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AI-Driven HR Optimization Strategies in Finance and Marketing: Methodological Framework and Applications

AI-Driven HR Optimization Strategies in Finance and Marketin...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： R.S. Lekshmi V. Sheela Mary G. Arasuraja V. Krishnamoorthy S. Kaliappan R. Selvameena Department of Management Studies St.Joseph's College of Engineering Chennai Tamil Nadu India Department of Management Studies Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation Chennai Tamil Nadu India Department of Management Studies Kongu Engineering College Erode Tamil Nadu India Division of Research and Development Lovely Professional University Punjab India Department of Computer Science and Engineering Dr.M.G.R Educational and ResearchChennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proposed systems that include AI and the IoT present innovative financial and marketing opportunities. AI analytics capabilities and networked devices with the capability to provide real-time data mean the possibility to significantly improve recruitment, employee engagement, performance management, and retention. It combines natural language processing and machine learning algorithms, offering personalized recommendations, and in the vision insights, and in the case of environmental sensors, smart devices, and wearable health monitors, it facilitates accumulation of significant data. This framework will integrate IoT with AI to enhance HR procedures: more engaging, efficient, and enjoyable work environment. Apart from better decision-making and automated occupations, it also advocates for a data-driven approach to HRM. Further, it can result in the formation of intelligent and responsive HR systems that have the potential of improving organizational effectiveness and work productivity.

关键词： Performance evaluation Productivity Decision making Real-time systems Internet of Things Artificial intelligence smart devices intelligent sensors Wearable sensors Recruitment

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IO-SEA: Storage I/O and data Management for Exascale Architectures 21

IO-SEA: Storage I/O and Data Management for Exascale Archite...

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21st ACM international conference on computing Frontiers, CF 2024

作者： Medeiros, Daniel Gregory, Eric B. Couvee, Philippe Hawkes, James Gougeaud, Sebastien Gilliot, Maike Bressand, Olivier Valeri, Yoann Jaeger, Julien Chapon, Damien Bournaud, Frederic Strafella, Loïc Caviedes-Voullième, Daniel Tashakor, Ghazal Zjupa, Jolanta Holicki, Max Ridley, Tom Müller, Yanik Guimarães, Filipe Souza Mendes Frings, Wolfgang Mirus, Jan-Oliver Zhukov, Ilya Borba, Eric Rodrigues Moti, Nafiseh Salkhordeh, Reza Derbey, Nadia Mimouni, Salim Derr, Simon Gursoy, Buket Benek Grogan, James Furmánek, Radek Golasowski, Martin Slaninová, Kateåina Martinovič, Jan Faltýnek, Jan Wong, Jenny Cakircali, Metin Quintino, Tiago smart, Simon Iffrig, Olivier Narasimhamurthy, Sai Happ, Sonja Rauh, Michael Krempel, Stephan Wiggins, Mark Nováček, Jiåí Brinkmann, André Markidis, Stefano Denier, Philippe KTH Royal Institute of Technology Sweden Forschungszentrum Jülich Germany Eviden France European Centre for Medium-Range Weather Forecasts United Kingdom Commissariat À l'Énergie Atomique et Aux Énergies Alternatives France ParTec AG Germany IT4Innovations VSB-Technical University of Ostrava Czech Republic SEAGATE United Kingdom Central European Institute of Technology Czech Republic Irish Centre for High-End Computing Ireland Johannes Gutenberg University Mainz Germany

ISBN: (纸本)9798400704925

The new emerging scientific workloads to be executed in the upcoming exascale supercomputers face major challenges in terms of storage, given their extreme volume of data. In particular, intelligent data placement, instrumentation, and workflow handling are central to application performance. The IO-SEA project developed multiple solutions to aid the scientific community in adressing these challenges: a Workflow Manager, a hierarchical storage management system, and a semantic API for storage. All of these major products incorporate additional minor products that support their mission. In this paper, we discuss both the roles of all these products and how they can assist the scientific community in achieving exascale performance. © 2024 Owner/Author.

