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Data mining technique is an application of the regular process for analyzing the huge size of existing data, excavating valuable information to support the decision-making process. The Earth’s upper atmosphere consists of an ionized part referred to as the ionosphere. It lies between eighty kilometre to one thousand kilometer height above the sea level, an area which comprises the parts of the thermosphere, mesosphere as well as the exosphere. The ionosphere is a shell of electrons and electrically stimulated atoms that ambiances the Earth. The target for Weka tool classification are these free electrons in the ionosphere. The performance analysis and experimental results carried out for five classifiers such as Naive Bayes, SVM, ANN, K-NN, and J48 are compared and evaluated in this study. The overall performance of these algorithms is analyzed based on the classification accuracy in which decision tree algorithm has achieved best performance compared to other algorithms. The above accuracy in ionospheric data classification is the focal idea of assessing the performance in data mining algorithms.

Data mining, Naive Bayes, SVM, ANN, K-NN, J48

[1] Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic (1996), "From Data Mining to Knowledge Discovery in Databases"
[2] K. Rawer, “Wave Propagation in the Ionosphere”. Kluwer Acad.Publ., Dordrecht 1993. ISBN 0-7923-0775-5
[3] Sigillito V G., Wing S P, Hutton L V and Baker K B, “Classification of radar returns from the ionosphere using neural networks” Johns Hopkins APL Technical Digest, 10, 262-266.
[4] Marie Fernandes , “Data Mining: A Comparative Study of its Various Techniques and its Process”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.19-23, 2017.
[5] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques”, International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.6, pp.5-8, 2017.
[6] P.Keerthana et al, “Performance Analysis of Data Mining Algorithms for Medical Image Classification” International Journal of Computer Science and Mobile Computing, Vol.5 Issue.3, March- 2016.
[7] Rokach, Lior, and Oded Maimon. "Decision Trees" 28. Web. 1 Feb. 2013.
[8] P Thamilselvana, Dr. J. G. R. Sathiaseelanb, “A Comparative Study of Data Mining Algorithms for Image Classification” Published Online June 2015 in MECS. DOI: 10.5815/ijeme.2015.02.01.

Chandrika, Divya. C, Gowramma. G. S, Varun. C. R, "A Comparative Analysis on Evaluation of Classification Algorithms Based on Ionospheric Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.636-640, 2018.

The study of abnormal cells is important to identify diseases like anemia, thalassemia and polycythemia. Cell features are important to identify abnormality in a given cell. The manual method used to identify abnormality of red blood cells is tedious, prone to human errors and time consuming. Hence, there is a need for fast and accurate system which can identify red blood cell abnormality and helps the doctor to quickly diagnose the diseases. Also, early detection of abnormality helps to prevent chronic diseases. This paper aims to develop such fast and accurate computer aided system which can automatically segment rbc in a given microscopic image. Further, the features of segmented cells are analyzed to detect abnormality. As the microscopic blood samples includes various cells like red blood cells, platelets, white blood cells, enzymes, biological debris, it is significant to segment only red blood cells by eliminating other unwanted cells in the given sample. So, this paper uses image segmentation technique to separate red blood cells in a given sample by eliminating white blood cells and platelets. These segmented cells are further used for feature extraction and classification.

Red blood cells, normal, abnormal, anemia, platelets, computer-aided

[1] Md. Ainul Haque, Mohammad Badrul Alam Miah, “Efficient approach to detect Hypo chromic and normochromic anemia through image processing”, International Journal of Computer Applications, Vol-159-No. 2, February 2017.
[2] Aditi K., Deepali.K, “Review on Separation of Red Blood Cells using Image Processing Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol 6, March 2016.
[3] Manpreet Singh Bawa, Er. Manjeet Singh, “A Red and White Blood Cell Counting From the Medical Images”, International Journal of Engineering & Science Research, Vol-5, July 2015.
[4] Jalil Bin Lias,”analysis of red blood cell (RBC) classification using Ni vision builder AI”, June 2015.
[5] Xudong Wei, Yiping Cao, Guangkai Fu and Yapin Wang, “A Counting method for complex overlapping erythrocytes-based microscopic imaging”, Journal of Innovative Optical Health Sciences, Vol-8-No.6, May 2015.
[6] Laghouiter Oussama, M.Mahadi Abdul Jamil, Wan Mahani Hafiza Bt. Wan Mahmud, “Image Segmentation Techniques for Red Blood Cell”, July 2015.
[7] Mausumi Maitra, Rahul Kumar Gupta, Manali Mukherjee, ”Detection and Counting of Red Blood Cells in Blood Cell Images using Hough Transform”, International Journal of Computer Applications, Vol-53-No.16, September 2012.
[8] Menika Sahu, Amit Kumar Biswas, K.Uma, “Detection of Sickle Cell Anemia in Red Blood Cell”, International Journal of Engineering and Applied Sciences, Vol-2, March 2015.

