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.
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This document discusses using machine learning techniques like logistic regression to analyze customer data and predict customer churn in the telecom industry. It proposes a system to build a churn prediction model using logistic regression on historical customer data to identify high-risk customers. The system would have options to view results, perform training and testing on new data, and analyze performance. It would also include a recommender system to recommend suitable plans for identified churn customers based on their usage patterns. The results show the model can predict churn with 80% accuracy and identify similar customers who may also churn.
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This document discusses using machine learning algorithms to predict loan approvals. It analyzes loan data using decision trees, logistic regression, and random forest algorithms. The random forest algorithm achieved the highest accuracy rate of 88.53% compared to 85% for decision trees and 83.04% for logistic regression. Therefore, the random forest algorithm is concluded to be best for loan approval prediction. Future work could involve applying these algorithms to other loan data sets and exploring additional machine learning methods.
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This document presents a study on using machine learning algorithms to predict minimum health insurance premium amounts based on individuals' health parameters. The study analyzes various health-related attributes like age, diabetes status, surgeries, etc. from datasets to train regression models. Random forest regression is identified as the best performing algorithm with an accuracy of around 85-90%. The proposed system aims to help individuals choose more appropriate insurance amounts by considering a wider range of health factors compared to existing solutions. It describes the system architecture involving data collection, model training, and premium prediction when users input their health details.
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This document discusses using machine learning classifiers to analyze credit risk. It examines various machine learning techniques for credit risk analysis, including Bayesian classifiers, naive Bayes, decision trees, k-nearest neighbors, multilayer perceptrons, support vector machines, and ensemble methods like bagging and boosting. Two credit datasets from the UCI machine learning repository were used to test the accuracy of these classifiers. The results showed decision trees had the highest accuracy at 89.9% and 71.25% on the two datasets, while k-nearest neighbors had the lowest. Future work could involve rebuilding the models with more accurate data to improve performance. The objective of credit risk analysis is to help banks and financial institutions balance approving loans to creditworthy borrowers
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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.
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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.
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This document discusses methods for enhancing data privacy through crypto clustering of heterogeneous and sensitive data. It first reviews existing literature on privacy-preserving techniques like local differential privacy and differential privacy-based clustering. It then proposes a method that uses cryptographic implementations based on sensitivity prediction and local differential privacy to automatically protect mixed data according to type and predicted sensitivity. Sensitive data is clustered and secured using these techniques to enhance privacy while maintaining data utility.
Loan Default Prediction Using Machine Learning TechniquesIRJET Journal
This document discusses using machine learning techniques to predict loan defaults. It begins with an abstract that outlines using data collection, cleaning, and performance assessment to predict loan defaulters. It then discusses implementing models like decision trees and KNN (K-nearest neighbors) for classification and regression. The document evaluates the performance of these models on loan default prediction and concludes the KNN model performs better. It proposes using both models and comparing their accuracy to improve prediction performance and minimize risk.
IRJET - Customer Churn Analysis in Telecom IndustryIRJET Journal
This document discusses using machine learning techniques like logistic regression to analyze customer data and predict customer churn in the telecom industry. It proposes a system to build a churn prediction model using logistic regression on historical customer data to identify high-risk customers. The system would have options to view results, perform training and testing on new data, and analyze performance. It would also include a recommender system to recommend suitable plans for identified churn customers based on their usage patterns. The results show the model can predict churn with 80% accuracy and identify similar customers who may also churn.
In Banking Loan Approval Prediction Using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict loan approvals. It analyzes loan data using decision trees, logistic regression, and random forest algorithms. The random forest algorithm achieved the highest accuracy rate of 88.53% compared to 85% for decision trees and 83.04% for logistic regression. Therefore, the random forest algorithm is concluded to be best for loan approval prediction. Future work could involve applying these algorithms to other loan data sets and exploring additional machine learning methods.
