Identification Error Rate Research Articles

Collaborative Robot Precision Task in Medical Microbiology Laboratory.

This study focuses on the feasibility of collaborative robot implementation in a medical microbiology laboratory by demonstrating fine tasks using kinesthetic teaching. Fine tasks require sub-millimetre positioning accuracy. Bacterial colony picking and identification was used as a case study. Colonies were picked from Petri dishes and identified using matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry. We picked and identified 56 colonies (36 colonies of Gram-negative Acinetobacter baumannii and 20 colonies of Gram-positive Staphylococcus epidermidis). The overall identification error rate was around 11%, although it was significantly lower for Gram-positive bacteria (5%) than Gram-negative bacteria (13.9%). Based on the identification scores, it was concluded that the system works similarly well as a manual operator. It was determined that tasks were successfully demonstrated using kinesthetic teaching and generalized using dynamic movement primitives (DMP). Further improvement of the identification error rate is possible by choosing a different deposited sample treatment method (e.g., semi-extraction, wet deposition).

Implementation of a Noise-Shaped Signaling System through Software-Defined Radio

Along with the development of electromagnetic weapons, Electronic Warfare (EW) has been rising as the future form of war. Especially in the area of wireless communications, high security defense systems such as Low Probability of Detection (LPD), Low Probability of Interception (LPI), and Low Probability of Exploitation (LPE) communication algorithms are being studied to prevent military force loss. One LPD, LPI, and LPE communication algorithm, physical-layer security, has been discussed and studied. We propose a noise signaling system, a type of physical-layer security, which modifies conventionally modulated I/Q data into a noise-like shape. To suggest the possibility of realistic implementation, we use Software-Defined Radio (SDR). Since there are certain hardware limitations, we present the limitations, requirements, and preferences of practical implementation of the noise signaling system. The proposed system uses ring-shaped signaling, and we present a ring-shaped signaling system algorithm, SDR implementation methodology, and performance evaluations of the system using the metrics of Bit Error Rate (BER) and Probability of Modulation Identification (PMI), which we obtain by using a Convolutional Neural Network (CNN) algorithm. We conclude that the ring-shaped signaling system can perform high LPI/LPE communication functioning because an eavesdropper cannot obtain the correct modulation scheme information. However, the performance can vary with the configurations of the I/Q data-modifying factors.

A multi-terminal pushing method for emergency information in a smart city based on deep learning

In order to overcome the problems of large information identification error and high error rate in traditional emergency information push methods, a multi-terminal push method of emergency information in a smart city based on deep learning is proposed. The convolution neural network based on deep learning is used to identify the real-time collection results of intelligent city operation monitoring information, and the combination of softmax loss function and centre loss function is used to reduce the identification error. Through the process of retrieval, adaptation and detection of crisis cases, crisis types are determined and emergency decision-making is selected. Based on XMPP protocol, a multi-terminal push network of emergency information is constructed to push monitoring information and emergency decision to each push terminal. The experimental results show that the information recognition rate of the proposed method is higher than 99%, and the emergency information push result has high precision.

Reinforced MCTS for non-intrusive online load identification based on cognitive green computing in smart grid.

Cognitive green computing (CGC) is widely used in the Internet of Things (IoT) for the smart city. As the power system of the smart city, the smart grid has benefited from CGC, which can achieve the dynamic regulation of the electric energy and resource integration optimization. However, it is still challenging for improving the identification accuracy and the performance of the load model in the smart grid. In this paper, we present a novel algorithm framework based on reinforcement learning (RL) to improve the performance of non-invasive load monitoring and identification (NILMI). In this model, a knowledge base of load power facilities (LPF-KB) architecture is designed to facilitate the load data-shared collection and storage; utilizing deep convolutional neural networks (DNNs) structure based on the attentional mechanism to enhance the representations learning of load features; using RL-based Monte-Carlo tree search (MCTS) method to construct an optimal strategy network, and to realize the online combined load prediction without relying on the prior knowledge. We use the massive experiment on the real-world datasets of household appliances to evaluate the performance of our method. The experimental results show that our approach has remarkable performance in reducing the load online identification error rate. Our model is a generic model, and it can be widely used in practical load monitoring identification and the power prediction system.

