Archive

Abstract:

The difficulty of fusion splicing hollow-core photonic Crystal fibers (HCPCFs) and solid-core (SC-PCFs) to conventional step index single mode fiber (SMF) has severely limited the implementation of PCFs. To make PCFs morefunctional, we have developed a method for splicing HC-PCF and SC-PCF toa SMF using a commercial arc splicer. A repeatable, robust, low-loss splice between the PCFs and SMF is demonstrated. In this paper, comprehensive theoretical, simulation and empirical -MZI based on splicing PCF between two single mode fibers. Adopting of MZI based on SMF and PCF is presented. Theoretical model of computing MFD and relative hole size is used to investigate losses with respect to splicing region. In addition, modeling of MZI using Opti Bpm yields a flexible solution to investigate the splicing effects and finding the optimum point of losses. Both MZI based on SC-PCF and HC-PCF are used in this article. In this section, optimization of splice loss of joints between PCF and SMF is carried out. For the analysis, we use two solvers OptiBPM and OptiMode, and codes written by MATLAB software.

Keywords:

Fusion splicing fibers,microstructure fabrication,photonic crystal fiber,

Refference:

I. A. D. Yablon and R. T. Bise, “Low-loss high-strength microstructured fiber
fusion splices using GRIN fiberlenses,” IEEE Photon. Technol. Lett. 17(1),
118–120 (2005).
II. A. Ishikura, Y. Kato, T. Ooyanagi, and M. Myauchi, “Loss factors analysis
for single-mode fiber splicing withoutcore axis alignment,” J. Lightwave
Technol. 7(4), 577–583 (1989).
III. A. Ortigosa-Blanch, J. C. Knight, W. J. Wadsworth, J. Arriaga, B. J. Mangan,
T. A. Birks, and P. St. J. Russell,“Highly birefringent photonic crystal fibers,”
Opt. Lett. 25(18), 1325–1327 (2000).
IV. B. Bourliaguet, C. Paré, F. Emond, A. Croteau, A. Proulx, and R. Vallée,
“Microstructured fiber splicing,” Opt.Express 11(25), 3412–3417 (2003).
V. T. S. Mansour, and F. M.Abdulhussein, “Dual measurements of pressure and
temperature with fiber Bragg grating sensor,” Al-Khwarizmi Engineering
Journal11(2), 86–91 (2015).
VI. G. E. Town and J. T. Lizier, “Tapered holey fibers for spot-size and
numerical-aperture conversion,” Opt. Lett.26(14), 1042–1044 (2001).
VII. G. Fu, W. Jin, X. Fu, and W. Bi, “Air-holes collapse properties of photonic
crystal fiber in heating process by CO2laser,” IEEE Photon. Jour. 4(3), 1028–
1034 (2012).
VIII. J. C. Knight, “Photonic crystal fibres,” Nature 424(6950), 847–851 (2003).
IX. F. Q. Mohammed, and T. S. Mansour, “Design and Implementation Tunable
Band Pass Filter based on PCF-Air Micro-cavity FBG Fabry-Perot
Resonator,” Iraqi Journal of Laser. 18(1), 13–23 (2019).
X. J. H. Chong and M. K. Rao, “Development of a system for laser splicing
photonic crystal fiber,” Opt. Express11(12), 1365–1370 (2003).
XI. T. S. Mansour, and D. H.Abbass, “Chemical Sensor Based on a Hollow-Core
Photonic Crystal Fiber,” Iraqi Journal of Laser. 12(A), 37–42 (2013).
XII. J. Lægsgaard and A. Bjarklev, “Reduction of coupling loss to photonic
crystal fibers by controlled hole collapse: anumerical study,” Opt. Commun.
237(4-6), 431–435 (2004).

XIII. J. T. Kristensen, A. Houmann, X. M. Liu, and D. Turchinovich, “Low-loss
polarization-maintaining fusion splicingof single-mode fibers and hollowcore
photonic crystal fibers, relevant for monolithic fiber laser
pulsecompression,” Opt. Express 16(13), 9986–9995 (2008).
XIV. J. T. Lizier and G. E. Town, “Splice losses in holey optical fiber,” IEEE
Photon. Technol. Lett. 13(3), 466–467(2001).
XV. L. M. Xiao, M. S. Demokan, W. Jin, Y. P. Wang, and C. L. Zhao, “Fusion
splicing photonic crystal fibers andconventional single-mode fibers:
microhole collapse effect,” J. Lightwave Technol. 25(11), 3563–3574 (2007).
XVI. L. Xiao, W. Jin, and M. S. Demokan, “Fusion splicing small-core photonic
crystal fibers and single mode fibers byrepeated arc discharges,” Opt. Lett.
32(2), 115–117 (2007).
XVII. M. L. V. Tse, H. Y. Tam, L. B. Fu, B. K. Thomas, L. Dong, C. Lu, and P. K.
A. Wai, “Fusion splicing holey fibersand Single-Mode Fibers: A simple
method to reduce loss and increase strength,” IEEE Photon. Technol. Lett.
21(3),164–166 (2009).
XVIII. P. J. Bennett, T. M. Monro, and D. J. Richardson, “Toward practical holey
fiber technology: fabrication, splicing,modeling, and characterization,” Opt.
Lett. 24(17), 1203–1205 (1999).
XIX. P. St. J. Russell, “Photonic-crystal fibers,” J. Lightwave Technol. 24(12),
4729–4749 (2006).
XX. R. Thapa, K. Knabe, K. L. Corwin, and B. R. Washburn, “Arc fusion splicing
of hollow-core photonic bandgapfibers for gas-filled fiber cells,” Opt.
Express 14(21), 9576–9583 (2006).
XXI. T. A. Birks, J. C. Knight, and P. S. Russell, “Endlessly single-mode photonic
crystal fiber,” Opt. Lett. 22(13), 961–963 (1997).
XXII. Z. Xu, K. Duan, Z. Liu, Y. Wang, and W. Zhao, “Numerical analyses of
splice losses of photonic crystal fibers,”Opt.Commun. 282(23), 4527–4531
(2009).

