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Apriori algorithm is an influential algorithm for mining frequent item sets for Boolean association rules. It uses a bottom up approach where frequent subset is extended one item at a time. It starts with an analysis of e‐commerce data and the driving forces behind the success of data mining in e‐commerce.

Data Mining, Data Clustering, Data Classification

[1] Scalable and Efficient Improved Apriori Algorithm, Miss. Nutan Dhange, Prof. Sheetal Dhande Dept. of CE, SCOET,Amravati Univercity, Amravati,India Dept. of CSE, SCOET,Amravati Univercity,Amravati, India.
[2] David Hand,Heikki Mannila,Padhraic Smyth. Principles of Data Mining. translater Yinkun Zhang. Beijing: Mechanical Industry Press. 2003: 272-284.
[3] Research of an Improved Apriori Algorithm in Data Mining Association Rules Jiao Yabing
[4] Association Rule Mining based on Apriori Algorithm in Minimizing Candidate Generation, Sheila A. Abaya 7, July-2012
[5] Recommendation of Books Using Improved Apriori Algorithm ,Nilkamal More (Assistant Professor, Department of Information Technology).
[6] APRIORI Algorithm by Professor Anita Wasilewska
[7] Study of various Improved Apriori Algorithms Deepali Bhende, Usha kosarke , Mnisha Gedam
[8] Rui Chang, Zhiyi Liu, “An Improved Apriori Algorithm”, 2011 International Conference on Electronics and Optoelectronics (ICEOE 2011)

Karan Kapoor, Shana Parveen, Praveen Gupta, Abhay Gupta , "Implementation of Apriori Algorithm in E-Commerce Application," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.251-254, 2018.

Opinion Mining is a process of automatic extraction of knowledge by means of opinion of others about some particular product, topic or problem. The idea of Opinion mining and Sentiment Analysis tool is to process a set of search results for a given item based on the quality and features. Opinion Mining is a useful tool for gathering information about customers’ opinions about products, brands or companies. Opinion mining is a type of natural language processing which could track the mood of the people about any particular product by review. Opinion mining is also called sentiment analysis due to large volume of opinion which is rich in web resources available online. Analyzing customer review is most important, by doing that we tend to rate the product and provide opinions for it which is been a challenging problem today. Most of the people watch the user reviews before buying the product. So in that aspect, here analyze and categorize the all the user review i.e opinion, in three types(positive, negative, or neutral). All the positive, negative and neutral comments are categorized in percentage with the pie-chart representation, from the total comments received. This paper focus on the opinion mining applications, challenges, tools and techniques for a particular product, from the customer review.