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning.

Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In this paper, we design, implement, and evaluate the performance of machine-learning-based NIDS in IoT networks. Specifically, we study six supervised learning methods that belong to three different classes: (1) ensemble methods, (2) neural network methods, and (3) kernel methods. To evaluate the developed NIDSs, we use the distilled-Kitsune-2018 and NSL-KDD datasets, both consisting of a contemporary real-world IoT network traffic subjected to different network attacks. Standard performance evaluation metrics from the machine-learning literature are used to evaluate the identification accuracy, error rates, and inference speed. Our empirical analysis indicates that ensemble methods provide better accuracy and lower error rates compared with neural network and kernel methods. On the other hand, neural network methods provide the highest inference speed which proves their suitability for high-bandwidth networks. We also provide a comparison with state-of-the-art solutions and show that our best results are better than any prior art by 1~20%.

Factor Graphs for Support Identification in Compressive Sensing Aided Wireless Sensor Networks

A new support identification technique based on factor graphs and belief propagation is proposed for compressive sensing (CS) aided wireless sensor networks (WSNs), which reliably estimates the locations of non-zero entries in a sparse signal through an iterative process. Our factor graph based approach achieves a support identification error rate of 10% at an signal to noise ratio (SNR) that is 6 dB lower than that required by the state-of-the-art relative frequency based support identification approach, as well as by the orthogonal matching pursuit (OMP) algorithm. We also demonstrate that our support identification technique is capable of mitigating the signal reconstruction noise by as much as 4 dB upon pruning the sparse sensing matrix. Furthermore, by intrinsically amalgamating the relative frequency based and the proposed factor graph based approach, we conceived a hybrid support identification technique for reducing communication between the sensor nodes and the fusion center (FC), while maintaining high-accuracy support identification and simultaneously mitigating the noise contaminating signal reconstruction.

A Synthesis of Juvenile Coastal Cutthroat Trout Emigration Studies: Considerations for Researchers and Resource Managers

AbstractFor the management of Coastal Cutthroat Trout Oncorhynchus clarkii clarkii, resource managers require abundance estimates and demographic structure of juvenile emigrants. However, Coastal Cutthroat Trout research and monitoring have been confounded by the variability in assessment approaches and ecological plasticity of the subspecies. The primary purpose of this study is to help address this current Coastal Cutthroat Trout fisheries management issue by conducting a synthesis of juvenile emigration studies and proposing solutions that should be considered by resource managers when designing juvenile emigration studies. A total of 100 studies conducted in British Columbia and Washington since the 1970s, representing 1,100 annual estimates, were reviewed. The results of this review highlighted inconsistencies in study timing, biological data collection, juvenile emigrant life stage assignments, species identification, and ecotype assumptions. Solutions to address these inconsistencies include increased recognition of juvenile emigrant studies targeting Coastal Cutthroat Trout, strengthened partnerships and data sharing between management agencies, implementation of a life stage assignment standard for juvenile emigration studies, implementation of a field‐based and genetic classification model to quantify study‐specific identification error rates of juvenile emigrants, and genetic research to determine ecotype expression at the population and individual scales. We highlight the practical issues related to juvenile emigration studies and provide recommendations for management actions to account for the great plasticity in Coastal Cutthroat Trout behavior across the subspecies’ range.

