Knee replacement operation frequently needed by hemophiliacs patients, which greatly improve their quality of life. The artificial knee is a prosthetic device which is made up of metals and polymers, which can be surgically implanted to replace a natural joint. A knee substitution is an arthroplasty surgical procedure to implant the weight bearing surface of the knee joint with less pain and move properly. This surgery consist of replacement of the diseased or damaged joint surface of the knee with metal and plastic component shaped to allow continued motion of the knee. In this project we using different sensors to monitoring the knee joints and leg movement. Exoskeleton devices are designed for applications such as rehabilitation, assistance, and haptic. Due to the nature of physical human machine interaction, designing and operating these devices is quite challenging. Optimization methods lessen the severity of these challenges and help designers develop the device they need. In this paper, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of exoskeletons.

Cloud memory management is a critical aspect of efficient data storage and retrieval in cloud computing environments. Hybrid algorithms have emerged as a promising solution to optimize memory management by combining the strengths of different approaches. This abstract explores the integration of hybrid algorithms in cloud memory management to enhance performance and scalability. Hash codes play a crucial role in data integrity verification and security. Comparing the hash codes generated by different algorithms, such as SHA1 and SHA2, provides insights into their efficiency, collision resistance, and cryptographic strength. This comparison aids in selecting the most suitable algorithm for specific security requirements in cloud environments. Data security is paramount in cloud computing, especially when sensitive information is stored or transmitted. Hybrid methods like homomorphic encryption combined with Proxy Re Encryption offer a robust approach to securing data while allowing for secure computation and sharing. This abstract delves into the implementation and benefits of using hybrid methods for enhancing data security in cloud environments. By exploring the integration of hybrid algorithms in cloud memory management, comparing hash codes for SHA1 and SHA2, and leveraging hybrid methods for data security, this abstract aims to provide valuable insights into optimizing cloud computing systems for improved performance, security, and efficiency.

This project focuses on the development of a machine learning model for the early prediction of neurovascular crises, commonly known as strokes. A Stroke is a health condition that causes damage by tearing the blood vessels in the brain. It can also occur when there is a halt in the blood flow and other nutrients to the brain. According to the World Health Organization (WHO), stroke is the leading cause of death and disability globally. Most of the work has been carried out on the prediction of heart stroke but very few works show the risk of a brain stroke. With this thought, various machine learning models are built to predict the possibility of stroke in the brain. Stroke is a destructive illness that typically influences individuals over the age of 65 years age. Prediction of stroke is a time consuming and tedious for doctors. Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques. Aim is to create an application with a user friendly interface which is easy to navigate and enter inputs.

In every aspect of human existence, automation has grown in significance. However, there are also a lot of places where more conventional approaches are being applied. One example of this is the ordering mechanism used in restaurants. Modern technology is developing and expanding at a breakneck pace .Innovation is another name for the technologies we are bringing. Our website is as easy to use as possible, and we are extracting the text from the uploaded image. The website will extract the text, search for the text on Google, and produce two columns for the image and the text name. This is the only work or activity we are doing; the real background work begins when they upload the image .Normally the already existed technology is if we scan the QR code, it directs us to a new website that is already linked and has all the information about the hotel, restaurant, etc. Although many restaurants are operating at full capacity, staffing is a very other story. As the pandemic slows down and other country become more confident about controlling the outbreak with the vaccination standard, diners have been itching to return to dining rooms. Process automation is the only way to keep restaurants operating at maximum efficiency while addressing staffing shortages. time, the system gives the restaurant employees the ability to change the menu, take orders, and create automatic invoices, which enables them to easily process customer payments.

Rather than a typical summary, consider framing the abstract around the paradigm shift 5G represents in the technological landscape. Emphasize how it’s not just a faster network but a foundation for the hyper connected future, with disruptive potential across nearly every industry. Highlight lesser known applications, like bio networking or digital twins, to showcase the visionary scope of 5G

In today‘s society, we have entered the era of big data and the Internet of Things. The rise of services such as artificial intelligence, cloud storage, and the metaverse has led to increased demands for data transmission and storage. People now require not only greater capacity but also higher efficiency, more powerful hardware, and advanced algorithms. Storage the Reed Solomon (RS) code, as a highly effective error correction code, is officially applied in fields such as data storage and data transmission. The Cauchy Reed Solomon (CRS) Code, which is based on the RS code, utilizes a Cauchy matrix instead of the original Vandermonde matrix and replaces the original matrix multiplication with XOR operations. This paper primarily explores how the XOR optimized Cauchy Reed Solomon Code improves the efficiency of distributed storage systems, It was discovered that using lookup tables to store frequently used data and operations can further enhance the efficiency of storage systems and its future applications.