opinion mining, statement analysis, text mining, web content mining

[1] Bakhtawar Seerat, Farouque Azam “Opinion Mining: Issues and Challenges (A Survery),” Itronational Journal of Computer Applications (0975-8887) Volume 49-No.9, July 2012
[2] Neeru Mago, “Opinion mining: Applications, Techniques, Tools, Challenges and Future Trends of Sentiment Analysis”, International Journal of Computer Engineering and Applications, Volume X, Issue IV, April 2016
[3] S. Kasthuri, Dr. L. Jayasimman, Dr. A. Nisha Jebaseeli, “An Opinion Mining and Sentiment Analysis Techniques: A Survey”, International Research Journal of Engineering and Technology(IRJET), Volume 03 Issue: 02, Feb 2016
[4] Dr. P. Perumal, M. Kasthuri, “A Survey on Opinion Mining from online Review Sentences” International Research Journal of Engineering and Technology.
[5] Nidhi Mishra, Dr. C.K. Jha, “Classification of Opinion Mining Techniques”, International Journal of Computer Applications, Volume 56, No.13, October 2012.
[6] Sumathi.T, Karthik.S, Marikannan.M “Performance Analysis of Classification Methods for Opinion”, International Journal of Innovations in Engineering and Technology (IJIET), Volume 2, Issue 4, August 2013.
[7] B. Liu “Sentiment Analysis and Opinion Mining”, April 22, 2012.
[8] Nidhi R. Sharma , Prof. Vidya D. Chitre “Opinion Mining, Analysis and its Challenges”, International Journal of Innovations & Advancement in Computer Science
[9] N.Mishra and C.K.Jha 2012, “An Insight into task of opinion mining”, Second International Joint Conference on Advances in Signal Processing and Information
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[11] Raisa Varghese, Jayasree. A Survey on Sentiment Analysis and Opinion Mining, International Journal of Research in Engineering and Technology (IJRET), Vol 2 Issue 11 Nov 2013..
[12] Nidhi Mishra et al. Classification of Opinion Mining Techniques, International Journal of Computer Applications, Vol 56, No 13,Oct 2012Pg No 1-6..
[13] G.Angulakshmi, Dr.R.ManickaChezian. An Analysis on Opinion Mining: Techniques and Tools , International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 7, July 2014.
[14] S. Yadav, K.Ahmad, J.Shekar. “Analysis of Web Mining Applications and Beneficial Areas” in proceedings of the IIUM Engineering Journal, Volume 12, no. 2, 2011
[15] U. Aggarwal, G. Aggarwal, “Sentiment Analysis: A Survey”, International Journal of Computer Science and Engineering, Volume-5, Issue-5, May 2017.
[16] Apoorva T, Pradeep N, “Aspect Based Sentiment Analysis with Text Compression”, Internatioanl Journal of Comuter Science and Engineering, Volume-5, Issue-8, Aug 2017

M. K. Prakash, G. Dinesh, "A Survey on Opinion Mining: Applications, Challenges, Tools and Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.255-260, 2018.

India has a wide network of roads and transportation playing a key role in the economic development of the nation. Traditional manual collection of tax at the toll plazas doesn’t provide an efficient and effective method for management of traffic on the highways leading to heavy congestions resulting in wastage of time and resources. Therefore in the paper a system is proposed which automatic toll collection, anti- theft system along with document verification of the vehicle. A system uses radio frequency identification (RFID), Arduino Uno microcontroller, GSM SIM 800, EM 18 reader and a computer host. Passive RFIDs are widely popular for its applications in the field of transportation and they are extensively used in motor vehicles for automated toll collection. They are easy to install and work for a lifetime based on getting its power supply from a reader module. In toll plaza collection it checks for blacklisted vehicles in the centralized database and verifies the documents, to ensure whether they are up to date or expired in terms of validity. This is a step towards digital India, where all the transaction are executed in an instant ensuring smoother flow of traffic and reducing the time spent in the ques of major toll plazas across the country. The system contributes to the existing methodology by ensuring the safety of the vehicle with anti-theft feature and keeping a check on the crime rate by maintaining a stolen vehicle directory. With document verification it manages all the documents and maintains a soft copy in the database along with an archive of modified records. It ensures that all the vehicles commuting on the roads have valid papers and rules & regulation are not violated and catching hold of people driving without proper documents at the toll booth. The proposed system increases efficiency in collecting taxes, reducing traffic congestion at the toll booths and resulting in lower fuel consumption.

