Daily Archives: January 29, 2025

Abstract-This paper aims to provide an in-depth understanding of A.I., its historical development, and its transformative influence on modern civilisation. We will discuss significant concepts, evolving technologies under influence, technological advancements, and ethical and unethical A.I..

Abstract-Facial expression detection has emerged as a transformative technology with applications in numerous fields such as healthcare, security, and entertainment. The proposed system aims to enhance user engagement by dynamically tailoring playlists based on the user’s emotional state. The proposed Emotion Recognition provides a foundation for further exploration and development of intelligent systems that adapt to users’ emotional states, fostering more immersive and personalized interactions in the realm of digital entertainment.

Abstract-This research introduces two innovative methods to convert a standard 16-control dental chair into an IoT-enabled dental chair at a minimal cost of under 2,000 INR. The first method involves directly interfacing the chair’s control wires with a 16-channel relay and an ESP32 microcontroller, enabling remote operation through the Blynk IoT platform. The second method leverages signal analysis by identifying the dental chair PCB’s communication lines, capturing control signals with a logic analyzer, and replicating them via the ESP32 for seamless functionality. Both approaches offer global control with minimal delay (<10ms) and enhance operational efficiency by enabling preemptive actions, such as heating water or cleaning the spit bowl remotely. This study provides a scalable, low-cost solution for modernizing dental chairs, ensuring ease of use and adaptability for dental clinics worldwide.

Abstract-One of the important and tedious tasks in agricultural practices is the detection of disease on crops. It requires time as well as skilled labor. This paper proposes a smart and efficient technique for the detection of crop disease which uses computer vision and machine learning techniques. Every year India loses a significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, a computer-aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, CNN was proposed to detect plant disease. It has three processing steps namely feature extraction, downsizing image, and classification. In CNN, the convolutional layer extracts the feature from the plant image. It helps to give personalized recommendations to farmers based on soil features, temperature, and humidity.

Abstract-Human-centered artificial intelligence (HCAI) emphasizes designing AI systems that prioritize human values, ethics, and usability, fostering trust and responsible adoption. This research explores the advancement of HCAI by addressing key challenges such as improving explainability, integrating ethical considerations, and optimizing real-world applications across diverse sectors. By investigating state-of- the-art methods for interpretable machine learning, the study aims to enhance user understanding and transparency in AI decision-making. It further examines frameworks for embedding ethical principles, including fairness, accountability, and privacy, into AI system design. Additionally, the research evaluates case studies from healthcare, education, and autonomous systems to illustrate the transformative potential of HCAI. This study underscores the need for interdisciplinary collaboration and innovation to ensure AI technologies align with human values and societal goals, paving the way for more inclusive and sustainable AI solutions.

Abstract-Drones are becoming indispensable tools in various critical sectors of India, such as agriculture, disaster management, land surveys, mining, and infrastructure mapping. Their ability to access remote, hazardous, or hard-to-reach areas makes them invaluable for tasks, such as crop monitoring, search and rescue, and real-time data collection. However, the effectiveness of drones in these mission-critical applications is often limited by their battery life and the need for frequent recharging, particularly in environments where human access is difficult or impossible. This project addresses this challenge by developing a wireless power transfer (WPT) system for drone charging. The system converted a standard 230V supply into a low-voltage DC output, which was then wirelessly transferred via a high-frequency (100kHz) inverter and coil setup. This WPT system is particularly suited for use in remote or inaccessible locations, where minimizing downtime is critical.