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Association rule mining represent a countenance up to in the field of big data. Association rule mining utilize conservative algorithms produce a big numeral of interviewee rules, with even use procedures such as preserve,consistency.There are still numerous rules to maintain, field authority are necessary to obtain out the rules of interest from the remaining rules. It paper is on we can straight provide rule rankings and appraise the relative relationship between the substance in the rules. this paper suggest a adapted FP-Growth algorithm called FP-GCID (novel FP-Growth algorithm based on Cluster IDs) to produce Association rule in accretion, this method called Mean-Product of Probabilities (MPP) is proposed to location rules and compute the ratio of substance for one rule. The research estranged into three phase DBSCAN (Density-Based Scanning Algorithm with Noise) algorithm is used to get mutually the geographic concern points and chart the gain cluster into comparable contract in succession; FP-GCID is used to produce Association rule.

association rules ,DBSCAN, FP-GCID,Mean-Product of Probabilities (MPP)

[1]. Maquee, A.; Shojaie, A.A.; Mosaddar, D. Clustering and association rules in analyzing the efficiency of maintenance system of an urban bus network. Int. J. Syst. Assur. Eng. Manag. 2012, 3, 175–183.
[2]. Sohrabi, M.; Javidi, M.M.; Hashemi, S. Detecting intrusion transactions in database systems: A novel approach. J. Intell. Inf. Syst. 2014, 42, 619–644.
[3]. Sharma, N.; Om, H. Significant patterns for oral cancer detection: Association rule on clinical examination and history data. Netw. Model. Anal. Health Inform. Bioinform. 2014, 3, 50.
[4]. Geng, X.; Chu, X.; Zhang, Z. An association rule mining and maintaining approach in dynamic database for aiding product–service system conceptual design. Int. J. Adv. Manuf. Technol. 2012, 62, 1–13.
[5]. Ma, X.; Wu, Y.-J.; Wang, Y.; Chen, F.; Liu, J. Mining smart card data for transit riders’ travel patterns.Transp. Res. Part C Emerg. Technol. 2013, 36, 1–12.
[6]. Edla, D.R.; Jana, P.K. A prototype-based modified DBSCAN for gene clustering. Procedia Technol. 2012,6, 485–492.
[7]. Usman, M.; Sitanggang, I.S.; Syaufina, L. Hotspot distribution analyses based on peat characteristics using density-based spatial clustering. Procedia Environ. Sci. 2015, 24, 132–140.
[8]. Lin, K.-C.; Liao, I.-E.; Chen, Z.-S. An improved frequent pattern growth method for mining association rules. Expert Syst. Appl. 2011, 38, 5154–5161. ISPRS Int. J. Geo-Inf. 2018, 7, 146 16 of 16
[9]. Lin, C.-W.; Hong, T.-P.; Lu,W.-H. An effective tree structure for mining high utility itemsets. Expert Syst. Appl.2011, 38, 7419–7424.
[10]. B.; Le, B. Interestingness measures for association rules: Combination between lattice and hash tables.Expert Syst. Appl. 2011, 38, 11630–11640.
[11]. Shaharanee, I.N.M.; Hadzic, F.; Dillon, T.S. Interestingness measures for association rules based on statistical validity. Knowl.-Based Syst. 2011, 24, 386–392.
[12]. Lee, I.; Cai, G.; Lee, K. Mining points-of-interest association rules from geo-tagged photos. In Proceedings of the 2013 46th International Conference on System Sciences (HICSS), Wailea, HI, USA, 7–10 January 2013.
[13]. Rajeswari AM, Sridevi M, Deisy C. Outliers detection on educational data using fuzzy association rule mining. In: Int. Conf. on Adv. In Computer,Communication and information Science (ACCIS-Elsevier Publications; 2014. p. 1e9.
[14]. Butincu CN, Craus M. An improved version of the frequent Itemset mining algorithm. In: 14th IEEE Int. Conf. Networking in Education andResearch, Craiova; 2015. p. 184e9.
[15]. Ban T, Eto M, Guo S, Inoue D, Nakao K, Huang R. A study on association rule mining of darknet big data. In: Proc IEEE Int Joint Conf on Neural Network (IJCNN); 2015. p. 1e7.
[16]. Dinesh J. Prajapati a,*, Sanjay Garg b, N.C. Chauhan c”Interesting association rule mining with consistent and inconsistent rule detection from big sales data in distributed environment” Department of Computer Science & Engineering, Institute of Technology, Nirma University, AhmedabadD.J. Prajapati et al. / Future Computing and Informatics Journal 2 (2017) 19-30
[17]. 1ms.j.omana, 2ms.s.monika, 3ms.b.deepika” survey on efficiency of association rule mining techniques” j.omana et al, International Journal of Computer Science and Mobile Computing, Vol.6 Issue.4, April- 2017, pg. 5-8
[18]. A Survey: On Association Rule Mining. Jeetesh Kumar Jain, Nirupama Tiwari and Manoj Ramaiya. International Journal of Engineering and Research and Application. February 2013. [2] An Extensive Survey on Association Rule Mining Algorithms. Mihir R Patel and Dipak Dabhi. International journal of Emerging technology and Advanced Engineering, January 2015.
[19]. Comparative Analysis of Association Rule Mining Algorithms Based on Performance Survey. K.Vani. International journal of Computer Science and Information Technologies, 2015.
[20]. Bhavesh M. Patel*, Vishal H. Bhemwala, Dr. Ashok R. Patel” Analytical Study of Association Rule Mining Methods in Data Mining” Department of Computer Science, Hemchandracharya North Gujarat University, Gujarat, India International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 3 | Issue 3 | ISSN : 2456-3307
[21]. Sohrabi, M.; Javidi, M.M.; Hashemi, S. Detecting intrusion transactions in database systems: A novel approach. J. Intell. Inf. Syst. 2014, 42, 619–644.

