Journal Vol – 15 No -1, January 2020

Abstract:

Flooding is considered to be one of the worst natural catastrophes effecting million of people throughout the world. Flooding is referred to as potentially destructive abundance of water in a normally dry location. Flooding occurs when water inundate the areas adjacent to the river channel called as the floodplain, causing potential damage to the inhabitants of that area. Thus, a proper flood forecasting system including the development of flood zoning maps, the right of river bed and extent of inundation of floodplain are required for these areas. A composite river hydraulic model provide basis for the development of forecasting system providing timely management of future flood events. Several computer programs are used for the simulation of these models based on either one- or two-dimensional modelling approach. As there are variety of performance capabilities and access to the data required for the development of these hydraulic models, thus it is essential to choose the best software related to those models. A review of various wellknownmodels developed on different software for flood forecasting has been presented in this paper that address the performance of software and the analysis techniques adopted to produce final results.

Keywords:

Flooding,Flood Forecasting,Floodplain Zoning,Hydraulic Model,Dimensional Approach,HEC RAS,MIKE,

Refference:

I. Abdollahi, A., Bajestan, M. S., Hasounizadeh, H. & Rostami, S. 2007.
Comparing the results of Hec-Ras & Mike 11 models in a Segment of
Karoon River. 7th International River Engineering Conference. Shahid
ChamranUniversity, Ahwaz.
II. Aerts, J.C.J.H., Major, D., Bowman, M. and Dircke, P., 2009, Connecting
Delta Cities: Coastal Cities, Flood Risk Management and Adaptation to
Climate Change, VU University Press, Amsterdam p. 96.
III. Ahmad ShahiriParsa, Mohammad Heydari and Noor FarahainbtMohd
Amin., 2013, Introduction to floodplain zoning simulation models through
dimensional approach: International Journal of Advancements Civil
Structural and Environmental Engineering – IJACSE v. 1, p. 20-23
IV. Aronica, G., B. Hankin, and K. Beven. “Uncertainty and equifinality in
calibrating distributed roughness coefficients in a flood propagation model
with limited data.” Advances in Water Resources, 1998: 349-365.
V. Aronica, G., B. Hankin, and K. Beven. “Uncertainty and equifinality in
calibrating distributed roughness coefficients in flood propagation model
with limited data.” Advances in Water Resources, 1998: 349-365.
VI. Bales, J.D., and C.R. Wagner. “Source of uncertainty in flood inundation
maps.” Journal of Flood Risk Management, 2009: 139-147.
VII. Barkhordar, M. &Chavoshian, S. A. 2001. Floodplain zoning. Technical
Workshop on Nonstructural flood management.
VIII. Bemani, M., Torani, M. &Chezgheh, S. 2012. Determination of floodplain
zoning by HEC-RAS Model. Journal of Geography and Environmental
Hazards, No. I, 16.
IX. Booij, M.J., 2005, Impact of climate change on river flooding assessed with
different spatial model resolutions: Journal of Hydrology, v. 303, p. 176–
198.
X. Bouwer, L.M., Crompton, R.P., Faust, E., Höppe, P. and Pielke, Jr., R.A.,
2007, ‘Confronting Disaster Losses’, Science, (318) 753.
XI. Bronstert, A., 2003, Floods and climate change: interactions and impacts:
Risk Analysis, v. 23(3), p. 545-557.
XII. Burby, R.J., 2001, ‘Flood insurance and floodplain management: The US
experience’, Environmental Hazards, (3/3–4) 111–122.
XIII. DHI. MIKE 21 Flow Model: Hydrodynamic Module Scientific
Documentation. MIKE by DHI, 2009.
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 275-282
Copyright reserved © J. Mech. Cont.& Math. Sci.
Engr Uzair Ali et al
282
XIV. Fayazi, M., Bagheri, A., Sedghi, H., Keyhan, K. & Kaveh, F. 2010 Flood
