False Classification Rate Research Articles

Using autonomous recording units to identify and monitor western yellow‐billed cuckoo habitat

AbstractAutonomous recording units (ARUs) paired with signal classification software can be used to detect species‐specific calls, making them useful for evaluating patterns in avian occurrence and activity. However, classification of target signals is not always reliable and may be especially challenging for species that vocalize infrequently. We assessed the use of ARUs to identify and monitor habitat for a cryptic and federally threatened distinct population segment, the western yellow‐billed cuckoo (Coccyzus americanus) in mountainous xeroriparian drainages. Using Kaleidoscope Pro, we developed a call‐classifier and processed acoustic data collected in sites also surveyed using traditional human‐observer methods, applying the same spatial and temporal detection criteria to estimate breeding territories for each method. The classifier detected a total of 4,061 true positive calls at 4 sites, had an overall precision score of 0.07, recall score of 0.09, and F‐score (beta = 1) of 0.08, indicating high false positive and false negative classification rates. Our results were, however, consistent with other ARU studies of rare and cryptic species and ARUs estimated occupancy as effectively as human surveys with as little as 2 hours of daily recording. Total detections varied among sites, likely due to differences in cuckoo population densities and the interaction between topography and ARU detection space. Our results suggest that despite performance shortcomings of call‐classifiers, ARUs can be effective for monitoring cuckoos, with potential for providing higher resolution temporal and spatial information on activity and habitat use and may be particularly effective in remote locations where cuckoos often occur.

A hyperdimensional framework: Unveiling the interplay of RBP and GSN within CNNs for ultra-precise brain tumor classification

This study presents the RBP-CNN model, a convolutional neural network specifically designed for the precise classification of brain tumors in medical imaging. Conventional methods often encounter difficulties in extracting image noise and texture features, which has led to the incorporation of regional binary patterns (RBP) and Gray Standard Normalization (GSN) preprocessing techniques in CNN. The research addresses fundamental inquiries regarding the impact of the model on accuracy, false classifications, and efficiency. The novelty of RBP-CNN lies in its distinctive approach to extracting texture features, which involves optimizing pixel values through GSN preprocessing and generating regional binary patterns based on integral images. The objective of this research is to bridge a critical gap by providing a more accurate and efficient model for classifying brain tumors. The key findings reveal the exceptional performance of RBP-CNN, achieving a classification accuracy of 96% with a reduced false classification ratio of 7% across a dataset of 3000 samples. Comparative analyses position RBP-CNN as superior to alternative models in terms of accuracy, false classification rates, and efficiency. The structural insights and hyperparameter values of the model, as well as its application to the FigShare dataset, demonstrate its robustness and scalability. RBP-CNN emerges as an innovative and effective solution, advancing the field of medical image categorization. The findings of this study contribute a novel methodology, paving the way for future exploration in hyperspectral image applications and positioning RBP-CNN as a potential state-of-the-art tool for medical image analysis.

Bi-Objective Crop Mapping from Sentinel-2 Images Based on Multiple Deep Learning Networks

Accurate assessment of the extent of crop distribution and mapping different crop types are essential for monitoring and managing modern agriculture. Medium and high spatial resolution remote sensing (RS) for Earth observation and deep learning (DL) constitute one of the most major and effective tools for crop mapping. In this study, we used high-resolution Sentinel-2 imagery from Google Earth Engine (GEE) to map paddy rice and winter wheat in the Bengbu city of Anhui Province, China. We compared the performance of different popular DL backbone networks with the traditional machine learning (ML) methods, including HRNet, MobileNet, Xception, and Swin Transformer, within the improved DeepLabv3+ architecture, Segformer and random forest (RF). The results showed that the Segformer based on the combination of the Transformer architecture encoder and the lightweight multilayer perceptron (MLP) decoder achieved an overall accuracy (OA) value of 91.06%, a mean F1 Score (mF1) value of 89.26% and a mean Intersection over Union (mIoU) value of 80.70%. The Segformer outperformed other DL methods by combining the results of multiple evaluation metrics. Except for Swin Transformer, which was slightly lower than RF in OA, all DL methods significantly outperformed RF methods in accuracy for the main mapping objects, with mIoU improving by about 13.5~26%. The predicted images of paddy rice and winter wheat from the Segformer were characterized by high mapping accuracy, clear field edges, distinct detail features and a low false classification rate. Consequently, DL is an efficient option for fast and accurate mapping of paddy rice and winter wheat based on RS imagery.

