This document presents a study on developing an algorithm to separate the heart rate signal of an unborn child from its mother during pregnancy. The algorithm first collects heart rate data from a pregnant woman, non-pregnant woman, and newborn baby. It then processes the signals by applying noise filtering. The filtered pregnant woman's signal is subtracted from the non-pregnant woman's signal to extract the fetal heart rate signal. This extracted signal is then compared to the newborn's signal to identify a match, indicating the presence of the fetal heart rate. If a match of 85-95% is found, the output is accepted, otherwise it is rejected for further analysis. The goal is to non-invasively monitor the fetal
IRJET- Ayurveda based Disease Diagnosis using Machine LearningIRJET Journal
The document describes a machine learning system for assisting Ayurvedic disease diagnosis. It uses optical sensors to capture pulse readings from three locations on the wrist. Artificial neural networks analyze the pulse data to predict the patient's prakriti (dosha balance). Questionnaires related to symptoms of anemia and hyperacidity are also used. Decision trees analyze the questionnaire responses to predict if the patient has those diseases. The system aims to help doctors make accurate diagnoses by integrating modern technology with ancient Ayurvedic pulse examination techniques.
Remote Consultation Using Pulse DetectionIRJET Journal
This document describes a system for remote health consultation using pulse detection from Ayurveda. The system uses sensors to detect the pulse at three locations (vatta, pitta, kapha) on the wrist. It sends the pulse data via Bluetooth to an online system where doctors can view the waveforms. The waveforms indicate dosha imbalances and can help doctors diagnose illnesses remotely. The system aims to incorporate the ancient practice of pulse detection into modern telemedicine for improved healthcare access.
Heart Disease Prediction using Data MiningIRJET Journal
This document describes a study that uses data mining techniques like neural networks and genetic algorithms to predict heart disease based on major risk factors. The proposed system initializes neural network weights using a genetic algorithm for feature selection and classification to build an intelligent clinical decision support system. It analyzes heart disease risk factors like age, cholesterol, blood pressure, smoking status and diabetes using a neuro-fuzzy model optimized with a genetic algorithm. The system is able to predict heart disease with 89% accuracy and can help detect the disease early to improve treatment outcomes.
Intelligent Healthcare Monitoring in IoTIJAEMSJORNAL
The developing of IoT-based health care systems must ensure and increase the safety of the patients, their quality of life and other health care activities. We may not be aware of the health condition of the patient during the sleeping hours. To overcome this problem. This paper proposes an intelligent healthcare monitoring system which monitors and maintains the patient health condition at regular intervals. The heart rate sensor and temperature sensor would help us analyze the patients’ current health condition. In case of major fluctuations in consecutive intervals a buzzer is run in order to notify the hospital staff and doctors. The monitored details are stored in the cloud "ThingSpeak". The doctor can view the patient health condition using Virtuino simulator. This system would help in reducing the random risks of tracing a patient medical highly. Arduino UNO is used to implement this intelligent healthcare monitoring system.
IRJET- Web-based Application to Detect Heart Attack using Machine LearningIRJET Journal
This document presents a web-based application that uses machine learning to detect heart attacks. The application builds an interactive risk prediction system that calculates an individual's vulnerability to having a heart attack based on their risk factor. It analyzes medical data using classification algorithms like logistic regression and Naive Bayes to predict whether someone has a high or low risk of a heart attack. The results are displayed to users through a simple web interface, and alerts are sent to patients' phones. The goal is to automate risk prediction to reduce the time and effort required from doctors.
Review on Diseases Diagnosing by using Nadi Parikshan IRJET Journal
1) Nadi Pariksha is an important Ayurvedic technique for diagnosing diseases by examining the pulse. It analyzes the vata, pitta, and kapha pulses which correspond to the three doshas (energies) in the body.
2) Various researchers have developed sensor-based systems to measure the pulse signals and aid in disease diagnosis. Early systems used piezoelectric or strain gauge sensors but had issues with noise. More advanced systems use improved sensors, signal conditioning, and data analysis techniques.
3) Simulated studies have also been conducted to generate sample pulse signals for different diseases and test signal processing methods. The goal of this research is to develop accurate, portable pulse
A data mining approach for prediction of heart disease using neural networksIAEME Publication
This document summarizes a research paper that developed a heart disease prediction system using neural networks. The system predicts the likelihood of patients getting heart disease based on 13 medical parameters like sex, blood pressure, and cholesterol. Two additional parameters of obesity and smoking were included for better accuracy. The neural network was able to predict heart disease with nearly 100% accuracy based on these parameters. The document also reviews several other studies that have used data mining techniques and artificial neural networks for heart disease diagnosis and prediction.