RFID, Arduino Uno Microcontroller, GSM SIM800, EM 18 Reader

[1] Prof. Amit Hatekar, Nikita Kokal, Tarun Jeswani, Pratik Lalwani, “RFID Based Automatic Toll Collection System using GSM”, International Research Journal of Engineering and Technology (IRJET), Vol.03, Issue.04, pp.1354-1357, April 2016.
[2] AungMyint Win, Chaw MyatNwe, KyawZinLatt, “RFID Based Automated Toll Plaza System”, International Journal of Scientific and Research Publications, Vol.04, Issue.06, pp.1-7, June 2014.
[3] Satyasrikanth P, Mahaveer Penna, Dileep Reddy Bolla, “Automatic Toll collection system using RFID”, International Journal of Computer Science and Mobile Computing (IJCSMC), Vol.05, Issue.08, pp.247-253, August 2016.
[4] R. Vinston Raja, K. Gopinath, K. Hemapriya, R. Agnel Joe, “RFID and Universal Number Vehicle Monitoring, Tracking and Traffic Free System”, Middle-East Journal of Scientific Research, Vol.24, Issue.S2, pp.306-312, 2016.
[5] K. Gowrisubadra, Jeevitha.S,Selvarasi.N, “A Survey on RFID Based Automatic Toll Gate Management”, International Conference on Signal Processing, Communications and Networking, Chennai, India, March 2017.
[6] Asif Iqbal Mulla, Sushanth K J, Shreya Rai, Ashritha Salian, Naziya, Kavitha Kalabar, “An Automatic Embedded Toll Plaza with Document Verification and Speed Detection System”, International Journal of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering, Vol.04, Issue.05, pp.316-319, May 2016.
[7] Surya.K.Narayanan, Thushara.C, Sandhya.C, Saranya.N, Sreepriya.P.V, “Automatic Toll Gate System Using RFID & GSM Technology”, International Journal of Science, Engineering and Technology, Vol.03, Issue.05, pp.1273-1279, 2015.
[8] Pritam Mhatre, Parag Ippar, Vinod Hingane, Yuvraj Sase, Sukhadev Kamble, “Advanced Tracking System with Automated Toll”, International Journal of Computational Engineering Research, Vol.03, Issue.04, pp.77-79, April 2013.
[9] Sunil Khatr, Shilpa Jadhwani, Chetan Basantani, Amit Makhija, Prof. Kajal Jewani, “Automatic Toll Collection with Stolen Vehicle Recognition”, International Journal of Engineering and Management Research, Vol.05, Issue.05, pp.407-410,October 2015.
[10] Rakhi Kalantri, Anand Parekar, Akshay Mohite, Rohan Kankapurkar, “RFID Based Toll Collection System”, International Journal of Computer Science and Information Technologies, Vol.05, Issue.02, pp. 2582-2585, 2014.
[11] Wei-Hsun Lee , Shian-Shyong Tseng , and Ching-Hung Wang, “Design And Implement Of Electronic Toll Collection System Based On Vehicle Positioning System Techniques”, Computer Communications,Vol.31, Issue.12, pp.2925–2933, July 2008.
[12] P. Shinde, Prof. Mr.Y.B.Mane, P. Tarange, “Real Time Vehicle Monitoring and Tracking System based on Embedded Linux Board and Android Application”, IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT),Tamil Nadu, India, 2015.
[13] Akshay Bhavke, Sadhana Pai, “Advance Automatic Toll Collection & Vehicle Detection During Collision using RFID”, IEEE International Conference on Nascent Technologies in the Engineering Field (ICNTE), Navi Mumbai, India, 2017.
[14] Dipali D. Pund, Sanjay G. Galande, “Congestion Free Toll Collection, Stolen Vehicle Detection and Tracking System for Authorised Person”, International Journal of Engineering Development and Research, Vol.04, Issue.02, pp. 1992- 1997, 2016.
[15] Rakhi Kalantri, Anand Parekar, Akshay Mohite, Rohan Kankapurkar, “RFID Based Toll Collection System”, International Journal of Computer Science and Information Technologies, Vol.05, Issue.02, pp.2582-2585, 2014.
[16] Amol A. Chapate, D.D. Nawgaje, “Electronic Toll Collection System Based on ARM”, International Journal of Science, Engineering and Technology Research (IJSETR), Vol.04, Issue.01, pp.46-49, January 2015.
[17] Ms. Galande S.D., Mr. Oswal S.J., Mr. Gidde V.A., Ms. Ranaware N.S., Prof. Bandgar S.B., “Automated Toll Cash Collection System for Road Transportation”, International Journal of Computer Science and Mobile Computing, Vol.04, Issue.02, pp.216-224, February 2015.
[18] Al Rashed, M.A., Oumar, O.A., Singh, D., “A real time GSM/GPS based tracking system based on GSM mobile phone”, IEEE Future Generation Communication Technology (FGCT), Vol.12, Issue.14, pp.65-68, Nov 2013.
[19] Kerav Shah1, Gourav Inani, Darshan Rupareliya, Rupesh Bagwe and Bharathi H N, “RFID Based Toll Automation System”, International Journal of Computer Science and Engineering, Vol.04, Issue.04, pp. 52-54, April 2016.

