Instance segmentation has drawn mounting attention due to its significant ***,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level *** this pa...
详细信息
Instance segmentation has drawn mounting attention due to its significant ***,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level *** this paper,we present a conceptually efficient contour regression network based on the you only look once(YOLO)architecture named YOLO-CORE for instance *** mask of the instance is efficiently acquired by explicit and direct contour regression using our designed multiorder constraint consisting of a polar distance loss and a sector *** proposed YOLO-CORE yields impressive segmentation performance in terms of both accuracy and *** achieves 57.9%AP@0.5 with 47 FPS(frames per second)on the semantic boundaries dataset(SBD)and 51.1%AP@0.5 with 46 FPS on the COCO *** superior performance achieved by our method with explicit contour regression suggests a new technique line in the YOLO-based image understanding ***,our instance segmentation design can be flexibly integrated into existing deep detectors with negligible computation cost(65.86 BFLOPs(billion float operations per second)to 66.15 BFLOPs with the YOLOv3 detector).
In recent years due to increase in the number of customers and organizations utilize cloud applications for personal and professionalization become greater. As a result of this increase in utilizing the Cloud services...
详细信息
Scientific workflows have gained the emerging attention in sophisti-cated large-scale scientific problem-solving *** pay-per-use model of cloud,its scalability and dynamic deployment enables it suited for executing scien...
详细信息
Scientific workflows have gained the emerging attention in sophisti-cated large-scale scientific problem-solving *** pay-per-use model of cloud,its scalability and dynamic deployment enables it suited for executing scientific workflow *** the cloud is not a utopian environment,failures are inevitable that may result in experiencingfluctuations in the delivered *** a single task failure occurs in workflow based applications,due to its task dependency nature,the reliability of the overall system will be affected *** rather than reactive fault-tolerant approaches,proactive measures are vital in scientific workfl*** work puts forth an attempt to con-centrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm(IWDA)combined with an efficient machine learning approach-Support Vector Regression(SVR)for task failure prognostica-tion which facilitates proactive fault-tolerance in the scheduling of scientific workflow *** failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workfl*** experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques.
During the COVID-19 crisis, the need to stay at home has increased dramatically. In addition, the number of sickpeople, especially elderly persons, has increased exponentially. In such a scenario, home monitoring of p...
详细信息
During the COVID-19 crisis, the need to stay at home has increased dramatically. In addition, the number of sickpeople, especially elderly persons, has increased exponentially. In such a scenario, home monitoring of patientscan ensure remote healthcare at home using advanced technologies such as the Internet of Medical Things (IoMT).The IoMT can monitor and transmit sensitive health data;however, it may be vulnerable to various attacks. In thispaper, an efficient healthcare security system is proposed for IoMT applications. In the proposed system, themedical sensors can transmit sensed encrypted health data via a mobile application to the doctor for ***, three consortium blockchains are constructed for load balancing of transactions and reducing transactionlatency. They store the credentials of system entities, doctors' prescriptions and recommendations according to thedata transmitted via mobile applications, and the medical treatment process. Besides, cancelable biometrics areused for providing authentication and increasing the security of the proposed medical system. The investigationalresults show that the proposed system outperforms existing work where the proposed model consumed lessprocessing time by values of 18%, 22%, and 40%, and less energy for processing a 200 KB file by values of 9%,13%, and 17%. Finally, the proposed model consumed less memory usage by values of 7%, 7%, and 18.75%. Fromthese results, it is clear that the proposed system gives a very reliable and secure performance for efficientlysecuring medical applications.
Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remain...
详细信息
Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various
This paper presents a research study on the use of Convolutional Neural Network (CNN), ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile models to efficiently detect brain tumors in order to reduce the time requi...
详细信息
This research provides a time series forecasting model that is hybrid which combines Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models with moving averages. For modelling statio...
详细信息
In the dynamic era of online education, the pursuit of a personalized and effective learning experience is paramount. In this research, a sophisticated smart system for learning video recommendations is proposed at th...
详细信息
Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism wit...
详细信息
Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective *** bots’patterns or features over the network have to be analyzed in both linear and non-linear *** linear and non-linear features are composed of high-level and low-level *** collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier ***,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor ***,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets *** simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so ***,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's *** F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.
Point clouds offer realistic 3D representations of objects and scenes at the expense of large data volumes. To represent such data compactly in real-world applications, Video-Based Point Cloud Compression (V-PCC) conv...
详细信息
暂无评论