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Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

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Computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Marwan Abu-Zanona Bassam Mohammad Elzaghmouri Farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of business intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

The future of competitive advantage in Oman: integrating green product innovation, Al, and intellectual capital in business strategies

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international Journal of innovation Studies 2024年第2期8卷 154-171页

作者： Fadi Abdelfattah Mohammed Salah Khalid Dahleez Riyad Darwazeh Hussam Al Halbusi Modern College of business and Science(MCBS) MuscatOman Dean College of business administration A'Sharqiyah UniversityIbraOman Head of Accounting Department Business SchoolAl-Ahliya Amman UniversityAmmanJordan Management Department Ahmed Bin Mohammed Military College(ABMMC)DohaQatar

This study delves into the dynamics of green product innovation,artificial intelligence(Al)adaption,and intellectual capital,investigating their impact on the competitiveness of firms in *** emphasizes the crucial role of government intervention and R&D investments in this *** on the responses of 214 top managers in Oman,the research employs structural equation modeling to analyze the intricate relationships between these *** findings underscore a significant positive correlation between green innovation,Al implementation,and intellectual capital,with government involvement and R&D investments as vital *** study provides a novel perspective on the synergy of technology,innovation,and intellectual capital in developing *** offers essential insights for business leaders,policymakers,and scholars,highlighting the necessity of integrating advanced technologies and sustainable practices in business strategies to achieve competitive *** research adds to the existing body of knowledge on innovation and *** offers practical implications for enhancing firm performance in Oman and similar emerging markets.

关键词： Green product innovation R&D investments Ai adoption intellectual capital Government involvement Knowledge-driven culture Oman

Analysis of Technical intelligence of Tumor Cell Therapy Based on Patent Map

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Asian Journal of Social Pharmacy 2024年第2期19卷 191-201页

作者： Liu Yue Zeng Yingying Li Xue Yuan Hongmei School of business administration Shenyang Pharmaceutical UniversityShenyang 110016China

Objective To analyze the technical information in the field of tumor cell therapy in China,and to provide reference for identifying technical trends and predicting technical *** Based on the patent data in the field of tumor cell therapy in China,the patent map method was used to construct a scientific technical information analysis ***,the technical status of new drug research and development in this field was explored to identify technical *** and Conclusion Studies have found that China’s tumor immunotherapy is in the growth *** technical innovation regions are mainly distributed in the east and innovative entities are *** hotspots are concentrated in areas such as A61P35,C12N5,and the patented technical efficacy is ***,the technical research and development opportunities identified are closely related to the background in this *** better promote the development of the industry,enterprises and research institutions should strengthen cooperation in technological innovation Meanwhile,they should pay attention to technical efficacy analysis to identify more technical opportunities,thereby effectively guiding innovation strategic decisions.

关键词： patent map tumor immunotherapy technical intelligence analysis technical opportunity

Preliminary Reflections on the Establishment of a New Energy System for the Achievement of Dual Carbon Goals

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China Oil & Gas 2024年第2期31卷 3-10页

作者： Zhang Yuqing National Energy administration

China has pledged to the world to achieve carbon peak in 10 years and carbon neutrality in 30 *** is an extremely arduous task,as it faces numerous challenges,including high energy consumption,heavy reliance on coal within its energy mix,and a large base of carbon emissions that must be *** this end,it is necessary to advance the new energy security strategy of“Four Revolutions,One Cooperation”to a deeper *** to interpretations from various parties,the new energy system is preliminarily summarized to have six features:new energy structure,new system form,new industrial system,new governance system,new system and mechanism,and new regulatory *** building a new energy system comprehensively,“Ten Commitments”have been proposed to help achieve the dual-carbon *** specific measures include:ensuring the security and stability of energy supply,accelerating the transformation to green and low-carbon energy,giving priority to energy conservation and efficiency improvement,promoting multi-energy complementation and synergistic and integrated development,enhancing the digital intelligence level in the energy industry,developing centralized and distributed energy,advancing the rural energy revolution,developing critical and core technological equipment and the comprehensive energy service industry,and promoting high-quality development of the Belt and Road initiative.

关键词： system Dual summarized

Predicting Hurricane Evacuation Decisions with interpretable Machine Learning Methods

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international Journal of Disaster Risk Science 2024年第1期15卷 134-148页

作者： Yuran Sun Shih‑Kai Huang Xilei Zhao Department of Civil and Coastal Engineering University of FloridaGainesvilleFL 32611USA Department of Emergency Management and Public administration Jacksonville State UniversityJacksonvilleAL 36265USA

Facing the escalating effects of climate change,it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency *** studies in this area often have relied on psychology-driven linear models,which frequently exhibited limitations in *** present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors,compared to existing models that mainly rely on psychological *** enhanced logistic regression model(that is,an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions(that is,univariate and bivariate threshold effects).Specifically,nonlinearity and interaction detection were enabled by low-depth decision trees,which offer transparent model structure and robustness.A survey dataset collected in the aftermath of Hurricanes Katrina and Rita,two of the most intense tropical storms of the last two decades,was employed to test the new *** findings show that,when predicting the households’ evacuation decisions,the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive *** outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.