Deepa T.P, N. Sai Ahladitha Reddy, K Jai Santhoshi, Priya. M, Lakshmi. N, "Segmentation of Red Blood Cells," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.641-643, 2018.

Over the decades, travel has toughened continuous growth and deepening diversification to become one amongst the quickest growing economic sectors within the world. Among the present move applications, solely one or two facilitate the flexibility to arrange a tour that is entirely supported user preferences, whereas giving an in-depth inspect the specified destination. Therefore, this analysis focuses on integration Semantic technologies and Collaborative filtering into the domain of travel and supply most well-liked user familiarized tour plans with superlative user satisfaction. Collaborative filtering is applied to spot a set of elective purpose of interested that maximize the potential satisfaction, whereas there are some pre-selected necessary points traveler should visit. The key issue that has to be understood is that the preferences or the behavior of one user is also entirely totally different from another. The system has introduced the idea of preferences and behavior based customized tour coming up with and also the approach of exploring desired routes.

Personalized Tour Plans; Semantic-Matching; Tour Suggestions; Collaborative Filtering

[1] S. Kotiloglu, T. Lappas, K. Pelechrinis, P.P. Repoussis, Personalized multi-period tour Recommendations, ELSEVIER (2017).
[2] D. I. De Silva1, I. U. Kaluthanthri2, K. S. Sudaraka3, U. P. D. Karunarathna4, J. M. T. I. Jayalath5 Scylax Preference based Personalized Tour Planner with Virtual Reality, IEEE-2016.
[3] Anacleto, R. Figueiredo, L., Almeida, A., & Novais, P. (2014). Mobile application to provide personalized sightseeing tours. Journal of Network and Computer Applications, 41, 56e64.
[4] T. Simcock, S. P. Hillenbrand and B. H. Thomas, Developing a Location Based Tourist Guide Application vol. 05, p. 7, 2003. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73.
[5] Jadhav Dipali, Sanap Pragati, Undre Pratik, Mahajan ogeshwari, Travel Package Recommendation System based on Package Locations and Rating, IJCA Volume 179 - Number 8.

Sonal Deshmukh, Dipali Jadhav, Pragati Sanap, Pratik V. Undre, Yogeshwari Mahajan, "Travel Package Recommendation System Based on Package Locations and Rating using Collaborative filtering algorithm.," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.644-648, 2018.

Classification and features selection play very important role to develop robust and computationally efficient model. In this paper, we have compared different classification techniques for classification of chronic kidney disease data. Two supervised classification learning algorithms are used to develop classifiers as Multilayer Perceptron Network (MLPN) and Radial Base Function Network (RBFN). The main focus of this research work is to reduce the number of features using different feature selection technique. We have also used five different classification techniques for select the relevant feature subsets and improve the accuracy of the classification through the Feature Selection Technique (FST). The RBFN classifier achieved the highest average percentage of performance in terms of accuracy. The results shows that both classification techniques given satisfactory accuracy rate in each different selected feature subset.