Sachin Kumar Pandey, "An Analysis of Association Rule Mining Algorithm Techniques Geographical Point of Interest in Big Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.776-781, 2018.

In recent year, data is emerging rapidly in each and every domain such as social media, news, education, etc. Due to data excessiveness, there is a need for an automatic text summarizer which will be having an ability to summarize the data. Since the research importance focusing on Natural Language Processing (NLP), text summarization can be used in several fields. Text summarization is a process of extracting data from a documents and generating summarized text of that documents. Thus presents an important data to the users in a relatively more concise form. The study of various extractive summarization of text is made and an essential text summarization method is proposed on the basis of Support-Vector-Machine (SVM). The proposed model tries to improve the quality as well as performances of the summary generated by the clustering technique by cascading it with Support-Vector-Machine (SVM). The documents are preprocessed to get the tokens that are obtained after tokenization, stop word removal, case folding and stemming. The various similarity measures are utilized in order to identify the similarity between the sentences of the document and then they are grouped in cluster on the basis of their term frequency and inverse document frequency (tf-idf) values of the words.

Text Summarization, Extractive Summarization, Natural language Processing (NLP), Clustering, Support-Vector-Machine (SVM), Advanced Encryption Standard (AES), Tokens.

[1] Shiva Kumar K M and Soumya R, “Text Summarization using Clustering Technique and SVM Technique”, International Journal of Applied Engineering Research, Vol. 10, No. 12, 2015.
[2] Mgbeafulike IJ nad Christopher, “CONDENZA: A System for Extracting Abstract from a Given Source Document”, Journal of Information Technology and Software Engineering, Vol. 8, Issue 1, 2018.
[3] Babar S, “Text Summarization”, an overview, 2013.
[4] Lehmam A, “Essential summarizer: Innovative automatic text summarization software in twenty languages”, ACM Digital Library, Personalization and Fusion of Heterogeneous Information, 2010.
[5] Ayush Agarwal and Utsav Gupta, “Extraction based approach for text summarization using k-means clustering”, International Journal of Scientific and Research Publications, Vol. 4, Issue 11, Nov 2014.
[6] Simran Kaur and wg.cdr Anil Chopra, “CLUSTERING BASED DOCUMENT SUMMARIZATION”, International Journal of Emerging Trends and Technology in Computer Science, Volume 5, Issue 1, January-February 2016.
[7] Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saied Safaei, Elizabeth D.Trippe, Juan B. Gutierrez, and Krys Kochut, “A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques”, August 2017.
[8] Chengzhi ZHANG, Huilin WANG, Yao LIU, Dan WU, Yi LIAO and Bo WANG, “Automatic Keyword Extraction from Documents Using Conditional Random Fields”, Journal of Computational Information Systems 4:3, 2008.
[9] Santosh Kumar Bharti, Korra Sathya Babu, and Anima Pradhan, “Automatic Keyword Extraction for Text Summarization in Multi-document e-Newspapers Articles”, European Journal of Advances in Engineering and Technology, 4(6), 2017.
[10] A. Kogilavani and Dr. P. Balasubramani, “CLUSTERING AND FEATURE SPECIFIC SENTENCE EXTRACTION BASED SUMMARIZATION OF MULTIPLE DOCUMENTS”, International Journal of Computer Science and Information Technology, Vol. 2, No. 4, August 2010.
[11] A. Srinivasa Roa, Dr. Ch. Divakar, Dr. A. Govardhan, “RANK BASED DOCUMENT CLUSTERING AND SUMMARIZATION APPROACH IN THE DISTRIBUTED P2P NETWORK”, Journal of Theoretical and Applied Information Technology, Vol. 78, No. 2, 20th August 2015.
[12] Ayushi Arya, “A Review Paper on Effective AES Implementation”, International Journal of Engineering and Computer Science, Vol. 4, Issue 12, Dec 2015.
[13] Baxendale P. B, “Man-made index for technical literature-an experiment”, IBM Journal of Research and Development, 2(4), 1958.
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M R Prathima, H R Divakar, "Automatic Extractive Text Summarization Using K-Means Clustering," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.782-787, 2018.

