This document discusses using machine learning to identify risk factors for running-related injuries. Data is collected from sensors on runners and questionnaires. Machine learning algorithms like support vector machines and random forests are used to analyze the data and detect patterns between variables that can predict injury risk. The results help identify biomechanical or training-related determinants of injuries and allow coaches to tailor training to reduce risks. Performance of the machine learning models is evaluated using techniques like cross-validation. The aim is to better understand multifactorial injury causes and allow prevention of running injuries.
Predicting the Maintenance of Aircraft Engines using LSTMijtsrd
What if apart of aircraft could let you know when the aircraft component needed to be replaced or repaired It can be done with continuous data collection, monitoring, and advanced analytics. In the aviation industry, predictive maintenance promises increased reliability as well as improved supply chain and operational performance. The main goal is to ensure that the engines work correctly under all conditions and there is no risk of failure. If an effective method for predicting failures is applied, maintenance may be improved. The main source of data regarding the health of the engines is measured during the flights. Several variables are calculated, including fan speed, core speed, quantity and oil pressure and, environmental variables such as outside temperature, aircraft speed, altitude, and so on. Sensor data obtained in real time can be used to model component deterioration. To predict the maintenance of an aircraft engine, LSTM networks is used in this paper. A sequential input file is dealt with by the LSTM model. The training of LSTM networks was carried out on a high performance large scale processing engine. Machines, data, ideas, and people must all be brought together to understand the importance of predictive maintenance and achieve business results that matter. Nitin Prasad | Dr. A Rengarajan "Predicting the Maintenance of Aircraft Engines using LSTM" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41288.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41288/predicting-the-maintenance-of-aircraft-engines-using-lstm/nitin-prasad
IRJET - Breast Cancer Risk and Diagnostics using Artificial Neural Network(ANN)IRJET Journal
This document describes research using an artificial neural network (ANN) to classify breast cancer as benign or malignant based on the Wisconsin Breast Cancer dataset. The ANN model was trained and tested on 683 instances from the dataset. The model achieved 97.8% accuracy on the training set and 97.5% accuracy on the test set. Various performance metrics including mean absolute error, root mean square error, and kappa statistics were used to evaluate the model, demonstrating low error rates. The ANN model outperformed other classification algorithms in related work and efficiently classified breast cancer with high accuracy and precision.
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This document presents a study on developing an algorithm for fault detection and classification of a DC motor using predictive maintenance. The study involves designing a DC motor hardware system to collect sensor data on temperature, vibration, RPM, etc. This data is then processed using MATLAB's machine learning algorithms to train predictive models. Specifically, a decision tree algorithm is able to accurately classify the motor's condition as healthy or faulty with over 95% accuracy based on the sensor data. The study demonstrates how predictive maintenance can help detect potential faults in DC motors to improve performance and reduce maintenance costs.
Insurance Premium Estimation: Data-Driven Modeling and Real-Time PredictionsIRJET Journal
This document summarizes a research project that developed a model to predict individual health insurance premiums. The researchers:
1. Cleaned and preprocessed a dataset from Kaggle containing 1,338 records and 6 attributes related to health and insurance charges.
2. Evaluated several regression models and found Gradient Boosting to have the best performance for predicting charges. They further optimized it using hyperparameter tuning.
3. Built a web application using Flask to provide real-time premium predictions to users based on their input data via the model.
4. Deployed the complete project including model, web app, and code on Heroku for continuous integration and public use, completing the transition from development to production.
IRJET- Early Detection of Sensors Failure using IoTIRJET Journal
This document summarizes research on early detection of sensor failures using IoT. It discusses how sensor failures can destabilize systems and the need for early failure detection. It then reviews literature on predictive maintenance and failure detection strategies for sensors, including using time-series analysis of sensor data and machine learning models to identify anomalies and predict failures. The paper presents a sensor failure prediction model that involves collecting sensor output data over time, identifying factors that contribute to failures, and using a predictive algorithm and test data to check the model's reliability for early failure detection.
