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For further details contact: N.RAJASEKARAN B.E M.S 9841091117,9840103301. IMPULSE TECHNOLOGIES, Old No 251, New No 304, 2nd Floor, Arcot road , Vadapalani , Chennai-26. www.impulse.net.in Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
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Tanaya resume
Tanaya Kavathekar is pursuing a Master of Science in Data Science from The George Washington University anticipated to graduate in May 2021. She has a Bachelor's in Computer Engineering from University of Pune in India. She has 3 years of experience as a Decision Scientist at Mu Sigma Business Solutions where she improved demand and sales forecasting accuracy for Fortune 500 clients using techniques like time series analysis, regression, and ensemble models. She is currently Associate Director of Graduate Affairs for her university's International Student Association.
Data analysis with pandas and scikit-learn
Definition and basic features of data analysis with python, pandas and scikit-learn. Brief explanation about most powerful features. Introduction part.
Amazon Neptune - visually more options
Amazon Neptune is a service that allows you to use graph structures and nodes to visualize stored data in an accessible way. You can find more in our blog entry: https://tinyurl.com/y623ff5j
All the sources are linked in the presentation.
Enjoy and don't forget to check out our blog and other social media!
LCloud Blog https://bit.ly/2Vgooz4
Facebook https://bit.ly/2tCqBJS
Twitter https://twitter.com/LCLOUD16
LinkedIn https://bit.ly/2syaQCr
YouTube https://bit.ly/2tGV62b
Questions? Feel free to ask:
kontakt@lcloud.pl
https://lcloud.pl/
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
A secure and dynamic multi keyword ranked search scheme over encrypted cloud data
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
Business proposal (2) (1)
SITA is an AI-powered digital assistant proposed for the steel industry in India. It aims to optimize resource use and decision-making by performing predictive analysis on the large amount of operational data generated. SITA will use advanced algorithms to generate insights and identify patterns in sales, procurement, production and maintenance data. This will allow for more accurate demand forecasting and predictive equipment maintenance, reducing costs and improving service. While the solution will require initial cycles of training and some companies may lack technical skills, the overall costs even for very large enterprises are estimated to be under Rs. 9 Crore annually with significant benefits in efficiency, margins, and planning.
Data lake
The document discusses the key differences between data lakes and data warehouses. It provides examples of how a retail company can use a data lake to store various structured and unstructured data sources together. This allows data scientists to more easily combine different data types into models to forecast product demand and for marketing experts to analyze sentiment and determine sales focus. The document also lists some SAP tools for administering and working with data in a data lake, including the SAP HANA Database Explorer, SAP HANA Cockpit, and SAP HANA Cloud Central.
Bayesian Networks Fact Sheet
CXAIR provides statistical analysis and modeling capabilities using Bayesian predictive analytics to identify relationships in data and key factors that affect outcomes. Users can create filtered datasets to build Bayesian network models using learning algorithms, which can then be visualized to show relationships and outcome probabilities based on variables. Models can identify data cohorts, be saved and applied to new data to validate outcomes for risk stratification, reporting, and analysis. CXAIR also offers extensive charting capabilities for visualizing models.
Machine Learning and Cultural Heritage: What Is It Good Enough For?
Funded through the AHRC’s Towards a National Collection Programme, the Science Museum Group (SMG) is collaborating with the V&A and School of Advanced Study, University of London, on a two-year project entitled “Heritage Connector: Transforming text into data to extract meaning and make connections”.
As with almost all data, museum collection catalogues are largely unstructured, variable in consistency and overwhelmingly composed of thin records. The form of these catalogues means that the potential for new forms of research, access and scholarly enquiry that range across multiple collections and related datasets remains dormant.
The Heritage Connector project is deploying a range of machine learning-based techniques to extract information from the SMG collection catalogue, link it to third-party sources – primarily Wikidata and the V&A’s collection – will then create a set of prototypes that demonstrate and explore the affordances of the resulting dataset.
Rather than attempting to deploy machine learning to create a perfect linked data model, Heritage Connector asks what’s “good enough” to provide useful functionality to different audiences.
https://www.aeolian-network.net/events/workshop-1-employing-machine-learning-and-artificial-intelligence-in-cultural-institutions/
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Tanaya resume
Tanaya Kavathekar is pursuing a Master of Science in Data Science from The George Washington University anticipated to graduate in May 2021. She has a Bachelor's in Computer Engineering from University of Pune in India. She has 3 years of experience as a Decision Scientist at Mu Sigma Business Solutions where she improved demand and sales forecasting accuracy for Fortune 500 clients using techniques like time series analysis, regression, and ensemble models. She is currently Associate Director of Graduate Affairs for her university's International Student Association.
