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Using Artificial Intelligence and Deep Learning Methods to Analysis the Marketing Analytics and Its Impact on Human Resource Management Systems 5th

Using Artificial Intelligence and Deep Learning Methods to A...

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5th International Conference on Emerging Technologies in computer engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

作者： Gambhir, Vinima Asnate-Salazar, Edwin Prithi, M. Alvarado-Tolentino, Joseph Tongkachok, Korakod SVIMS Business School Wadala Maharashtra Mumbai India Computer Science and Engineering Universidad Nacional Santiago Antunez de Mayolo Huaraz Peru Computer Science Periyar University Tamil Nadu Vellore India Science and Engineering with a Major in Audit and Computer Security Universidad Nacional Santiago Antunez de Mayolo Huaraz Peru Faculty of Law Thaksin University Songkhla Thailand

ISBN: (纸本)9783031070112

In recent years, all firms have been concerned with marketing analytics. They’re using numerous advanced technologies to analyse marketing analytics. Artificial intelligence (AI) and Deep Learning (DL) technology are highly capable of examining large databases for patterns and insights. These technologies enable the marketing function to encompass its reach and analytics empower a deeper understanding of how the market responds to actions. With the help of Deep learning, businesses are now able to connect a wide range of datasets to better understand what customers want with greater sophistication and analytic capacity, and then use that information to gain a competitive advantage. In addition, Deep learning uses numerous technical tools that excel at extracting perceptions and patterns from huge amounts of data and then predicting the future for marketing. Thus, Deep learning can potentially be used to create products that are tailored to what customers want. It has been found that marketing analytics play a significant role in HRM. It helps in examining the employees’ skill sets and developing a training programme based on the market demands. AI assists firms in determining target audiences and devising a strategy to meet its objectives. Also, AI technology adapts and learns from data to make data-driven decisions. Many time-consuming and managerial chores will be automated by HR software that includes artificial intelligence. A lot of administrative activities are automated and speeded up using AI. Moreover, to gain in-depth knowledge of Artificial Intelligence and Deep learning this paper is conducted. Furthermore, the article examines how artificial intelligence and deep learning technologies are utilised to assess marketing statistics, as well as their impact on human resource management systems. For this paper, descriptive research methodology has been used for this study, and secondary data has been used to obtain reliable conclusions. © 2022, The Author(s),

关键词： Marketing

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DeepUserLog: Deep Anomaly Detection on User Log Using Semantic Analysis and Key-Value Data

DeepUserLog: Deep Anomaly Detection on User Log Using Semant...

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International Symposium on Software Reliability engineering (ISSRE)

作者： Wenjing Wang Shida Lu Jianhui Luo Chengrong Wu Network Information Security Audit and Monitoring Engineering Research Center of the Ministry of Education School of Computer Science Fudan University Shanghai China State Grid Shanghai Information & Telecommunication Company Shanghai China

Numerous studies have proven that abnormal behaviors related to business and transactions can be detected from user logs. In actual use, we discover that user logs are often formatted in complex ways, and there are challenges to analyzing them: (1) errors in log parsing that necessitate significant human intervention to resolve accurately, and (2) insufficient information mining. These two issues often result in increased human investment and reduced accuracy. To address these challenges, we propose DeepUserLog, a framework for anomaly detection of user logs containing a large number of key-value pairs. Our approach sidesteps the necessity of cumbersome preprocessing and a log parsing step that risks introducing noise. DeepUserLog retrieves the key-value pairs within the log and extracts the semantic features of the content after removing the values in key-value pairs, representing them as semantic vectors. In addition, the framework categorizes key-value pairs into four types while leveraging and identifying the temporal keys to uncover deeper connections between logs. Furthermore, it conducts a more thorough analysis of the information associated with numeric and text-based key-value pairs. DeepUserLog has been validated on real-world user log datasets from industry and public system log datasets, yielding promising results confirming its efficacy.

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Differentiating Malicious and Benign Android App Operations Using Second-Step Behavior Features

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Chinese Journal of Electronics 2019年第5期28卷 944-952页

作者： LI Pengwei FU Jianming XU Chao CHENG Binlin ZHANG Huanguo School of Information Engineering Nanjing Audit University School of Cyber Science and Engineering Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan University College of Computer Science and Technology Hubei Normal University

security-sensitive operations in Android applications(apps for short) can either be benign or malicious. In this work, we introduce an approach of static program analysis that extracts "second-step behavior features", i.e., what was triggered by the security-sensitive operation, to assist app analysis in differentiating between malicious and benign operations. Firstly, we summarized the characteristics of malicious operations, such as spontaneity, independence, stealthiness and continuity,which can be used to classify the malicious operations and benign ones. Secondly, according to these characteristics,Second step behavior features(SSBFs for short) have been presented, including structural features and semantic features. Thirdly, an analysis prototype named SSdroid has been implemented to automatically extract SSBFs of security-sensitive operations. Finally, experiments on 9285 operations from both benign and malicious apps show that SSBFs are effective and usefulness. Our evaluation results suggest that the second-step behavior can greatly assist in Android malware detection.

