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AWeb Application Fingerprint Recognition Method Based on Machine Learning

Computer Modeling in Engineering & Sciences 2024年第7期140卷 887-906页

作者： Yanmei Shi Wei Yu Yanxia Zhao Yungang Jia school of Integrated Circuit Science and Engineering Tianjin University of TechnologyTianjin300382China College of international business Zhejiang Yuexiu UniversityShaoxing312030China school of Public Administration Zhejiang Gongshang UniversityHangzhou310018China Tianjin Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China Tianjin300100China

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and *** fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web *** response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering *** method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality *** core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated *** transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint *** experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad *** novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

关键词： Web application fingerprint recognition unsupervised learning clustering algorithm feature extraction automated testing network security

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Is economic fitness helpful in achieving environmental sustainability in BRICS economies?A risk exposure perspective

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Chinese Journal of Population,Resources and Environment 2024年第2期22卷 109-117页

作者： Jabbar Ul-Haq Hubert Visas Muhlis Can Qazi Muhammad Adnan Hye Noreen Aslama Department of economics University of SargodhaSargodha 40100Pakistan school of international Trade&economics University of International Business and EconomicsBeijing 100029China Middle East University Amman 11831Jordan Vishi Higher Institute DoualaCameroon BETA Akademi Social Sciences Research Lab(SSR Lab)Istanbul 34000Turkey Academic Research and Development Wing DubaiUnited Arab Emirates

A country’s ability to create complex goods and diversify its lines of products is essential for addressing all types of *** a country’s vulnerability to extreme climatic events,such as droughts,superstorms,and other natural disasters,and its capacity for successful adaption,is an essential global need that has been *** study examines the role of economic fitness(EF)in addressing climate change risk ex‐posure in BRICS countries in the context of the environmental Kuznets curve using panel data from 1995 to *** threshold methodology is employed to ascertain the nonlinear relationship between EF and climate change risk exposure(i.e.,Notre Dame Global Adaptation Initiative Country Index(ND-GAIN)).In addition,empirical associations were estimated using panel-corrected standard errors,Driscoll-Kraay standard errors,and feasible generalized least squares estimation *** findings demonstrated an inverted N shaped link between EF and ***,even after controlling for significant ND-GAIN influencing variables such as gross domestic product per capita,financial development,and urbanization,our robustness checks revealed significant and consistent findings.

关键词： Economic fitness Climate change BRICS countries Environmental Kuznets Curve Notre Dame Index

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Forecasting measured responses of structures using temporal deep learning and dual attention

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 832-850页

作者： Viet-Hung DANG Trong-Phu NGUYEN Thi-Lien PHAM Huan X.NGUYEN Department of Structural Mechanics Hanoi University of Civil EngineeringHanoi 11600Vietnam Faculty of Applied Science University of Economics-Technology for IndustriesHanoi 11600Vietnam London Digital Twin Research Centre Faculty of Science and TechnologyMiddlesex UniversityLondon NW44BTUK international school Vietnam National UniversityHanoi 11600Vietnam

The objective of this study is to develop a novel and efficient model for forecasting the nonlinear behavior of structures in response to time-varying random *** key idea is to design a deep learning architecture to leverage the relationships,between external excitations and structure's vibration signals,and between historical values and future values,within multiple time-series *** proposed method consists of two main steps:the first step applies a global attention mechanism to combine multiple-measured time series and time-varying excitation into a weighted time series before feeding it to a temporal architecture;the second step utilizes a self-attention mechanism followed by a fully connected layer to predict multi-step future *** viability of the proposed method is demonstrated via two case studies involving synthetic data from a three-dimensional(3D)reinforced concrete structure and experimental data from an 18-story steel ***,comparison and robustness studies are carried out,showing that the proposed method outperforms conventional methods and maintains high performance in the presence of noise with an amplitude of less than 10%.