plains simulation of Kashkanriver, Lorestan, Iran with MIKE11& MIKE
FLOOD. 8th International River Engineering Conference. Shahid
ChamranUniversity, Ahwaz.
XV. Fleenor, W. E. Evaluation of Numerical Models… HEC-RAS and
DHIMIKE 11.
XVI. Frank, E., A. Ostan, M. Coccato, and G.S. Stelling. “Use of an integrated
one-dimensional/two-dimensional hydraulic modelling approach for flood
hazard and risk mapping.” In River Basin Management, by R.A. Falconer
and W.R Blain, 99-108. Southhampton, UK: WIT Press, 2001.
XVII. Garrote, L. and Bras, R.L., 1995. A distributed model for real-time flood
forecasting using digital elevation models. Journal of Hydrology, 167(1-4):
279-306.
XVIII. Lin, B., J.M. Wicks, R.A. Falconer, and K. Adams. “Integrating 1D and 2D
hydrodynamic models for flood simulation.” Preceedings of the Institution
of Civil Engineers. Water Management Incorporated, 2006. 19-25.
XIX. Mashhadi, S. S., Rad, M. A., Memari, A. R. & Pour, S. J. 2012.
Determining of limits of river bed and its flow by using HEC-HMS 3.1.0
and Arcview 3.3 software (case study: Kakhk river in Gonabad). The first
National Conference on Desertification.
XX. Mason, D.C., D.M. Cobby, M.S. Horritt, and P.D. Bates. “Floodplain
friction parameterization in two-dimensional river flood models using
vegetation heights derived from airborne scanning laser altimetry.”
Hydrological Processes, 2003: 1711-1732.
XXI. National Disaster Management Authority (NDMA), annual report 2010:
Available at http:// www.ndma.gov.pk/.
XXII. Pappenberger, F., K. Beven, M. Horritt, and S. Blazkova. “Uncertainty in
the calibration of effective roughness parameters in HEC-RAS using
inundation and downstream level observations.” Journal of Hydrology,
2004: 46-69.
XXIII. Patro, S., C. Chatterjee, S. Mohanty, R. Singh, and N.S. Raghuwanshi.
“Flood Inundation Modeling using MIKE FLOOD and Remote Sensing
Data.” Journal of the Indian Society of Remote Sensing, 2009: 107-118.
XXIV. S. Néelz and G Pender. 2009, Desktop review of 2D hydraulic modelling
packages.
XXV. S. Néelz and G Pender. 2010, Benchmarking of 2D hydraulic modeling
packages
XXVI. Smemoe, C.M., E.J. Nelson, A.K. Zundel, and A.W. Miller. “Demonstrating
Floodplain Uncertainty Using Flood Probability Maps.” Journal of the
American Water Resources Association, 2007: 359-371.
XXVII. USGS. The National Map Seamless Server. August 19, 2008.
http://seamless.usgs.gov/products/3arc.php (accessed June 2015).
XXVIII. Werner, M.G.F. “Impact of Grid Size in GIS Based FLood Extent Mapping
Using a 1D Flow Model.” Phys. Chem. Earth Part B-Hydrol. Oceans
Atmos., 2001: 517-522.

View Download

Abstract:

Spectral and spatial two-dimensional optical code division multiple access (2D-OCDMA) systems cannot support low cost passive optical networks (PON) due to the extensive use of optical fiber media. Consequently, spectral and temporal OCDMA systems are explored to provide the required system capacity. Maintaining an efficient cross correlation values between the adjacent codes is of primal importance to ensure required system capacity at relatively simple architecture. To develop such a system this paper focuses on the design of a 2D spectral and temporal OCDMA coding scheme. The proposed scheme mitigates the effect of interfering users by utilizing fixed in phase cross correlation code called diagonal eigenvalue unity (DEU) code along the spectral domain; whereas zero cross correlation (ZCC) code is adapted at the temporal domain. Analysis shows that the proposed combination significantly mitigates the contribution of interfering users and reduce the impact of cross correlation. This can lead to a system with relatively high transmission capacity and simple architecture for implementation at the cost sensitive access domain.

