Utilizing Support Vector Machine Research Articles

Development support vector machine, artificial neural network and artificial neural network – genetic algorithm hybrid models for estimating erodible fraction of soil to wind erosion

ABSTRACT The index of Erodible Fraction (EF) component against wind is well known as a criteria of soil wind erodibility. This study was conducted to estimate the index of EF by utilizing support vector machine (SVM), artificial neural network (ANN) and genetic algorithm (GA-ANN) methods. For this reason, 95 soil samples were collected 10 cm above the soil surface of Allahabad plain in Qazvin province. Then the percentage of aggregates with a diameter of less than 0.84 mm, pH, EC, soil saturation capacity, SAR, equivalent calcium carbonate, organic matter, clay, sand and silt were measured and used in SVM, ANN and GA-ANNs model construction and testing. Results revealed that the EF had a significant correlation at the level of 1% with five soil characteristics, including pH, EC, SAR, clay and organic matter. On the other hand, the ANNs model developed for EF prediction had higher accuracy (R 2 = 0.49) than the GA-ANNs model (R 2 = 0.37) and SVMs model (0.02). All models were approximately overestimated and the GMER values for SVMs, ANNs and GA-ANNs models were 1.11, 1.15 and 1.08, respectively. However, according to the Akaike information criterion (AIC), all of them had similar power model estimation. Although SVMs offer good accuracy and perform fast prediction in this study, it did not perform well. This issue may be related to the complexity of the relationship between EF and soil properties.

Drilling resistance testing combined with SonReb methods for nondestructive estimation of concrete strength

This study explores a drilling-based test methodology for nondestructive estimation of in-situ concrete strength. An experimental campaign has been carried out to develop a relationship between the drilling-resistance parameter, DR, and compressive strength of concrete. Rebound hammer (RH) and ultrasonic pulse velocity (UPV) tests were also performed and multivariate regression models complementing DR with RH and UPV data have been developed. A machine learning approach utilizing support vector machines (SVM) was implemented. The experimental data support that combined usage of DR with UPV and/or RH provides a robust tool for compressive strength prediction. Even with the limited data available, the support vector regression model shows promising performance.

Screening model of candidate drugs for breast cancer based on ensemble learning algorithm and molecular descriptor

Breast cancer is one of the leading killers of women around the world. Finding compounds with good bioactivity, metabolic dynamics and safety, including Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET for short), is a long and challenging task in breast cancer therapy. In the paper, molecular descriptor data of compounds was analyzed by the ensemble learning algorithm, and important features were selected for the development and validation of ADMET classification models. The overall process includes data cleaning, data splitting to training and testing sets, feature selection and classification model evaluation. A Two-Level Stacking Algorithm (TLSA) based on ensemble learning is proposed for ADMET classification. Various performance measures like classification accuracy, precision, recall, confusion matrix, F1-score, Receiver Operating Characteristic (ROC) curves and the Area Under the ROC Curves (AUC) are reported to show the superiority of the proposed method as compared to different classifiers. The experimental results show that the second level algorithm for TLSA utilizes Logistic Regression is better than other classifiers for the properties of Absorption, Distribution and Excretion, with accuracy of 94.6037%, 94.9410% and 88.1956% respectively. For the properties Metabolism and Toxicity, the second level algorithm utilizes Support Vector Machine to achieve the best classification performance, with accuracy of 88.8702% and 96.7960% respectively. The results show that the proposed approach works well with the classification of compound properties and can be a good alternative for the well-known machine learning program.

Integration of differential expression and network structure for ‘omics data analysis

Differential expression (DE) analysis has been routinely used to identify molecular features that are statistically significantly different between distinct biological groups. In recent years, differential network (DN) analysis has emerged as a powerful approach to uncover molecular network structure changes from one biological condition to the other where the molecular features with larger topological changes are selected as biomarkers. Although a large number of DE and a few DN-based methods are available, they have been usually implemented independently. DE analysis ignores the relationship among molecular features while DN analysis does not account for the expression changes at individual level. Therefore, an integrative analysis approach that accounts for both DE and DN is required to identify disease associated key features. Although, a handful of methods have been proposed, there is no method that optimizes the combination of DE and DN. We propose a novel integrative analysis method, DNrank, to identify disease-associated molecular features that leverages the strengths of both DE and DN by calculating a weight using resampling based cross validation scheme within the algorithm. First, differential expression analysis of individual molecular features is carried out. Second, a differential network structure is constructed using the differential partial correlation analysis. Third, the molecular features are ranked in the order of their significances by integrating their DE measures and DN structure using the modified Google's PageRank algorithm. In the algorithm, the optimum combination of DE and DN analyses is achieved by evaluating the prediction performance of top-ranked features utilizing support vector machine classifier with Monte Carlo cross validation. The proposed method is illustrated using both simulated data and three real data sets. The results show that the proposed method has a better performance in identifying important molecular features with respect to predictive discrimination. Also, as compared to existing feature selection methods, the top-ranked features selected by our method had a higher stability in selection. DNrank allows the researchers to identify the disease-associated features by utilizing both expression and network topology changes between two groups.