This document discusses how IoT technology can provide value for maintenance of HVAC systems. It notes that IoT allows for remote monitoring of systems, automatic alarm notifications, and detailed analysis of operational data. This enables more proactive maintenance by identifying issues early, optimizing maintenance scheduling, reducing unnecessary service visits, and transforming unexpected repairs into routine maintenance through predictive analytics of equipment performance metrics.
IRJET- Ayurveda based Disease Diagnosis using Machine LearningIRJET Journal
The document describes a machine learning system for assisting Ayurvedic disease diagnosis. It uses optical sensors to capture pulse readings from three locations on the wrist. Artificial neural networks analyze the pulse data to predict the patient's prakriti (dosha balance). Questionnaires related to symptoms of anemia and hyperacidity are also used. Decision trees analyze the questionnaire responses to predict if the patient has those diseases. The system aims to help doctors make accurate diagnoses by integrating modern technology with ancient Ayurvedic pulse examination techniques.
Remote Consultation Using Pulse DetectionIRJET Journal
This document describes a system for remote health consultation using pulse detection from Ayurveda. The system uses sensors to detect the pulse at three locations (vatta, pitta, kapha) on the wrist. It sends the pulse data via Bluetooth to an online system where doctors can view the waveforms. The waveforms indicate dosha imbalances and can help doctors diagnose illnesses remotely. The system aims to incorporate the ancient practice of pulse detection into modern telemedicine for improved healthcare access.
Heart Disease Prediction using Data MiningIRJET Journal
This document describes a study that uses data mining techniques like neural networks and genetic algorithms to predict heart disease based on major risk factors. The proposed system initializes neural network weights using a genetic algorithm for feature selection and classification to build an intelligent clinical decision support system. It analyzes heart disease risk factors like age, cholesterol, blood pressure, smoking status and diabetes using a neuro-fuzzy model optimized with a genetic algorithm. The system is able to predict heart disease with 89% accuracy and can help detect the disease early to improve treatment outcomes.
Intelligent Healthcare Monitoring in IoTIJAEMSJORNAL
The developing of IoT-based health care systems must ensure and increase the safety of the patients, their quality of life and other health care activities. We may not be aware of the health condition of the patient during the sleeping hours. To overcome this problem. This paper proposes an intelligent healthcare monitoring system which monitors and maintains the patient health condition at regular intervals. The heart rate sensor and temperature sensor would help us analyze the patients’ current health condition. In case of major fluctuations in consecutive intervals a buzzer is run in order to notify the hospital staff and doctors. The monitored details are stored in the cloud "ThingSpeak". The doctor can view the patient health condition using Virtuino simulator. This system would help in reducing the random risks of tracing a patient medical highly. Arduino UNO is used to implement this intelligent healthcare monitoring system.
IRJET- Web-based Application to Detect Heart Attack using Machine LearningIRJET Journal
This document presents a web-based application that uses machine learning to detect heart attacks. The application builds an interactive risk prediction system that calculates an individual's vulnerability to having a heart attack based on their risk factor. It analyzes medical data using classification algorithms like logistic regression and Naive Bayes to predict whether someone has a high or low risk of a heart attack. The results are displayed to users through a simple web interface, and alerts are sent to patients' phones. The goal is to automate risk prediction to reduce the time and effort required from doctors.
Review on Diseases Diagnosing by using Nadi Parikshan IRJET Journal
1) Nadi Pariksha is an important Ayurvedic technique for diagnosing diseases by examining the pulse. It analyzes the vata, pitta, and kapha pulses which correspond to the three doshas (energies) in the body.
2) Various researchers have developed sensor-based systems to measure the pulse signals and aid in disease diagnosis. Early systems used piezoelectric or strain gauge sensors but had issues with noise. More advanced systems use improved sensors, signal conditioning, and data analysis techniques.
3) Simulated studies have also been conducted to generate sample pulse signals for different diseases and test signal processing methods. The goal of this research is to develop accurate, portable pulse
A data mining approach for prediction of heart disease using neural networksIAEME Publication
This document summarizes a research paper that developed a heart disease prediction system using neural networks. The system predicts the likelihood of patients getting heart disease based on 13 medical parameters like sex, blood pressure, and cholesterol. Two additional parameters of obesity and smoking were included for better accuracy. The neural network was able to predict heart disease with nearly 100% accuracy based on these parameters. The document also reviews several other studies that have used data mining techniques and artificial neural networks for heart disease diagnosis and prediction.
