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Data mining is the process of extracting hidden interesting patterns from massive database. It is used to extract the hidden information/knowledge /inference from the real-life database. In this paper an effort has been made to implement the concept of data mining in Chronic Kidney Disease. Chronic Kidney Disease contains heterogeneous data that can be mined properly to provide a variety of useful information for the physicians to detect a disease and predict the severity of the disease and above all survivability of the patients who have this disease. The concepts of clustering and data mining have been used to design the knowledge base for the prediction of chronic kidney disease based on the new data. The concept of factor analysis has been used for the selection of the best factor of this data set and there after the concept of clustering has been used to predict the output of new data element of same data set.

Data mining, clustering, Hierarchical clustering, Chronic Kidney Disease, Clustering, Distance function

[1] Maryam SoltanpourGharibdousti, Kamran Azimi, Saraswathi Hathikal, Dae H Won, “Prediction of Chronic Kidney Disease Using Data Mining Techniques”, In the Proceedings of Industrial and Systems Engineering Conference, in the year 2017.
[2] S.DilliArasu, Dr.R.Thirumalaiselvi , “Review of Chronic Kidney Disease based on Data Mining Techniques”, International Journal of Applied Engineering Research , Volume 12, Number 23 (2017) pp. 13498-13505 , ISSN 0973-4562
[3]Sahana B J, Dr Minavathi, “Kidney Disease Prediction Using Data Mining Classification Techniquesand ANN”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 4, April 2017.
[4] SirageZeynu, Shruti Patil, “Survey on Prediction of Chronic Kidney Disease Using Data Mining Classification Techniques and Feature Selection”International Journal of Pure and Applied Mathematics, Volume 118, No. 8 2018, 149-156, ISSN: 1311-8080
[5] Tabassum S, Mamatha Bai B G, Jharna Majumdar, “Analysis and Prediction of Chronic Kidney Disease using Data Mining Techniques”, International Journal of Engineering Research in Computer Science and Engineering, Vol 4, Issue 9, September 2017, ISSN (Online) 2394-2320
[6] GuneetKaur, Ajay Sharma, “PREDICT CHRONIC KIDNEY DISEASE USING DATA MINING ALGORITHMS IN HADOOP”, International Journal of Advances in Electronics and Computer Science, Volume-5, Issue-4, Apr.-2018, ISSN: 2393-2835
[7]Dr.S.Vijayarani,Mr.S.Dhayanand, “DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION”, International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 4, August 2015
[8] D. P., J. P. Choudhury and M. De, “An Enhance DE algorithm for analysis in data set”, International Journal of Data Science (IJDS), 2016 (Accepted).
[9] Dharmpal Singh, Abhishek Banerjee, Gopal Purkait , “Assessment of Heart DiseaseTypes Using Clustering Techniques Under the Domain of Data Mining”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol.6 Issue 03 pp. 10-16, Print ISSN: 2231-2021 e-ISSN: 2231-0312 .
[10] D. P. Singh, S. Sahana, SK Saddam Ahmed, "A Comparative Study to Assess the Crohn’s Diseasetype using Statistical and Fuzzy Logic Methodology", International Journal of Computer Sciences and Engineering, Volume-04, Issue-06, Page No (41-46), Aug -2016, E-ISSN: 2347-2693
[11] D. P. Singh, J. P. Choudhury and M. De, “An effort to select a preferable meta heuristic model for knowledge discovery in Data mining”, International Journal of mataheuristics, Vol. 4 No. 1, pp.57-90, September, 2015. 1755-2184, ISSN print: 1755-2176 DBLP Index
[12] Sk. Saddam and D. P.Singh, “Genotype based classification of Crohn’s disease using various BPN training algorithms”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 5 No. 2, pp.22-29, July, 2015, Print ISSN: 2231-2021 e-ISSN: 2231-0312.
[13] D. P. Singh, J. P. Choudhury and M. De, “An Effort to Compare the Clustering Technique on Different Data Set Based On Distance Measure Function in the Domain of Data Mining”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 5, No. 1, pp. 1-8,January, 2015, Print ISSN: 2231-2021 e-ISSN: 2231-0312.
[14] D. P. Singh, J. P. Choudhury and M. De, “A Comparative Study to Select a Soft Computing Model for Developing the Knowledge Base of Data mining with Association Rule Formation by Factor Analysis”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 3, No. 3, pp.18-23, October, 2013, Print ISSN: 2231-2021 e-ISSN: 2231-0312.
[15] D. P. Singh, J. P. Choudhury and M. De, “An Effort to Developing the Knowledge Base in Data Mining by Factor Analysis and Soft Computing Methodology”, International Journal of Scientific & Engineering Research (IJSER), Vol. 4, No. 9, pp. 1912-1923, September, 2013, ISSN 2229-5518.
[16] D. P. Singh, J. P. Choudhury and M. De, “A comparative study on the performance of Fuzzy Logic, Bayesian Logic and neural network towards Decision Making” International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 4, No. 2, pp. 205-216, April, 2012. SSN online: 1755-8069 ISSN print: 1755-8050, Scopus Index
[17] D. P. Singh, J. P. Choudhury and M. De, “A Comparative Study to Select a Soft Computing Model for Knowledge Discovery in Data Mining”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 2, No. 2, pp. 6-19, April, 2012.
[18] D.P. Singh, J.P. Choudhury and M. De, “A Comparative Study on the performance of Soft Computing models in the domain of Data Mining,” International Journal of Advancements in Computer Science and Information Technology, Vol. 1, No. 1, pp. 35-49, September, 2011, ISSN 2277-9140.
[20] D.P. Singh, J.P. Choudhury and M. De, “Optimization of Fruit Quantity by comparison between Statistical Model and Fuzzy Logic by Bayesian Network”, PCTE, Journal of Computer Sciences, Vol. 8, No.1, Punjab, pp. 91-95, June-July, 2010.
[21] D. P. Singh, J. P. Choudhury and M. De, “Prediction Based on Statistical and Fuzzy Logic Membership Function”, PCTE, Journal of Computer Sciences, Vol. 8, No. 1, Punjab, pp. 86-90, June-July, 2010.
[22] D.P. Singh, J. P. Choudhury and M. De, “Performance Measurement of Neural Net Work Model Considering Various Membership Functions under Fuzzy Logic”, International Journal of Computer and Engineering, Vol. 1, No. 2, pp. 1-5, 2010, ISSN-0976-9587.
[23] D. P. Singh, J. P. Choudhury, “Assessment of Exported Mango Quantity by Soft Computing Model”, International Journal of Information Technology and Knowledge Management, Kurukshetra University, Vol. 2, No. 2, pp. 393-395, June-July, 2009, ISSN: 0973-4414.
[24] D.P Singh, “A Modified Bio Inspired BAT algorithm,” International Journal of Applied Metaheuristic Computing (IJAMC), Vol. 9 No. 1, pp. 60-77, 2018. Scopus Index.
[25] D. P. Singh, J. P. Choudhury and M. De, “A modified ACO for classification on different data set” International Journal of Computer Application, Vol.123, No. 6, pp-39-52, August 2015, ISSN 0975-8887.
[26]D. P. Singh, J. P. Choudhury and M. De, “Performance measurement of Soft Computing models based on Residual Analysis” International Journal for Applied Engineering and Research, Vol. 6, No. 5, Delhi, India, pp. 823-832, Jan-July, 2011, Print ISSN 0973-4562. Online ISSN 1087-1090. Scopus Index.