IRJET- Deep Feature Fusion for Iris Biometrics on Mobile DevicesIRJET Journal
This document proposes a deep neural network-based system for iris recognition and authentication on mobile devices. The system uses feature fusion with multiple feature extraction algorithms to recognize iris patterns. It then uses a backpropagation neural network for classification and authentication. The proposed system aims to more accurately recognize iris patterns, reduce processing time, and provide appropriate authentication outputs compared to existing CNN-based systems. It segments the iris from input images, extracts features using algorithms like FAST, SURF, and Harris, and feeds these into a neural network for classification and authentication. Performance charts are generated to evaluate the system. The proposed system seeks to securely authenticate users for mobile applications and transactions.
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This document discusses using machine learning models to gain insights into patient satisfaction. Specifically, it proposes a framework that transforms heterogeneous patient data into interpretable features that can be used to build a machine learning model. The model aims to achieve good performance while maintaining interpretability, allowing for real-world applications. It discusses shortcomings of existing approaches that focus on single data sources or use limited modeling techniques. The proposed framework performs feature transformation, variable selection, and coefficient learning using a mixed-integer programming model to build an intrinsically interpretable model for analyzing factors that influence patient satisfaction.
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This document describes a study that used machine learning to develop an e-healthcare monitoring system for diagnosing heart disease. The researchers used a modified support vector machine (SVM) algorithm to analyze cardiovascular disease data and predict whether patients have heart disease. They evaluated the performance of their modified SVM against other machine learning models like random forest, gradient boosting, and AdaBoost. The modified SVM achieved the highest accuracy of 88.8%, outperforming the other models. The study concludes that machine learning and deep learning methods can help enable early detection, classification, and prediction of cardiovascular disease.
Predicting the Maintenance of Aircraft Engines using LSTMijtsrd
What if apart of aircraft could let you know when the aircraft component needed to be replaced or repaired It can be done with continuous data collection, monitoring, and advanced analytics. In the aviation industry, predictive maintenance promises increased reliability as well as improved supply chain and operational performance. The main goal is to ensure that the engines work correctly under all conditions and there is no risk of failure. If an effective method for predicting failures is applied, maintenance may be improved. The main source of data regarding the health of the engines is measured during the flights. Several variables are calculated, including fan speed, core speed, quantity and oil pressure and, environmental variables such as outside temperature, aircraft speed, altitude, and so on. Sensor data obtained in real time can be used to model component deterioration. To predict the maintenance of an aircraft engine, LSTM networks is used in this paper. A sequential input file is dealt with by the LSTM model. The training of LSTM networks was carried out on a high performance large scale processing engine. Machines, data, ideas, and people must all be brought together to understand the importance of predictive maintenance and achieve business results that matter. Nitin Prasad | Dr. A Rengarajan "Predicting the Maintenance of Aircraft Engines using LSTM" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41288.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41288/predicting-the-maintenance-of-aircraft-engines-using-lstm/nitin-prasad
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This document describes research using an artificial neural network (ANN) to classify breast cancer as benign or malignant based on the Wisconsin Breast Cancer dataset. The ANN model was trained and tested on 683 instances from the dataset. The model achieved 97.8% accuracy on the training set and 97.5% accuracy on the test set. Various performance metrics including mean absolute error, root mean square error, and kappa statistics were used to evaluate the model, demonstrating low error rates. The ANN model outperformed other classification algorithms in related work and efficiently classified breast cancer with high accuracy and precision.
Developing Algorithm for Fault Detection and Classification for DC Motor Usin...IRJET Journal
This document presents a study on developing an algorithm for fault detection and classification of a DC motor using predictive maintenance. The study involves designing a DC motor hardware system to collect sensor data on temperature, vibration, RPM, etc. This data is then processed using MATLAB's machine learning algorithms to train predictive models. Specifically, a decision tree algorithm is able to accurately classify the motor's condition as healthy or faulty with over 95% accuracy based on the sensor data. The study demonstrates how predictive maintenance can help detect potential faults in DC motors to improve performance and reduce maintenance costs.