This document discusses how IoT technology can provide value for maintenance of HVAC systems. It notes that IoT allows for remote monitoring of systems, automatic alarm notifications, and detailed analysis of operational data. This enables more proactive maintenance by identifying issues early, optimizing maintenance scheduling, reducing unnecessary service visits, and transforming unexpected repairs into routine maintenance through predictive analytics of equipment performance metrics.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The document discusses how the Data Distribution Service (DDS) middleware standard can enable integration of medical devices and systems. DDS allows devices and applications to share information through a global data space and supports real-time performance, reliability, and interoperability across platforms. Examples shown include using DDS for integrated control of medical imaging devices like CT and MRI scanners, particle therapy systems, surgical robotics, and connecting monitoring devices at the hospital, ambulance, and clinical levels. DDS provides a common information model and quality of service policies to ensure safe and effective data sharing across disconnected medical systems.
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.
IRJET - Prediction and Analysis of Multiple Diseases using Machine Learni...IRJET Journal
This document discusses using machine learning techniques to predict and analyze multiple diseases. It presents research using KNN, support vector machine, random forest, and decision tree algorithms applied to a medical database to predict future and previous diseases. The goal is to provide a smart card method for easily and accurately diagnosing disease by storing an individual's full medical record. It reviews related work applying various machine learning classifiers like decision trees, naive Bayes, and logistic regression to diseases such as heart disease, diabetes, and cancer. The conclusion is that machine learning applied to medical data can help predict disease and save time for patients and doctors.
IRJET- Hiding Sensitive Medical Data using EncryptionIRJET Journal
This document summarizes a research paper about securely hiding sensitive medical data using encryption. The paper proposes a system that distributes a patient's data across multiple encrypted data servers. It uses Paillier cryptosystems to allow statistical analysis of the encrypted patient data without compromising privacy. The system includes wireless medical sensors that monitor patients and transmit encrypted data to a database. It uses an access control system and statistical analysis protocols that employ Paillier encryption to allow authorized users like doctors to access and analyze the encrypted patient data without revealing its contents. The goal is to protect sensitive medical information from privacy breaches while still enabling useful analysis of aggregated patient data.
1) The document describes an IoT-based e-prognosis system that monitors patients' temperature and heartbeat using sensors. The sensors send the medical data over the Internet to be accessed by medical professionals.
2) If the system detects a constant rise in the patient's temperature, it will diagnose the issue and send treatment information and remedies to the patient, caretaker, or doctor via IoT.
3) The system is meant to help disabled or elderly people who need monitoring but may not have constant caretaker assistance. It allows remote monitoring using wearable sensors connected to the Internet.
Wearable technology and blood pressure monitoring: Addressing the global hype...Valencell, Inc
High blood pressure is one of the largest public health epidemics in the world, affecting over one billion people according the World Health Organization and presenting significant risk factors for stroke, heart failure, coronary artery disease, diabetes and kidney disease. Monitoring blood pressure is the first step toward improving it, but it remains challenging to consistently monitor blood pressure in ways that don’t disrupt people’s lives. There are numerous efforts underway to address some of those challenges through wearable technology and devices, from watches to earbuds to clothing and more.
In this webinar, leaders from Omron and Valencell will explore the latest technologies and initiatives designed to navigate the technical challenges, regulatory environment, consumer expectations, and opportunities to make a dent in the global hypertension epidemic.
Child health care monitoring using sensor technologyIRJET Journal
This document proposes a framework for monitoring children's health using sensor technology. The framework uses sensors embedded in student ID cards to monitor heart rate and body temperature. It sends this health data to a server, which checks for abnormal readings and notifies parents and school administrators via SMS or a web application if issues are detected. The goal is to provide accurate, real-time child health information to concerned parties in order to promote early detection of potential problems and improve child growth, development, and performance in school. Key aspects of the proposed system include the use of sensors, centralized data collection and analysis, and automated notification of authorized individuals via multiple channels.
IRJET - Digital Assistance: A New Impulse on Stroke Patient Health Care using...IRJET Journal
1) The document presents a study that uses deep learning algorithms and artificial bee colony optimization to predict stroke using medical dataset features.
2) A neural network architecture is developed to classify patients' risk of stroke based on 13 variables from their medical records, with the artificial bee colony algorithm used to preprocess data and extract meaningful features.
3) The random forest algorithm achieved the highest prediction accuracy of over 88% based on metrics like precision, recall, and F1 score compared to other models like logistic regression, naive bayes, and decision trees.
IRJET- Predicting Heart Disease using Machine Learning AlgorithmIRJET Journal
1) The document discusses using machine learning algorithms like Naive Bayes and Decision Trees to predict heart disease using a dataset from Kaggle.
2) It describes preprocessing the dataset, training models, and evaluating accuracy. Decision Trees were found to more accurately predict heart disease than Naive Bayes.
3) The models use 13 attributes like age, sex, cholesterol levels, and examine classification performance to identify individuals at risk of heart disease.