A neighborhood median weighted fuzzy c-means method for soil pore identification

Complex soil pore geometry and heterogeneity determines the ability of a soil to retain moisture and conduction. These soil properties are widely recognized as key factors of essential ecological functions and services. However, until recently, the existing pore identification methods have the problems of low identification accuracy and operating efficiency, which has restricted the development of soil science. The objective of this study was to propose a neighborhood median weighted fuzzy c-means method based on grayscale-gradient feature (NMFCM-G) to identify soil pore structure automatically and accurately. By combining three-dimensional (3D) printing technology with X-ray computed tomography technology, a 3D simulation model with known aperture (10-cm inner diameter) was adopted to evaluate the pore identification error rate of the NMFCM-G method quantitatively. Compared to the methods commonly applied in previous studies, the NMFCM-G method had the smallest average area relative error (2.98%), which was only 1/6 of that of the Image J method with the largest area relative error (18.46%). The NMFCM-G method also had the smallest average perimeter (5.46%), about 3/5 of that of the Image-Pro Plus method with the largest perimeter relative error (8.35%). Meanwhile, the NMFCM-G method was successfully tested on undisturbed cylindrical soil samples, providing encouraging results in terms of identifying irregular pore structure from the complex hierarchical organization of soil. The results show that the NMFCM-G method had the smallest distribution entropy (0.81), the smallest inter-class correlation (0.164 0), and the largest distribution coefficient (0.11), which proves that the NMFCM-G method performed the best in identifying different soil pore structures. Overall, the NMFCM-G method provides new insights into identifying pore structures and thus could provide an automatic and high-efficiency technique for studying the effects of tillage and freeze-thaw cycles on pore structure and soil quality in the future.

Evaluating the Language ENvironment Analysis System for Korean.

Purpose The algorithm of the Language ENvironment Analysis (LENA) system for calculating language environment measures was trained on American English; thus, its validity with other languages cannot be assumed. This article evaluates the accuracy of the LENA system applied to Korean. Method We sampled sixty 5-min recording clips involving 38 key children aged 7-18 months from a larger data set. We establish the identification error rate, precision, and recall of LENA classification compared to human coders. We then examine the correlation between standard LENA measures of adult word count, child vocalization count, and conversational turn count and human counts of the same measures. Results Our identification error rate (64% or 67%), including false alarm, confusion, and misses, was similar to the rate found in Cristia, Lavechin, et al. (2020). The correlation between LENA and human counts for adult word count (r = .78 or .79) was similar to that found in the other studies, but the same measure for child vocalization count (r = .34-.47) was lower than the value in Cristia, Lavechin, et al., though it fell within ranges found in other non-European languages. The correlation between LENA and human conversational turn count was not high (r = .36-.47), similar to the findings in other studies. Conclusions LENA technology is similarly reliable for Korean language environments as it is for other non-English language environments. Factors affecting the accuracy of diarization include speakers' pitch, duration of utterances, age, and the presence of noise and electronic sounds.

Extraction Method of Microscopic Image Feature of Cotton Fiber Cross Section

The micro-section image analysis is an effective method to measure cotton fiber maturity directly. However, the accuracies of the existing image segmentation and contour extraction algorithms were limited by the extraction of cotton fiber cross-section features. Therefore, the paper first binarized the cotton fiber cross-section micro image with the image processing software programmed by VC++, and then extracted the contours of its outer layer, inner layer and cavity, and finally calculated its geometric features. Based on statistics of these geometric features, a judgment model was established to remove the low quality cross sections such as pseudo-cross sections and separate adhesion cross sections, so as to improve the geometric feature extraction effect and the maturity estimation accuracy of cotton fiber cell. The results showed that, compared with the artificial subjective judgment, the cotton fiber cross-section feature extraction method proposed in this paper could not only solve the problem of unable to determine the geometric characteristics caused by cotton fiber adhesion and other interference factors, but also reduce the identification error rate from 0.073 to 0.038, so as to better judge the quality of cotton fiber at harvest time, and infer the possible influencing factors.