Minimum Health Insurance Premium prediction using health parametersIRJET Journal
This document presents a study on using machine learning algorithms to predict minimum health insurance premium amounts based on individuals' health parameters. The study analyzes various health-related attributes like age, diabetes status, surgeries, etc. from datasets to train regression models. Random forest regression is identified as the best performing algorithm with an accuracy of around 85-90%. The proposed system aims to help individuals choose more appropriate insurance amounts by considering a wider range of health factors compared to existing solutions. It describes the system architecture involving data collection, model training, and premium prediction when users input their health details.
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IRJET - Breast Cancer Risk and Diagnostics using Artificial Neural Network(ANN)IRJET Journal
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This document discusses using machine learning techniques to analyze employee performance. Specifically, it proposes using a support vector machine (SVM) algorithm to identify employee performance based on factors like quality, timeliness, and cost. The document reviews related literature on using both traditional and data-driven approaches to performance assessment. It then outlines the proposed system for building a software tool to manage employee performance data using SVM. Key steps in the SVM algorithm are described. The document concludes that improving individual performance can boost business results and SVM is effective for differentiating between two groups of data.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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This document discusses using deep learning models for churn prediction in the telecommunications industry. It begins with an introduction to churn prediction and feature selection challenges. It then provides an overview of deep learning techniques, including artificial neural networks, convolutional neural networks, and their applications. The document proposes three deep learning architectures for churn prediction and experiments with them on two telecom datasets. The results show deep learning models can achieve performance comparable to traditional models without manual feature engineering.
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This document discusses predicting loan defaults through machine learning models. It begins by introducing the business problem of banks suffering losses from customer loan defaults. It then describes preprocessing the loan dataset, which includes handling missing data, label encoding categorical variables, and balancing the dataset using SMOTE and SMOTEENN techniques. Logistic regression, decision trees, AdaBoost and random forest algorithms are applied to both the original and balanced datasets. The random forest model on the balanced data using SMOTEENN achieved the best accuracy of 92%. The model is then pickled and integrated into a web application using Flask for users to predict loan defaults.
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This document discusses how business analytics can help improve the Indian power sector. It explains that business analytics can help better manage the power sector to make it more financially viable and promote competition, in line with objectives of the Indian Electricity Act of 2003. The document outlines the role of integrating business analytics with big data in the power sector. It also discusses software requirements and attributes needed for business analytics in power generation, transmission and distribution utilities. Finally, it provides examples of how business analytics can help analyze customer data, transmission losses, revenue realization, plant efficiencies, load forecasting, and support faster decision making.
This document discusses how business analytics can help improve the management and financial viability of India's power sector. It describes how analytics can be applied across generation, transmission, and distribution to better understand operations, reduce losses, improve revenue, and enhance decision making. The integration of business analytics with big data from the power sector is seen as important for developing power quality services. Overall, business analytics has the potential to improve various aspects of the power sector in India through more effective analysis of the large amount of operational and customer data that is now available.
DIFFERENCES OF CLOUD-BASED SERVICES AND THEIR SAFETY RENEWAL IN THE HEALTH CA...IRJET Journal
The document discusses the benefits and risks of cloud-based services for the healthcare system. It begins by introducing how cloud computing has impacted various sectors including healthcare by enabling storage of large amounts of patient data and easy access. It then categorizes existing cloud applications and services used in healthcare. The document also analyzes security and privacy risks of cloud-based healthcare services and compares the risks of secure vs insecure cloud systems. It proposes that adopting cloud services in healthcare requires addressing security issues.
DIFFERENCES OF CLOUD-BASED SERVICES AND THEIR SAFETY RENEWAL IN THE HEALTH CA...IRJET Journal
The document discusses the benefits and risks of cloud-based services for healthcare systems. It begins by outlining how cloud computing has enabled new diagnostic technologies and easy access to patient data. However, it also notes security and privacy risks, such as data breaches and unauthorized access. The document then reviews existing literature on revolutionary impacts of cloud solutions, predictive threat analysis using big data, and risk analysis of cloud models. It proposes a methodology for categorizing cloud benefits and risks to help healthcare workers and IT professionals. The methodology aims to securely manage data exchange while addressing challenges like cyberattacks and lack of technical knowledge.