Data analysis with pandas and scikit-learn
Definition and basic features of data analysis with python, pandas and scikit-learn. Brief explanation about most powerful features. Introduction part.
Amazon Neptune - visually more options
Amazon Neptune is a service that allows you to use graph structures and nodes to visualize stored data in an accessible way. You can find more in our blog entry: https://tinyurl.com/y623ff5j
All the sources are linked in the presentation.
Enjoy and don't forget to check out our blog and other social media!
LCloud Blog https://bit.ly/2Vgooz4
Facebook https://bit.ly/2tCqBJS
Twitter https://twitter.com/LCLOUD16
LinkedIn https://bit.ly/2syaQCr
YouTube https://bit.ly/2tGV62b
Questions? Feel free to ask:
kontakt@lcloud.pl
https://lcloud.pl/
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
A secure and dynamic multi keyword ranked search scheme over encrypted cloud data
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
Business proposal (2) (1)
SITA is an AI-powered digital assistant proposed for the steel industry in India. It aims to optimize resource use and decision-making by performing predictive analysis on the large amount of operational data generated. SITA will use advanced algorithms to generate insights and identify patterns in sales, procurement, production and maintenance data. This will allow for more accurate demand forecasting and predictive equipment maintenance, reducing costs and improving service. While the solution will require initial cycles of training and some companies may lack technical skills, the overall costs even for very large enterprises are estimated to be under Rs. 9 Crore annually with significant benefits in efficiency, margins, and planning.
Data lake
The document discusses the key differences between data lakes and data warehouses. It provides examples of how a retail company can use a data lake to store various structured and unstructured data sources together. This allows data scientists to more easily combine different data types into models to forecast product demand and for marketing experts to analyze sentiment and determine sales focus. The document also lists some SAP tools for administering and working with data in a data lake, including the SAP HANA Database Explorer, SAP HANA Cockpit, and SAP HANA Cloud Central.
Bayesian Networks Fact Sheet
CXAIR provides statistical analysis and modeling capabilities using Bayesian predictive analytics to identify relationships in data and key factors that affect outcomes. Users can create filtered datasets to build Bayesian network models using learning algorithms, which can then be visualized to show relationships and outcome probabilities based on variables. Models can identify data cohorts, be saved and applied to new data to validate outcomes for risk stratification, reporting, and analysis. CXAIR also offers extensive charting capabilities for visualizing models.
Machine Learning and Cultural Heritage: What Is It Good Enough For?
Funded through the AHRC’s Towards a National Collection Programme, the Science Museum Group (SMG) is collaborating with the V&A and School of Advanced Study, University of London, on a two-year project entitled “Heritage Connector: Transforming text into data to extract meaning and make connections”.
As with almost all data, museum collection catalogues are largely unstructured, variable in consistency and overwhelmingly composed of thin records. The form of these catalogues means that the potential for new forms of research, access and scholarly enquiry that range across multiple collections and related datasets remains dormant.
The Heritage Connector project is deploying a range of machine learning-based techniques to extract information from the SMG collection catalogue, link it to third-party sources – primarily Wikidata and the V&A’s collection – will then create a set of prototypes that demonstrate and explore the affordances of the resulting dataset.
Rather than attempting to deploy machine learning to create a perfect linked data model, Heritage Connector asks what’s “good enough” to provide useful functionality to different audiences.
https://www.aeolian-network.net/events/workshop-1-employing-machine-learning-and-artificial-intelligence-in-cultural-institutions/
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IEEE Big data 2016 Title and Abstract
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Website: http://www.tsys.co.in/
Mail Id: tsysglobalsolutions2014@gmail.com.
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This document provides summaries for several M.Phil Computer Science Data Mining Projects written in C#. The projects cover topics such as bridging virtual communities, mood recognition during online tests, surveying the size of the World Wide Web, knowledge sharing in virtual organizations, adaptive provisioning of human expertise in service-oriented systems, cost-aware rank joins with random and sorted access, improving data quality with dynamic forms, targeted data delivery algorithms, and sentiment classification using feature relation networks.
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Big Data e tecnologie semantiche - Utilizzare i Linked data come driver d'int...
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Bio: Michiel Hildebrand received his PhD from University of Amsterdam (at CWI) in 2010 for his research on access to Linked Data. He worked as a researcher at VU University and CWI. In 2014 he joined Spinque to apply the company’s search by strategy approach to Linked Data.
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Ph: 9841103123, 044-42607879
Website: http://www.tsys.co.in/
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M.E Computer Science Data Mining Projects
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At33264269
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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IJET-V3I2P2
The previous research has focused on quick and efficient generation of wrappers; the
development of tools for wrapper maintenance has received less attention. This is an important research
problem because Web sources often change in ways that prevent the wrappers from extracting data
correctly. Present an efficient algorithm that extract unstructured data to structural data from web. The
wrapper verification system detects when a wrapper is not extracting correct data, usually because the
Web source has changed its format. The Verification framework automatically recovers data using
Dimension Reduction Techniques from changes in the Web source by identifying data on Web pages.
