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2. Logistic regression, random forest, and balanced random forest classifiers were evaluated on a dataset of 25,000 customers described by 111 variables.
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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.
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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.
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This document presents a new hybrid algorithm called ISSA that combines the Squirrel Search Algorithm (SSA) and Invasive Weed Optimization (IWO) algorithm to classify and predict skin cancer diseases. ISSA introduces the population generation approach of IWO into SSA. It is evaluated on skin lesion disease datasets and compared to other algorithms like SSA, IWO, PSO, DA, and ALO. The results show ISSA has longer runtime but higher accuracy than SSA for skin cancer classification tasks. Further improvements to SSA and combining it with other swarm intelligence algorithms could enhance its performance.
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This document presents a proposed CAD system for cancer detection using SVM classification. The system aims to automatically detect, segment, and classify breast masses in mammograms. It first extracts the region of interest from mammograms and performs segmentation using fuzzy C-means clustering. It then extracts texture and geometric features from segmented masses. Feature selection is used to select the most important features, which are then classified as benign or malignant using an SVM classifier. The proposed system seeks to develop a fully automated CAD system for breast cancer detection and classification without manual intervention.
IRJET - Survey on Analysis of Breast Cancer PredictionIRJET Journal
This document compares three machine learning techniques - Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) - for predicting breast cancer using a dataset of 198 patient records. It finds that SVM achieved the highest accuracy of 96.97% for classification, followed by RF at 96.45% and NB at 95.45%. SVM also had the highest recall rate at 0.97, indicating it was best at correctly identifying malignant tumors. While NB had the lowest precision of 0.92, meaning it incorrectly identified some benign cases as malignant, all three techniques showed high performance in predicting breast cancer.
Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...IRJET Journal
1. The document presents a comparative analysis of machine learning algorithms for predicting customer churn in the telecom industry.
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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.
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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.
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.
Breast Cancer Detection Using Machine LearningIRJET Journal
The document proposes using machine learning classifiers like k-Nearest Neighbors (KNN) and Naive Bayes to classify breast cancer as benign or malignant based on features in the Wisconsin Breast Cancer Dataset, and finds that the KNN algorithm achieved 97.13% accuracy in classification; it also describes developing a web application with doctor and patient login for entering patient details and viewing cancer reports and classification results.
Fuzzy Rule Base System for Software Classificationijcsit
This document describes a fuzzy rule-based system for classifying Java applications using object-oriented metrics. Key features of the system include automatically extracting OO metrics from source code, a configurable set of fuzzy rules, and classifying software at both the application and class level. The system is designed to address limitations of existing OO metric tools by providing an automated, unified analysis and classification without requiring complex post-processing methods. The document outlines the system design, including subsystems for the fuzzy rules engine and extracting OO metrics, and defines membership functions and fuzzy rules for classification.
MACHINE LEARNING CLASSIFIERS TO ANALYZE CREDIT RISKIRJET Journal
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
IRJET- Improving Prediction of Potential Clients for Bank Term Deposits using...IRJET Journal
This document summarizes research on improving predictions of potential clients for bank term deposits using machine learning approaches. The researchers analyzed bank customer data using logistic regression, support vector machines, random forests, and XGBoost models. They found that XGBoost performed best with an area under the ROC curve of 0.7368, an F1 score of 0.9291, and test accuracy of 0.8351. The study aimed to identify the most effective predictive model that can be used in bank telemarketing campaigns to target potential clients.
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This document discusses machine learning classification algorithms and their applications for predictive analysis in healthcare. It provides an overview of data mining techniques like association, classification, clustering, prediction, and sequential patterns. Specific classification algorithms discussed include Naive Bayes, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Neural Networks, and Bayesian Methods. The document examines examples of these algorithms being used for disease diagnosis, prognosis, and healthcare management. It analyzes their predictive performance on datasets for conditions like breast cancer, heart disease, and ICU readmissions. Overall, the document reviews how machine learning techniques can enhance predictive accuracy for various healthcare problems.
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BREAST CANCER DETECTION USING MACHINE LEARNINGIRJET Journal
1) The document presents a study on using machine learning techniques for breast cancer detection from mammography images.
2) It proposes a methodology using CNN-based feature extraction with transfer learning and Gaussian kernel-based segmentation for classification.
3) The proposed method achieved 96% accuracy in classifying mammography images as cancerous or non-cancerous, an improvement over prior methods.
Analysis of Common Supervised Learning Algorithms Through Applicationaciijournal
This document analyzes and compares the performance of common supervised learning algorithms (decision trees, boosting, support vector machines) on two datasets (breast cancer and company bankruptcy prediction). For each algorithm, the author applies hyperparameter tuning to optimize performance. Validation and learning curves are generated to analyze algorithm behavior at different hyperparameters and dataset sizes. Overall, the research finds that hyperparameter tuning can improve algorithm accuracy and that different algorithms perform best depending on the specific dataset. The analysis provides guidance for researchers on selecting and applying supervised learning algorithms in practice.
ANALYSIS OF COMMON SUPERVISED LEARNING ALGORITHMS THROUGH APPLICATIONaciijournal
Supervised learning is a branch of machine learning wherein the machine is equipped with labelled data
which it uses to create sophisticated models that can predict the labels of related unlabelled data.the
literature on the field offers a wide spectrum of algorithms and applications.however, there is limited
research available to compare the algorithms making it difficult for beginners to choose the most efficient
algorithm and tune it for their application.