Trellis coded modulation is a technique in which coding and modulation are done jointly unlike earlier uncoded modulation in which coding and modulation were done separately and hence TCM gives more coding gain without escalating the signal bandwidth required.TCM uses set portioning based signal labeling to achieve a high free Euclidian distance. However decoding and demodulation are complex process in Trellis coded modulation. Bit interleaved coded modulation (BICM) reduced the decoding complexity of TCM by incorporating random interleavers to the coded scheme. BICM combines convolution codes with bit interlavers. Then BICM is modified for iterative decoding and demodulation and this modified BICM technique is known as BICM-ID. This scheme uses signal portioning as in TCM but enhances the BICM free Euclidian distance space. so better utilization of bandwidth with less complexity can be achieved with above defined schemes. In this research work a comparison of coded modulation schemes (TCM, BICM, BICM-ID) is done for finding better BER performance. According to results iterative bit interleavers are better choice than other two.

BICM, TCM, BICM-ID, AWGN, BER, SNR

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[5] A. Alvarado, L. Szczecinski, and E. Agrell, “On BICM receivers forTCM transmission," IEEE Trans. Commun., vol. 59, no. 10, pp. 2692{2702, Oct. 2011. ! pages 3, 4, 5, 9, 10, 16, 17, 18, 19, 21, 25, 32, 39,50, 51, 53, 57, 59
[6] C. Stierstorfer, R. F. H. Fischer, and J. B. Huber, “Optimizing BICMwith convolutional codes for transmission over the AWGN channel," inProc.of International Zurich Seminar Commun., March 2010. ! pages3, 9
[7] LEE – FANG WEI, “trellis coded modulation with multidimensional constellation”, IEEE Transactions onet al, International Journal of Advanced Research in Computer Science, 8(9), Nov–Dec, 2017,797-800© 2015-19, IJARCS All Rights Reserved 800Information Theory, volume IT-33,No.4, pp 483- 501, July1987.
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[9] Vincent K.N. Lau, “Performance of variable bit interleaved coding for high bandwidth efficiency”, The 51st IEEE V T SVehicular technology conference proceedings, spring, Tokyo,japan, vol.3, pp 2054-2058, 15-18 May 2000.
[10] Xiaodong Li, Aik Chindapol, James A. Ritcey, “bit interleaved coded modulation with iterative decoding and 8 PSK signalling”, IEEE Transactions on communication Vol. 50,No.8, august 2002.
[11] Naghi H. Tran and Ha H. Nguyen, “signal mapping of 8 – ary constellation for bit interleaved coded modulation with iterative decoding”. IEEE Transactions on Broad casting, Volume 52, No.1, pp 92-99, March 2006.
[12] Md. Jahangir Hossian, Alex Alvarado, Laszek Szczecinski, “Constellation and Interleaver design for bit interleaved coded modulation”, Global Telecommunication Conference(GLOBECOM 2011) , IEEE, 5-9 December 2011.
[13] Hongzhong Yan and Ha H. Nguyen,“Bit interleaved coded modulation with Iterative decoding in two way relaying communication”, IEEE International Conference on communications (ICC) ,22-27 may 2016.
[14] J. Barrueco, C.Reguerio, J. Montalban, M.Velez, P. Angueria, Heung-mook, sung-IK Park, Jae-young lee,“Combining advanced constellation and SSD techniques for optimal BICM capacity”, IEEE international symposium on broadband multimedia systems and broadcasting, 6 august 2015.
[15] Rugui Yao, Lu Li , Juan Xu, Fanqi Gao and Yongjia Zhu “Improving BER Performance with a BICM System of 3D-Turbo code and Rotated Mapping QAM”, 2015 IEEE 26th International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): Fundamentals and PHY