A Precise Medical Imaging Approach for Brain MRI Image Classification.

Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.

Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT.

The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection. In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. The proposed AMLSDM framework develops an SDN-enabled security mechanism for IoT devices with the support of an adaptive machine learning classification model to achieve the successful detection and mitigation of DDoS attacks. The proposed framework utilizes machine learning algorithms in an adaptive multilayered feed-forwarding scheme to successfully detect the DDoS attacks by examining the static features of the inspected network traffic. In the proposed adaptive multilayered feed-forwarding framework, the first layer utilizes Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers to build a model for detecting DDoS attacks from the training and testing environment-specific datasets. The output of the first layer passes to an Ensemble Voting (EV) algorithm, which accumulates the performance of the first layer classifiers. In the third layer, the adaptive frameworks measures the real-time live network traffic to detect the DDoS attacks in the network traffic. The proposed framework utilizes a remote SDN controller to mitigate the detected DDoS attacks over Open Flow (OF) switches and reconfigures the network resources for legitimate network hosts. The experimental results show the better performance of the proposed framework as compared to existing state-of-the art solutions in terms of higher accuracy of DDoS detection and low false alarm rate.

Locomotion Modes and Environmental Features Recognition Using Laser Distance Sensors

Recognition of locomotion mode (LM) has been used to control the lower limb powered prosthetic and orthotic devices for navigating autonomously on a variety of terrains. In addition, the knowledge of environmental features (EFs) can be utilized to modulate the joint kinetics of prosthetic devices in a precise manner. In this article, a novel LM recognition strategy has been proposed along with EFs estimation utilizing a combination of laser distance sensors (LDSs) and inertial measurement units (IMUs). LM was classified from outputs of eight sensors, i.e., six LDSs and two IMUs, utilizing support vector machine (SVM) followed by a majority voting scheme. A cloud of points was generated in the sagittal plane, which was further used to determine the EFs leveraging curve fitting techniques. The efficiency of the proposed method was tested with six healthy subjects and for a variety of LMs, such as walking on the level-ground, ramp, and stairs. The overall accuracy of LM classification and EF estimation was found to be equal to 97.85% and 95.36%, respectively. The proposed study is an accurate, computationally inexpensive, and cost-effective solution and can be used efficiently to control lower limb prosthetic devices. The novelty of this study is the simplification of the LM classification and EF estimation methods compared to a camera-based technique to facilitate real-time control of powered prostheses.

Predicting Microbe‐Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

Numerous microbes inhabit human body, making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study, we develop a prediction method by learning global graph feature on the heterogeneous network (called HNGFL). Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple similarities. Based on microbe Gaussian interaction profile (GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

Disease-Specific Imaging Utilizing Support Vector Machine Classification of H-Scan Parameters: Assessment of Steatosis in a Rat Model.

In medical imaging, quantitative measurements have shown promise in identifying diseases by classifying normal versus pathological parameters from tissues. The support vector machine (SVM) has shown promise as a supervised classification algorithm and has been widely used. However, the classification results typically identify a category of abnormal tissues but do not necessarily differentiate progressive stages of a disease. Moreover, the classification result is typically provided independently as a supplement to medical images, which contributes to an overload of information sources in the clinic. Hence, we propose a new imaging method utilizing the SVM to integrate classification results into medical images. This framework is called disease-specific imaging (DSI) that produces a color overlaid highlight on B-mode ultrasound images indicating the type, location, and severity of pathology from different conditions. In this article, the SVM training was performed to construct hyperplanes that can differentiate normal, fibrosis, steatosis, and pancreatic ductal adenocarcinoma (PDAC) metastases in livers based on ultrasound echoes. Also, cluster centroids for specific diseases define unique disease axes, and the inner product between measured features and any disease axis selected by the SVM quantifies the disease progression. The features were measured from 2794 ultrasound frames using the H-scan analysis, attenuation estimation, and B-mode image analysis. The performance of our proposed DSI method was evaluated for a preclinical model of steatosis ( n = 400 frames). The contribution of each feature was assessed, and the results were compared with ground truth from histology. Moreover, the images generated by our DSI were compared with earlier imaging methods of B-mode, H-scan, and histology. The comparisons demonstrate that DSI images yield higher sensitivity to monitor progressive steatosis than B-mode and H-scan and provide a comparable performance with the histology. For the parameter comparison, DSI and H-scan resulted in similar correlation with histology ( rs = 0.83 ) but higher than attenuation ( rs = 0.73 ) and B-mode ( rs = 0.47 ). Therefore, we conclude that DSI utilizing the SVM applied to steatosis can visually represent the classification results with color highlighting, which can simplify the interpretation of classification compared to the traditional SVM result. We expect that the proposed DSI can be used for any medical imaging modality that can estimate multiple quantitative parameters at high resolution.

