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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 proposes an automated resume classification system using clustering techniques. It aims to help HR departments more efficiently sort through large numbers of resumes by calculating a score for each resume based on skills and assigning resumes to clusters. The system would allow employers to customize job postings and weight desired skills. It would provide candidates a portal to upload resumes and receive scores. K-means clustering would then group resumes, giving HR a classified view. This could reduce the time and effort spent on manual resume sorting while increasing accuracy. The document outlines the system architecture, algorithms and benefits of automating and customizing the resume classification process.
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This document presents a cloud computing task scheduling algorithm based on a modified genetic algorithm. It begins with an abstract discussing scalable cloud computing and the need for efficient task scheduling and virtual machine allocation. It then discusses the problem of existing scheduling algorithms having high overhead and slow convergence. The proposed methodology uses a heuristic-based prediction model with a logistic normal distribution technique to improve data transmission prediction. Simulation results show the proposed approach has better throughput and computation time than existing algorithms for different data packet sizes. The conclusion discusses overcoming drawbacks of earlier algorithms and future work focusing on algorithms with better tradeoffs between performance characteristics.
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This document describes a machine learning model to predict road accident hotspots in Bangalore, India. The researchers collected accident data from government websites and other sources. They used K-means clustering to group similar data points and label them as high or low risk zones. The dataset was preprocessed and split into training and testing sets. A K-means clustering algorithm was trained on the larger training set to create clusters of accident-prone areas based on factors like weather, road conditions, etc. The model can then predict whether new locations belong to a high or low risk cluster. The user interface allows emergency responders and city planners to input a location and get a prediction to help prevent future accidents.
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This document discusses the implementation of the K-means clustering algorithm using R programming. It begins with an introduction to machine learning and the different types of machine learning algorithms. It then focuses on the K-means algorithm, describing the steps of the algorithm and how it is used for cluster analysis in unsupervised learning. The document then demonstrates implementing K-means clustering in R by generating sample data, initializing random centroids, calculating distances between data points and centroids, assigning data points to clusters based on closest centroid, recalculating centroids, and plotting the results. It concludes that K-means clustering is useful for gaining insights into dataset structure and was successfully implemented in R.
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This document provides a review of data mining methods used in manufacturing and how warehousing impacts manufacturing. It discusses how data mining techniques can be applied to extract patterns from manufacturing data to improve quality, optimize processes, and predict demands. Common data mining methods used include decision trees, neural networks, clustering, and regression. The document also examines the role of warehousing in managing inventory and supporting efficient manufacturing operations. Issues like demand variability, labor costs, and inventory inaccuracies are discussed. Automating warehouse processes through technologies like RFID is presented as a way to improve performance.
IRJET- Logistics Network Superintendence Based on Knowledge EngineeringIRJET Journal
This document describes a proposed web-based system for automatically predicting demand using machine learning. The system would collect input data from companies, preprocess the data, train machine learning models on historical data, and use the models to predict future demand values. The predictions would be presented to users in tables, graphs and charts for easy interpretation. The system aims to more accurately and efficiently predict demand compared to traditional manual methods, in order to help companies with planning and goal-setting.
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.
IRJET- Automated CV Classification using Clustering TechniqueIRJET Journal
This document proposes an automated resume classification system using clustering techniques. It aims to help HR departments more efficiently sort through large numbers of resumes by calculating a score for each resume based on skills and assigning resumes to clusters. The system would allow employers to customize job postings and weight desired skills. It would provide candidates a portal to upload resumes and receive scores. K-means clustering would then group resumes, giving HR a classified view. This could reduce the time and effort spent on manual resume sorting while increasing accuracy. The document outlines the system architecture, algorithms and benefits of automating and customizing the resume classification process.
Cloud Computing Task Scheduling Algorithm Based on Modified Genetic AlgorithmIRJET Journal
This document presents a cloud computing task scheduling algorithm based on a modified genetic algorithm. It begins with an abstract discussing scalable cloud computing and the need for efficient task scheduling and virtual machine allocation. It then discusses the problem of existing scheduling algorithms having high overhead and slow convergence. The proposed methodology uses a heuristic-based prediction model with a logistic normal distribution technique to improve data transmission prediction. Simulation results show the proposed approach has better throughput and computation time than existing algorithms for different data packet sizes. The conclusion discusses overcoming drawbacks of earlier algorithms and future work focusing on algorithms with better tradeoffs between performance characteristics.