关键词： structural dynamic time-varying excitation deep learning signal processing response forecasting

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Leveraging Zibo Barbecue’s Success to Develop Hebei’s Culinary Brand:A Strategic Path

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Proceedings of business and Economic Studies 2024年第3期7卷 48-52页

作者： Chengguo E Yang Yang Ali Mao Duo Pan Foundation Department Hebei University of Environmental EngineeringQinhuangdao 066102China Hebei Institute of international business and economics Qinhuangdao 066311China

In 2023,Zibo barbecue culture exploded across the entire country,allowing people nationwide to experience and enjoy Zibo *** phenomenon injected new vitality into the economic development of *** promote the economic development of Hebei Province,this paper fully analyzes the reasons behind the popularity of Zibo barbecue,combines these insights with the characteristics of traditional cuisine in Hebei,and draws lessons from Zibo barbecue’s success as a business card for *** paper then outlines a strategy for building a culinary business card for Hebei.

关键词： Zibo barbecue Hebei traditional food Online platform City business card Government-enterprise cooperation

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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Stock Price Prediction Based on the Bi-GRU-Attention Model

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Journal of Computer and Communications 2024年第4期12卷 72-85页

作者： Yaojun Zhang Gilbert M. Tumibay Graduate school Angeles University Foundation Angeles Philippines SolBridge international school of business Woosong University Daejeon South Korea

The stock market, as one of the hotspots in the financial field, forms a data system with a huge volume of data and complex relationships between various factors, making stock price prediction an area of keen interest for further in-depth mining and research. Mathematical statistics methods struggle to deal with nonlinear relationships in practical applications, making it difficult to explore deep information about stocks. Meanwhile, machine learning methods, particularly neural network models and composite models, which have achieved outstanding results in other fields, are being applied to the stock market with significant results. However, researchers have found that these methods do not grasp the essential information of the data as well as expected. In response to these issues, researchers are exploring better neural network models and combining them with other methods to analyze stock data. Thus, this paper proposes the ABiGRU composite model, which combines the attention mechanism and bidirectional gated recurrent unit (GRU) that can effectively extract data features for stock price prediction research. Models such as LSTM, GRU, and Bi-LSTM are selected for comparative experiments. To ensure the credibility and representativeness of the research data, daily stock price indices of BYD are chosen for closing price prediction studies across different models. The results show that the ABiGRU model has a lower prediction error and better fitting effect on three index-based stock prices, enhancing the learning efficiency of the neural network model and demonstrating good prediction stability. This suggests that the ABiGRU model is highly adaptable for stock price prediction.

关键词： Machine Learning Attention Mechanism LSTM Neural Network ABiGRU Model Stock Price Prediction

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Valorization of Agricultural Residues for Hydrogen-Based Electricity Generation towards Circular Bioeconomy

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Open Journal of Energy Efficiency 2024年第2期13卷 57-78页

作者： Patience Afi Seglah Komikouma Apelike Wobuibe Neglo international Education school III Guangzhou College of Technology and Business Foshan China school of Foreign Languages Guangzhou College of Technology and Business Guangzhou China Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing China Institute of Agricultural economics and Development Chinese Academy of Agricultural Sciences Beijing China

Global crises, notably climate shocks, degraded ecosystems, and growing energy demand, enforce sustainable production and consumption pathways. A circular bioeconomy offers the opportunities to actualize resource and eco-efficiency enhancement, valorization of waste streams, reduction of fossil energy and greenhouse gas (GHG) emissions. Albeit biomass resources are a potential feedstock for bio-hydrogen (bio-H2) production, Ghana’s agricultural residues are not fully utilized. This paper examines the economic and environmental impact of bio-H2 electricity generation using agricultural residues in Ghana. The bio-H2 potential was determined based on biogas steam reforming (BSR). The research highlights that BSR could generate 2617 kt of bio-H2, corresponding to 2.78% of the global hydrogen demand. Yam and maize residues contribute 50.47% of the bio-H2 produced, while millet residues have the most negligible share. A tonne of residues could produce 16.59 kg of bio-H2 and 29.83 kWh of electricity. A total of 4,705.89 GWh of electricity produced could replace the consumption of 21.92% of Ghana’s electricity. The economic viability reveals that electricity cost is $0.174/kWh and has a positive net present value of $2135550609.45 with a benefit-to-cost ratio of 1.26. The fossil diesel displaced is 1421.09 ML, and 3862.55 kt CO2eq of carbon emissions decreased corresponding to an annual reduction potential of 386.26 kt CO2eq. This accounts for reducing 10.26% of Ghana’s GHG emissions. The study demonstrates that hydrogen-based electricity production as an energy transition is a strategic innovation pillar to advance the circular bioeconomy and achieve sustainable development goals.

