Social and juristic challenges of artificial intelligence

Perc, Matjaž; Özer, Mahmut; Hojnik, Janja

doi:10.1057/s41599-019-0278-x

Cited by 120 publications

(79 citation statements)

References 37 publications

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“…From the analysis in Fig. 6, it is clear that the accuracy, precision and recall of C4.5 are higher compared to other machine learning algorithms [7,23,47]. So, for traffic classification, we use the already existing C4.5 decision tree Fig.…”

Section: Performance Analysismentioning

confidence: 99%

Intelligent Reward-Based Data Offloading in Next-Generation Vehicular Networks

Raja

Ganapathisubramaniyan²,

Anbalagan

et al. 2020

IEEE Internet Things J.

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A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle's data traffic from congested cellular network's licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module.

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Section: Performance Analysismentioning

confidence: 99%

Intelligent Reward-Based Data Offloading in Next-Generation Vehicular Networks

Raja

Ganapathisubramaniyan²,

Anbalagan

et al. 2020

IEEE Internet Things J.

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“…In recent years, with the continuous development of technology and flexible changes in the organization [4], individuals are achieving their goals by organizing and managing various types of projects. A project is defined as a procedure in an individual's or a group's effort over a particular time period for a particular goal [5].…”

Section: Project Management Strategies In Multitaskingmentioning

confidence: 99%

A Sustainable Project Management Strategy against Multitasking Situations from the Viewpoints of Cognitive Mechanism and Motivational Belief

Park

2019

Sustainability

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In modern society, multitasking is necessary for a worker to accomplish a final goal by their deadline, which could be pursued for either a single goal or multiple goals. Moreover, a worker who has the authority to prioritize their tasks can make plans about the process of behavioral strategies to perform each task by making a to-do list. This strategy is a way of unpacking that which seems to affect the expectancy of goal attainment and heighten the value and importance of the goal. Otherwise, a worker could write a to-do list without specific action plans. These effects of unpacking and packing can be used as management strategies for multitasking engagement and could impact a worker’s cognition differently depending on the goal relations, including if there is a single goal or multiple goals. On the one hand, in pursuit of a single goal, unpacking can facilitate a worker’s judgment of the importance of the task. On the other hand, in pursuit of multiple goals, a worker’s judgment of one task’s importance can conflict with another task due to contradictory unpacking guidelines. Additionally, self-regulation as an intrinsic motivation empowers conscious intentions to neglect the cognitive effects of the to-do list. Therefore, those with low self-regulation tend to be encouraged by the effect of unpacking, but those with high self-regulation have the effects of unpacking inhibited. This theoretical model was constructed to identify the cognitive mechanism and the role of self-regulation on boundary conditions in regard to the different effects on unpacking. This study was confirmed via the two-way experiment (single- and multiple-goal x packing and unpacking) to explore the effects of the cognitive mechanism on task importance. The following test was performed via the three-way experiment, using an additional variable, the levels of self-regulation (low self-regulation and high self-regulation), to verify whether they inhibit cognitive effects. This study suggests that the judgment of task importance is different in accordance with goal relations, packing and unpacking, and self-regulations for sustainable management strategies of multitasking.

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“…The main objective of this article is to analyze the random process describing the sharing economy system at any moment of time in equilibrium. Versatile scientific approaches using modern technologies, including an artificial intelligence and a complex information systems, have been adapted for resolving social and economic challenges [18,19]. The theoretical approach to find the optimization of a sharing economy platform might be a basic clue for analyzing social and economic challenges to understand vital factors of the problems before adapting complex intelligent systems.…”

Section: Introductionmentioning

confidence: 99%

A Versatile Queuing System For Sharing Economy Platform Operations

Kim¹,

Yeun²

2019

Mathematics

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The paper deals with a sharing economy system with various management factors by using a bulk input G/M/1 type queuing model. The effective management of operating costs is vital for controlling the sharing economy platform and this research builds the theoretical background to understand the sharing economy business model. Analytically, the techniques include a classical Markov process of the single channel queueing system, semi-Markov process and semi-regenerative process. It uses the stochastic congruent properties to find the probability distribution of the number of contractors in the sharing economy platform. The obtained explicit formulas demonstrate the usage of functional for the main stochastic characteristics including sharing expenses due to over contracted resources and optimization of their objective function.AMS Classification: 60G10, 60J10, 60K15, 60K25, 60K20, 90B05, 90B22, 90B50, 90C15 * The paper has been submitted to an international journal in Economics area and currently under review.

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Social and juristic challenges of artificial intelligence

Cited by 120 publications

References 37 publications

Intelligent Reward-Based Data Offloading in Next-Generation Vehicular Networks

Intelligent Reward-Based Data Offloading in Next-Generation Vehicular Networks

A Sustainable Project Management Strategy against Multitasking Situations from the Viewpoints of Cognitive Mechanism and Motivational Belief

A Versatile Queuing System For Sharing Economy Platform Operations

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