Background: Electrocardiogram (ECG) is defined as an electrical signal, which represents cardiac activity. Heart rate variability (HRV) as the variation of interval between two consecutive heartbeats represents the balance between the sympathetic and parasympathetic branches of the autonomic nervous system. Objective: In this study, we aimed to evaluate the efficiency of discrete wavelet transform (DWT) based features extracted from HRV which were further selected by genetic algorithm (GA), and were deployed by support vector machine to HRV classification. Materials and Methods: In this paper, 53 ECGs including 3 different beat types (ventricular fibrillation (VF), atrial fibrillation (AF) and also normal sinus rhythm (NSR)), were selected from the MIT/BIH arrhythmia database. The approach contains 4 stages including HRV signal extraction from each ECG signal, feature extraction using DWT (entropy, mean, variance, kurtosis and spectral component β ), best features selection by GA and classification of normal and abnormal ECGs using the selected features by support vector machine (SVM). Results: The performance of the classification procedure employing the combination of selected features were evaluated using several measures including accuracy, sensitivity, specificity and precision which resulted in 97. 14%, 97. 54%, 96. 9% and 97. 64%, respectively. Conclusion: A comparative analysis with the related existing methods illustrates the proposed method has a higher potential in the classification of AF and VF. The attempt to classify the ECG signal has been successfully achieved. The proposed method has shown a promising sensitivity of 97. 54% which indicates that this technique is an excellent model for computer-aided diagnosis of cardiac arrhythmias.

With the advent of quantum computing theory and quantum communication networks, establishing confidential and secure communication has become challenging. Quantum audio signal steganalysis is one of the interesting subfields in the field of quantum signal processing and quantum computing, which tries to identify hidden communications in the context of quantum communication networks by using feature extraction techniques and quantum machine learning algorithms. Since steganography causes inevitable changes in the statistical characteristics of the frequency domain of the host signal, it can be used as an efficient and effective tool to build comprehensive and accurate steganalysis. So; At first, the proposed method uses quantum Fourier transform on QRDS audio signal to extract statistical features. For this purpose, the proposed quantum circuit network of these features, quantum spectral center and quantum spectral bandwidth has been designed and implemented. Finally, the Quantum Support Vector Machine (QSVM) algorithm, using the extracted features, separates clean and stego data sets with an accuracy of more than 95%.

Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, features subsets are selected due to some measures like inter-class distance, features statistical independence or information theoretic measures. Even though, wrapper methods use a classifier to evaluate features subsets by their predictive accuracy (on test data) by statistical resampling or cross-validation. Filter methods usually use only one measure for feature selection that does not necessarily produce the best result. In this paper, we proposed to use the classification error measures besides to filter measures where our classifier is support vector machine (SVM). To this end, we use multi objective genetic algorithm. In this way, one of our feature selection measure is SVM classification error. Another measure is selected between mutual information and Laplacian criteria which indicates informative content and structure preserving property of features, respectively. The evaluation results on the UCI datasets show the efficiency of this method.

Nanoflares are the small impulsive sudden energy releases, due to the explosion of solar background. Thus, determination of their energies and distributions is important. Recent observations and simulation models have shown that the frequency of their energies follows power-law. According to Parker hypothesis, if these exponents are greater than critical value 2, the contributions of nanoflares to the heating of solar corona is more significan. Here, the extreme ultra-violet (EUV) emission radiances of corona observed by STEREO/EUVI taken on 11 and 12 Jun 2007 are analyzed. To simulate the EUV irradiance, a simple nanoflare model with three key parameters (the flare rate, the flare duration time, and the exponent of the power- law) is applied. Based on genetic algorithm, the lengths of data points are reduced. The resultant light curves are fed to the Support Vector Machine (SVM) classifier. The produced light curves of quiet and active regions of the solar corona are classified and the set of power- law exponent, the flare duration time and the flare rate parameters are obtained. The flare duration time is estimated about 80 minutes. The power-low exponents range about 2.5-2.7.