MLP, RBFN,CKD, Feature Selection Techniques

[1] M. Kumar, “Prediction of Chronic Kidney Disease Using Random Forest Machine Learning Algorithm,” Int. J. Comput. Sci. Mob. Comput., vol. 5, no. 2, pp. 24–33, 2016.
[2] J. Han, M. Kamber, and J. Pei, Data mining: concepts and techniques, Third. Elsevier, 2012.
[3] J. Novakovic, P. Strbac, and D. Bulatovic, “Toward optimal feature selection using ranking methods and classification algorithms,” Yugosl. J. Oper. Res., vol. 21, no. 1, pp. 119–135, 2011.
[4] D. N. R. S.Ramya, “Diagnosis of Chronic Kidney Disease Using Machine Learning Algorithms,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 4, no. 1, pp. 812–820, 2016.
[5] M. Arora and E. A. Sharma, “Chronic Kidney Disease Detection by Analyzing Medical Datasets in Weka,” Int. J. Comput. Appl., vol. 6, no. 4, pp. 20–26, 2016.
[6] P. sinha; P. Sinha, “Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM,” vol. 4, no. 12, pp. 608–612, 2015.
[7] A. Marcano-Cedeño, P. Chausa, A. García, C. Cáceres, J. M. Tormos, and E. J. Gómez, “Data mining applied to the cognitive rehabilitation of patients with acquired brain injury,” Expert Syst. Appl., vol. 40, no. 4, pp. 1054–1060, 2013.
[8] D. Oreski and T. Novosel, “Comparison of Feature Selection Techniques in Knowledge Discovery Process,” vol. 3, no. 4, pp. 285–290, 2014.
[9] S. I. Ali and W. Shahzad, “A feature subset selection method based on symmetric uncertainty and Ant Colony Optimization,” pp. 1–6, 2012.
[10] Sivanandam and Deepa, Principles of Soft Computing, Second. wiley, 2014.
[11] S. Haykin, Neural Networks and Learning Machines, vol. 3. 2008.
[12] R. Kala, H. Vazirani, N. Khanwalkar, and M. Bhattacharya, “Evolutionary radial basis function network for classificatory problems,” Int. J. Comput. Sci. Appl., vol. 7, no. 4, pp. 34–49, 2010.
[13] “UCI Machine Learning Repository of machine learning databases,” 2015. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease. [Accessed: 01-Jan-2016].

A. K. Shrivas, Sanat Kumar Sahu, "Classification of Chronic Kidney Disease using Feature Selection Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.649-653, 2018.

As information is rising each day, thus it`s terribly tough task to regulate storage devices for this volatile development of digital information. Information reduction has developed into terribly important drawback. Deduplication moves toward plays an important role to get rid of redundancy in massive scale cluster massive information storage. Existing deduplication strategies don`t work effectively in several things. Overlapping and slicing formula is employed for deduplication method in existing system absorb with high memory with a lot of time interval. Recently, the info deduplication cluster has matured to be a significant want of most profitable and investigate backup systems. Information deduplication cluster become accepted in storage system for information backup and archiving. Several researchers specialize in deduplication cluster by that to cut back alternative redundant information. Particularly pattern matching deduplication cluster becomes well-liked. We have a tendency to projected multi graph pattern matching formula (MGPMA) in reduplication in massive information with higher potency. The technique of mixing similarity with neighborhood is applying to the deduplication cluster with bloom filter. As associate economical information removal move toward it exploits information redundancy. As a result, deduplication systems improve storage consumption whereas reducing time delay. Finally, the experimentation shows the system have a decent performance.

deduplication; Slicing; Overlapping Clustering; multi graph pattern matching algorithm (MGPMA); Bloom filter

[1]. Yucheng Zhang, Dan Feng,Hong Jiang, Wen Xia, Min Fu’A Fast Asymmetric Extremum Content Defined Chunking Algorithm for Data Deduplication in Backup Storage Systems’ IEEE Transaction on Computers, July 2016
[2]. Salim El Rouayheb ‘Synchronization and Deduplication in Coded Distributed Storage Networks’ IEEE/ACM Transactions on Networking, December 2015 
[3]. Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding ‘Data Mining with Big Data’ IEEE Transaction on Knowledge and Data Engineering, Jan-2014
[4]. Franc¸ois Goasdoue´ and Marie-Christine Rousset ‘Robust Module-Based Data Management’ IEEE Transaction on Knowledge and Data Engineering, March-2013
[5]. Ekaterini Ioannou and Minos Garofalakis ‘Query Analytics over Probabilistic Databases with Unmerged Duplicates’ IEEE Transaction on Knowledge and Data Engineering,Feburary-2015
[6]. Guanfeng Liu, Kai Zheng, Yan Wang, Mehmet A. Orgun, An Liu, Lei Zhao, and Xiaofang Zhou ‘Multi-Constrained Graph Pattern Matching in Large-Scale Contextual Social Graphs’ IEEE International Conference,April-2015
[7]. Wenfei Fan, Jianzhong Li, Jizhou Luo, Zijing Tan, Xin Wang, Yinghui Wu ‘Incremental Graph Pattern Matching’ ACM SIGMOD International Conference on Management of data, June 2011
[8]. Guanfeng Liu, Kai Zheng, Yan Wang, Mehmet A. Orgun, An Liu, Lei Zhao ‘Multi-Constrained Graph Pattern Matching in Large-Scale Contextual Social Graphs’ IEEE International Conference, April-2015
[9]. Guilherme Dal Bianco, Renata Galante, Marcos Andr_e Gonc¸alves, Sergio Canuto and Carlos A. Heuser ‘A Practical and Effective Sampling Selection Strategy for Large Scale Deduplication’ IEEE Transaction on Knowledge and Data Engineering, September 2015
[10]. Arindam Banerjee Chase Krumpelman, Sugato Basu Raymond J. Mooney ‘Model based Overlapping Clustering’ ACM International Conference on Knowledge Discovery and Data Mining, August 2015.
[11]. Vina M. Lomte, Hemlata B. Deorukhakar ‘Review of Slicing Approach: Data Publishing with Data Privacy and Data Utility’ International Journal of Science and Research (IJSR),June 2014
[12]. S. Indirakumari, A. Thilagavathy “A Secure Verifiable Storage Deduplication Scheme on Bigdata in Cloud”- International Journal of Scientific Research in Computer Science, Engineering and Information Technology –April 2017
[13]. P. Balasubhramanyam Reddy, G. Nagappan ‘A Survey on Secure Cloud Storage with Techniques Like Data Deduplication and Convergent Key management’- International Journal of Scientific Research in Computer Science, Engineering and Information Technology –August 2016

