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crossrefFuture Internet2025-01-20Cited by 27

The Time Machine: Future Scenario Generation Through Generative AI Tools

Jan Ferrer i Picó, Michelle Catta-Preta, Alex Trejo Omeñaca, Marc Vidal, Josep Maria Monguet i Fierro

Contemporary society faces unprecedented challenges—from rapid technological evolution to climate change and demographic tensions—compelling organisations to anticipate the future for informed decision-making. This case study aimed to design a digital system for end-users called the Time Machine, which enables a generative artificial intelligence (GAI) system to produce prospective future scenarios based on the input information automatically, proposing hypotheses and prioritising trends to streamline and make the formulation of future scenarios more accessible. The system’s design, development, and testing progressed through three versions of prompts for the OpenAI GPT-4 LLM, with six trials conducted involving 222 participants. This iterative approach allowed for gradual adjustment of instructions given to the machine and encouraged refinement. Results from the six trials demonstrated that the Time Machine is an effective tool for generating future scenarios that promote debate and stimulate new ideas in multidisciplinary teams. Our trials proved that GAI-generated scenarios could foster discussions on +70% of generated scenarios with appropriate prompting, and more than half included new ideas. In conclusion, large language models (LLMs) of GAI, with suitable prompt engineering and architecture, have the potential to generate useful future scenarios for organisations, transforming future intelligence into a more accessible and operational resource. However, critical use of these scenarios is essential.

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crossrefFuture Internet2025-01-21Cited by 4

Sixth Generation Enabling Technologies and Machine Learning Intersection: A Performance Optimization Perspective

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The fifth generation (5G) of wireless communication is in its finalization stage and has received favorable reception in many nations. However, research is now geared towards the anticipated sixth-generation (6G) wireless network. The new 6G promises even more severe performance…

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crossrefFuture Internet2025-07-18Cited by 14

Enhancing Security in 5G and Future 6G Networks: Machine Learning Approaches for Adaptive Intrusion Detection and Prevention

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The evolution from 4G to 5G—and eventually to the forthcoming 6G networks—has revolutionized wireless communications by enabling high-speed, low-latency services that support a wide range of applications, including the Internet of Things (IoT), smart cities, and critical infrastr…

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crossrefFuture Internet2023-07-30Cited by 379

A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks

Vaia I. Kontopoulou, Athanasios D. Panagopoulos, Ioannis Kakkos, George K. Matsopoulos

In the broad scientific field of time series forecasting, the ARIMA models and their variants have been widely applied for half a century now due to their mathematical simplicity and flexibility in application. However, with the recent advances in the development and efficient de…

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crossrefFuture Internet2025-02-18Cited by 6

Beyond Firewall: Leveraging Machine Learning for Real-Time Insider Threats Identification and User Profiling

Saif Al-Dean Qawasmeh, Ali Abdullah S. AlQahtani

Insider threats pose a significant challenge to organizational cybersecurity, often leading to catastrophic financial and reputational damages. Traditional tools such as firewalls and antivirus systems lack the sophistication needed to detect and mitigate these threats in real ti…

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crossrefFuture Internet2024-10-28Cited by 6

Predicting the Duration of Forest Fires Using Machine Learning Methods

Constantina Kopitsa, Ioannis G. Tsoulos, Vasileios Charilogis, Athanassios Stavrakoudis

For thousands of years forest fires played the role of a regulator in the ecosystem. Forest fires contributed to the ecological balance by destroying old and diseased plant material; but in the modern era fires are a major problem that tests the endurance not only of government a…

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crossrefFuture Internet2025-03-19Cited by 3

A Distributed Machine Learning-Based Scheme for Real-Time Highway Traffic Flow Prediction in Internet of Vehicles

Hani Alnami, Imad Mahgoub, Hamzah Al-Najada, Easa Alalwany

Abnormal traffic flow prediction is crucial for reducing traffic congestion. Most recent studies utilized machine learning models in traffic flow detection systems. However, these detection systems do not support real-time analysis. Centralized machine learning methods face a num…

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