IRJET- An Information Forwarder for Healthcare Service and analysis using Big...IRJET Journal
This document proposes a system for collecting and analyzing healthcare big data using cloud computing. It discusses how healthcare data is growing rapidly in volume and variety due to data from various sources like sensors, and how traditional storage and processing may not be suitable. The proposed system has three layers - a data acquisition layer to collect data from sensors, a transmission layer to send the data to the cloud, and a computational layer in the cloud to analyze and classify the data using clustering and fuzzy rule-based classifiers. This would allow real-time remote healthcare by efficiently storing and processing the large amounts of heterogeneous healthcare data in the cloud. Evaluation metrics like response time, accuracy, cost and false positives are used to compare the proposed system to existing techniques.
An Integrated Approach of Blood Pressure and Heart Rate Measurement SystemsIRJET Journal
This document describes a new integrated device that can measure both blood pressure and heart rate through the wrist and fingertip using a single device. The device uses infrared sensors placed on the wrist and fingertip to detect blood flow and pulses. It counts the pulses to determine the heart rate and blood pressure. The measurements from this new device were compared to manual measurements on 100 subjects, and it was found to have a negligible error rate. The goal was to create an affordable, easy-to-use device that could help both literate and illiterate people regularly monitor their vital signs without visiting a doctor.
This document proposes a remote monitoring system for ECG signals using cloud computing and wireless networks. The system allows ECG signals from patients to be monitored simultaneously by experts. If an abnormality is detected, a message is sent to the cloud and doctor. This could help reduce delays in treatment for heart patients and lower mortality rates. The system uses electrocardiogram signals sent via ZigBee to the cloud where doctors can access the data remotely. This provides availability and reliability of critical patient data through cloud-based storage and access.
Smart Pulse Cardiac Health Monitor from Medi-Coreavivahealth
Smart Pulse is a patented diagnostic device that is very innovative and portable.
Now from the convenience of your home or office, you can quickly run a health check-up for yourself to understand how much progress you have made with your current lifestyle.
http://www.avivahealth.com/shop/products.asp?itemid=11338
Risk Of Heart Disease Prediction Using Machine LearningIRJET Journal
This document describes a study that uses machine learning algorithms to predict the risk of heart disease. It analyzes a dataset containing characteristics of 270 patients using algorithms like logistic regression, naive Bayes, support vector machine, k-nearest neighbors, decision tree, random forest, XGBoost and artificial neural network. The random forest algorithm achieved the highest prediction accuracy of 95%. The model takes patient attributes as input and outputs a prediction of 0 or 1 indicating the presence or absence of heart disease risk. It aims to help detect risk early to reduce death rates from heart disease, which is a leading cause of death worldwide.
1. The document describes a multiple disease prediction system that uses machine learning to predict three diseases: heart disease, liver disease, and diabetes.
2. It aims to build a single system that can predict multiple diseases, unlike existing systems that typically only predict one disease. This would allow users to predict different diseases without needing multiple different tools.
3. The system is designed to take user inputs related to symptoms and features of the selected disease and use machine learning algorithms like KNN, random forest and XGBoost trained on disease datasets to predict the likelihood of the disease. The models would be integrated into a web interface using Django for users to get predictions.
This document describes a project to develop a stress detection system using Arduino. The system would measure stress levels through physiological sensors like heart rate, skin temperature, and galvanic skin response. It aims to address gaps in existing stress detection apps and create a more beneficial system for patients and healthcare providers. The document outlines the implementation plan, including the sensors and hardware that would be used, and presents results from testing the system. It was found that physiological signals can accurately detect stress levels and the system has the potential to help people better manage their stress.
IRJET - Prediction and Detection of Diabetes using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict and detect diabetes. It first provides background on diabetes and different types. It then reviews related work applying algorithms like SVM, KNN, and random forest to diabetes prediction. The document describes datasets and algorithms used in the proposed system, including Naive Bayes, support vector machines, and gradient boosting. It presents results showing gradient boosting achieved the highest accuracy of 96% and discusses using a voting classifier to combine algorithms. The proposed system aims to help people understand their diabetes risk and condition.
IRJET- Health Monitoring System using ArduinoIRJET Journal
This document describes a health monitoring system using Arduino that monitors patients' pulse rate, vital signs, and saline level. Sensors are used to collect this medical data, which is then sent to the cloud for storage and access by doctors. The system is meant to continuously monitor patients and alert doctors if any parameters exceed thresholds, to save lives in emergencies or when doctors are not present. It discusses how technologies like the Internet of Things and wireless sensors can help create remote health monitoring systems.
