RCS-YOLO improves brain tumor detection speed and accuracy.
problem Efficiently detecting brain tumors with high accuracy and speed.
method Proposes RCS-YOLO, a fast and accurate brain tumor detector using YOLO with Reparameterized Convolution and channel Shuffle.
result RCS-YOLO outperforms other YOLO versions in speed and accuracy on brain tumor detection.
SAAD enhances anomaly detection in automotive systems with high accuracy.
problem Anomaly detection in automotive systems.
method Integrates statistical techniques with machine learning.
result SAAD achieves 88.3% accuracy and 0.921 F1 score.
Improved YOLOv5 model detects mask-wearing with enhanced accuracy.
problem Detecting mask-wearing in high traffic public places.
method Improved YOLOv5l with Multi-Head Attentional Self-Convolution, Swin Transformer Block, I-CBAM module, and enhanced feature fusion.
result 1.1% improvement in mAP(0.5) and 1.3% improvement in mAP(0.5:0.95) compared to YOLOv5l.
Paper improves transaction-based Ponzi detection on Ethereum.
problem Low accuracy of current transaction-based Ponzi detection models.
method Employ time-series features to improve accuracy.
result Up to 30% higher F1-scores achieved with new features.
TimeVQVAE-AD detects anomalies in time series data with high accuracy and provides explainable results.
problem Detecting and explaining anomalies in time series data accurately.
method Masked latent generative modeling in time-frequency domain.
result TimeVQVAE-AD outperforms existing methods in anomaly detection and explainability.
Algorithm speeds up search for stationary targets with guaranteed accuracy.
problem Minimize search time while ensuring high detection accuracy of stationary targets.
method Multi-fidelity Gaussian process model and EMTS algorithm.
result Guaranteed performance in target detection accuracy and search time.
Out-of-distribution (OOD) detection approaches usually present special requirements (e.g., hyperparameter validation, collection of outlier data) and produce side effects (e.g., classification accuracy drop, slower energy-inefficient inferences). We argue that these issues are a consequence of the SoftMax loss anisotro…
A graph neural network detects beneficial feature interactions for recommender systems.
problem Feature interactions are crucial but not all are beneficial for recommendation accuracy.
method Graph neural network with L0 activation regularization for edge prediction.
result The model outperforms baselines and automatically identifies beneficial feature interactions.
Deep learning model improves X-ray disease detection accuracy in Thai patients.
problem Lack of large-scale validation of AI algorithms for medical image diagnosis in Thailand.
method Development and testing of a deep learning algorithm using 421,859 local chest radiographs.
result Convolutional neural networks achieve remarkable performance in detecting 13 common abnormality conditions on chest X-ray.
TinyML models detect RF and cyber threats in spacecraft with low latency.
problem Detecting cyber-RF threats in autonomous spacecraft with low latency.
method Analysis of classical models (RF, LR, SVM, MLP) for latency-accuracy trade-offs.
result Logistic Regression achieves microsecond-level inference with minimal accuracy loss.
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However,…
Adaptive anomaly detection for IoT data reduces delay without sacrificing accuracy.
problem Real-time anomaly detection for IoT data in distributed edge computing systems.
method Adaptive anomaly detection approach using contextual bandit and reinforcement learning.
result Significantly reduces detection delay (e.g., 71.4% for univariate data) without sacrificing accuracy.
Changepoint detection is a central problem in time series and genomic data. For some applications, it is natural to impose constraints on the directions of changes. One example is ChIP-seq data, for which adding an up-down constraint improves peak detection accuracy, but makes the optimization problem more complicated.…
The Normalized Mutual Information (NMI) has been widely used to evaluate the accuracy of community detection algorithms. However in this article we show that the NMI is seriously affected by systematic errors due to finite size of networks, and may give a wrong estimate of performance of algorithms in some cases. We gi…
SVMs improve forest fire detection accuracy on challenging datasets.
problem Rapid and accurate detection of forest fires.
method Training SVMs on labeled fire image datasets, focusing on data preprocessing, feature extraction, and model training.
result SVMs enhance detection accuracy on complex datasets, revealing key parameters affecting performance.
