Paper quarantines unreliable Yelp users by detecting review spam.
problem Unreliable and spamming users deceive Yelp's users.
method Used RSD and spam detection techniques on key features.
result More than 80% of Yelp's accounts are unreliable, and highly-rated businesses are often spammed.
Graph Neural Networks improve financial fraud detection.
problem Complex financial transactions pose challenges in fraud detection.
method Unified framework of GNN methodologies applied to financial fraud detection.
result GNNs excel at capturing complex relational patterns in financial networks.
Paper proposes spamGAN to detect and generate opinion spam using limited labeled data.
problem Detecting and preventing opinion spam in online reviews with limited labeled data.
method Generative adversarial network (GAN) trained on semi-supervised data.
result spamGAN outperforms existing techniques in detecting opinion spam with limited labeled data.
Paper detects review abuse using tensor decomposition.
problem Detecting review abuse by sellers and reviewers.
method Semi-supervised binary multi-target tensor decomposition.
result The model achieves higher precision and recall.
ScoreGAN uses GANs with IGM to detect bot-generated reviews based on text and scores.
problem Lack of labeled data and bot-generated reviews in fraud review detection.
method ScoreGAN incorporates review text and scores into a GAN framework for data augmentation and detection.
result ScoreGAN outperforms existing methods by 7% and 5% on Yelp and TripAdvisor datasets.
This review examines techniques for detecting anomalies in time series data.
problem Detecting anomalies in time series data.
method Taxonomy based on main aspects of outlier detection techniques.
result Structured and comprehensive state-of-the-art on outlier detection techniques in time series.
This review examines deep learning in financial fraud detection over 5 years.
problem Improving deep learning techniques for financial fraud detection.
method Systematic literature review of 57 studies using performance metrics.
result Deep learning models enhance fraud detection across various financial domains.
Review of machine learning methods for detecting depression from resting EEG.
problem Detecting depression from resting-state EEG recordings.
method Machine learning combined with nonlinear analysis on EEG data.
result Improved reliability of depression detection models.
This review explores ML and DL techniques for detecting distracted driving across various modalities.
problem Improving detection of complex distraction patterns, especially cognitive distractions.
method Categorizes and evaluates studies based on modality, data accessibility, and methodology.
result Multimodal systems outperform single-modal systems in detecting complex distraction patterns.
Proposes RTL model for sentiment classification and key word detection in online reviews.
problem Sentiment classification and key word detection in online reviews for hospitality industry.
method Regularized Text Logistic (RTL) regression model.
result RTL model achieves satisfactory classification performance and identifies key word features.
Automates detecting problem statements in peer assessments.
problem Identifying problem statements in peer assessment reviews.
method Used machine learning models including neural networks and traditional classifiers.
result Hierarchical Attention Network classifier achieved 93.1% accuracy.
Systematic review of ML models for detecting social media deception.
problem Detecting fake news, spam, and fake accounts on social media.
method 36 studies evaluated using PROBAST tool, identifying biases and limitations.
result Over-reliance on accuracy in imbalanced data settings is a flaw.
Paper tackles non-credible reviews by deriving consistency features from limited data.
problem Identifying credible online reviews amidst fake, incompetent, and biased reviews.
method Uses latent topic models to derive consistency features from review texts, item ratings, and timestamps.
result Improves credibility detection over state-of-the-art baselines on real-world datasets.
Review of machine learning methods for detecting depression from resting EEG.
problem Improving depression diagnosis from EEG data.
method Analysis of machine learning approaches in detecting depression from resting-state EEG.
result Discussion of various machine learning models for depression detection.
New method detects evasive product reviews that evade traditional spam detection.
problem Hardened spammers evade behavior-based opinion spam detection systems.
method Proposes EMERAL to generate evasive spams and DETER to defend against them.
result DETER outperforms traditional methods in detecting evasive reviews.
Deep learning improves anomaly detection across various fields.
problem Detecting anomalies in data with advanced approaches.
method Survey of deep learning methods for anomaly detection.
result Advancements in deep anomaly detection address unique challenges.