Neena Sidhu, Akshita Jain, Yashashwita Shukla, T.B. Patil, S.T.Sawant-Patil, "Automated Toll Collection coupled with Anti-theft & Vehicle Document Verification System using RFID and Arduino Uno," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.261-266, 2018.

Our project is an approach towards Smart Home automation using Internet of things (IOT). In this era of increasing technology our day to day life becomes simpler and much easier in all aspects. Today’s generation preferred automated system over manual system, and IOT is latest and emerging technology. In our project we control our home appliances using computer or mobile devices through internet from anywhere around the world. Our project is meant to save the electric power and human energy. Use of Android application software in home appliances renders. Our project is controllable and monitor able from remote places. Our project is suitable for physically challenged people and it is also helpful for those who are sick and on bed rest. In this paper we are going to present various scenario of controlling home appliance over Internet .

Home Automation System, Android Phone, Arduino, ESP8266 Wi-Fi Module, Relay

[1] Atukorala K., Wijekoon D., Tharugasini M., Perera I., Silva C., (2009), “SmartEye Integrated Solution to Home Automation, Security and Monitoring Through Mobile hones”, Next Generation Mobile Applications, Services and Technologies, IEEE Third International Conference on, pp.64–69.
[2] Zhai Y., Cheng X., (2011), “Design of Smart Home Remote Monitoring System Based on Embedded System”, Control and Industrial Engineering, IEEE 2nd International Conference, pp.41-44 .
[3] Gurek A., Gur C., Gurakin C., Akdeniz M., Metin S. K., Korkmaz I., (2013), “An Android Based Home Automation System”, High Capacity Optical Networks and Enabling Technologies, IEEE 10th International Conference on, pp.121- 125.
[4] Tan K. K., Lee T. H., Soh C. Y., (2002), “Internet Based Monitoring of Distributed Control Systems an Undergraduate Experiment”, Education, IEEE Transactions on, vol.45, no.2, pp.128–134.
[5] R.Naresh Naik , P.Siva Nagendra Reddy, S.Nanda Kishore and K.Tharun Kumar Reddy published a Paper Titled “Arduino Based LPG gas Monitoring & Automatic Cylinder booking with Alert System” in IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) , Volume 11, Issue 4, Ver. I (Jul.-Aug .2016), PP 06-12 e-ISSN: 2278-2834,p- ISSN: 2278-8735.
[6] Yüksekkaya B., Kayalar A. A., Tosun M. B., Özcan M. K., Alkar A. Z., (2006), “A GSM, Internet and Speech Controlled Wireless Interactive Home Automation System”, Consumer Electronics, IEEE Transactions on, vol.52, no.3, pp.837-843.
[7] Yamazaki T., (2006), “Beyond The Smart Home”, Hybrid Information Technology, IEEE International Conference on, vol.2, pp.350-355.
[8] Ogawa M., Tamura T., Yoda M., Togawa T., (1997), “Fully Automated Biosignal Acquisition System For Home Health Monitoring”, Engineering in Medicine and Biology Society, IEEE Proceedings of the 19th Annual International Conference on, vol.6, pp.2403-2405.
[9] URL:_ https://www.irjet.net/archives/V2/i3/Irjet-v2i3317.pdf
[10] Al-Ali A. R., Al-Rousan M., (2004), “Java Based Home Automation System”, Consumer Electronics, IEEE Transactions on, vol.50, no.2, pp.498-504.
[11] Deepti S., (2014), “Home Automation System with Universally Used Mobile Application Platform”, IOSR Journal of Electronics and Communication Engineering, vol.9, no.2, pp.01- 06.
[12] Piyare R., Tazil M., (2011), “Bluetooth Based Home Automation System Using Cell Phone”, Consumer Electronics, IEEE 15th International Symposium on, vol.45, no.3, pp.192-195.