View Download

Abstract:

In recent days the fast-growing disease in most of the world's is breast cancer especially in women and, according to global statistics, represents a different level of cases that are hitting cancer and illnesses associated with related diseases, rendering it a major public health issue currently in the community. The diagnosis and treatment for this significantly contributed by the machine learning techniques that can be applied for patient data to detect the cancer stage at earlier stages can help patients receive appropriate medical treatment. In this paper, four classification methods have been used in the context of Bayes Net, Adaboost, Simple Logistic and Stochastic Gradient Descent, successfully. The primary goal is to test in terms of accuracy, uncertainty matrix, MAE and RMSE, consistency in the identification of information concerning efficiency and effectiveness of each algorithm.

Keywords:

Classification,Machine learning,Stochastic Gradient Descent,Breast cancer,

Refference:

I. B.Kranthi kiran, Padmaja.Pulicherla, Classification and Enrichment of Unlabeled Feedback Data using Machine Learning. International Journal of Engineering and Advanced Technology,ISSN: 2249 – 8958, Volume-9 Issue-1, October 2019.
II. Chen, W.; Zheng, R.; Baade, P.D.; Zhang, S.; Zeng, H.; Bray, F.; Jemal, A.; Yu, X.Q.; He, J. Cancer statistics inChina, 2015. CA Cancer J. Clin. 2016, 66, 115–132.
III. Dr.Padmaja.Pulicherla, Job Shifting Prediction and Analysis Using Machine Learning(2019), et al 2019 J. Phys.: Conf. Ser. 1228 012056.
IV. Elias Zafiropoulos, Ilias Maglogiannis, and Ioannis Anagnostopoulos. 2006. A support vector machine approach to breast cancer diagnosis and prognosis. Artificial Intelligence Applications and Innovations (2006), 500–507.
V. Gouda I Salama, M Abdelhalim, and Magdy Abd-elghany Zeid. 2012. Breast cancer diagnosis on three different datasets using multi-classifiers. Breast Cancer (WDBC) 32, 569 (2012), 2.
VI. Padmaja.Pulicherla, Image Map: Alternative for Password Based Authentication, International Journal of Recent Technology and Engineering, ISSN: 2277-3878, Volume-8 Issue-3, September 2019.
VII. Padmaja.Pulicherla, Retrieving Songs By Lyrics Query Using Information Retrieval, International Journal of Engineering and Advanced Technology,ISSN: 2249 – 8958, Volume-8 Issue-6S, August 2019.
VIII. Stéfan van der Walt, S Chris Colbert, and Gael Varoquaux. 2011. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering 13, 2 (2011), 22–30.
IX. William H Wolberg, W Nick Street, and Olvi L Mangasarian. 1992. Breast cancer Wisconsin (diagnostic) data set. UCI Machine Learning Repository [http://archive. ics. uci. edu/ml/] (1992).
X. Wenbin Yue, Zidong Wang, Hongwei Chen, Annette Payne Xiaohui Liu, “Machine Learning with Applications in BreastCancer Diagnosis and Prognosis”, mdpi design, May 2018.
XI. Yichuan Tang. 2013. Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239 (2013).

View Download

Abstract:

Cancer is a disease which is usually happens among the individuals everywhere throughout the world. There are numerous reasons to happen the malignant growth like as various habitats, environmental disorders and so forth. Cancer growth being identified at beginning periods can saves a large number of peoples, if viable cure is specified. It can make harm any piece of body. Generally the cancer occurs in breast of ladies. When a breast cells divide rapidly, it creates a group of mass which is called tumor . It is very difficult to detect the breast cancer tumor, it is very challenging task. Also the structure of the cancer cells are very complicated. In this article a prediction of breast cancer is present. In this a deep learning support-vector-method (D-SVM) is used to identify the breast cancer tumor. Also, In a early stages of an mammographic cancer a segmentation to threshold method is used. For the classification and for the feature extraction purpose this DSVM method is used. In this method we integrates conventional support vector machine (SVM) & classifier deep-neural-network. Likewise, probability of the lump to differentiate its sort is additionally taken in this paper for example amiable, suspicious or harmful.

Keywords:

Malignant,Image Processing,Support Vector Machine,Feature Extraction,Deep Neural Network,

Refference:

I. A. Davis, G. Irving , J. Chen, J. Dean, M. Abadi, M. Devin, M. Isard, P. Barham,
S. Ghemawat, and Z. Chen, and “TensorFlow: A system for large-scale machine
learning.”
II. A. G. Lynch, Curtis, D. Speed, G. Turashvili, M. J. Dun-ning, O. M. Rueda, S. P.
Shah, S.-F. Chin, S. Samarajiwa, and Y. Yuan, “The genomic and transcriptomic
architecture of 2,000 breast tumours reveals novel subgroups,” Nature, vol. 486,
no. 7403, pp. 346-352, 2012.