关键词： Artifcial intelligence(Ai) Decision-making modeling Hurricane evacuation interpretable machine learning Nonlinearity and interaction detection

Multi-modal knowledge graph inference via media convergence and logic rule

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CAAi Transactions on intelligence Technology 2024年第1期9卷 211-221页

作者： Feng Lin Dongmei Li Wenbin Zhang Dongsheng Shi Yuanzhou Jiao Qianzhong Chen Yiying Lin Wentao Zhu School of information Science and Technology Beijing Forestry UniversityBeijingChina Engineering Research Center for Forestry-oriented intelligent information Processing National Forestry and Grassland AdministrationBeijingChina Michigan Technological University HoughtonMichiganUSA

Media convergence works by processing information from different modalities and applying them to different *** is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less *** address the issue,an inference method based on Media Convergence and Rule-guided Joint inference model(MCRJi)has been *** authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link ***,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic ***,logic rules of different lengths are mined from knowledge graph to learn new entity ***,knowledge graph inference is performed based on representing entities that converge multi-media *** experimental results show that MCRJi outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJi provides an excellent approach for knowledge graph inference with converged multi-media features.

关键词： logic rule media convergence multi-modal knowledge graph inference representation learning

An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

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Journal of Mountain Science 2024年第8期21卷 2739-2753页

作者： CHEN Jun HU Wang ZHANG Yu QiU Hongzhi WANG Renchao Sichuan Artificial intelligence Research institute Yibin 644000China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Architecture and Civil Engineering Chengdu UniversityChengdu 610106China

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and ***,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same *** order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MiC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement *** MiC,a suitable input sequence length is selected for the LSTM *** investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different *** teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological administration(CMA)and Reliable Prognosis(https://***)to improve the model’s expression capability,and the student model learns sequence information from other time *** attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention ***,the predicted displacement is obtained through a linear *** proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention *** achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PC

关键词： Landslide prediction MiC LSTM Attention mechanism Teacher Student model Prediction stability and interpretability

A core scientific problem in the treatment of central nervous system diseases:newborn neurons

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Neural Regeneration Research 2024年第12期19卷 2588-2601页

作者： Peng Hao Zhaoyang Yang Kwok-Fai So Xiaoguang Li Department of Neurobiology School of Basic Medical SciencesCapital Medical UniversityBeijingChina Guangdong-HongKong-Macao institute of CNS Regeneration Ministry of Education CNS Regeneration Collaborative Joint LaboratoryJinan UniversityGuangzhouGuangdong ProvinceChina Bioland Laboratory(Guangzhou Regenerative Medicine and Health Guangdong Laboratory) GuangzhouGuangdong ProvinceChina Department of Ophthalmology and State Key Laboratory of Brain and Cognitive Sciences The University of Hong KongHong Kong Special Administration RegionChina Center for Brain Science and Brain-inspired intelligence Guangdong-Hong Kong-Macao Greater Bay AreaGuangzhouGuangdong ProvinceChina Co-innovation Center of Neuroregeneration Nantong UniversityNantongJiangsu ProvinceChina Department of Biomedical Engineering School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina

it has long been asserted that failure to recover from central nervous system diseases is due to the system's intricate structure and the regenerative incapacity of adult *** over recent decades,numerous studies have established that endogenous neurogenesis occurs in the adult central nervous system,including humans'.This has challenged the long-held scientific consensus that the number of adult neurons remains constant,and that new central nervous system neurons cannot be created or ***,we present a comprehensive overview of the alterations and regulatory mechanisms of endogenous neurogenesis following central nervous system injury,and describe novel treatment strategies that to rget endogenous neurogenesis and newborn neurons in the treatment of central nervous system *** nervous system injury frequently results in alterations of endogenous neurogenesis,encompassing the activation,proliferation,ectopic migration,diffe rentiation,and functional integration of endogenous neural stem *** of the unfavorable local microenvironment,most activated neural stem cells diffe rentiate into glial cells rather than ***,the injury-induced endogenous neurogenesis response is inadequate for repairing impaired neural *** have attempted to enhance endogenous neurogenesis using various strategies,including using neurotrophic factors,bioactive materials,and cell reprogramming *** alone or in combination,these therapeutic strategies can promote targeted migration of neural stem cells to an injured area,ensure their survival and diffe rentiation into mature functional neurons,and facilitate their integration into the neural *** can integration re plenish lost neurons after central nervous system injury,by improving the local *** regulating each phase of endogenous neurogenesis,endogenous neural stem cells can be harnessed to promote effective regeneration of newborn ***

关键词： bioactive materials brain trauma endogenous neurogenesis hippocampal dentate gyrus neural stem cells neurotrophic factors newborn neurons spinal cord injury stroke subventricular zone

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise information Systems

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Computers, Materials & Continua 2024年第8期80卷 3123-3138页

作者： Saket Sarin Sunil K.Singh Sudhakar Kumar Shivam Goyal Brij Bhooshan Gupta Wadee Alhalabi Varsha Arya Chandigarh College of Engineering and Technology Chandigarh160019India Department of Computer Science and information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for information Technology(SCiT) Symbiosis International UniversityPune411057India Center for interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of business administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut11022801Lebanon UCRD Chandigarh UniversityChandigarh140413India

in the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial intelligence(Ai)and Algorithmic *** in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable Ai(XAi)within Fintech,aiming to refine Algorithmic Trading *** meticulous examination,we uncover the nuanced interactions of Ai-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading *** Ai-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital *** research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital ***,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

关键词： Neurodynamic Fintech multi-agent reinforcement learning algorithmic trading digital financial frontier