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Emerging Topics in Artificial Intelligence (AI): Architectures and Techniques for...
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Emerging Topics in Artificial Intelligence (AI): Architectures and Techniques for Real-World Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 5337

Special Issue Editors

Prof. Dr. Kah Phooi Seng

E-Mail Website
Guest Editor
1. School of AI & Advanced Computing, Xi’an Jiaotong Liverpool University, Suzhou 215123, China
2. School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
Interests: artificial intelligence; deep learning; machine learning and data analytics; intelligent systems; Internet of Things and edge computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Li-Minn Ang

E-Mail Website
Guest Editor
School of Science and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia
Interests: electrical and computer engineering; intelligent and smart sensor systems; IoT and data analytics for environmental sensing; innovative technologies for real world systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is a technological innovation that is transforming the world and changing the relationship between humans and machines. The past decade has seen tremendous progress in the area of AI, with giant leaps in the advancement of machine learning and deep learning. In the near future, most technology applications will harness or incorporate the output of some form of AI. AI turns autonomous vehicles and robots into reality and enables them to sense their environments, learn, adapt, and respond on their own. AI has transformed healthcare and become a critical part of the healthcare industry. AI will also disrupt business models and create new ways of working and facilitate digital transformation in many applications.

The techniques for the AI of the future are also changing to meet new requirements and applications. In a traditional approach, training a machine learning model requires building a large dataset locally and keeping it on the local machine or a data center. This is a centralized approach where data are gathered in a centralized server and machine learning models are trained over them. One future trend is distributed AI and performing AI on edge devices. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location of the device. Edge AI is growing in popularity and is the next frontier of development for the Intelligent Internet of Things or artificial intelligence IoT (AIoT).

This Special Issues focusses on the recent development of AI and aims to collect the latest research works for emerging topics in AI, intelligent systems, machine learning, and deep learning. The potential topics include but are not limited to the following:

  • AI and intelligent systems;
  • Edge intelligence or Edge AI and computing;
  • Distributed AI or distributed deep learning;
  • On-device machine learning/deep learning/AI;
  • AI and Internet of Things for smart cities;
  • Embedded Intelligence on GPU/ FPGA/ SoC;
  • Collaborative AI;
  • Generative AI;
  • AI in software engineering;
  • Scalable AI for big data;
  • Trusted AI or trustworthy AI;
  • Explainable AI;
  • Future AI threats and security;
  • Future AI and digital health;
  • Future AI data-driven technology;
  • The future of AI in transportation;
  • The future of mobile AI;
  • The future of human-centered AI.

Prof. Dr. Kah Phooi Seng
Prof. Dr. Li-Minn Ang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • intelligent systems
  • intelligent sensing
  • deep learming
  • edge AI
  • generative AI
  • trustworthy AI

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Published Papers (2 papers)

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Research

25 pages, 5163 KiB  
Article
Towards an End-to-End Personal Fine-Tuning Framework for AI Value Alignment
by Eleanor Watson, Thiago Viana, Shujun Zhang, Benjamin Sturgeon and Lukas Petersson
Electronics 2024, 13(20), 4044; https://doi.org/10.3390/electronics13204044 - 14 Oct 2024
Cited by 1 | Viewed by 2857
Abstract
This study introduces a novel architecture for value, preference, and boundary alignment in large language models (LLMs) and generative AI systems, accompanied by an experimental implementation. It addresses the limitations in AI model trustworthiness stemming from insufficient comprehension of personal context, preferences, and [...] Read more.
This study introduces a novel architecture for value, preference, and boundary alignment in large language models (LLMs) and generative AI systems, accompanied by an experimental implementation. It addresses the limitations in AI model trustworthiness stemming from insufficient comprehension of personal context, preferences, and cultural diversity, which can lead to biases and safety risks. Using an inductive, qualitative research approach, we propose a framework for personalizing AI models to improve model alignment through additional context and boundaries set by users. Our framework incorporates user-friendly tools for identification, annotation, and simulation across diverse contexts, utilizing prompt-driven semantic segmentation and automatic labeling. It aims to streamline scenario generation and personalization processes while providing accessible annotation tools. The study examines various components of this framework, including user interfaces, underlying tools, and system mechanics. We present a pilot study that demonstrates the framework’s ability to reduce the complexity of value elicitation and personalization in LLMs. Our experimental setup involves a prototype implementation of key framework modules, including a value elicitation interface and a fine-tuning mechanism for language models. The primary goal is to create a token-based system that allows users to easily impart their values and preferences to AI systems, enhancing model personalization and alignment. This research contributes to the democratization of AI model fine-tuning and dataset generation, advancing efforts in AI value alignment. By focusing on practical implementation and user interaction, our study bridges the gap between theoretical alignment approaches and real-world applications in AI systems. Full article
(This article belongs to the Special Issue Emerging Topics in Artificial Intelligence (AI): Architectures and Techniques for Real-World Applications)
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12 pages, 1359 KiB  
Article
Image to Label to Answer: An Efficient Framework for Enhanced Clinical Applications in Medical Visual Question Answering
by Jianfeng Wang, Kah Phooi Seng, Yi Shen, Li-Minn Ang and Difeng Huang
Electronics 2024, 13(12), 2273; https://doi.org/10.3390/electronics13122273 - 10 Jun 2024
Cited by 1 | Viewed by 1473
Abstract
Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and natural language understanding, but this worsens data scarcity in Med-VQA, hindering [...] Read more.
Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and natural language understanding, but this worsens data scarcity in Med-VQA, hindering clinical application and advancement. This paper proposes the ITLTA framework for Med-VQA, designed based on field requirements. ITLTA combines multi-label learning of medical images with the language understanding and reasoning capabilities of large language models (LLMs) to achieve zero-shot learning, meeting natural language module needs without end-to-end training. This approach reduces deployment costs and training data requirements, allowing LLMs to function as flexible, plug-and-play modules. To enhance multi-label classification accuracy, the framework uses external medical image data for pretraining, integrated with a joint feature and label attention mechanism. This configuration ensures robust performance and applicability, even with limited data. Additionally, the framework clarifies the decision-making process for visual labels and question prompts, enhancing the interpretability of Med-VQA. Validated on the VQA-Med 2019 dataset, our method demonstrates superior effectiveness compared to existing methods, confirming its outstanding performance for enhanced clinical applications. Full article
(This article belongs to the Special Issue Emerging Topics in Artificial Intelligence (AI): Architectures and Techniques for Real-World Applications)
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