Nisha, Manoj Ahlawat, "BER Performance Comparison of TCM and BICM for fast Recovery of Data at the Receiver," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.788-792, 2018.

Under the National E- Governance Plan (NeGP) and with the aim of providing quality services to its citizens, government of India established around one hundred thousand common service centres (CSCs) across India. These Common Service Centres were established with the objective of delivering various types of services to its citizens. The services included government-to-citizen services (G2C), business-to-consumer services (B2C) and other services. Like other states in India, the scheme was extended to the state of Jammu & Kashmir as well and these CSCs were popularly referred as Khidmet Centres in the state. The objective of the present study is to find out whether any significant improvement has occurred in delivery of government-to-citizen services since these Khidmet centres came into existence in the state of Jammu & Kashmir. While making use of the secondary as well as primary data, the study finds that after the establishment of Khidmet Centres, there has been a remarkable improvement in delivery of services to citizen in terms of timeliness, transparency and cost. However the findings also suggest that these Khidmet centres have become more of a business-to-consumer than government-to-citizen delivery points.

Common Service Centres, Khidmet Centres, Citizen Services etc

[1] “The Role of ICT in Advancing Growth in Least Developed Countries: - Trends, Challenges and Opportunities,” International Telecommunication Union, 2011.
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[5] “United Nations E-government Survey 2014,” UNO, 2014.
[6] J. Grace and C. Kenny, “Information and Communication Technologies and Broad-Based Development: A Partial Review of the Evidence,” Worldbank, Washington DC, 2004.
[7] Gomez and H. P, “Telecentres Evaluation and research: A global perspective,” International Development Research Centre., Ottawa, Cannada, 1999.
[8] M. Islam and M. Hassan, “Multipurpose community telecentres in Bangladesh: problems and prospects.,” The Electronic Library Emerald Group Publishing Ltd.
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[10] Banjamin, “Does "Telecentres" mean the center is far away: Telecenter development in South,” The South Africal Journal of Information and Communication, p. 1.
[11] “About The scheme,” [Online]. Available: http://csc.gov.in/index.php?option=com_content&view=article&id=106&Itemid=249. [Accessed 10 11 2015].
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Mohammad Asif Naqshbandi, Asif Iqbal Fazili, "Impact Assessment of Common Service Centres (Telecenters) on Citizen Services: - Findings from Jammu and Kashmir," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.793-798, 2018.

Majority of earth’s surface is covered with water but less than 5% is useful. So water conserving has become a major issue so certain water management steps are to be taken. Measuring water level is an important task from government and residence side. Thus, existing management systems has to be updated [1]. In this paper, we investigate the water level management using ultrasonic sensor which detects the amount of water present in the tank and returns the percentage of water present in it. This system has an Arduino, motor pump, LCD display, over-head tank, resource tank, buzzer and an LED. All components are interfaced with the Arduino and works by automation as per uploaded code. We divided the over–head tank by mean of percentages likely 10%, 20%...100%. 10% is the condition of the tank where the quantity of water present in it is very less and finally 100% is maximum condition. We have to monitor and maintain the tank when the water in it is getting less, but in this we make use of a buzzer so whenever the water level percentage is about 10% the buzzer, makes sense and automatically the relay based motor starts and standstill up to reach of 100% of the tank. So no one is required to monitor the tank and for switching of the motor. The main thing we employed is echo, which can be easily understood by an example consider you are in a silent cave when you produce sound you will listen the same thing by high intensity and this is called echo. Like example the ultrasonic sensor has two small openings on it. In which one opening sends the high frequency sound pulse called as ultrasonic waves like a small speaker (sender) and other opening receives them like a small microphone (receiver) [2] the explanation can be better understood by fig (1).