IRJET -Improving the Accuracy of the Heart Disease Prediction using Hybrid Ma...IRJET Journal
This document describes research on improving the accuracy of heart disease prediction using hybrid machine learning techniques. The researchers collected data on patient biomarkers and risk factors from hospitals and online repositories. They applied data preprocessing, feature selection, and various classification models like decision trees, support vector machines, random forests, and K-nearest neighbors. Evaluating the models showed that a hybrid of fuzzy K-nearest neighbor and K-nearest neighbor achieved the highest accuracy rate of 94% for heart disease prediction. The researchers then built a web application using this hybrid model to allow users to predict their risk of heart disease online with high accuracy. The study demonstrates that machine learning can effectively analyze medical data and help predict diseases.
IRJET- Health Monitoring and Stress Detection SystemIRJET Journal
This paper proposes a system that combines health monitoring and stress detection to analyze physiological data and detect a person's stress levels. The system measures health parameters like heart rate, skin conductance, blood pressure and brainwaves using sensors. These parameters are known stress markers. The data is analyzed by a microcontroller and an alert is provided if stress is detected. The system aims to accurately detect stress levels using multiple stress-related data sources, with an integrated health monitoring approach allowing for reduced costs compared to separate systems. Analysis of brainwaves through EEG provides particularly accurate assessment of mental state for stress detection. The system is intended to help manage stress, which has become highly prevalent in modern society.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The document discusses how the Data Distribution Service (DDS) middleware standard can enable integration of medical devices and systems. DDS allows devices and applications to share information through a global data space and supports real-time performance, reliability, and interoperability across platforms. Examples shown include using DDS for integrated control of medical imaging devices like CT and MRI scanners, particle therapy systems, surgical robotics, and connecting monitoring devices at the hospital, ambulance, and clinical levels. DDS provides a common information model and quality of service policies to ensure safe and effective data sharing across disconnected medical systems.
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.
IRJET - Prediction and Analysis of Multiple Diseases using Machine Learni...IRJET Journal
This document discusses using machine learning techniques to predict and analyze multiple diseases. It presents research using KNN, support vector machine, random forest, and decision tree algorithms applied to a medical database to predict future and previous diseases. The goal is to provide a smart card method for easily and accurately diagnosing disease by storing an individual's full medical record. It reviews related work applying various machine learning classifiers like decision trees, naive Bayes, and logistic regression to diseases such as heart disease, diabetes, and cancer. The conclusion is that machine learning applied to medical data can help predict disease and save time for patients and doctors.
IRJET- Hiding Sensitive Medical Data using EncryptionIRJET Journal
This document summarizes a research paper about securely hiding sensitive medical data using encryption. The paper proposes a system that distributes a patient's data across multiple encrypted data servers. It uses Paillier cryptosystems to allow statistical analysis of the encrypted patient data without compromising privacy. The system includes wireless medical sensors that monitor patients and transmit encrypted data to a database. It uses an access control system and statistical analysis protocols that employ Paillier encryption to allow authorized users like doctors to access and analyze the encrypted patient data without revealing its contents. The goal is to protect sensitive medical information from privacy breaches while still enabling useful analysis of aggregated patient data.
1) The document describes an IoT-based e-prognosis system that monitors patients' temperature and heartbeat using sensors. The sensors send the medical data over the Internet to be accessed by medical professionals.
2) If the system detects a constant rise in the patient's temperature, it will diagnose the issue and send treatment information and remedies to the patient, caretaker, or doctor via IoT.
3) The system is meant to help disabled or elderly people who need monitoring but may not have constant caretaker assistance. It allows remote monitoring using wearable sensors connected to the Internet.
Wearable technology and blood pressure monitoring: Addressing the global hype...Valencell, Inc
High blood pressure is one of the largest public health epidemics in the world, affecting over one billion people according the World Health Organization and presenting significant risk factors for stroke, heart failure, coronary artery disease, diabetes and kidney disease. Monitoring blood pressure is the first step toward improving it, but it remains challenging to consistently monitor blood pressure in ways that don’t disrupt people’s lives. There are numerous efforts underway to address some of those challenges through wearable technology and devices, from watches to earbuds to clothing and more.
In this webinar, leaders from Omron and Valencell will explore the latest technologies and initiatives designed to navigate the technical challenges, regulatory environment, consumer expectations, and opportunities to make a dent in the global hypertension epidemic.
Child health care monitoring using sensor technologyIRJET Journal
This document proposes a framework for monitoring children's health using sensor technology. The framework uses sensors embedded in student ID cards to monitor heart rate and body temperature. It sends this health data to a server, which checks for abnormal readings and notifies parents and school administrators via SMS or a web application if issues are detected. The goal is to provide accurate, real-time child health information to concerned parties in order to promote early detection of potential problems and improve child growth, development, and performance in school. Key aspects of the proposed system include the use of sensors, centralized data collection and analysis, and automated notification of authorized individuals via multiple channels.
IRJET - Digital Assistance: A New Impulse on Stroke Patient Health Care using...IRJET Journal
1) The document presents a study that uses deep learning algorithms and artificial bee colony optimization to predict stroke using medical dataset features.