Topological feature vectors for chatter detection in turning processes

Machining processes are most accurately described using complex dynamical systems that include nonlinearities, time delays and stochastic effects. Due to the nature of these models as well as the practical challenges which include time-varying parameters, the transition from numerical/analytical modeling of machining to the analysis of real cutting signals remains challenging. Some studies have focused on studying the signals of cutting processes using machine learning algorithms with the goal of identifying and predicting undesirable vibrations during machining referred to as chatter. These tools typically decompose the signal using Wavelet Packet Transforms (WPT) or Ensemble Empirical Mode Decomposition (EEMD). However, these methods require a significant overhead in identifying the feature vectors before a classifier can be trained. In this study, we present an alternative approach based on featurizing the time series of the cutting process using its topological features. We first embed the time series as a point cloud using Takens embedding. We then utilize Support Vector Machine, Logistic Regression, Random Forest and Gradient Boosting classifier combined with feature vectors derived from persistence diagrams, a tool from persistent homology, to encode chatter’s distinguishing characteristics. We present the results for several choices of the topological feature vectors, and we compare our results to the WPT and EEMD methods using experimental turning data. Our results show that in two out of four cutting configurations the Topological Data Analysis (TDA)-based features yield accuracies as high as 97%. We also show that combining Bézier curve approximation method and parallel computing can reduce runtime for persistence diagram computation of a single time series to less than a second thus making our approach suitable for online chatter detection.

Electricity Theft Identification by Tracking Usage Data Utilizing Support Vector Machine Algorithm

Electricity Theft Identification by Tracking Usage Data Utilizing Support Vector Machine Algorithm

Lithological mapping enhancement by integrating Sentinel 2 and gamma-ray data utilizing support vector machine: A case study from Egypt

Hybrid data fusion mostly gives a better diagnosis to lithological units compared to single-source mapping techniques. Rock unit discrimination depends mainly on variations in the concentrations of chemical elements. Remote sensing datasets reflect these variations as different spectral reflectances, while gamma-ray spectrometric measurements enable recording the varied concentrations of K, Th, and U in these rock units. Accordingly, in this study, we use Support-Vector Machine (SVM) learning algorithm to classify combined high spectral resolution Sentinel 2 data with K, Th, and U content of the rocks to better differentiate a lithologically complex area in Egypt. SVM classifier has been trained and tested on a reference map (built from FCCs, principal and independent component analysis of remote sensing images, as well as previous geological maps) to allocate 13 lithological targets. K, Th, U, and total count maps are interpolated using the inverse distance weighted (IDW) method, cubically resampled, and fused with Sentinel 2 data. We concluded that incorporating any single chemical concentration in the allocation gives better results than using remote sensing data solely and raised the Overall Accuracy by 4.14%, 5.11%, and 6.83% by adding U, K, and Th, respectively. Moreover, blending the total count band (K + Th + U) with Sentinel 2 data outstandingly boosts the classification accuracy by 7.77 %. We performed field reconnaissance to verify the classification results. The study demonstrates the effectiveness of integrating Sentinel 2 data with airborne geophysical spectrometric data, and the proposed approach may prove a more precise and sophisticated lithological map.