Optimal classification and generalized prevalence estimates for diagnostic settings with more than two classes.

An accurate multiclass classification strategy is crucial to interpreting antibody tests. However, traditional methods based on confidence intervals or receiver operating characteristics lack clear extensions to settings with more than two classes. We address this problem by developing a multiclass classification based on probabilistic modeling and optimal decision theory that minimizes the convex combination of false classification rates. The classification process is challenging when the relative fraction of the population in each class, or generalized prevalence, is unknown. Thus, we also develop a method for estimating the generalized prevalence of test data that is independent of classification of the test data. We validate our approach on serological data with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) naïve, previously infected, and vaccinated classes. Synthetic data are used to demonstrate that (i) prevalence estimates are unbiased and converge to true values and (ii) our procedure applies to arbitrary measurement dimensions. In contrast to the binary problem, the multiclass setting offers wide-reaching utility as the most general framework and provides new insight into prevalence estimation best practices.

On optimal biomarker cutoffs accounting for misclassification costs in diagnostic trilemmas with applications to pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is the most deadly cancer and currently there is strong clinical interest in novel biomarkers that contribute to its early detection. Assessing appropriately the accuracy of such biomarkers is a crucial issue and often one needs to take into account that many assays include biospecimens of individuals coming from three groups: healthy, chronic pancreatitis, and PDAC. The ROC surface is an appropriate tool for assessing the overall accuracy of a marker employed under such trichotomous settings. A decision/classification rule is often based on the so-called Youden index and its three-dimensional generalization. However, both the clinical and the statistical literature have not paid the necessary attention to the underlying false classification (FC) rates that are of equal or even greater importance. In this article we provide a framework to make inferences around all classification rates as well as comparisons. We explore the trinormal model, flexible models based on power transformations, and robust non-parametric alternatives. We provide a full framework for the construction of confidence intervals, regions, and spaces for joint inferences or for clinically meaningful points of interest. We further discuss the implications of costs related to different FCs. We evaluate our approaches through extensive simulations and illustrate them using data from a recent PDAC study conducted at the MD Anderson Cancer Center.

Fault diagnosis in mobile computing using TwinSVM

Introduction: Mobile computing systems (MCS) comes up with the challenge of low communication bandwidth and energy due to the mobile nature of the network. These features sometimes may come up with the undesirable behaviour of the system that eventually affects the efficiency of the system.
 Problem: Fault tolerance in MCS will increase the efficiency of the system even in the presence of faults.
 Objective: The main objective of this work is the development of the Monitoring Framework and Fault Detection and Classification.
 Methodology: For the Node Monitoring and for the detection and classification of faults in the system a neighbourhood comparison-based technique has been proposed. The proposed framework uses Twin Support Vector Machine (TWSVM) algorithm has been applied to build classifier for fault classification in the mobile network.
 Results: The proposed system has been compared with the existing techniques and has been evaluated towards calculating the detection accuracy, latency, energy consumption, packet delivery ratio, false classification rate and false positive rate.
 Conclusion: The proposed framework performs better in terms of all the selected parameters.

C SVM Classification and KNN Techniques for Cyber Crime Detection

In the digital age, cybercrime is spreading its root widely. Internet evolution has turned out to a boon as well as curse for those confronting the issues of privacy, national security, social decency, IP rights, child protection, fighting, detecting, and prosecuting cybercrime. Hence, there arises a need to detect the cybercriminal. Cybercrime identification utilizes dataset that is taken from CBS open dataset. For identifying the cybercriminal, support vector machine (SVM) in the C SVM classification and K -nearest neighbor (KNN) models is utilized for determining the cybercrime information. The evaluation of the performance is done taking the following metrics into consideration: true positive, false positive, true negative and false negative, false alarm rate, detection rate, accuracy, recall, precision, specificity, sensitivity, classification rate, and Fowlkes-Mallows Scores. Expectation maximization (EM) calculation is utilized for evaluating the presentation of the Gaussian mixture model. The performance of classifier’s presentation is also done. Accuracy is accomplished in the event of grouping by means of SVM classifier as 89% in the supervised method.

