Detecting and confirming MasterCard fraud (also known as “master card fraud detection”) is a popular course topic amongst students due to the usefulness of data processing and machine learning strategies in combatin...
Detecting and confirming MasterCard fraud (also known as “master card fraud detection”) is a popular course topic amongst students due to the usefulness of data processing and machine learning strategies in combating cybercrime. The three main parts of the proposed method are preprocessing, feature extraction, and model training. Preprocessing techniques are used to mine crucial information for identifying MasterCard fraud. Only by using the most advanced and efficient algorithms, the future transactions can be predicted with any degree of certainty. By employing these techniques, the proposed approach may confirm a variety of facts, including the correctness of authentication and the regularity of dealings. To accomplish feature extraction, the proposed approach used the PCA and SKM algorithms. In order to train the models following feature extraction, the proposed approach uses CNN, BiGRU, and BiGRU-CNN. Both the CNN and the BiGRU models are outperformed by the proposed model. With an approximate 98.87 percent accuracy, the proposed method is superior to other methods such as CNN and BiGRU.
Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between diffe...
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Due to flexibility and low-cost, unmanned aerial vehicles (UAVs) are increasingly crucial for enhancing coverage and functionality of wireless networks. However, incorporating UAVs into next-generation wireless commun...
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Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to *** as a key metric of community resilience is the social well-being of a community du...
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Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to *** as a key metric of community resilience is the social well-being of a community during a disaster,which is made up of mental and physical social *** factors influencing community resilience directly or indirectly are emotional health,emergency services,and the availability of critical infrastructures services,such as food,agriculture,water,transportation,electric power,and communications *** turns out that in computational social science literature dealing with community resilience,the role of these critical infrastructures along with some important social characteristics is not *** address these weaknesses,we develop a new multi-agent based stochastic dynamical model,standardized by overview,design concepts,details,and decision(ODD+D)protocol and derived from neuro-science,psychological and social sciences,to measure community resilience in terms of mental and physical *** this model,we analyze the micro-macro level dependence between the emergency services and power systems and social characteristics such as fear,risk perception,informationseeking behaviour,cooperation,flexibility,empathy,and experience,in an artificial ***,we simulate this model in two case studies and show that a high level of flexibility,experience,and cooperation enhances community *** for both theory and practice are discussed.
In recent years, Hong Kong has been experiencing severe traffic problems and especially traffic congestion. In this paper, we proposed a traffic monitoring system which is PC-based, no infrastructure requirement but s...
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The proliferation of smart mobile devices has catalyzed the growth of Mobile CrowdSourcing (MCS) as a distributed problem-solving paradigm. MCS platforms heavily rely on advanced truth inference techniques to extract ...
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Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network’s prediction mechanism must be interpretable to establish trus...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network’s prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB’s input regions with high mutual information with the network’s output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets—CNMC, ISBI2016, and BreaKHis—demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains.
Edge computing is now widely deployed across the globe. Although the Internet serves as the foundation for edge computing, the actual usefulness of this type of computing is seen when it is combined with the process o...
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Edge computing is now widely deployed across the globe. Although the Internet serves as the foundation for edge computing, the actual usefulness of this type of computing is seen when it is combined with the process of obtaining data from sensors and deriving relevant information from that data. It is predicted that in the not-too-distant era, most edge devices will be equipped with intelligent systems powered by deep learning. Unfortunately, in order to train, methods based on deep learning require a significant quantity of data of a high standard, and these methods are quite costly in terms of the amount of processing, memory, and power that they use. Distributed deep neural networks, or DDNNs, are something suggested by using distributed computing hierarchies. The cloud, fog, and devices make up these tiers. Although a DDNN can facilitate the interpretation of a DNN in the cloud, it also allows for interpretation to be carried out quickly and precisely on the edges and on end devices by making use of shallow parts of the neural network. With the help of a scalable cloud-based infrastructure, a DDNN can expand both in terms of volume of its neural network and the number of users it serves around the world. For DNN applications, the distributed nature of DDNNs results in improvements to sensor fusion, fault tolerance in the system, and data privacy. In order to implement DDNN, first the portions of a DNN are mapped onto a dispersed computing structure. By learning both components together, the devices' need become limited for both connectivity and energy while the model provided the value of the selected features in the cloud. The final product includes provision for instinctive sensor fusion as well as fault tolerance that has been built directly into the system. This study demonstrates as a proof of concept that a DDNN may make use of the geographical variety of sensors to improve the accuracy of object detection and lower the cost of communication. The suggested
Globally, surgical site infections are common complications that are both serious and costly. While telemedicine has enhanced the remote assessment of surgical wounds, it still faces limitations. This study introduces...
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ISBN:
(数字)9798331532352
ISBN:
(纸本)9798331532369
Globally, surgical site infections are common complications that are both serious and costly. While telemedicine has enhanced the remote assessment of surgical wounds, it still faces limitations. This study introduces a convolutional neural network (CNN) model designed to automatically classify digital images of surgical wounds as either altered or unaltered. The study utilized a dataset of 4,262 segmented and expert-labeled images. The CNN model achieved an accuracy of 83.46%, a sensitivity of 81.54%, and an AUROC of 92.22%. Although the MobileNet model demonstrated acceptable performance, it was less effective in comparison. The findings s uggest t hat C NNs a re e ffective for classifying images of surgical wounds, with potential for further improvement using advanced techniques and a multidisciplinary expert panel.
In heterogeneous SDN networks, controllers deploying different protocols need to cooperate with each other and establish a global network view that can be passed to the network applications for unified management and ...
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