IRJET- Contradicting the Hypothesis of Data Analytics with the Help of a Use-...IRJET Journal
This document discusses how data analytics techniques can be used to analyze manufacturing industry data and help with decision making. It presents a case study analyzing expenses data from a manufacturing company from 1950 to 2020. Descriptive analytics on the data show trends in number of employees, working hours, costs of raw materials, machinery, overhead, labor, and profit over time. Diagnostic analytics provide reasons for these trends, such as increases in employees and costs correlating with new technologies and production increases. Predictive analytics are not discussed in the summary. The document suggests prescriptive analytics using advanced Industry 4.0 technologies like ultrafast 3D printing could help maximize profit and minimize employees and costs.
A Novel Approach for Forecasting Disease Using Machine LearningIRJET Journal
This document discusses using machine learning models to predict diseases. It analyzes several supervised machine learning algorithms, including Naive Bayes, Decision Trees, K-Nearest Neighbors, Logistic Regression, and Convolutional Neural Networks. The key findings are:
1) K-Nearest Neighbors performed best at predicting kidney disease, Parkinson's disease, and heart disease based on the analyses.
2) Logistic Regression and Convolutional Neural Networks predicted breast cancer and common diseases accurately, respectively.
3) Supervised machine learning algorithms show potential for early disease detection when applied to electronic health data, which can help clinicians and improve patient outcomes.
Machine Learning Approaches to Predict Customer Churn in Telecommunications I...IRJET Journal
This project aimed to develop machine learning models to predict customer churn in the telecommunications industry. Four algorithms were evaluated - logistic regression, support vector machine, decision tree, and random forest. Logistic regression performed best with an accuracy of 79.25% and AUC score of 84.08%. The models analyzed customer attribute data to identify patterns and predict churn, helping telecom companies understand churn reasons and develop retention strategies. The results provide insights to improve customer experience and reduce costly customer churn.
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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.
A Compendium of Various Applications of Machine LearningIRJET Journal
This document provides a review of various applications of machine learning. It begins with an introduction to machine learning and discusses its applications in fields such as energy efficiency, intrusion detection, anomaly detection, quantitative finance, and cancer prediction and prognosis. Specific machine learning algorithms and techniques discussed include decision trees, naive Bayes, k-nearest neighbors, artificial neural networks, support vector machines, and more. The document also provides examples of machine learning applications in each field and references various research papers to support the discussed applications.
IRJET - Job Portal Analysis and Salary Prediction SystemIRJET Journal
This document presents a job portal analysis and salary prediction system that uses web scraping and machine learning techniques. The system has two main modules: 1) A data visualization module that analyzes job posting data to identify trending skills in different companies and locations. 2) A salary prediction module that uses regression analysis to predict average salaries based on factors like domain, job title, skills, and experience. The system aims to help job seekers and students choose career paths by providing insights into in-demand skills and predicted salaries. It collects data from various job portals and uses that data to train machine learning models for its analysis and predictions.
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.
Bank Customer Segmentation & Insurance Claim PredictionIRJET Journal
This document summarizes a research project that aims to help a bank segment their customers and help an insurance company predict insurance claims. The project uses data mining techniques like clustering and predictive modeling with machine learning algorithms. For the bank customer segmentation problem, the document describes applying hierarchical and k-means clustering on customer credit card usage data to identify customer segments. For the insurance claim prediction problem, the document outlines applying classification models like CART, random forest and artificial neural networks on historical claims data to predict future claims and compares their performance. The results from both problems can provide business insights like tailored promotional strategies for different customer segments and recommendations to reduce claim frequency and improve sales for the insurance company.
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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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.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.