India is one of the countries with scarcest water resources in the world; due to poor utilization of the water resources, some parts of our country are facing the risk of draught. In order to conserve existing water resources and efficiently manage it for agriculture, recent advances in technology can be used. Internet of Things is one of such new technology which can help our country to reduce the overall impact of faulty water management in agriculture sector. In this paper, we have designed and developed a new framework for multilevel farming in urban area where cultivation space is limited. We have provided local node for each level with its individual local decision making system, sensors and actuators which is customized to the selected crop. These local nodes communicate to a centralized node via wireless communication. This centralized node is connected to a Cloud Server where the received data will be stored and processed. Cloud based data analysis and monitoring allows the user to analyze and monitor the irrigation system through internet providing ubiquitous access. Our Experimental results show reduced water consumption and better power utilization.

According to the recent statistics about 7.5% of Indians are speech challenged and 5.8% of the population is hearing impaired and Indian Sign Language is the only mode of communication used by them. In this paper we have presented an approach that gives a technique for improving Sign Language Recognition system. In the proposed method; we will be using sensors which are incorporated on a glove to detect the gestures and convert it to speech with the help of a Bluetooth module and an Android Smart Phone. The gloves will help in producing artificial speech which provides an environment similar to daily communication which is hard to achieve for speech impaired people.

Background: Dentistry is undergoing a digital transformation driven by emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Digital Twins (DTs), and Large Language Models (LLMs). These advancements offer new paradigms in clinical diagnostics, patient monitoring, treatment planning, and medical education. However, integrating these technologies also raises critical questions around security, privacy, ethics, and trust. Objective: This review aims to provide a structured synthesis of the recent literature exploring AI, IoT, DTs, and LLMs in dentistry, with a specific focus on their application domains and the associated ethical, privacy, and security concerns. Methods: A comprehensive literature search was conducted across PubMed, IEEE Xplore, and SpringerLink using a custom Boolean query string targeting publications from 2020 to 2025. Articles were screened based on defined inclusion and exclusion criteria. In total, 146 peer-reviewed articles and 18 technology platforms were selected. Each article was critically evaluated and categorized by technology domain, application type, evaluation metrics, and ethical considerations. Results: AI-based diagnostic systems and LLM-driven patient support tools were the most prominent technologies, primarily applied in image analysis, decision-making, and health communication. While numerous studies reported high performance, significant methodological gaps exist in evaluation design, sample size, and real-world validation. Ethical and privacy concerns were mentioned frequently, but were substantively addressed in only a few works. Notably, IoT and Digital Twin implementations remained largely conceptual or in pilot stages, highlighting a technology gap in dental deployment. Conclusions: The review identifies significant potential for converged intelligent dental systems but also reveals gaps in integration, security, ethical frameworks, and clinical validation. Future work must prioritize cross-disciplinary development, transparency, and regulatory alignment to realize responsible and patient-centered digital transformation in dentistry.

The Internet of Things (IoT) has emerged as a transformative technology in managing Alzheimer’s Disease (AD), offering novel solutions for early diagnosis, continuous patient monitoring, and assistive care. This review presents a comprehensive analysis of IoT-enabled systems tailored to AD care, focusing on wearable biosensors, cognitive monitoring tools, smart home automation, and Artificial Intelligence (AI)-driven analytics. A systematic literature survey was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify, screen, and synthesize 236 relevant studies primarily published between 2020 and 2025 across IEEE Xplore, PubMed, Scopus and Web of Science. The inclusion criteria targeted peer-reviewed articles that proposed or evaluated IoT-based solutions for AD detection, progression monitoring, or patient assistance. Key findings highlight the effectiveness of the IoT in detecting behavioral and cognitive changes, enhancing safety through real-time alerts, and improving patient autonomy. The review also explores integration challenges such as data privacy, system interoperability, and clinical adoption. The study reveals critical gaps in real-world deployment, clinical validation, and ethical integration of IoT-based systems for Alzheimer’s care. This study aims to serve as a definitive reference for researchers, clinicians, and developers working at the intersection of the IoT and neurodegenerative healthcare.

With an ever-increasing number of smart connected devices for various applications, there is a need for finding a new, smarter way of communicating with all the homogeneous and heterogeneous devices in a particular network. Conversational Bots, also known as Chatbots, are currently a popular solution in many applications, as they provide a user-friendly interface and more intuitive recommendations to user queries. In this work, the domain of home automation is considered from the area of the Internet of Things, and a Chatbot application built using technologies like Natural Language Processing, Machine Learning, and Service-Oriented Computing is designed as an intuitive user-interface for Smart home products. The aim of this paper is to build easy to implement and integrate DIY Smart Home Assistant using available technologies. The proposed Conversational Artificial Intelligence system can aid the user in smart decision making, predictive and preventive analytics, and showed promising results during experimental evaluation.