Keywords:

Optical code division multiple access,two dimensional codes,diagonal eigenvalue unity code,zero cross correlation code,

Refference:

I. H. Ghafouri-Shiraz, M.M. Karbassian, “Optical CDMA networks:
principles, analysis and applications”, John Wiley & Sons, 2012.
II. K. S. Nisar, “Construction of zero cross correlation code using a type of
anti-diagonal-identity-column block matrices.”Optik-International Journal
for Light and Electron Optics. vol. 125, no. 21, pp. 6586-6588, 2014.

Mansoor Qadir et al
290
III. M. Najjar, N. Jellali, “Spectral/spatial optical CDMA code based on
Diagonal Eigenvalue Unity”, Optical Fiber Technol.,vol. 38, pp. 61 –69,
2017.
IV. N.D. Keraf, S. Aljunid, A. Arief, P. Ehkan, “The evolution of double weight
codes family in spectral amplitude coding ocdma”, Advanced Computer and
Communication Engineering Technology, Springer, pp. 129 –140, 2015.
V. R. Kadhim, H.A. Fadhil, S.A. Aljunid, M. Razalli, “A new two dimensional
spectral/ spatial multi-diagonal code for non-coherent OCDMA systems”,
Opt. Commun., vol. 329, pp. 28 –33, 2014.
VI. W.A. Imtiaz, H.Y. Ahmed, M. Zeghid, Y. Sharief, M. Usman, “Design and
implementation of two-dimensional enhanced multi-diagonal code for high
cardinality OCDMA-PON”, Arab. J. Sci. Eng., vol. 3, pp. 1 –18, 2019.

View Download

Abstract:

This paper is about the technique used for removal of ECG Signals Artifacts Using Multistage Adaptive Filtering. Electrocardiogram (ECG) is the diagnostic tool to monitor rhythm of heart activity. it is of low amplitude and contain numerous noise which includes power line interference, baseline drift , movement artifacts and electrosurgical noise. For better diagnostic and treatment of cardiac patient the removal of such noise are very much important. Initially various method were proposed to remove the artifacts for better understanding of cardiac problem. These were static or fixed filters i.e. Band pass Low pass or High pass which based on the nature of the noise. The static filters possess fixed filter coefficients which makes it strenuous to eliminate time varying noise from the signals. To overcome this shortcoming of the fixed filters, various adaptive filtering procedures have been introduced. Since the ECG signal suffers from several artifacts at a time, which makes a single stage adaptive filter unsuitable for multiple noise signals removal. This paper presents a Multistage Modified Normalized Least Mean Square (MNLMS) algorithm for the eradication of multiple artifacts from signals of ECG. The results of the suggested algorithm are compared with existing adaptive algorithms including Multistage LMS,MNLS ,CNN,DNN including Signal to Noise ratio (SNR), convergence rate as well as the computational time, which elaborate the effectiveness of the suggested algorithm. After the removal of noise, db’6 wavelets are used for the detection of features (PQRST) of ECG wave because wavelet tree offers a very good time-frequency resolution analysis which is not possible with the Fourier transform.

Keywords:

ECG,Noise Removal,Adaptive filtering algorithms,Feature Extraction,Neural Networks,

Refference:

I. A. B. Sankar, D. Kumar, and K. Seethalakshmi, “Performance study of various
adaptive filter algorithms for noise cancellation in respiratory signals,” Signal
processing: An international journal (SPIJ), vol. 4, no. 5, p. 267, 2010.
II. Brij N. Singh and Arvind K. Tiwari, “Optimal selection of wavelet basis
function applied to ECG signal denoising,” Journal of Digital Signal Processing,
vol. 16, no. 3, pp. 275-287, May 2006.
III. Bandi, IK “Simulation of Adaptive Noise Canceller for an ECG signal
Analysis,” ACEEE Int. J. on Signal & Image Processing, vol. 3, no. 1, June
2012
IV. Dr. K. L. Yadav and Sachin Singh, ““Performance evaluation of different
adaptive filters for ECG signal processing,” International Journal On Computer
Science and Engineering, vol. 40, no. 5, pp. 1880-1883, 2010
V. Ervin Domazet, Marjan Gusev and Sasko Ristov Ss. Cyril and Methodius
“Dataflow DSP Filter for ECG Signals” University, Faculty of Computer
Science and Engineering,1000 Skopje, Macedonia
VI. Gabriel Khan, “Rapid ECG Interpretation,” Humana press, vol. 5, no. 10, pp.
185-195, 2008
VII. G.B. Moody and R.G. Mark, “The MIT-BIH Arrhythmia Database,” in
International conference on Computers in Cardiology, Chicago, September
1990, pp. 185-188.
VIII. J. A. Sharp, Data flow computing: theory and practice. Intellect Books, 1992
IX. Journal of Electro cardiology Volume 51, Issue 2, March–April 2018, Pages
265-275
X. Multistage Adaptive filter for ECG Signal Processing .Conference Paper ·
March 2017
XI. T. He, G. Clifford, and L. Tarassenko, “Application of independent component
analysis in removing artifacts from the electrocardiogram,” Neural Computing
& Applications, vol. 15, no. 2, pp. 105–116, 2006.

View Download

Abstract:

In this research the steel fibers replaced with 1% of coarse aggregate and 20% of fine aggregate with bacillus subtilis and calcium lactate. Compressive tests on three sets of specimen, control with no replacement, specimens with bacillus subtilis and specimens with bacillus and steel fibers. Experimental results show that the compressive strength loss of bacterial specimens compared to control specimens was up to 40 % which was regain in the third sets of cylinders containing steel fibers up to 35% showing that the compressive strength of the steel fiber specimens is 90% of the control specimens.

Keywords:

Bacilluss subtilis,steel fibers,calcium lactate,concrete mix,expanded clay particles,control specimen,

Refference:

I. Ahn, T., & Kishi, T. (2010). Crack Self-healing Behavior of Cementitious
Composites Incorporating Various Mineral Admixtures. 8(2), 171–186.
II. Breugel, K. Van. (2012). SELF-HEALING MATERIAL CONCEPTS AS
SOLUTION FOR AGING SELF-HEALING MATERIAL CONCEPTS AS
SOLUTION FOR AGING.
III. Depaa, R. A. B., & Kala, T. F. (2015). Experimental Investigation of Self
Healing Behavior of Concrete using Silica Fume and GGBFS as Mineral
Admixtures. 8(December). https://doi.org/10.17485/ijst/2015/v8i36/87644
IV. Jonkers, H M. (2011). Bacteria-based self-healing concrete. 56(1), 1–12.
V. Jonkers, Henk M., & Schlangen, E. (2008). Development of a bacteria-based
self healing concrete. Proceedings of the International FIB Symposium 2008
– Tailor Made Concrete Structures: New Solutions for Our Society, 109.
https://doi.org/10.1201/9781439828410.ch72
VI. Luhar, S., & Gourav, S. (2015). A Review Paper on Self Healing Concrete.
5(3), 53–58. https://doi.org/10.5923/j.jce.20150503.01

View Download

Abstract:

Climate change is variation in patterns of weather that lasts from decades to centuries. The increasing levels greenhouse gases are playing key role in producing global warming and it has decisive impact on hydrological cycle of earth. The resulting temporal and spatial availability of water makes it imperative to exercise innovative way of water conservation strategy .In this study ground water rehabilitation strategy is proposed for groundwater recharge using excess storm water to be injected in recharge wells at Narai Khuwar. The current groundwater levels of tube wells and well logs of Hayatabad were obtained from Peshawar Development Authority for over 70 tube wells. The hydrological study has been performed using rainfall-runoff model WIN TR 20 by using rainfall data of last 40 years and other hydrological and hydro-geological parameters. The UHG of different time intervals (2 to 200 years) were obtained to determine the availability of water. The subsurface geology was determined by conducting resistivity test. Extensive numerical modeling was performed for current and future water levels in these wells using MODFLOW. The calibrated model was then used in simulation mode and an estimate of water levels in the study area was made for 30 years (2019 - 2049) with and without application of proposed recharge strategy. The study indicates that mitigation measures are required to arrest the rapid water decline in Hayatabad.

