False Detection Rate Research Articles

Detecting temporal lobe seizures in ultra long-term subcutaneous EEG using algorithm-based data reduction

ObjectiveUltra long-term monitoring with subcutaneous EEG (sqEEG) offers objective outpatient recording of electrographic seizures as an alternative to self-reported epileptic seizure diaries. This methodology requires an algorithm-based automatic seizure detection to indicate periods of potential seizure activity to reduce the time spent on visual review. The objective of this study was to evaluate the performance of a sqEEG-based automatic seizure detection algorithm. MethodsA multicenter cohort of subjects using sqEEG were analyzed, including nine people with epilepsy (PWE) and 12 healthy subjects, recording a total of 965 days. The automatic seizure detections of a deep-neural-network algorithm were compared to annotations from three human experts. ResultsData reduction ratios were 99.6% in PWE and 99.9% in the control group. The cross-PWE sensitivity was 86% (median 80%, range 69–100% when PWE were evaluated individually), and the corresponding median false detection rate was 2.4 detections per 24 hours (range: 2.0–13.0). ConclusionsOur findings demonstrated that step one in a sqEEG-based semi-automatic seizure detection/review process can be performed with high sensitivity and clinically applicable specificity. SignificanceUltra long-term sqEEG bears the potential of improving objective seizure quantification.

Detection of microplastics based on spatial heterodyne Raman spectroscopy

As a new type of pollutant, microplastics have attracted extensive attention due to its characteristics such as small size and degradation difficulty under natural circumstance. Though there are many detection methods nowadays, there are still some limitations like long detection time, high false detection rate and expensive detection equipment, which make the detection of microplastics exists in natural environment hard to carry out. To improve the environmental conditions, rapid, efficient, and accurate detection methods for microplastics are needed urgently. In this paper, a method combined the Spatial heterodyne technology and Raman spectroscopy was proposed to detect the microplastics. A spatial heterodyne spectrometer (SHS) was built, and the algorithms used to process the spectrum data were introduced. An optical system used to collect the Raman signal of the sample was built and connected with the SHS. Four different kinds of microplastic samples were detected by the complete system and identification can be achieved according to the Raman spectrum. The samples were also detected by using a normal spectrometer for comparison. A comparison of Raman spectrum using conventional dispersion spectrometer was carried out and the result shows that SHS is of higher signal-to-noise ratio. The results indicate that SHRS technology has good performance and development prospects in the field of microplastics detection.

Combination of continuous wavelet transform and genetic algorithm-based Otsu for efficient mass spectrometry peak detection

Mass spectrometry (MS) data is susceptible to random noises and alternating baseline, posing great challenges to spectral peak detection, especially for weak peaks and overlapping peaks. Herein, an efficient peak detection algorithm combining continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (denoted as WSTGA) for mass spectrometry was proposed. Firstly, Mexican Hat wavelet was selected as the mother wavelet by comparing the matching degree between the difference of Gaussian (DOG) and different wavelets. Subsequently, the ridges and valleys were identified from 2D wavelet coefficient matrix. Afterward, an improved threshold segmentation method, Otsu method based on genetic algorithm, was introduced to find optimal segmentation threshold and achieve better image segmentation, overcoming the deficiency of traditional Otsu method that cannot handle long-tailed unimodal histograms. Finally, the characteristic peaks were successfully identified by utilizing the ridge-valley lines in wavelet space and original spectrum. Receiver operating characteristic (ROC) curve, area under curve (AUC) and F₁ measure are used as criterions to evaluate performance of peak detection algorithms. Compared with multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS) methods, all the results showed that WSTGA can achieve better peak detection. More importantly, the experimental results from MALDI-TOF spectra demonstrated that WSTGA can effectively detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate than MSPD and CWT-IS methods, indicating its great advantages in characteristic peak identification.

Performance of a Rasch-based method for group comparisons of longitudinal change and response shift at the item level in PRO data: A simulation study.

