This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation...
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The earthquake early warning (EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is...
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The earthquake early warning (EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific information. In Japan's earthquake magnitude dataset, there is a chance of a high imbalance concerning the earthquakes above strong impact. This imbalance causes a high prediction error while training advanced machine learning or deep learning models. In this work, Conditional Tabular Generative Adversarial Networks (CTGAN), a deep machine learning tool, is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information. The result obtained using actual and mixed (synthetic and actual) datasets will be used for training the stacked ensemble magnitude prediction model, MagPred, designed specifically for this study. There are 13295, 3989, and 1710 records designated for training, testing, and validation. The mean absolute error of the test dataset for single station magnitude detection using early three, four, and five seconds of P wave are 0.41, 0.40, and 0.38 MJMA. The study demonstrates that the Generative Adversarial Networks (GANs) can provide a good result for single-station magnitude prediction. The study can be effective where less seismic data is available. The study shows that the machine learning method yields better magnitude detection results compared with the several regression models. The multi-station magnitude prediction study has been conducted on prominent Osaka, Off Fukushima, and Kumamoto earthquakes. Furthermore, to validate the performance of the model, an inter-region study has been performed on the earthquakes of the India or Nepal region. The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods. This has a high potential
Recent years have witnessed notable progressions in facial recognition technology which have been led by the inception of deep learning models-primarily Siamese Neural Networks. This article delves into the use of Sia...
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This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic *** in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from ...
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This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic *** in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault *** biologically inspired strategies allow for effective solutions to intricate physical *** its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization *** utility and benefits have found traction in numerous academic *** its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference *** paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization *** trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.
作者：
A.E.M.EljialyMohammed Yousuf UddinSultan AhmadDepartment of Information Systems
College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science
College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia
Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks i...
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Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.
Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-...
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Purpose-The Internet of Things(IoT)cloud platforms provide end-to-end solutions that integrate various capabilities such as application development,device and connectivity management,data storage,data analysis and dat...
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Purpose-The Internet of Things(IoT)cloud platforms provide end-to-end solutions that integrate various capabilities such as application development,device and connectivity management,data storage,data analysis and data *** high use of these platforms results in their huge availability provided by different ***,choosing the optimal IoT cloud platform to develop IoT applications successfully has become *** key purpose of the present study is to implement a hybrid multi-attribute decision-making approach(MADM)to evaluate and select IoT cloud ***/methodology/approach-The optimal selection of the IoT cloud platforms seems to be dependent on multiple ***,the optimal selection of IoT cloud platforms problem is modeled as a MADM problem,and a hybrid approach named neutrosophic fuzzy set-Euclidean taxicab distance-based approach(NFS-ETDBA)is implemented to solve the ***-ETDBA works on the calculation of assessment score for each alternative,*** cloud platforms,by combining two different measures:Euclidean and taxicab ***-A case study to illustrate the working of the proposed NFS-ETDBA for optimal selection of IoT cloud platforms is *** results obtained on the basis of calculated assessment scores depict that“Azure IoT suite”is the most preferable IoT cloud platform,whereas“Salesman IoT cloud”is the least ***/value-The proposed NFS-ETDBA methodology for the IoT cloud platform selection is implemented for the first time in this *** is highly capable of handling the large number of alternatives and the selection attributes involved in any decision-making ***,the use of fuzzy set theory(FST)makes it very easy to handle the impreciseness that may occur during the data collection through a questionnaire from a group of experts.
Diabetes has become one of the significant reasons for public sickness and death in worldwide. By 2019, diabetes had affected more than 463 million people worldwide. According to the International Diabetes Federation ...
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Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encounte...
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Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.
Forensic science is the application of Scientific methods to resolve crime and legal issues. It involves various disciplines, such as computerscience, Biology, Chemistry and Anthropology. Forensic scientists examine ...
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Forensic science is the application of Scientific methods to resolve crime and legal issues. It involves various disciplines, such as computerscience, Biology, Chemistry and Anthropology. Forensic scientists examine and analyze evidence from crime scenes, such as fingerprints, DNA, blood, or weapons. Digital proof is one of the forms of forensic evidence. It provide real time eye witness of the incident. Video recordings enable investigators to find out what exactly has transpired. Investigators use video evidence as a source for witness statements, and it aids in the search for the missing person or suspect. Video evidence is also used to testify in court and help with investigations and prosecutions. Failure of forensic science results in wrong judgement convicting innocent people and escaping criminals [1]. For most crimes high quality video recordings are often not available. video quality issues such as blurry, speckled, pixelated and low-resolution videos captured at low light are a real challenge in forensic analysis. To address such issues in this research a hybrid model using set of filters including triplemask spatial linear filter, median filter and bilateral filters are used. For denoising images, a novel image filter using sliding window convolution is proposed. For image sharpening a triplemask spatial linear filter is proposed. Triplemask spatial linear filter is created by cascading a series of filters. Identity, shift and fraction-based approach is used in mask processing. For image smoothing and to preserve the edges bilateral filter is used [2]. The performance of convolution operation is compared with distinct convolution, shift rotational convolution and scipy convolution. To handle uncertainty, imprecision, and ambiguity in real-world image data in a precise manner neutrosophic science is used in image analysis. By the generated neutrosophic set of the given input image ambiguous regions in the image are detected. Feature selection is made by
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