STOCK MARKET ANALYZING AND PREDICTION USING MACHINE LEARNING TECHNIQUESIRJET Journal
This document discusses predicting stock market movements using machine learning techniques. It begins by reviewing previous research on fundamental analysis, technical analysis and applying machine learning to stock prediction. It then proposes a methodology using machine learning algorithms like support vector machine, decision trees and classification to analyze stock market data, extract features, segment data and build a mathematical model to forecast stock prices. The goal is to help investors make better decisions by predicting stock behavior.
Accident Prediction System Using Machine LearningIRJET Journal
This document describes a machine learning model to predict road accident hotspots in Bangalore, India. The researchers collected accident data from government websites and other sources. They used K-means clustering to group similar data points and label them as high or low risk zones. The dataset was preprocessed and split into training and testing sets. A K-means clustering algorithm was trained on the larger training set to create clusters of accident-prone areas based on factors like weather, road conditions, etc. The model can then predict whether new locations belong to a high or low risk cluster. The user interface allows emergency responders and city planners to input a location and get a prediction to help prevent future accidents.
Machine Learning, K-means Algorithm Implementation with RIRJET Journal
This document discusses the implementation of the K-means clustering algorithm using R programming. It begins with an introduction to machine learning and the different types of machine learning algorithms. It then focuses on the K-means algorithm, describing the steps of the algorithm and how it is used for cluster analysis in unsupervised learning. The document then demonstrates implementing K-means clustering in R by generating sample data, initializing random centroids, calculating distances between data points and centroids, assigning data points to clusters based on closest centroid, recalculating centroids, and plotting the results. It concludes that K-means clustering is useful for gaining insights into dataset structure and was successfully implemented in R.
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This document provides a review of data mining methods used in manufacturing and how warehousing impacts manufacturing. It discusses how data mining techniques can be applied to extract patterns from manufacturing data to improve quality, optimize processes, and predict demands. Common data mining methods used include decision trees, neural networks, clustering, and regression. The document also examines the role of warehousing in managing inventory and supporting efficient manufacturing operations. Issues like demand variability, labor costs, and inventory inaccuracies are discussed. Automating warehouse processes through technologies like RFID is presented as a way to improve performance.
A Machine learning based framework for Verification and Validation of Massive...IRJET Journal
This document presents a machine learning based framework for verification and validation of massive scale image data. It discusses the challenges of managing and analyzing large image datasets. The proposed framework uses techniques like data augmentation, feature extraction and selection, decision trees, cross-validation and test cases to systematically manage massive image data and validate machine learning algorithms and systems. It uses Cell Morphology Analysis (CMA) as a case study to demonstrate how the framework can verify and validate large datasets, software systems and algorithms. The effectiveness of the framework is shown through its application to CMA, which involves classifying cell images using machine learning.
BIG MART SALES PREDICTION USING MACHINE LEARNINGIRJET Journal
This document describes a study that uses machine learning to predict sales at Big Mart stores. The researchers collected data on 8542 products from Kaggle and used the XGBoost regressor model to predict sales. They preprocessed the data by handling missing values, removing unnecessary attributes, data visualization, cleaning, label encoding, and splitting into training and testing sets. The XGBoost model was trained on the preprocessed data and evaluated using metrics like RMSE and R-squared. The model achieved accurate sales predictions that can help Big Mart better plan strategies to increase profits and outcompete rivals.
Smart E-Logistics for SCM Spend AnalysisIRJET Journal
This document discusses applying predictive analytics and machine learning techniques like LSTM models to supply chain management problems. It focuses on spend analysis and extracting fields from invoices and proofs of delivery using optical character recognition. The key points are:
1. LSTM models are applied to time series spend analysis data and shown to provide more accurate predictions than ARIMA models.
2. A technique is proposed to extract fields from printed and handwritten documents using models trained on Form Recognizer and then cleaning the extracted data.
3. The technique aims to reconcile invoices and proofs of delivery by comparing extracted data fields and calculating a match confidence score.
This document describes a business utility application that was developed to help business owners manage multiple branches more easily. The application allows cashiers to generate bills and tokens, and analyze sales data like best selling items on a daily, weekly or monthly basis. It also allows managers to add/remove items and categories, and view transaction reports. The application was built using React Native and uses a MySQL database. It was tested across different functions and helped business owners reduce waste and better monitor cashiers' work.
