Innovations in Education

Advances in educational technologies and instructional design book series, Journal Year: 2024, Volume and Issue: unknown, P. 19 - 52

Published: Oct. 11, 2024

As the digital revolution transforms education, Explainable AI (XAI) plays a key role in advancing educational intelligence. This chapter examines how XAI is reshaping education by making machine learning processes transparent. Unlike traditional AI's “black boxes,” clarifies algorithms make recommendations, assessments, and personalized pathways. transparency helps educators understand trust tools, them effective partners education. The also explores XAI's practical uses adaptive platforms intelligent tutoring systems, showing clarity can enhance environments. It allows to address biases, customize strategies, track outcomes more precisely. Through real-world case studies theoretical insights, illustrates bridges advanced technology with teaching practices, promoting transparent equitable system.

Language: Английский

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Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Information Fusion, Journal Year: 2024, Volume and Issue: 110, P. 102472 - 102472

Published: May 16, 2024

Language: Английский

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Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction

Neurocomputing, Journal Year: 2024, Volume and Issue: 599, P. 128111 - 128111

Published: Sept. 1, 2024

Language: Английский

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Trust, Explainability and AI

Philosophy & Technology, Journal Year: 2025, Volume and Issue: 38(1)

Published: Jan. 8, 2025

Abstract There has been a surge of interest in explainable artificial intelligence (XAI). It is commonly claimed that explainability necessary for trust AI, and this why we need it. In paper, I argue some notions it plausible indeed condition. But these kinds are not appropriate AI. For thus conclude AI matters.

Language: Английский

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A Survey on Cybersecurity in IoT

Future Internet, Journal Year: 2025, Volume and Issue: 17(1), P. 30 - 30

Published: Jan. 11, 2025

The proliferation of the Internet Things (IoT) has transformed digital landscape, enabling a vast array interconnected devices to communicate and share data seamlessly. However, rapid expansion IoT networks also introduced significant cybersecurity challenges. This paper presents comprehensive survey in ecosystem, examining current state research, identifying critical security vulnerabilities, exploring advanced strategies for mitigating threats. covers various facets security, including device authentication, integrity, privacy, network emerging role artificial intelligence (AI) bolstering defenses. By synthesizing existing research highlighting ongoing challenges, this aims provide holistic understanding guide future endeavors.

Language: Английский

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Internet of Robotic Things: Current Technologies, Challenges, Applications, and Future Research Topics

Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 765 - 765

Published: Jan. 27, 2025

This article focuses on the integration of Internet Things (IoT) and Robotic Things, representing a dynamic research area with significant potential for industrial applications. The (IoRT) integrates IoT technologies into robotic systems, enhancing their efficiency autonomy. provides an overview used in IoRT, including hardware components, communication technologies, cloud services. It also explores IoRT applications industries such as healthcare, agriculture, more. discusses challenges future directions, data security, energy efficiency, ethical issues. goal is to raise awareness importance demonstrate how this technology can bring benefits across various sectors.

Language: Английский

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Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

International Journal of Intelligent Systems, Journal Year: 2023, Volume and Issue: 2023, P. 1 - 41

Published: Oct. 26, 2023

Given the tremendous potential and influence of artificial intelligence (AI) algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse fields, including education, business, healthcare industries, government, justice sectors. While AI DM offer significant benefits, they also carry risk unfavourable outcomes for users society. As a result, ensuring safety, reliability, trustworthiness becomes crucial. This article aims to provide comprehensive review synergy between DM, focussing on importance trustworthiness. The addresses following four key questions, guiding readers towards deeper understanding this topic: (i) why do we need trustworthy AI? (ii) what are requirements In line with second question, that establish been explained, explainability, accountability, robustness, fairness, acceptance AI, privacy, accuracy, reproducibility, human agency, oversight. (iii) how can data? (iv) priorities in terms challenging applications? Regarding last six different discussed, environmental science, 5G-based IoT networks, robotics architecture, engineering construction, financial technology, healthcare. emphasises address before their deployment order achieve goal good. An example is provided demonstrates be employed eliminate bias resources management systems. insights recommendations presented paper will serve as valuable guide researchers seeking applications.

Language: Английский

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A review on fault detection and diagnosis of industrial robots and multi-axis machines

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102397 - 102397

Published: June 11, 2024

- Industrial Robots and Multi-axis Machines have become increasingly popular in recent years, a diverse range of industries. These complex expensive machines are vulnerable to variety problems that could put the robot or its surroundings danger. To keep system running, these issues must be discovered diagnosed quickly. Although numerous related review papers been increasing over time, none describe techniques fault diagnosis isolation (FDI) for smart manufacturing industrial robotic systems their rotating components. This work reviews this issue expands discussion existing cover FDI Multi DOF robots. The study excludes some types autonomous robots like multi-robot systems, swarms, UAVs out our domain while including associated components involved such as gearbox, actuators, controllers. A few previous studies discussed current-signature data-driven approaches but either single motor, actuator, one joint not whole manipulator faults. literature outcome concluded methods can identify faults only two DOFs it is advisable present an approach repetitive benefit from limitations conducting on automatic enhanced by reference mathematical model each task.

Language: Английский

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Multiple Intrusion Detection Using Shapley Additive Explanations and a Heterogeneous Ensemble Model in an Unmanned Aerial Vehicle’s Controller Area Network

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(13), P. 5487 - 5487

Published: June 25, 2024

Recently, methods to detect DoS and spoofing attacks on In-Vehicle Networks via the CAN protocol have been studied using deep learning models, such as CNN, RNN, LSTM. These studies produced significant results in field of Network attack detection models. However, these typically addressed single-model intrusion verification drone networks. This study developed an ensemble model that can multiple types simultaneously. In preprocessing, patterns within payload measure Feature Importance are distinguished from normal data. As a result, this improved accuracy model. Through experiment, both score F1-score were verified for practical utility through 97% performance measurement.

Language: Английский

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Artificial intelligence – Human intelligence conflict and its impact on process system safety

Digital Chemical Engineering, Journal Year: 2024, Volume and Issue: 11, P. 100151 - 100151

Published: April 5, 2024

In the Industry 4.0 revolution, industries are advancing their operations by leveraging Artificial Intelligence (AI). AI-based systems enhance automating repetitive tasks and improving overall efficiency. However, from a safety perspective, operating system using AI without human interaction raises concerns regarding its reliability. Recent developments have made it imperative to establish collaborative between humans AI, known as Intelligent Augmentation (IA). 5.0 focuses on developing IA-based that facilitate collaboration AI. potential conflicts in controlling process plant pose significant challenge IA systems. Human-AI conflict operation can arise due differences observation, interpretation, control action. Observation may when disagree with observed data or information. Interpretation occur decision-making based data, influenced learning ability of intelligence (HI) Control action AI-driven differs operator Conflicts introduce additional risks operation. Therefore, is crucial understand concept human-AI perform detailed risk analysis before implementing system. This paper aims investigate following: 1. Human possible during collaboration. 2. Formulate an 3. Provide case study identify conflict.

Language: Английский

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