IJRAIDS

1. Ethics-First AI: Designing Bias-Aware Algorithms from the Ground UpS

Balachandar.P, Jeyasurya.M, S. Bharathiraja, R. Poornima

Artificial Intelligence (AI) is no longer just a dream of the future; it's a part of our digital life. AI systems are having more and more of an impact on the choices that shape our lives, from the suggestions we see online to the decisions that affect our job applications, loans, parole, and even medical diagnoses.

2. Fusion of Edge AI and Federated Learning in Smart Cities

Mrs.D. Jayasree, D. Deepika, N. Prabha devi

The emergence of smart cities has necessitated the development of data processing systems that are real-time, decentralized, and privacy-conscious. This paper analyzes the integration of Edge Artificial Intelligence (Edge AI) with Federated Learning (FL) as an innovative approach to enhance data management, security, and decision-making in intelligent urban environments.

3. Multimodal AI for Predicting how people Feel in Real Time on Educational Platforms

Mohamed Kasim

Smart schools now need to be able to recognise emotions since they affect how motivated students are, how individualised their learning is, and how well they learn. This article talks about a whole multimodal artificial intelligence (AI) framework that can predict how people will feel in real time on educational platforms.

4. Personalized Healthcare Diagnostics Using Multi-Source Genomic and Behavioral Data

Ishwarya, Archana

In the rapidly changing world of modern medicine, a groundbreaking change is happening: personalised healthcare diagnostics that use genomic and behavioural data from many sources. Traditional diagnostic systems, while beneficial, frequently utilise a "one-size-fits-all" approach that does not consider the intricate uniqueness of human biology and lifestyle.

5. Self-Healing AI Systems in Industrial IoT Environments

Gopika P, Gayathri S, Dhanapriya K

AI and the Industrial Internet of Things (IIoT) have teamed up to build a new sort of self-healing technology that can cure itself. These systems find faults, figure out what caused them, and fix them right away. This cuts down on the time individuals have to wait and the necessity for them to get up and move.

6. Zero-Shot Learning for Autonomous Vehicles Capable of Adapting to Unstructured Terrain

Musadaq Hanandi

Zero-shot learning (ZSL) is a new way of doing machine learning that lets models use what they already know to new classes or scenarios without obtaining tagged data for those classes. As self-driving cars (AVs) go through more difficult and unstructured places, such forests, deserts, snowy terrain, and disaster zones, they need adaptive intelligence more than ever.