关键词： Android malware security-sensitive operations Static program analysis Classification

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Study of the efficiency of gas purification systems for metallurgical productions 4

Study of the efficiency of gas purification systems for meta...

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4th International Conference on Applied Physics, Information Technologies and engineering 2022, APITECH 2022

作者： Zolkin, A.L. Kolesnikov, A.S. Bityutskiy, A.S. Saradzheva, O.V. Stepina, I.V. Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics L. Tolstogo Street 23 Samara443010 Russia Department of Science of Production and Innovation M. Auezov South-Kazakhstan University Av. Tauke khan 5 Shymkent160012 Kazakhstan Invent Technology LLP AlmatyA10E5P4 Kazakhstan Economic Security Controlling and Audit Department A.N. Kosygin Russian State University Moscow115035 Russia National Research Moscow State University of Civil Engineering Yaroslavskoe Shosse 26 Moscow129337 Russia

One of the main tools used to study the technological processes of waste gases purification from metallurgical production is physical simulation. The problems of large-scale transitions that arise in the course of such a study can be circumvented by conducting experiments on industrial and (or) semi-industrial units. The article presents possible options for reduction of emissions of chlorine-containing components, taking into account the chemistry of the reactions occurring throughout the entire operational cycle of neutralization. © Published under licence by IOP Publishing Ltd.

关键词： Air purification

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Multipath based privacy protection method for data transmission in SDN 1st

Multipath based privacy protection method for data transmiss...

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1st EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019

作者： Dong, Na Han, Zhigeng Wang, Liangmin School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Zhenjiang China School of Information Engineering Nanjing Audit University Nanjing211815 China

ISBN: (纸本)9783030229702

With the development of Software-Defined Networking (SDN), privacy and security issues have become an urgent problem to be solved. Although there are many ways to solve these problems, the existing technology represented by encryption cannot effectively deal with traffic analysis attacks, and there are also key management problems. For this reason, we propose a privacy protection method for SDN data transmission based on multipath, including path searching procedure for searching for all paths between the sender and the receiver, and path filtering procedure for filtering out paths to reduce path correlation, and path selection procedure for randomly selecting one path to disturbed the traffic similarity between multiple transmission. The experiment results show that our method is more effective, less similarity of traffic compared with Multipath-Floyd method and single-path method, respectively. Moreover, it is difficult for attackers to capture the traffic feature and do not need key management, which reduces the cost of the controller. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2019 Published by Springer Nature Switzerland AG 2019. All Rights Reserved.

关键词： Data privacy

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Calculation of damage from discharges on example of ferroalloy plant

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AIP Conference Proceedings 2024年第1期2969卷

作者： V. V. Kuleshov A. L. Zolkin Yu. N. Koval O. V. Saradzheva S. V. Shamina Department of Industrial Ecology and Safety FSBEI HE Omsk State Technical University 11 Mira avenue Omsk 644050 Russia Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics L.Tolstogo Street 23 Samara 443010 Russia Department of forensic engineering and criminal science FSBEI HE Siberian Fire and Rescue Academy EMERCOM of Russia Severnaya Street 1 Zheleznogorsk Krasnoyarsk Krai 662972 Russia Economic security controlling and audit department A.N. Kosygin Russian State University Moscow 115035 Russia Department Natural science disciplines South Ural state agrarian University Gagarin street 13 Troitsk Chelyabinsk region 457100 Russia

Every enterprise faces the issues of waste generation and its disposal during implementation of the production process and manufacturing of finished products. Due to the fact that waste can be different, the process of disposal and recycling is different. Along with this, there is also waste that has an adverse impact on the environment, and consequently on the Earth's ecology. In this regard, there are mandatory rules for calculation of the caused damage. This study provides an analysis of the negative impact of various industrial wastes. Along with this, the analysis of emissions of adverse substances into the atmosphere and water bodies has been carried out on the example of the ferroalloy plantain the course of the study it was revealed that among the pollutants emitted into the atmosphere by the enterprise per year, the first place is taken by coal ash, followed by nitrogen dioxide. The selected organization in the process of calculation is classified as the first hazard class. Also the losses of the enterprise from atmospheric pollution have been calculated and measures to reduce them have been proposed.

