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8th IEEE International Conference on Big Data Security on Cloud, 8th IEEE International Conference on High Performance and Smart computing, and 8th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Zhong, Luoyifan QILU Institute of Technology School of Computing Science and Information Engineering Jinan China

ISBN: (纸本)9781665480697

Tourism is one of the main sources of income in Australia. The number of tourists will affect airlines, hotels and other stakeholders. Predicting the arrival of tourists can make full preparations for welcoming tourists. This paper selects Queensland Tourism data as intelligent data. Carry out data visualization around the intelligent data, establish seasonal ARIMA model, find out the characteristics and predict. In order to improve the accuracy of prediction. Based on the tourism data around Queensland, build a 10 layer Back Propagation neural network model. It is proved that the network shows good performance for the data prediction of this paper. © 2022 IEEE.

关键词： Forecasting

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Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1552-1562页

作者： Dao H.Vu Kashem M.Muttaqi Ashish P.Agalgaonkar Arian Zahedmanesh Abdesselam Bouzerdoum School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongAustralia Information and Computing Technology Division College of Science and EngineeringHamad Bin Khalifa UniversityDohaQatar IEEE

The incorporation of weather variables is crucial in developing an effective demand forecasting model because electricity demand is strongly influenced by weather *** dependence of demand on weather conditions may change with time during a ***,the time stamped weather information is *** this paper,a multi-layer moving window approach is proposed to incorporate the significant weather variables,which are selected using Pearson and Spearman correlation *** multi-layer moving window approach allows the layers to adjust their size to accommodate the weather variables based on their significance,which creates more flexibility and adaptability thereby improving the overall performance of the proposed ***,a recursive model is developed to forecast the demand in multi-step *** electricity demand data for the state of New South Wales,Australia are acquired from the Australian Energy Market Operator and the associated results are reported in the *** results show that the proposed approach with dynamic incorporation of weather variables is promising for day-ahead and week-ahead load demand forecasting.

关键词： Autoregressive(AR)model load forecasting multi-layer moving window Pearson correlation Spearman correlation

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A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

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Computer Modeling in Engineering & sciences 2024年第4期139卷 783-800页

作者： Lejun Zhang Junjie Zhang Kentaroh Toyoda Yuan Liu Jing Qiu Zhihong Tian Ran Guo College of Information Engineering Yangzhou UniversityYangzhou225127China Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou510006China Research and Development Center for E-Learning Ministry of EducationBeijing100039China Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A∗STAR)Singapore138632Singapore School of Physics and Materials Science Guangzhou UniversityGuangzhou510006China

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment *** the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model *** engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small *** this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and *** innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping ***,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization *** final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as *** testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful *** addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

关键词： Bitcoin multi-service classification graph maximization consensus data security

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Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter Data

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Computers, Materials & Continua 2023年第4期75卷 81-97页

作者： Bahjat Fakieh Abdullah S.AL-Malaise AL-Ghamdi Farrukh Saleem Mahmoud Ragab Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia

The outbreak of the pandemic,caused by Coronavirus Disease 2019(COVID-19),has affected the daily activities of people across the *** COVID-19 outbreak and the successive lockdowns,Twitter was heavily used and the number of tweets regarding COVID-19 increased *** studies used Sentiment Analysis(SA)to analyze the emotions expressed through tweets upon ***,in current study,a new Artificial Bee Colony(ABC)with Machine Learning-driven SA(ABCMLSA)model is developed for conducting Sentiment Analysis of COVID-19 Twitter *** prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made *** involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature *** identification and classification of the sentiments,the Support Vector Machine(SVM)model is *** last,the ABC algorithm is applied to fine tune the parameters involved in *** demonstrate the improved performance of the proposed ABCML-SA model,a sequence of simulations was *** comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches.

关键词： Sentiment analysis twitter data data mining COVID-19 machine learning artificial bee colony

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An Efficient Association Rule Mining from Distributed Medical Database for Predicting Heart Disease 6

An Efficient Association Rule Mining from Distributed Medica...

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6th International Conference on computing Methodologies and Communication, ICCMC 2022

作者： Kumar, Aswin Gowri, S. David, John Wilifred Bevish Jinila, Y. Sathyabama Institute of Science and Technology School of Computing Department of Information Technology Chennai600 119 India

ISBN: (纸本)9781665410281

Naive Bayes classification categorization in machine learning is employed to check the patient's entire heart illness in this proposed work. As a result, the percentage of patients that contract disease as both positive and negative data is used. Most database management systems and desktop analytics and visualization applications make working with big data difficult. As a result of this machine learning can be employed from the standpoint of data mining, and the proposal displays a machine learning methodology. The classifiers are used to process heart percentages, and the results are given as a confusion matrix. In the presence of a training dataset, a unique classification strategy is introduced that can effectively increase classification performance. Heart disease stent diagnostic In addition, the generated method has a high identification of rates, making It's a useful tool for junior cardiologists to check the cardio vascular patients with a high risk for certain diseases and refer them to expert cardiologists for further evaluation. © 2022 IEEE.

