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Machine learning internship - Random Seeds for ML

As part of a machine learning lab at the University of Siegen[2], the code of the paper "The Effect of Random Seeds for Data Splitting on Recommendation Accuracy"[6] was replicated and reproduced. While the original paper focused on recommender systems, the code was adapted to investigate the effect of random seeds on general machine learning ...
Jouhaina Salsabil El Euch, Ikram Boudi, Jirapat Woranij   +2 more
openaire   +1 more source

Artificial intelligence (AI) and machine learning (ML) for beyond 5G/6G communications

EURASIP Journal on Wireless Communications and Networking, 2023
Mohammad Abdul Matin, Sotirios K. Goudos, Shaohua Wan, Panagiotis Sarigiannidis, Emmanouil M. Tentzeris   +4 more
doaj   +1 more source

Machine learning in big data: A performance benchmarking study of Flink-ML and Spark MLlib

Applied Computer Science
Machine learning (ML) in big data frameworks plays a critical role in real-time analytics, decision making, and predictive modeling. Among the most prominent ML libraries for large-scale data processing are Flink-ML, the machine learning extension of ...
Messaoud MEZATI, Ines AOURIA
doaj   +1 more source

Using Machine Learning (ML) Models to Predict Risk of Venous Thromboembolism (VTE) Following Spine Surgery. [PDF]

Clin Spine Surg, 2023
Katiyar P, Chase H, Lenke LG, Weidenbaum M, Sardar ZM.   +4 more
europepmc   +1 more source

A proficiency assessment of integrating machine learning (ML) schemes on Lahore water ensemble. [PDF]

Sci Rep, 2023
Shahid N.
europepmc   +1 more source

Design of an Integrated Model Combining CycleGAN, PPO, and Vision Transformer for Adaptive Scene Rendering in the Metaverse

IEEE Access
The emergence of the metaverse demands adaptive rendering systems that produce high-quality scenes, balancing the dimensions of visual fidelity with computational efficiency.
Durga Prasad Kavadi, Kailash Chandra Mishra, Sai Babu Veesam, Mudimela Madhusudhan, Palacharla Ravi Kumar, Pannangi Naresh, Yuvaraju Chinnam   +6 more
doaj   +1 more source

Applications of machine learning and deep learning in musculoskeletal medicine: a narrative review

European Journal of Medical Research
Artificial intelligence (AI), with its technologies such as machine perception, robotics, natural language processing, expert systems, and machine learning (ML) with its subset deep learning, have transformed patient care and administration in all fields
Martina Feierabend, Julius Michael Wolfgart, Maximilian Praster, Marina Danalache, Filippo Migliorini, Ulf Krister Hofmann   +5 more
doaj   +1 more source

ML-STIM: Machine Learning for SubThalamic nucleus Intraoperative Mapping

Journal of Neural Engineering
Abstract Objective. Deep Brain Stimulation (DBS) of the SubThalamic Nucleus (STN) is effective in alleviating motor symptoms in medication-refractory patients with Parkinson’s Disease (PD). Intraoperative identification of the STN relies on MicroElectrode Recordings (MERs), typically analyzed ...
Fabrizio Sciscenti, Valentina Agostini, Laura Rizzi, Michele Lanotte, Marco Ghislieri   +4 more
openaire   +1 more source

Retracted: Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM). [PDF]

Comput Math Methods Med, 2023
Methods In Medicine CAM.
europepmc   +1 more source

Knockoff-ML: a knockoff machine learning framework for controlled variable selection and risk stratification in electronic health record data

npj Digital Medicine
Effective risk stratification is essential in clinical practice, enabling better resource allocation and improved patient outcomes. Although machine learning models have been widely used for risk prediction and stratification in electronic health record (
Qi Wang, Linyan Li, Yi Yang
doaj   +1 more source