Artificial intelligence and knowledge management: A partnership between human and AI

Jarrahi, Mohammad Hossein; Askay, David A.; Eshraghi, Ali; Smith, Preston G.

doi:10.1016/j.bushor.2022.03.002

Cited by 122 publications

(45 citation statements)

References 25 publications

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“…Knowledge storage processes mean: Retention, perpetuation, searching, accessing, retrieval, and place. The process of storing knowledge indicates to the importance of organizational memory (Jarrahi et al, 2022). The institutions face a great danger as a result of losing a great amount of knowledge because of individuals leaving for various reasons (Peña, 2014).…”

Section: Storagementioning

confidence: 99%

Applying Knowledge Management Processes to ‎Improve Institutional Performance ‎

Massoudi

Birdawod

2023

Cihan U Erbil J H SOC SCI

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This study aimed to identify the role of knowledge management ‎processes in improving ‎the level of institutional performance in Cihan ‎University- Erbil, Iraq. For the purpose of ‎achieving the study objectives, a hypothesis has been formulated with two sub-‎hypotheses. The study relied on a descriptive analytical approach. The data were collected ‎by a questionnaire distributed randomly to 90 academic staff at Cihan University-Erbil ‎Campus, 74 valid responses were received. The results showed that knowledge ‎management ‎processes play an important role in improving the institutional performance ‎‎level of the institution studied. Also, the processes of ‎generating and storing knowledge ‎show have stronger correlation than the ‎processes of distributing and applying knowledge ‎in improving the institutional ‎performance level in the University. ‎The novelty of this ‏article comes from ‏its results and the set of ‎recommendations that will contribute to ‎raising the level of ‎institutional performance. ‎

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Section: Storagementioning

confidence: 99%

Applying Knowledge Management Processes to ‎Improve Institutional Performance ‎

Massoudi

Birdawod

2023

Cihan U Erbil J H SOC SCI

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“…However, for simplification, (2) describes the behavior of molecules in terms of parameters w i,r,m,j , w i,r,m,j = σ r,m,j H i,r,m,j , and H 0,r,m,j = 1. Given the cost function in (16) [32], partial error derivative with respect to parameter w i,r,m,j can be calculated using the chain rule, as shown in (17).…”

Section: Training With Amsgradmentioning

confidence: 99%

“…Finally, and having calculated all the partial derivatives, it is possible to use (17) to obtain the partial derivative of the error with respect to the parameters w i,r,m,j . The results are shown in Fig (21).…”

Section: Training With Amsgradmentioning

confidence: 99%

“…Therefore, appropriate training is a fundamental requirement for correct functioning of artificial neural networks. The most efficient deep-learning algorithms often use a supervised approach that uses large amounts of labelled data to enable accurate prediction of future invisible data [17]. Therefore, to train an artificial neural network propeller, three important parameters must be specified: an optimizer, which is the training algorithm that allows the neural network to gradually modify itself to fit the training data; a cost function, which is a function that allows us to determine the performance of the neural network based on the training data; and finally, metrics to visualize during the training and test phases, in which precision is usually used as a monitoring measure [18].…”

Section: Introductionmentioning

confidence: 99%

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A PID Control Algorithm With Adaptive Tuning Using Continuous Artificial Hydrocarbon Networks for a Two-Tank System

Sanchez-Palma

Ordonez-Avila

2022

IEEE Access

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Owing to their ease of implementation, proportional-integral-derivative (PID) control systems are widely used to control physical systems. However, when environmental disturbances or changes in system parameters occur, the complexity of tuning the gains of PID controllers increases because, in these cases, their performance decreases. To solve this problem, an AI-based online self-tuning algorithm adjusts the PID gains when system parameters are changed. Thus, the objective of this study was to develop a PID control algorithm with adaptive parameter tuning using artificial hydrocarbon networks, which is a supervised learning artificial intelligence technique inspired by hydrocarbon networks. Artificial hydrocarbon networks were designed to be trained using a prior set of data. Therefore, the motivation of this study was to demonstrate that they can be trained in real time for use in the control of nonlinear systems. Because this type of network has not been commonly used for this specific application, existing studies on adaptive control based on artificial neural networks were taken as a reference. The AMSGrad optimization algorithm was used to train the parameters in real time, for which a "continuous" model of artificial hydrocarbon networks was also proposed. Finally, an algorithm capable of adapting to variations in the operating conditions of a tank system was successfully designed, although its performance was similar to that of the Ziegler-Nichols method.

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“…This is especially important because machine learning is based on learning [8]. The nature of knowledge is said to be intimately tied to the capabilities and limits of AI and KM [9]. This study utilizes the natural language processing (NLP) and machine learning approach to develop a meaningful topics from a standard construction specific document.…”

Section: Introductionmentioning

confidence: 99%