S.A. Amala Nirmal Doss, Mrs.P.Jeevitha, "Large Scale Deduplication Analysis Using Multigraph Pattern Matching Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.654-658, 2018.

This paper deals in Web Content Mining for extraction of structured data. While perusing the web, the client needs to experience numerous pages of the Internet, channel the information and download related records and documents. This errand of seeking and downloading is tedious. Now and again the look inquiries call for particular choice, say, restricting inquiry to few connections. To lessen the time spent by clients, a web extraction and capacity apparatus has been composed and executed in .Net framework, that robotizes the downloading task from a given client question. The Test Scenario has been given different catchphrases. The present work can be a valuable contribution to Web Manipulators, Staff, Students and Web Administrators in an Academic Environment.

Web Content Mining, Structured Data, Web Data Extraction, HTML, Data mining, Web Mining

[1] U. Moulali, V. Sasidhar, “Competent pattern innovation designed for textual content mining”, 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 572 – 577.
[2] Farman Ali, Pervez Khan, Kashif Riaz, Daehan Kwak, Tamer Abuhmed, Daeyoung Park, Kyung Sup Kwak, “A Fuzzy Ontology and SVM–Based Web Content Classification System”, IEEE Access, Vol. 5, pp. 25781 – 25797.
[3] Yeongsu Kim, Seungwoo Lee, “SVM-based web content mining with leaf classification unit from DOM-tree”, 2017 9th International Conference on Knowledge and Smart Technology (KST), pp. 359 – 364.
[4] Tak-Lam Wong, Wai Lam, “Learning to Adapt Web Information Extraction Knowledge and Discovering New Attributes via a Bayesian Approach” IEEE Trans. on Knowledge and Data Engineering, Vol. 22, No. 4, pp. 523 – 536, 2010.
[5] Charu C. Aggarwal, Yuchen Zhao, Philip S. Yu, “On the Use of Side Information for Mining Text Data”, IEEE Trans. on Knowledge and Data Engineering, Vol. 26, No. 6, pp. 1415 – 1429, 2014.
[6] Kaveh Hassani, Won-Sook Lee, “Adaptive animation generation using web content mining”, 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1 – 8.
[7] G. Dhivya, K. Deepika, J. Kavitha, V. Nithya Kumari, “Enriched content mining for web applications”, 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1 – 5.
[8] Tao Jiang, Ah-hwee Tan, Ke Wang, “Mining Generalized Associations of Semantic Relations from Textual Web Content”, IEEE Trans. on Knowledge and Data Engineering, Vol. 19, No. 2, pp. 164 – 179.
[9] Hung-Yu Kao, Shian-Hua Lin, Jan-Ming Ho, Ming-Syan Chen, “Mining Web informative structures and contents based on entropy analysis”, IEEE Trans. on Knowledge and Data Engineering, Vol. 16, No. 1, pp. 41 – 55, 2004.
[10] F. de la Rosa Troyano, S. del Pozo Hidalgo, R. Martinez Gasca, “Analysis and Visualization of Scientific Communities with Information Extracted from the Web”, IEEE Latin America Transactions, Vol. 3, No. 1, pp. 56 – 61.
[11] I-Jen Chiang, Charles Chih-Ho Liu, Yi-Hsin Tsai, Ajit Kumar, “Discovering Latent Semantics in Web Documents Using Fuzzy Clustering”, IEEE Trans. on Fuzzy Systems, Vol. 23, No. 6, pp. 2122 – 2134.
[12] Hao Ma, Irwin King, Michael R. Lyu, “Mining Web Graphs for Recommendations”, IEEE Trans. on Knowledge and Data Engineering, Vol. 24, No. 6, pp. 1051 – 1064, 2012.
[13] Tak-Lam Wong, Wai Lam, “Learning to Adapt Web Information Extraction Knowledge and Discovering New Attributes via a Bayesian Approach”, IEEE Trans. on Knowledge and Data Engineering, Vol. 22, No. 4, pp. 523 – 536.
[14] Wei Liu, Xiaofeng Meng, Weiyi Meng, “ViDE: A Vision-Based Approach for Deep Web Data Extraction”, IEEE Trans. on Knowledge and Data Engineering, Vol. 22, No. 3, pp. 447 – 460.

