The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and ...The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and Euler deconvolution technology.The objective was to identify the distribution of igneous rocks in the China Seas and neighboring regions and investigate their relationships with petroliferous basins.Our results reveal that igneous rocks are widely scattered throughout the China Seas and neighboring regions,with the highest concentration in the northwest(NW)and the second highest concentration in the east-northeast(ENE).The largest-scale igneous rocks are those with a north-south(N-S)orientation,followed by those with northeast(NE),NW,and ENE orientations.The depths of igneous rocks within petroliferous basins typically range from 3 km to 9 km and are associated with hydrocarbon resource distributions characterized by deep oil and shallow gas.The proportions of igneous rocks in different types of basins exhibit varying correlations with the total hydrocarbon resources.In particular,the proportion of igneous rocks in rift-type basins in the China Seas exhibits a strong linear correlation with the total hydrocarbon resources.These research findings provide valuable guidance for studying the relationship between igneous rock distribution and petroliferous basins,offering insights that can inform future hydrocarbon exploration endeavors.展开更多
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and...This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.展开更多
The Internet of Things (IoT) has gained popularity and is widely used in modern society. The growth in the sizes of IoT networks with more internet‑connected devices has led to concerns regarding privacy and security....The Internet of Things (IoT) has gained popularity and is widely used in modern society. The growth in the sizes of IoT networks with more internet‑connected devices has led to concerns regarding privacy and security. In particular, related to the routing protocol for low‑power and lossy networks (RPL), which lacks robust security functions, many IoT devices in RPL networks are resource‑constrained, with limited computing power, bandwidth, memory, and bat‑tery life. This causes them to face various vulnerabilities and potential attacks, such as DIO neighbor suppression attacks. This type of attack specifcally targets neighboring nodes through DIO messages and poses a signifcant security threat to RPL‑based IoT networks. Recent studies have proposed methods for detecting and mitigating this attack;however, they produce high false‑positive and false‑negative rates in detection tasks and cannot fully protect RPL networks against this attack type. In this paper, we propose a novel fuzzy logic‑based intrusion detection scheme to secure the RPL protocol (FLSec‑RPL) to protect against this attack. Our method is built of three key phases consecu‑tively: (1) it tracks attack activity variables to determine potential malicious behaviors;(2) it performs fuzzy logic‑based intrusion detection to identify malicious neighbor nodes;and (3) it provides a detection validation and blocking mechanism to ensure that both malicious and suspected malicious nodes are accurately detected and blocked. To evaluate the efectiveness of our method, we conduct comprehensive experiments across diverse scenarios, including Static‑RPL and Mobile‑RPL networks. We compare the performance of our proposed method with that of the state‑of‑the‑art methods. The results demonstrate that our method outperforms existing methods in terms of the detection accuracy, F1 score, power consumption, end‑to‑end delay, and packet delivery ratio metrics.展开更多
基金The National Key Research and Development Program of China under contract No.2017YFC0602202.
文摘The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and Euler deconvolution technology.The objective was to identify the distribution of igneous rocks in the China Seas and neighboring regions and investigate their relationships with petroliferous basins.Our results reveal that igneous rocks are widely scattered throughout the China Seas and neighboring regions,with the highest concentration in the northwest(NW)and the second highest concentration in the east-northeast(ENE).The largest-scale igneous rocks are those with a north-south(N-S)orientation,followed by those with northeast(NE),NW,and ENE orientations.The depths of igneous rocks within petroliferous basins typically range from 3 km to 9 km and are associated with hydrocarbon resource distributions characterized by deep oil and shallow gas.The proportions of igneous rocks in different types of basins exhibit varying correlations with the total hydrocarbon resources.In particular,the proportion of igneous rocks in rift-type basins in the China Seas exhibits a strong linear correlation with the total hydrocarbon resources.These research findings provide valuable guidance for studying the relationship between igneous rock distribution and petroliferous basins,offering insights that can inform future hydrocarbon exploration endeavors.
基金funded by FCT/MECI through national funds and,when applicable,co-funded EU funds under UID/50008:Instituto de Telecomunicacoes.
文摘This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.
基金funded by a Royal Scholarship from Her Royal Highness Prin‑cess Maha Chakri Sirindhorn Education Project to Cambodia for 2020,faculty of College of Computing,Khon Kaen University.
文摘The Internet of Things (IoT) has gained popularity and is widely used in modern society. The growth in the sizes of IoT networks with more internet‑connected devices has led to concerns regarding privacy and security. In particular, related to the routing protocol for low‑power and lossy networks (RPL), which lacks robust security functions, many IoT devices in RPL networks are resource‑constrained, with limited computing power, bandwidth, memory, and bat‑tery life. This causes them to face various vulnerabilities and potential attacks, such as DIO neighbor suppression attacks. This type of attack specifcally targets neighboring nodes through DIO messages and poses a signifcant security threat to RPL‑based IoT networks. Recent studies have proposed methods for detecting and mitigating this attack;however, they produce high false‑positive and false‑negative rates in detection tasks and cannot fully protect RPL networks against this attack type. In this paper, we propose a novel fuzzy logic‑based intrusion detection scheme to secure the RPL protocol (FLSec‑RPL) to protect against this attack. Our method is built of three key phases consecu‑tively: (1) it tracks attack activity variables to determine potential malicious behaviors;(2) it performs fuzzy logic‑based intrusion detection to identify malicious neighbor nodes;and (3) it provides a detection validation and blocking mechanism to ensure that both malicious and suspected malicious nodes are accurately detected and blocked. To evaluate the efectiveness of our method, we conduct comprehensive experiments across diverse scenarios, including Static‑RPL and Mobile‑RPL networks. We compare the performance of our proposed method with that of the state‑of‑the‑art methods. The results demonstrate that our method outperforms existing methods in terms of the detection accuracy, F1 score, power consumption, end‑to‑end delay, and packet delivery ratio metrics.