This paper reviews self-supervised learning methods for time series anomaly detection.
problem Challenges in traditional unsupervised methods for time series anomaly detection.
method Self-supervised learning techniques for time series anomaly detection.
result Enhanced performance of anomaly detectors through self-supervised learning.
This paper reviews traditional and modern methods for detecting structural damage using vibrations.
problem Early warning of structural damage to maintain civil structures.
method Vibration-based methods and ML/DL algorithms.
result ML and DL algorithms show superior performance in detecting structural damage.
This review unifies deep and shallow anomaly detection methods.
problem Improving anomaly detection on complex datasets.
method Systematic comparison of deep and shallow approaches.
result Identifies common principles and assumptions in anomaly detection.
Deep learning improves Android malware detection.
problem Detecting and preventing Android malware.
method Review of static, dynamic, and hybrid deep learning approaches.
result Identifies strengths and weaknesses of deep learning methods.
This review explores automated pain detection from facial expressions using FACS.
problem Automated detection of pain from facial expressions is challenging and underexplored.
method Examines progress in AFER and APD, focusing on FACS-based methods and deep learning.
result Limited studies on APD, highlighting the need for clinical applications.
Auto-detection system identifies safety issues in baby products from reviews.
problem Early detection of safety issues in baby products to reduce injuries and deaths.
method Text cleaning, feature extraction, dimensionality reduction, classifier analysis.
result Logistic regression model with 66% precision in identifying top 50 safety issues.
The paper reviews techniques for detecting errors in semantic segmentation models.
problem Detecting false positives and false negatives in semantic segmentation models.
method Uncertainty quantification techniques applied to semantic segmentation.
result Techniques for detecting false positives and false negatives are proposed and discussed.
This review explores methods for detecting Adverse Drug Events in EHRs.
problem Detecting Adverse Drug Events (ADEs) in EHRs using temporal data.
method Examines 5 main approaches: temporal abstraction, graph-based, learning weights, and time series data.
result Challenges remain in exploiting heterogeneous temporal data for ADE detection.
Deep learning predicts mismatched ratings in Amazon reviews.
problem Identifying reviews with mismatched ratings on Amazon.
method Converted reviews to vectors using paragraph vector, trained a recurrent neural network with gated recurrent unit, incorporated semantic relationships.
result Model accurately predicts rating mismatches and provides feedback.
Summarizes changepoint detection models and their applications.
problem Detect abrupt changes in time-series data.
method Summarizes and analyzes traditional and alternative changepoint detection models.
result Explores future research directions in changepoint detection.
This review covers learning under concept drift, including detection, understanding, and adaptation.
problem Unforeseeable changes in data distribution over time impact machine learning performance.
method Reviews and analyzes methodologies and techniques for concept drift detection, understanding, and adaptation.
result Establishes a framework for learning under concept drift with three main components.
Machine learning models for COVID-19 detection and prognosis from chest images are flawed and unreliable.
problem Developing reliable machine learning models for COVID-19 diagnosis and prognosis from chest images.
method Systematic review of machine learning models published in 2020.
result None of the models identified are of clinical use due to methodological flaws and biases.
This paper reviews metrics to assess AI model calibration accuracy.
problem AI model probabilities do not always match their true accuracy.
method Comprehensive review of 82 probability calibration metrics.
result Identified 4 classifier families and 1 object detection family of metrics.
Improved aspect detection from few seed keywords.
problem Fine-grained aspect detection from user reviews is labor-intensive.
method Weakly supervised co-training with student-teacher approach.
result Significant improvement in F1 scores over previous methods.
This article reviews ranking problems and their solutions.
problem Ranking problems in statistical learning.
method Systematic review of ranking problems and optimization techniques.
result Unified notation for optimization problems and identification of strengths and limitations of algorithms.
