Journal: Sensors (Basel, Switzerland)

Article Title: At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

doi: 10.3390/s23031639

Figure Lengend Snippet: Qualitative comparison of smart environment-related works.

Article Snippet: , , [ ] , Distributed data analysis for air prediction , Preprocessing , K-means SVM, MLP, DT, KNN, NB , U.S. Pollution Data Kaggle , Edge devices, cloud , Distributed computing , IFogSim toolkit-YAFS- , AccuracyPrecision recallF1-Score , Data reductionLow response time reduction , Not consider mobility of nodes.

Techniques: Comparison, Transmission Assay, Western Blot

Journal: Sensors (Basel, Switzerland)

Article Title: At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

doi: 10.3390/s23031639

Figure Lengend Snippet: Qualitative comparison of smart education related works.

Article Snippet: , , [ ] , Distributed data analysis for air prediction , Preprocessing , K-means SVM, MLP, DT, KNN, NB , U.S. Pollution Data Kaggle , Edge devices, cloud , Distributed computing , IFogSim toolkit-YAFS- , AccuracyPrecision recallF1-Score , Data reductionLow response time reduction , Not consider mobility of nodes.

Techniques: Comparison

Journal: Sensors (Basel, Switzerland)

Article Title: At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

doi: 10.3390/s23031639

Figure Lengend Snippet: Qualitative comparison of smart industry related works.

Article Snippet: , , [ ] , Distributed data analysis for air prediction , Preprocessing , K-means SVM, MLP, DT, KNN, NB , U.S. Pollution Data Kaggle , Edge devices, cloud , Distributed computing , IFogSim toolkit-YAFS- , AccuracyPrecision recallF1-Score , Data reductionLow response time reduction , Not consider mobility of nodes.

Techniques: Comparison, Selection, Extraction, Transmission Assay

Journal: Sensors (Basel, Switzerland)

Article Title: At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

doi: 10.3390/s23031639

Figure Lengend Snippet: Qualitative comparison of smart transportation-related works.

Article Snippet: , , [ ] , Distributed data analysis for air prediction , Preprocessing , K-means SVM, MLP, DT, KNN, NB , U.S. Pollution Data Kaggle , Edge devices, cloud , Distributed computing , IFogSim toolkit-YAFS- , AccuracyPrecision recallF1-Score , Data reductionLow response time reduction , Not consider mobility of nodes.

Techniques: Comparison, Preserving, Biomarker Discovery

Journal: PeerJ Computer Science

Article Title: A systematic review of literature on credit card cyber fraud detection using machine and deep learning

doi: 10.7717/peerj-cs.1278

Figure Lengend Snippet: Comparisons of selected article on cyber fraud detection in credit card.

Article Snippet: A95 , DT, RF, KNN, LR K-means, DBSCAN, MLP, NB, XGBoost Gradient boost , Accuracy Precision Recall F1-score , The result showed that RF yielded perfect performance result with accuracy 99.995. RF is suitable for large datasets. , Real dataset/Europeans cardholders/Kaggle , NA.

Techniques: Sampling, Isolation, Plasmid Preparation, Injection, Preserving, Selection, Produced, Construct, Comparison

Journal: Frontiers in Neurology

Article Title: Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review

doi: 10.3389/fneur.2024.1413071

Figure Lengend Snippet: Summary of included studies on the machine learning algorithms for early detection of NCDs and NDDs.

Article Snippet: The following algorithms were used for detection, screening and progression of AD, all of which were successful for the purposes: Sequential minimal optimization (SMO), Naive Bayes (NB), tree augmented Naive Bayes (TAN), K2, MATLAB PatternRecognition toolbox, TF-IDF, CountVectorizer (CV), Word2Vec, FastText, VGG16 with XGB, stacked fusion models//hybrid stacked fusion model, PRS, AAO, KNN, decision tree, random forest, ANN, 3D-CNN model, Boruta FS algorithm, Gradient, Information Gain (IG), Multi-view Separable Pyramid Network (MiSePyNet), PyWinEA using Mono-objective and Multi-objective Genetic Algorithms (NSGAII), Elastic Net (EN), Gaussian Processes (GP), kNN, (LR), Linear Discriminant, Support Vector Machine, Voting classifiers, Multi-Classifier Network (MCN), Gradient Boosted Trees (GBTs), basic three-layer Neural Network architecture using the OASIS, Sparse K-means w/Resampling, a deep neural network architecture, Adaboost, graph convolutional and recurrent neural network (graph-CNN-RNN), Single hidden layer neural network, Single-layer bidirectional, LSTM, Three-layer CNN, Deep Belief Network (DBN), stacked auto-encoder (SAE), SVR, SVC, PLSR, Shallow Models, Feature Pyramid Network (FPN) and temporally structured SVM (TS-SVM).

Techniques: Biomarker Discovery, Construct, Derivative Assay, Infection, Gene Expression, Plasmid Preparation, Selection, Diagnostic Assay, Control, Introduce, Extraction, Modification, Functional Assay, Positron Emission Tomography, Raman Spectroscopy, Sequencing, Clinical Proteomics, Expressing, Quantitative Proteomics, Phospho-proteomics, Magnetic Resonance Imaging, Imaging, Tomography, Mutagenesis, Single Photon Emission Computed Tomography, DNA Methylation Assay, Comparison, Binding Assay, Software, Activity Assay