Biometric Identification and Authentication Providence using Fingerprint for ...IJECEIAES
The raise in the recent security incidents of cloud computing and its challenges is to secure the data. To solve this problem, the integration of mobile with cloud computing, Mobile biometric authentication in cloud computing is presented in this paper. To enhance the security, the biometric authentication is being used, since the Mobile cloud computing is popular among the mobile user. This paper examines how the mobile cloud computing (MCC) is used in security issue with finger biometric authentication model. Through this fingerprint biometric, the secret code is generated by entropy value. This enables the person to request for accessing the data in the desk computer. When the person requests the access to the authorized user through Bluetooth in mobile, the Authorized user sends the permit access through fingerprint secret code. Finally this fingerprint is verified with the database in the Desk computer. If it is matched, then the computer can be accessed by the requested person.
Biometric Identification and Authentication Providence using Fingerprint for ...IJECEIAES
The raise in the recent security incidents of cloud computing and its challenges is to secure the data. To solve this problem, the integration of mobile with cloud computing, Mobile biometric authentication in cloud computing is presented in this paper. To enhance the security, the biometric authentication is being used, since the Mobile cloud computing is popular among the mobile user. This paper examines how the mobile cloud computing (MCC) is used in security issue with finger biometric authentication model. Through this fingerprint biometric, the secret code is generated by entropy value. This enables the person to request for accessing the data in the desk computer. When the person requests the access to the authorized user through Bluetooth in mobile, the Authorized user sends the permit access through fingerprint secret code. Finally this fingerprint is verified with the database in the Desk computer. If it is matched, then the computer can be accessed by the requested person.
Identification of important features and data mining classification technique...IJECEIAES
Employees absenteeism at the work costs organizations billions a year. Prediction of employees’ absenteeism and the reasons behind their absence help organizations in reducing expenses and increasing productivity. Data mining turns the vast volume of human resources data into information that can help in decision-making and prediction. Although the selection of features is a critical step in data mining to enhance the efficiency of the final prediction, it is not yet known which method of feature selection is better. Therefore, this paper aims to compare the performance of three well-known feature selection methods in absenteeism prediction, which are relief-based feature selection, correlation-based feature selection and information-gain feature selection. In addition, this paper aims to find the best combination of feature selection method and data mining technique in enhancing the absenteeism prediction accuracy. Seven classification techniques were used as the prediction model. Additionally, cross-validation approach was utilized to assess the applied prediction models to have more realistic and reliable results. The used dataset was built at a courier company in Brazil with records of absenteeism at work. Regarding experimental results, correlationbased feature selection surpasses the other methods through the performance measurements. Furthermore, bagging classifier was the best-performing data mining technique when features were selected using correlation-based feature selection with an accuracy rate of (92%).
Data Science Training | Data Science Tutorial for Beginners | Data Science wi...Edureka!
***** Data Science Training - https://www.edureka.co/data-science *****
This Edureka tutorial on "Data Science Training" will provide you with a detailed and comprehensive training on Data Science, the real-life use cases and the various paths one can take to become a data scientist. It will also help you understand the various phases of Data Science.
Data Science Blog Series: https://goo.gl/1CKTyN
http://www.edureka.co/data-science
An Extensible Web Mining Framework for Real KnowledgeIJEACS
With the emergence of Web 2.0 applications that bestow rich user experience and convenience without time and geographical restrictions, web usage logs became a goldmine to researchers across the globe. User behavior analysis in different domains based on web logs has its utility for enterprises to have strategic decision making. Business growth of enterprises depends on customer-centric approaches that need to know the knowledge of customer behavior to succeed. The rationale behind this is that customers have alternatives and there is intense competition. Therefore business community needs business intelligence to have expert decisions besides focusing customer relationship management. Many researchers contributed towards this end. However, the need for a comprehensive framework that caters to the needs of businesses to ascertain real needs of web users. This paper presents a framework named eXtensible Web Usage Mining Framework (XWUMF) for discovering actionable knowledge from web log data. The framework employs a hybrid approach that exploits fuzzy clustering methods and methods for user behavior analysis. Moreover the framework is extensible as it can accommodate new algorithms for fuzzy clustering and user behavior analysis. We proposed an algorithm known as Sequential Web Usage Miner (SWUM) for efficient mining of web usage patterns from different data sets. We built a prototype application to validate our framework. Our empirical results revealed that the framework helps in discovering actionable knowledge.
Clustering Prediction Techniques in Defining and Predicting Customers Defecti...IJECEIAES
With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several ecommerce sites and compare their competitors‟ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-RecencyFrequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macroaveraging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers‟ performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection.