Debolina Dalui, Priyabrata Karmakar, Dharmpal Singh, Sonali Bhattacharyya, "Assessment of Chronic Kidney Disease using clustering techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.18, pp.53-61, 2019.

Purpose: MFCLs have been a better choice for myopia control followed by Ortho K lenses presumably by inducing a relative peripheral myopia. The study was done in emmetropes and myopes to assess the peripheral refraction (PR) profile at all possible eccentricities to have a better understanding of myopia control with the help of MATLAB. Methods: 5 emmetropes and 18 myopic adults of age 18-30 years with -0.50 to -6.00 D spherical component and less than 1.00 D astigmatism were fitted with commercially available center near multifocal soft contact lenses with low and high add. Center and peripheral refraction were measured under cyclopleigia at all possible eccentricities ranging from 0-180, 90-270, 30-210, 60-240, 120-300, 150-330 under four conditions: baseline (No lens wear); single vision spherical(SVCL); MFCL low add and MFCL high add by using Grand-Seiko WAM 5500 autorefractor with the help of MATLAB programming. Measurements are taken in Single Click Mode and High Speed Mode. Results are interpreted as a change in relative PR profile as refractive power vector components; M, J0 and J45 and analysed using a separate MATLAB program. Results: The results show statistically significant differences (p<0.05) between each condition for means of High speed mode and M value in each meridian. MFH showed a myopic defocus nasally but temporally there was hyperopic defocus. Conclusion: In comparison to high add, center near multifocal low add showed a more myopic periphery both temporally and nasally in young myopes.

Peripheral refraction, Relative peripheral defocus, Myopia control, Multifocal contact lens

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[17]. Gifford P, Gifford KL. The Future of Myopia Control Contact Lenses. Optom Vis Sci 2016 Apr [cited 2017 Jan 7];93(4):336–43
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[25]. Fedtke C, Ehrmann K, Falk D, Bakaraju RC, Holden BA. The BHVI-EyeMapper: peripheral refraction and aberration profiles. Optom Vis Sci 2014 Oct [cited 2017 Jan 6];91(10):1199–207
[26]. Hartwig A, Charman WN, Radhakrishnan H. Baseline peripheral refractive error and changes in axial refraction during one year in a young adult population. J Optom Jan [cited 2017 Jan 6];9(1):32–9
[27]. Giner A, Aldaba M, Arjona M, Vilaseca M, Pujol J. Assessment of multifocal contact lens over-refraction using an infrared, open-field autorefractor: A preliminary study. Cont Lens Anterior Eye [Internet]. 2015 Oct [cited 2017 Jan 7];38(5):322–6
[28]. Berntsen DA, Kramer CE. Peripheral defocus with spherical and multifocal soft contact lenses. Optom Vis Sci [Internet]. 2013 Nov [cited 2017 Jan 7];90(11):1215–24
[29]. Daniela Lopes-Ferreira et al. Peripheral refraction with dominant design multifocal contact lenses in young myopes.Journalof Optometry(2013) 6, 85-94
[30]. Gifford P, Gifford KL. The Future of Myopia Control Contact Lenses. Optom Vis Sci 2016 Apr [cited 2017 Jan 7];93(4):336–43
[31]. Queirós A, Lopes-Ferreira D, González-Méijome JM. Astigmatic Peripheral Defocus with Different Contact Lenses: Review and Meta-Analysis. Curr Eye Res [Internet]. 2016 Aug [cited 2017 Jan 9];41(8):1005–15
[32]. Huang J, Wen D, Wang Q, McAlinden C, Flitcroft I, Chen H, et al. Efficacy Comparison of 16 Interventions for Myopia Control in Children: A Network Meta-analysis. Ophthalmology 2016 Apr [cited 2016 Nov 21];123(4):697–708

K Mitra, V Ramasubramanian, "Matlab mapping of full field peripheral refraction profile with multifocal contact lenses in myopes", International Journal of Computer Sciences and Engineering, Vol.07, Issue.18, pp.62-74, 2019.