A. Pandey, A. Azhar, A. Gautam, M. Tiwari, "IOT Based Home Automation Using Arduino and ESP8266," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.267-270, 2018.

We classify data quality problems that are directed by data cleaning and provide an overview of the principal Solution approaches.Data cleansing is particularly needed when integrating heterogeneous data sources and Should be directed together with schema-related data transformations. We also discuss current tool support for data cleanup. Data profiling is a specific form of data analysis customer data to detect and characterize important features of data sets. Data Analysis offers a delineation of data structure, content, rules and relationships by using statistical methodologies to deliver a lot of standard characteristics about data -data types, field lengths and cardinality of columns, granularity, value sets, format patterns, content patterns, implied rules, and cross-column and cross-file data relationships and cardinality of those relationships. Data deduplication has been advocated as a promising and effective technique to save the digital space by removing the duplicated data from the data centres or clouds. Data deduplication is a process of identifying the redundancy in data and then removing it. The resulting unique data/Consolidate data into single format using data cleansing and Data standardization. Use scorecards to measure data quality progress and shared URL link to the stakeholder.

Data Analysis, Data Profiling, Data Cleansing, Data Standardization, Data Score Cards

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[7]. Cohen, W.: Integration of Heterogeneous Databases without Common Domains Using Queries Based Textual Similarity. Proc. ACM SIGMOD Conf. on Data Management, 1998.
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A. Ghouse Mohiddin S. Ramakrishna, "TACTICS FOR DYNAMIC DATA CLEANSING AND DATA PROFILING USING DIMENSIONS FOR DATA QUALITY ASSESSMENT," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.271-276, 2018.

The aim of this research work is to develop a best optimal procedure to send the emails and get the high open rate. This research work is focused on email address verification whether it is email sender address or receiver address. The success of an email campaign depends on two major factors, Email sender address, and Email subject line. The importance of subject line is less over email sender address as it is essential to send an email to user inbox folder to start the subject line role to play. And if we get fail in pushing the email into inbox folder then subject line would not have any significance even after it is made best. Study of email sender address could be an effective area to dig out the impact of email sender address over successful email campaign. We tried to analyze the effects of email addresses over email sending by sending live emails to the users. In the email sender verification, we analyze the impact of domain, SPF, DKIM, and came up with some useful results. In email receiver verification process we developed a code to filter the email addresses of the receiver where we checked the email address for MX, disposable, duplicate, role email, and domain. After this email cleaning process, we found an extra increment in the email open and deliver rate whereas email bounce rate got decreased.

email marketing, email cleaning, spam, and bounce, SPF, DKIM, email template, email campaign.

[1]. Response Goodarz Javadian Dehkordi1, Samin Rezvani1, Muhammad Sabbir Rahman1, Firoozeh Fouladivanda1 Neda Nahid1A Conceptual Study on E-marketing and Its Operation on Firm`s Promotion and Understanding Customer’s & Samaneh Faramarzi Jouya2 International Journal of Business and Management; page5, Vol. 7, No. 19; 2012 ISSN 1833-3850 E-ISSN 1833-8119 Published by Canadian-Center-of-science-and-Education.

[2]. Manish Kumar* A Countermeasure Technique for Email Spoofing Volume4, No. 2, Jan-Feb 2013,page3, International Journal of Advanced Research in Computer Science ISSNNo. 0976-5697, Dr.M.T.V.Suresh Kumar.

[3]. Smart Insight Email marketing engagement and response statistics 2018, The best email statistics sources to benchmark open and clickthrough rates for your email campaigns in your industry sector.

[4]. Rekha 1,International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 6 - May 2014 ISSN: 2231-5381 http://www.ijettjournal.org Page 1 A Review on Different Spam Detection-Approaches,SandeepNegi2

[5].Siti-Hajar-Aminah Ali1, Seiichi Ozawa1Journal of Intelligent Learning 2015.72005 How to cite this paper: Ali, S.-H.-A., Ozawa, S., Nakazato, J., Ban, T. and Shimamura, J. (2015) An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing. Journal of Intelligent Learning Systems and Applications, 7, 42-57. http://www.scirp.org/journal/jilsahttp://dx.doi.org/10.4236/jilsa.

