This document describes a study that uses machine learning algorithms to analyze flood data and predict flood impacts. The study collected flood data from various states in India containing information on start/end dates, duration, causes, affected districts/states, and casualties including human injuries and deaths as well as animal fatalities. Various machine learning models like decision trees, random forests, SVMs, and neural networks were trained on the data. The models' performance was evaluated based on metrics like accuracy, precision, recall, and F1-score. The results showed that some states experienced higher numbers of human/animal casualties from floods compared to others. Graphs and charts were used to analyze relationships between variables in the data and compare flood impacts like casualties and
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This document summarizes a research paper that aims to predict water quality using machine learning. It discusses how water quality is an important issue due to contamination negatively impacting human and environmental health. The researchers developed a machine learning model using artificial neural networks and time series analysis to forecast water quality index and categorization. They trained the model on historical water quality data from 2014 in the United States. The study aims to improve current techniques for managing water quality by developing a more effective, reliable and accurate prediction model.
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This document presents a machine learning system for flood forecasting using rainfall data. It aims to predict flood occurrence with high accuracy. The proposed system uses machine learning algorithms like K-Nearest Neighbors and XGBoost to develop a prediction model from historical rainfall and flood data. This approach is more computationally efficient and accurate than existing methods. The system allows users to upload data, select a model, input values to predict flooding, and view results. The developed model and system can help provide early flood warnings.
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This document summarizes a research paper that evaluates different machine learning algorithms for detecting blood diseases from laboratory test results. It first introduces the objective to classify and predict diseases like anemia and leukemia. It then evaluates three algorithms: Gaussian, Random Forest, and Support Vector Classification (SVC). SVC achieved the highest accuracy of 98% for anemia detection. The models are deployed using Streamlit so users can access them online or offline. Benefits include low hardware requirements and mobile access. Future work will add more disease predictions and integrate nutritional guidance.
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This document presents a study on rainfall forecasting in Solapur District, Maharashtra, India using machine learning algorithms. The study aims to develop an integrated water resources management system using artificial neural networks to more accurately predict rainfall based on parameters like temperature, humidity, and wind speed. The document provides background on the importance of accurate rainfall forecasting for agriculture. It also reviews previous studies on rainfall prediction using methods like convolutional neural networks, artificial neural networks, and ensemble models. The proposed methodology uses a dataset from the UCI repository to train and test logistic regression, K-nearest neighbors, random forest, and decision tree algorithms for rainfall classification. The models are evaluated based on accuracy, precision, recall, and F1 score, with random
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This document discusses using deep learning to predict breast cancer based on tumor data. It proposes using a neural network model to classify tumors as malignant or benign. The key steps are:
1. Collecting and preprocessing tumor cell data to remove noise and inconsistencies.
2. Developing a neural network model and training it on labeled training data to learn patterns.
3. Testing the trained model on unlabeled testing data to evaluate its accuracy in classifying tumors.
The goal is to develop an accurate model to help doctors determine the critical condition of patients and classify difficult tumors.
The document describes a mobile application called Plant Disease Doctor App that uses convolutional neural networks and deep learning to identify plant diseases from images. The app allows users to take photos of diseased plant leaves or import images and receives a disease diagnosis along with management tips. The system was trained on a dataset of over 20,000 images of 15 plant species with 5 diseases each. It aims to make disease identification easier, faster and less reliant on experts physically examining plants. The application architecture involves users uploading images, a CNN model analyzing them and returning results, which are then matched to information from a database for display.
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This document presents research on using machine learning models to predict environmental quality by analyzing data from environmental sensors. The researchers implemented classification algorithms like decision trees, logistic regression, naive bayes, random forest and support vector machines to predict air quality using metrics like accuracy. They found that the decision tree algorithm achieved the highest accuracy of 99.8% among the other models. The proposed system aims to develop a more effective machine learning model for air quality prediction compared to existing image-based techniques, in order to help monitor pollution levels and protect public health.
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This document summarizes a research paper that aims to predict water quality using machine learning. It discusses how water quality is an important issue due to contamination negatively impacting human and environmental health. The researchers developed a machine learning model using artificial neural networks and time series analysis to forecast water quality index and categorization. They trained the model on historical water quality data from 2014 in the United States. The study aims to improve current techniques for managing water quality by developing a more effective, reliable and accurate prediction model.
