Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning by Mohamed Massaoudi (16888710)

“…Subsequently, state-of-the-art research that envisions the use of clustering-based machine learning and deep learning-based solutions for PGP is presented. …”

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects by Mohamed Massaoudi (16888710)

“…In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. …”

Sentiment Analysis of the Emirati Dialect text using Ensemble Stacking Deep Learning Models by AL SHAMSI, ARWA AHMED

“…Once the dataset was constructed and validated, I then conducted a performance evaluation and comparison of various basic Machine Learning algorithms, Deep Learning models, and stacking deep learning models on different datasets of Sentiment Analysis of Arabic Dialects. …”
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Assessment of static pile design methods and non-linear analysis of pile driving by Abou-Jaoude, Grace G.

“…Pile foundations are used to transfer loads from the superstructure to deep layers in a soil deposit. Depending on the installation method, piles can either preserve the original soil density and stress state to a certain degree (e.g., bored piles), or induce changes that cannot be easily quantified, leading to greater challenges in obtaining accurate estimates of pile resistance (e.g., driven or jacked piles). …”
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DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs by Bahareh Jafari (22501715)

“…As such, in addition to avoiding coverage holes, we should also make the outage time as small as possible. By deploying a deep reinforcement learning algorithm, we find optimal UAV paths based on the two families of trajectories: spiral and oval curves, to tackle different design considerations and constraints, in terms of QoS, energy consumption and coverage hole avoidance. …”

Evolutionary algorithms for state justification in sequential automatic test pattern generation by El-Maleh, Aiman H.

“…A common search operation in sequential Automatic Test Pattern Generation is to justify a desired state assignment on the sequential elements. State justification using deterministic algorithms is a difficult problem and is prone to many backtracks, which can lead to high execution times. …”
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Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier by JAIN, SWETA

“…The hyper parameters are optimized with DM optimization algorithm which improves the accuracy of classifier. …”
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A method for optimizing test bus assignment and sizing for system-on-a-chip by Harmanani, Haidar M.

“…We present experimental results that demonstrate the effectiveness of our method while outperforming reported techniques.…”
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A New Hamiltonian Semi-Analytical Approach to Vibration Analysis of Piezoelectric Multi-Layered Plates by Andrianarison, O.

“…The whole piezoelectric multilayered plate’s dynamic stiffness is then built, from which its circular frequencies are computed with the help of the Wittrick-Williams algorithm. A detailed discussion is provided on the implementation aspects, followed by some numerical examples to assess the robustness, accuracy and effectiveness of the proposed method. …”
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Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks by Mohammed Almehdhar (22046597)

“…We highlight the transition from traditional signature-based to anomaly-based detection methods, emphasizing the significant advantages of AI-driven approaches in identifying novel and sophisticated intrusions. …”

Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis by Hassan, Ali

“…Deep Learning (DL) algorithms have shown wonders in many Natural Language Processing (NLP) tasks such as language-to-language translation, spam filtering, fake-news detection, and comprehension understanding. …”
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Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis by Hassan Ali (3348749)

“…<p>Deep Learning (DL) algorithms have shown wonders in many Natural Language Processing (NLP) tasks such as language-to-language translation, spam filtering, fake-news detection, and comprehension understanding. …”

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. …”

Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands by Peng, Wang

“…A novel evolving solution based on flower pollination algorithm is also proposed to solve the problem optimally. …”
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Joint energy-distortion aware algorithms for cooperative video streaming over LTE networks by Yaacoub, Elias

“…The cluster heads receive the data on the long-range LTE links, either via unicasting or multicasting. …”
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ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification by Md. Iqbal Hossain (723299)

Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices by Neder Karmous (19743430)

Cyberbullying Detection and Abuser Profile Identification on Social Media for Roman Urdu by Ayesha Atif (22155133)

Integrated Energy Optimization and Stability Control Using Deep Reinforcement Learning for an All-Wheel-Drive Electric Vehicle by Reza Jafari (3494018)

“…<p dir="ltr">This study presents an innovative solution for simultaneous energy optimization and dynamic yaw control of all-wheel-drive (AWD) electric vehicles (EVs) using deep reinforcement learning (DRL) techniques. To this end, three model-free DRL-based methods, based on deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and TD3 enhanced with curriculum learning (CL TD3), are developed for determining optimal yaw moment control and energy optimization online. …”

Prediction of EV Charging Behavior Using Machine Learning by Shahriar, Sakib

“…Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. …”
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