LLmFPCA-detect detects anomalies in sparse longitudinal text data using LLMs and mFPCA.
problem Challenges in detecting patterns and anomalies in sparse longitudinal textual data.
method Pairs LLM-based text embeddings with mFPCA to detect clusters and anomalies.
result LLmFPCA-detect outperforms state-of-the-art baselines on Amazon and Wikipedia datasets.
Deep learning improves retinal fundus image analysis for eye diseases.
problem Improving accuracy in diagnosing eye diseases using retinal images.
method Review of deep learning models and datasets for retinal image analysis.
result Deep learning enhances detection and classification of eye diseases.
Systematic review of electronic health record phenotyping approaches.
problem Detecting patient cohorts using electronic health records.
method Comprehensive literature review of preprocessing and modeling approaches.
result Natural language processing shows promise for electronic phenotyping.
This article reviews datasets for COVID-19 detection using ML.
problem Lack of accessible datasets for COVID-19 detection research.
method Analyzed 96 papers on COVID-19 detection from January 2020 to June 2020.
result Identified and summarized datasets used in COVID-19 detection studies.
Review of deep learning techniques for audio signal processing.
problem Improving audio signal processing using deep learning.
method Analysis of various deep learning models and techniques.
result Advancements in speech, music, and environmental sound processing.
Survey of data augmentation methods for improving deep learning on time series data.
problem Limited labeled data in real-world time series applications.
method Review and comparison of data augmentation methods for time series.
result Empirical comparison of data augmentation methods for various time series tasks.
Two machine learning models detect anomalies in ER claims, saving up to 40% in improper payments.
problem Improper health insurance payments from fraud and upcoding.
method Two machine learning models: an upcoding model based on severity code distributions and a random forest model for claim sorting.
result Random forest model saved 12% to 40% in improper payments compared to a baseline approach.
The paper reviews methods for determining the number of communities in network data.
problem Determining the number of communities in network data.
method Statistical methods for hypothesis testing and clustering in network models.
result SCORE and NCV methods evaluated for clustering in Degree-Corrected Block Models, with NCV facing challenges.
Project analyzes drug reviews to predict ratings using machine learning.
problem Predicting drug ratings from text reviews.
method Implemented supervised machine learning algorithms with TFIDF and Count Vectors.
result Good results in predicting test data sets for popular conditions.
This review compares extractive and abstractive summarization methods.
problem Improving abstractive summarization in natural language processing.
method Compared various approaches including supervised and unsupervised methods, deep learning, and NLP.
result Current research uses combinations of approaches, but abstractive summarization remains unsolved.
Survey of methods for detecting fraud in networks.
problem Detecting anomalies in graph-based data for fraud detection.
method Anomaly detection techniques using graph structure and attributes.
result Survey of various methods for fraud detection in networks.
Survey categorizes time series anomaly detection methods.
problem Need for anomaly detection in time series data.
method Process-centric taxonomy of anomaly detection methods.
result Meta-analysis of time series anomaly detection trends.
Analyzes methods for detecting communities in networks.
problem Detecting communities in networks.
method Examined graph theory, reviewed community detection methods, classified them, and evaluated community quality.
result Developed a classification of community detection methods and evaluated their strengths and weaknesses.
Neural nets detect alarming student responses for quick review.
problem Identifying alarming student responses in online assessments.
method Developed neural network models to flag potentially concerning responses.
result Neural nets can flag alarming responses more efficiently than manual review.
Combining training and post-training methods improves OOD detection accuracy.
problem Deep networks struggle with OOD detection.
method Divided OOD detection methods into training and post-training, then combined them.
result State-of-the-art results in OOD detection achieved.
Scoping review finds EEG key in MCI research, identifying ERP/EEG, QEEG, and machine learning.
problem Identifying MCI early and accurately.
method Scoping review with co-occurrence analysis and PAGER framework.
result Main research themes identified: ERP/EEG, QEEG, and EEG-based machine learning.
This review chronicles AI algorithms for cervical cancer screening.
problem Automated screening of cervical cancer using AI methods.
method Analysis of various machine learning algorithms and clustering techniques.
result Holistic review of computational methods over time.