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CUPPING THERAPY
Traditional cupping therapy (CT) has been around for a long time and is still used today to treat many different kinds of medical issues. Still, researchers have yet to pin down exactly how (CT) works. Looking at CT through the lens of contemporary medicine, this review set out to determine how it might work and provide some explanations for its effects. A search was conducted using keywords in the English publications in PubMed, Cochrane Library, and Google Scholar. From the 223 articles that were found, 149 records were reviewed and 74 articles were deemed irrelevant and removed. Of the 75 full-text publications that were considered for this review, 64 were ultimately included. There are six competing explanations for the benefits of cupping therapy. Nitric Oxide theory may provide an explanation for the calming effects on muscles, alterations in local tissue structures, and improvements in blood circulation. The Activation of immune system theory may explain the hormonal changes and immunological effects. According to the "Blood Detoxification Theory" heavy metals and waste are eliminated while toxins are released. These theories can complement one another or even be used interchangeably to treat different diseases and ailments. It would appear that the many impacts of cupping cannot be adequately explained by a single theory. The aforementioned theories require further investigation to either confirm or disprove them, and future research should also aim to develop novel conceptualizations of CT.
Brain Tumor Detection from MRI Image using Deep Learning
In this project, we propose a deep learning-based approach for the automatic detection of brain tumors from magnetic resonance imaging (MRI) scans. The model utilizes convolutional neural networks (CNNs) to analyze intricate patterns within the images, distinguishing between tumor and non-tumor regions. A diverse dataset, encompassing a range of tumor types and sizes, is employed for robust model training. Through meticulous preprocessing and augmentation, the system enhances its ability to generalize to new and unseen cases. The model is rigorously evaluated using established metrics on a separate test set, demonstrating its efficacy in accurate tumor detection. This research contributes to the advancement of automated medical image analysis, offering a potential tool for timely diagnosis and intervention in neurological healthcare settings.
Additive Manufacturing in Biomedical Application
Additive Manufacturing (AM), commonly known as 3D printing, has revolutionized the field of biomedical applications by offering innovative solutions for personalized and complex structures. This technology enables the fabrication of patient-specific implants, prosthetics, and tissues with enhanced precision and customization. The versatility of additive manufacturing allows the incorporation of biocompatible materials, fostering the development of implants tailored to individual anatomical requirements. Additionally, the rapid prototyping capabilities of 3D printing facilitate the creation of intricate models for surgical planning and education. The use of bioinks and biomaterials in additive manufacturing has paved the way for the fabrication of functional tissues and organs, advancing the prospects of regenerative medicine. Furthermore, the scalability and cost-effectiveness of 3D printing in the biomedical field hold significant promise for widespread adoption and accessibility. In conclusion, additive manufacturing stands as a transformative force in biomedical applications, offering unparalleled opportunities for personalized healthcare and advancing the frontiers of medical technology.
Transportation and Disposal of Medical Waste Using Block Chain Technology
This project proposes a blockchain-based solution to enhance the regulation and supervision of medical waste disposal, addressing critical loopholes in current management practices. By introducing decentralized system architecture and smart contracts, the model ensures the authenticity and credibility of medical waste data throughout its lifecycle. Digital credentials are employed to protect operator information privacy, while blockchain technology facilitates information sharing and transfer among stakeholders. The integration of digital credentials for physical certificates enhances security and privacy protection. The model offers authoritative evidence for regulatory bodies, supporting the establishment of a new generation of medical waste regulatory information systems. Through insightful discussions and case studies, the practicability of blockchain in waste management is highlighted, paving the way for a more efficient and accountable approach to medical waste disposal.
Smart Warming: Lab VIEW- Powered Patient Mattress for Ultimate Comfort
Exposure to cold temperatures is, often, a neglected problem in ICUs, CT, AND MRI scans it may cause several discomforts such as shivering, lung diseases, and artifacts in scan reports, etc., one of the leading influences of the overall sensation of cold discomfort is the cooling of the back. This study aimed to evaluate the effect of a heated ambulance mattress- prototype on body temperatures and thermal comfort in an experimental study. The endotherm patient warming mattress and ambulatory patient mattress are the preexisting projects. The Inditherm mattress is designed for use by all patients undergoing anesthesia who may be at risk of inadvertent pre-operative hypothermia, and for any patients who require warming in pre-operative, intra-operative, and postoperative settings the ambulatory mattress is generally a warming mattress. The main disadvantage is that these two technologies are not automated the operator has to set all parameters. In this project, we are designing a mattress that liberates heat depending on room temperature. The Lab VIEW controls on and off of the system it compares the room temperature with the present value and produces a suitable result. The main advantage is the project is fully automated and warms the patient only at the required time.
