Special Issue on AI4IOT: Accelerating Innovation in Engineering Science

Closes 28 July, 2024

Issue Pre-order form

Journal: Recent Advances in Electrical & Electronic Engineering
Guest editor(s):Dr. Sai Kiran Oruganti
Co-Guest Editor(s): Eugenio Vocaturo,Dimitrios Karras,Asif Ekbal


The Internet of Things (IoT) and artificial intelligence (AI) are two of the most transformative technologies of our time. IoT connects devices and systems to the internet, while AI allows machines to learn and make decisions without human intervention. These technologies are already having a major impact on industries such as healthcare, manufacturing, and transportation. The combination of AI and IoT, known as AI4IOT, is poised to accelerate innovation in even more industries, including engineering science. AI4IOT can be used to improve the efficiency and productivity of engineering projects, develop new products and services, and create new business models. It can also be used to improve the quality of life for people in India and around the world. This special issue will explore the latest research and trends in AI4IOT in the context of engineering science. We are seeking papers that address the following topics: The application of AI4IOT in engineering projects, such as design, manufacturing, and maintenance. The challenges and opportunities of AI4IOT in engineering science, such as security, privacy, and ethics. The latest advances in AI4IOT algorithms and technologies for engineering applications. Natural Language processing Training Models & Algorithms for Speech, Text and Video Analytics • Exploration of Advanced Algorithms and Models in Speech, Text, and Video Analytics, including Context-aware Conversational Models. • Applications of Natural Language Processing (NLP) Techniques for Text Analysis and Dialogue Intent Detection. • Deep Learning Approaches for Video and Speech Recognition, contributing to Multi-modal Conversational AI. • Multimodal Analytics Integrating Speech, Text, and Video Data, enhancing Conversational Recommender Systems. • Ethical Considerations in AI-driven Analytics, encompassing Privacy and Ethics in Conversational Systems. • Real-world Applications of AI Analytics in Diverse Industries, with a focus on Dialogue Systems in Low-resource Settings. • Creating Datasets for Conversational AI, supporting Knowledge-aware Conversational Systems. • Developing Dialogue Models and Natural Language Generation for Empathetic Conversational Systems. • Zero-shot and Meta-learning Techniques in Dialogue Systems, contributing to Personalization in Dialogue Systems. • Addressing Knowledge Graph-driven Dialogue Management and Personalization in Dialogue Systems. • Implementing Question-Answering Capabilities in Conversations, exploring Emotion Recognition and Sentiment Analysis. • Analyzing Sentiments in Conversational Contexts, emphasizing Ethics, Privacy, and Bias in Conversational Systems.


IoT, AI, NLP, Video Analytics, Text Analytics, Speech Anayltics

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