Introduction: Jaundice is one of the most common problems in the neonatal period, affecting about 60% of full-term and 80% of premature infants in their first week of life. The present study aimed to develop a system for predicting neonatal jaundice within the first 24 to 72 hours post-delivery by using the Support Vector Machine (SVM) algorithm. Methods: This applied-developmental study employed a quantitative method. First, based on a literature review, a questionnaire containing effective factors for predicting jaundice in newborns was designed. Data analysis was performed using descriptive statistics, and factors that were recognized as necessary by at least 50% of the experts were included in the model. Then, data from 1178 newborns delivered at Lolagar hospital in Tehran were extracted from birth records, and several machine learning algorithms were used to predict neonatal jaundice. Results: The findings of this research showed that the proposed model based on the SVM algorithm is the best output due to the distance between classes. Therefore, the final model of the SVM algorithm was created using the Gaussian kernel, with a sigma value of 1. 2360605. Thirty percent of the samples (354 cases) were tested, and 321 cases were correctly predicted. In the proposed SVM model, parameters such as precision, the area under the Receiver Operating Characteristic (ROC), and F1 score were 92. 7%, 93%, and 88% respectively. Conclusion: Incorporating SVM into a system for predicting jaundice in newborns can aid doctors with timely prediction of jaundice in newborns and provide the possibility of taking preventive measures and preventing possible risks caused by jaundice in newborns.

Foreground-background image segmentation has been an important research problem. It is one of the main tasks in the field of computer vision whose purpose is detecting variations in image sequences. It provides candidate objects for further attentional selection, e. g., in video surveillance. In this paper, we introduce an automatic and efficient Foreground-background segmentation. The proposed method starts with the detection of visually salient image regions with a saliency map that uses Fourier transform and a Gaussian filter. Then, each point in the maps classifies as salient or non-salient using a binary threshold. Next, a hole filling operator is applied for filling holes in the achieved image, and the area-opening method is used for removing small objects from the image. For better separation of the foreground and background, dilation and erosion operators are also used. Erosion and dilation operators are applied for shrinking and expanding the achieved region. Afterward, the foreground and background samples are achieved. Because the number of these data is large, K-means clustering is used as a sampling technique to restrict computational efforts in the region of interest. K cluster centers for each region are set for training of Support Vector Machine (SVM). SVM, as a powerful binary classifier, is used to segment the interest area from the background. The proposed method is applied on a benchmark dataset consisting of 1000 images and experimental results demonstrate the supremacy of the proposed method to some other foreground-background segmentation methods in terms of ER, VI, GCE, and PRI.

By expanding Internet-based services and developing websites, cyber threats are also increasing. One of these threats is to perform denial-of-service attacks and interfere with the services of a website. Web or application-layer service blocking attacks by creation of artificial traffic impose a heavy traffic on the web server and thus disrupt the Web service. In this research, to detect these attacks, Web server logs are classified by applying 20 second time windows and calculating the activity level and the entropy of different IPs in each time window. Using entropy variance, time windows with continuity are determined. In the next stage, through the backup machine algorithm, the network is trained to store abnormal time windows, and ultimately IP addresses that lead to blocked service attacks or service disruptions are classified and labelled. The proposed model was implemented on the EPA-HTTP standard dataset indicating improvement compared to previous studies.