POSTURE MONITORING
Due to a widespread use of electronic goods and an increasing trend towards desk-bound work environments, the prevalence of sedentary behaviors has reached alarming levels in the present rapidly evolving technological landscape. Poor posture, a common result of inactivity, has negative effects on your mental as well as physical wellness and overall quality of life. The third most prevalent reason why people go to the doctor on a worldwide scale is for back pain, which is often caused by poor posture. This paper explores the negative effects of sitting for long periods of time and slouching, and how important it is to maintain good posture for health and wellbeing in general.
Parkinson’s Disease Management Via Wearable Sensors
This study presents an innovative IoT-based system for the real-time management of Parkinson's Disease using a combination of Flex Sensor, Node MCU, SCR Sensor, and ADXL335. The Flex Sensor captures hand tremors, the ADXL335 monitors overall motion, and the SCR Sensor measures physiological stress levels. These components collectively enable continuous monitoring of Parkinson's symptoms. The data is wirelessly transmitted to a centralized server for analysis, allowing healthcare providers to remotely assess patient conditions. Machine learning algorithms offer insights into disease progression and predict potential exacerbations. The system provides timely alerts, enhancing patient care and reducing healthcare costs. This research contributes to the evolving landscape of IoT applications in healthcare for neurodegenerative disorders.
Medical Sensors and their Integration in Wireless Body area Networking for Pervasive Healthcare Delivery
Wireless Body Area Networks (WBANs) integrate diverse sensors like heart rate monitors, electrocardiograms, pulse oximeters, respiration rate sensors, gyroscopes, and skin conductance sensors into a network for pervasive healthcare delivery. This interconnected system enables real-time monitoring and data fusion, ensuring accurate and comprehensive health assessments. Efficient communication protocols, cloud integration, and power management enhance the reliability and longevity of wearable devices. Security measures and user-friendly interfaces prioritize the protection of sensitive health data and ease of access. Machine learning algorithms contribute to predictive analytics for early detection of health issues. WBANs offer a holistic approach to personalized healthcare, allowing for continuous and remote monitoring of physiological parameters. The seamless integration of these sensors establishes a foundation for timely intervention and improved patient outcomes.
IoT Based Air Quality Monitoring and Controlling System for Hospital
This project introduces an IoT based Air Quality Monitoring and Controlling System tailored for hospital environments. Utilizing a variety of sensors, including those for particulate matter, carbon dioxide, and volatile organic compounds, the system ensures continuous real-time monitoring of indoor air quality. NodeMCU facilitates wireless communication, enabling the transmission of data to a central server or cloud platform. Hospital staff can remotely monitor air quality conditions, receiving threshold alerts in the event of pollutant levels surpassing predefined limits. The system integrates a controlling mechanism, allowing for the activation of ventilation systems or air purifiers based on the collected data. Energy efficiency is prioritized through intelligent control of air quality devices. Historical data analysis enables administrators to identify patterns and improve overall air quality management. The system seamlessly integrates with existing hospital infrastructure and is designed for scalability, ensuring adaptability to evolving needs. Overall, this IoT-based solution offers a smart and automated approach to maintaining a healthy indoor environment in hospital settings.
HYPERSOMNIA
Hypersomnia, a sleep disorder characterized by excessive daytime sleepiness and prolonged nocturnal sleep, poses significant challenges to individuals' daily functioning. Afflicted individuals struggle to stay awake during waking hours despite extended periods of sleep, often experiencing difficulties waking up in the morning. Cognitive functions, including memory and concentration, may be impaired, leading to social and occupational hindrances. While short naps provide minimal relief, hypersomnia's impact on overall well-being necessitates timely diagnosis and intervention. Diagnosis involves a comprehensive assessment of medical history and, if required, polysomnography. Treatment strategies, ranging from lifestyle modifications to pharmacological interventions, aim to address the underlying causes, which may be primary or secondary to other medical conditions. Timely recognition and management of hypersomnia are crucial to mitigating its adverse effects on daily life and fostering improved sleep quality and daytime alertness.
Abrasion Curative Using HerboGel
Our project aims to develop a hydrogel-based wound healing formulation utilizing the medicinal properties of three potent herbs: Curcuma longa (turmeric), Ocimumtenuiflorum (holy basil), and Azadirachtaindica (neem). These herbs are renowned for their antimicrobial and anti-inflammatory properties, which are crucial for effective wound management. The hydrogel will serve as a vehicle for delivering the active compounds extracted from these herbs to the site of injury, promoting faster healing and reducing the risk of infection. Through a series of extraction, formulation, and characterization steps, we will optimize the herbal hydrogel for its physical, chemical, and biological properties. In vitro studies will assess its antimicrobial efficacy against common wound pathogens, while in vivo experiments will evaluate its ability to accelerate wound closure and reduce inflammation in animal models. Safety and stability assessments will ensure the viability of the formulation for clinical translation. Ultimately, this research holds promise for the development of a natural, effective, and accessible solution for wound care, addressing a critical need in healthcare.