Crowdsourced data helps detect incidents faster, balancing accuracy and practicality.
problem Detecting incidents from crowdsourced data is challenging due to noise and uncertainty.
method CROME (Crowdsourced Multi-objective Event Detection) uses CNN and Pareto optimization.
result The approach outperforms existing methods in incident detection and practicality.
With the increased affordability and availability of whole-genome sequencing, large-scale and high-throughput gene expression is widely used to characterize diseases, including cancers. However, establishing specificity in cancer diagnosis using gene expression data continues to pose challenges due to the high dimensio…
Real-time fuel leakage detection framework MOCPD improves accuracy.
problem Early detection of fuel leakage to prevent hazards and losses.
method Memory-based Online Change Point Detection (MOCPD) framework.
result MOCPD outperforms baseline methods in detection accuracy.
Recently, advances in machine learning techniques have attracted the attention of the research community to build intrusion detection systems (IDS) that can detect anomalies in the network traffic. Most of the research works, however, do not differentiate among different types of attacks. This is, in fact, necessary fo…
A novel approach ODAR detects outliers for clustering.
problem Outliers interfere with clustering algorithms, leading to unreliable results.
method Feature transformation to separate outliers and normal objects into distinct clusters.
result ODAR improves clustering accuracy on 7 out of 10 datasets.
Anomaly detection of time series plays an important role in reliability systems engineering. However, in practical application, there is no precisely defined boundary between normal and anomalous behaviors in different application scenarios. Therefore, different anomaly detection algorithms and processes ought to be ad…
We introduce a Bayesian defect detector to facilitate the defect detection on the motion blurred images on rough texture surfaces. To enhance the accuracy of Bayesian detection on removing non-defect pixels, we develop a class of reflected non-local prior distributions, which is constructed by using the mode of a distr…
This paper improves anomaly detection in lane rendering images for safer navigation.
problem Anomalies in lane rendering images can mislead drivers, posing safety risks.
method Proposes a four-phase pipeline using Transformer models, self-supervised pre-training, and fine-tuning.
result The pipeline enhances detection accuracy and reduces training time.
Paper uses ensemblers to predict sepsis early from patient records.
problem Early detection of sepsis in patients.
method Imputation and weak ensembler technique applied to 40k patient records.
result Model achieved 93.45% accuracy and 0.271 utility score.
Hierarchical GANs reduce anomaly detection costs.
problem Balancing anomaly detection accuracy and sampling costs.
method Hierarchical GANs for nonuniform sampling and buffer zones.
result Proposed GAN-based detector outperforms baseline in detection delay and average cost of error.
Study shows latent space OOD detection isn't a reliable proxy for model performance.
problem Evaluating and interpreting deep learning systems on real-world data.
method Empirical investigation of latent space OOD detection and classification accuracy using SAR datasets.
result OOD detection cannot be used as a proxy measure for model performance.
Often, more time is spent on finding a model that works well, rather than tuning the model and working directly with the dataset. Our research began as an attempt to improve upon a simple Recurrent Neural Network for answering "simple" first-order questions (QA-RNN), developed by Ferhan Ture and Oliver Jojic, from Comc…
Hidden Markov Models detect hand gestures from wearable sEMG signals.
problem Detecting activity regions in continuous sEMG signals.
method Hidden Markov Models applied to sEMG signals for gesture recognition.
result Average accuracy of 96.25% for activity onsets and 87.5% for activity terminations.
Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can barely afford complex DNN models due to limited computational power and energy supply. While one can offload anomaly detection tasks to the cloud, it incurs long delay and requires large bandwidth …
A new OOD detector using an overlap index improves accuracy without high computational costs.
problem Effective OOD detection for machine learning models in open-world scenarios.
method Proposes an overlap index-based confidence score function for OOD detection.
result The proposed method achieves competitive accuracy with lower computational costs compared to state-of-the-art detectors.
Implantable, closed-loop devices for automated early detection and stimulation of epileptic seizures are promising treatment options for patients with severe epilepsy that cannot be treated with traditional means. Most approaches for early seizure detection in the literature are, however, not optimized for implementati…
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage fr…
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.
XGBoost detects unlawful insider trading with high accuracy.
problem Detecting unlawful insider trading from large volumes of transactions.
method Applying eXtreme Gradient Boosting (XGBoost) for identifying and ranking key features.
result XGBoost achieves 97% accuracy in detecting unlawful transactions.