The growing interest in patient perception and experience in healthcare has led to an increase in the use of patient-reported outcomes (PRO) data. However, chronically ill patients may regularly adapt to their disease and, as a consequence, might change their perception of the PRO being measured. This phenomenon named response shift (RS) may occur differently depending on clinical and individual characteristics. The RespOnse Shift ALgorithm at the Item level (ROSALI), a method for RS analysis at the item level based on Rasch models, has recently been extended to explore heterogeneity of item-level RS between two groups of patients. The performances of ROSALI in terms of RS detection at the item level and biases of estimated differences in latent variable means were assessed. A simulation study was performed to investigate four scenarios: no RS, RS in only one group, RS affecting both groups either in a similar or a different way. Performances of ROSALI were assessed using rates of false detection of RS when no RS was simulated and a set of criteria (presence of RS, correct identification of items and groups affected by RS) when RS was simulated. Rates of false detection of RS were low indicating that ROSALI satisfactorily prevents from mistakenly inferring RS. ROSALI is able to detect RS and identify the item and group(s) affected when RS affects all response categories of an item in the same way. The performances of ROSALI depend mainly on the sample size and the degree of heterogeneity of item-level RS.

Enhanced Spectrum Handoff Security Mechanism in Cognitive Radio Network

Abstract: In this work we introduce a novel cognitive user emulation attack (CUEA) in a cognitive radio network (CRN), which can be exploited by intruders during spectrum handoff. Then, we propose a secure handoff mechanism that can successfully counter such an attack by introducing a coordinating cognitive user that computes the level of trust of each cognitive user based on its behavioral characteristics. Malicious users can be effectively identified by the coordinating cognitive user by looking up the trust values. The performance of the proposed mechanism is validated using MATLAB simulations. The simulation results show that the utility of the proposed mechanism in terms of its probability in correctly identifying false authentication, detection rate, throughput rate, and transmission delay.

Effects of elevated serum urate on cardiometabolic and kidney function markers in a randomised clinical trial of inosine supplementation

In observational studies, serum urate positively associates with cardiometabolic and kidney diseases. We analyzed data from a randomised placebo-controlled trial to determine whether moderate hyperuricemia induced by inosine affects cardiometabolic and kidney function markers. One hundred and twenty post-menopausal women were recruited into a 6-month randomised, double-blind, placebo-controlled trial of inosine for bone health. Change from baseline in the following pre-specified endpoints was analyzed: body mass index; blood pressure; lipid profile; C-reactive protein; fasting glucose; insulin; HbA1c; serum creatinine; and estimated glomerular filtration rate (eGFR). Despite increases in serum urate levels (+ 0.17 mmol/L at week 6, P < 0.0001), no significant between-group differences were observed in cardiometabolic markers, with the exception of lower fasting glucose concentrations with inosine at week 19. In the inosine group, change in serum urate correlated with change in serum creatinine (r = 0.41, P = 0.0012). However, there was no between-group difference in serum creatinine values. Over the entire study period, there was no significant difference in eGFR (ANCOVA P = 0.13). Reduction in eGFR was greater in the inosine group at Week 13 (mean difference − 4.6 mL/min/1.73 m2, false detection rate P = 0.025), with no between-group difference in eGFR at other time points. These data indicate that increased serum urate does not negatively influence body mass index, blood pressure, lipid profile, or glycaemic control. Serum urate changes associated with inosine intake correlate with changes in serum creatinine, but this does not lead to clinically important reduction in kidney function over 6 months.Clinical trial registration number: Australia and New Zealand Clinical Trials Registry (ACTRN12617000940370), registered 30/06/2017.

