Recent Trends in Artificial Intelligence & It’s Applications (e-ISSN: 2583-4819, p-ISSN: 3107-7234)

Assisting Blind Individuals in Navigation Using Multimodal LLM and Voice Assistance

2026-09-03T04:59:17+00:00

Visual impairment can considerably limit a person’s ability to move through unfamiliar surroundings safely and independently. Recent developments in Artificial Intelligence (AI), Computer Vision, Deep Learning, Multimodal Large Language Models (LLMs), and smartphone-based sensing technologies have facilitated the emergence of intelligent assistive navigation systems designed to support safer and more independent mobility. These systems employ object detection, distance estimation, scene understanding, voice guidance, and haptic feedback to improve environmental awareness and obstacle avoidance. This survey reviews recent research on AI-based assistive navigation systems for visually impaired individuals, with particular emphasis on object detection models, multimodal LLMs, LiDAR-based sensing, distance estimation, and real-time audio guidance. The reviewed approaches demonstrate improvements in object recognition, environmental understanding, and navigation assistance; however, several limitations remain, including dependence on specialized hardware, high computational requirements, response latency, limited offline functionality, and insufficient integration of multiple assistive features within a single application. The survey identifies these research gaps and discusses future directions toward lightweight, affordable, smartphone-based assistive navigation solutions. Integration of multimodal AI, efficient object detection, smartphone sensors, real-time distance estimation, voice assistance, vibration feedback, emergency support, and offline processing can contribute to safer, more reliable, and accessible navigation for visually impaired individuals.

Arogyabodhini: An AI-Powered Multilingual Healthcare Assistant for Disease Prediction and Smart Medical Support

2026-09-05T12:04:25+00:00

Healthcare accessibility remains a major challenge, particularly in multilingual regions where patients often struggle to communicate their symptoms due to language barriers, limited medical knowledge, and restricted access to specialized healthcare services. Existing digital healthcare systems generally support a limited number of languages and lack an integrated platform for multilingual communication, intelligent symptom analysis, doctor recommendations, and medical report processing. To address these challenges, this article proposes Arogyabodhini, an AI-powered multilingual healthcare assistant that provides intelligent medical support through voice-enabled interaction, disease prediction, and smart healthcare services. The proposed system enables patients to describe their health conditions using voice or text in regional Indian languages, where speech input is converted into a professional language (English) using speech recognition and translation techniques to ensure standardized processing. Natural Language Processing (NLP) is employed to extract relevant symptoms from patient descriptions, which are then analyzed using a Decision Tree-based machine learning model to predict possible diseases. Furthermore, Arogyabodhini integrates a centralized doctor recommendation system that identifies suitable specialists based on predicted diseases, specialization, and availability while supporting online appointment booking, digital prescription management, and emergency alerts for critical health conditions. A secure and user-friendly dashboard enables patients to access their health records, consultation history, uploaded reports, and medical recommendations. By integrating Artificial Intelligence, Machine Learning, Natural Language Processing, speech recognition, and modern web technologies into a unified healthcare platform, the proposed system aims to improve healthcare accessibility, reduce communication barriers, support early disease identification, and simplify patient–doctor interaction, thereby contributing to the development of inclusive, intelligent, and technology-driven healthcare services, particularly for rural and linguistically diverse communities.

Neuromorphic Computing to make AI Energy-efficient: A Review of Contemporary Architectures, Difficulties and Applications

2026-08-31T07:24:09+00:00

Artificial intelligence is placing increasingly demanding requirements, especially in data-intensive and real-time applications, on the efficiency and scalability of traditional Von Neumann computing architectures, hence causing huge energy demands and computational bottlenecks. This article gives a detailed overview of neuromorphic computing, which is a relatively new paradigm that finds its inspiration in highly efficient systems and provides a potential solution to these limitations. The work defines the foundational principles of neuromorphic systems, such as the details of Spiking Neural Networks (SNNs), the benefits of event-driven processing, and the synaptic plasticity processes, which, together, will allow never-before-seen energy efficiency to be achieved. It gives a more detailed analysis of various hardware implementations that include digital and analog neuromorphic chips, etc. In addition, the review also discusses the wide range of applications in which neuromorphic systems outperform, such as real-time edge AI and autonomous robotics. The major achievements are the description of modern neuromorphic hardware and an overview of all existing and potential applications.