III. A.Krizhevsky, G. E. Hinton, I. Sutskever, N. Srivastava, and R. Sala-khutdinov,
“Dropout: a simple way to prevent neural networks from overfitting,” Journal of
Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.
IV. A. Li, C. Peng, M. Wang and Y. Zhang, “Improve glioblastoma multiforme
prognosis prediction by using feature selection and multiple kernel learning,”
IEEE/ACM transactions on computational biology and bioinformatics, vol. 13,
pp. 825-835, 2016.
V. A. Li, H. Feng, M. Wang, W. Fan, X. Xu and Y. Shen, “Prediction of protein
kinase-specific phosphorylation sites in hierarchical structure using functional
information and random forest,” Amino acids, vol. 46, no. 4, pp. 1069-1078,
2014.
VI. A. Petrosian, D. D. Adler, H. P. Chan, M. A. Helvie, and M. M. Goodsitt,
,“Computer-aided diagnosis in mammography: Classification of mass and normal
tissue by texture analysis,” Phys. Med. Biol., vol. 39, pp. 2273–2288, 1994. 2002.
VII. A. R.Webb, Statistical Pattern Recognition, 2nd ed. New York,NY, USA: Wiley,
2002, 0470845147.
VIII. A. Tsigginou, C. Dimitrakakis, F. Zagouri, I. Papaspyrou, et al., M. Gazouli, T.
N. Sergentanis, “HSP90, HSPA8, HIF-1 alpha and HSP70-2 polymorphisms in
breast cancer: a case–control study,” Molecular biology reports, vol. 39, pp.
10873-10879, 2012.
IX. C. R. Jung and J. Scharcanski, “Denoising and enhancing digital mammographic
images for visual screening,” Computerized Med. Imag. Graphics, vol. 30, no. 4,
pp. 243–254, Jun. 2006, 10.1016/j.compmedimag. 2006.05.002, 0895-6111.
X. C. Szegedy and S. Ioffe, “Batch normalization: Accelerating deep net-work
training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167,
2015.
XI. C. Varela, N. Karssemeijer AND S. Timp, “Temporal change analysis for
characterization of mass lesions in mammography,” IEEE Trans. Med. Imag.,
vol. 26, no. 7, pp. 945–953, Jul. 2007, 10.1109/TMI.2007. 897392.
XII. C. Zhang, C. Ré, D. L. Rubin, G. J. Berry, K.-H. Yu, R. B. Altman, and M.
Snyder, “Predicting non-small cell lung cancer prognosis by fully au-tomated
microscopic pathology image features,” Nature communica-tions, vol. 7, 2016.
XIII. D. Whiteson , P. Baldi and P. Sadowski, “Searching for exotic particles in highenergy
physics with deep learning,” Nature communications, vol. 5, pp. 4308,
2014.
XIV. Guliato, J. A. Zuffo, J. E. L. Desautels, R. M. Rangayyan, W. A. Carnielli
“Segmentation of breast tumors in mammograms by fuzzy region growing,” in
Proc. 20th Annu. Int. Conf. IEEE Engineering Medicine Biology Soc., Hong
Kong, Oct. 29–Nov. 1 1998, pp. II:1002–II:1004.
XV. https://www.google.com/search?q=B.%09Deep+Neural+Network+(DNN)&sxsrf
=ACYBGNR9ajuXEg2YRsUfTPNn4uUItUQ6IA:1569690915343&source=lnms
&tbm=isch&sa=X&ved=0ahUKEwiG5KmrgvTkAhW06XMBHUDZDH4Q_AU
IESgB&biw=1366&bih=613#imgrc=DoJ-mUab_fPKcM:

XVI. https://www.google.com/search?biw=1366&bih=613&tbm=isch&sxsrf=ACYBG
NQdbxRXPoHmh86l_YlYycnMNS-dnw%3A1569693459094&sa=1&ei=E5-
PXbuyBZO5rQH7yqeIBA&q=stages+of+breast+cancer&oq=stages+of+breast+
&gs_l=img.3.0.0l10.1489820.1495922..1497398…0.0..0.430.4269.0j4j10j2j1……
0….1..gws-wiz-img…….0i67.d5aeLR-5FxM#imgrc=_KhL65hb1in4MM:
XVII. J. E. L. Desautels, N. M. El-Faramawy, O. A. Alim, R. M. Rangayyan,
“Measures of acutance and shape for classification of breast tumours,” IEEE
Trans. Med. Imag., vol. 16, no. 12, pp. 799–810, Dec.1997.
XVIII. J. M. Brady, L. Tarassenko, S. L. Kok, “The detection of abnormalities in
mammograms,” in Proc. 2nd Int. Workshop Digital Mammography, York, U.K.,
Jul. 10–12, 1994, pp. 261–270.
XIX. J. R. Wakeling, J.-G. Liu, M. Medo, T. Zhou, Y.-C. Zhang, and Z. Kuscsik,
“Solving the apparent diversity-accuracy dilemma of recom-mender systems,”
Proceedings of the National Academy of Sciences, vol. 107, no. 10, pp. 4511-
4515, 2010.
XX. J. Park and S. Jeong, “Wnt activated β-catenin and YAP proteins enhance the
expression of non-coding RNA component of RNase MRP in colon cancer cells,”
Oncotarget, vol. 6, p. 34658, 2015.
XXI. K. B. Haskard, L. R. Martin, M. R. DiMatteo, and S. L. Williams, “The challenge
of patient adherence,” Ther Clin Risk Manag, vol. 1, pp. 189- 199, 2005.
XXII. K. Tomczak, M. Wiznerowicz, and P. Czerwinska, “The Cancer Genome Atlas
(TCGA): an immeasurable source of knowledge,” Contemp Oncol (Pozn), vol.
19, pp. A68-A77, 2015.
XXIII. M. A. Horan, M. F. Jefferson, N. Pendleton and S. B. Lucas, “Compari-son of a
genetic algorithm neural network with logistic regression for predicting outcome
after surgery for patients with nonsmall cell lung carcinoma,” Cancer, vol. 79, no.
7, pp. 1338-1342, 1997.
XXIV. M. Wang, X. Xu, and Y. Jiang, “A novel method for predicting posttranslational
modifications on serine and threonine sites by using sitemodification network
profiles,” Molecular BioSystems, vol. 11, pp. 3092- 3100, 2015.
XXV. N. Karssemeijer, “Adaptive noise equalization and recognition of
microcalcification clusters in mammograms,” Int. J. Pattern Recog. Artif. Intell.,
vol. 7, pp. 135713–135776, 1993.
XXVI. R. M. Haralick, “Textural features for image classification,” IEEE Trans. Syst.,
Man, Cybern., vol. 3, pp. 610–621, Dec. 1973.
XXVII. S. Gupta, S. S. Chandra, S. Raman, S. S. Channap-payya, and V. A. Kumar,
“No-reference quality assessment of tone mapped High Dynam-ic Range (HDR)
images using transfer learning.” pp. 1-3.
XXVIII. S.R. Burke, “Hybrid recommender systems: Survey and experiments,” User
modeling and user-adapted interaction, vol. 12, no. 4, pp. 331-370, 2002.
XXIX. T. Joachims, “Making large-scale SVM learning practical,” Technical Report,
SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität
Dortmund1998.