As automobiles become intelligent, automobile theft methods are evolving intelligently. Therefore automobile theft detection has become a major research challenge. Data-mining, biometrics, and additional authentication methods have been proposed to address automobile theft, in previous studies. Among these methods, dat…
Locally private methods detect changes in time series data.
problem Detecting distributional changes in time series data under local differential privacy.
method Proposed locally differentially private algorithms based on randomized response and binary mechanisms.
result Theoretical performance bounds and empirical validation of detection accuracy.
AI enhances pollen recognition in veterinary imaging using holographic microscopy.
problem Challenges in recognizing pollen in holographic images due to speckle noise and artifacts.
method Training YOLOv8s and MobileNetV3L on dual-modality dataset, employing WGAN-SN for synthetic data augmentation.
result GAN-based augmentation improves object detection and classification in holographic images, closing the performance gap.
EagerNet detects network attacks quickly with less resources.
problem Efficiently detecting network attacks with minimal resources.
method Proposes a new architecture that trades prediction speed for accuracy, evaluating only a subset of layers.
result Comparable accuracies to simple FCNNs achieved with early predictions, saving energy and computational efforts.
OneShotSTL efficiently decomposes time series online, improving speed and accuracy.
problem Real-time analysis of time series data with low processing delay.
method Online seasonal-trend decomposition algorithm with O(1) update time complexity.
result 1,000 times faster than batch methods with comparable accuracy.
Balancing graph summarization and change detection in streaming data.
problem Balancing compression rate in graph summarization and accuracy in change detection.
method Introducing a probabilistic hierarchical latent variable model and optimizing parameters based on the minimum description length principle to balance the trade-off.
result Guaranteed suppression of Type I error probability (false alarms) in change detection.
This paper evaluates algorithms for classification and outlier detection accuracies in temporal data. We focus on algorithms that train and classify rapidly and can be used for systems that need to incorporate new data regularly. Hence, we compare the accuracy of six fast algorithms using a range of well-known time-ser…
Paper builds ML classifier to detect crypto-ransomware.
problem Detecting crypto-ransomware with high accuracy and low false positives.
method Behavior-based detection using input/output activities and file-content entropy. Deep-learning classifier with adversarial research and Integrated Gradient method for explanation.
result Deep-learning classifier achieves high accuracy and low false positive rate in detecting crypto-ransomware.
Personalized deep learning reduces inappropriate shocks in VA detection.
problem High inappropriate shock rate in traditional VA detection methods.
method Personalized deep learning framework using CNN for real-time VA detection and collaborative inference.
result 6.6% reduction in inappropriate shock rate compared to traditional methods.
TLRF improves timely COVID-19 outbreak detection with small sample size counties.
problem Balancing accuracy and speed in estimating COVID-19 case growth rates.
method Transfer Learning Random Forest (TLRF) framework for growth rate estimation.
result TLRF outperforms existing methods in predicting case growth rates and timely outbreak detection.
Bayesian autoencoders quantify anomaly uncertainty for safer machine learning.
problem Lack of uncertainty quantification in autoencoders for anomaly detection.
method Formulated Bayesian autoencoders to quantify epistemic and aleatoric anomalies.
result Demonstrated effectiveness of BAEs on benchmark and real datasets.
Improved fraud detection in finance with quantum-enhanced federated learning.
problem Challenges in detecting financial fraud with traditional methods.
method Hybrid quantum-enhanced federated learning framework combining quantum LSTM with privacy-preserving techniques.
result Approximately 5% improvement in performance metrics compared to conventional models.
T2IVAE detects anomalies in time series data with high accuracy.
problem Detecting anomalies in noisy, complex time series data.
method Unsupervised model based on NVAE and GANs, transforming 1D to 2D images.
result T2IVAE outperforms other models on various datasets.
A novel semi-supervised outlier detection model detects anomalies with few labels.
problem Efficiently detecting group anomalies with limited labeled data.
method RCC-Dual-GAN model that combines RCC and M-GAN components for semi-supervised outlier detection.
result Significantly improved accuracy in outlier detection with few labeled anomalies.