Deploying model obfuscation: towards the privacy of decision-making models on shared platforms by Sadhukhan, Payel

“…The implementation nuances involve data and model sharing among allies and partners working on the same domain. …”
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A Novel Genetic Algorithm Optimized Adversarial Attack in Federated Learning for Android-Based Mobile Systems by Faria Nawshin (21841598)

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Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment by Farhat Mahmood (15468854)

“…First, an analytical model based on mass and energy balance and a data-driven model based on an artificial neural network is developed, and their prediction performance is compared. …”

Optimized Load-Scheduling Algorithm for CubeSat's Electric Power System Management Considering Communication Link by Bayan Hussein (16904856)

Estimating Construction Project Duration Using a Machine Learning Algorithm by Nasr, Joshua

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A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model by Md. Nazmul Islam Shuzan (16888827)

Correlation Clustering via s-Club Cluster Edge Deletion by Makarem, Norma

Subjects: “…Cluster analysis -- Data processing…”
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Heuristic Algorithm for Obtaining Approximate Optimum Stratification With Mixture of Ratio and Product Estimators by S. E. H. Rizvi (22052105)

Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control by Farhat Mahmood (15468854)

“…The proposed model predictive control framework is flexible and can be applied to other greenhouse systems by tuning the model on the new data set.…”

A Secure and Decentralized Blockchain Based EV Energy Trading Model Using Smart Contract in V2G Network by Atif Iqbal (5504636)

Predicting Patient ICU Readmission Using Recurrent Neural Networks With Long Short-Term Memory by Nadia Rasheed (22997257)

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Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study by Mutasim Baba, Fuad

“…This methodology includes a variance-based sensitivity analysis to determine building parameters that significantly influence indoor air temperatures, the Multi-Objective Genetic Algorithm to calibrate different rooms simultaneously based on the significant param eters identified by the sensitivity analysis, and new evaluation criteria to achieve a high-accuracy calibrated model. …”
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Large language models for code completion: A systematic literature review by Rasha Ahmad Husein (19744756)

Evaluation of Aerosol Optical Depth and Aerosol Models from VIIRS Retrieval Algorithms over North China Plain by Jun Zhu (84054)

“…The VIIRS Environmental Data Record data (VIIRS_EDR) is produced operationally by NOAA, and is based on the MODIS atmospheric correction algorithm. …”

Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning by Zulfiqar Ali (117651)

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A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method by Amit Kumar Balyan (18288964)

“…Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. …”

Fault-Tolerant Network Topologies for Datacenters by Attia, Heba Mahmoud Helal

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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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Acoustic Based Localization of Partial Discharge Inside Oil-Filled Transformers by Hamidreza Besharatifard (16904823)

Label dependency modeling in Multi-Label Naïve Bayes through input space expansion by PKA Chitra (21749216)

“…To accommodate the heterogeneity of the expanded input space, we refine the likelihood parameters of iMLNB using a joint density function, which is adept at handling the amalgamation of data types. We subject our enhanced iMLNB model to a rigorous empirical evaluation, utilizing six benchmark datasets. …”