Insurance Premium Estimation: Data-Driven Modeling and Real-Time PredictionsIRJET Journal
This document summarizes a research project that developed a model to predict individual health insurance premiums. The researchers:
1. Cleaned and preprocessed a dataset from Kaggle containing 1,338 records and 6 attributes related to health and insurance charges.
2. Evaluated several regression models and found Gradient Boosting to have the best performance for predicting charges. They further optimized it using hyperparameter tuning.
3. Built a web application using Flask to provide real-time premium predictions to users based on their input data via the model.
4. Deployed the complete project including model, web app, and code on Heroku for continuous integration and public use, completing the transition from development to production.
IRJET- Early Detection of Sensors Failure using IoTIRJET Journal
This document summarizes research on early detection of sensor failures using IoT. It discusses how sensor failures can destabilize systems and the need for early failure detection. It then reviews literature on predictive maintenance and failure detection strategies for sensors, including using time-series analysis of sensor data and machine learning models to identify anomalies and predict failures. The paper presents a sensor failure prediction model that involves collecting sensor output data over time, identifying factors that contribute to failures, and using a predictive algorithm and test data to check the model's reliability for early failure detection.
IRJET- Deep Feature Fusion for Iris Biometrics on Mobile DevicesIRJET Journal
This document proposes a deep neural network-based system for iris recognition and authentication on mobile devices. The system uses feature fusion with multiple feature extraction algorithms to recognize iris patterns. It then uses a backpropagation neural network for classification and authentication. The proposed system aims to more accurately recognize iris patterns, reduce processing time, and provide appropriate authentication outputs compared to existing CNN-based systems. It segments the iris from input images, extracts features using algorithms like FAST, SURF, and Harris, and feeds these into a neural network for classification and authentication. Performance charts are generated to evaluate the system. The proposed system seeks to securely authenticate users for mobile applications and transactions.
Gaining Insights into Patient Satisfaction through Interpretable Machine Lear...IRJET Journal
This document discusses using machine learning models to gain insights into patient satisfaction. Specifically, it proposes a framework that transforms heterogeneous patient data into interpretable features that can be used to build a machine learning model. The model aims to achieve good performance while maintaining interpretability, allowing for real-world applications. It discusses shortcomings of existing approaches that focus on single data sources or use limited modeling techniques. The proposed framework performs feature transformation, variable selection, and coefficient learning using a mixed-integer programming model to build an intrinsically interpretable model for analyzing factors that influence patient satisfaction.
E-Healthcare monitoring System for diagnosis of Heart Disease using Machine L...IRJET Journal
This document describes a study that used machine learning to develop an e-healthcare monitoring system for diagnosing heart disease. The researchers used a modified support vector machine (SVM) algorithm to analyze cardiovascular disease data and predict whether patients have heart disease. They evaluated the performance of their modified SVM against other machine learning models like random forest, gradient boosting, and AdaBoost. The modified SVM achieved the highest accuracy of 88.8%, outperforming the other models. The study concludes that machine learning and deep learning methods can help enable early detection, classification, and prediction of cardiovascular disease.
IRJET - Detection and Classification of Brain TumorIRJET Journal
This document presents a novel method for classifying brain MRI images as normal or abnormal using tumor detection. The method first uses wavelet transforms to extract features from images. It then applies principal component analysis to reduce the feature dimensions. The reduced features are input to a kernel support vector machine for classification. A k-fold cross validation strategy is used to enhance the generalization of the support vector machine model. The proposed system takes MRI brain images as input, detects any tumors by highlighting the affected area, and specifies tumor characteristics like dimensions and type (benign or malignant).
IRJET- Damage Assessment for Car InsuranceIRJET Journal
This document describes a system for automating damage assessment for car insurance claims using deep learning techniques. The system trains convolutional neural network models to classify damage types from images of 10 car parts. It extracts the vehicle registration number from images to retrieve vehicle information from a database. The models classify each car part in an image as damaged, undamaged, or missing and a report is generated estimating repair costs. The system was tested on over 3000 images with high accuracy for classifying damage and estimating repair costs, which has the potential to streamline the insurance claims process.