2) A neural network architecture is developed to classify patients' risk of stroke based on 13 variables from their medical records, with the artificial bee colony algorithm used to preprocess data and extract meaningful features.
3) The random forest algorithm achieved the highest prediction accuracy of over 88% based on metrics like precision, recall, and F1 score compared to other models like logistic regression, naive bayes, and decision trees.
IRJET- Predicting Heart Disease using Machine Learning AlgorithmIRJET Journal
1) The document discusses using machine learning algorithms like Naive Bayes and Decision Trees to predict heart disease using a dataset from Kaggle.
2) It describes preprocessing the dataset, training models, and evaluating accuracy. Decision Trees were found to more accurately predict heart disease than Naive Bayes.
3) The models use 13 attributes like age, sex, cholesterol levels, and examine classification performance to identify individuals at risk of heart disease.
IRJET- An Information Forwarder for Healthcare Service and analysis using Big...IRJET Journal
This document proposes a system for collecting and analyzing healthcare big data using cloud computing. It discusses how healthcare data is growing rapidly in volume and variety due to data from various sources like sensors, and how traditional storage and processing may not be suitable. The proposed system has three layers - a data acquisition layer to collect data from sensors, a transmission layer to send the data to the cloud, and a computational layer in the cloud to analyze and classify the data using clustering and fuzzy rule-based classifiers. This would allow real-time remote healthcare by efficiently storing and processing the large amounts of heterogeneous healthcare data in the cloud. Evaluation metrics like response time, accuracy, cost and false positives are used to compare the proposed system to existing techniques.
An Integrated Approach of Blood Pressure and Heart Rate Measurement SystemsIRJET Journal
This document describes a new integrated device that can measure both blood pressure and heart rate through the wrist and fingertip using a single device. The device uses infrared sensors placed on the wrist and fingertip to detect blood flow and pulses. It counts the pulses to determine the heart rate and blood pressure. The measurements from this new device were compared to manual measurements on 100 subjects, and it was found to have a negligible error rate. The goal was to create an affordable, easy-to-use device that could help both literate and illiterate people regularly monitor their vital signs without visiting a doctor.
This document proposes a remote monitoring system for ECG signals using cloud computing and wireless networks. The system allows ECG signals from patients to be monitored simultaneously by experts. If an abnormality is detected, a message is sent to the cloud and doctor. This could help reduce delays in treatment for heart patients and lower mortality rates. The system uses electrocardiogram signals sent via ZigBee to the cloud where doctors can access the data remotely. This provides availability and reliability of critical patient data through cloud-based storage and access.
Smart Pulse Cardiac Health Monitor from Medi-Coreavivahealth
Smart Pulse is a patented diagnostic device that is very innovative and portable.
Now from the convenience of your home or office, you can quickly run a health check-up for yourself to understand how much progress you have made with your current lifestyle.
http://www.avivahealth.com/shop/products.asp?itemid=11338
Risk Of Heart Disease Prediction Using Machine LearningIRJET Journal
This document describes a study that uses machine learning algorithms to predict the risk of heart disease. It analyzes a dataset containing characteristics of 270 patients using algorithms like logistic regression, naive Bayes, support vector machine, k-nearest neighbors, decision tree, random forest, XGBoost and artificial neural network. The random forest algorithm achieved the highest prediction accuracy of 95%. The model takes patient attributes as input and outputs a prediction of 0 or 1 indicating the presence or absence of heart disease risk. It aims to help detect risk early to reduce death rates from heart disease, which is a leading cause of death worldwide.
1. The document describes a multiple disease prediction system that uses machine learning to predict three diseases: heart disease, liver disease, and diabetes.
2. It aims to build a single system that can predict multiple diseases, unlike existing systems that typically only predict one disease. This would allow users to predict different diseases without needing multiple different tools.
3. The system is designed to take user inputs related to symptoms and features of the selected disease and use machine learning algorithms like KNN, random forest and XGBoost trained on disease datasets to predict the likelihood of the disease. The models would be integrated into a web interface using Django for users to get predictions.
This document describes a project to develop a stress detection system using Arduino. The system would measure stress levels through physiological sensors like heart rate, skin temperature, and galvanic skin response. It aims to address gaps in existing stress detection apps and create a more beneficial system for patients and healthcare providers. The document outlines the implementation plan, including the sensors and hardware that would be used, and presents results from testing the system. It was found that physiological signals can accurately detect stress levels and the system has the potential to help people better manage their stress.
IRJET - Prediction and Detection of Diabetes using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict and detect diabetes. It first provides background on diabetes and different types. It then reviews related work applying algorithms like SVM, KNN, and random forest to diabetes prediction. The document describes datasets and algorithms used in the proposed system, including Naive Bayes, support vector machines, and gradient boosting. It presents results showing gradient boosting achieved the highest accuracy of 96% and discusses using a voting classifier to combine algorithms. The proposed system aims to help people understand their diabetes risk and condition.