View Download

Abstract:

The present work reports an experimental investigation for the convective boiling heat transfer of R-134a in vertical tube filled with metal foam. High porosity (0.95-0.98) with PPI (40-80) metal foams (open-cell) are being considered to improve heat transfer process. Both of hydrodynamic and heat thermal performance are investigated. The results indicate that the metal foams significantly increases both heat transfer coefficient and Nusselt number but at the expense of increasing the pressure drop with mass flux rang 3-40 kg/m2.s. New correlations are proposed to predict the pressure drop and Nusselt number and show good agreements with previous experimental and numerical works.

Keywords:

Metal Foam,Forced Convection,Boiling,Experimental Study,Pressure Drop,Vertical Tube,

Refference:

I. A. Diani, S. Mancin, L. Doretti, L. Rossetto, “Low-GWP refrigerants flow
boiling heat transfer in a 5 PPI copper foam”, Int. J. Multiph. Flow 76 – 111–
121, 2015
II. Ali Hassan Lafta “AN INVESTAGATION OF FLOW BOILING HEAT
TARNSFER IN METAL FOAM FILLED TUBES “, Ph.D. Dissertation,
College of Engineering, University of Baghdad, Mechanical Engineering
Department \ Thermo-Fluids,2015
III. Ali Samir and Ihsan Y. Hussain “Simulation of natural convection boiling
heat transfer for refrigerant R-134a flow in a metal foam filled vertical tube”
Case Studies in Thermal EngineeringVolume 13, 100390.March 2019
IV. Ali Samir and Ihsan Y. Hussain ” Investigation of Forced Convection Boiling
Heat Transfer for R-134a Flow in a Vertical Tube Filled by Metal Foam “,
International Journal of Mechanical & Mechatronics Engineering IJMMEIJENS
Vol:19 No:03, Paper ID: 191303-2828-IJMME-IJENS.
V. Dukhan, N., “Metal Foams: Fundamentals and Applications”,1 edition,
Lancaster, DEStech Publications, Inc. 2013.
VI. Gholamreza Bamorovat Abadi, Chanhee Moon, Kyung Chun Kim. “Flow
boiling visualization and heat transfer in metal-foam-filled mini tubes – Part I:
Flow pattern map and experimental data”, International Journal of Heat and
Mass Transfer 98 – 857–867.2016
VII. Gholamreza Bamorovat Abadi, Chanhee Moon, Kyung Chun Kim “Flow
boiling visualization and heat transfer in metal-foam-filled mini tubes – Part
II: Developingpredictive methods for heat transfer coefficient and pressure
drop”, International Journal of Heat and Mass Transfer 98-868–878, 2016
VIII. Gholamreza Bamorovat Abadi, Kyung Chun Kim “Enhancement of phasechange
evaporators with zeotropic refrigerant mixture using metal foams”,
International Journal of Heat and Mass Transfer 106 – 908–919,2017
IX. Holman, J. P., “Heat Transfer”, 6th edition, McGraw-Hill, 1979.
X. Kashif Nawaz, Jessica Bock, Anthony M. Jacobi “Thermal-hydraulic
performance of metal foam heat exchangers under dry operating conditions”,
Applied Thermal Engineering 119 – 222–232, 2017
XI. Madani, B., F. Topin and L. Tadrist,” Convective Boiling in Metallic Foam:
Experimental Analysis of the Pressure Loss”, J. FDMP, Vol.6, No.4, pp.351-
367, 2010.

XII. Nield, D. A. and Bejan A. “Convection in Porous Media” New York:
Springer-Verlag 2006.
XIII. R.K. Shah, D.P. Sekulic, “Fundamentals of Heat Exchanger Design”, John
Wiley & Sons, Inc., ISBN 0-471-32171-0, 2003
XIV. S. Mancin, A. Diani, L. Doretti, L. Rossetto, “R134a and R1234ze(E) liquid
and flow boiling heat transfer in a high porosity copper foam”, Int. J. Heat
Mass Transfer 74-77–87. 2014
XV. Y. Wang and P. Cheng, “Multiphase flow and heat transfer in porous media”,
Advances in Heat Transfer, vol. 30, pp. 93–196, 1997.
XVI. Y.Y. Hsieh, L.J. Chiang, T.F. Lin, “Subcooled flow boiling heat transfer of
R-134a and the associated bubble characteristics in a vertical plate heat
exchanger”, Int.J. Heat Mass Transfer 45 -1791–1806.2002
XVII. Zhua, Y., Haitao H., Dinga, G.,Suna, S.and Jinga,Y., “Influence of Metal
Foam on Heat Transfer Characteristics of Refrigerant-Oil Mixture Flow
Boiling Inside Circular Tubes”, Applied Thermal Engineering, Vol.50, No.1,
pp. 1246–1256, 2013.

View Download

Abstract:

The concentration of carbon monoxide (CO) gas was measured at different traffic stressed areas. This study aims to find out the air quality CO concentration in the city of Karachi, Pakistan from 2002 to 2018. More than 300 sites were observed in the year 2002 and 2018. Those observations were segregated with respect of type of the day, time of the day and, at different elevations. Type of the day is then categories on weekdays and weekends. Time of the day considered as morning, afternoon and, evening. Elevations of observation were taken as 3.0 feet and 4.5 feet above the ground. A CO Index was also checked for every combination. Geographic Information System (GIS) maps were also crafted for every combination of days, times and, heights to visualize the situation. At, 3.0 feet height for both cases of working and weekdays it is observed that CO concentration is nearly half of that of 2002. At the elevation of 4.5 feet it is also going down but about 10% as compared to 2002. Even after having a decrement trend the area under study is unhealthy for living. CO concentration was then predicted for years 2020, 2022 and 2025. Even have a decrement trend, the living condition was not good for any of the projected year for time of the day and type of the day. The main reason for having a decrement pattern is changing fuel type and removal of old carriage buses.