M.Florence Dayana, Dr.M.Chidambaram, "Implementation of Web Content Extraction of Structured Data Using DotNet Framework," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.659-663, 2018.

With the revolutionary advent of the Semantic Web and the corresponding technologies, Ontologies have lately gained tremendous patronage and popularity from a large cross-section of users from the realm of knowledge management. This paper seeks to make an insightful comparative analysis of a few select software tools, associated with ‘Semantic Web’. As a matter of fact, this study attempts an insightful comparative description of six ontology-editors, viz. Protégé, Swoop, Vitro and Fluent. Also, it describes the key structural aspects of the above editors, including its basic features, apart from the methods of use. In essence, convenience for the user as well as the multiple possibilities of applications is considered as the primary criterion for comparison among these editors. Diverse user groups prefer to use Ontology Building/Management tools while handling several tasks. Even though, each tool defines a specific functionality, many users prefer to use it only for a single purpose for migration within their host ontologies, from one tool to another. Additionally, we evaluated the compatibility among various ontologies by applying many development and management tools. Finally, it detects the many similarities and diversities found among the analyzed ontologies, both within a particular domain (application area) as well as with other domains.

Semantic Web, Intelligent Web, Ontology, Ontology building tools

[1] Bhaskar Kapoor, Savitha Sharma, “ A Comparative Study Ontology Building Tools for Semantic Web Applications”, International Journal of Web & Semantic Technology (IJWesT), 1(3), 2010.
[2] Emhimed Alatrish, “Comparison of ontology Editors. Management Information Systems”, 8(2), 18-24, 2013.
[3] S.C.Buraga, L.Cojocaru,O.C. Nichifor, “Survey on web Ontology Editing Tools”. Retrieved February 25,2018 from Alexandru loan Cuza University,2006.
[4] A.Funk,V.Tablan,K. Bontcheva,H. Cunningham, B.Davis,S. Handschuh, “Clone: Controlled language for ontology editing”, Sixth International Semantic Web Conference, (ISWC). Berlin: Springer Berlin Heidelberg. (pp. 142-155).2007.
[5] A. Harith ,O. kieron, S.Nigel, “Common Features of Killer apps : A comparison with Protégé”. 8th International Prot Conference, 18-21 July 2005. Madrin ,Spain.2005.
[6] https://sourceforge.net/p/vivo/vitro/home/
[7] vitro.mannlib.cornell.edu/
[8] www.cognitum.eu/Semantics/FluentEditor
[9] https://www.w3.org/2001/sw/wiki/Fluent_Editor

D. Ramya, V. Kiran Kumar, "Ontology Editing Tools: A Comparative Perspective," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.664-667, 2018.

High risk of illness is observed for the patients admitted in hospital’s cardiac intensive care units (ICU). Patient’s dead/alive categorical outcome prediction would benefits for patients as well as medical professionals in creating awareness and making clinical decisions respectively. In this work, a model is proposed for predicting life outcomes of cardiac patients admitted in ICU. The model is prepared on the basis of data collected from the regular medication treatments and clinical laboratory test results. A logistic regression model is prepared and compared with two standard algorithms in machine learning such as artificial neural network (ANN) and random forest algorithms, which are the classifiers of decision tree. The performance parameters were compared for both Synthetic Minority Oversampling Technique and stratified sampling for all predictive learning models. It is concluded that logistic regression with stratified sampling techniques would be preferable as a predictive model for the inconsistent time series data set.