[6]. Bryan Klimt and Yiming Yang ,The Enron Corpus: A New Dataset for Email Classification Research Language Technologies Institute Carnegie

[7]. Communicating Through Email Chapter contributed by Peggie CHAN and LEE Gek Ling Baker, A. (2003). Email etiquette. Retrieved on June 15, 2009, from http://oit.wvu.edu/email/ Email%20Etiquette.pdf Lesikar, R., Flatley, M.E., & Rentz, K. (2008). Email. Business communication – Making connections in a digital world (11th ed.), pp. 96-109. New York: McGraw-Hill Irwin. The OWL at Purdue (2008). OWL Materials: Email etiquette. Retrieved on June 15, 2009, from http:// owl.english.purdue.edu/owl/resource/636/01/


[8]. A Trend Micro Research Paper Concerns Regarding Flaws in the New DKIM Standard Douglas Otis (Forward-Looking Threat Research Team Dave Crocker. (June 21, 2011). CircleID. “Searching Under Lampposts with DKIM. ”LastaccessedSeptember11,2013. http://www.circleid.com/posts/searching_under_lampposts_with_dkim/.

[9]. E. Fariborzi and M. Zahedifard, International Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 2, No. 3, June 2012E-mail Marketing: Advantages, Disadvantage Sand Improving Techniques

[10]. An Experian Marketing Services’ study. 2013 Email Market StudyHowtoday’semailmarketersareconnecting,engagingandinspiringtheircustomersDecember2013,page3.

[11]. Five new failings of email marketing with best practice solutions Australian research into email marketing ACMA, Australia in the DigitalEconomy,page2,onlineparticipation report, Roy Morgan graph, Cheetah Mail Professional Services Department Study, 2008. http://www.acma.gov.au/webwr/aba/about/recruitment/online_participation_aust_in_digital_economy.pdf

[12]. How It Is Hurting Email and Degrading Life on the Internet For release at 6 p.m. [Eastern] October 22 2003 Deborah Fallows, Senior Research Fellow PEW INTERNET & AMERICAN LIFE PROJECT 1100 CONNECTICUT AVENUE, page2,NW – SUITE 710 WASHINGTON-D.C.-20036-202-296-0019,//www.pewinternet.org/ Spam How It Is .



[13]. Yingjie Zhou, Mark Goldberg, Malik Magdon-Ismail, and William A. Wallace Strategies for Cleaning Organizational Emails with an Application to Enron Email Dataset page3, Yingjie Zhou Rensselaer Polytechnic Institute zhouy5@rpi.edu .

[14]. The Economics of Spam_ Justin M. Rao Microsoft Research David H. ReileyGoogle,page 1, Inc. Keywords: spam, externalities, email, arms race, screening JEL Codes: D02, D23, D62

[15]. Anurag Tiwari, Sonal Rai, An Optimal Approach To Achieve Effective Email Marketing: Review, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-6 Issue-12, August 2017

[16]. Constant Contact industry open rate as of 2018, Average Industry Rates for Email as of February 2018, Average open, click-through, and bounce rates of Constant Contact customers by industry


[17]. R. Miller, E.Y.A. Charles Department of Computer Science, University of Jaffna, Sri Lanka Millerfeeds@gmail.com. 2016 International Conference on Advances in ICT for Emerging Regions (ICTer): 058 - 065 978-1-5090-6078-8/16/$31.00 ©2016 IEEE A psychological based analysis of Marketing Email Subject Lines.

Anurag Tiwari, Mohd. Aquib Ansari, Rachana Dubey, "An Effective Email Marketing using Optimized Email Cleaning Process," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.277-285, 2018.