关键词： Agricultural Residues Biogas Steam Reforming Bio-Hydrogen Electricity Circular Bioeconomy

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Exploring the impacts of major events on the systemic risk of the international energy market

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Petroleum Science 2024年第2期21卷 1444-1457页

作者： Ming-Tao Zhao Su-Wan Lu Lian-Biao Cui school of Statistics and Applied Mathematics Anhui University of Finance and EconomicsBengbu233030AnhuiChina Anhui Province Key Laboratory of Philosophy and Social Science of Low-Carbon Development and Carbon Finance Anhui University of Finance and EconomicsBengbu233030AnhuiChina

This study examines the systemic risk caused by major events in the international energy market(IEM)and proposes a management strategy to mitigate it. Using the tail-event driven network(TENET)method, this study constructed a tail-risk spillover network(TRSN) of IEM and simulated the dynamic spillover tail-risk process through the cascading failure mechanism. The study found that renewable energy markets contributed more to systemic risk during the Paris Agreement and the COVID-19pandemic, while fossil energy markets played a larger role during the Russia-Ukraine conflict. This study identifies systemically important markets(SM) and critical tail-risk spillover paths as potential sources of systemic risk. The research confirms that cutting off the IEM risk spillover path can greatly reduce systemic risk and the influence of SM. This study offers insights into the management of systemic risk in IEM and provides policy recommendations to reduce the impact of shock events.

关键词： international energy market Tail-risk spillover Cascading failure mechanism Systemic risk management

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Analysis of factors influencing carbon emissions in the Yangtze River Delta region and projections of carbon peak scenarios

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Ecological Economy 2024年第1期20卷 2-24页

作者： SHI Xiong-tian WU Feng-qing CHEN Yang DAI Li-li school of business Administration and Tourism Management Yunnan UniversityKunming 650500CHINA school of economics and Management Yanshan UniversityQinhaungdao 066000CHINA business school University of SouthamptonSouthampton SO152UAUK

Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon emissions in the Yangtze River Delta(YRD)through path analysis,and to forecast carbon emissions in the YRD from the baseline scenario,factor regulation scenario and integrated scenario to reach the *** results show that:(1)Jiangsu's high carbon emission pattern is the main reason for the YRD hindering the synergistic regulation of carbon emissions.(2)Human factors,institutional factors and economic growth factors can all contribute to carbon emissions in the YRD region,while technological and industrial factors can generally suppress carbon emissions in the YRD region.(3)Under the capital regulation scenario,the YRD region has the highest level of carbon emission synergy,with Jiangsu reaching its peak five years *** the balanced regulation scenario,the YRD region as a whole,Jiangsu,Zhejiang and Anhui reach the peak as scheduled.

关键词： Yangtze River Delta carbon peaking scenario forecasting STIRPAT model

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Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

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Digital Communications and Networks 2024年第2期10卷 498-508页

作者： Zhenkun Jin Yixuan Geng Chenlu Zhu Yunzhi Xia Xianjun Deng Lingzhi Yi Xianlan Wang Wuhan business University 430056China Wuhan Research Institute of Post and Telecommunication Wuhan430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecuritySchool of Cyber Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China Hubei Chutian Expressway Digital Technology Co.Ltd Wuhan430074China school of Information and Safety Engineering Zhongnan University of Economics and LawWuhan430073China

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited *** energy harvesting WSN is a novel network architecture to address the limitation of traditional ***,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical *** considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual *** Confident Information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor *** the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation *** simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

关键词： Energy harvesting WSN Deployment optimization Confident information coverage(CIC) Target perpetual coverage

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