Data mining is the process of extracting hidden interesting patterns from massive database. It is used to extract the hidden information/knowledge from the real-life database. It also has use in the medical field. Thus want to apply concept of data mining into breast cancer prediction. According to many surveys, it has been observed that all over the world most of the women are dying in breast cancer in recent days. So, we have made an effort to design the knowledge base using clustering technique on the data. We have applied the hierarchical clustering and found the error is 2.13%. The K means and fuzzy C means will be applied in the future to minimize the error further.

Breast cancer, Data mining, Clustering, Hierarchical clustering

[1] Shweta kharya, ‘Using data mining techniques for diagnosis and prognosis of cancer disease’, INTERNATIONAL JOURNAL OF COMPUTER SCIENCE, ENGINEERING AND INFORMATION TECHNOLOGY (IJCSEIT), vol.2, no.2, april 2012.
[2] A.Priyanga, Dr.S.Prakasam, ‘ The role of data mining-based cancer prediction system (dmbcps) in cancer awareness’, INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND ENGINEERING COMMUNICATIONS- IJCSEC, vol.1 issue.1, december 2013.
[3] P.ramachandran, N. Girija, T.bhuvaneswari, ‘Early detection and prevention of cancer using data mining techniques’, INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS (0975 – 8887) volume 97– no.13, july 2014.
[4] Niharika saxena, prof. Meeta kumar, ‘Comprehensive study on data clustering for breast cancer prognosis and risk exposure’, INTERNATIONAL JOURNAL OF PURE AND APPLIED MATHEMATICS, volume 118 no. 24 2018.
[5] S.Syed Shajahaan, S.Shanthi , V.Manochitra, ‘Application of data mining techniques to model breast cancer data’, INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGY AND ADVANCED ENGINEERING, ISSN 2250-2459, iso 9001:2008 certified journal, volume 3, issue 11, november 2013.
[6] Peter Adebayo Idowu, Kehinde Oladipo williams, Jeremiah Ademola Balogun, Adeniran Ishola Oluwaranti, ‘Breast cancer risk prediction using data mining classification techniques’, SOCIETY FOR SCIENCE AND EDUCATION,UNITED KINGDOM,volume-3,issue-2,ISSN : 2054-7420.
[7] G . Purkait and D.P Singh, “An effort to optimize the error using statistical and soft computing methodologies" , Journal of Applied Computer Science & Artificial Intelligence Vol .1 No. 1, pp. 15-20, 2017.
[8] D. P., J. P. Choudhury and M. De, “An Enhance DE algorithm for analysis in data set”, International Journal of Data Science (IJDS), 2016 (Accepted).
[9] Dharmpal Singh, Abhishek Banerjee, Gopal Purkait , “Assessment of Heart DiseaseTypes Using Clustering Techniques Under the Domain of Data Mining”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol.6 Issue 03 pp. 10-16, Print ISSN: 2231-2021 e-ISSN: 2231-0312 .
[10] D. P. Singh, S. Sahana, SK Saddam Ahmed , "A Comparative Study to Assess the Crohn’s Diseasetype using Statistical and Fuzzy Logic Methodology", International Journal of Computer Sciences and Engineering, Volume-04, Issue-06, Page No (41-46), Aug -2016, E-ISSN: 2347-2693
[11] D. P. Singh, J. P. Choudhury and M. De, “An effort to select a preferable meta heuristic model for knowledge discovery in Data mining”, International Journal of mataheuristics, Vol. 4 No. 1, pp.57-90, September, 2015. 1755-2184, ISSN print: 1755-2176 DBLP Index
[12] Sk. Saddam and D. P.Singh, “Genotype based classification of Crohn’s disease using various BPN training algorithms”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 5 No. 2, pp.22-29, July, 2015, Print ISSN: 2231-2021 e-ISSN: 2231-0312.
[13] D. P. Singh, J. P. Choudhury and M. De, “An Effort to Compare the Clustering Technique on Different Data Set Based On Distance Measure Function in the Domain of Data Mining”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 5, No. 1, pp. 1-8,January, 2015, Print ISSN: 2231-2021 e-ISSN: 2231-0312.
[14] D. P. Singh, J. P. Choudhury and M. De, “A Comparative Study to Select a Soft Computing Model for Developing the Knowledge Base of Data mining with Association Rule Formation by Factor Analysis”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 3, No. 3, pp.18-23, October, 2013, Print ISSN: 2231-2021 e-ISSN: 2231-0312.
[15] D. P. Singh, J. P. Choudhury and M. De, “An Effort to Developing the Knowledge Base in Data Mining by Factor Analysis and Soft Computing Methodology”, International Journal of Scientific & Engineering Research (IJSER), Vol. 4, No. 9, pp. 1912-1923, September, 2013, ISSN 2229-5518.
[16] D. P. Singh, J. P. Choudhury and M. De, “A comparative study on the performance of Fuzzy Logic, Bayesian Logic and neural network towards Decision Making” International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 4, No. 2, pp. 205-216, April, 2012. SSN online: 1755-8069 ISSN print: 1755-8050, Scopus Index
[17] D. P. Singh, J. P. Choudhury and M. De, “A Comparative Study to Select a Soft Computing Model for Knowledge Discovery in Data Mining”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 2, No. 2, pp. 6-19, April, 2012.
[18] D. P. Singh, J. P. Choudhury and M. De, “ A Comparative Study on the performance of Soft Computing models in the domain of Data Mining,” International Journal of Advancements in Computer Science and Information Technology, Vol. 1, No. 1, pp. 35-49, September, 2011, ISSN 2277-9140.
[19] D. P. Singh, J. P. Choudhury and M. De, “Optimization of fruit quantity by different types of cluster technique”, Journal of Computer Sciences, Punjab, Vol. 9, No.1, pp. 17-28, June-July, 2011, ISSN 0973-4058.
[20] D. P. Singh, J. P. Choudhury and M. De, “Optimization of Fruit Quantity by comparison between Statistical Model and Fuzzy Logic by Bayesian Net work”, PCTE, Journal of Computer Sciences, Vol. 8, No.1, Punjab, pp. 91-95, June-July, 2010.
[21] D. P. Singh, J. P. Choudhury and M. De, “Prediction Based on Statistical and Fuzzy Logic Membership Function”, PCTE, Journal of Computer Sciences, Vol. 8, No. 1, Punjab, pp. 86-90 , June-July, 2010.
[22] D.P. Singh, J. P. Choudhury and M. De, “Performance Measurement of Neural Net work Model Considering Various Membership Functions under Fuzzy Logic”, International Journal of Computer and Engineering, Vol. 1, No. 2, pp. 1-5, 2010, ISSN-0976-9587.
[23] D. P. Singh, J. P. Choudhury, “Assessment of Exported Mango Quantity by Soft Computing Model”, International Journal of Information Technology and Knowledge Management, Kurukshetra University, Vol. 2, No. 2, pp. 393-395, June-July, 2009, ISSN : 0973-4414.
[24] D.P Singh, “ A Modified Bio Inspired BAT algorithm," International Journal of Applied Metaheuristic Computing (IJAMC), Vol. 9 No. 1,pp. 60-77, 2018. Scopus Index.
[25] D. P. Singh, J. P. Choudhury and M. De, “A modified ACO for classification on different data set” International Journal of Computer Application, Vol.123, No. 6, pp-39-52, August 2015, ISSN 0975-8887.
[26] D. P. Singh, J. P. Choudhury and M. De, “Performance measurement of Soft Computing models based on Residual Analysis”, International Journal for Applied Engineering and Research, Vol. 6, No. 5, Delhi, India, pp. 823-832, Jan-July, 2011, Print ISSN 0973-4562. Online ISSN 1087-1090. Scopus Index.