Keywords:

CO Concentration,Karachi Metropolis,Air Quality Index,Trafficrelated air Pollution,

Refference:

I A. Hasan, “Environment and Urbanization,” The urban resource
centre, Karachi., vol. 19, no. 1, pp. 275-292, 2007.
II B. R. J. A. S. A. A. A. G. P. N. A. S. &. L. J. Gurjar, “Human health
risks in megacities due to air pollution,” Atmospheric Environment,
vol. 44, no. 36, pp. 4606-4613, 2010.
III C. J. F. R. S. P. M. B. G. C. Motterlini R., “Carbon Monoxide-
Releasing Molecules: Characterization of Biochemical and Vascular
Activities,” Circulation Research, vol. 90, no. 2, pp. 17-24, 2002.
IV D. G. &. W. S. T. Streets, “Present and future emissions of air
pollutants in China:: SO2, NOx, and CO.,” Atmospheric Environment,
vol. 34, no. 3, pp. 363-374, 2000.
V D. &. Z. O. Schwela, Urban traffic pollution, CRC Press, 1998

VI E. B. V. D. J. A. D. A. P. W. A. .. &. R. J. B. Bérard, “Expired-air
carbon monoxide as a predictor of 16-year risk of all-cause,
cardiovascular and cancer mortality,” Preventive medicine, pp. 195-
200, 2015.
VII E. J. &. K. E. Calabrese, Air toxics and risk assessment, CRC Press,
1991.
VIII F. H. G. M. &. W. U. A. Matin, “Factors affecting traffic jam in
Karachi and its Impact on performance of economy.,” KASBIT Journal
of Management & Social Science, vol. 5, pp. 25-32, 2012.
IX H. A. F. Z. M. D. A. Z. K. A. S. A. &. C. D. O. Khwaja, ” Effect of air
pollution on daily morbidity in Karachi, Pakistan.,” Journal of Local
and Global Health Science, 2012.
X H. S. S. S. K. A. S. &. N. T. Yashiro, “Temporal and spatial variations
of carbon monoxide over the western part of the Pacific Ocean,”
Journal of Geophysical Research: Atmospheres, vol. 114, no. D8,
2009.
XI J. &. G. M. Kobza, “Do the pollution related to high-traffic roads in
urbanised areas pose a significant threat to the local population?,”
Environmental monitoring and assessment, vol. 189, no. 1, 2017.
XII J. &. G. M. Kobza, “Do the pollution related to high-traffic roads in
urbanised areas pose a significant threat to the local population?189,”
Environmental monitoring and assessment, vol. 189, no. 1, 2017.
XIII J. D. K. S. A. H. D. H. M. E. &. B. P. J. Antuni, “Increase in exhaled
carbon monoxide during exacerbations of cystic fibrosis.,” Thorax, vol.
55, no. 2, pp. 138-142, 2000.
XIV M. A. K. &. R. R. A. Khalil, “Carbon monoxide in the earth’s
atmosphere: increasing trend.,” Science, pp. 54-56, 1984.
XV M. A. G. S. A. S. &. A. M. R. K. Hussain, “Developing a
Mathematical Model to Assess the Liveablity inBlighted Mega City.,”
Journal of Basic & Applied Sciences., vol. 9, 2013.
XVI M. H. ). ARSALAN, “MONITORING SPATIAL’P ATTERNS OF
AIR POLLUTION IN KARACHI• METROPOLIS: A GIS AND
REMOTE SENSING PERSPECTIVE,” Doctoral dissertation,
UNIVERSITY OF KARACHI KARACHI-PAKISTAN, 2002.
XVII N. L. S. A. S. a. R. F. Rizvi, ” Distribution and circumstances of
injuries in squatter settlements of Karachi, Pakistan.,” Accident
Analysis & Prevention, pp. 526-531, 2006.
XVIII N. Qureshi, “Atmospheric Pollution in Karachi 40 Percent Higher than
other Cities.,” Frontier Post, 1997.

XIX P. L. I. K. S. K. N. &. K. A. Klepac, ” Ambient air pollution and
pregnancy outcomes: A comprehensive review and identification of
environmental public health challenges.,” Environmental research, vol.
167, pp. 144-159, 2018.
XX P. G. Flachsbart, “Models of exposure to carbon monoxide inside a
vehicle on a Honolulu highway.,” Journal of Exposure Science and
Environmental Epidemiology., vol. 9, no. 3, p. 245, 1999.
XXI P. Brown, “Karachi –The Poison City,” Daily Dawn, January 01,
Karachi, 1998.
XXII R. M. &. H. R. Harrison, Air pollution: sources, concentrations and
measurements. Pollution: causes, effects and control, 2001.
XXIII S. W. &. O. L. E. Ryter, “Carbon monoxide in biology and medicine.
Bioessays,” vol. 26, no. 3, pp. 270-280, 2004.
XXIV S. J. M. J. &. V. M. Alm, “Urban commuter exposure to particle matter
and carbon monoxide inside an automobile.,” Journal of Exposure
Science and Environmental Epidemiology,, vol. 9, no. 3, p. 237, 1999.
XXV S. R. D. S. &. S. S. K. Mehta, ” Carbon monoxide poisoning.,”
Medical journal, Armed Forces India, vol. 63, no. 4, p. 362, 2007.
XXVI S. S. G. S. J. P. B. N. . Zevin S., “Cardiovascular Effects of Carbon
Monoxide and Cigarette Smoking,” Journal of American Colegel of
Cardiology, vol. 38, no. 6, pp. 1633-8, 2001.
XXVII S. Z. Ilyas, “A review of transport and urban air pollution in Pakistan.,”
Journal of Applied Sciences and Environmental Management, vol. 11,
no. 2, 2007.
XXVIII T. J. N. M. Z. M. A. &. B. Z. A. Ishtiaq, ” Comparative Study of air
particulate matter concentrations in different traffic stressed sites of
Quetta City, with special reference to their harmful impact on human
health.,” Biological Forum , vol. 7, no. 2, p. 357, 2015.
XXIX WHO, The World Health Organization. Environmental Indicator
Report, WHO European Environment Agency., Copenhagen., 2012.

View Download

Abstract:

In the present era, we have a new method called cloud computing, in which the services are shared over the Internet. There are many organizations that provide cloud processing services. Cloud processing allows users and developers to use these services without interfering with the technical knowledge or control of the technology they require, but on the other hand, day to day, more information of individuals and companies is stored inside clouds, which puts the challenge of data security ahead of users. Information security in virtual environments and new area of cloud computing has always been emphasized as one of the basic infrastructures and essential requirements for ICT-intensive use. Although absolute security is unattainable both in the real environment and in the virtual environment, it is possible to create a level of security that is sufficiently adequate in almost all environmental conditions. In cloud computing, there are many security challenges that must be addressed by cloud service providers to convince users to use this technology. One of the most important issues is ensuring the user's data is inaccurate and unavailable. For the user, the security process used to store data in the cloud is very obscure, long, and vague. In this research, a security approach based on abnormal behavior is designed to detect events that are unusual and abnormal in relation to other system behaviors. The focus of this paper is the use of evolutionary algorithms such as genetics or other new algorithms such as an imperialist competitive algorithm to detect these abnormal behaviors with intelligence agents. In similar studies, various optimization methods such as genetic algorithms, pso have been used. The proposed algorithm can be compared and evaluated with previous methods.

Keywords:

abnormal behavior,security,cloud computing,evolutionary algorithm,imperialist competitive algorithm,

Refference:

I. A. Vakili, N.J. Navimipour. Comprehensive and systematic review of the
service composition mechanisms in the cloud environments. J. Netw.
Comput. Appl., 81 pp. 24-36, 2017.
II. B. Wang, Y. Zheng, W. Lou, and Y. T. Hou, “DDoS attack protection in the
era of cloud computing and software-defined networking,” Computer
Networks, vol. 81, pp. 308-319, 2015.
III. Beiter, Michael, et al. “End-to-end policy based encryption techniques for
multi-party data management.” Computer Standards & Interfaces, 36.4 689-
703, 2014.
IV. Bose, Ranjit, Xin Luo, and Yuan Liu. “The Roles of Security and Trust:
Comparing Cloud Computing and Banking.” Procedia-Social and Behavioral
Sciences, 73: 30-34, 2013.
V. Che, Jianhua, et al. “Study on the security models and strategies of cloud
computing.” Procedia Engineering , 23: 586-593, 2011.
VI. Dastjerdi, Amir Vahid, Kamalrulnizam Abu Bakar, and Sayed Gholam
Hassan Tabatabaei. “Distributed intrusion detection in clouds using mobile
agents.” Advanced Engineering Computing and Applications in Sciences,
2009. ADVCOMP’09. Third International Conference on. IEEE, 2009.
VII. Govinda, K., and E. Sathiyamoorthy. “Agent Based Security for Cloud
Computing using Obfuscation.” Procedia Engineering 38 (2012): 125-129.
VIII. Jalal Nouri,D.,Saniee Abadeh, M.&Ghareh Mohammadi, F. HYEI: A New
Hybrid Evolutionary Imperialist Competitive Algorithm for Fuzzy
Knowledge Discovery. Advances in Fuzzy Systems. vol. 14, no. 11, 387-395,
2014.
IX. Jin, Hai, et al. “A guest-transparent file integrity monitoring method in
virtualization environment.” Computers & Mathematics with Applications,
60, 2: 256-266, 2010.
X. K. Vieira, A. Schulter, C. Westphall, and C. Westphall, “Intrusion detection
techniques in grid and cloud computing environment,” IT Professional, IEEE
Computer Society, vol. 12, pp. 38-43, 2010.
XI. Kim, Junghan, Inhyuk Kim, and Young Ik Eom. “Nopfit: File system
integrity tool for virtual machine using multi-byte nop injection.”
Computational Science and Its Applications (ICCSA), 2010 International
Conference on. IEEE, 2010.
XII. Kong, Jinzhu, AdjointVM: a new intrusion detection model for cloud
computing. Elsevier, 2011.
XIII. Kousiouris, George, et al. “Dynamic, behavioral-based estimation of resource
provisioning based on high-level application terms in Cloud platforms.”
Future Generation Computer Systems, 32: 27-40, 2014.
XIV. Kshetri, Nir. “Privacy and security issues in cloud computing: The role of
institutions and institutional evolution.” Telecommunications Policy, 37, 4:
372-386, 2013.
XV. Lai, Yung-Liang. “Analyzing strategies of mobile agents on malicious cloud
platform with Agent-Based Computational Economic Approach.” Expert
Systems with Applications, 40, 7: 2615-2620, 2013..

XVI. Lo, Chi-Chun, Chun-Chieh Huang, and Joy Ku. “A cooperative intrusion
detection system framework for cloud computing networks.” Parallel
processing workshops (ICPPW), 2010 39th international conference on.
IEEE, 2010.
XVII. M. Haresh, S. Kalady, and V. Govindan, “Agent based dynamic resource
allocation on federated clouds,” in Recent Advances in Intelligent
Computational Systems (RAICS), IEEE, 2011, pp. 111-114, 2011.
XVIII. M. Tang, X. Dai, J. Liu, J. Chen. Towards a trust evaluation middleware for
cloud service selection. Future Gener. Comput. Syst., 74, pp. 302-312, 2017.
XIX. Ms. Parag K. Shelke, Ms. Sneha Sontakke, Dr.A. D. Gawande, Intrusion
Detection System for Cloud Computing. International Journal of Scientific &
Technology Research, 2012.
XX. N. Dot. Anomaly Detection via Online Over-Sampling Principal Component
Analysis| FTJ1314, 2015.
XXI. N. Ghadiri, A. Baraani-Dastjerdi, N. Ghasem-Aghaee, and M. A.
Nematbakhsh, “Optimizing the performance and robustness of type-2 fuzzy
group nearest-neighbor queries,” Mobile Information Systems, vol. 7, pp.
123-145, 2011.
XXII. Pearson Siani. Toward accountability in the cloud. EEE Internet
Comput;15(4):64–99, 2011.
XXIII. R.Roustaei and F.Yousefi Fakhr. A Hybrid Meta-Heuristic Algorithm based
on Imperialist Competition Algorithm. Journal of AIand Data Mining Vol 6,
No 1, 59-67, 2018.
XXIV. Rasheed, Hassan. “Data and infrastructure security auditing in cloud
computing environments.” International Journal of Information Management
34.3: 364-368, 2014.
XXV. Rong Chunming, Nguyen Son T. Cloud trends and security challenges. In:
Proceedings of the 3rd international workshop on security and computer
networks (IWSCN 2011), 2011.
XXVI. Shin, Dong-Hee. “User centric cloud service model in public sectors: policy
implications of cloud services.” Government Information Quarterly 30.2:194-
203, 2013.
XXVII. Sood, Sandeep K. “A combined approach to ensure data security in cloud
computing.” Journal of Network and Computer Applications 35.6, 1831-
1838, 2012.
XXVIII. T. Garfinkel and M. Rosenblum, “A Virtual Machine Introspection Based
Architecture for Intrusion Detection,” in Ndss, pp. 191-206, 2003.
XXIX. Tarigonda, Siva, A. Ganesh, and Srinivasulu Asadi. “Providing Data Security
in Cloud Computing using Novel and Mixed Agent based Approach.”
International Journal of Computer Applications 112.6, 2015.
XXX. Tianfield, Huaglory. “Cloud computing architectures.” Systems, Man, and
Cybernetics (SMC), 2011 IEEE International Conference on. IEEE, 2011.
XXXI. Turki Alharkan, Patrick Martin, IDSaaS: Intrusion Detection System as a
Service in Public Clouds. IEEE/ACM International Symposium on Cluster,
Cloud and Grid Computing, 2012.

XXXII. Waqar, Adeela, et al. “A framework for preservation of cloud users’ data
privacy using dynamic reconstruction of metadata.” Journal of Network and
Computer Applications 36.1, 235-248, 2013.
XXXIII. Y. Wang, J. Wei. Toward protecting control flow confidentiality in cloudbased
computation. Comput. Secur., 52, pp. 106-127, 2015.
XXXIV. Z. Cai, X. Guan, P. Shao, Q. Peng, and G. Sun, “A rough set theory based
method for anomaly intrusion detection in computer network systems,”
Expert Systems, vol. 20, pp. 251-259, 2003.
XXXV. Zhang, Xuyun, et al. “An efficient quasi-identifier index based approach for
privacy preservation over incremental data sets on cloud.” Journal of
Computer and System Sciences 79.5: 542-555, 2013.
XXXVI. Zhao Gansen, Rong Chunming, Li Jin, Zhang Feng, Tang Yong. Trusted
data sharing over untrusted cloud storage providers. In: Proceedings of the
2nd IEEE international conference on cloud computing technology and
science (CloudCom 2010), 2010.
XXXVII. Zissis, Dimitrios, and Dimitrios Lekkas. “Addressing cloud computing
security issues.” Future Generation Computer Systems, 28, 3: 583-592, 2012.

View Download

Abstract:

This paper presents a multi-objective optimization model to optimize the performance of the Combined cooling, heat & power (CCHP) strategy in different climates based on cost, energy consumption and carbon gas production. In order to ensure the reliability of the CCHP's performance strategy and potential load power, potential constraints are added to the contingency model and the impact of increasing the level of reliability on contingency constraints on cost, energy consumption and carbon gas production is analyzed. To develop the proposed multi-objective analysis, a model is proposed to reduce primary energy consumption and carbon emissions, and for different atmospheric conditions, values of energy consumption and carbon gas production are determined. Finally, the proposed problem was applied to the cities of San Francisco, Boston, Miami, Minneapolis and Columbus and coded in the GAMS optimization software environment. Then, based on the numerical results, the capabilities of the proposed scheme in support of optimal CCHP performance planning are evaluated.

Keywords:

Multi Objective Modeling,Power generation,CCHP Heating Unit,Combined Systems,

Refference:

I. W. El-Khattam and M. M. A. Salama, “Distributed generation
technologies, definitions and benefits,” Electr. power Syst. Res., vol. 71,
no. 2, pp. 119–128, 2004.
II. C. Shang, D. Srinivasan, and T. Reindl, “Generation and storage
scheduling of combined heat and power,” Energy, vol. 124, pp. 693–705,
2017.
III. X. Chu, D. Yang, X. Li, and R. Zhou, “Evaluation of CCHP system
performance based on operational cost considering carbon tax,” Energy
Procedia, vol. 142, pp. 2930–2935, 2017.