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This document presents a machine learning system for flood forecasting using rainfall data. It aims to predict flood occurrence with high accuracy. The proposed system uses machine learning algorithms like K-Nearest Neighbors and XGBoost to develop a prediction model from historical rainfall and flood data. This approach is more computationally efficient and accurate than existing methods. The system allows users to upload data, select a model, input values to predict flooding, and view results. The developed model and system can help provide early flood warnings.
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This document summarizes a research paper that evaluates different machine learning algorithms for detecting blood diseases from laboratory test results. It first introduces the objective to classify and predict diseases like anemia and leukemia. It then evaluates three algorithms: Gaussian, Random Forest, and Support Vector Classification (SVC). SVC achieved the highest accuracy of 98% for anemia detection. The models are deployed using Streamlit so users can access them online or offline. Benefits include low hardware requirements and mobile access. Future work will add more disease predictions and integrate nutritional guidance.
Integrated Water Resources Management Using Rainfall Forecasting With Artific...IRJET Journal
This document presents a study on rainfall forecasting in Solapur District, Maharashtra, India using machine learning algorithms. The study aims to develop an integrated water resources management system using artificial neural networks to more accurately predict rainfall based on parameters like temperature, humidity, and wind speed. The document provides background on the importance of accurate rainfall forecasting for agriculture. It also reviews previous studies on rainfall prediction using methods like convolutional neural networks, artificial neural networks, and ensemble models. The proposed methodology uses a dataset from the UCI repository to train and test logistic regression, K-nearest neighbors, random forest, and decision tree algorithms for rainfall classification. The models are evaluated based on accuracy, precision, recall, and F1 score, with random
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The document describes a machine learning-based crop recommendation system that analyzes soil and climate data to predict the most suitable crops for farmers to grow. It evaluates several machine learning algorithms (decision tree, support vector machine, logistic regression, random forest) and finds that random forest has the highest accuracy at 99.09%. The system is implemented as a website using the random forest model to help farmers select optimal crops.
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1. Collecting and preprocessing tumor cell data to remove noise and inconsistencies.
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The document describes a mobile application called Plant Disease Doctor App that uses convolutional neural networks and deep learning to identify plant diseases from images. The app allows users to take photos of diseased plant leaves or import images and receives a disease diagnosis along with management tips. The system was trained on a dataset of over 20,000 images of 15 plant species with 5 diseases each. It aims to make disease identification easier, faster and less reliant on experts physically examining plants. The application architecture involves users uploading images, a CNN model analyzing them and returning results, which are then matched to information from a database for display.
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This document presents research on using machine learning models to predict environmental quality by analyzing data from environmental sensors. The researchers implemented classification algorithms like decision trees, logistic regression, naive bayes, random forest and support vector machines to predict air quality using metrics like accuracy. They found that the decision tree algorithm achieved the highest accuracy of 99.8% among the other models. The proposed system aims to develop a more effective machine learning model for air quality prediction compared to existing image-based techniques, in order to help monitor pollution levels and protect public health.
This document describes a web application called Agro-Farm Care that aims to help farmers by predicting crops, fertilizers, and detecting diseases from images using machine learning models. The application takes in soil parameters and recommends suitable crops. It also takes in soil and crop details to recommend fertilizers. Additionally, it can detect diseases by analyzing leaf images. The system was developed using tools like Flask, Python, HTML, CSS and aims to benefit farmers by providing crop, fertilizer and disease predictions and recommendations.
This presentation provides an overview of a flood and rainfall prediction system. The system aims to increase awareness and reduce loss by allowing users to search rainfall ranges and flood histories in different areas. It uses machine learning models like artificial neural networks trained on historical rainfall and flood data to provide real-time flood predictions and early warnings. The system has features like fast performance, hazard mapping, and update capabilities. It faces challenges in data collection, model selection, and accuracy improvement with limited data.
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There is an exponential increase in the use of conversational bots. Conversational bots can be
described as a platform that can chat with people using artificial intelligence. The recent advancement has
made A.I capable of learning from data and produce an output. This learning of data can be performed by using
various machine learning algorithm. Machine learning techniques involves construction of algorithms that can
learn for data and can predict the outcome. This paper reviews the efficiency of different machine learning
algorithm that are used in conversational bot.
1) The document describes a study that compares the performance of three classification models - Random Forest, Decision Tree (J48), and Logistic Regression - on term deposit subscription prediction tasks.
2) The study uses 20 datasets from the UCI repository containing 150 to 20,000 instances each to test the models. Random Forest generally had better performance than Decision Tree on larger datasets with more instances, while Decision Tree performed better on smaller datasets.