关键词： Supercomputers

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Zero-touch security automation mechanisms for edge NFV: the π-Edge approach 18

Zero-touch security automation mechanisms for edge NFV: the ...

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18th international conference on Network and Service Management (CNSM) - intelligent Management of Disruptive Network Technologies and Services

作者： Valantasis, Alexandros Psaromanolakis, Nikos Theodorou, Vasileios Intracom Telecom Athens Greece

ISBN: (纸本)9783903176515

The shift towards distributed computing architectures that push data storage and processing to the edge of the network, is resulting into a convergence of cloud-computing services and next generation mobile network technologies. In order to uniformly manage resources and services in the formed cloud/core to edge/devices continuum and to handle the diversity of multi-party underlying infrastructure technologies in a latency-aware, reliable and trustworthy fashion, management automation has become more crucial than ever. In this work, we present the security analytics mechanisms of the p-Edge platform-our edge management platform that embodies zerotouch automation features for interoperability, Quality of Service (QoS) assurance, resilience and trust. To this end, we introduce a declarative NFV MANO Information Model (IM) and methods for automatically enhancing Network Slices at the edge, with security services that i) continuously monitor user-plane traffic on the links between Virtual Network Functions (VNFs), ii) detect possible network vulnerabilities or malicious behaviour and iii) apply relevant actions to effectively observe and mitigate identified threats. The implementation of such mechanisms is evaluated through experimentation on a use case of DDoS attacking scenarios, showcasing the usability and the benefits of our proposed solution.

关键词： Edge computing Network slice security automation Platform as a Service Edge lifecycle management

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Production Scheduling for Energy Cost Optimization with Constraints: Comparison of Genetic Algorithm and PSO Techniques 19th

Production Scheduling for Energy Cost Optimization with Cons...

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19th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Mota, Bruno Faria, Pedro Ramos, Carlos GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9783031750120

intelligent production scheduling has become predominant in smart factories that aim to greatly reduce energy costs. For this purpose, scheduling systems need to not only consider multiple machines, PV energy, and electricity price volatility but also take into account constraint applicability and flexible job shop layouts to meet smart factories’ demands. To solve these complex scheduling problems, Artificial Intelligence (AI) is the ideal approach. Accordingly, the premise of the present paper is to implement and compare quantitatively three AI algorithms for intelligent production scheduling: a proposed Genetic Algorithm (GA), and a conventional GA and Particle Swarm Optimization (PSO) from the DEAP Python library. Different manufacturing concepts are considered to solve the aforementioned issues, including tasks, machines, products, and manufacturing orders. In addition, three constraints are explored: schedule completion deadline, manufacturing order deadline, and time leap. Validation of the AI algorithms for production scheduling is done through a real case study with real-production data. Results show that the proposed GA has, on average, lower energy costs of about 20.2% and 22.2% when compared to the conventional GA and PSO, respectively. Furthermore, when constraints were imposed, only the proposed GA was effective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Particle swarm optimization (PSO)

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Task Offloading in Edge computing System with Deep Reinforcement Learning and Reward Shaping

Task Offloading in Edge Computing System with Deep Reinforce...

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IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Huishi Zhao Yitong Liu Xingcheng Liu School of Electronics and Information Technology (SEIT) Sun Yat-sen University Guangzhou China Department of Electrical & Computer Engineering (ECE) University of Waterloo Waterloo Canada School of Information and Intelligent Engineering Guangzhou Xinhua University Guangzhou China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

The evolution of mobile internet has brought challenges to smart devices such as smartphones and VR headsets, only having limited computational power to handle intensive tasks. The advent of edge computing provides a solution, in which the smart devices are enabled to offload tasks to nearby servers, thus facilitating the necessary computations with reduced latency and energy consumption. This study focuses on the challenge of task offloading. In our proposed scheme, edge computing is considered as a Markov decision process (MDP). Hence, the deep Q-networks with reward shaping (DQN-RS) can be utilized to improve training effectiveness for sparse reward conditions. Experimental results show that this method DQN-RS significantly enhances DRL algorithm performance compared to a well hand-crafted greedy algorithm, demonstrating the efficacy of reinforcement learning in discovering optimal offloading strategies.