Learning niche features to improve image-based species identification

Species identification is a critical task of ecological research. Having accurate and intelligent methods of species identification would improve our ability to study and conserve biodiversity with saving much time and effort. But image-based deep learning methods have rarely taken account of domain knowledge, and perform poorly on imbalanced training dataset and similar species. Here, we propose NicheNet which combines ecological niche model and image-based deep learning model together expanding from the joint model framework invented by previous research. We incorporate an optimization process for NicheNet, and examine its performance on identifying Chinese Galliformes comparing with image-only model and Geo-prior-based model. For assessing the ability of both NicheNet and image-only models on distinguishing similar species, we initiate a new criterion named Near Error Rate in this study which decomposes identification error rate on each species along its classification. We show that the joint models gain significantly improvement on identifying our study species compared with image-only model. Against on an image-only baseline 82.5%, we observe 7.73% improvement in top-1 accuracy for NicheNet, and a 6.21% increase for Geo-prior-based model. NicheNet gains a 15.3% increment of average F1-Score and gets a − 8.5% mean decrement in Near Species Error Rate while −4.4% in Near Genus Error Rate. Further cases analysis shows the background mechanism of NicheNet to improve image-only models. We demonstrate that NicheNet can learn the features from images and niche-prior, which is generated by niche model and finer than geo-prior by Geo-prior-based model, together to promote its ability on identifying species, especially on those with small training dataset and similar outlook. It is a flexible model framework, and we can introduce more biological features and domain models to strengthen its accuracy and robust in the future work. Our study shows that NicheNet can be widely adopted and will accurately accelerate automatic species identification task for biodiversity research and conservation.

Low-complexity and highly robust barcodes for error-rich single molecular sequencing.

DNA barcodes are frequently corrupted due to insertion, deletion, and substitution errors during DNA synthesis, amplification and sequencing, resulting in index hopping. In this paper, we propose a new DNA barcode construction scheme that combines a cyclic block code with a predetermined pseudo-random sequence bit by bit to form bit pairs, and then converts the bit pairs to bases, i.e., the DNA barcodes. Then, we present a barcode identification scheme for noisy sequencing reads, which uses a combination of cyclic shifting and traditional dynamic programming to mark the insertion and deletion positions, and then performs erasure-and-error-correction decoding on the corrupted codewords. Furthermore, we verify the identification error rate of barcodes for multiple errors and evaluate the reliability of the barcodes in DNA context. This method can be easily generalized for constructing long barcodes, which may be used in scenarios with serious errors. Simulation results show that the bit error rate after identifying insertions/deletions is greatly reduced using the combination of cyclic shift and dynamic programming compared to using dynamic programming only. It indicates that the proposed method can effectively improve the accuracy for estimating insertion/deletion errors. And the overall identification error rate of the proposed method is lower than when the probability of each base mutation is less than 0.1, which is the typical scenario in third-generation sequencing.

Dual-rotor misalignment fault quantitative identification based on DBN and improved D-S evidence theory

Misalignment fault is the main factor that affects the normal running of dual-rotor system. Quantitative identification the misalignment fault is an important way to ensure the safe and stable service of the dual-rotor system, while the identification accuracy of traditional methods is low. Aiming at the above problems, this paper proposed a dual-rotor misalignment fault quantitative identification method based on DBN and D-S evidence theory improved by mutual information measure (MIMD-S). Seven groups experiments were conducted and several vibration signals were collected. By comparing it with the traditional methods D-S, and Pignistic improved D-S (PD-S) evidence theory, the results show that the method proposed in this paper improves the accuracy of the misalignment fault quantitative identification of the dual-rotor, the identification error rate was only 0.36%.

A congestion attack behaviour recognition method for wireless sensor networks based on a decision tree

To overcome the problems of low attack recognition rates and high congestion identification errors in traditional congestion attack behaviour judgement methods for wireless sensor networks (WSNs), a new congestion attack behaviour judgement method based on decision trees is proposed in this paper. First, the traffic is processed, a congestion attack detection model is constructed as a decision tree, and the sensor nodes with artificial labels are classified and evaluated. According to the classification results, the network congestion attack behaviour is identified according to an attack recognition threshold. The experimental results show that the average recognition rate of congestion attacks is 99.82%, the error rate of congestion identification is only 0.00284, and the packet loss rate is low, indicating that this method can effectively recognise network congestion attack behaviours.