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Distortion technique for hiding sensitive association rules

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous engineering 2014年第10期9卷 57-66页

作者： Srinivasa Rao, K. Suresh Babu, C.H. Damodaram, A. Kim, Tai-Hoon Department of Computer Science and Engineering Vignan’s University Vadlamudi Guntur Dist India Department of Computer Science and Engineering Sree Vaanmayi Institute of Science and Technology Bibinagar Nalgonda India Department of Computer Science and Engineering Academic Audit Cell JNTUH Kukatpally Hyderabad India Department of Convergence Security Sungshin Women's University 249-1 Dongseon-dong 3-ga Seoul Korea Republic of

Data mining is the process of extracting hidden patterns of data. Association rule mining is an important data mining task that finds interesting association among a large set of data item. It may disclose pattern and various kinds of sensitive information. Such information may be protected against unauthorized access. Association rule hiding is one of the techniques of privacy preserving data mining to protect the association rules generated by association rule mining. This paper adopts data distortion technique for hiding sensitive association rules. Algorithms based on this technique either hide a specific rule using data alteration technique or hide the rules depending on the sensitivity of the items to be hidden. In the proposed technique, positions of sensitive items are altered while maintaining the support. The proposed technique uses the idea of representative rules to prune the rules first and then hides the sensitive rules. © 2014 SERSC.

关键词： Association rules

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Data Storage Media Sanitization Procedures at the U.S. Agency For International Development (USAID)

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EDPACS 2006年第8期29卷 11-19页

作者： James Craft William Cleveland The Information Systems Security Officer at the United States Agency for International Development (USAID) Bureau for Management Information Resources Management (M/IRM) Information Systems Security Team. He has a BS in business management from George Mason University and has 22 years of experience focusing primarily on systems engineering cybersecurity and enterprise operations. He has played a major role in the implementation of the U.S. Government's Model Information Security Policy. The USAID PRIME Security Program Manager Computer Sciences Corporation in Arlington Virginia. He has MS degrees in information and telecommunications systems management and in military science. Cleveland has over 20 years of experience in the planning and management of a wide variety of functions including business process reengineering and IT security.

This article is derived from a Best security Practice (BSP) developed by the U.S. Agency for International Development (USAID), Bureau for Management, Information Resources Management (M/IRM), Information Systems security Team. The USAID is an independent agency that provides economic, development, and humanitarian assistance around the world in support of the foreign policy goals of the United States. It is located in the Ronald Reagan Building in Washington, D.C.

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An approach to the direct analysis of transient stability

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European Transactions on Electrical Power 1993年第3期3卷 241-248页

作者： Djukanovic, M.B. Sobajic, D.J. Pao, Y.‐H. Miodrag B. Djukanovic (1959) received his B.S. M.Sc. and Ph.D. degrees in Electrical Engineering from the University of Belgrade/Yugoslavia in 1982 1985 and 1992 respectively specializing in electric power systems. In 1984 he joined the Electrical Engineering Institute “Nikola Tesla” in Belgrade where he was working on the scientific studies in the field of power systems planning operation and control. In 1985 and 1990 he was appointed as a research scholar at the Royal Institute of Technology Stockholm and Case Western Reserve University Cleveland Ohio. His major in- terests are in the area of power system analysis steady-state and dynamic security and application of neural networks in electric power systems. (Electrical Engineering Institute “Nicola Tesla” ul. Koste Glavinica 8A YU-11000 Belgrad T +3811/2351-619 Fax + 3811/2351-823) Dejan J. Sobajic (1949) received the B.S.E.E. and the M.S.E.E. degrees from the University of Belgrade/Yugoslavia in 1972 and 1976 respectively and the Ph.D. degree from Case Western Reserve University Cleveland Ohio in 1988. At present he is with the Department of Electrical Engineering and Applied Physics Case Western Reserve University Cleveland. He is also the Engineering Manager of A1 WARE Inc. Cleveland. His current research interests include power system operation and control neuralnet systems and adaptive control. He is a member of the IEEE Task Force on Neural-Network Applications in Power Systems and of the IEEE Intelligent Controls Committee. He is the Chairman of the International Neural-Networks Society Special Interest Group on Power Engineering. (Case Western Reserve University Department of Electrial Engineering and Computer Sciences Glennan Building Ohio 44 106 USA T + 1216/421-2380 Fax +1216/368-8776) Yoh-Han Pao (1922) has been a Professor of Electrical Engineering and Computer Science at Case Westem Reserve University (CWRU) Cleveland Ohio since 1967. He has served as chairman of the University's Electrical Engineering Department

The Transient Energy Function (TEF) method has been intensely investigated over the last decade as a reliable and accurate tool for transient stability assessment of multimachine power systems. In this paper we propose the Potential Energy Boundary Surface (PEBS) based method for direct analysis of transient stability. The critical value of transient energy and the Critical Clearing Time (T_CCT) are evaluated without the need for successive calculation and memorization of the system kinetic and potential energy values along the sustained fault trajectory. The method is used for determination of output features in the training phase of the neural‐net based transient security assessment. The proposed approach is tested on the well‐known ten‐generator New England test system demonstrating the validity and accuracy of obtained results. Copyright © 1993 John Wiley & Sons, Ltd.

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