关键词： Greedy algorithms Training Energy consumption Real-time systems Servers Task analysis smart devices

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Edge computing and 5G Integration for Real-time analytics in Interoperable smart Grids

Edge Computing and 5G Integration for Real-time Analytics in...

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international conference on Power Energy, Environment and intelligent Control (PEEIC)

作者： Kskn Venkata Ramana Devi Aravinda Koithyar Sorabh Lakhanpal Ashish Parmar Vandana Arora Sethi Adnan Allwi Ftaiet Institute of Aeronautical Engineering Dundigal Hyderabad Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Lloyd Institute of Management and Technology Greater Noida Uttar Pradesh India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

In the rapidly evolving landscape of power and energy systems, the smart Grid (SG) emerges as a dynamic and adaptive solution to cater to the ever-increasing energy demands while ensuring sustainable growth. The core essence of a smart Grid is its ability to provide real-time analytics, facilitating efficient energy management and distribution. With the influx of a myriad of heterogeneous devices and systems in the grid, interoperability emerges as a crucial factor to ensure seamless operations. This paper delves deep into the symbiotic relationship between Edge computing (EC) and the fifth generation (5G) mobile networks, illustrating their potential to revolutionize real-time analytics in interoperable smart Grids. Edge computing, with its decentralized data processing capabilities, caters to the latency-sensitive needs of SG systems. On the other hand, 5G, with its high bandwidth and ultra-reliable low latency communication, offers robust support to EC operations. Their integration promises unprecedented improvements in the efficiency, reliability, and resilience of SGs. Through an exhaustive study, this paper highlights the architectural enhancements, challenges, and potential solutions, casting a vision for the future of intelligent energy systems.

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Park4U: A Blockchain-Based Parking Reservation and Management for smart Cities

Park4U: A Blockchain-Based Parking Reservation and Managemen...

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Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Amjad Aldweesh Mohammad Alauthman Ahmad Al-Qerem Mohammad Al Khaldy Saad Alateef Someah Alangari College of computing and IT Shaqra University Shaqra Saudi Arabia Faculty of Information Technology University of Petra Amman Jordan Faculty of Information Technology Zarqa University Zarqa Jordan Department of Business Intelligence and Data Analytics University of Petra Amman Jordan School of Computing Science Newcastle University Newcastle UK College of Computing and IT Shaqra University Aldawadmi Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Urbanization and increasing vehicles in smart cities have created challenges in managing parking efficiently. Traditional systems face issues like lack of transparency, security vulnerabilities, and poor stakeholder coordination. This paper introduces Park4U, a blockchain-based parking reservation and management system for smart cities. It takes advantage of blockchain's decentralized, secure, and transparent nature to improve efficiency, accessibility, and reliability of parking services. Park4U integrates IoT sensors, smart contracts, and a mobile application facilitating seamless reservation, payment, and management. The system aims to enhance resource utilization, reduce congestion, and provide a secure and transparent platform for stakeholders: drivers, operators, and authorities. An evaluation of performance and comparison with traditional systems are presented, showcasing blockchain-based approach's benefits. It also explores potential challenges and future research directions.

关键词： smart cities smart contracts Blockchains Mobile applications Stakeholders Security Reliability intelligent sensors Faces Vehicles

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computing task offloading based on multi-hop V2V optimization algorithm research

Computing task offloading based on multi-hop V2V optimizatio...

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international conference on intelligent Traffic Systems and smart City (ITSSC)

作者： Wu, Jiaxin Chen, Weizhe Zhu, Shunzhi Liu, Yang Xiamen Univ Technol Coll Comp & Informat Engn Xiamen Peoples R China

ISBN: (数字)9781510652071

ISBN: (纸本)9781510652071;9781510652064

In today's era when the original data is huge, useful information can only be extracted from the data through calculations and corresponding processing. Nowadays, the computing power of wireless access points, laptops, and mobile phones is comparable to that of computers more than a decade ago. However, most of the time, these computing resources are often idle, which greatly causes a waste of resources. In the Internet of Vehicles scenario, this problem has always been the focus of attention by scholars. The V2V (Vehicle to Vehicle) technology has solved this problem to a certain extent. This article considers the link failure time LET (Link Expiration Time) and the limit of the task allowable delay. Most of the equipment on the road uses external power supply and user equipment is powered by energy. If the transmission of the task cannot be completed within the effective time of the link, it will cause more energy consumption. This article aims at link failure time and task allowable delay Under the limitation of V2V multi-hop task, the total energy loss in the unloading process of the V2V multi-hop task is minimized.