IRJET- Recommendation System based on Graph Database TechniquesIRJET Journal
This document proposes a recommendation system based on graph database techniques. It uses Neo4j to develop a recommendation approach using content-based filtering, collaborative filtering, and hybrid filtering. The system recommends restaurants and meals to customers based on reviews and friend recommendations. It stores data about restaurants, meals, customers and their reviews in a graph database to allow for complex queries and recommendations. The implementation and results of the proposed recommendation system are also discussed.
IRJET- A Review on K-Means++ Clustering Algorithm and Cloud Computing wit...IRJET Journal
This document provides an overview of K-means++ clustering algorithm and how it can be implemented using MapReduce in cloud computing. It first discusses cloud computing and its ability to handle big data through flexibility and scalability. It then explains Hadoop and MapReduce, which provide a framework to parallelize processing of large datasets. The document describes the K-means++ clustering algorithm, which improves upon standard K-means by better initializing cluster centroids. Finally, it outlines how K-means++ can be implemented in MapReduce by splitting data and computing distances across mappers and reducers.
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This document proposes using Hive and R to perform data stream mining on big data. Hive is used to query and analyze large datasets stored in Hadoop. Test and trained datasets are extracted from the data using Hive queries. The Support Vector Machine (SVM) classifier algorithm analyzes the data to produce a statistical report in R, comparing the accuracy of linear and nonlinear models. The proposed method aims to improve data processing speed and ability to analyze large volumes of data as compared to other tools.
1) The document describes an automatic courier management system that was developed to handle a large volume of parcels efficiently.
2) Key features of the system include allowing customers to register and track parcels online, calculating delivery costs automatically, and finding optimal delivery routes to reduce costs.
3) The system was designed using technologies like Angular, Spring Boot, Hibernate, and MySQL. It allows different user types like customers and employees to perform tasks like registration, tracking, and management of parcel delivery.
IRJET- Iot Applied to Logistics using Intelligent CargoIRJET Journal
This document discusses using IoT to track cargo shipments through the supply chain. Sensors would monitor cargo weight and GPS location to detect any tampering during transport. If a weight change occurred, the GPS coordinates, time, and alert would be sent via SMS or to a remote database. The system aims to increase transport efficiency and security by continuously monitoring cargo status. It was found to accurately detect any manipulation attempts and trigger alerts. Future work could integrate real-time traffic and condition data to improve route optimization and cargo monitoring.
IRJET- Big Data Processes and Analysis using Hadoop FrameworkIRJET Journal
This document discusses issues with analyzing sub-datasets in a distributed manner using Hadoop, such as imbalanced computational loads and inefficient data scanning. It proposes a new approach called Data-Net that uses metadata about sub-dataset distributions stored in an Elastic-Map structure to optimize storage placement and queries. Experimental results on a 128-node cluster show that Data-Net provides better load balancing and performance for various sub-dataset analysis applications compared to the default Hadoop implementation.
IRJET - A Framework for Tourist Identification and Analytics using Transport ...IRJET Journal
This document presents a framework for identifying and analyzing tourists using transport data. Big data technologies are used to monitor tourist movement and evaluate travel behavior in scenic areas. Transport data is isolated using Hadoop tools like HDFS, MapReduce, Sqoop, Hive and Pig. This allows processing large transport data sets without data loss issues. The data is analyzed to represent tourist hotspots, locations and preferences. Visualization tools like R are then used to provide insights into the analytics results. The framework aims to provide better information and perspectives to stakeholders like tour companies and transport operators using transport data.
Geo Spatial Data And it’s Quality AssessmentIRJET Journal
This document discusses assessing the quality of geospatial data generated from unmanned aerial vehicle (UAV) images. The study area was approximately 5-6 km of the Banaras Hindu University campus in Varanasi, India. Images were captured using a DJI Mavic Pro Platinum drone and processed using ArcGIS Pro and Pix4dmapper to generate orthophotos, point clouds, and 3D models. The horizontal and vertical accuracies of the UAV solution were analyzed. Statistical analysis, including a paired t-test, showed the differences between UAV-derived data and reference GPS points were within acceptable limits of accuracy. The analysis demonstrated that accurate geospatial data can be produced from UAV images.