Predictive learning, logistic regression, SMOTE sampling, stratified sampling, time series data

[1] R Awang, S Palaniappa, “Intelligent heart disease prediction system using data mining techniques”, IEEE/ACS International Conference on Computer Systems and Applications, ISSN: 2161-5322,2008.
[2] J.Sun, S. Ebadollahi, D. Gotz, J. Hu, D. Sow and C.Neti , “Predicting Patient’s Trajectory of Physiological Data using Temporal Trends in Similar Patients: A System for Near-Term Prognostics”, AMIA Symposium Proceedings, pp-192-195, 2010.
[3] S.Wang , F.Huang, and C.Chan , “Predicting Disease By Using Data Mining Based on Healthcare Information System”, IEEE International Conference on Granular Computing, 2012.
[4] M. Rouzbahman, R. Kealey, E. Yu, M. Chignell ,R. Samavi and T. Sieminowski, “Development of Non-Confidential Patient Types for Use in Emergency Medicine Clinical Decision Support,” IEEE Securiy & Privacy, vol. 11, pp. 12-18, 2013.
[5] L.Morissette and S.Chartier, “The k-means clustering technique: General considerations and implementation in Mathematica”, Tutorials in Quantitative Methods for Psychology, Vol. 9 (1), p. 15-24,2013.
[6] M. Rouzbahman, R. Kealey, E. Yu, M. Chignell ,R. Samavi and T. Sieminowski, “Development of Non-Confidential Patient Types for Use in Emergency Medicine Clinical Decision Support,” IEEE Security & Privacy, vol. 11, pp. 12-18, 2013.
[7] J.Lee, H.Lim, D.Kim, S.Shin, J.Kim,B.Yoo, and K.Cho, “The development and implementation of stroke risk prediction model in National Health Insurance Service`s personal health record”, Computer Methods and Programs in Biomedicine, Vol 153, pp. 253-257, 2018.
[8] M.Rouzbahman, A. Jovicic, and M.Chignell, “Can Cluster-Boosted Regression Improve Prediction of Death and Length of Stay in the ICU?”, IEEE Journal of Biomedical and Health Informatics, Vol 21, pp. 851 – 858, 2016.
[9] Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, W. Philip Kegelmeyer , “SMOTE: Synthetic Minority Over-sampling Technique”, Journal of Artificial Intelligence Research, vol.16, pp 321-357, 2002.
[10] Kevin Lang, Edo Liberty, Konstantin Shmakov, “Stratified Sampling Meets Machine Learning”, International Conference on machine Learning, vol.48, pp 2320-2329, 2016.

K V Sruthi, K Manju, R K Rashmi, R Krishnamouli, "Mortality Rate Prediction in ICU Using Logistic Regression Method," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.668-674, 2018.

Garbage management is a major issue for almost every country, developing or developed alike. Innumerable garbage bins and dust bins are left unattended due to lack of management which leads to unhygienic conditions and creates an unhealthy environment. It could also result in spread of diseases and illnesses. To avoid such problems, we have designed an "IOT Based Garbage Management System". The main idea of this system is to discern whether the garbage bin is full or not in order to make efficient use of time and man-power by providing instant cleaning of the dustbins. Arduino UNO, interfaced with the ultra-sonic sensor is mounted on every garbage bin which checks the level of the garbage in the bin and sends the alert signal to the municipal web server forthwith if the garbage bin fills up to a set minimum threshold level. After cleaning the dustbin, the status of the bin is automatically set to empty in the municipal web server. The whole process contains an embedded module integrated with RFID and IOT facilitation.Also, the real time status of how much garbage has been filled can be monitored by a municipal authority which controls the system. We have also developed an android application which will be linked to the municipal web server to notify the alerts from the microcontrollers to the driver to take necessary action thereby reducing the manual process of monitoring and verification. The notifications are sent to the android application available to the driver.[1,2]

Garbage Management, Arduino UNO, Wi-Fi, Ultrasonic Sensor, GPS, IoT, e-Monitoring

