Hasil untuk "Management information systems"

Y. Duan, J. Edwards, Yogesh Kumar Dwivedi

Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.

A. Shapiro, Darinka Dentcheva, A. Ruszczynski

Natasha Gude, T. Koponen, Justin Pettit et al.

Renée B. Adams, Daniel Ferreira

V. Sambamurthy, R. Zmud

Robert S. Kaplan, D. Norton

E. Wagner, Brian T. Austin, C. Davis et al.

R. Studer, Richard Benjamins, D. Fensel

Lukasz Golab, M. Tamer Özsu

Wil M.P. van der Aalst, A. Weijters, L. Măruşter

E. Wagner, Brian T. Austin, M. Korff

N. Damianou, Naranker Dulay, Emil C. Lupu et al.

Viet T. Dao, Ian M. Langella, Jerry Carbo

Dave Yates, Scott Paquette

Tamara Dinev, Heng Xu, Jeff Smith et al.

Tony Davila

Naresh Adhikari, Abdul Shaikh, Hafedh Al Shihi et al.

Background Ensuring the security and trustworthiness of a digitized and automated electoral process remains a significant challenge in democratic systems. As digital voting systems are increasingly being investigated worldwide, ensuring the integrity of the process using robust security measures is of great importance. This paper presents a simplified model to enhance electoral integrity by leveraging Blockchain technology in the context of Oman’s digital voting system. The model uses Blockchain technology to create a secure and trustworthy voting environment, addressing key vulnerabilities in digital electoral systems. Methods The research utilized a quantitative approach, employing an experimental design methodology using open-source software to simulate voting systems. Synthetic population data is used to operate these systems, while advanced biometric authentication technologies verify voter identities. Blockchain technology is leveraged to ensure secure vote recording, with smart contracts used to authenticate voters and securely record votes. Additionally, synchronous transactions are executed for both voter registration and voting processes, enhancing the overall security and efficiency of the system. Results The experimental results show that Blockchain enhances electoral integrity and security in Oman’s voting system, improving elections’ transparency and reliability. The performance evaluation of the model focuses on efficiency, reliability, and scalability metrics. Asynchronous transactions are utilized to improve processing time for voter registration and voting. Election administrators can manage, monitor, and certify election results, while Ethereum nodes ensure decentralized verification and transparency in the voting process. Conclusion This research offers insights for policymakers to consider Blockchain for electoral reforms, addressing issues like data integrity, fraud prevention, and transparency to boost voter trust. A strong regulatory framework and public awareness are crucial for successful implementation. Pilot projects are needed to assess Blockchain’s practical impact. Oman could lead global innovation in electoral technology, though infrastructure and public resistance challenges must be managed.

Yebao Wang, Wenhao Liu, Chuntao Chen et al.

Typhoons pose a significant threat to China’s coastal regions, resulting in substantial economic losses and casualties. Understanding the vulnerability of these areas to typhoon stress is crucial for effective disaster management and risk mitigation. This study assesses the vulnerability of China’s coastal provinces to typhoon disasters by integrating three key factors: exposure, sensitivity, and adaptability. The primary methodologies employed are the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) techniques. A comprehensive risk assessment framework is developed using 17 indicators, with AHP applied for indicator weighting and GIS used for spatial analysis and visualization of vulnerability patterns. The findings indicate considerable spatial variation in vulnerability, with southern provinces such as Guangdong, Guangxi, and Hainan exhibiting high vulnerability due to frequent typhoons, dense populations, and lower adaptive capacity. Southeastern regions, like Fujian and Zhejiang, show moderate to high vulnerability, while northern provinces such as Jiangsu, Hebei, and parts of Shandong and Liaoning experience lower vulnerability, attributed to reduced exposure and stronger disaster preparedness systems. These results underscore the importance of targeted disaster management strategies tailored to the specific vulnerabilities of each region.

Akhil Premkumar

Diffusion models transform noise into data by injecting information that was captured in their neural network during the training phase. In this paper, we ask: \textit{what} is this information? We find that, in pixel-space diffusion models, (1) a large fraction of the total information in the neural network is committed to reconstructing small-scale perceptual details of the image, and (2) the correlations between images and their class labels are informed by the semantic content of the images, and are largely agnostic to the low-level details. We argue that these properties are intrinsically tied to the manifold structure of the data itself. Finally, we show that these facts explain the efficacy of classifier-free guidance: the guidance vector amplifies the mutual information between images and conditioning signals early in the generative process, influencing semantic structure, but tapers out as perceptual details are filled in.

Antonina Kosikova, Apostolos Psaros, Andrew Smyth

System identification under unknown external excitation is an inherently ill-posed problem, typically requiring additional knowledge or simplifying assumptions to enable reliable state and parameter estimation. The difficulty of the problem is further amplified in structural systems subjected to natural hazards such as earthquakes or windstorms, where responses are often highly transient, nonlinear, and spatially distributed. To address this challenge, we introduce Spectrum and Physics-Informed Neural Networks (SaPINNs) for efficient input--state--parameter estimation in systems under complex excitations characteristic of natural hazards. The proposed model enhances the neural network with governing physics of the system dynamics and incorporates spectral information of natural hazards by using empirically derived spectra as priors on the unknown excitations. This integration improves inference of unmeasured inputs, system states, and parameters without imposing restrictive assumptions on their dynamics. The performance of the proposed framework is demonstrated through comparative studies on both linear and nonlinear systems under various types of excitation, including the El Centro earthquake, where the seismic spectrum is assumed to be not precisely known. To account for predictive uncertainty, the proposed architecture is embedded within a Deep Ensemble (DEns) networks architecture, providing distributions over possible solutions. The results demonstrate that the proposed approach outperforms conventional PINNs, as the incorporation of spectral information introduces an inductive bias that guides the network more effectively through the solution space and enhances its ability to recover physically consistent state and parameter estimates with realistic uncertainty levels.