Social Network Analysis for Precise Friend Suggestion for Twitter by Associating Multiple Networks Using ML by Dharmendra Kumar Singh Singh (19517710)

“…The information gathered to make these predictions includes the user’s friends, tags, tweets, language spoken, ID, etc. Based on these features, the authors trained their models using supervised learning methods. …”

Automatic Video Summarization Using HEVC and CNN Features by Issa, Obada

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Fast Text Classification using Lean Gradient Descent Feed Forward Neural Network for Category Feature Augmentation by Attieh, Joseph

“…This is done by embedding the TF-ICF matrix in a one-layer feed-forward neural network. The latter is trained using the gradient descent algorithm allowing to iteratively update the term-category matrix until reaching convergence. …”
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Soft Sensor for NOx Emission using Dynamical Neural Network by Shakil, M.

“…Neural network model is trained using real data logs of an industrial boiler. …”
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Diagnosing failed distribution transformers using neural networks by Farag, A.S.

“…The ANN was trained utilizing backpropagation algorithm using a real (out of the field) data obtained from utilities distribution networks transformer's failures. …”
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Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test by Hasan T. Abbas (8115014)

“…Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. …”

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

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Lung cancer medical images classification using hybrid CNN-SVM by Abdulrazak Yahya Saleh (18520026)

“…This paper presents an image classification method based on the hybrid Convolutional Neural Network (CNN) algorithm and Support Vector Machine (SVM). …”

Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring by Muhammad E.H. Chowdhury (17151154)

“…Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). …”

Design and implementation of a deep learning-empowered m-Health application by Akhan Akbulut (17380285)

“…Later, the web service classifies using the pre-trained model built based on a deep learning algorithm. …”

Semi-supervised Clustering of Facial Expressions by Jalal, Ahsan

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Cohen syndrome and early-onset epileptic encephalopathy in male triplets: two disease-causing mutations in VPS13B and NAPB by Alice AbdelAleem (17753799)

“…Sanger sequencing verified the segregation of the two recessive gene variants with the phenotype in family members. The prediction algorithms support the pathogenicity of these variants. …”

A hybrid model to predict the pressure gradient for the liquid-liquid flow in both horizontal and inclined pipes for unknown flow patterns by Md Ferdous Wahid (13485799)

“…The achieved MAPE using the proposed hybrid model is superior to existing mechanistic or correlation-based models reported in the literature (between 26 and 69%). …”

The Assessment and Allocation of Public Private Partnership Risks in the UAE by Shaban, Mazhd

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Electric Vehicles Charging Station Load Forecasting Integration With Renewable Energy Using Novel Deep EfficientBiLSTMNet by Vineet Dhanawat (22361395)

“…The EfficientBiLSTMNet model, which integrates the EfficientNet and BiLSTM layers, is trained on the preprocessed datasets. The model’s hyperparameters are optimized using an Enhanced Firefly Algorithm (EFA). …”

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying by Tekli, Joe

“…Many efforts have been deployed by the IR community to extend freetext query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. …”
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