[1]. Kumar, N. Sathish,et al."IOT based smart garbage alert system using Arduino UNO."
[2]. Karadimas, Dimitris,et al."An integrated node for Smart City applications based onactive RFID tags; Usecaseonwaste-bins."Region10 Conference (TENCON), 2016 IEEE. IEEE, 2016
[3]. Medvedev, Alexey, et al. "Waste management as an IoT-enabled service in smart cities". Conference on Smart Spaces. Springer International Publishing, 2015.
[4]. Mohammed Rafeeq, Ateequrahman, Sanjar Alam, Mikdad. "Automation of plastic, metal and glass waste materials segregation using arduino in scrap industry", 2016 International Conference on Communication and Electronics. "International Conference on Intelliget Computing and Applications",Springer Nature,2018.
[5]. Naveen Kishore Gattim, M. Gopi Krishna, B. Raveendra Nadh, N. Madhu, C. Lokanath Reddy. "Chapter 27 IoT-Based Green Environment for Smart Cities", Springer Nature, 2018
[6]. "Microelectronics, Electromagnetics and Telecommunications", Springer Nature, 2018

Vedant Dhamde, Ameya Pacholi, Shreyas Ragit, Heena Agrawal, "IOT based Garbage Management System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.675-680, 2018.

Data Retrieval can be characterized as the movement of acquiring data or information significant to data needs from a gathering of data assets. In like manner, Resources are accessible in colossal, huge and unstructured. From the accessible Resources, just a section or some is needed by the clients. Hence some algorithm has to be derived to carry out this retrieving process. There are many previously proposed algorithms. Almost they are unique in nature of implementing the retrieving process. Many algorithms retrieve information based on term similarity where the result is not quite accurate to the user need. Here in this newly proposed algorithm called “Value Model for Text Mining (VM)” the Information retrieving process is performed based on semantic similarity. Hence this algorithm results in better performance in retrieving more related information by ranking the terms in the Fuzzy set.

Data mining, Value Model, Ranking Tool, Fuzzy Set

[1] http://en.wikipedia.org/wiki/Information_retrieval
[2] Fayyad, Usama, Piatetsky-Shapiro, Gregory, Smyth, Padhraic (1996), "From Data Mining to Knowledge Discovery in Databases", Retrieved 17 December 2008
[3] "Data Mining Curriculum",ACM SIGKDD. 2006-04-30, Retrieved 2011-10-28.
[4] Clifton, Christopher (2010), "Encyclopædia Britannica: Definition of Data Mining", Retrieved 2010-12-09.
[5] Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction". Retrieved 2012-08-07.
[6] Wei Song • Soon Cheol Park. “Latent semantic analysis for vector space expansionand fuzzy logic-based genetic clustering”. © Springer-Verlag London Limited 2009.
[7] Lotfi A. Zadeh *,1.” Is there a need for fuzzy logic?”. _ 2008 Elsevier Inc
[8] Novák, V., Perfilieva, I. and Močkoř, J. (1999)Mathematical principles of fuzzy logic Dodrecht: Kluwer Academic. ISBN 0-7923-8595-0
[9] Ahlawat, Nishant, Ashu Gautam, and Nidhi Sharma (International Research Publications House 2014) "Use of Logic Gates to Make Edge Avoider Robot."International Journal of Information & Computation Technology (Volume 4, Issue 6; page 630) ISSN 0974-2239 (Retrieved 27 April 2014)
[10] Philip Resnik, Mari Broman Olsen and MONA DIAB The Bible as a Parallel Corpus: Annotating the “Book of 2000 Tongues” Computers and the Humanities 33: 129–153, 1999.© 1999 Kluwer Academic Publishers. Printed in the Netherlands. 129
[11] Min Lu a, YaLou Huang a,b, MaoQiangXieb,c,, Jie Liu a “Rank hash similarity for fast similarity search “,Information Processing and Management 49 (2013) 158–168
[12] Wei Song a,b, Jiu Zhen Liang a, Soon Cheol Park b “Fuzzy control GA with a novel hybrid semantic similarity strategy for text clustering”, Information Sciences 273 (2014) 156–170
[13] R. ThamaraiSelvi, Dr. E. George Dharma Prakash Raj. “UFSBIR:A Semantic based Boolean Information Retrieval Algorithm with User Feedback “.
[14] http://en.wikipedia.org/wiki/Fuzzy_set.
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