Improved UNet Framework with attention for Semantic Segmentation of Tumor Reg...IRJET Journal
The document proposes an improved UNet framework with attention for semantic segmentation of tumor regions in brain MRI images. The authors develop a variation of the UNet model that incorporates batch normalization after each convolution layer. They train the model in batches and evaluate it using the Intersection over Union metric, which is well-suited for foreground/background segmentation tasks. With their proposed methodology, they achieve an averaged IoU of 84.3% and dice coefficient value of 91.4%, demonstrating the effectiveness of their improved UNet model for segmenting tumor regions in brain MRI images.
IRJET- Confidential Data Access through Deep Learning Iris BiometricsIRJET Journal
This document describes a study that explores using iris recognition and deep learning as a biometric authentication method for sensitive mobile transactions. The proposed system uses a deep neural network classifier and edge detection with adaptive contour segmentation to identify individuals from iris images. It authenticates website access through MATLAB. The system is said to enhance security compared to existing methods by fusing information from iris and surrounding eye region features. Evaluation shows it reduces computation time and improves specificity, sensitivity and accuracy compared to region-based segmentation alone.
MTCNN BASED AUTOMATIC ATTENDANCE SYSTEM USING FACE RECOGNITIONIRJET Journal
This document summarizes a research paper that proposes an automated attendance system using facial recognition technology. It begins by outlining the limitations of current manual and RFID card-based attendance systems. It then describes a new system that uses MTCNN for face detection and CNN for facial recognition. The system captures images and identifies recognized students as present by matching faces to a database of stored images. The document provides details on the various stages of the proposed method, including face detection using MTCNN, face alignment, feature extraction with FaceNet, and classification with SVM. It presents the overall algorithm and concludes by discussing modelling and analysis.
IRJET - Airplane Crash Analysis and Prediction using Machine LearningIRJET Journal
This document discusses research on analyzing and predicting airplane crashes using machine learning techniques. The researchers conducted an analysis of airplane crash data, correlating it with accident factors. They used supervised machine learning algorithms like SVM, K-NN, AdaBoost and XGBoost for classification and prediction. Feature selection was used to choose the most relevant features for improving accuracy. The algorithms were trained and tested on datasets, with the most accurate one used for prediction to determine if a flight was "safe" or at "crash" risk based on input specifications. The goal was to help the aviation industry improve safety by better understanding factors that contribute to crashes.
Comparative Study of Pre-Trained Neural Network Models in Detection of GlaucomaIRJET Journal
The document presents a comparative study of various pre-trained neural network models for the early detection of glaucoma from fundus images. Six pre-trained models - Inception, Xception, ResNet50, MobileNetV3, DenseNet121 and DenseNet169 - were analyzed based on their accuracy, loss graphs, confusion matrices and performance metrics like precision, recall, F1 score and specificity. The DenseNet169 model achieved the best results among the models based on these evaluation parameters.
Brain Tumor Classification using EfficientNet ModelsIRJET Journal
This document discusses using EfficientNet models to classify brain tumors in MRI images. It evaluates the performance of EfficientNet B0, B1, B2, and B3 models on a dataset of MRI brain images. The EfficientNet B3 model achieved the highest accuracy, with 98.8% accuracy on the training set and 93.1% on the test set. This study found that EfficientNet B3 performed best for the task of brain tumor classification and detection using MRI images.
IRJET - A Survey on Machine Learning Intelligence Techniques for Medical ...IRJET Journal
This document discusses machine learning techniques for classifying medical datasets. It provides an overview of various artificial intelligence and machine learning algorithms that have been applied for medical dataset classification, including artificial neural networks, support vector machines, k-nearest neighbors, and decision trees. The document surveys works that have used these techniques for diseases like breast cancer, heart disease, and diabetes. It also describes common pre-processing steps for medical datasets like data normalization and feature selection methods like F-score and PCA that are used to select the most important features for classification. The classification algorithms are then evaluated based on accuracy metrics like sensitivity, specificity, and accuracy.