Introductionlandslide risk assessment provides a systematic framework for evaluating the likelihood and potential consequences of landslides in a given area. It involves the identification and analysis of key factors contributing to landslide occurrence, such as slope characteristics, geological formations, land use patterns, rainfall patterns, and human activities. By integrating these factors into a comprehensive risk assessment methodology, stakeholders can better understand the vulnerability of areas and populations at risk and develop appropriate strategies and measures to mitigate and manage landslide hazards. Advancements in geospatial technologies, such as geographic information systems (GIS), remote sensing, and machine learning algorithms, have significantly enhanced the accuracy and efficiency of landslide risk assessment. These tools enable the integration and analysis of diverse data sources, including topographic data, satellite imagery, and historical landslide records, to create detailed landslide susceptibility and hazard maps. These maps provide valuable information for prioritizing risk-prone areas, implementing land-use regulations, designing engineering structures, and formulating early warning systems. This study aims to contribute to the field of landslide risk assessment by evaluating the key factors influencing landslide occurrence and developing a comprehensive methodology for assessing landslide risks in the Chesb Watershed, Zanjan Province. The research findings will provide valuable insights for land managers, policymakers, and stakeholders involved in disaster risk reduction, land-use planning, and infrastructure development. By understanding and effectively managing landslide risks, communities can build resilience, protect lives and property, and ensure sustainable development in landslide-prone regions. Materials and MethodsThis research was conducted in the catchment area of Chesb, which is located in the city of Eejrud, Zanjan province, between geographical longitudes 36.13 to 36.27 degrees and geographical latitudes 48.1 to 48.41 degrees. To begin, a comprehensive review of literature was conducted to gather existing knowledge and identify influential factors related to landslides. Additionally, field visits were conducted to gather on-site information and observations. Based on the collected information, various data layers were prepared using a GIS. These layers included slope, slope direction, elevation classes, geology, distance from the drainage network to the river, distance from roads, distance from faults, topographic indices (such as stream power index (SPI), topographic wetness index (TWI), and slope length factor (LS), geomorphological indices (such as topographic position index (TPI), topographic roughness index, and curvature index), land use, normalized difference vegetation index (NDVI), and precipitation.After data preparation, a total of 81 landslide occurrences were identified in the study area through field surveys and previous studies. For landslide risk modeling, 70% of the landslide points were used to train the support vector machine (SVM) model, while the remaining 30% were used for model validation. Using the SVM model, a sensitivity map for landslides occurrence was generated. The model utilized the prepared data layers to identify areas with varying levels of sensitivity to landslides, ranging from very low to very high. Results and DiscussionThe results of the study revealed important findings related to landslides and their risk assessment in the Chesb Watershed, Zanjan Province. The sensitivity map generated by the SVM provided valuable insights into the areas prone to landslides. According to the sensitivity map, approximately 30.63% of the watershed area fell into the very low sensitivity class, indicating a lower likelihood of landslides in these areas. The low sensitivity class covered 17.82% of the area, suggesting a relatively lower risk of landslides. The moderate sensitivity class covered 15.43% of the area, indicating a medium level of landslide risk. The high sensitivity class encompassed 17.33% of the area, reflecting a considerable risk of landslides. Lastly, the very high sensitivity class covered 18.5% of the area, representing the highest risk of landslides. The efficiency of the SVM model was also evaluated using the Receiver Operating Characteristic (ROC) curve, and the area under the ROC curve (AUC) in the validation phase was found to be 0.874. This AUC value indicates a very good capability of the model in classifying and identifying landslide-prone areas in the Chesb catchment area.These findings were consistent with previous research on landslides and demonstrated the effectiveness of the SVM model in identifying landslide-prone areas. The sensitivity map derived from the model can be instrumental in land-use planning, disaster risk management, and decision-making processes aimed at minimizing the impact of landslides. ConclusionLandslides occur when masses of soil, rocks, and debris rapidly move downhill under the influence of gravity. it can be triggered by various factors, including heavy rainfall, seismic activities, slope instability, geological conditions, and human activities. Landslides can result in devastating consequences such as loss of life, property damage, disruption of transportation networks, and ecological disturbances. To address these challenges, landslide risk assessment provides a systematic framework for evaluating the likelihood and potential consequences of landslides in a given area. It involves the identification and analysis of key factors contributing to landslide occurrence, such as slope characteristics, geological formations, land use patterns, rainfall patterns, and human activities. By integrating these factors into a comprehensive risk assessment methodology, stakeholders can better understand the vulnerability of areas and populations at risk and develop appropriate strategies and measures to mitigate and manage landslide hazards.The research identified the most influential factors in landslides occurrence and developed a sensitivity map using a SVM. The findings highlighted the areas with varying levels of sensitivity to landslides, ranging from low to very high. These results can inform land-use planning strategies, allowing policymakers and stakeholders to better manage and mitigate the risk of landslides in the study area. The outcomes of this study contribute to the broader knowledge on landslides and provide valuable insights for disaster risk reduction efforts in the Chesb Watershed. The obtained sensitivity map can guide land managers, decision-makers, and authorities in implementing appropriate mitigation measures and ensuring the safety of the population and infrastructure in the area.