An Indexing Algorithm Based on Clustering of Minutia Cylinder Codes for Fast Latent Fingerprint Identification

Latent fingerprint identification is one of the leading forensic activities to clarify criminal acts. However, its computational cost hinders the rapid decision making in the identification of an individual when large databases are involved. To reduce the search time used to generate the fingerprint candidates' order to be compared, fingerprint indexing algorithms that reduce the search space while minimizing the increase in the error rate (compared to the identification) are developed. In the present research, we propose an algorithm for indexing latent fingerprints based on minutia cylinder codes (MCC). This type of minutiae descriptor presents a fixed structure, which brings advantages in terms of efficiency. Besides, in recent studies, this descriptor has shown an identification error rate, at the local level, lower than the other descriptors reported in the literature. Our indexing proposal requires an initial step to construct the indices, in which it uses k-means++ clustering algorithm to create groups of similar minutia cylinder codes corresponding to the impressions of a set of databases. K-means++ allows for a better outcome over other clustering algorithms because of the selection of the proper centroids. The buckets associated with each index are populated with the background databases. Then, given a latent fingerprint, the algorithm extracts the minutia cylinder codes associated with the clusters' indices with the lowest distance respect to each descriptor of this latent fingerprint. Finally, it integrates the votes represented by the fingerprints obtained to select the candidate impressions. We conduct a set of experiments in which our proposal outperforms current rival algorithms in presence of different databases and descriptors. Also, the primary experiment reduces the search space by four orders of magnitude when the background database contains more than one million impressions.

Evaluation of the Autof MS1000 mass spectrometer in the identification of clinical isolates

BackgroundTo evaluate the accuracy and performance of the Autof MS1000 mass spectrometer in bacteria and yeast identification, 2342 isolates were obtained from microbial cultures of clinical specimens (e.g. blood, cerebrospinal fluid, respiratory tract samples, lumbar puncture fluid, wound samples, stool, and urine) collected in 2019 in Henan Provincial People’s Hospital. Repetitive strains from the same patient were excluded. We tested the Autof MS1000 and Bruker Biotyper mass spectrometry systems and the classical biochemical identification system VITEK 2/API 20C AUX. Inconsistencies in strain identification among the three systems were identified by 16S rDNA and gene sequencing.ResultsAt the species level, the Autof MS1000 and Bruker Biotyper systems had isolate identification accuracies of 98.9 and 98.5%, respectively. At the genus level, the Autof MS1000 and Bruker Biotyper systems were 99.7 and 99.4% accurate, respectively. The instruments did not significantly differ in identification accuracy at either taxonomic level. The frequencies of unreliable identification were 1.1% (26/2342) for the Autof MS1000 and 1.5% (34/2342) for the Bruker Biotyper. In vitro experiments demonstrated that the coincidence rate of the Autof MS1000 mass spectrometer in the identification of five types of bacteria was > 93%, the identification error rate was < 3%, and the no identification rate was 0. This indicates that the Autof MS1000 system is acceptable for identification.ConclusionsThe Autof MS1000 mass spectrometer can be utilised to identify clinical isolates. However, an upgradation of the database is recommended to correctly identify rare strains.

Image Normalization and Weighted Classification Using an Efficient Approach for SVM Classifiers

The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.