Multi-information fingerprint identification method based on interactive genetic algorithm

In order to overcome the shortcomings of traditional fingerprint identification methods, such as low computational efficiency and high false classification rate, a new multi-information fingerprint identification method based on interactive genetic algorithm is proposed. Firstly, the multi-information fingerprint image is pre-processed to extract the feature points. Then, combined with the interactive genetic algorithm, the rotation angle of the two fingerprint images is determined. At the same time, the fingerprint offset code is coded, and the matching setting function is set. The matching degree of the two fingerprints is determined by the interactive genetic algorithm, which effectively realises the multi-information fingerprint identification. Finally, the simulation experiment is carried out. The experimental results show that the proposed method can effectively reduce the false classification rate, reduce the average matching time of fingerprint image, and improve the operation efficiency. The minimum error rate is only 1.02%.

Composite fault diagnosis methodology for urban vehicular ad hoc network

Vehicular Ad-hoc NETwork (VANET) is a meteoric growing research area due to the technological advancement in sensing, computation, communication, and radio wireless technology. The demand for VANET is increasing day by day due to the numerous diverse applications. Applications of VANET include safety applications like reduction in the road accident by broadcasting messages to the driver, convenience applications like automatic parking service, commercial applications like selling and buying of products through a network of wheels, productive applications like automatic environmental parameters monitoring, vehicular cloud service like secure toll transaction, etc. The aforementioned VANET applications will become non-operational if the communication unit of the vehicle becomes inoperative or malfunctioning. This paper proposes a composite fault diagnosis methodology by detecting and classifying the fault of the On Board Unit (OBU) of the vehicular ad hoc network. The fault detection includes the detection of two categories of the fault i.e. hard fault in which the communication unit is in the inactive state and not able to communicate with other communication units and soft fault in which the communication unit is in the active state but communicates incorrect data with other communication devices. The hard fault is detected by the repeated time out mechanism and by a statistical method called Kolmogorov–Smirnov test (K-S test). The detection of soft fault (permanent, intermittent, and transient) is performed by a statistical method called the Chi-square test. Apart from the detection of the soft fault, the classification of soft fault is carried out by ECOC-SVM. Finally, the result of the fault is transmitted to the faulty vehicle. The parameters such as Fault Detection Accuracy (FDA) and False Alarm Rate (FAR) are used to validate the proposed fault detection protocol. The parameter False classification rate (FCR) is used to measure the performance of the soft fault classification. Finally, the performance of the transmission of the result to the faulty vehicle is evaluated through the parameters end-to-end (E2E) delay, number of hops, and network gaps by comparing the proposed work with existing routing protocols of VANET like GSR, A-STAR, Bhoi et al.

Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability

Time-dependent reliability-based design optimization is an effective tool to guarantee a high reliability of the product during the full life cycle. However, the necessarily repeated probabilistic constraint evaluations bring big computational burden when this tool is applied to the complex engineering systems. To reduce the computational cost, this work employs Kriging model to approximate the limit states of time-consuming probabilistic constraints, and proposes the global and local Kriging modeling methods respectively based on the wrong-classification probability. The global one aims to reduce the wrong-classification probability in the vicinity of the whole limit states, while the local one focuses on the limits states that are potentially visited by the optimum. Based on the wrong-classification probability, two indices, i.e. false classification rate and estimation error of failure probability, are derived to measure the global and local accuracies of limit states respectively. For the global or local Kriging modeling, the approximated Kriging constraint maximizing the false classification rate or the estimation error of failure probability will be updated by the point with the maximum wrong-classification probability. Results of four case studies demonstrate the efficacy of the proposed methods.

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier’s effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability.

Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety.

To monitor road safety, billions of records can be generated by Controller Area Network bus each day on public transportation. Automation to determine whether certain driving behaviour of drivers on public transportation can be considered safe on the road using artificial intelligence or machine learning techniques for big data analytics has become a possibility recently. Due to the high false classification rates of the current methods, our goal is to build a practical and accurate method for road safety predictions that automatically determine if the driving behaviour is safe on public transportation. In this paper, our main contributions include (1) a novel feature extraction method because of the lack of informative features in raw CAN bus data, (2) a novel boosting method for driving behaviour classification (safe or unsafe) to combine advantages of deep learning and shallow learning methods with much improved performance, and (3) an evaluation of our method using a real-world data to provide accurate labels from domain experts in the public transportation industry for the first time. The experiments show that the proposed boosting method with our proposed features outperforms seven other popular methods on the real-world dataset by 5.9% and 5.5%.

Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine

AbstractIn this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.

Detection, Classification and Boundary Regularization of Buildings in Satellite Imagery Using Faster Edge Region Convolutional Neural Networks

With the development of effective deep learning algorithms, it became possible to achieve high accuracy when conducting remote sensing analyses on very high-resolution images (VHRS), especially in the context of building detection and classification. In this article, in order to improve the accuracy of building detection and classification, we propose a Faster Edge Region Convolutional Neural Networks (FER-CNN) algorithm. This proposed algorithm is trained and evaluated on different datasets. In addition, we propose a new method to improve the detection of the boundaries of detected buildings. The results of our algorithm are compared with those of other methods, such as classical Faster Region Convolution Neural Network (Faster R-CNN) with the original VGG16 and the Single-Shot Multibox Detector (SSD). The experimental results show that our methods make it possible to obtain an average detection accuracy of 97.5% with a false positive classification rate of 8.4%. An additional advantage of our method is better resistance to shadows, which is a very common issue for satellite images of urban areas. Future research will include designing and training the neural network to detect small buildings, as well as irregularly shaped buildings that are partially obscured by shadows or other occlusions.

The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine

Background: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems. Methods: A total of 200 individuals participated in this study and were further divided into a modeling group and a test group. Their physiological parameters (height, weight, age, the four parameters of abdominal impedance and body fat mass) were measured using the body composition tester (the universal INBODY measurement device) based on BIA. Intelligent algorithms were used in the modeling group to build predictive models and the test group was used in model performance evaluation. Firstly, the optimal boundary C and parameter gamma were optimized by the particle swarm algorithm. We then developed an algorithm to classify human abdominal adiposity according to the parameter setup of the SVM algorithm and constructed the prediction model using this algorithm. Finally, we designed experiments to compare the performances of the proposed method and the other methods. Results: There are different abdominal obesity prediction models in the 1 KHz and 250 KHz frequency bands. The experimental data demonstrates that for the frequency band of 250 KHz, the proposed method can reduce the false classification rate by 10.7%, 15%, and 33% in relation to the sole SVM algorithm, the regression model, and the waistline measurement model, respectively. For the frequency band of 1 KHz, the proposed model is still more accurate. (4) Conclusions: The proposed method effectively improves the prediction accuracy and reduces the sample size dependence of the algorithm, which can provide a reference for abdominal obesity.

RETRACTED ARTICLE: Automatic epilepsy detection using hybrid decomposition with multi class support vector method

The epilepsy has been detected from the electroencephalogram (EEG) by utilizing the complex wavelet transform with support vector machine. These methods successfully examine each and every frequency in the EEG signal for detecting the epilepsy with effective manner because epilepsy is one of the most important brain abnormalities which affect the entire brain function. The epilepsy is occurring due to the brain stroke, lack of blood flow, brain fever and so on, these lead to create the number of human deaths. So, the brain epilepsy needs to be analyzed and it has to be detected for improving the epilepsy recognition rate. But the major problem with the epilepsy recognition is the accuracy and efficiency of the classifier because of the traditional approximation entropy only extracts the minimum number of features which is difficult to detect the epilepsy with effective manner. These problems increase the false classification rate while analyzing the brain features. So, brain abnormalities has been automatically recognized by using the various machine learning steps like preprocessing, signal decomposition, feature extraction, feature selection and classification. In this research, the brain Epilepsy is recognized by applying the Hybrid Multi Class Support Vector Machine (HMCSVM). Then the performance of the system is analyzed using the experimental results and discussions.

Underlying and Persistence Fault Diagnosis in Wireless Sensor Networks Using Majority Neighbors Co-ordination Approach