A Two-Stage Low-Complexity Human Sleep Motion Classification Method Using IR-UWB

With increasing concern about sleep quality, human sleep motion recognition has come into sight. Recent years have witnessed that almost all researches use deep neural networks for human motion recognition based on radar sensors. However, it is difficult to deploy these methods in edge computing in the case of limited hardware resources. This paper proposes a two-stage low-complexity method for real-time human sleep motion classification using an ultra-wideband (UWB) radar, which can be applied in edge computing. Firstly, we implement power burst curve (PBC) detection to decide whether sleep motions occur. Secondly, a method named zero-forcing processing for radar spectrum is proposed in order to reduce the number of parameters of classifiers. Later, in stage one, we propose a three-feature extraction approach based on micro-Doppler signatures and utilize support vector machine (SVM) to roughly classify micro-motion and body-movement motions. In stage two, a long short-term memory (LSTM) neural network with fewer parameters is applied to further classify body movements. Experimental results show that the proposed method has a low computational complexity, and can reach the average accuracy of the five typical sleep motions over 97% using train and test split samples, and 91.50% using the leave-two-subject-out (LTSO) samples.

Utilizing support vector machines for predictive analytics in chronic kidney diseases

Chronic kidney disease (CKD), due to rising patients, the high probability of deterioration towards end-stage renal disease and inaccurate estimates of morbidity and mortality, constitutes a heavy burden on the sanitary infrastructure. The aim of this research is to build a model for machine-learning, which uses comorbidity and data on drugs and predicts population prevalence. Predictive health care prediction using machine learning is a daunting activity to help clinicians assess the precise therapies for life-saving. In this paper, the study applies machine learning method in combination with ensemble learning for estimation of chronic kidney disease with clinical evidence. They are based on chronic kidney disease datasets and the efficiency of these models is compared to choose the best classifier for chronic kidney disease prediction. The comparative analysis is estimated in terms of various metrics like classification accuracy, f-measure, percentage error, etc. The results of simulation shows that the proposed ensemble machine learning classifier namely Ensemble Support Vector Machine predicts well the chronic kidney disease from the datasets than other existing ensemble methods.

Geometric model for toppling-prone deformation of layered reverse-dip slope

This paper presents the analyses of toppling response of layered reverse-dip slopes under the influence of geometric factors of slope angle, strata thickness and dip. The toppling response of reverse-dip slopes under different geometric factors is obtained by using the discrete element code UDEC. Then, a prediction model for toppling deformation is developed by utilizing support vector machine and the toppling-prone combinations of these geometric factors are determined from a total of 120 samples of reverse-dip slopes. The study has shown that the geometric model is a quarter of a spheroid with a slope angle of 80°, a strata dip of 80°, and a strata thickness of 0.19 m as the center. The length of the long equatorial radius of the spheroid is 31° (strata dip axial), that of the short equatorial radius is 21° (slope angle axial), and the polar radius is 0.075 m (strata thickness axial). And the ratio of long equatorial radius, short equatorial radius, and polar radius is 2.48:1.68:1.00.

A Detection Framework Against CPMA Attack Based on Trust Evaluation and Machine Learning in IoT Network

Internet of Things (IoT) network is vulnerable to various cyberattacks, especially insider attacks. Most existing studies mainly detect nontargeted insider attackers, who manipulate all packets forwarded by them with a probability. Compared with nontargeted attackers, targeted attackers only manipulate specific packets, which makes them more efficient and covert. In this article, we propose a targeted insider attack model called conditional packets manipulation attack (CPMA), in which attackers maliciously manipulate the packets whose attribute values meet specific conditions with a probability. When resisting the CPMA attack, most existing detection algorithms are inefficient to find such malicious behavior. Also, they detect malicious nodes by collecting and analyzing the overall behavior of nodes, which are not appropriate for energy-constrained nodes in the IoT network. To solve these problems, we present CPMAED, a malicious nodes detection framework against CPMA attack. CPMAED maintains some partial trust metrics for each relay node, which indicate the probability of launch attacks when forwarding the packets with different attribute values. Also, our scheme leverages regression and clustering algorithms to evaluate the trust values of nodes and classify them into benign or malicious. In order to obtain higher detection accuracy, we optimize the routing of transmitted packets and inject the packets to collect more information about nodes to enhance detection. The experimental results show that our proposed scheme utilizing support vector machine and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means can achieve good detection performance and identify malicious nodes’ attack modes with high accuracy.

