

Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study by Alaa Abd-alrazaq (17058018)

“…Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.…”

Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain by Farshad Rahimi Ghashghaei (20880995)

“…<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. …”

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects by Majdi Mansouri (16869885)

“…Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. …”

A Digital DNA Sequencing Engine for Ransomware Analysis using a Machine Learning Network by KHAN, FIROZ

“…The data is finally classified as either ransomware or goodware using the learning methodologies. The results are found to be promising and reconfirm the fact that the developed method has efficiently detected ransomware when compared to other methods. …”
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Reinforcement Learning for Resilient Aerial-IRS Assisted Wireless Communications Networks in the Presence of Multiple Jammers by Zain Ul Abideen Tariq (17984107)

“…Experimental results demonstrate the effectiveness of our proposed DDPG-based approach in outperforming other RL algorithms. It achieves a near-optimal solution compared to the benchmark technique within the close gap and improves both achievable transmission rates and energy efficiency compared to related works by 50-70%.…”

Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

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

“…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. …”

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. …”

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

“…The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). …”

Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review by Sarah Aqel (17787809)

“…There’s a significant focus on the integration of AI and ML in prehospital emergency care, particularly in using ML algorithms for predicting outcomes in COVID-19 patients and enhancing the recognition of out-of-hospital cardiac arrest (OHCA). …”

Modeling and thermoeconomic analysis of new polygeneration system based on geothermal energy with sea water desalination and hydrogen production by Wulaer Shaersaikai (21436652)

“…The Grey Wolf Optimization (GWO) algorithm, which directs the system's optimization process, demonstrates a competitive trade-off between exergy efficiency, freshwater production, costs, NPV, and environmental impact. …”

Edge intelligence for network intrusion prevention in IoT ecosystem by Mansura, Habiba

“…This paper proposes a deep learning-based algorithm to protect the network against Distributed Denial-of-Service (DDoS) attacks, insecure data flow, and similar network intrusions. …”
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The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions by Abdulmalik Alwarafy (17984104)

“…Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. …”