Regression estimation for continuous-time functional data processes with missing at random response by Mohamed Chaouch (17983846)

Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data by Rajesh Kumar Dhanaraj (19646269)

“…During this process, the input patterns are categorized into different clusters. …”

A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT by Dhananjay Bisen (19482454)

Modelling fatigue uncertainty by means of nonconstant variance neural networks by Mohamad Shadi Nashed (21363557)

“…First, we model the fatigue life of cover‐plated beams under constant amplitude loading, and then we model the relationship between random vibration velocity and equivalent stress in process pipework. The two case studies demonstrate that PNNs with nonconstant variance can model the distribution of the data while also considering the variability of both distribution parameters (mean and standard deviation). …”

Assessing the risk of vibration-induced fatigue in process pipework using convolutional neural networks by Ahmed Mohamed (628889)

“…In contrast, vibration data can be efficiently collected using accelerometers and single-channel data loggers, providing a more feasible solution for initial screening. …”

Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection by Sulaiman Khan (12585349)

“…<h3>Objective</h3><p dir="ltr">During the last few years, underwater object detection and marine resource utilization have gained significant attention from researchers and become active research hotspots in underwater image processing and analysis domains. This research study presents a data fusion-based method for underwater salient object detection and ocean environment monitoring by utilizing a deep model.…”

Optimizing malicious website prediction: An advanced XGBoost-based machine learning model by Sumaira Hussain (19259669)

Experimental Verification of Low-Pressure Kinetics Model for Direct Synthesis of Dimethyl Carbonate Over CeO₂ Catalyst by G. Ibrahim (7601063)

“…The kinetic model predictions closely aligned with experimental data, demonstrating a 17% mean absolute percentage error and indicating a high level of predictability. …”

On the Use of Deep Learning for Video Classification by Atiq ur Rehman (14153391)

Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection by Faizul Rakib Sayem (21399890)

Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter by Mena S. ElMenshawy (17983807)

“…A few research studies focused on developing data filtering algorithm for the load forecasting process using approaches such as Kalman filter, which has good tracking capability in the presence of noise in the data collection process. …”

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

Traffic fatality trends in four continents based micro level data for three decades by Hashim M. N. Al-Madani (19807239)

“…It took over three years to gather such data. Such large gathered data for such long period of time are yet not observed in the literature using the common means. …”

MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model by Md Abrar Jahin (20108252)

“…MCDFN utilizes Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs) to extract spatial and temporal features from time series data. Comparative benchmarking against seven other deep-learning models validates MCDFN’s efficacy, showing it outperforms its counterparts across key metrics with a mean squared error (MSE) of 23.5738, root mean squared error (RMSE) of 4.8553, mean absolute error (MAE) of 3.9991, and mean absolute percentage error (MAPE) of 20.1575%. …”

Supporting secure dynamic alert zones using searchable encryption and graph embedding by Sina Shaham (17707254)

“…However, serious privacy concerns arise when location data are used in clear in the process. Several solutions employ searchable encryption (SE) to achieve secure alerts directly on encrypted locations. …”

SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework by Daksh Dave (17949239)

“…These systems need access to user data, which is a serious security violation. While users seek accurate opinions, they do not want to compromise their privacy in the process. …”

Effect of cold and hot compactions on corrosion behavior of p- and n-type bismuth telluride-based alloys developed through microwave sintering process by Aicha S. Lemine (17148352)

Condition Monitoring and Fault Diagnosis of Wind Turbine: A Systematic Literature Review by Musavir Hussain (20725062)

“…Additionally, this review considers the distinctive concept of machine learning model building which includes data input and its processing, feature selection, model building and its evaluation to analyze the research issues. …”

Household-Level Energy Forecasting in Smart Buildings Using a Novel Hybrid Deep Learning Model by Dabeeruddin Syed (16864260)

“…The proposed framework consists of two stages, namely, data cleaning, and model building. The data cleaning phase applies pre-processing techniques to the raw data and adds additional features of lag values. …”

Improving MRI Resolution: A Cycle Consistent Generative Adversarial Network-Based Approach for 3T to 7T Translation by Zakaria Shams Siam (22048001)