IRJET- Health Monitoring System using ArduinoIRJET Journal
This document describes a health monitoring system using Arduino that monitors patients' pulse rate, vital signs, and saline level. Sensors are used to collect this medical data, which is then sent to the cloud for storage and access by doctors. The system is meant to continuously monitor patients and alert doctors if any parameters exceed thresholds, to save lives in emergencies or when doctors are not present. It discusses how technologies like the Internet of Things and wireless sensors can help create remote health monitoring systems.
IRJET -Improving the Accuracy of the Heart Disease Prediction using Hybrid Ma...IRJET Journal
This document describes research on improving the accuracy of heart disease prediction using hybrid machine learning techniques. The researchers collected data on patient biomarkers and risk factors from hospitals and online repositories. They applied data preprocessing, feature selection, and various classification models like decision trees, support vector machines, random forests, and K-nearest neighbors. Evaluating the models showed that a hybrid of fuzzy K-nearest neighbor and K-nearest neighbor achieved the highest accuracy rate of 94% for heart disease prediction. The researchers then built a web application using this hybrid model to allow users to predict their risk of heart disease online with high accuracy. The study demonstrates that machine learning can effectively analyze medical data and help predict diseases.
IRJET- Health Monitoring and Stress Detection SystemIRJET Journal
This paper proposes a system that combines health monitoring and stress detection to analyze physiological data and detect a person's stress levels. The system measures health parameters like heart rate, skin conductance, blood pressure and brainwaves using sensors. These parameters are known stress markers. The data is analyzed by a microcontroller and an alert is provided if stress is detected. The system aims to accurately detect stress levels using multiple stress-related data sources, with an integrated health monitoring approach allowing for reduced costs compared to separate systems. Analysis of brainwaves through EEG provides particularly accurate assessment of mental state for stress detection. The system is intended to help manage stress, which has become highly prevalent in modern society.
IRJET- Heart Rate Monitoring System using Finger Tip through IOTIRJET Journal
This document describes a heart rate monitoring system that measures heart rate through the fingertip using a pulse sensor and displays the results on an LCD screen and online using WiFi. The system works by using a photoplethysmography sensor to detect changes in blood volume in the fingertip with each heartbeat. The heartbeat signal is amplified and sent to an Arduino board, which processes the data and displays the heart rate in beats per minute on the LCD. The WiFi module then transmits the data to a local server webpage and online server to view the results remotely over a network. The system provides a low-cost way to continuously monitor heart rate for healthcare applications.
This document presents a health analyzer system that uses machine learning to predict multiple diseases from user-input data. The system was designed to predict diabetes, stroke, breast cancer, fetal health, liver disease, and heart disease. It uses various machine learning algorithms like random forest, SVM, logistic regression, naive bayes and decision trees. Models for each disease were trained on different datasets and the best performing algorithm was selected for each disease. A Flask API with user interfaces was created to allow users to input data and receive predictions. The system aims to provide a cost-effective solution compared to separate systems for each disease. It analyzes diseases by considering all relevant parameters to detect effects more accurately.
IRJET- Review Paper on Patient Health Monitoring System using Can ProtocolIRJET Journal
This document summarizes a research paper on a patient health monitoring system using CAN protocol. The system measures the heart rate and body temperature of one or more patients at a time using sensors connected to a microcontroller. It sends the measured data over a CAN bus to a receiving node, which then transmits the data serially to be displayed on a single monitor. This allows doctors to remotely monitor multiple patients' vital signs from one location in real-time. The system aims to reduce monitoring time and increase flexibility for doctors compared to traditional monitoring methods.
This document summarizes a research paper that designed a smart waist belt for health monitoring. The belt tracks steps, posture, heart rate and classifies activities using sensors and a random forest machine learning algorithm. It achieved high accuracy rates between 90-95% for classifying activities like sitting, walking and standing. The smart waist belt addresses issues with current fitness trackers and promotes an active lifestyle. It provides real-time health data to a mobile app and cloud for access and analysis. This allows users to conveniently self-monitor health metrics and get notifications about posture.
IRJET - Machine Learning for Diagnosis of DiabetesIRJET Journal
This document describes a study that uses machine learning models to predict whether a person has diabetes based on patient data. The researchers created several classification models using algorithms like logistic regression and support vector machines on a diabetes dataset. The models with the highest accuracy at predicting diabetes were random forest and gradient boosting. An Android app was also developed to input patient data, run the predictions from the trained models, and display the results to help diagnose diabetes. The goal is to help reduce diabetes rates and healthcare costs by improving diagnosis.
