Recent Trends in Artificial Intelligence & It’s Applications

From Intelligence to Adaptability: The Role of Reconfigurable Services in 6G Evolution

Manikanta Kalyan, Manas Kumar Yogi — Fri, 24 May 2024 00:00:00 +0000

The advent of 6G technology heralds a new era of connectivity, promising unprecedented speed, reliability, and connectivity for the digital world. At the core of this evolution are two key pillars: Artificial Intelligence (AI) and Reconfigurable Intelligent Services (RIS). This proposed chapter explores the intricate interplay between these two transformative technologies and their pivotal roles in shaping the landscape of 6G networks. The chapter will explore Artificial Intelligence in the context of 6G networks, elucidating its significance in enabling autonomous decision-making, predictive analytics, and network optimization. The chapter will underscore their potential to revolutionize network management, resource allocation, and user experience in 6G environments through a comprehensive review of AI-driven solutions such as machine learning, deep learning, and natural language processing. Subsequently, the focus will shift to Reconfigurable Intelligent Services, shedding light on their dynamic nature and adaptability to diverse network requirements. By elucidating the principles of reconfigurability and intelligence in service provisioning, the paper will illustrate how these services empower 6G networks to respond to fluctuating demands swiftly, optimize resource utilization, and enhance overall network efficiency. This paper provides a comprehensive understanding of how Artificial Intelligence and Reconfigurable Intelligent Services synergistically drive the evolution of 6G networks towards unprecedented performance and versatility through a synthesis of theoretical frameworks, case studies, and prospects.

Multi-Class Skin Disease Classification Using Convolutional Neural Network

Namitha Krishnan, Surya Gayathri U, Ardra P.V, Aparna A.K, Shibitha K.P — Wed, 12 Jun 2024 00:00:00 +0000

Our skin protects our body’s critical organs from potential harm by acting as an outer layer. This vital body part is prone to several illnesses caused by dust, allergies, bacteria, viruses, and fungi. Millions of people suffer from skin illnesses, which can result in discrimination and societal shame. Effective therapy for skin disorders requires a prompt and accurate diagnosis. Although advanced technologies like lasers and photonics have been used to diagnose skin diseases, they are frequently expensive and unaffordable for areas with low resources. As a result, image-based techniques present a quick and affordable substitute. Prior research has explored image-based diagnostic approaches for skin diseases. This study utilizes Convolutional Neural Networks (CNN) to classify various skin diseases, including Acne and rosacea, Lupus, Psoriasis, Tinea and Ringworm, Bullous disease, Cellulitis and Impetigo, Atopic dermatitis, vasculitis, Herpes, and Candidiasis. The image data underwent standardization in size, conversion to grayscale, and intensity balancing. Augmentation techniques were also applied. A comparative analysis used established CNN architectures like MobileNetV2 and ResNet 50. This project proposes a CNN architecture called Sequential Model, which is more accurate and reliable.

Review on AI-Driven Automation for Autonomous Vehicles

Mamatha G, Neha Subrahmanya Hegde — Wed, 19 Jun 2024 00:00:00 +0000

The advancement of Artificial Intelligence (AI) has revolutionized the automotive industry, particularly in the field of Autonomous vehicles. This paper explores the integration of sophisticated AI algorithms and Sensor systems to enable the automation of Cars that are leveraged with techniques such as Machine Learning, Deep Learning, and Computer vision. Vehicles can autonomously perceive their surroundings, make informed decisions, and navigate complex environments without human intervention. The evolution of autonomous driving is examined through various levels of automation, with a focus on achieving fully autonomous capabilities.
Further, the paper discusses the benefits and challenges of AI-driven car automation. Despite promising advancements, challenges like regulatory frameworks, safety concerns, and infrastructure limitations persist. Ongoing research and development efforts are aimed at addressing challenges and enhancing Autonomous Vehicles' safety, efficiency, and reliability. By embracing AI technology, the automotive industry is poised to usher in a new era of transportation characterized by convenience, improved safety, and reduced environmental impact.