3) The key metrics used for comparison were correctly classified instances, incorrectly classified instances, precision, recall, and F-measures. The results show that Random Forest accuracy increased from 69% to 96% as the number of instances in the term deposit dataset increased from 285 to 698
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This document describes a machine learning model that predicts disease based on symptoms. It uses classification algorithms like decision tree, random forest, and naive Bayes. The model is built using Python libraries like NumPy, Pandas, Scikit-learn, and Flask. Symptoms are input by the user and preprocessed before being fed to the trained algorithms. The model outputs the most likely disease based on the symptoms. It aims to help physicians diagnose patients more accurately and efficiently.
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This document discusses using a random forest algorithm to predict air quality index (AQI) levels. It involves collecting historical data on air pollution parameters from monitoring stations. The data is preprocessed, relevant features are selected, and the data is split into training and test sets. A random forest model is trained on the training data and hyperparameters are tuned. Random forest is well-suited for this task as it can make accurate predictions while reducing overfitting by combining decision trees trained on different subsets of the data. The model performance is evaluated using error metrics to test how well it can predict AQI levels. Accurate AQI forecasting could help reduce the health impacts of air pollution.
IRJET- Titanic Survival Analysis using Logistic RegressionIRJET Journal
This document describes a study that uses logistic regression to analyze survival data from the Titanic disaster and predict which passengers were most likely to survive. The study uses a publicly available dataset from Kaggle to train logistic regression and other machine learning models. Data preprocessing is applied to handle missing values. The logistic regression model achieves 95% accuracy according to the confusion matrix analysis. The study concludes that logistic regression is well-suited for problems with a binary dependent variable like survival data.
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This document discusses using convolutional neural networks to detect diseases in organic products. The researchers classify fruits and their diseases using a mixed deep neural network and contour feature-based technique. They train their model on a dataset of 6509 rice leaf photos and test on 2000 images, achieving 99.6% accuracy. Their proposed system uses pre-trained ResNet50 models to extract deep features and classify plant diseases, offering advantages of lower cost, scalability, and time savings compared to manual inspection methods.
IRJET- Crop Yield Prediction and Disease Detection using IoT ApproachIRJET Journal
This document proposes a system to predict crop yields and detect diseases using an IoT approach. The system would use sensors to monitor soil moisture levels, weather conditions, and other environmental factors. This data would be sent to a Raspberry Pi controller and stored in a database. Farmers could access this information through a mobile app to make informed decisions. The system would also automatically predict potential crop diseases based on changing conditions and provide prevention methods to farmers. This precision agriculture approach aims to help farmers save time and resources through better decision making supported by real-time sensor data analysis.
Android application for detection of leaf disease (Using Image processing and...IRJET Journal
This document describes an Android application for detecting leaf diseases using image processing and neural networks. The application uses a convolutional neural network (CNN) model trained on a dataset of images of healthy and unhealthy plant leaves. The CNN classifies leaf images uploaded by users to identify the disease and provide an accurate diagnosis. The application aims to help farmers and students quickly identify plant diseases to control their spread and reduce agricultural losses. It analyzes leaf images using techniques like preprocessing, augmentation, feature extraction, and classification with a CNN architecture. The trained model is integrated into an Android application using TensorFlow Lite to enable real-time disease detection from smartphone photos of leaves.
IRJET - Enlightening Farmers on Crop YieldIRJET Journal
This document discusses using data mining techniques to predict crop yields to help farmers. It proposes using a random forest regression algorithm on past agricultural data from 2000-2014 to build a prediction model. The model would help farmers select optimal crops, understand weather patterns, and maximize yields. The system is described as gathering data, preprocessing it, training a random forest model on 60% of the data and testing it on 20%. It would then provide yield predictions and recommendations to farmers through a visualization tool. The goal is to help guide farmers' decisions around fertilizer use, soil management, and crop selection to improve production levels.
IRJET- Analyze Weather Condition using Machine Learning AlgorithmsIRJET Journal
This document discusses using machine learning algorithms to analyze weather conditions and predict the weather. Historical weather data from 2016 containing features like temperature, humidity, wind speed is used to train models. Three algorithms - simple linear regression, gradient boosting, and random forest classification - are applied and their accuracy is compared. The data is first preprocessed by cleaning null values and outliers before being divided into training and test sets. The models are trained on the training set and tested on the test set. The goal is to utilize the predictions to support areas affected by climate change like agriculture and water resources.