A Review on Novel Approach for Load Balancing in Cloud Computingijtsrd
Cloud computing is an interconnection between the networks such as in private or public networks through internet in order to provide access to the application, data and file storage. It not only decreases the computational cost, hosting application, content storage and delivery rate. It is also a practical approach in which data centre is transferred from a capital intensive set up to a variable priced environment. As compared to traditional concepts, cloud computing coveys the concept of the grid computing, distributed computing, utility computing or autonomic computing. When any virtual machine gets overloaded, fault may occur in the cloud environment. With the help of BFO algorithm, technique of adaptive task scheduling is proposed. Using this method, it becomes easy to transfer the task to the most reliable virtual machine. In this research work, the technique will be proposed which will select the most reliable virtual machine for the load balancing. The proposed improvement leads to reduce execution time and resource consumption. Sukhdeep Kaur | Preeti Sondhi "A Review on Novel Approach for Load Balancing in Cloud Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26361.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/26361/a-review-on-novel-approach-for-load-balancing-in-cloud-computing/sukhdeep-kaur
Stacked Generalization of Random Forest and Decision Tree Techniques for Libr...IJEACS
The huge amount of library data stored in our modern research and statistic centers of organizations is springing up on daily bases. These databases grow exponentially in size with respect to time, it becomes exceptionally difficult to easily understand the behavior and interpret data with the relationships that exist between attributes. This exponential growth of data poses new organizational challenges like the conventional record management system infrastructure could no longer cope to give precise and detailed information about the behavior data over time. There is confusion and novel concern in selecting tools that can support and handle big data visualization that deals with multi-dimension. Viewing all related data at once in a database is a problem that has attracted the interest of data professionals with machine learning skills. This is a lingering issue in the data industry because the existing techniques cannot be used to remove or filter noise from relevant data and pad up missing values in order to get the required information. The aim is to develop a stacked generalization model that combines the functionality of random forest and decision tree to visualization library database visualization. In this paper, the random forest and decision tree techniques were employed to effectively visualize large amounts of school library data. The proposed system was implemented with a few lines of Python code to create visualizations that can help users at a glance understand and interpret the behavior of data and its relationships. The model was trained and tested to learn and extract hidden patterns of data with a cross-validation test. It combined the functionalities of both models to form a stacked generalization model that performed better than the individual techniques. The stacked model produced 95% followed by the RF which produced a 95% accuracy rate and 0.223600 RMSE error value in comparison with the DT which recorded an 80.00% success rate and 0.15990 RMSE value.
IEEE Projects 2013 For ME Cse Seabirds ( Trichy, Thanjavur, Karur, Perambalur )SBGC
ieee projects 2013 for me cse trichy, ieee projects 2013 for me cse Karur, ieee projects 2013 for me cse chennai, ieee projects 2013 for me cse, ieee projects, ieee projects for cse, ieee projects 2013, ieee projects 2013 for me cse Thanjavur, ieee projects 2013 for me cse Perambalur,
Identification of important features and data mining classification technique...IJECEIAES
Employees absenteeism at the work costs organizations billions a year. Prediction of employees’ absenteeism and the reasons behind their absence help organizations in reducing expenses and increasing productivity. Data mining turns the vast volume of human resources data into information that can help in decision-making and prediction. Although the selection of features is a critical step in data mining to enhance the efficiency of the final prediction, it is not yet known which method of feature selection is better. Therefore, this paper aims to compare the performance of three well-known feature selection methods in absenteeism prediction, which are relief-based feature selection, correlation-based feature selection and information-gain feature selection. In addition, this paper aims to find the best combination of feature selection method and data mining technique in enhancing the absenteeism prediction accuracy. Seven classification techniques were used as the prediction model. Additionally, cross-validation approach was utilized to assess the applied prediction models to have more realistic and reliable results. The used dataset was built at a courier company in Brazil with records of absenteeism at work. Regarding experimental results, correlationbased feature selection surpasses the other methods through the performance measurements. Furthermore, bagging classifier was the best-performing data mining technique when features were selected using correlation-based feature selection with an accuracy rate of (92%).
Data Science Training | Data Science Tutorial for Beginners | Data Science wi...Edureka!
***** Data Science Training - https://www.edureka.co/data-science *****
This Edureka tutorial on "Data Science Training" will provide you with a detailed and comprehensive training on Data Science, the real-life use cases and the various paths one can take to become a data scientist. It will also help you understand the various phases of Data Science.
