Analyzing the Ecosystem Contexts in the AI Literature Using Latent Dirichlet Allocation and Exploratory Factor Analysis

WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS, Год журнала: 2025, Номер 22, С. 344 - 357

Опубликована: Май 19, 2025

This study aims to explore the major topics in recent artificial intelligence (AI) ecosystem literature and identify categorize those into categories of AI ecosystems. The analyzed 149 publications from Google Scholar using two text mining techniques: latent Dirichlet allocation (LDA) exploratory factor analysis (EFA). LDA identified 12 topics, while EFA grouped them six common factors: (a) human resources-driven, (b) technology algorithm-based, (c) business entrepreneurial-driven, (d) legal, ethical, privacy, regulatory framework, (e) innovation-based, (f) government-supported. goal is suggest various ecosystems their best fit for a country or region based on its characteristics resources. Understanding these types can provide valuable insights government agencies, policymakers, businesses, educational institutions, other stakeholders align strategies with resources developing successful AI-driven

Язык: Английский

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Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller

Sensors, Год журнала: 2022, Номер 22(14), С. 5106 - 5106

Опубликована: Июль 7, 2022

The Internet of Things (IoT) era is mainly dependent on the word "Smart", such as smart cities, homes, and cars. This aspect can be achieved through merging machine learning algorithms with IoT computing models. By adding Artificial Intelligence (AI) to IoT, result Cognitive (CIoT). In automotive industry, many researchers worked self-diagnosis systems using deep learning, but most them performed this process cloud due hardware limitations end-devices, devices obtain decision via servers. Others simple traditional solve these processing capabilities end-devices. paper, a device introduced fast responses little overhead Multi-Layer Perceptron Neural Network (MLP-NN) technique. MLP-NN stage Tensorflow framework generate an MLP model's parameters. Then, model implemented parameters low cost end-device ARM Cortex-M Series architecture. After implementing model, implementation built publish results. With proposed method for device, output based sensors values taken by node itself without returning cloud. For comparison, another solution cloud-based architecture, where Cloud. results clarify successful has lower traffic latency than solution.

Язык: Английский

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Advancing Industry 5.0: An Extensive Review of AI Integration

Studies in systems, decision and control, Год журнала: 2024, Номер unknown, С. 3 - 21

Опубликована: Янв. 1, 2024

Язык: Английский

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Solar Irradiation and Wind Speed Forecasting Based on Regression Machine Learning Models

Lecture notes in networks and systems, Год журнала: 2023, Номер unknown, С. 31 - 51

Опубликована: Янв. 1, 2023

Язык: Английский

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Effectual Text Classification in Data Mining: A Practical Approach

Mesopotamian Journal of Big Data, Год журнала: 2023, Номер unknown, С. 46 - 52

Опубликована: Май 10, 2023

Text classification is the process of setting records into classes that have already been set up based on what they say. It automatically puts texts in natural languages categories up. most crucial part text retrieval systems, which find user requests, and understanding change some way, like by making summaries, answering questions, or pulling out data. Existing algorithms use supervised learning to classify need enough examples learn well. The for data mining are used texts, as well a review work has done classifying texts. Design/Methodology/Approach: Data were talked about, studies looked at how these at, with focus comparative studies. Findings: No classifier can always do best job because different datasets situations lead accuracy. Implications Real Life: When using documents, it's important keep mind conditions will affect documents classified. For this reason, should be organized.

Язык: Английский

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Personalized device authentication scheme using Q-learning-based decision-making with the aid of transfer fuzzy learning for IIoT devices in zero trust network (PDA-QLTFL)

Computers & Electrical Engineering, Год журнала: 2024, Номер 118, С. 109435 - 109435

Опубликована: Июль 17, 2024

Язык: Английский

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Roles of Big Data and AI in Manufacturing

Studies in systems, decision and control, Год журнала: 2024, Номер unknown, С. 3 - 25

Опубликована: Янв. 1, 2024

Язык: Английский

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