Variable Selection in High Dimensional Data with Interactions

A common research area in statistical machine learning has been variable selection in high dimensional settings. In recent years, numerous effective approaches have been created to deal with these challenges. In order to improve the prediction accuracy of the model for the given dataset, this study sought to present a double approach variable selection method when pairwise interactions between the explanatory variables exist and to choose the smallest explanatory variable set (considering interactions among them). In this study, a double step method consolidating Random Forest and Adaptive Elastic Net was further examined to mimic potential health effects of environmental contamination. When there were existing interactions in the data or none at all, the double step approach was compared to the single-step adaptive elastic net method and two-step CART paired with the adaptive elastic net method. Using significant statistical tests like RMSE, R2 , and the quantity of the variable chosen for the final model, the success of the strategies was measured. The double step RF+AENET approach produces a simple, constrained model. Despite the complex association between exposure variables, it has the lowest false detection rate for null interactions. A set of variables that have correlation with the result are effectively retained by the screening and variable reduction processes in the RF step of the RF+AENET approach. The double step RF+AENET performs prediction better than a single technique and chooses a sparse model that is close to the true model. Thus, it can be said that when there are pairwise interactions between variables in the simulated biological dataset, the double step technique is a better method for model prediction and parameter estimation. Keywords: Adaptive Elastic Net, Random Forest, Variable Selection, CART.

Data Mining Technology for Agricultural Equipment Machinery and Information Network Data Resources

In order to realize the automation and intelligence of agricultural equipment mechanical state detection and improve the efficiency and accuracy of agricultural machinery equipment detection technology, this paper proposes an intelligent agricultural machinery equipment state detection method based on computer data mining. This method uses support vector machine to classify the equipment status and uses ant colony algorithm to solve the optimization problem. Its basic logic is to collect the status data of agricultural machinery equipment, extract the status detection characteristics of agricultural machinery equipment, and use computer data mining to establish an automatic state detection model of agricultural machinery equipment. The application results show that the correct rate of the state detection of agricultural machinery equipment is more than 95%, and the false detection rate of the state of agricultural machinery equipment is far lower than that of other current state detection methods of agricultural machinery equipment. Conclusion. This method can realize high-precision real-time detection of agricultural machinery equipment and has higher practical value.

ARCMT: Anchor node-based range free cooperative multi trusted secure underwater localization using fuzzifier

Secure and precise node localization is a critical challenge for the underwater acoustic sensor network. The sensor node distribution in the aquatic environment is the most vulnerable to many external attacks, interrupting the nodes to find the precise location information and causing complete network failure due to excessive, unnecessary communication and power consumption. This paper proposes an anchor nodes-based range-free cooperative multi-trust management system using fuzzy logic to provide a secure and reliable underwater localization. The multi-trust-based scheme protects the sensor nodes from untrusted or malicious nodes, and the lightweight Mamdani fuzzy model is used to reduce circuit complexity and memory consumption. The proposed scheme does not require the cluster head to perform trust-related tasks, which reduces the heavy overburden on the cluster heads with their regular task and energy budget to enhance their lifetime. Therefore, the proposed method uses Anchor node-based joint trust calculation and localization of unlocalized cluster members, providing a better reliable trust evaluation and better accuracy and stability in terms of the localization error. The simulation report shows that the proposed scheme improves the average accuracy by 10.77%, average stability by 23.7%, average malicious node detection rate by 42.4%, and average false detection rate by 38.25% compared to other single-cluster head-based and traditional energy trust-based systems.

Optimization of patient-based real-time quality control based on the Youden index