Priyabrata Karmakar, Debolina Dalui, Dharmpal Singh, Ira Nath, "Breast Cancer Prediction Using Clustering Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.18, pp.75-81, 2019.

According to the WHO there is 39 million blind present across the globe. These visually challenged group faces lot of difficulties in various task including basic orientation and mobility. We worked in two phase. In phase one this paper describes the design and implementation of a smart cane which is consists of Arduino and Ultrasonic Sensor. The stick is made up of a microcontroller, GPS Module, Buzzer, Vibrator, Vibrator goggles, Bluetooth and head phone connections. The Arduino can control the surroundings environmental obstacles by receiving the input signals. A buzzer is operated by a transducer and it converts an electric, oscillating signal in the audible range of 20Hz to 20 kHz. A vibrator motor is included along with buzzer to enhance its capacity to receive information’s from the environment in various formats. In addition to this the sound signal can also be transferred to the user’s ears with the help of output to an earphone. This walking stick can give response in various conditions like obstacle detections, wet surface detection, Heat detection, IR sensor, LDR sensor. In phase two we used our newly made smart cane for the practical demonstration. For this, we have used National Orientation and Mobility checklist (NOMA) pre and post usage of the cane among 20 blind individuals. Paired t test results shows there is significant changes (p<0.0001) in their mobility performances with this newly made cane. The features of light weight, easy to use and cheaper cost will make it more acceptable to the users.

Visually Challenged, Smart Cane, Arduino, Ultrasonic Sensor, NOMA

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C S Monira, V Biswas, S Adhikary, S Maity, P R Chakraborty, "Design and Implementation of Arduino and Ultrasonic Sensors based Smart Cane for Visually Challenged:A Practical approach", International Journal of Computer Sciences and Engineering, Vol.07, Issue.18, pp.82-90, 2019.

Assessment plays a major role in the field of prediction. If the Assessment cannot be selected properly, the prediction information becomes incorrect and this scope of work of futuristic planning becomes lost. Therefore it is needed to select an appropriate technique for the purpose of forecasting. A lot of soft computing model is being used in various application systems for the purpose of forecasting. The performance of fuzzy logic, in the field of soft computing, is being examined for the purpose of Assessment on the basis of average error. Here an effort is being used to select the proper soft computing technique to predict the futuristic information of quantity of exported tea to be exported in near future. Initially, the concept of least square base linear equation has been applied and therefore after concept of Fuzzy logic with Membership functions of soft computing has been used to optimize the error.

Logic, Soft Computing, Least Square Base Linear Equation

[1]. International Journal of Applied Evolutionary Computation (IJAEC), Vol. 8 No. 3 pp. 13-52, 2017 ACM Index.
[2]. G . Purkait and D.P Singh, “An effort to optimize the error using statistical and soft computing methodologies" , Journal of Applied Computer Science & Artificial Intelligence Vol .1 No. 1, pp. 15-20, 2017.
[3]. D.P Singh, “ A Modified Bio Inspired BAT algorithm," International Journal of Applied Metaheuristic Computing (IJAMC), Vol. 9 No. 1,pp. 60-77, 2018. Scopus Index
[4]. D. P., J. P. Choudhury and M. De, “An Enhance DE algorithm for analysis in data set”, International
[5]. Journal of Data Science (IJDS), 2016 (Accepted). Dharmpal Singh, Abhishek Banerjee, Gopal Purkait , “Assessment of Heart DiseaseTypes Using Clustering Techniques Under the Domain of Data Mining”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol.6 Issue 03 pp. 10-16, Print ISSN: 2231-2021 e-ISSN: 2231-0312 .
[6]. D. P. Singh, S. Sahana, SK Saddam Ahmed , "A Comparative Study to Assess the Crohn’s Diseasetype using Statistical and Fuzzy Logic Methodology", International Journal of Computer Sciences and Engineering, Volume-04, Issue-06, Page No (41-46), Aug -2016, E-ISSN: 2347-2693
[7]. D. P. Singh, J. P. Choudhury and M. De, “An effort to select a preferable meta heuristic model for knowledge discovery in Data mining”, International Journal of mataheuristics, Vol. 4 No. 1, pp.57-90, September, 2015. 1755-2184, ISSN print: 1755-2176 DBLP Index
[8]. Sk. Saddam and D. P.Singh, “Genotype based classification of Crohn’s disease using various BPN training algorithms”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 5 No. 2, pp.22-29, July, 2015, Print ISSN: 2231-2021 e-ISSN: 2231-0312.
[9]. D. P. Singh, J. P. Choudhury and M. De, “A modified ACO for classification on different data set” International Journal of Computer Application, Vol.123, No. 6, pp-39-52, August 2015, ISSN 0975-8887..
[10]. D. P. Singh, J. P. Choudhury and M. De, “An Effort to Compare the Clustering Technique on Different Data Set Based On Distance Measure Function in the Domain of Data Mining”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 5, No. 1, pp. 1-8,January, 2015, Print ISSN: 2231-2021 e-ISSN: 2231-0312.
[11]. D. P. Singh, J. P. Choudhury and M. De, “A Comparative Study to Select a Soft Computing Model for Developing the Knowledge Base of Data mining with Association Rule Formation by Factor Analysis”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 3, No. 3, pp.18-23, October, 2013, Print ISSN: 2231-2021 e-ISSN: 2231-0312.
[12]. D. P. Singh, J. P. Choudhury and M. De, “An Effort to Developing the Knowledge Base in Data Mining by Factor Analysis and Soft Computing Methodology”, International Journal of Scientific & Engineering Research (IJSER), Vol. 4, No. 9, pp. 1912-1923, September, 2013, ISSN 2229-5518.
[13]. D. P. Singh, J. P. Choudhury and M. De, “A comparative study on the performance of Fuzzy Logic, Bayesian Logic and neural network towards Decision Making” International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 4, No. 2, pp. 205-216, April, 2012. SSN online: 1755-8069 ISSN print: 1755-8050, Scopus Index
[14]. D. P. Singh, J. P. Choudhury and M. De, “A Comparative Study to Select a Soft Computing Model for Knowledge Discovery in Data Mining”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 2, No. 2, pp. 6-19, April, 2012.
[15]. D. P. Singh, J. P. Choudhury and M. De, “ A Comparative Study on the performance of Soft Computing models in the domain of Data Mining,” International Journal of Advancements in Computer Science and Information Technology, Vol. 1, No. 1, pp. 35-49, September, 2011, ISSN 2277-9140.
[16]. D. P. Singh, J. P. Choudhury and M. De, “Optimization of fruit quantity by different types of cluster technique”, Journal of Computer Sciences, Punjab, Vol. 9, No.1, pp. 17-28, June-July, 2011, ISSN 0973-4058.
[17]. D. P. Singh, J. P. Choudhury and M. De, “Performance measurement of Soft Computing models based on Residual Analysis”, International Journal for Applied Engineering and Research, Vol. 6, No. 5, Delhi, India, pp. 823-832, Jan-July, 2011, Print ISSN 0973-4562. Online ISSN 1087-1090. Scopus Index
[18]. D. P. Singh, J. P. Choudhury and M. De, “Optimization of Fruit Quantity by comparison between Statistical Model and Fuzzy Logic by Bayesian Net work”, PCTE, Journal of Computer Sciences, Vol. 8, No.1, Punjab, pp. 91-95, June-July, 2010.
[19]. D. P. Singh, J. P. Choudhury and M. De, “Prediction Based on Statistical and Fuzzy Logic Membership Function”, PCTE, Journal of Computer Sciences, Vol. 8, No. 1, Punjab, pp. 86-90 , June-July, 2010.
[20]. D.P. Singh, J. P. Choudhury and M. De, “Performance Measurement of Neural Net work Model Considering Various Membership Functions under Fuzzy Logic”, International Journal of Computer and Engineering, Vol. 1, No. 2, pp. 1-5, 2010, ISSN-0976-9587.
[21]. D. P. Singh, J. P. Choudhury, “Assessment of Exported Mango Quantity by Soft Computing Model”, International Journal of Information Technology and Knowledge Management, Kurukshetra University, Vol. 2, No. 2, pp. 393-395, June-July, 2009, ISSN : 0973-4414 .

A. Roy, S. Kundu, J. Bhattacharya, D. Singh, S. Sahana, Ira Nath, "Assessment of Exported Tea Quantity by Soft Computing Model", International Journal of Computer Sciences and Engineering, Vol.07, Issue.18, pp.91-97, 2019.

The Internet of things (IoT) refers to the concept of extending Internet connectivity beyond conventional computing platforms such as personal computers and mobile devices, and into any range of traditionally "dumb" or non-internet-enabled physical devices and everyday objects. Embedded with electronics, Internet connectivity, and other forms of hardware (such as sensors), these devices can communicate and interact with others devices over the Internet, and they can be remotely monitored and controlled. Specifically, when the automations are needed to be controlled using just voice commands from a remote location, that may be a large distance. This paper also includes detection of obstacles and the device come to a stop/waiting for the voice command from the control room (which is in remote location) and then take next decision.

IOT, wi-fi, Motionestimation, IR

[1] Christie, Derek & Koymans, Anne & Chanard, Thierry & Lasgouttes, Jean-Marc & Kaufmann, Vincent. (2016). Pioneering Driverless Electric Vehicles in Europe: The City Automated Transport System (CATS)
[2] Kohl, Christopher & Knigge, Marlene & Koleva, Galina & Böhm, Markus & Krcmar, Helmut. (2018). Anticipating acceptance of emerging technologies using twitter: the case of self-driving cars. Journal of Business Economics. 10.1007/s11573-018-0897-5.
[3] Pendleton, Scott & Uthaicharoenpong, Tawit & Jie Chong, Zhuang & Ming James Fu, Guo & Qin, Baixue & Liu, Wei & Shen, Xiaotong & Weng, Zhiyong & Kamin, Cody & Adam Ang, Mark & Tetsuya Kuwae, Lucas & Marczuk, Katarzyna & Andersen, Hans & Feng, Mengdan & Butron, Gregory & Chong, Zhuang Zhi & Jr, Marcelo & Frazzoli, Emilio & Rus, Daniela. 2015). Autonomous Golf Cars for Public Trial of Mobility-on-Demand Service. 10.1109/IROS.2015
[4] J. Haboucha, Chana & Ishaq, Robert & Shiftan, Yoram. (2017). User preferences regarding autonomous vehicles. Transportation Research Part C: Emerging Technologies. 78. 37-49. 10.1016/j.trc.2017.01.010.
[5] Szigeti, Szilárd & Csiszar, Csaba & Földes, Dávid. (2017). Information Management of Demand-responsive Mobility Service Based on Autonomous Vehicles. Procedia Engineering. 187. 483-491. 10.1016/j.proeng.2017.04.404.
[6] Monjezi Kouchak, Shokoufeh & Gaffar, Ashraf. (2018). Determinism in Future Cars: Why Autonomous Trucks are Easier. 10.1109/UIC-ATC.2017.8397598.

Swastik Bhattacharya, Moloy Dhar, Srotoswini Chanda, "Design of Driverless Pod Using Voice Commands : A Novel Approach", International Journal of Computer Sciences and Engineering, Vol.07, Issue.18, pp.98-100, 2019.

The near omni-directional hexapod vehicle is an autonomous robotic kit which can move in three-dimensional space. It is able to rotate any angle at its any state of movement without compromising its speed. Rotary-to-linear motion conversion is concerned with taking the rotational motion and torque from an actuator and producing a linear motion and force on the output. In this paper, an effort has been made to design the mechanical system of a robotic vehicle having six legs which can serve as a compliant mobile platform. The design has been validated through simulation. It has a limited number of degree-of-freedom to minimize the mechanical motion constraints as well as lower power consumption. The hexapod design of the vehicle offers great static stability during walking.

Hexapod vehicle, Omni-directional robotic kit, Pantograph leg, Stability margin

[1] K. P. Valavanis, G. J. Vachtsevanos, P. J. Antsaklis, “Technology and Autonomous Mechanisms in the Mediterranean: From Ancient Greece to Byzantium”, Proceedings of the European Control Conference, Greece, pp.263-270, 2007.
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[8] A. M. Hoover, E. Steltz, R. S. Fearing, “RoACH: An autonomous 2.4g crawling hexapod robot”, IEEE/RSJ International Conference on Intelligent Robots and Systems, France, pp.26-33, 2008.
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[12] M. O. Sorin, M. Niţulescu, “Hexapod Robot. Virtual Models for Preliminary Studies”, 15th International Conference on System Theory, Control and Computing, Sinaia, pp.1-6, 2011.
[13] G Jianhua, “Design and Kinematic Simulation for Six-DOF Leg Mechanism of Hexapod Robot” Proceedings of the IEEE International Conference on Robotics and Biomimetics, China, pp.625-629, 2006.

H. Masum, "Cost Effective Rotary to Linear Motion Conversion for a Near Omni-Directional Robotic Vehicle", International Journal of Computer Sciences and Engineering, Vol.07, Issue.18, pp.101-105, 2019.

A new HS (Harmony Search) meta-heuristic algorithm has been conceptualized using the musical process of searching or a perfect state of harmony. Musical performances seek to find pleasing harmony (a perfect state) as determined by an aesthetic standard, just as the optimization process seeks to find a global solution (a perfect state) as determined by an objective function. The pitch of each musical instrument determines the aesthetic quality; just as the objective function value as determined by the set of values assigned to each decision variable. The new HS (Harmony Search) meta-heuristic algorithm has been derived based on natural musical performance processes that occur when a musician searches for a better state of harmony, such as during jazz improvisation. In this paper, the concept of harmony search algorithm has been used in feature selection and based on it; literature survey for the recent few years has been done.

Harmony search, feature selection, optimization, pitch adjustment, bandwidth and harmony memory

[1]. Koti, P & Dhavachelvan, P & Kalaipriyan, T & Arjunan, S & J, Uthayakumar & Pothula, Sujatha. “Heart disease prediction using hybrid harmony search algorithm with levi distribution”. International Journal of Mechanical Engineering and Technology. 9. 980-994, 2018
[2]. Lomoush, Alaa & Alsewari, Abdul Rahman & Alamri, Hammoudeh & Z. Zamli, Kamal. “Comprehensive Review of the Development of the Harmony Search Algorithm and its Applications”. IEEE Access. PP. 1-1. 10.1109/ACCESS.2019.2893662, 2019.
[3]. L. A. M. Pereira, J. P. Papa and A. N. de Souza, "Harmony search applied for support vector machines training optimization," Eurocon 2013, Zagreb, pp. 998-1002, 2013.
[4]. Zhang, L., Xu, Y., Xu, G., et al. “A Catfish Effect Inspired Harmony Search Algorithm for Optimization”. International Journal of Nonlinear Sciences and Numerical Simulation, 14(6), pp. 413-422, 2013.
[5]. B. Aboulissane et al., "An Improved Harmony Search Algorithm for a Planar Parallel Robot Synthesis", International Journal of Engineering Research in Africa, Vol. 35, pp. 185-197, 2018.
[6]. Ouyang, H., Kong, X., Hu, B., Li, Z., & Liu, G. “Competition harmony search algorithm with dimension selection for continuous optimization problems”. 2018 Chinese Control And Decision Conference (CCDC), 6032-6037, 2018.
[7]. Toğan, Vedat & Daloglu, Ayşe & Karadeniz, Halil. “Optimization of trusses under uncertainties with harmony search”. Structural Engineering and Mechanics. 37. 10.12989/sem.2011.37.5.543, 2011.
[8]. Salcedo et al. “One-way urban traffic reconfiguration using a multi-objective harmony search approach”. Expert Systems with Applications. 40. 3341–3350. 10.1016/j.eswa.2012.12.043, 2013.
[9]. K. Lenin, B. Ravindranath Reddy, and M. Surya Kalavathi, "Harmony Search (HS) Algorithm for Solving Optimal Reactive Power Dispatch Problem," International Journal of Electronics and Electrical Engineering, Vol. 1, No. 4, pp. 269-274, December 2013.
[10]. Kumar, Dinesh & Shrutika. “Harmony Search Algorithm for Feature Selection in Face Recognition”. 250. 554-559. 10.1007/978-3-642-25734-6_95, 2011.
[11]. Geem, Zong Woo & Yong Chung, Sung & Kim, Jin-Hong. “Improved Optimization for Wastewater Treatment and Reuse System Using Computational Intelligence”. Complexity. 1-8. 10.1155/2018/2480365, 2018.
[12]. X. Z. Gao, V. Govindasamy, H. Xu, X. Wang, and K. Zenger. “Harmony search method: theory and applications”. Intell. Neuroscience 2015, Article 39, January 2015.
[13]. Tuo, Shouheng & Yong, Longquan & Deng, Fang`an. “A Novel Harmony Search Algorithm Based on Teaching-Learning Strategies for 0-1 Knapsack Problems”. The Scientific World Journal. 2014. 637412. 10.1155/2014/637412, 2014
[14]. Sabar, Nasser & Kendall, Graham. “Using harmony search with multiple pitch adjustment operators for the portfolio selection problem”. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. 499-503. 10.1109/CEC.2014.6900384, 2014.
[15]. Manjarres et al. “A survey on applications of the harmony search algorithm”. Engineering Applications of Artificial Intelligence. 26. 1818-1831. 10.1016 /j.engappai.2013.05.008, 2014.
[16]. Lee, Kang & Geem, Zong Woo. “A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice”. Computer Methods in Applied Mechanics and Engineering. 194. 3902-3933. 10.1016/j.cma.2004.09.007, 2005.
[17]. Scalabrin, Marlon & Parpinelli, Rafael & Benitez, Cesar & Lopes, Heitor. “Population-based harmony search using GPU applied to protein structure prediction”. International Journal of Computational Science and Engineering. 9. 106-118. 10.1504/IJCSE.2014.058703, 2014
[18]. Samraj, Andrews & Banu, Nizar. “Harmony Search PSO Clustering for Tumor and Cancer Gene Expression Dataset”. International Journal of Swarm Intelligence Research. 5. 1-22, 2014.
[19]. Al-Betar M.A., Khader A.T., Liao I.Y. “A Harmony Search with Multi-pitch Adjusting Rate for the University Course Timetabling”. In: Geem Z.W. (eds) Recent Advances In Harmony Search Algorithm. Studies in Computational Intelligence, vol 270. Springer, Berlin, Heidelberg, 2010.
[20]. Bagyamathi M., Inbarani H.H. “A Novel Hybridized Rough Set and Improved Harmony Search Based Feature Selection for Protein Sequence Classification”. In: Hassanien A., Azar A., Snasael V., Kacprzyk J., Abawajy J. (eds) Big Data in Complex Systems. Studies in Big Data, vol 9. Springer, Cham, 2015.
[21]. Yadav, Parikshit & Kumar, Rajesh & Panda, Sanjib & S. Chang, C. “An Improved Harmony Search Algorithm for Optimal Scheduling of the Diesel Generators in Oil Rig Platforms”. Energy Conversion and Management, Elsevier. 52. 893-902, 2011.
[22]. Zhao, Shi-Zheng & Suganthan, Ponnuthurai & Das, Swagatam. “Dynamic multi-swarm particle swarm optimizer with subregional harmony search”. IEEE CEC. 1-8. 10.1109/CEC.2010.5586323, 2010.
[23]. Zou, Dexuan & Gao, Liqun & Wu, Jianhua & Li, Steven & Li, Yang. “A novel global harmony search algorithm for reliability problems”. Computers & Industrial Engineering. 58. 307-316. 10.1016/j.cie.2009.11.003, 2010.