Ultrasonic waves, echo, trig, Arduino, smart water, water level management, automation, relay, water level, trig, echo

[1] Yogita patil, Ramandeep Singh, “Smart tank Management System for Residential Colonies Using Atmega 128A Microcontroller”, International Journal of Scientific & Engineering Research, Volume 5, Issue 6, June-2014.
[2] http://arduino-info.wikispaces.com/Ultrasonic+Distance+Sensor
[3] Asaad Ahmed Mohammedahmed Eltaieb, Zhang Jian Min, “Automatic Water level control System”, International Journal of Science and Research, volume 4 issue 12 December 2015.
[4] Partik Roy, “Construction of Digital Water Level Indicator and Automatic Pump Control System”, International journal of research, volume-3 issue-13 September 2016.
[5] http://arduino-info.wikispaces.com
[6] https://components101.com/ultrasonic-sensor-working-pinout-datasheet
[7] Beza Negash Getu, Hussian A. Attia, Automatic Water Level Sensor and Controller System, IEEE volume-1 issue-3 2016.
[8] https://circuitdigest.com/microcontroller-projects/arduino-ultrasonic-sensor-based-distance-measurement
[9] J.A Jodice, “Relay Performance Testing”, IEEE, volume- 12 issue- 1, 1997.
[10] https://www.electronics-tutorials.ws/resistor/potentiometer.html
[11] Abhishek Saini, Shikhar Rana, Simranjeet Singh, Mohit, Harpreet Kaur Channi, “Designing and Modeling of Water Level Indicator”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology,
Volume 2, Issue 6, November-December 2017.
[12] Supriya R. Khaire, Revati M. Wahul, “Water Quality Data Transfer and Monitoring System in IOT Environment: A Survey”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Volume 2, Issue 6, November-December 2017.

Kodathala Sai Varun, Kandagadla Ashok Kumar, Vunnam Rakesh Chowdary, C. S. K. Raju, "Water Level Management Using Ultrasonic Sensor(Automation)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.799-804, 2018.

Now a day’s prediction of diseases is gaining importance in a hospital management system, rather than giving treatment for the diseases after it is being diagnosed. It is better to avoid that in an initial stage, by taking proper suggestions from the doctor. Artificial intelligence and machine learning are used for this purpose, so in this project we are using decision tree technique based on prediction mechanism to predict the future occurring diseases and avoiding that before it is forthcoming. The main aim of using artificial intelligence in healthcare is to analyse relationships between prevention and treatment of the diseases. We survey the current status of artificial intelligence applications in healthcare and discuss its future. This proposed system highlights the use of artificial intelligence for decision making and Prediction of diseases in the medical field. Decision makers should be aware of giving proper treatment for the particular disease and which will be the evolution of the patient during the treatment.

Artificial intelligence, machine learning, deep learning, prediction, Image Net, decision tree technique

[1]. Paritosh Khubchandani, Kundan Jha, Rohit Bijani, Surah Lala, Pallavi Saindane, ”Medical Prediction using Artificial Intelligence” research article vol.7 issue No 3 2017.
[2]. Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, Yongjun Wang “artificial intelligence in healthcare: past, present and future” 22 June 2017
[3]. Daniel flagella “machine learning healthcare applications 2018 and beyond” 01 June 2018
[4]. Carlo combi “artificial intelligence in medicine”
[5]. Eman Saleem Ibrahem Harba “Computer-Aided Diagnosis for Lung Diseases based on Artificial Intelligence: A Review to Comparison of Two-Ways: BP Training and PSO Optimization” IJCSMC, Vol. 4, Issue. pg.1121 – 1138 6, June 2015
[6]. Jonathan H. Chen, Steven M. Asch, “Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectation” June 2017
[7]. “Artificial Intelligence for Health and Health Care “ December 2017
[8]. Daniel B. Neill, “Using Artificial Intelligence to Improve Hospital Inpatient Care” March/April 2013
[9]. Gianluca bontempi,“machine learning for predicting in a big data world” August 2017
[10]. Ashish Khare, Moongu Jeon, Ishwar K. Sethi,and Benlian Xu, “Machine Learning Theory and Applications for Healthcare” Volume 2017, Article ID 5263570, 27 September 2017
[11]. Yong Xia, Zexuan Ji, Andrey Krtlov,Hang Chang, and Weidong cai, “machine learning in multimodal medical imaging” volume 2017 article ID 1278329 5 march 2017
[12]. Qing-yun Dai, Chun-ping Zhang and Hao Wu, “Research of decision tree classification algorithm in data mining” vol.9, no.5 2016
[13]. Shikha Chourasia, “Survey paper on improved methods of ID3 decision tree classification” vol.3, issue 12, December 2013
[14]. J y chung,”a new decision tree classification algorithm for machine learning” 06 august 2002
[15]. Alan brown, “Machine learning with decision trees” 14 February 2018

S. Divyashree , H.R. Divakar, "Prediction of Human Health using Decision Tree Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.805-808, 2018.