Data Science Blog Series: https://goo.gl/1CKTyN
http://www.edureka.co/data-science
An Extensible Web Mining Framework for Real KnowledgeIJEACS
With the emergence of Web 2.0 applications that bestow rich user experience and convenience without time and geographical restrictions, web usage logs became a goldmine to researchers across the globe. User behavior analysis in different domains based on web logs has its utility for enterprises to have strategic decision making. Business growth of enterprises depends on customer-centric approaches that need to know the knowledge of customer behavior to succeed. The rationale behind this is that customers have alternatives and there is intense competition. Therefore business community needs business intelligence to have expert decisions besides focusing customer relationship management. Many researchers contributed towards this end. However, the need for a comprehensive framework that caters to the needs of businesses to ascertain real needs of web users. This paper presents a framework named eXtensible Web Usage Mining Framework (XWUMF) for discovering actionable knowledge from web log data. The framework employs a hybrid approach that exploits fuzzy clustering methods and methods for user behavior analysis. Moreover the framework is extensible as it can accommodate new algorithms for fuzzy clustering and user behavior analysis. We proposed an algorithm known as Sequential Web Usage Miner (SWUM) for efficient mining of web usage patterns from different data sets. We built a prototype application to validate our framework. Our empirical results revealed that the framework helps in discovering actionable knowledge.
Clustering Prediction Techniques in Defining and Predicting Customers Defecti...IJECEIAES
With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several ecommerce sites and compare their competitors‟ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-RecencyFrequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macroaveraging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers‟ performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection.
A Review on Novel Approach for Load Balancing in Cloud Computingijtsrd
Cloud computing is an interconnection between the networks such as in private or public networks through internet in order to provide access to the application, data and file storage. It not only decreases the computational cost, hosting application, content storage and delivery rate. It is also a practical approach in which data centre is transferred from a capital intensive set up to a variable priced environment. As compared to traditional concepts, cloud computing coveys the concept of the grid computing, distributed computing, utility computing or autonomic computing. When any virtual machine gets overloaded, fault may occur in the cloud environment. With the help of BFO algorithm, technique of adaptive task scheduling is proposed. Using this method, it becomes easy to transfer the task to the most reliable virtual machine. In this research work, the technique will be proposed which will select the most reliable virtual machine for the load balancing. The proposed improvement leads to reduce execution time and resource consumption. Sukhdeep Kaur | Preeti Sondhi "A Review on Novel Approach for Load Balancing in Cloud Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26361.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/26361/a-review-on-novel-approach-for-load-balancing-in-cloud-computing/sukhdeep-kaur
Stacked Generalization of Random Forest and Decision Tree Techniques for Libr...IJEACS
The huge amount of library data stored in our modern research and statistic centers of organizations is springing up on daily bases. These databases grow exponentially in size with respect to time, it becomes exceptionally difficult to easily understand the behavior and interpret data with the relationships that exist between attributes. This exponential growth of data poses new organizational challenges like the conventional record management system infrastructure could no longer cope to give precise and detailed information about the behavior data over time. There is confusion and novel concern in selecting tools that can support and handle big data visualization that deals with multi-dimension. Viewing all related data at once in a database is a problem that has attracted the interest of data professionals with machine learning skills. This is a lingering issue in the data industry because the existing techniques cannot be used to remove or filter noise from relevant data and pad up missing values in order to get the required information. The aim is to develop a stacked generalization model that combines the functionality of random forest and decision tree to visualization library database visualization. In this paper, the random forest and decision tree techniques were employed to effectively visualize large amounts of school library data. The proposed system was implemented with a few lines of Python code to create visualizations that can help users at a glance understand and interpret the behavior of data and its relationships. The model was trained and tested to learn and extract hidden patterns of data with a cross-validation test. It combined the functionalities of both models to form a stacked generalization model that performed better than the individual techniques. The stacked model produced 95% followed by the RF which produced a 95% accuracy rate and 0.223600 RMSE error value in comparison with the DT which recorded an 80.00% success rate and 0.15990 RMSE value.
IEEE Projects 2013 For ME Cse Seabirds ( Trichy, Thanjavur, Karur, Perambalur )SBGC
ieee projects 2013 for me cse trichy, ieee projects 2013 for me cse Karur, ieee projects 2013 for me cse chennai, ieee projects 2013 for me cse, ieee projects, ieee projects for cse, ieee projects 2013, ieee projects 2013 for me cse Thanjavur, ieee projects 2013 for me cse Perambalur,
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.