AimThis study sets out to investigate the utility of exponentially weighted moving average (EWMA) as patient-based real-time quality control (PBRTQC) by conducting a simulation study and subsequent real-patient data implementation to determine optimal EWMA features (weighting factors, control limits, and truncation methods) based on the Youden index. MethodsA simulation experiment was conducted in the first stage to investigate optimal EWMA features for the tests, including aspartate aminotransferase, blood urea nitrogen, and glucose, calcium, creatinine, potassium, sodium, triglycerides, thyroid - stimulating hormone (TSH), and vitamin B12 tests. In the second stage of the study, EWMA was applied to real patient data to elucidate practical utility and achieve final optimal EWMA features. Different degrees of systematic errors (SE) including total allowable error (TEa) as a maximum error level were added to both simulation and patient results, and then the EWMA performance was assessed for different EWMA features. We calculated Youden’s index for each combination of EWMA features to find their optimal features to achieve minimum false positive rate (FPR) and maximum error detection rate at the SE level corresponding to TEa. ResultsEWMA implementation on real patient data revealed optimal EWMA features for each test. FPR values of creatinine and glucose were 18.48% and 10.17%, respectively, which exceeded the acceptable criteria for FPR (10%). The remaining six analytes showed acceptable FPR. ConclusionsWe showed the implementation of EWMA as PBRTQC, and optimization of its features based on the Youden index by conducting extensive performance evaluations and simulations in this study.

Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects

In order to solve the problem that the traditional detection technology can not meet the requirements of online detection, a visual detection device for bearing inner ring defects based on image processing and pattern recognition is proposed in this paper. The device systematically designs an image acquisition device of bearing inner ring based on CCD. In the hardware scheme, the appropriate lens, camera, light source, and other related hardware are selected according to the actual needs, a complete image acquisition platform of bearing inner ring is built, and the software function design is completed. The simulation results show that the qualified rate of machine detection is 98.6%, the missed detection rate is 0, and the false detection rate is 1.4%, which are better than manual detection. Conclusion. The test results show that the bearing inner ring defect detection system can detect the surface defects of bearing inner ring quickly, stably, and reliably, and the detection efficiency and accuracy are higher than the original manual detection method, so it has a good application prospect.

The Detection Scheme Against Selective Forwarding of Smart Malicious Nodes With Reinforcement Learning in Wireless Sensor Networks

Wireless sensor networks (WSNs) are extremely vulnerable to different attacks because of open communication, and distribution in unattended areas. The selective forwarding attack is one of the most difficult inside attacks to be detected for two reasons. The node in a harsh environment has to drop some data packets, and the smart malicious node frequently eludes detection. In this paper, we model a selective forwarding attack of smart malicious nodes with a reinforcement learning (RL) algorithm. To effectively detect the selective forwarding attack under a harsh environment, we design the double-threshold density peaks clustering (DT-DPC) algorithm. Abnormal nodes are identified as malicious and isolated owing to continuous abnormalities. Suspicious nodes are determined by the neighbor voting method because malicious behaviors show up separately and a harsh environment universally disturbs agglomerate nodes. Even if smart malicious nodes elude the detection by an RL algorithm, DT-DPC improves the network throughput. The simulation results show that DT-DPC has a low false detection rate (FDR) of around 1% and a missed detection rate (MDR) of around 10%. The network throughput increases by about 4% under a harsh environment.

IDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection

Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence on inputs outside their training distribution. The deployment of DNNs in safety-critical domains requires detection of out-of-distribution (OOD) data so that DNNs can abstain from making predictions on those. A number of methods have been recently developed for OOD detection, but there is still room for improvement. We propose the new method iDECODe, leveraging in-distribution equivariance for conformal OOD detection. It relies on a novel base non-conformity measure and a new aggregation method, used in the inductive conformal anomaly detection framework, thereby guaranteeing a bounded false detection rate. We demonstrate the efficacy of iDECODe by experiments on image and audio datasets, obtaining state-of-the-art results. We also show that iDECODe can detect adversarial examples. Code, pre-trained models, and data are available at https://github.com/ramneetk/iDECODe.

AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision detection smart accident alert and rescue system will help us to minimize delays in a rescue operation that could save many lives. With the growing popularity of smart cities, intelligent transportation systems (ITS) are drawing major interest in academia and business, and are considered as a means to improve road safety in smart cities. This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence system (AI). An IoT kit is developed that detects the accident and collects all accident-related information, such as position, pressure, gravitational force, speed, etc., and sends it to the cloud. In the cloud, once the accident is detected, a deep learning (DL) model is used to validate the output of the IoT module and activate the rescue module. Once the accident is detected by the DL module, all the closest emergency services such as the hospital, police station, mechanics, etc., are notified. Ensemble transfer learning with dynamic weights is used to minimize the false detection rate. Due to the dataset’s unavailability, a personalized dataset is generated from the various videos available on the Internet. The proposed method is validated by a comparative analysis of ResNet and InceptionResnetV2. The experiment results show that InceptionResnetV2 provides a better performance compared to ResNet with training, validation, and a test accuracy of 98%, respectively. To measure the performance of the proposed approach in the real world, it is validated on the toy car.

Information Perception Method for Fruit Trees Based on 2D LiDAR Sensor

To solve the problem of orchard environmental perception, a 2D LiDAR sensor was used to scan fruit trees on both sides of a test platform to obtain their position. Firstly, the two-dimensional iterative closest point (2D-ICP) algorithm was used to obtain the complete point cloud data of fruit trees on both sides. Then, combining the lightning connection algorithm (LAPO) and the density-based clustering algorithm (DBSCAN), a fruit tree detection method based on density-based lightning connection clustering (LAPO-DBSCAN) was proposed. After obtaining the point cloud data of fruit trees on both sides of the test platform using the 2D-ICP algorithm, the LAPO-DBSCAN algorithm was used to obtain the position of fruit trees. The experimental results show that the positive detection rate was 96.69%, the false detection rate was 3.31%, and the average processing time was 1.14 s, verifying the reliability of the algorithm. Therefore, this algorithm can be used to accurately find the position of fruit trees, meaning that it can be applied to orchard navigation in a later stage.

Automated identification and quantification of invisible microplastics in agricultural soils

Microplastics in agricultural soils have become the research hotspot in recent years, however, the quantitative methods based on the traditional visual inspection may have a high false detection rate. Here we combined the laser direct infrared (LDIR) and Fourier–transform infrared (FTIR) methods to investigate the microplastics in farmland with long–term agricultural activities. The results showed that the total abundance of microplastics reached 1.98 ± 0.41 × 105, 1.57 ± 0.28 × 105, 1.78 ± 0.27 × 105, and 3.20 ± 0.41 × 105 particles/kg soil in cotton fields with film mulching of 5, 10, 20, and >30 years, respectively. LDIR results indicated that microplastics ranging from 10 to 500 μm accounted for 96.5–99.9 % of the total microplastic amounts in the soils. Additionally, a total of 26 polymer types of microplastics were detected, among which polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polyamide (PA), and polytetrafluoroethylene (PTFE) were dominantly observed. For the microplastics detected by FTIR (500 μm–5 mm), PE polymer was majorly observed (88.0–98.9 %). Most microplastics were films (88.2 %), while fibers and pellets were also found. The reclaimed water from sewage treatment plants, the drip irrigation utilities, and the residual plastic film are the potential sources of microplastics in the farmland soils. By using the automated quantitative and identifiable approaches, this study suggested that the commonly used visual counting method may underestimate the microplastic contamination in agricultural soils.

Intelligent Cyber Security Framework Based on SC-AJSO Feature Selection and HT-RLSTM Attack Detection