A mother, with new born baby if needs to be away from baby due to employment, household work, shopping, etc. in that case health status intensifies the stress for mothers. The number of approx. 7,00,000 life births in the world is overshadowed by a large number of infant deaths for various reasons like apparently life threatening events (ALTE) or sudden infants death syndrome (SIDS). Continues monitoring of physiological parameters and notification updates is need of current scenario. Child health status is important aspect and health monitoring system is ultimate solution for that. The new era wearable technologies can be easily adoptable for monitoring systems. Wearable health monitoring system with fully integrated sensors can sense the physiological parameters and accordingly, synchronize the real time data to user application. The mobile application will help user to get real time body temperature and heartbeat count of the baby. It will also provide health and report analysis as well as emergency notifications.
SMART PRENATAL HEALTH CARE MONITORING SYSTEM FOR PREGNANCY WOMEN IN RURAL ARE...IRJET Journal
The document describes a smart prenatal health care monitoring system for rural pregnant women using IoT. It aims to address the lack of access to medical care in rural areas. The system uses sensors to measure the fetal heartbeat, kicks and the mother's blood pressure, temperature. These parameters are transmitted via IoT and displayed on a mobile phone. The system is lightweight and sensitive. It is intended to help reduce maternal and infant mortality rates in rural areas by enabling remote health monitoring.
IRJET - Smart Maternal Real Time Monitoring using IoT-TechniqueIRJET Journal
This document describes a smart maternal real-time monitoring system using IoT techniques. The system includes sensors placed on an abdomen belt to measure the fetal heart rate, temperature, movement, and indicators of labor pain like sweat and contractions. The measured parameters are sent to the cloud for storage and analysis. An alert message will be sent via GSM if any parameters exceed thresholds. The system aims to improve prenatal care and reduce maternal mortality rates by enabling real-time remote monitoring of high-risk pregnancies.
Predicting Heart Disease Using Machine Learning Algorithms.IRJET Journal
This document summarizes a research paper that predicts heart disease using machine learning algorithms. It compares the performance of three algorithms - logistic regression, decision trees, and random forests - on a heart disease dataset. Logistic regression achieved the highest accuracy at 92%, outperforming decision trees and random forests. The paper outlines developing a heart disease prediction web application using logistic regression that allows users to input their medical details and get a prediction of their heart disease risk level.
IRJET - Heart Health Classification and Prediction using Machine LearningIRJET Journal
1) The document discusses using machine learning models to classify heart health based on medical records and heart sounds.
2) Support vector machines, logistic regression, random forest, and ensemble learning algorithms were used to predict heart disease risk based on medical records.
3) Convolutional neural networks were used to develop models for heart sound segmentation and classification. The models classify heart sounds as normal, murmur, or extrasystole based on the timing of the S1 and S2 heart sounds.
Design of Self-Learning System for Diagnosing Health Parameters using ANFISIRJET Journal
The document describes the design of a self-learning system using ANFIS (Adaptive Neuro-Fuzzy Inference System) to diagnose health parameters and predict heart diseases by monitoring electrocardiogram (ECG), pulse oximeter, and temperature sensor readings transmitted from a patient in real-time via an Arduino, ESP8266 WiFi module and ThingSpeak internet of things server. The system is intended to identify preliminary stages of heart diseases so they can be addressed early through continuous remote monitoring of vital signs and alerts sent to doctors if abnormal readings are detected.
Heart Disease Prediction Using Machine Learning TechniquesIRJET Journal
This document describes a study that used five machine learning algorithms to predict heart disease: Random Forest classification, Support Vector Machine, AdaBoost Classifier, Logistic Regression, and Decision Tree Classifier. The algorithms were tested on a dataset of 270 patients described by 14 attributes. Random Forest classification achieved the highest test accuracy of 85.22%, compared to accuracies ranging from 67.43% to 81.23% for the other algorithms. Therefore, the study concludes that Random Forest classification is the best performing algorithm for predicting heart disease based on this dataset and analysis.
Prediction of Dengue, Diabetes and Swine Flu using Random Forest Classificati...IRJET Journal
This document describes a disease prediction system that uses the Random Forest classification algorithm to predict Dengue, diabetes, and swine flu. The system trains on labeled datasets for each disease. It then takes user-entered symptoms as input and predicts the likelihood of each disease. If a disease is predicted to be positive, the system recommends a specialized doctor. The document discusses related work on disease prediction using data mining techniques. It provides an overview of how the Random Forest algorithm works for classification problems and ensemble learning. The proposed system aims to help users predict diseases and find appropriate doctors for treatment.
Similar to IRJET- Discrete Heart Rate Segregation of an Unborn Child from its Mother During Pregnancy (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
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Artificial intelligence (AI) | Definitio