Fractal Robots is a developing new administration that guarantees to alter each part of human innovation. This innovation has the potential to enter each field of human work like development, medication, research, and others. Fractal robots can empower structures to be worked inside a day, help perform touchy therapeutic operations and can aid research centre trials. Fractal Robots have worked in self-repair which implies they can proceed without human intercession. In this paper, I have shown a research article about an attractive technology called as fractal robot, this new technology has enough amazing features which can attracts many researchers and scientists for continuing their research work in this area.

Fractals, Fractal Robot, Digital Matter Control, Real Life Fractal, Man-made FractalFractals, Fractal Robot, Digital Matter Control, Real Life Fractal, Man-made FractalFractals, Fractal Robot, Digital Matter Control, Real Life Fractal, Man-made Fractal

[1]. Arifmohammad Attar, Loukik Kulkarni, and S. G. Bhatwadekar, "Fractal Robots – Smart Future of Manufacturing Industry," Vol. 2, Issue. 4, pp. 103-105. 2013.
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[7]. Prateeksha Tiwari, Ruchika Nagarkar, Surbhi Shrivastava and Shweta Uikey, "New Era Fractals,"IJETMR, Vol. 1, Issue. 1, pp. 261-266, 2013.

Ketan Jha, "Fractal Robots: An Intelligent Futuristic Machine," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.286-288, 2018.

Internet of Things (IoT) has provided enormous opportunities to make prevailing smart environment by influencing the increasing ubiquity of Radio Frequency Identification Devices (RFID), wireless network, and sensor devices. Recently, a large number of industrial IoT applications have embarked their presence. Rapid technological growth introduces tremendous information on the network. Big Data is an idea to assemble huge amount of data from IoT enabled devices like sensors, actuators in IoT smart environment to help monitor specific conditions, procedures, and system performance. In this new generation, it becomes more challenging to extract most relevant information quickly and efficiently. To solve this problem, a data mining technique widely known as automatic text summarization may also prove to be fruitful. Text summarization creates summarized information from a large text corpus. Various latest techniques used for text summarization viz. Classification, Particle Swarm Optimization, Genetic Algorithms, clustering, neural network and various hybridized approaches are presented in this paper. The latest and relevant algorithms may be customized in the context of IoT applications. This paper is aimed at reviewing these techniques and also discusses the challenges as well as other related research issues.

Data mining in IoT, challenges, Multilingual text summarization, clustering, particle swarm optimization

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Deepti Sehrawat and Nasib Singh Gill, "Data Mining in IoT and its Challenges," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.289-295, 2018.

A software system design was said to be succeeded only when it satisfies the purpose for which it was intended. Requirement Engineering (RE) is the progression of determining that purpose, by recognizing stakeholders and their desires and recording these in a form that is adaptable to analysis, communication and implementation. Agent–oriented perceptions are growing very popular in software engineering as demonstrating frameworks for RE. On the other hand, Data Warehouse (DW) is a field of computer science that is used to capture the historical information to provide decisions to be taken by the management. In this paper, we used a blend of these technologies with Agent-Goal-Decision-Information (AGDI) model for the RE of DW. Based on the proposed AGDI model, a new RE approach has been proposed.

Requirement Engineering, software engineering, Data Warehouse, AGDI

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S. Bharadwaj, A.K. Goyal, "Requirement Engineering for the Development of Agent Oriented Data Warehouse," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.296-300, 2018.

Data mining is the area of research, which means that useful information or knowledge is extracted from previous data. Data mining defines large amounts of data as a process of finding information such as super market data for various technologies used for data mining, such as science, research, medicine, media, web, entertainment and many other areas, which is implemented with various goods, data mining model data warehouses and online analytical resources. Data mining has made a immense advancement in recent year but the problem of lost data has remained a big challenge for data mining algorithms. This paper analyzed the predictive and descriptive techniques such as classification, regression time series analysis ,predication and clustering, summarization, association rules, sequence discovery techniques on the basis of algorithms which is used to predict previously unidentified class of objects.

Data mining, Data mining techniques: Predictive and Descriptive DM techniques

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Shaganpreet Kaur, Chinu, "Comparative Analysis of Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.301-304, 2018.