This research aims to analyse the performance of common supervised learning algorithms when applied to
sample datasets along with the effect of hyper-parameter tuning.for the research, each algorithm is applied
to the datasets and the validation curves (for the hyper-parameters) and learning curves are analysed to
understand the sensitivity and performance of the algorithms.the research can guide new researchers
aiming to apply supervised learning algorithm to better understand, compare and select the appropriate
algorithm for their application. Additionally, they can also tune the hyper-parameters for improved
efficiency and create ensemble of algorithms for enhancing accuracy.
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.
Analysis of Common Supervised Learning Algorithms Through Applicationaciijournal
Supervised learning is a branch of machine learning wherein the machine is equipped with labelled data
which it uses to create sophisticated models that can predict the labels of related unlabelled data. the
literature on the field offers a wide spectrum of algorithms and applications. However, there is limited
research available to compare the algorithms making it difficult for beginners to choose the most efficient
algorithm and tune it for their application.
This research aims to analyse the performance of common supervised learning algorithms when applied to
sample datasets along with the effect of hyper-parameter tuning. for the research, each algorithm is
applied to the datasets and the validation curves (for the hyper-parameters) and learning curves are
analysed to understand the sensitivity and performance of the algorithms. The research can guide new
researchers aiming to apply supervised learning algorithm to better understand, compare and select the
appropriate algorithm for their application. Additionally, they can also tune the hyper-parameters for
improved efficiency and create ensemble of algorithms for enhancing accuracy.
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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.
IRJET- Comparison of Classification Algorithms using Machine LearningIRJET Journal
This document compares several machine learning classification algorithms. It first provides background on machine learning and describes common algorithms like linear regression, support vector machines, and decision trees. It then outlines an experimental framework in Python using libraries like Pandas, Scikit-Learn, and Matplotlib. Various classification algorithms are applied to a dataset and their test and train errors are calculated and compared to determine the most accurate algorithm. The proposed algorithm is found to have the lowest test and train errors compared to other algorithms like ridge regression, KNN, Bayesian regression, decision trees, and SVM.
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
Performance Comparisons among Machine Learning Algorithms based on the Stock ...IRJET Journal
This document compares the performance of various machine learning algorithms for predicting stock market performance based on stock market data and news data. It applies algorithms like linear regression, random forest, decision tree, K-nearest neighbors, logistic regression, linear discriminant analysis, XGBoost classifier, and Gaussian naive Bayes to datasets containing stock market values, news articles, and Reddit posts. It evaluates the algorithms based on metrics like accuracy, recall, precision and F1 score. The results suggest that linear discriminant analysis achieved the best performance at predicting stock market values based on the given datasets and evaluation metrics.
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This document discusses the use of machine learning and deep learning methods for cybersecurity and network intrusion detection. It provides an overview of various algorithms including convolutional neural networks, support vector machines, k-nearest neighbors, decision trees, deep belief networks, and recurrent neural networks. For each algorithm, it describes the basic process and provides an example of its application to intrusion detection. It also includes a literature review summarizing research on applying these methods to intrusion detection using various datasets and evaluating their accuracy. Finally, it compares the results, limitations, and opportunities for future enhancements in using machine learning for cybersecurity.
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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.
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research available to compare the algorithms making it difficult for beginners to choose the most efficient
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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.
IRJET- Comparison of Classification Algorithms using Machine LearningIRJET Journal
This document compares several machine learning classification algorithms. It first provides background on machine learning and describes common algorithms like linear regression, support vector machines, and decision trees. It then outlines an experimental framework in Python using libraries like Pandas, Scikit-Learn, and Matplotlib. Various classification algorithms are applied to a dataset and their test and train errors are calculated and compared to determine the most accurate algorithm. The proposed algorithm is found to have the lowest test and train errors compared to other algorithms like ridge regression, KNN, Bayesian regression, decision trees, and SVM.
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
Performance Comparisons among Machine Learning Algorithms based on the Stock ...IRJET Journal
This document compares the performance of various machine learning algorithms for predicting stock market performance based on stock market data and news data. It applies algorithms like linear regression, random forest, decision tree, K-nearest neighbors, logistic regression, linear discriminant analysis, XGBoost classifier, and Gaussian naive Bayes to datasets containing stock market values, news articles, and Reddit posts. It evaluates the algorithms based on metrics like accuracy, recall, precision and F1 score. The results suggest that linear discriminant analysis achieved the best performance at predicting stock market values based on the given datasets and evaluation metrics.
IRJET- Machine Learning and Deep Learning Methods for CybersecurityIRJET Journal
This document discusses the use of machine learning and deep learning methods for cybersecurity and network intrusion detection. It provides an overview of various algorithms including convolutional neural networks, support vector machines, k-nearest neighbors, decision trees, deep belief networks, and recurrent neural networks. For each algorithm, it describes the basic process and provides an example of its application to intrusion detection. It also includes a literature review summarizing research on applying these methods to intrusion detection using various datasets and evaluating their accuracy. Finally, it compares the results, limitations, and opportunities for future enhancements in using machine learning for cybersecurity.
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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.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
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.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
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Artificial intelligence (AI) | Definitio