Psdot 14 using data mining techniques in heartZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
Psdot 14 using data mining techniques in heartZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
prediction of heart disease using machine learning algorithmsINFOGAIN PUBLICATION
The successful experiment of data mining in highly visible fields like marketing, e-business, and retail has led to its application in other sectors and industries. Healthcare is being discovered among these areas. There is an opulence of data available within the healthcare systems. However, there is a scarcity of useful analysis tool to find hidden relationships in data. This research intends to provide a detailed description of Naïve Bayes and decision tree classifier that are applied in our research particularly in the prediction of Heart Disease. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classification.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
One of the major purposes manufacturers incorporate AI or ML in their applications is to ease software computations and to predict precise results. I think compared to any other application, a medical application requires a lot of precise computations and therefore, AI is a perfect solution to enhance performance and productivity. While reading the health-tech news, I came across recent research in this regard, the use of AI in predicting a potential stroke or cardiac arrest. ..
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
Prognosis of Cardiac Disease using Data Mining Techniques A Comprehensive Surveyijtsrd
The Healthcare exchange generally clinical diagnosis is ended commonly by doctor's knowledge and practice. Computer Aided Decision Support System plays a major task in the medical field. Data mining provides the methodology and technology to modify these rises of data into valuable data for decision making. By utilizing data mining techniques it requires less time for the prediction of the diseases with more accuracy. Among the expanding research on coronary diseases predicting system, it has happened significant to classifications the exploration results and gives readers with a layout of the current coronary diseases forecast strategies in every discussion. Data mining tools can respond to exchange addresses that expectedly being used much time over riding to decide. In this paper we study different papers in which at least one algorithm of data mining used for the prediction of coronary diseases. As of the study it is observed that Naïve Bayes Technique increase the accuracy of the coronary diseases prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are outlined in this paper. D. Haripriya | Dr. M. Lovelin Ponn Felciah "Prognosis of Cardiac Disease using Data Mining Techniques: A Comprehensive Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26605.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26605/prognosis-of-cardiac-disease-using-data-mining-techniques-a-comprehensive-survey/d-haripriya
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
prediction of heart disease using machine learning algorithmsINFOGAIN PUBLICATION
The successful experiment of data mining in highly visible fields like marketing, e-business, and retail has led to its application in other sectors and industries. Healthcare is being discovered among these areas. There is an opulence of data available within the healthcare systems. However, there is a scarcity of useful analysis tool to find hidden relationships in data. This research intends to provide a detailed description of Naïve Bayes and decision tree classifier that are applied in our research particularly in the prediction of Heart Disease. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classification.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
One of the major purposes manufacturers incorporate AI or ML in their applications is to ease software computations and to predict precise results. I think compared to any other application, a medical application requires a lot of precise computations and therefore, AI is a perfect solution to enhance performance and productivity. While reading the health-tech news, I came across recent research in this regard, the use of AI in predicting a potential stroke or cardiac arrest. ..
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
Prognosis of Cardiac Disease using Data Mining Techniques A Comprehensive Surveyijtsrd
The Healthcare exchange generally clinical diagnosis is ended commonly by doctor's knowledge and practice. Computer Aided Decision Support System plays a major task in the medical field. Data mining provides the methodology and technology to modify these rises of data into valuable data for decision making. By utilizing data mining techniques it requires less time for the prediction of the diseases with more accuracy. Among the expanding research on coronary diseases predicting system, it has happened significant to classifications the exploration results and gives readers with a layout of the current coronary diseases forecast strategies in every discussion. Data mining tools can respond to exchange addresses that expectedly being used much time over riding to decide. In this paper we study different papers in which at least one algorithm of data mining used for the prediction of coronary diseases. As of the study it is observed that Naïve Bayes Technique increase the accuracy of the coronary diseases prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are outlined in this paper. D. Haripriya | Dr. M. Lovelin Ponn Felciah "Prognosis of Cardiac Disease using Data Mining Techniques: A Comprehensive Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26605.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26605/prognosis-of-cardiac-disease-using-data-mining-techniques-a-comprehensive-survey/d-haripriya
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
Propose a Enhanced Framework for Prediction of Heart DiseaseIJERA Editor
Heart disease diagnosis requires more experience and it is a complex task. The Heart MRI, ECG and Stress Test etc are the numbers of medical tests are prescribed by the doctor for examining the heart disease and it is the way of tradition in the prediction of heart disease. Today world, the hidden information of the huge amount of health care data is contained by the health care industry. The effective decisions are made by means of this hidden information. For appropriate results, the advanced data mining techniques with the information which is based on the computer are used. In any empirical sciences, for the inference and categorisation, the new mathematical techniques to be used called Artificial neural networks (ANNs) it also be used to the modelling of the real neural networks. Acting, Wanting, knowing, remembering, perceiving, thinking and inferring are the nature of mental phenomena and these can be understand by using the theory of ANN. The problem of probability and induction can be arised for the inference and classification because these are the powerful instruments of ANN. In this paper, the classification techniques like Naive Bayes Classification algorithm and Artificial Neural Networks are used to classify the attributes in the given data set. The attribute filtering techniques like PCA (Principle Component Analysis) filtering and Information Gain Attribute Subset Evaluation technique for feature selection in the given data set to predict the heart disease symptoms. A new framework is proposed which is based on the above techniques, the framework will take the input dataset and fed into the feature selection techniques block, which selects any one techniques that gives the least number of attributes and then classification task is done using two algorithms, the same attributes that are selected by two classification task is taken for the prediction of heart disease. This framework consumes the time for predicting the symptoms of heart disease which make the user to know the important attributes based on the proposed framework.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.