This transformation allows the model to capture the relationships between different health indicators and their correlation with CKD risk. At the core of the predictive analysis lies a Random Forest Classifier, a powerful ensemble learning method known for its accuracy and robustness in classification tasks. The model is trained on a comprehensive dataset encompassing various health metrics associated with CKD. By analysing this data, the classifier predicts the likelihood of a user developing CKD based on their input, enabling early detection and timely intervention. In addition to predicting CKD risk, the application utilizes K means clustering to categorize users into distinct stages of CKD, based on their health data patterns. This clustering approach aids in providing a clearer understanding of an individual’s kidney health status, which is essential for appropriate treatment planning and management. Furthermore, to enhance user experience and support proactive healthcare decisions, the application employs the K Nearest Neighbors (KNN) algorithm to offer personalized recommendations for nearby hospitals. This web application is designed to predict chronic kidney disease (CKD) through an intuitive user interface that enables individuals to securely log in and input their health details. The application employs a robust data processing pipeline to ensure the reliability of the predictions. Initially, it utilizes interpolation techniques for data cleaning, addressing any missing values in the user input to enhance dataset integrity. This is crucial, as incomplete data can lead to inaccurate predictions and hinder effective analysis. To facilitate the handling of categorical variables, the application incorporates Label Encoder, which converts these categories into numerical values that machine learning algorithms can process effectively.

The project focuses on building a mental health tracker. You will try to get an idea of the mental state of your user (in the least intrusive ways), find out if they are suffering and then suggest measures they can take to get out of their present condition. A user answers some questions and based on the answers that they provide, you will suggest tasks to them and maintain a record of their mental state for displaying on a dashboard. Mental disorders are widespread in countries all over the world. Nevertheless, there is a global shortage in human resources delivering mental health services. Leaving people with mental disorders untreated may increase suicide attempts and mortality. To address this matter of limited resources, conversational agents have gained momentum in the last years. In this work, we introduce a mobile application with integrated Chabot that implements methods from cognitive behavior therapy (CBT) to support mentally ill people in regulating emotions and dealing with thoughts and feelings. Application asks the user on a daily basis on events that occurred and on emotions. It determines automatically the basic emotion of a user from the natural language input using natural language processing and a lexicon based approach. Depending on the emotion, an appropriate measurement such as activities or mindfulness exercises is suggested by application.

Accurate early stage detection of crop diseases is essential for maintaining crop quality and yield by enabling timely and effective treatments. However, disease detection often requires specialized expertise in plant pathology and significant experience. Therefore, an automated crop disease detection system is invaluable in agriculture, as it supports the development of an early warning system. To build this system, we developed a stepwise disease detection model utilizing images of diseased and healthy plants, along with a CNN based algorithm incorporating five pre trained models. The detection process involves three stages: crop classification, disease detection, and disease classification. An ‘unknown’ category was included to enhance model generalization for broader applications. In validation tests, the model achieved high accuracy in classifying crops and disease types (97.09%). Additionally, by adding non model crops to the training dataset, we improved accuracy for these crops, demonstrating the model’s scalability. This model has significant potential for application in smart farming, particularly for Solanaceae crops, and could be adapted to other crop varieties by expanding the training dataset.

Online social media is the dominant force in today’s world in many ways, and its user base is expanding daily. Social media usage is rapidly increasing. The main advantage is the ease and effectiveness with which we may communicate with others through online social media.  This created a new potential attack vector, including a fake identity, misleading information, and so forth. A new study indicates that there are far more accounts among social media users than there are users themselves. Online social network providers find it challenging to identify these fraudulent accounts.  Recognizing these fraudulent accounts is crucial since social media is overflowing with advertisements, misleading information, and other kinds of content. We provide a technique for identifying fraudulent accounts using a dataset of online social media. Instead of using normal machine learning classifiers, we used boosting techniques to increase the accuracy of the usual methodology. This approach has led to a significant increase in accuracy by strengthening poor learners. This research will compare the accuracy of the Gradient Boosting Classifier and the Xgboost Classifier. Xgboost did a fantastic job in comparison to the earlier work.