Vehicle re-identification based on unsupervised local area detection and view discrimination

Vehicle re-identification is an important part of intelligent transportation. Although much work has been done on this subject in recent years, vehicle re-identification is still a challenging task due to its obvious illumination change, high similarity between inter-class and great changes under different views. As discriminatory local areas and vehicle view information is the key to improving the above issues, it is desirable to create a model which considers both local details and cross-view situation. In this paper, we built a vehicle re-identification framework based on unsupervised local area detection and view discrimination. First, we combine the convolution features of multiple vehicle images by channel to generate joint channel features, and constructs a discriminating region detector unsupervised by clustering the channel covariance vectors generated between the joint channel features. In the next stage, the irregular shape detection results are converted to rectangular discriminative region by using a novel local region integration algorithm. These rectangular discriminative regions are fed into a multi-branch network to extract the local–global features which contains rich detail information. Furthermore, we generate the view features by regarding the detected discriminative areas as keypoints and construct the unsupervised view discriminator. By using the view information of the vehicle, we designed a view-discrimination based reranking algorithm, which effectively reduces the error rate of identification due to view variant. In order to prove the validity of our method, we have done extensive experiments on VehicleID and VERI-Wild dataset. Experimental results show that our method is superior to other existing vehicle re-identification algorithms.

Evaluating Likelihood Ratio (LR) for firearm evidence identifications in forensic science based on the Congruent Matching Cells (CMC) method

Firearm evidence identification has been challenged by the 2008 and 2009 National Research Council (NRC) reports and by legal proceedings on its fundamental assumptions, its procedure involving subjective interpretations, and the lack of a statistical foundation for evaluation of error rates or other measures for the weight of evidence. To address these challenges, researchers of the National Institute of Standards and Technology (NIST) recently developed a Congruent Matching Cells (CMC) method for automatic and objective firearm evidence identification and quantitative error rate evaluation. Based on the CMC method, a likelihood ratio (LR) procedure is proposed in this paper aiming to provide a scientific basis for firearm evidence identification and a method for evaluation of the weight of evidence. The initial LR evaluations using two sets of 9mm cartridge cases’ breech face impression images with different sample sizes, imaging methods and ammunition showed that for all the declared identifications of the tested 2D and 3D image pairs, the evaluated LRs for the least favorable scenario were well above an order of 106, which provides Extremely Strong Support for a prosecution proposition (e.g. a same-source proposition) in a Bayesian frame. The LR evaluations also showed that for all the declared exclusions of the tested 3D image pairs, the evaluated LRs for the least favorable scenario were above an order of 102, which provides Moderately Strong Support for a defense proposition (e.g. a different-source proposition) in a Bayesian frame.

Efficacy of smartphone-based Mobile learning versus lecture-based learning for instruction of Cephalometric landmark identification

BackgroundConsidering the increasing popularity of electronic learning, particularly smartphone-based mobile learning, and its reportedly optimal efficacy for instruction of complicated topics, this study aimed to compare the efficacy of smartphone-based mobile learning versus lecture-based learning for instruction of cephalometric landmark identification.MethodsThis quasi-experimental interventional study evaluated 53 dental students (4th year) in two groups of intervention (n = 27; smartphone instruction using an application) and control (n = 26, traditional lecture-based instruction). Two weeks after the instructions, dental students were asked to identify four landmarks namely the posterior nasal spine (PNS), orbitale (Or), articulare (Ar) and gonion (Go) on lateral cephalograms. The mean coordinates of each landmark identified by orthodontists served as the reference point, and the mean distance from each identified point to the reference point was reported as the mean consistency while the standard deviation of this mean was reported as the precision of measurement. Data were analyzed using SPSS version 18 via independent sample t-test.ResultsNo significant difference was noted between the two groups in identification of PNS, Ar or Go (P > 0.05). However, the mean error rate in identification of Or was significantly lower in smartphone group compared with the traditional learning group (P = 0.020).ConclusionsSmartphone-based mobile learning had a comparable, and even slightly superior, efficacy to lecture-based learning for instruction of cephalometric landmark identification, and may be considered, at least as an adjunct, to enhance the instruction of complicated topics.Trial registration numberThis is not a human subject research. https://ethics.research.ac.ir/ProposalCertificateEn.php?id=33714&Print=true&NoPrintHeader=true&NoPrintFooter=true&NoPrintPageBorder=true&LetterPrint=true.