Fuzzy set theory and rough set theory are two formal mathematical tools to handle vagueness, imperfection or incompleteness in data. Fuzzy rough set theory is an embodiment of the prime features of both the theories. This hybrid theory has been proved to be an effective tool for data mining, particularly for feature selection. In this paper, generalized fuzzy rough approximations based on divergence measure of fuzzy sets in an information system is defined using a fuzzy implicator and a fuzzy t-norm. Also, the properties of the fuzzy rough approximations are investigated. Further, an algorithm for feature selection using the fuzzy boundary region of the proposed approximations is presented and experimented with twelve real data sets.

Information System, Approximations, Divergence Measure, Fuzzy rough set, Feature selection

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Sheeja T.K., Sunny Kuriakose A., "Divergence Based Generalized Fuzzy Rough Sets," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.809-815, 2018.

It is very difficult to develop an efficient protocol for message broadcast in vehicular ad-hoc network that supports communication between vehicles and road side units with minimum delay, minimum overhead and maximum reachability. In this paper we show that if we study graph theoretic characteristics of VANETs because they don’t follow scale free and small world network properties, we can improve message broadcast in VANETs. We study urban and highway VANETs graph scenario for different capacities and different network densities. We study properties of graph like average shortest path length, node degree distribution, clustering coefficient and connectivity. For different parameters we develop separate analytical model for both urban and highways VANETs and study VANETs from graph structure point of view. After that we observe that clustering coefficient do not depend upon network density and network size. We study the impact of network density on connectivity and also study VANETs node degree distribution. We then show how this information can be used to reduce overhead in urban vehicular broadcasting protocol (UVCAST) with no major reduction in performance of existing protocol.

Connectivity, Node Degree, Vehicular Adhoc Networks, Routing Protocols

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Jagtar Singh, Sanjay Singla, Surender Jangra, "Improving VANET Protocols using Graph Structure Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.816-821, 2018.

This Paper presents the design and simulation of a low profile planar triple mode dipole antenna which is designed for wireless applications. Three symmetric arms are etched on metallic layer of a single sided printed circuit board (PCB). By changing the size of the arms of the antenna we can adjust the resonant frequency. Computer Simulation Technology (CST) studio software is used for design the antenna which has its peak gain at 900/1800/2400 MHz. The effect of changing length of antenna arms on resonant frequency and parameters S11 (return loss) are described. Proposed antenna can also be work as sensor application at different frequency 900/1800/2400 MHz.

Computer Simulation Technology (CST), Printed Circuit Board (PCB), Voltage Standing Wave Ratio (VSWR)

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Pawan, A. Sangwan, "Design of Planar Triple Mode Dipole Antenna for 900/1800/2400 MHz Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.822-825, 2018.

Recommendation system is used to generate the recommendations on the basis of the input and processed data. This study develops a web usage data mining based recommendation system. To perform the classification and pattern matching is the major task of a recommendation system. For the purpose of classification, traditional recommendation system prefers the KNN classifiers but the issue was that the KNN performs the classification and pattern matching on the basis of the nearest neighbour and thus in this manner, it lacks the scalability and fails to perform the exact matches for the recommendation system. Therefore the Naïve Bayes classifier is implemented and analyzed in this study for the recommendation system and after simulation, it is found that the Naïve Bayes classifier generates the highly accurate and error-free recommendations for the users. The JAVA platform is used for simulation and the results are evaluated in the form of Accuracy, Error Rate, RMSE and Precision.

Data Mining, Web Usage Data mining, Classification, Pattern Matching, Naïve Bayes Classifiers

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Hutashan Vishal Bhagat, Shashi Bhushan, Sachin Majithia, "Consummate Approach for Classification and Pattern Matching for a Web usage based Recommendation System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.826-830, 2018.