The faults in wireless sensor network are classified according to the underlying causes, behavior, and persistence with respect to the observation time. Due to underlying causes, faults are classified as fail and stop, crash, omission, timing, and incorrect computation fault. Due to behavior, faults are classified as hard and soft fault. Due to persistence, faults are classified as permanent, intermittent, and transient fault. As the recent state-of-art fault diagnosis is a significant requirement for each application of wireless sensor network. In this research paper, we have proposed a fault diagnosis protocol using majority neighbors coordination based approach for wireless sensor network. Precisely, a multiple-hop data received technique, timeout period mechanism, timeout request and response message exchange, timeout early and delay message exchange, and degree of belongingness using Gaussian function mechanism are used for the detection of faults such as fail and stop, crash, omission, timing, and incorrect computation. The mean difference and standard error comparison with different threshold condition are used for soft (permanent, intermittent, and transient) fault detection, and timeout response mechanism with different threshold condition is used for hard (permanent, intermittent, and transient) fault detection. After fault detection, the actual fault status of the sensor node is confirmed by the one-hop majority neighbor sensor nodes. For validation of the proposed fault detection algorithms, simulation experiments are conducted by the network simulator NS-2.35. The experimental results show the substantially parameters performance such as fault detection accuracy, false alarm rate, false positive rate, and false classification rate with increasing the fault probability for different average degree of the sensor nodes in the network.

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

SummaryThe fault diagnosis in wireless sensor networks is one of the most important topics in the recent years of research work. The problem of fault diagnosis in wireless sensor network can be resembled with artificial immune system in many different ways. In this paper, a detection algorithm has been proposed to identify faulty sensor nodes using clonal selection principle of artificial immune system, and then the faults are classified into permanent, intermittent, and transient fault using the probabilistic neural network approach. After the actual fault status is detected, the faulty nodes are isolated in the isolation phase. The performance metrics such as detection accuracy, false alarm rate, false‐positive rate, fault classification accuracy, false classification rate, diagnosis latency, and energy consumption are used to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm gives superior results as compared with existing algorithms in terms of the performance metrics. The fault classification performance is measured by fault classification accuracy and false classification rate. It has also seen that the proposed algorithm provides less diagnosis latency and consumes less energy than that of the existing algorithms proposed by Mohapatra et al, Panda et al, and Elhadef et al for wireless sensor network.

Forensic genealogy—A comparison of methods to infer distant relationships based on dense SNP data

The concept forensic genealogy was discussed already in 2005 but has recently emerged in relation to the use of public genealogy databases to find relatives of the donor of a crime stain. In this study we explored the results and evaluation of searches conducted in such databases. In particular, we focused on the statistical classification that entails from the search and study the variation observed for different relationship classes. The forensic guidelines advocate the use of the likelihood ratio (LR) as a mean to measure the weight of evidence, which requires exact formulation of competing hypotheses. We contrast the LR approach with alternative approaches relying on identical by state (IBS) measures to estimate the total length of shared genomic segments as well as identical by descent (IBD) coefficients for a pair of individuals.We used freely accessible data from the 1000 Genome project to perform extensive simulations, generating data for a number of distinct relationships. Specifically we studied some overarching relationship classes and the performance of the above-mentioned evaluative approaches to classify a known pair of relatives into each class.The results indicate that the traditional LR approach as a single source of classification is as good as, and in some cases even better than, the alternative approaches. In particular the true classification rate is higher for some distant relationship. However, the LR approach is both computer-intensive and sensitive to population frequencies as well as genetic maps (positions of the markers). We further showed that when combining different classification approaches, a lower false classification rate was achieved while still maintaining a high true classification rate.

A complete diagnosis of faulty sensor modules in a wireless sensor network

Wireless sensor network (WSN) is a collection of sensor devices, which collects information from the environment and sends to the base station by single-hop or multi-hop communication towards achieving some predefined objectives. Due to the multi-functional applications in the adventurer environment, the sensor modules are damaged by various external and internal sources, which leads to a failure sensor network. Therefore, automated fault diagnosis and fault tolerance are of utmost importance in WSN. In this article, a complete fault diagnosis methodology is proposed for faulty sensors in the failed WSN. The proposed methodology consists of four phases of functioning such as initialization, fault detection, fault classification, and fault tolerance phase. The hard fault and link fault are detected by checksum and Fletcher’s checksum method. The soft faults (permanent, intermittent, and transient) are detected by Mann-Whitney U statistical test. The Gaussian transformation function with a classification factor is used for soft faults classification. The fault tolerance is attained in the sensor system by the stepwise regressional method. The proposed methodology is implemented in a real testbed experiment with simulation setup. The parameters like fault detection accuracy, false alarm rate, false positive rate, fault classification rate, false classification rate, R-squared value, standard error, fault probability, and degree of sensors are considered for the evaluation of the proposed diagnosis methodology.