Wireless Fault Detection System for an Industrial Robot Based on Statistical ...IJECEIAES
Industrial robots are now commonly used in production systems to improve productivity, quality and safety in manufacturing processes. Recent developments involve using robots cooperatively with production line operatives. Regardless of application, there are significant implications for operator safety in the event of a robot malfunction or failure, and the consequent downtime has a significant impact on productivity in manufacturing. Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation and thus reducing the maintenance costs. Developments in electronics and computing have opened new horizons in the area of condition monitoring. The aim of using wireless electronic systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to develop an online and wireless fault detection system for an industrial robot based on statistical control chart approach. An experimental investigation was accomplished using the PUMA 560 robot and vibration signal capturing was adopted, as it responds immediately to manifest itself if any change is appeared in the monitored machine, to extract features related to the robot health conditions. The results indicate the successful detection of faults at the early stages using the key extracted parameters.
PARKINSON’S DISEASE DETECTION USING MACHINE LEARNINGIRJET Journal
This document discusses using machine learning to detect Parkinson's disease. It presents the results of several studies that used techniques like random forests, support vector machines, logistic regression, and neural networks. The best performing model was found to be random forest, achieving 97.43% accuracy, 96.55% precision, and 98.24% F1 score. The study concludes that machine learning shows promise for early detection of Parkinson's disease using features extracted from voice and image data.
Vibration based condition monitoring of rolling element bearing using xg boo...Conference Papers
This document summarizes a study on using vibration-based condition monitoring to detect faults in rolling element bearings. The study used the XGBoost machine learning algorithm and Orange data mining software to analyze vibration signals from bearings. Both statistical and image embedding methods were used to extract features from the signals. The image embedding approach improved classification accuracy compared to statistical features. Of the machine learning algorithms tested, XGBoost performed best, achieving a 91.25% classification accuracy using both feature extraction approaches on a bearing dataset from Case Western Reserve University.
This document discusses using artificial neural networks to detect oral cancer from images. It proposes using recurrent neural network (RNN) and artificial neural network (ANN) classifiers to segment, extract features from, and classify images of oral tissue as benign or malignant. The existing methods for oral cancer detection have limitations like low accuracy, high complexity, and difficulty detecting early-stage cancer. The proposed system would use image preprocessing to remove noise, feature extraction to analyze characteristics of the image, and classification algorithms like RNN and ANN to automatically diagnose cancers. It presents data flow diagrams and use case diagrams for the proposed system, and discusses implementing RNN and ANN algorithms to classify images. System testing would evaluate the performance and accuracy of the oral cancer detection system
Student Attendance Management Automation Using Face Recognition AlgorithmIRJET Journal
The document describes a proposed student attendance management system that uses real-time face recognition. The system uses a camera to capture student faces and the Haar cascading algorithm to detect faces. It then applies the Local Binary Pattern Histogram (LBPH) algorithm to recognize the faces and mark attendance in the database. The system provides automated attendance tracking and reporting to replace manual attendance marking. Administrators can access attendance reports and add new student details. The proposed system aims to save time, increase accuracy and prevent fraudulent attendance recording compared to traditional methods.
This document describes a driver monitoring system that uses various sensors and cameras to continuously monitor the driver and provide alerts if suspicious activity is detected, such as speeding, alcohol consumption, fatigue, etc. The goals are to track driving patterns, generate reports, and help reduce accidents caused by reckless driving. It discusses implementing such a system for truck and bus drivers, as the Maharashtra State Road Transport Corporation is facing significant losses from vehicle maintenance costs due to careless driving. The proposed system would use sensors to monitor speed, driving patterns, and other metrics, and alert the driver or vehicle owner if thresholds are exceeded. It would analyze the collected data to identify risky drivers and report them to transportation authorities.