After apply wrapped data to One Class Classification in Numerical features for avoid classification
problem. Finally, the result data apply in Top-K query for provide best rank based on probabilities
scores. Wrapper verification system relies on one-class classification techniques to beat previous
weaknesses to identify the problem by analysing both the signature and the classifier output. If there are
sufficient mislabelled slots, a technique to find a pattern could be explored.
Annotation for query result records based on domain specific ontology
The World Wide Web is enriched with a large collection of data, scattered in deep web databases and web
pages in unstructured or semi structured formats. Recently evolving customer friendly web applications
need special data extraction mechanisms to draw out the required data from these deep web, according to
the end user query and populate to the output page dynamically at the fastest rate. In existing research
areas web data extraction methods are based on the supervised learning (wrapper induction) methods. In
the past few years researchers depicted on the automatic web data extraction methods based on similarity
measures. Among automatic data extraction methods our existing Combining Tag and Value similarity
method, lags to identify an attribute in the query result table. A novel approach for data extracting and
label assignment called Annotation for Query Result Records based on domain specific ontology. First, an
ontology domain is to be constructed using information from query interface and query result pages
obtained from the web. Next, using this domain ontology, a meaning label is assigned automatically to each
column of the extracted query result records.
facilitating document annotation using content and querying value
LOGIC SYSTEMS
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9533694296,9703109334
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A Novel Data Extraction and Alignment Method for Web Databases
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
keyword query routing
LOGIC SYSTEMS
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9533694296,9703109334
logicsystemsprojects@gmail.com
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Ph: 9841103123, 044-42607879
Website: http://www.tsys.co.in/
Mail Id: tsysglobalsolutions2014@gmail.com.
2014 IEEE JAVA DATA MINING PROJECT Keyword query routing
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 JAVA DATA MINING PROJECTS Keyword query routing
The document discusses keyword query routing for keyword search over multiple structured data sources. It proposes computing top-k routing plans based on their potential to contain results for a given keyword query. A keyword-element relationship summary compactly represents keyword and data element relationships. A multilevel scoring mechanism computes routing plan relevance based on scores at different levels, from keywords to subgraphs. Experiments on 150 public sources showed relevant plans can be computed in 1 second on average desktop computer. Routing helps improve keyword search performance without compromising result quality.
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Enabling Verifiable and Dynamic Ranked Search Over Outsourced Data
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To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
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18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
17
This document discusses efficient rendezvous algorithms for wireless sensor networks with mobile base stations. It proposes an approach where select sensor nodes act as rendezvous points, buffering and aggregating data from other sensors. These rendezvous points then transfer the collected data to the base station when it arrives, combining the advantages of controlled mobility and in-network caching. Algorithms are presented for rendezvous design with mobile base stations having variable or fixed tracks. Both theoretical analysis and simulations validate that this approach can achieve a good balance between energy savings and reduced data collection latency in the network.
16
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
15
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
25
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
24
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
23
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
22
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
21
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
20
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
17
This document discusses preventing private information inference attacks on social networks. It explores how released social networking data could be used to predict undisclosed private information about individuals, such as their political affiliation or sexual orientation. It then describes three sanitization techniques that could be used to decrease the effectiveness of such attacks. An experiment is conducted applying these techniques to a Facebook dataset to attempt to discover sensitive attributes through collective inference and show that the sanitization methods decrease the effectiveness of local and relational classification algorithms.
16
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
15
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
14
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
13
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
12
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
11
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
10
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
9
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
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https://app.box.com/s/tacvl9ekroe9hqupdnjruiypvm9rdaneChapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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- 1. Impulse Technologies Beacons U to World of technology 044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in Combining Tag and Value Similarity for Data Extraction and Alignment Abstract Web databases generate query result pages based on a user's query. Automatically extracting the data from these query result pages is very important for many applications, such as data integration, which need to cooperate with multiple web databases. We present a novel data extraction and alignment method called CTVS that combines both tag and value similarity. CTVS automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the QRRs are not contiguous, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested structure that may exist in the QRRs. We also design a new record alignment algorithm that aligns the attributes in a record, first pairwise and then holistically, by combining the tag and data value similarity information. Experimental results show that CTVS achieves high precision and outperforms existing state-of-the-art data extraction methods. Your Own Ideas or Any project from any company can be Implemented at Better price (All Projects can be done in Java or DotNet whichever the student wants) 1