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MyJournals.org - Science - 'Algorithms, Vol. 16, Pages 288: An Adaptive Deep Learning Neural Network Model to Enhance Machine-Learning-Based Classifiers for Intrusion Detection in Smart Grids' (Algorithms)

MyJournals.org - Science - Algorithms, Vol. 16, Pages 288: An Adaptive Deep Learning Neural Network Model to Enhance Machine-Learning-Based Classifiers for Intrusion Detection in Smart Grids (Algorithms) ....

Network Security Laboratory , Adaptive Deep Learning , Network Security Laboratory Knowledge Discovery , Machine Learning Based ,

Instagram Chief Explains How Reels And Photos Are Ranked

Instagram has unveiled new information about its ranking algorithms, with new details in a blog post about the unique algorithms behind the Feed, Stories, Explore, Reels, and Search features. Instagram also described what is shadowbanning and cleared the misconceptions surrounding it. ....

Adam Mosseri , Instagram Ranks Feed , Account Status , Instagram Head , Instagram Algorithms , Instagram Ranking , Hat Is Shadowbanning , Mi Shadowbanned , Nstagram Reels Algorithm , Ow To Get More View On Instagram Reels , Social Media Platforms , Anking Algorithms , Adam Mosseri , User Metrics , Ser Connections , Explore Tab ,

MyJournals.org - Science - 'Prediction of disease-related miRNAs by voting with multiple classifiers' (BMC Bioinformatics)

MyJournals.org - Science - Prediction of disease-related miRNAs by voting with multiple classifiers (BMC Bioinformatics) ....

Disease Related ,

Ultrafine selection at your fingertips

Loïc Pottier and Philippe Niel, Fives, detail the development of a new generation of classifiers designed to work with ultrafine mineral loads. ....

Philippe Niel , Ultra High Fineness ,

"Machine learning models for classification and identification of signi" by Koushik Chandra Howlader, Md Shahriare Satu et al.

Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a res ....

Pima Indian , Pima Indian Diabetes Dataset , Generalized Boosted Regression , Sparse Distance Weighted Discrimination , Generalized Additive Model , Boosted Generalized Additive Models , Eature Selection Sets , Machine Learning Models , Prediction Model ,