Cyber security is identified as an emerging concern for information technology management in business and society, owing to swift advances in telecommunication and wireless technologies. Cyberspace security has had a tremendous impact on numerous crucial infrastructures. Along with current security status data, historical data should be acquired by the system to implement the latest cyber security defense and protection. It also makes intelligent decisions that can provide adaptive security management and control. An intelligent cyber security framework using Hyperparameter Tuning based on Regularized Long Short-Term Memory (HT-RLSTM) technique was developed in this work to elevate the security level of core system assets. To detect various attacks, the proposed framework was trained and tested on the collection of data. Owing to missing values, poor scaling, imbalanced and overlapped data, the data was primarily incomplete and inconsistent. To elevate the decision making for detecting attacks, the inconsistent or unstructured data issue was addressed. The missing values were handled by this work along with scaling performance using the developed Kernelized Robust Scaler (KRS). Using the developed Random Over Sample-Based Density-Based Spatial Clustering Associated with Noise (ROS-DBSCAN), the imbalanced and overlapped data were handled, which was followed by the relevant feature selection of data utilizing the Sine Cosine-Based Artificial Jellyfish Search Optimization (SC-AJSO) technique. The data were split under the provision of Stratified K-Fold cross-validation along being trained in the proposed HT-RLSTM. The experimental analysis depicted that better accuracy was attained in detecting attacks by the proposed work for different datasets. When analogized with prevailing state-of-the-art methods, a low false detection rate, as well as computation time, was attained by the proposed scheme.

HollowBox: An anchor‐free UAV detection method

Unmanned aerial vehicles (UAV) “black flight” incidents cause serious security risks and economic losses to airports. Additionally, existing UAV detection methods are mainly radar technology at airports. Whereas, it is unable to correctly identify the number of UAVs and visualise their size, which undoubtedly poses a serious security risk. Accordingly, an anchor-free UAV detection method HollowBox is proposed to supplement radar detection equipment for the airport “black flight” problem. It is inspired by the FoveaBox object detection method, the object detection feature layers are reset and the allocation ratio of positive and negative samples in the training phase are redefined, the HollowBox UAV detection is proposed according to the multi-size characteristics. Extensive experiments show that, the approach achieves 90.1% AP, 6% false detection rate and 17.2 FPS inference speed, which accomplished a satisfactory performance, as verified by the real-shot data collected of an airport in Tianjin. This work is of great significance for the application in airport UAV detection.

Robust Continuous Authentication Using Cardiac Biometrics From Wrist-Worn Wearables

Traditional one-time user authentication is vulnerable to attacks when an adversary can obtain unauthorized privileges after a user’s initial login. Continuous user authentication (CA) has recently shown its great potential by enabling seamless user authentication with few users’ participation. We devise a low-cost system that can exploit users’ pulsatile signals from photoplethysmography (PPG) sensors in commodity wearable devices to perform CA. Our system requires zero user effort and applies to practical scenarios that have nonclinical PPG measurements with human motion artifacts (MAs). We explore the uniqueness of the human cardiac system and develop adaptive MA filtering methods to mitigate the impacts of transient and continuous activities from daily life. Furthermore, we identify general fiducial features and develop an adaptive classifier that can authenticate users continuously based on their cardiac characteristics with little additional training effort. Experiments with our wrist-worn PPG sensing platform on 20 participants under practical scenarios demonstrate that our system can achieve a high CA accuracy of over 90% and a low false detection rate of 4% in detecting random attacks. We show that our MA mitigation approaches can improve the CA accuracy by around 39% under both transient and continuous daily activity scenarios.

Power distribution abnormal data identification and processing system based on correlation classification

when the conventional system classifies the power distribution data, the data category is not fine enough, resulting in low accuracy of abnormal data identification, high false detection rate and large correction error. A power distribution abnormal data identification and processing system based on correlation classification is proposed. In terms of hardware, multi-layer framework is adopted, and the overall hardware structure of the system is composed of communication modules, so as to optimize the power distribution data and signal acquisition circuit and suppress signal noise; In terms of software, set the data classification convergence conditions, iteratively increase the number of distribution data clustering centers until the convergence conditions are met, realize the fine correlation classification of data, draw the classified data curve, obtain the normal data feasible region, judge that there are abnormalities in the data beyond the feasible region, and use the vertical comparison method for correction. The results show that the designed system improves the accuracy of abnormal data identification, reduces the false detection rate and correction error, and the accuracy of abnormal data identification and processing is better than that of the conventional system.