Journal of Computational and Experimental Science

Research on Intelligent Safety Management Innovation Enabled by Artificial Intelligence and Forklift Internet of Things: A Data-Driven Risk Prediction Perspective

Qirong Shen — 2026-09-04

To address the prominent pain points of high accident frequency, lagging hazard early warning, and extensive empirical management in traditional forklift operation safety management, this paper proposes a full-process intelligent safety management innovation framework enabled by artificial intelligence (AI) and forklift Internet of Things (IoT), from the perspective of data-driven risk prediction. Against the background of deep integration of Industry 4.0 and intelligent logistics, the framework takes industrial safety governance theory and systems engineering methodology as theoretical guidance, and realizes deep integration of multi-source forklift IoT perception data, standardized full-link data governance, and AI-driven quantitative risk prediction models through a four-layer edge-cloud collaborative architecture. On the basis of data standardization and quality control, it constructs four core mathematical models: multi-dimensional operation risk assessment, improved XGBoost collision risk prediction, dynamic early warning resource scheduling optimization, and comprehensive safety performance evaluation, and builds a closed-loop management mechanism of "perception-analysis-warning-disposal-evaluation". Experimental validation based on real large-scale warehouse logistics scenarios shows that the framework significantly improves data integrity and consistency, reduces the overall accident rate of forklift operations by more than 65%, shortens the response time of safety incidents by over 80%, and enhances the scientificity and foresight of safety management decisions. The research results provide a feasible technical implementation path for the digital and intelligent transformation of industrial site safety management, and have important reference value for promoting the application of AI and IoT technologies in the field of industrial safety.

AI-Amplified Cyber Warfare and Critical Infrastructure Resilience in the Gulf Cooperation Council: An Evidence-Based Analytical Review, 2021-2025

Muhammad Saqib Butt — 2026-07-07

This study conducts an original analytical review of AI-enabled cyber warfare and critical infrastructure resilience in the Gulf Cooperation Council (GCC) region for the period of 2021-2025 based on the available evidence. The aim is to explore the effect of all these on the cyber risk landscape in the region and their interaction to form the new landscape. The 45 sources included in the sample have been coded by a clear evidence matrix, using academic and standards sources. The method is a structured review, coding and weighing of variables, and descriptive analysis and regression analysis simulating. The main factors are the intensity of the attacks, ransomware development, vulnerabilities in the supply chain, social media vulnerabilities, critical infrastructure vulnerabilities, development of defence capabilities, and coordination of policies. The expected results are that attack pressure increased from 49 to 84 on a 0-100 scale, and defence readiness increased from 42 to 68, resulting in a continuous resilience gap of 16 points by 2025. The innovation is that the model takes a sector-level approach, combines cognitive cyber operations, AI-enabled offensive acceleration, and resilience to cyber threats at a regional level, and is applicable across the GCC region, rather than focusing on cyber warfare, cybercrime, and cybergovernance. The implications include the importance of AI-aware threat intelligence, zero-trust implementation, regional incident sharing, supply chain assurance, harmonised regulation of critical infrastructure, and improved verification, all aimed at combating the threats posed by synthetic identity and executive impersonation.

A Comprehensive Review of Cybersecurity Threats, Vulnerabilities, and Mitigation Techniques in the Internet of Things (IoT)

Muhammad Sami Intizar — 2026-08-05

Internet of Things (IoT) has become a new paradigm that combines physical devices with computing and networking features to form intelligent systems that can accomplish their tasks with minimal human interaction. It is estimated that by 2030, there will be more than 30 billion interconnected IoT devices, which will transfer more than 40 zettabytes of data each year. Nevertheless, this exponential increase has brought surprising cybersecurity issues, and recent evaluations show that over 70% of IoT devices are susceptible to hacking. This review examines the complex security environment of IoT ecosystems, evaluates vulnerabilities at each layer of architecture, explores new threat vectors, and assesses new defense solutions. This paper introduces a systematic review of IoT security issues based on a five-layer architecture and specifically focuses on the vulnerabilities of the network and application layers. It examines the ways in which artificial intelligence, blockchain technology, edge computing, and Zero Trust Architecture will transform IoT security paradigms. This review helps reveal long-standing issues such as resource limitations, device heterogeneity, and scaling challenges through the analysis of actual attack cases and defenses in the field of smart healthcare, industrial IoT, smart cities, and other vital infrastructures. Recommendations on future research directions are then given at the end of the paper with an emphasis on quantum-resistant cryptography, 6G network security, and standardized security frameworks required to construct resilient IoT ecosystems.