“Detection of Diseases using Machine Learning”IRJET Journal
This document describes a machine learning-based disease prediction system. The system was developed as a web application using the Flask framework. It uses logistic regression and random forest classifiers trained on disease-related health parameters to predict diseases. The system allows users to login and submit their health details, generates a prediction report, and stores all user data in a MySQL database for admin access and record keeping. The goal is to help doctors detect diseases earlier and improve healthcare system quality by leveraging machine learning models.
ANALYSIS AND PREDICTION OF RAINFALL USING MACHINE LEARNING TECHNIQUESIRJET Journal
The document discusses using machine learning techniques like multiple linear regression and support vector regression to analyze and predict rainfall. It analyzes over 100 years of monthly rainfall data from India to train and test the models. The support vector regression model achieved the highest accuracy in predicting rainfall compared to the multiple linear regression model. Accurately predicting rainfall is important for agriculture in India since much of the economy depends on rainfall and farming. The models extract patterns from historical weather data to forecast future rainfall levels.
Covid Face Mask Detection Using Neural NetworksIRJET Journal
The document describes a study that developed a convolutional neural network (CNN) model using the MobileNetV2 architecture to detect if people in images are wearing face masks properly, improperly, or not at all. The model was trained on a dataset containing these three classes and achieved an accuracy of 97.25% for classifying images. The developed model can be implemented in real-world applications like public transportation stations, hospitals, offices, and schools to help monitor mask compliance and reduce the spread of COVID-19.
IRJET- Comparative Analysis of Various Tools for Data Mining and Big Data...IRJET Journal
This document compares and analyzes various tools for data mining and big data mining. It discusses traditional open source data mining tools like Orange, R, Weka, Shogun, Rapid Miner and KNIME. Each tool has different capabilities for data preprocessing, machine learning algorithms, visualization, platforms and programming languages. The document aims to help researchers select the most appropriate data mining tool for their needs and research.
Agricultural Product Price and Crop Cultivation Prediction based on Data Scie...IRJET Journal
This document summarizes a research paper that examines using machine learning techniques to predict agricultural crop prices and crop cultivation based on available data. It discusses using supervised machine learning classification algorithms like decision trees, random forests, naive bayes, and support vector machines to analyze crop dataset variables and predict crop yields. The goal is to develop an accurate machine learning model for crop prediction to help farmers and the agricultural economy by replacing updatable supervised models and predicting results with the best accuracy by comparing algorithms.
IRJET - Detection of Malaria using Image CellsIRJET Journal
The document describes a study on detecting malaria using deep learning techniques to analyze images of blood cell samples under a microscope. The researchers used convolutional neural network models like VGGNet and a machine learning algorithm called Support Vector Machine to classify images as healthy or infected with malaria. They found that the deep learning models achieved over 95% accuracy, higher than the SVM, as deep learning has an advantage in automatically learning from large amounts of data. The proposed best model was to fine-tune a pre-trained VGG-19 convolutional neural network model with image augmentation techniques.
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 presentation provides an overview of a flood and rainfall prediction system. The system aims to increase awareness and reduce loss by allowing users to search rainfall ranges and flood histories in different areas. It uses machine learning models like artificial neural networks trained on historical rainfall and flood data to provide real-time flood predictions and early warnings. The system has features like fast performance, hazard mapping, and update capabilities. It faces challenges in data collection, model selection, and accuracy improvement with limited data.
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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.
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
methods that face significant bottlenecks. However, the complexity
of NoC design presents numerous challenges related to
performance metrics such as scalability, latency, power
consumption, and signal integrity. This project addresses the
issues within the router's memory unit and proposes an enhanced
memory structure. To achieve efficient data transfer, FIFO buffers
are implemented in distributed RAM and virtual channels for
FPGA-based NoC. The project introduces advanced FIFO-based
memory units within the NoC router, assessing their performance
in a Bi-directional NoC (Bi-NoC) configuration. The primary
objective is to reduce the router's workload while enhancing the
FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
suggested. Simulation and synthesis results demonstrate
guaranteed throughput, predictable latency, and equitable
network access, showing significant improvement over previous
designs
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Determination of Equivalent Circuit parameters and performance characteristic...pvpriya2
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
methodology behind the Levelized Cost of
Hydrogen (LCOH) calculator. Moreover, this
manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity