Biomaterial Strategies for Immune System Enhancement and Tissue Healing
Qualitative and Quantitative Performance Optimization of Simple Gas Turbine Power Plant using Three Different Types of Fuel
Efficient Shopping: RFID-Powered Cart with Automated Billing System
Medical Drone System for Automated External Defibrillator Shock Delivery for Cardiac Arrest Patients
A Critical Review on Biodiesel Production, Process Parameters, Properties, Comparison and Challenges
Review on Deep Learning Based Image Segmentation for Brain Tumor Detection
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
A Series of Tool-Life Studies on Aluminium Matrix Hybrid Composites
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 injuries can experience significant impairments in their ability to control their environment, including challenges in 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 that utilizes triple blinking is a novel approach showing great potential for improving the accuracy and efficiency of eye tracking technology. By employing advanced machine learning algorithms, this method 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 people engage with digital devices, making them more accessible and user-friendly for individuals with disabilities or impairments. The findings related to AI-enhanced eye tracking using triple blinking suggest 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 triple blinking is a promising innovation that could contribute to a more inclusive and accessible digital world. With continued research and development, even more innovative solutions and applications for this technology are expected in the future.
This healthcare initiative leverages advanced technology to enhance bone fracture recovery through the implementation of a specialized cuff and a companion mobile application. The application integrates an interactive 3D human model, allowing patients to pinpoint areas of discomfort during ambulation exercises. Upon detecting movement, the cuff activates nerve vibrators to alleviate pain, thereby facilitating a more comfortable rehabilitation process. This innovative approach targets specific anatomical areas to improve patient outcomes and revolutionize bone fracture rehabilitation. Real-time data tracking within the application enables healthcare professionals to remotely monitor patient progress and make informed adjustments to treatment protocols. The specialized cuff features adjustable settings, providing personalized support tailored to individual rehabilitation requirements. The mobile application includes guided exercise tutorials, real-time feedback, and comprehensive progress analytics, promoting patient engagement and adherence to prescribed rehabilitation regimens. Advanced machine learning algorithms are employed to adapt the system to patients' evolving needs, ensuring optimal pain management throughout the recovery period. The ergonomically designed cuff, discreet for everyday wear, enhances patient compliance. Secure cloud storage of patient data addresses privacy concerns and establishes a robust framework for the adoption of this innovative solution in clinical practice. This technology-driven initiative offers a novel approach to bone fracture rehabilitation by combining personalized patient care, real-time monitoring, and adaptive pain management, significantly improving recovery outcomes and setting new standards in healthcare.
The discovery and use of medicinal plants are essential for both conventional and modern health systems. This study introduces a unique deep learning technique using EfficientNetB3 models for the detection and extraction of medicinal plants. For the chemical models, the version is trained on specialized datasets, including various plant species, to ensure classification accuracy. Through deep learning, this proposed technique provides a reliable and efficient solution for identifying medicinal plants based on specific characteristics. The EfficientNetB3 model demonstrates better overall performance in classification tasks, even with limited computing resources. The application of deep learning in plant chemical identification holds promise in fields such as medicine, ethnobotany, and conservation biology, enabling researchers, health professionals, and enthusiasts to quickly catalog medicinal plants and gain insights into their healing properties. In particular, the EfficientNetB3 model facilitates the efficient identification and classification of medicinal plants, thereby advancing plant research and improving health practices.
To keep pace with revolutionary advancements in VLSI design, a Programmable System on Chip (PSoC) has been developed to monitor ECG signals, utilizing innovative Analog and Mixed Signal (AMS) technology. Electrocardiographic signals are very weak and have ultra-high impedance, making traditional analog amplifiers unsuitable. The AMS-based PSoC devices from Cypress Semiconductors demonstrate the capability to provide a suitable solution for this issue. The analog section of the PSoC device CY8C55 has ultra-high input impedance. Therefore, by deploying the on-chip resources of this device, an embedded system has been designed to detect and monitor ECG signals. This paper interprets the issues related to the design and validation of both on-chip hardware and embedded firmware. Using electrodes placed only off the chip, the signals are read into the chip with an appropriate reference level. The on-chip PGAs are configured for signal extraction. The analog signals are digitized with 10-bit resolution and then digitally filtered with a low-pass filter configured for Fc = 150 Hz. The signals are recorded in real-time on the DSO. Upon investigating the ECG signals monitored on the DSO, it can be concluded that the current SoC is highly suitable for medical applications.
The field of Machine Learning (ML) demands a comprehensive exploration encompassing research advancements, industry applications, and emerging regulatory considerations. This article delves into these multifaceted aspects, identifying key trends and challenges that are shaping the landscape of ML. The literature reveals that machine learning is rapidly transforming various industries. For instance, in healthcare, ML algorithms achieve accuracy rates exceeding 90% in medical image analysis, leading to earlier diagnoses and improved patient outcomes. Similarly, in nanotechnology, ML is employed to design and optimize novel materials, enhancing properties by approximately 50% compared to traditional methods. However, the ethical and legal implications of Artificial Intelligence (AI) and machine learning necessitate careful consideration. The article explores ongoing discussions surrounding regulations and responsible development in this domain. By offering a comprehensive perspective that integrates advancements, applications, and regulatory considerations, this analysis aims to serve as a valuable resource for academics and policymakers navigating the complexities and opportunities associated with machine learning.