Building an efficient intrusion detection system using grasshopper optimization algorithm for anomaly detection

Intrusion detection is one of the most crucial activities for security infrastructures in network environments, and it is widely used to detect, identify and track malicious threats. A common approach in intrusion detection systems (IDSs) specifically in anomaly detection is evolutionary algorithm that works as intrusion detector. Still, it has been challenging to design a precise and reliable IDS to determine security threats due to the large capacity of network data which contains redundant and irrelevant features. It does not only decrease the process of classification but also prevents a classifier from making precise decisions. To increase the accuracy and reduce the false alarm rate, in this study integration of ensemble feature selection (EFS) and grasshopper optimization algorithm (GOA), called EFSGOA is developed. Firstly, EFS method is applied to rank the features for selecting the top subset of relevant features. Afterward, GOA is utilized to identify significant features from the obtained reduced features set produced by EFS technique that can contribute to determine the type of attack. Furthermore, GOA utilizes support vector machine (SVM) as a fitness function to obtain the noteworthy features and to optimize penalty factor, kernel parameter, and tube size parameters of SVM for maximizing the classification performance. The experimental results demonstrate that EFSGOA method has performed better and obtained high detection rate of 99.69%, accuracy of 99.98% and low false alarm rate of 0.07 in NSL-KDD and high detection rate of 99.26%, accuracy of 99.89% and low false alarm rate of 0.097 in KDD Cup 99 data. Moreover, the proposed method has succeeded in achieving higher performance compared to other state-of-art techniques in terms of accuracy, detection rate, false alarm rate, and CPU time.

RETRACTED ARTICLE: Classification of audio signals using SVM-WOA in Hadoop map-reduce framework

Audio classification is the reason for the multimedia gratified examination which is the utmost significant and generally utilized application these days. For huge information bases, programmed classification procedure utilizing Artificial Intelligence (AI) is more viable than the manual classification. Various sorts of AI calculations have proposed in writing like K-Nearest Neighbors Principal Component Analysis, Gaussian Mixture Model, and Hidden Markov Model, etc. By utilizing the above methods, the audio classification can be done with no class related pre-information. However, they require huge training data with no real segregation results. To beat these insufficiencies, this paper proposed a general structure for audio classification. In this paper, another audio classification algorithm utilizing Support Vector Machine (SVM) in view of Whale Optimization Algorithm (WOA) is given where WOA-SVM utilizes the class mark of the info test as the real yield. WOA is utilized for conquering the inconvenience of SVM, for example, high computational multifaceted nature as a result of the explaining of enormous scale quadratic programming in parameter iterative learning methodology. The audio sign has shown up in huge volumes on account of its tendency. With the goal that we have utilized the MapReduce approach which is one of the sorts of big data investigation to play out the classification on the unstructured information. The proposed audio classification algorithm has contrasted with a few existing classification algorithms with demonstrating its productivity and the exactness.

Mitigation of nonlinearities in analog radio over fiber links using machine learning approach

Machine learning (ML) techniques are looked upon as an innovative and realistic direction to cope up with nonlinearity issues in fiber optics communication. In this paper, a 64-quadrature amplitude modulation (QAM) based radio over fiber (RoF) system is demonstrated for 10 km of standard single mode fiber length utilizing support vector machine (SVM) method to indicate an effective nonlinearity mitigation in front-hauls. The comparison of SVM is drawn with conventional ML classifiers to optimize symbol decision boundary that will reduce the RoF link impairments. The results are reported in terms of BER, Eye-linearity and Quality factor.

A Machine Learning‐Based Detection of Earthquake Precursors Using Ionospheric Data

AbstractDetection of precursors of strong earthquakes is a challenging research area. Recently, it has been shown that strong earthquakes affect electron distribution in the regional ionosphere with indirectly observable changes in the ionospheric delays of GPS signals. Especially, the total electron content (TEC) estimated from GPS data can be used in the seismic precursor detection for strong earthquakes. Although physical mechanisms are not well understood yet, GPS‐based seismic precursors can be observed days prior to the occurrence of the earthquake. In this study, a novel machine learning‐based technique, EQ‐PD, is proposed for detection of earthquake precursors in near real time based on GPS‐TEC data along with daily geomagnetic indices. The proposed EQ‐PD technique utilizes support vector machine (SVM) classifier to decide whether an observed spatiotemporal anomaly is related to an earthquake precursor or not. The data fed to the classifier are composed of spatiotemporal variability map of a region. Performance of the EQ‐PD technique is demonstrated in a case study over a region covering Italy in between the dates of 1 January 2014 and 30 September 2016. The data are partitioned into three nonoverlapping time periods, that are used for training, validation, and test of detecting precursors of earthquakes with magnitudes above 4 in Richter scale. The EQ‐PD technique is able to detect precursors in 17 out of 21 earthquakes while generating 7 false alarms during the validation period of 266 days and 22 out of 24 earthquakes while generating 13 false alarms during the test period of 282 days.