关键词： Diseases

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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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BEVUDA++: Geometric-Aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 5109-5122页

作者： Zhang, Rongyu Liu, Jiaming Li, Xiaoqi Chi, Xiaowei Wang, Dan Du, Li Du, Yuan Zhang, Shanghang Nanjing University School of Electronic Science and Engineering Nanjing210093 China The Hong Kong Polytechnic University Department of Computing Hung Hom Hong Kong Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science Beijing100871 China The Hong Kong University of Science and Technology Academy of Interdisciplinary Studies Hung Hom Hong Kong

Vision-centric Bird’s Eye View (BEV) perception holds considerable promise for autonomous driving. Recent studies have prioritized efficiency or accuracy enhancements, yet the issue of domain shift has been overlooked, leading to substantial performance degradation upon transfer. We identify major domain gaps in real-world cross-domain scenarios and initiate the first effort to address the Domain Adaptation (DA) challenge in multi-view 3D object detection for BEV perception. Given the complexity of BEV perception approaches with their multiple components, domain shift accumulation across multi-geometric spaces (e.g., 2D, 3D Voxel, BEV) poses a significant challenge for BEV domain adaptation. In this paper, we introduce an innovative geometric-aware teacher-student framework, BEVUDA++, to diminish this issue, comprising a Reliable Depth Teacher (RDT) and a Geometric Consistent Student (GCS) model. Specifically, RDT effectively blends target LiDAR with dependable depth predictions to generate depth-aware information based on uncertainty estimation, enhancing the extraction of Voxel and BEV features that are essential for understanding the target domain. To collaboratively reduce the domain shift, GCS maps features from multiple spaces into a unified geometric embedding space, thereby narrowing the gap in data distribution between the two domains. Additionally, we introduce a novel Uncertainty-guided Exponential Moving Average (UEMA) to further reduce error accumulation due to domain shifts informed by previously obtained uncertainty guidance. To demonstrate the superiority of our proposed method, we execute comprehensive experiments in four cross-domain scenarios, securing state-of-the-art performance in BEV 3D object detection tasks, e.g., 12.9% NDS and 9.5% mAP enhancement on Day-Night adaptation. © 1991-2012 IEEE.

关键词： Teaching

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Binary Image Steganalysis Based on Symmetrical Local Residual Patterns

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Chinese Journal of Electronics 2022年第4期31卷 752-763页

作者： LUO Junwei YU Mujian YIN Xiaolin LU Wei School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security TechnologyMinistry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University

Residual computation is an effective method for gray-scale image steganalysis. For binary images, the residual computation calculated by the XOR operation is also employed in the local residual patterns(LRP) model for steganalysis. A binary image steganalytic scheme based on symmetrical local residual patterns(SLRP) is proposed. The symmetrical relationships among residual patterns are introduced that make the features more compact while reducing the dimensionality of the features set. Multi-scale windows are utilized to construct three SLRP submodels which are further merged to construct the final features set instead of a single *** with higher probability to be modified after embedding are emphasized and selected to construct the feature sets for training the support vector machine classifier. The experimental results show that the proposed steganalytic scheme is effective for detecting binary image steganography.

关键词： information hiding Steganalysis Symmetrical local residual patterns Multi-scale model

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VVNet:Underwater Object Detection Network Based on Vision Transformer and Vision Retnet for Underwater Robot Picking

VVNet:Underwater Object Detection Network Based on Vision Tr...

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2024 International Symposium on Digital Home, ISDH 2024

作者： Liu, Jiaji Liu, ZhiTao Wang, YePeng Li, Fang School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China College of Engineering and Computing University of South Carolina Columbia United States

ISBN: (纸本)9798331509873

Underwater object detection is an important task in computer vision, but it is usually difficult due to the complex underwater environment. In recent years, underwater object detection methods based on deep learning have been proposed and have achieved promising results. However, there still exist issues with weaker extraction of global features from images. To tackle this issue, we propose a novel neural network, VVNet, which incorporates the newly introduced RMTBFA and CVit modules, achieving superior performance in extracting global features. RMTBFA integrates the core module of the Vision Retinet model, the Manhattan Attention Mechanism, while CVit incorporates the Vision Transformer module, which facilitates the extraction of global features from images. Furthermore, we introduce Shape-IoU as a loss function for IoU, which further enhances the accuracy of our model. Finally, we tested on the two datasets of URPC2017 and URPC2018. The results showed that our network model improved the underwater object detection task indicators. © 2024 IEEE.

关键词： Deep learning

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Analysis of 5G+ Internet of Vehicles Application Based on SPN 12

Analysis of 5G+ Internet of Vehicles Application Based on SP...

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12th International Conference on information technology in Medicine and Education, ITME 2022

作者： Ting, Li Bin, Liu Fuzhou Institute of Technology School of Computing and Information Science Fuzhou350506 China

ISBN: (纸本)9798350310153

SPN is an innovative technology system proposed for 5G transmission. SPN can realize comprehensive service transmission with large bandwidth, low delay and high efficiency in Internet of Vehicles, but there are still problems to be resolved. In this paper, the slicing function and service carrying capacity of SPN system are analyzed by constructing SPN test network. The test data show that the management and control planes of the slice function can greatly reduce the complexity of service opening and maintenance, and the forwarding plane can realize the function of isolation and flexible bandwidth scheduling of the IoV service based on FlexE technology, and the combination of service bearing and Service Router can effectively reduce the risk of service interruption and improve the network security performance. © 2022 IEEE.

关键词： Network security

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