AI Enhanced Eye Detection Wheelchair with Smart Monitoring using Deep Learning
Pain Alleviating and Movement Assistive Device for Bone Fractured Patients
Nature’s Pharmacy: A Deep Learning approach for Identification of Medicinal Plants
Design and Implementation of an Analog and Mixed-Signal System on Chip for ECG Monitoring
Machine Learning: A Multifaceted Exploration of Trends, Regulations, and Global Impact
Chemistry and Chemical Engineering: Approaches, Observations, and Outlooks
Integration of PMS Software and Decision Matrix Tool Based on Data Acquired from Latest IT Advanced Sensors and 3D CAD Models in Marine Operations Field
Dynamic Changes in Mangrove Forest and Lu/Lc Variation Analysis over Indian Sundarban Delta in West Bengal (India) Using Multi-Temporal Satellite Data
The Impacts of Climate Change on Water Resources in Hilly Areas of Nepal
Development and Performance Evaluation of Magnetic Abrasive Finishing Setup for Thick Cylinders
An Analysis of Machining Forces On Graphite/Epoxy, Glass/Epoxy and Kevlar/Epoxy Composites Using a Neural Network Approach
Deformation Behaviour of Fe-0.8%C-1.0%Si-0.8%Cu Sintered P/M Steel during Powder Preform Forging
A Series of Tool-Life Studies on Aluminium Matrix Hybrid Composites
Achieving Manufacturing Excelence by Applying LSSF Model – A Lean Six Sigma Framework
Design and Analysis of Piezo- Driven Valve-Less Micropump
People with high-level cervical spinal cord injury can have significant impairments in their ability to control their environment, including challenges operating a smartphone or navigating a power wheelchair. The use of eye-tracking technology has been crucial in improving communication and control for individuals with tetraplegia. However, traditional eye-tracking systems often have limitations in terms of accuracy, calibration time, and practicality. To overcome these limitations, researchers have explored the use of Convolutional Neural Networks (CNNs) in AI-enhanced eye-tracking technology. CNNs are a type of deep learning algorithm that can learn complex patterns in image data, allowing for more accurate and reliable eye-tracking. AI-enhanced eye tracking using three times eye blinking is a novel approach that has shown great potential in improving the accuracy and efficiency of eye tracking technology. By using advanced machine learning algorithms, this approach can detect and track eye movements based on the number of blinks, providing a more reliable and efficient way to interact with digital devices. This technology has the potential to revolutionize the way we interact with digital devices, making them more accessible and user-friendly for people with disabilities or impairments. The results and discussions related to AI-enhanced eye tracking using three times eye blinking have shown that it can be a viable alternative to traditional eye tracking technology, which can be costly, time-consuming, and difficult to use. Furthermore, this approach is highly customizable and can be adapted to meet the specific needs and preferences of individual users. As such, it has the potential to significantly enhance the quality of life for individuals with motor impairments, visual impairments, or other disabilities that affect their ability to use traditional eye tracking technology. AI-enhanced eye tracking using three times eye blinking is a promising technology that has the potential to create a more inclusive and accessible digital world. With continued research and development, we can expect to see even more innovative solutions and applications for this technology in the future.
This healthcare initiative leverages advanced technology to optimize bone fracture recovery, introducing a specialized cuff and accompanying app. The app, featuring a 3D human model, allows patients to identify discomfort during walking exercises. When the device detects movement, nerve vibrators are activated to alleviate pain, ensuring a more comfortable rehabilitation experience. This innovative solution targets specific areas, enhancing overall patient outcomes and revolutionizing the rehabilitation process for bone fracture recovery.Additionally, the project incorporates real-time data tracking, enabling healthcare professionals to monitor patients’ progress remotely.
The discovery and use of medicinal plants is essential for each conventional and present- day health structures. This looks at introduces a unique deep gaining knowledge of technique the usage of EfficientNetB3 models for medicinal plant detection and extraction. For chemical models, the version is trained on special datasets, inclusive of plant species, to make certain class accuracy. Through deep gaining knowledge of this proposed technique gives a dependable and efficient solution for figuring out medicinal flora based on specific characteristics. The EfficientNetB3 model checks for better overall performance in category programs, in spite of restricted computing assets. The application of deep learning in plant chemical identity holds promise in fields including medication, ethnobotany, and conservation biology offering researchers, health professionals, and lovers are able to quickly catalog medicinal plants and advantage perception into their healing properties. In deep learning techniques, particularly the EfficientNetB3 version, facilitates the green identification and type of medicinal plant life, thereby advancing plant research and improving fitness practices are powerful.
To keep pace with the revolutionary advancement taking place in the field of VLSI design, a Programmable System on Chip (PSoC) is designed to monitor the ECG signal, wherein an innovative Analog and Mixed Signal (AMS) technology is deployed. The electrocardiographic signals are very weak and are available at ultra high impedance for which traditional analog amplifiers found unsuitable. The AMS based PSoC devices from Cypress Semiconductors exhibit the capability to provide suitable solution on this problem. The analog section of the PSoC devices CY8C55 has ultra high input impedance. Therefore, deploying on chip resources of this device, an embedded system is designed on chip to detect and monitor the ECG signal. The issues of designing and validation of both on chip hardware and embedded firmware are interpreted in this paper. Employing the electrodes, only off the chip, the signals are read into the chip with proper reference level. The on chip PGAs are configured for extraction of the signal. The analog signals are digitized with 10 bit resolution and then digital filtered with low pass filter configured for Fc= 150Hz. The signals in real time are recorded on the DSO. On investigation of ECG signal monitored on the DSO, it can be said that the present SoC is mostly suitable for medical applications.
The rise of machine learning (ML) necessitates a multifaceted exploration encompassing regulations, research advancements, and global trends. Hence, this article identifies important trends and issues while offering a thorough overview of how machine learning is affecting different industries. With major applications in industries including healthcare, nanotechnology, and pharmaceuticals, it is evident that machine learning is developing quickly. Since the moral and legal ramifications of AI and ML are still developing, it is equally critical to emphasize regulatory perspectives. Overall, your assessment highlights the necessity for ongoing cooperation and innovation in the field of machine learning and offers insightful information to academics and policy makers alike. Thus by offering this comprehensive perspective, the analysis aims to serve as a reference point for navigating the complexities and opportunities associated with machine learning.