Keywords:

Climate change,Groundwater recharge,Numerical modeling,Rehabilitation strategy,UHG (Unit Hydrograph),Resistivity Test,Rainfall runoff model WIN TR2,

Refference:

I. Abulohom, M.S., Shah, S.M.S. and Ghumman, A.R., 2001. Development of a
rainfall-runoff model, its calibration and validation. Water resources
management, 15(3), pp.149-163.
II. B.L.Mansouri and L.EL Mezouary. (2015). Enhancement of groundwater
potential by aquifer artificial recharge techniques: an adaptation to climate
change. Proceedings of the 11th Kovacs Colloquium, Paris, France, June
2014). IAHS Publ. 366, 2015
III. Dr G Rasul , M Afzal . Maida Z. Syed Ahsan A B. “Climate change in
Pakistan Focused on Sindh Province” Pakistan Metrological Department.
Technical Report No. PMD-25/2012
IV. Farooqi, Anjum Bari, Azmat Hayat Khan, and Hazrat Mir. “Climate change
perspective in Pakistan.” Pakistan J. Meteorol 2.3 (2005).
V. Ghazaw, Y.M., Ghumman, A.R., Al-Salamah, I. and Khan, Q.U., 2014.
Investigations of impact of recharge wells on groundwater in Buraydah by
numerical modeling. Arabian Journal for Science and Engineering, 39(2),
pp.713-724.
VI. Harbaugh, A.W., Banta, E.R., Hill, M.C. and McDonald, M.G., 2000.
MODFLOW-2000, The U. S. Geological Survey Modular Ground-Water
Model-User Guide to Modularization Concepts and the Ground-Water Flow
Process. Open-file Report. U. S. Geological Survey, (92), p.134.
VII. Hashemi, H. and Berndtsson, R. .2012 “Natural vs. artificial groundwater
recharge, quantification through inverse modeling”.Hydrol. Earth Syst. Sci.,
17, 637–650.
VIII. Hojat, A., S. K. Nasab and S. Makooni. 2011. Successful use of Geoelectrical
survey in Area 3 of Gol-e-Gohar Iron ore mine. Mine Water Environ.
30:208–215.
IX. M. C. Sashikkumar, S. Selvam , V. Lenin Kalyanasundaram and
J. Colins Johnny 2017 “GIS based groundwater modeling study to assess the
effect of artificial recharge: A case study from Kodaganar river basin,
Dindigul district, Tamil Nadu” Journal of the Geological Society of India
,January 2017, Volume 89, Issue 1, pp 57–64
X. ManouchehrChitsazan, Ali Movahedian. (2015). Evaluation of Artificial
Recharge on Groundwater Using MODFLOW Model (Case Study: Gotvand
Plain-Iran). Journal of Geoscience and Environment Protection, 2015, 3, 122-
132
XI. Pakistan Metrological Department “Monthly Rainfall Record of Peshawar”
(2015)
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 312-327
Copyright reserved © J. Mech. Cont.& Math. Sci.
Engr Syed Shujaat Ali et al
327
XII. Pennan Chinnasamy, Gourav Misra, Tushaar Shah, Basant Maheshwari,
Sanmugam Prathapar. (2015). Evaluating the effectiveness of water
infrastructures for increasing groundwater recharge and agricultural
production – A case study of Gujarat, India. Agricultural Water Management
158 (2015) 179–188
XIII. Peshawar Development Authority “Official Record of Water level in
Hayatabad tube wells”pg 1-4 (2014)
XIV. S. Selvam, Farooq A. Dar N. S. Magesh C. Singaraja S. Venkatramanan and
S. Y. Chung 2016 “Application of remote sensing and GIS for delineating
groundwater recharge potential zones of Kovilpatti Municipality, Tamil Nadu
using IF technique” une2016, Volume 9, Issue 2, pp 137–150
XV. User manual Processing MODFLOW 8.04

View Download

Abstract:

Rainfall is an extrinsic factor for the collapse of sloping terrain in Western Ghats of Kerala. Careful analysis of rainfall induced landslide is very important as people in the area have serious threat from landslides. In-depth assessments of variation of pore water pressure change in slopes during avalanche rainfalls are required for the purpose of mitigation. Soil water characteristic curve was prepared by field and laboratory tests so that various properties of unsaturated soil could be estimated. As suction distribution and rainfall infiltration were influenced by the ratio of rainfall intensity to saturated hydraulic conductivity, numerical models were analysed for various ratio of rainfall intensities. Variations of pore water pressure across different sections of the slope and reduction of factor of safety with respect to time and rainfall intensities were analysed. The results of the analyses can be applied in practice for determining the probability of landslide hazards in areas vulnerable to heavy rainfall and consequently damage from landslides.

Keywords:

Rainfall induced landslide,water pressure,suction distribution,numerical assessments,slope failure,

Refference:

I. Anderson MG, Lloyd DM. 1991. Using a combined slope hydrology-stability
model to develop cut slope design charts. Proc Inst Civ Eng. 2:705_718.
II. Chen L, Young MH (2006) Green-Ampt infiltration model for sloping
surface. Water Resour Res 42:1–9
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 328-338
Copyright reserved © J. Mech. Cont.& Math. Sci.
Kavin kumar C et al
338
III. De Campos, L.E.P. & Menezes, M.S.S. 1991. A proposed procedure for slope
stability analysis in tropical soils. In Proc., 6th Int. Symp. on Landslides,
Christchurch, New Zealand, Balkema, Rotterdam, The Netherlands, Vol. 2:
1351-1355.
IV. Dhakal, A.S., and Sidle, R.C., (2004), ” Pore water pressure assessment in a
forest watershed: simulations and distributed field measurements related to
forest practices”, Water Resources Research. 40, W02405.
V. Fredlund, D.G., Xing, A. and Huang, S. (1994). Predict-ing the Permeability
Function for Unsaturated Soils Using the Soil-Water Character Curve.
Canadian Geo-technical Journal. 31(3): 533-546
VI. Green, W. H., and C. A. Ampt. 1911. “Studies on Soil Physics: Flow of Air
and Water through Soils.” Journal of Agricultural Science 4: 1–24.
VII. Griffiths, D. V., Huang, J., and de Wolfe, G. F. (2011). “Numerical and
analytical observations on long and infinite slopes.” Int. J. Numer. Anal.
Methods Geomech., 35(5), 569–585.
VIII. Muntohar, A. M., and H. J. Liao. 2010. “Rainfall Infiltration: Infinite Slope
Model for Landslides Triggering by Rainstorm.” Natural Hazards 54:967–
984.
IX. Ng, C. W. W., and Shi, Q. (1998). “Influence of rainfall intensity and
duration on slope stability in unsaturated soils.” Q. J. Eng. Geol.
Hydrogeol.31 (2), 105–113.
X. Rahardjo H, Ong TH, Rezaur RB, Leong EC. 2007. Factors controlling
instability of homogeneous soil slopes under rainfall. J Geotech Geoenviron
Eng ASCE. 133:1532_1543
XI. Rahardjo H., Leong E. C., Deutsher M.S., Gasmo J.M. and Tang S.K. (2000)
Rainfall-Induced Slope Failures, Geotechnical Engineering Monograph 3,
NTU-PWD Geotechnical Research Centre, NTU, Singapore.
XII. Rahardjo, H. & Fredlund, D.G. 1995a. Procedures for slope stability analyses
involving unsaturated soils. In Developments in deep foundations and ground
improvement schemes, Balkema, Rotterdam, The Netherlands: 33-56.
XIII. Van Beek, L.P.H (2002), “Assessment of the influence of changes in Landuse
and Climate on Landslide Activity in a Mediterranean Environment” [PhD
thesis]: Utrecht, The Netherlands, University of Utrecht.
XIV. Van Genuchten, M Th. 1980. “A Closed-Form Equation Predicting the
Hydraulic Conductivity of Unsaturated Soils.” Soil Science Society of
America 44: 892–898.