Network Slicing in 5G Systems: Challenges, Opportunities and Implementation Approaches
The introduction of the fifth-generation (5G) of network technology has radically transformed the telecommunications landscape by providing high-speed, low-latency communication suitable for a range of innovative applications. However, this transformation also introduces novel network complexity and resource management challenges. An emerging solution to these formidable challenges is 'Network Slicing,' a powerful technology that plays a crux role in the efficient management of 5G network systems. Network slicing, enabled by key technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), allows the creation of multiple virtual and independent networks operating on a shared physical infrastructure. This ability contributes to a more flexible, scalable, and efficient network system, making it aptly suited for diverse 5G applications. In this paper, we conduct an in-depth examination of network slicing in 5G systems, its implementation strategies, associated challenges, and potential solutions. Two real-world case studies underline its practical applications, while a discussion on the future outlook anticipates advances in AI and ML to refine network slicing management. The paper posits that while network slicing brings its own set of complexities, its continuous evolution and relentless innovations gear towards overcoming such challenges, paving the way to a future of 5G networking marked by versatility, reliability, and efficiency of unprecedented levels.
Design of Driver Seat in Automobile Car
Driver seat in the automobile is an important system. There are so many parts in the driver seat which made it a complicated system. Adjustments as well as safety systems. A seat adjustment includes height adjustment, fore and aft adjustment, back recline adjustments. Safety systems include seat belts, air bags and advanced head restraints. Generally professional driver works more than eight hours per day of a week, therefore driver seat must be designed by considering all the parameters. Also uneven road condition induces the vibration in a vehicle transmitted to driver body. Poorly designed driver seat affects the driver health and psychological conditions of mind. Three main objectives of any driver seats are safety, health and comfort of driver. This paper presents all the parameters of driver seat such as anthropometry of human, ergonomics related parameters, seat materials, safety related parameters, comfort related parameters as well as weight and aesthetics with classifications and basics of driver seat.
Optimization of Motor Bracket for Enhanced Performance in Electric Vehicles - A Review
As electric vehicles (EVs) continue to gain prominence in the automotive industry, the demand for efficient and lightweight components becomes crucial for overall vehicle performance. This study focuses on the optimization of the bracket used for mounting the motor in electric vehicles, aiming to enhance structural integrity, reduce weight, and improve overall efficiency. The research employs a multi-faceted approach, combining advanced materials analysis, structural simulations, and manufacturing techniques to achieve an optimal design. Initially, a comprehensive materials selection process is conducted, considering factors such as strength, weight, and cost. Finite Element Analysis (FEA) is then employed to simulate the bracket's structural behavior under various loading conditions, ensuring that it meets stringent performance and safety standards. The optimization process involves fine-tuning the bracket's geometry to strike a balance between weight reduction and structural robustness, thereby improving the overall efficiency of the electric motor system. Additionally, advanced manufacturing methods, such as additive manufacturing and precision machining, are explored to facilitate the production of the optimized bracket. These techniques not only contribute to the reduction of material waste but also allow for complex geometries that further enhance the bracket's performance.
Design and Analysis of Universal Joint to Enhanced Performance in Electric Vehicles - A Review
The rapid evolution of electric vehicles (EVs) has necessitated a comprehensive reevaluation of critical components, particularly the universal joint (U-joint), which plays a pivotal role in transmitting power from electric motors to the wheels. This review meticulously examines the existing literature on the design and analysis of U-joints, with a primary focus on enhancing performance in the context of electric vehicles. Challenges unique to EVs, including higher torque demands and variable speeds, underscore the urgency for optimized U-joint designs. Material advancements and manufacturing techniques, analytical tools, and innovative designs are explored, considering their potential to address these challenges. Smart technologies, such as sensors and condition monitoring systems, are integrated into the discussion, offering insights into real-time monitoring and predictive maintenance strategies. Environmental considerations, case studies, and practical implementations are also deliberated upon. The literature review concludes with a forward-looking perspective, identifying future trends and recommending areas for continued research and development in the pursuit of achieving enhanced U-joint performance for electric vehicles. This synthesis of research findings serves as a valuable resource for engineers, researchers, and industry professionals engaged in advancing the efficiency and reliability of electric propulsion systems.