P. Chatterjee, D. Singh, A.B. Samaddar, B. Pal, "Literature Survey on Harmony Search for feature selection", International Journal of Computer Sciences and Engineering, Vol.07, Issue.18, pp.106-111, 2019.

Mode of locomotion of a robot can be chosen according to the condition of the terrain; these are wheeled, Legged and Crawler or Hybrid. Legged mobile robots are superior to conventional wheeled mobile robot (WMR) for rough and marshy terrain due to its terrain adaptability. They have also the ability to raise or lower bodies or tilt them by varying the length of its legs by bending knees. However, unlike WMR, legged robots are much complex and needs more comprehensive analysis for developing a realistic autonomous system. The goal of this research is to develop a path planning of powered and autonomous hexapod robotic kit which is capable of navigating different terrain within the mechanical motion limits. It has been observed that the kit is capable to take turn at sharp corner or when it needs to turn at a large angle at any instant.

Legged robots, Trajectory planning, Wheeled mobile robot

[1] J. A. T. Machado, M. F. Silva, “An overview of Legged Robots”, MME 2006–International Symposium on Mathematical Methods in Engineering, Turkey, 2006.
[2] R.B. McGhee, “Vehicular legged Locomotion”, Advance in Automation and Robotics, 1983.
[3] R. Altendorfer, N. Moore, H. Komsuoglu, M. Buehler, H.B. Brown Jr., D. Mcmordie, U. Saranli, R. Full, D.E. Koditschek, “RHex: A Biologically Inspired Hexapod Runner”, Autonomous Robots, Vol.11, pp.207–213, 2001.
[4] S. Zhang, X. Rong, Y. Li, B. Li, “A Composite COG Trajectory Planning Method for the Quadruped Robot Walking on Rough Terrain”, International Journal of Control and Automation, Vol.8, No 9, pp. 101-118, 2015.
[5] K. Hauser, T. Bretl, J. C Latombe, K. Harada, B. Wilcox, “Motion Planning for Legged Robots on Varied Terrain”, The International Journal of Robotics Research, Vol. 27, No. 11–12, pp. 1325–1349, 2008
[6] M. Wermelinger, P. Fankhauser, R Diethelm, M Hutter, P. Krusi, R. Siegwart, “Navigation Planning for Legged Robots in Challenging Terrain”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.
[7] M. H. Raibert, “Legged Robots”, Communications of the ACM, Vol. 29,No.6, pp.499-514,1986.
[8] S. M. Song, K. J. Waldron, “Machines that walk: the adaptive suspension vehicle”, MIT press, 1989.

H. Masum, "Trajectory Planning of a Hexapod Robotic Kit", International Journal of Computer Sciences and Engineering, Vol.07, Issue.18, pp.112-115, 2019.

This paper presents a method for solving posynomial geometric programming with fuzzy coefficients in a context of structural design model. We have been developed a two bar truss design model in fuzzy environment. By utilizing comparison of fuzzy numbers with different approaching method, the programming with fuzzy coefficients is reduced to the programming with constant coefficient. Then we can solve the two bar truss problem with fuzzy coefficients using a method to posynomial geometric programming. Finally, one comparative example is used to illustrate the advantage of the new method.

Fuzzy posynomial geometric programming, Yager’s method, A new approach for ranking of trapezoidal fuzzy numbers

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Samir Dey, Souvik Mukherjee, Rohit Nath, "Two Bar Truss Optimization using Fuzzy Posynomial Geometric Programming Technique", International Journal of Computer Sciences and Engineering, Vol.07, Issue.18, pp.116-120, 2019.