Comparative Study of Enchancement of Automated Student Attendance System Usin...IRJET Journal
This document discusses developing an automated student attendance system using facial recognition and deep learning algorithms. It begins with an overview of how facial recognition can be used to take attendance accurately and efficiently. It then describes the methodology, which involves using a convolutional neural network (CNN) to detect and recognize faces. Dimensionality reduction techniques like principal component analysis (PCA) and linear discriminant analysis (LDA) are also used to improve recognition accuracy. The goal is to build a system that can identify students in real-time with a high degree of accuracy, even in varying lighting conditions. It aims to automate the entire attendance tracking process for both students and teachers.
Comparative Analysis of Various Algorithms for Fetal Risk PredictionIRJET Journal
This document compares various machine learning algorithms to accurately predict fetal risk levels based on performance metrics. It discusses collecting and preprocessing data from an online repository to build models using Random Forest, Bagging, AdaBoostM1, SMO, Kstar, Naive Bayes, Hoeffding Tree, and Classification via Regression algorithms. These algorithms are evaluated based on precision, recall, F-score, and training time. Random Forest is found to have the highest accuracy of 99.9% and is the preferred algorithm for fetal risk prediction based on its performance metrics.
IRJET - Biometric Identification using Gait Analyis by Deep LearningIRJET Journal
1. The document discusses using gait analysis and deep learning for biometric identification. Gait analysis examines a person's walking pattern or "gait cycle" as a biometric for identification.
2. The proposed system would use deep learning models trained on video frames of a person's gait cycle to generate identification vectors, which would then be used to authenticate individuals based on a match to their gait pattern.
3. If a sample video's gait cycle matches the trained identification vectors, the person would be authenticated. This could eliminate manual entry and allow automatic identification via security cameras based on a person's walking pattern.
IRJET- GDPS - General Disease Prediction SystemIRJET Journal
The document describes a General Disease Prediction System (GDPS) that uses machine learning and data mining techniques to predict diseases based on patient symptoms.
The GDPS first collects patient data, preprocesses it, and extracts relevant features. It then implements the ID3 decision tree algorithm to generate a predictive model and classify diseases. As an admin, one can train the model using sample data. As a user, one can enter symptoms and the trained model will predict the likely disease and recommend precautions.
The GDPS was tested on a dataset of 120 patients and achieved 86.67% accuracy in disease prediction. The system currently covers common diseases but future work involves expanding it to predict more serious or fatal diseases like various cancers
Accident Prediction System Using Machine LearningIRJET Journal
This document describes a machine learning model to predict road accident hotspots in Bangalore, India. The researchers collected accident data from government websites and other sources. They used K-means clustering to group similar data points and label them as high or low risk zones. The dataset was preprocessed and split into training and testing sets. A K-means clustering algorithm was trained on the larger training set to create clusters of accident-prone areas based on factors like weather, road conditions, etc. The model can then predict whether new locations belong to a high or low risk cluster. The user interface allows emergency responders and city planners to input a location and get a prediction to help prevent future accidents.
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
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This document discusses using artificial neural networks to detect oral cancer from images. It proposes using recurrent neural network (RNN) and artificial neural network (ANN) classifiers to segment, extract features from, and classify images of oral tissue as benign or malignant. The existing methods for oral cancer detection have limitations like low accuracy, high complexity, and difficulty detecting early-stage cancer. The proposed system would use image preprocessing to remove noise, feature extraction to analyze characteristics of the image, and classification algorithms like RNN and ANN to automatically diagnose cancers. It presents data flow diagrams and use case diagrams for the proposed system, and discusses implementing RNN and ANN algorithms to classify images. System testing would evaluate the performance and accuracy of the oral cancer detection system
Student Attendance Management Automation Using Face Recognition AlgorithmIRJET Journal
The document describes a proposed student attendance management system that uses real-time face recognition. The system uses a camera to capture student faces and the Haar cascading algorithm to detect faces. It then applies the Local Binary Pattern Histogram (LBPH) algorithm to recognize the faces and mark attendance in the database. The system provides automated attendance tracking and reporting to replace manual attendance marking. Administrators can access attendance reports and add new student details. The proposed system aims to save time, increase accuracy and prevent fraudulent attendance recording compared to traditional methods.