IoT Cloud Based Low-Cost Temperature, Humidity and Dust Monitoring System to Prevent Food Poisoning
This project presents a cost-effective IoT-based system designed for monitoring temperature, humidity, and dust levels in food storage environments to mitigate the risk of food poisoning. Utilizing sensors and a microcontroller, real-time data is collected and transmitted to a cloud platform through a secure communication protocol. The cloud-based solution incorporates a backend server and a database for efficient data processing and storage. A user-friendly web or mobile interface enables stakeholders to monitor conditions, set custom thresholds, and receive timely alerts. The system's design emphasizes scalability, adaptability, and ease of deployment, making it suitable for various food storage facilities. Rigorous testing ensures the reliability and accuracy of the system, and its implementation is intended to enhance food safety practices. Ongoing maintenance and updates are incorporated to ensure the sustained effectiveness of the monitoring solution in preventing food borne illnesses.
IoT Based Drug Delivery System
This IoT-based medicine dispenser system presents a smart medication management system designed to enhance user adherence and automate dispensing processes. This project proposes a NodeMCU and Arduino-based Drug Delivery System utilizing a buzzer, RTC DS3231 module, LCD display, IR sensor, and GSM technology. The system is designed to enhance medication adherence and patient care. The NodeMCU and Arduino facilitate seamless integration of components, while the RTC DS3231 module ensures precise timekeeping for medication schedules. An LCD display provides real-time information on dosage and timings. An IR sensor enables user interaction, allowing patients to confirm dosage retrieval. Additionally, a buzzer provides audible alerts for medication reminders. The incorporation of GSM technology allows remote monitoring and alerts for healthcare providers, ensuring efficient and timely drug delivery. This system aims to improve patient outcomes and simplify medication management.
Image Restoration
Image restoration is an integral component of computer vision that tries to restore pictures that have been deteriorated or corrupted to their original or enhanced condition. In this study, we look into the wide-ranging terrain of picture restoration techniques, which includes both conventional filter-based approaches and cutting-edge deep learning models. There are certain circumstances in which traditional approaches, such as Wiener filtering and bilateral filtering, perform quite well, particularly when it comes to smoothing and noise reduction. On the other hand, the fact that they rely on handcrafted filters restricts their adaptation to more complicated forms of degradation. Visual restoration has been revolutionized by deep learning, which is led by convolutional neural networks (CNNs). Deep learning involves learning sophisticated representations of visual data. It is because of this that CNNs are able to deal with a wide variety of degradations, such as noise, blurring, artifacts, and missing data. Generative adversarial networks, often known as GANs, are continually pushing the limits of what is possible by utilizing adversarial training to accomplish spectacular outcomes in the areas of in painting and picture super-resolution. Despite amazing development, there are still obstacles to overcome: Understanding the inner workings of deep learning models continues to be a challenge, thanks to the limited interpretability of the data. Dependence on data: Acquiring large quantities of high-quality data is necessary for the training of successful models. Costs associated with computation: The process of training and deploying deep learning models may be quite computationally rigorous. The improvement of camera vision for autonomous cars in order to make navigation safer and more dependable overall. Image restoration technology has the potential to continue to revolutionize image processing and analysis, ultimately contributing to advancements across a wide range of scientific and technological domains. This can be accomplished by addressing the challenges that are currently being faced and concentrating on the promising research directions that are currently being pursued.
Feedback Analyzer for Employee Progress Graph
In contemporary workplace environments, the assessment and improvement of employee performance are integral components of organizational success. This abstract introduces a sophisticated Feedback Analyzer designed to streamline the process of evaluating and visualizing employee progress through dynamic and interactive graphs. The Feedback Analyzer leverages advanced data analytics and machine learning techniques to analyze feedback data collected from various sources, including performance reviews, peer evaluations, and managerial assessments. The system employs natural language processing algorithms to extract valuable insights feedback, transforming unstructured comments into quantifiable metrics. In this research we focus on the need for effective employee performance evaluation and feedback mechanisms is paramount. Traditional methods of assessing employee progress often fall short in providing real-time insights and actionable data for both employees and managers.
Customer Service Automation Using Machine learning
The abstract for a customer service automation using machine learning might focus on the application of machine learning techniques to enhance and streamline customer service processes. It could highlight how these technologies aim to improve response accuracy, speed, and personalization by leveraging algorithms for sentiment analysis, natural language processing, and automated decision-making. The abstract could also discuss the potential benefits of such automation, including increased efficiency, cost-effectiveness, and improved customer satisfaction. This research paper explores the integration of machine learning techniques in automating customer service processes. As businesses strive to enhance efficiency and customer satisfaction, the application of machine learning algorithms in handling customer queries, sentiment analysis, and personalized interactions has gained significant traction. This study investigates the utilization of natural language processing (NLP) models, sentiment analysis, recommendation systems, and chatbots to streamline and personalize customer service experiences. Various machine learning approaches are examined for their effectiveness in handling diverse customer inquiries across different industries. Moreover, the challenges, ethical considerations, and future prospects of employing machine learning for customer service automation are also discussed. The findings demonstrate the potential of machine learning-driven automation in revolutionizing customer service, paving the way for more efficient, responsive, and tailored customer interactions.