关键词： V2V multi-hop algorithm task offload energy loss time delay link failure time

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Design and Analysis of Key Technologies for intelligent Parking Based on NB-Iot and High Precision Positioning Technology

Design and Analysis of Key Technologies for Intelligent Park...

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2023 international conference on Power, Communication, computing and Networking Technologies, PCCNT 2023

作者： Hu, Yingkun Pan, Zhongliang Chen, Pinzhong School of Computer and Information Engineering Guangdong Polytechnic of Industry and Commerce 963 Guangzhou Avenue North Tianhe District Guangzhou510510 China School of Electronics and Information Engineering South China Normal University Foshan528225 China

ISBN: (纸本)9781450399951

Traffic congestion and parking difficulties are currently urgent issues that need to be addressed in urban development. The development of smart parking has good prospects for solving this problem, and more and more cities are considering smart transportation construction as an important component of urban construction. Through extensive research, it has been found that the problem of parking difficulties is mainly reflected in two aspects: Firstly, it is difficult to find parking lots with available parking spaces. Due to the lack of a unified platform or channel for information dissemination in various parking lots in the city, drivers are not clear which parking lot has available parking spaces, and can only search for parking lots randomly, arbitrarily, and blindly;the second issue is that the parking position in parking lots cannot be accurately measured, and most of them can only be guided roughly by guidance screens, which cannot achieve precise navigation. When picking up a car, due to the lack of precise navigation, it is very inconvenient to pick up a car in a closed parking lot. Therefore, how to quickly and accurately find a suitable parking space is also a challenging task. Based on this, how to achieve high-precision indoor and outdoor parking location positioning, accurately and automatically identify the idle status of parking spaces, and build a low-cost, easy to deploy, safe, stable, and easy to maintain interconnected smart parking system is currently the key technology of smart parking systems. In response to the current issue of inaccurate positioning accuracy of parking spaces in large indoor parking lots, which seriously affects the experience of car owners' navigation and parking, this article uses Beidou high-precision differential technology and indoor positioning technology to accurately measure parking spaces, and integrates parking space precise positioning data into the NB IoT parking space positioning detector to improve parking space

关键词： Urban growth

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Blockchain-based decentralized federated learning: A secure and privacy-preserving system 23

Blockchain-based decentralized federated learning: A secure ...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-smartCity-DependSys 2021

作者： Zhao, Shuang Wu, Yalun Sun, Rui Qian, Xiaoai Zi, Dong Xie, Zhiqiang Tong, Endong Niu, Wenjia Liu, Jiqiang Han, Zhen Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing China

ISBN: (纸本)9781665494571

To solve the data silos and data security dilemma faced by machine learning (ML), the concept of federated learning (FL) was first proposed, and federated learning has also attracted great attention from the information security community in recent years, where the most concerned issue is the security of data and models in federated learning. The current centralized federated learning scheme is prone to single point of failure as well as central server evil problem, so blockchain is used to replace the central server, but the blockchain network is not immune to poisoning attacks by participants on the global model and inference attacks on participants' local data. To this end, in this paper, we propose a blockchain-based federated learning security and privacy protection framework (BFLSP), design a model storage scheme on the blockchain, and add a node scoring mechanism as well as a node election scheme to ensure the security of the model and data during the training process while guaranteeing the accuracy of the model. Finally, we design experiments to confirm the feasibility as well as the security of the framework. © 2021 IEEE.

关键词： Training smart cities Voting data security Resists Collaborative work data models

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