1. The document describes a deep learning model to analyze and classify rice quality using images of rice paddies. Rice paddies are photographed and the images are analyzed by a model trained on custom datasets to classify rice purity levels.
2. A convolutional neural network model is built using TensorFlow to classify rice paddies as pure, impure, or partially impure based on image analysis. The model achieves comparable accuracy to state-of-the-art systems.
3. The model can be used by rice mills to automatically analyze rice purity from images and categorize rice without manual inspection, improving efficiency over traditional methods.
Visualizing and Forecasting Stocks Using Machine LearningIRJET Journal
This document discusses using machine learning techniques like regression and LSTM models to predict stock market returns. It first provides background on the challenges of predicting the stock market due to its unpredictable nature. It then describes obtaining stock price data from Yahoo Finance to use as the dataset. The document outlines using regression analysis to build a relationship between stock prices and time and using LSTM due to its ability to learn from sequence data. It then reviews related work applying machine learning like neural networks and genetic algorithms to optimize stock prediction. The methodology section provides more detail on preprocessing the dataset and using regression and LSTM models to make predictions and compare results.
Web Development Using Cloud Computing and Payment GatewayIRJET Journal
This document summarizes a research paper on developing a website for an engineering company using cloud computing and a payment gateway. It discusses developing a website to showcase the company's products manufactured using CNC machines. The website allows customers to get instant answers from a chatbot, submit queries through an online form, and make online payments to purchase products. The website was created using HTML, CSS, Bootstrap, JavaScript, RazorPay for payments, and Firebase for cloud data storage. The system architecture and screenshots of the developed website are provided.
Fast Range Aggregate Queries for Big Data AnalysisIRJET Journal
The document proposes a fast range aggregate query (Fast RAQ) method to efficiently analyze large banking transaction datasets for the purpose of identifying tax violators. It divides data into partitions and generates local estimates for each partition. When a query is received, results are obtained by aggregating the local estimates from all partitions. The method is tested on banking transaction data from multiple banks partitioned and stored in Hadoop. It aims to track transactions across banks for a user using their unique ID to find individuals depositing over 50,000 rupees annually in 3 or more banks. The Fast RAQ method provides accurate results for large datasets more efficiently than existing approaches.
Performance Comparison of Dimensionality Reduction Methods using MCDRAM Publications
The recent blast of dataset size, in number of records and in addition of attributes, has set off the improvement of various big data platforms and in addition parallel data analytic algorithms. In the meantime however, it has pushed for the utilization of data dimensionality reduction systems. Mobile Telecom Industry competition has become more and more fierce. In order to improve their services and business in the competitive world, they are ready to analyse the stored data by several data mining technologies to retain customers and maintain their relationship with them. Mobile Call Detail Record (MCDR) comprises diversity and complexity information containing information like Voice Call, Text Message, Video Calls, and other Data Services usages. It is proposed to evaluate and compare the performance of different dimensionality reduction methods such as Chi-Square (Chi2) Method, Principal Component Analysis (PCA), Information Gain Attribute Evaluator, Gain-Ratio Attribute Evaluator (GRAE), Attribute Selected Classifier (ASC) and Quantile Regression (QR) Methods.
IRJET- Smart Railway System using Trip Chaining MethodIRJET Journal
This document proposes a smart railway system using trip chaining and big data analysis of passenger information from smart cards. The system would collect data like passenger name, age, travel time, source and destination stations from smart cards. It would then use k-means clustering to group passengers by age and travel patterns. A naïve bayes classifier would predict passenger counts at each station. This analysis of passenger data could help the railway department improve infrastructure and services based on demand.
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.
Visualizing and Forecasting Stocks Using Machine LearningIRJET Journal
This document discusses using hidden Markov models to visualize and forecast stock prices using machine learning. It presents the results of using hidden Markov models and support vector regression to predict stock prices for Tata Motors, Reliance, and YES Bank. The hidden Markov model achieved prediction accuracies greater than 80% for short-term forecasts, outperforming support vector regression as measured by mean absolute percentage error. While both methods tracked stock price patterns well, the hidden Markov model was found to be more sensitive to changes in stock price. The document concludes the hidden Markov model is effective for stock price prediction and minimizing the impact of factor selection compared to other methods.
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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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.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
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.
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.
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.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.