This document describes a driver monitoring system that uses various sensors and cameras to continuously monitor the driver and provide alerts if suspicious activity is detected, such as speeding, alcohol consumption, fatigue, etc. The goals are to track driving patterns, generate reports, and help reduce accidents caused by reckless driving. It discusses implementing such a system for truck and bus drivers, as the Maharashtra State Road Transport Corporation is facing significant losses from vehicle maintenance costs due to careless driving. The proposed system would use sensors to monitor speed, driving patterns, and other metrics, and alert the driver or vehicle owner if thresholds are exceeded. It would analyze the collected data to identify risky drivers and report them to transportation authorities.
Comparative Study of Enchancement of Automated Student Attendance System Usin...IRJET Journal
This document discusses developing an automated student attendance system using facial recognition and deep learning algorithms. It begins with an overview of how facial recognition can be used to take attendance accurately and efficiently. It then describes the methodology, which involves using a convolutional neural network (CNN) to detect and recognize faces. Dimensionality reduction techniques like principal component analysis (PCA) and linear discriminant analysis (LDA) are also used to improve recognition accuracy. The goal is to build a system that can identify students in real-time with a high degree of accuracy, even in varying lighting conditions. It aims to automate the entire attendance tracking process for both students and teachers.
Comparative Analysis of Various Algorithms for Fetal Risk PredictionIRJET Journal
This document compares various machine learning algorithms to accurately predict fetal risk levels based on performance metrics. It discusses collecting and preprocessing data from an online repository to build models using Random Forest, Bagging, AdaBoostM1, SMO, Kstar, Naive Bayes, Hoeffding Tree, and Classification via Regression algorithms. These algorithms are evaluated based on precision, recall, F-score, and training time. Random Forest is found to have the highest accuracy of 99.9% and is the preferred algorithm for fetal risk prediction based on its performance metrics.
IRJET - Biometric Identification using Gait Analyis by Deep LearningIRJET Journal
1. The document discusses using gait analysis and deep learning for biometric identification. Gait analysis examines a person's walking pattern or "gait cycle" as a biometric for identification.
2. The proposed system would use deep learning models trained on video frames of a person's gait cycle to generate identification vectors, which would then be used to authenticate individuals based on a match to their gait pattern.
3. If a sample video's gait cycle matches the trained identification vectors, the person would be authenticated. This could eliminate manual entry and allow automatic identification via security cameras based on a person's walking pattern.
IRJET- GDPS - General Disease Prediction SystemIRJET Journal
The document describes a General Disease Prediction System (GDPS) that uses machine learning and data mining techniques to predict diseases based on patient symptoms.
The GDPS first collects patient data, preprocesses it, and extracts relevant features. It then implements the ID3 decision tree algorithm to generate a predictive model and classify diseases. As an admin, one can train the model using sample data. As a user, one can enter symptoms and the trained model will predict the likely disease and recommend precautions.
The GDPS was tested on a dataset of 120 patients and achieved 86.67% accuracy in disease prediction. The system currently covers common diseases but future work involves expanding it to predict more serious or fatal diseases like various cancers
Accident Prediction System Using Machine LearningIRJET Journal
This document describes a machine learning model to predict road accident hotspots in Bangalore, India. The researchers collected accident data from government websites and other sources. They used K-means clustering to group similar data points and label them as high or low risk zones. The dataset was preprocessed and split into training and testing sets. A K-means clustering algorithm was trained on the larger training set to create clusters of accident-prone areas based on factors like weather, road conditions, etc. The model can then predict whether new locations belong to a high or low risk cluster. The user interface allows emergency responders and city planners to input a location and get a prediction to help prevent future accidents.
Similar to IDENTIFICATION OF RISK FACTORS FOR RUNNING RELATED INJURIES USING MACHINE LEARNING (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.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
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.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.