View Download

Abstract:

The problem of mining numerical data and to propose different approaches to efficiently apply clustering to such data According to an aspect of the method the Mean Base Divisive Clustering (MB-DivClues) method is developed to categories unstructured data into various groups. The a constructive mean-based divisive clustering method is developed to reduce comparison includes several steps which includes identification of mean value from a given dataset, to find the arithmetic mean value of base cluster-infrequent attribute and storing the found mean value in a tree which is represented as root. Further the steps include comparing the objects in the dataset with the said mean value and stored in the nearest cluster. A new cluster is created to place the sorted object in new cluster. In the process of proposed method includes steps of shifting the object value to the left cluster when it is less than the mean value, shifting the object value to right cluster when it is greater than the mean value and repeating the above procedure until singleton cluster is picked from the given dataset. Wherein before applying divisive Clustering method, initially all the data objects are available in a single cluster and a mean value is calculated on the dataset.

Keywords:

Classification,Clustering,Data mining,Divisive Methods,Mean Based Divisive Method (MB-DivClues),

Refference:

I. A. Babenko and V. Lempitsky, “Tree Quantization for Large-Scale Similarity
Search and Classification,” in CVPR, 2015.
II. A. K. Agogino and K. Tumer. Ensemble clustering with voting active labels. Pattern
Recognition Letters, 29(14):1947–1953, 2008.
III. Brandt, “Transform coding for fast approximate nearest neighbor search in high
dimensions,” in IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 2010, pp. 1815–1822.
IV. J. Wang, T. Zhang, J. Song, N. Sebe, and H. T. Shen, “A Survey on Learning to
Hash,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13,
no. 9, 2017.
V. M. Laszlo and S. Mukherjee, “Minimum Spanning Tree Partitioning
Algorithm for Micro aggregation”, IEEE Trans. on Knowledge and Data
Engg., 17(7), 2005, 902- 911.
VI. Mohamed A. Mahfouz, d M. A. Ismail. Fuzzy Relatives of the CLARANS
Algorithm With Application to Text Clustering. International Journal of
Electrical and Computer Engineering. 2009 370-377.
VII. N. Paivinen, “Clustering with a Minimum Spanning Tree of Scale- free-like
Structure”, Pattern Recognition Letters, Elsevier, 26(7), 2005, 921-930
VIII. P.Praveen, B.Rama, “An Efficient Smart Search Using R Tree on Spatial Data”,
Journal of Advanced Research in Dynamical and Control Systems, Issue
4,ISSN:1943-023x
IX. P. Praveen, B. Rama and T. Sampath Kumar, “An efficient clustering algorithm
of minimum Spanning Tree,” 2017 Third International Conference on Advances
in Electrical, Electronics, Information, Communication and Bio-Informatics
(AEEICB), Chennai, 2017, pp. 131135. doi: 10.1109/AEEICB.2017.7972398
X. Pushpa.R. Suri, Mahak. Image Segmentation With Modified K-Means Clustering
Method. International Journal of Recent Technology and Engineering 2012 176-
179.
XI. S.Vijayarani, S.Nithya. An Efficient Clustering Algorithm for Outlier Detection.
International Journal of Computer Applications. 2011 22-27.
XII. Xiaochun Wang, Xiali Wang and D. Mitchell Wilkes, “A Divide-and conquer
Approach for Minimum Spanning Tree-based Clustering”, IEEE Transactions on
Knowledge and Data Engg., 21, 2009.
XIII. Y. Chen, T. Guan, and C. Wang, “Approximate nearest neighbor search by
residual vector quantization,” Sensors, vol. 10, no. 12, pp. 11259– 11273, 2010.
XIV. Yu-Chen Song, J.O’Grady, G.M.P.O’Hare, Wei Wang, A Clustering Algorithm
incorporating Density and Direction, IEEE Computer Society, CIMCA 2008
XV. Zhang, D. Chao, and J. Wang, “Composite Quantization for Approximate
Nearest Neighbor Search,” in ICML, 2014.

View Download