IV. H. Moradi, I. G. Moghaddam, M. P. Moghaddam, and M. R. Haghifam,
“Opportunities to improve energy efficiency and reduce greenhouse gas
emissions for a cogeneration plant,” in 2010 IEEE International Energy
Conference, pp. 785–790,2010.
V. C.-Z. Li, Y.-M. Shi, S. Liu, Z. Zheng, and Y. Liu, “Uncertain
programming of building cooling heating and power (BCHP) system
based on Monte-Carlo method,” Energy Build., vol. 42, no. 9, pp. 1369–
1375, 2010.
VI. S. Bracco, G. Dentici, and S. Siri, “Economic and environmental
optimization model for the design and the operation of a combined heat
and power distributed generation system in an urban area,” Energy, vol. 55,
pp. 1014–1024, 2013.
VII. A. Smith, R. Luck, and P. J. Mago, “Analysis of a combined cooling,
heating, and power system model under different operating strategies with
input and model data uncertainty,” Energy Build., vol. 42, no. 11, pp.
2231–2240, 2010.
VIII. D. Maraver, A. Sin, J. Royo, and F. Sebastián, “Assessment of CCHP
systems based on biomass combustion for small-scale applications through
a review of the technology and analysis of energy efficiency parameters,”
Appl. Energy, vol. 102, pp. 1303–1313, 2013.
IX. J. Wang, P. Zhao, X. Niu, and Y. Dai, “Parametric analysis of a new
combined cooling, heating and power system with transcritical CO2
driven by solar energy,” Appl. Energy, vol. 94, pp. 58–64, 2012.
X. S. Li, J. Sui, H. Jin, and J. Zheng, “Full chain energy performance for a
combined cooling, heating and power system running with methanol and
solar energy,” Appl. Energy, vol. 112, pp. 673–681, 2013.
XI. E. Cardona, A. Piacentino, and F. Cardona, “Matching economical,
energetic and environmental benefits: an analysis for hybrid CHCP-heat
pump systems,” Energy Convers. Manag., vol. 47, no. 20, pp. 3530–3542,
2006.
XII. A. A. Jalalzadeh-Azar, “A comparison of electrical-and thermal-loadfollowing
CHP systems,” ASHRAE Trans., vol. 110, p. 85, 2004.
XIII. M. Liu, Y. Shi, and F. Fang, “A new operation strategy for CCHP systems
with hybrid chillers,” Appl. Energy, vol. 95, pp. 164–173, 2012.
XIV. A. Rong and R. Lahdelma, “An efficient linear programming model and
optimization algorithm for trigeneration,” Appl. Energy, vol. 82, no. 1, pp.
40–63, 2005.
XV. J.-J. Wang, Y.-Y. Jing, and C.-F. Zhang, “Optimization of capacity and
operation for CCHP system by genetic algorithm,” Appl. Energy, vol. 87,
no. 4, pp. 1325–1335, 2010.
XVI. J. Wang, Z. J. Zhai, Y. Jing, and C. Zhang, “Particle swarm optimization
for redundant building cooling heating and power system,” Appl. Energy,
vol. 87, no. 12, pp. 3668–3679, 2010.
XVII. W. Jiang-Jiang, Z. Chun-Fa, and J. You-Yin, “Multi-criteria analysis of
combined cooling, heating and power systems in different climate zones in
China,” Appl. Energy, vol. 87, no. 4, pp. 1247–1259, 2010.

XVIII. B. Shi, L.-X. Yan, and W. Wu, “Multi-objective optimization for
combined heat and power economic dispatch with power transmission loss
and emission reduction,” Energy, vol. 56, pp. 135–143, 2013.
XIX. E. Thorin, H. Brand, and C. Weber, “Long-term optimization of
cogeneration systems in a competitive market environment,” Appl. Energy,
vol. 81, no. 2, pp. 152–169, 2005.
XX. S. G. Tichi, M. M. Ardehali, and M. E. Nazari, “Examination of energy
price policies in Iran for optimal conFiguration of CHP and CCHP
systems based on particle swarm optimization algorithm,” Energy Policy,
vol. 38, no. 10, pp. 6240–6250, 2010.
XXI. G. Abdollahi and H. Sayyaadi, “Application of the multi-objective
optimization and risk analysis for the sizing of a residential small-scale
CCHP system,” Energy Build., vol. 60, pp. 330–344, 2013.
XXII. K. C. Kavvadias and Z. B. Maroulis, “Multi-objective optimization of a
trigeneration plant,” Energy Policy, vol. 38, no. 2, pp. 945–954, 2010.
XXIII. M. Liu, Y. Shi, and F. Fang, “Optimal power flow and PGU capacity of
CCHP systems using a matrix modeling approach,” Appl. Energy, vol. 102,
pp. 794–802, 2013.
XXIV. L. Wang and C. Singh, “Stochastic combined heat and power dispatch
based on multi-objective particle swarm optimization,” in 2006 IEEE
Power Engineering Society General Meeting, 8, pp 72-87, 2006.
XXV. L. Coles and R. W. Beck, “Distributed generation can provide an
appropriate customer price response to help fix wholesale price volatility,”
in 2001 IEEE Power Engineering Society Winter Meeting. Conference
Proceedings (Cat. No. 01CH37194), vol. 1, pp. 141–143, 2001.
XXVI. J. F. Blinn, “A generalization of algebraic surface drawing,” ACM Trans.
Graph., vol. 1, no. 3, pp. 235–256, 1982.
XXVII. C. Z. Li, Y. M. Shi, and X. H. Huang, “Sensitivity analysis of energy
demands on performance of CCHP system,” Energy Convers. Manag., vol.
49, no. 12, pp. 3491–3497, 2008.
XXVIII. S. Gamou, R. Yokoyama, and K. Ito, “Optimal unit sizing of cogeneration
systems in consideration of uncertain energy demands as continuous
random variables,” Energy Convers. Manag., vol. 43, no. 9–12, pp. 1349–
1361, 2002.

View Download

Abstract:

This paper presents a new approach based upon Perturb and Observe (P&O) to track Maximum Power Point (MPP) in Photovoltaic (PV) systems. This algorithm has a wide step length range which results in a very high response time to changing ambient condition. This method is analogue based and thus no DSP and/or A/D are employed in this system which greatly reduces the complexity and cost. To further increase the total efficiency, a soft switching converter as an interface circuit is applied. Semiconductor devices in underutilized converter entirely are fully soft switched. The proposed system is analyzed and the simulation results are presented. A 135W prototype system is implemented and the stated experimental results confirm the veracity of the theoretical analysis.

Keywords:

Maximum Power Point Tracking,Perturb and Observe,Photovoltaic Panel,Zero Current Switching Converters,

Refference:

I. Adib, E. and H. Farzanehfard. “Family of Zero-Current Transition PWM
Converters.” IEEE Transactions on Industrial Electronics, 55(8): 3055-3063,
2008.
II. Banu, I. V., et al. Comparative analysis of the perturb-and-observe and
incremental conductance MPPT methods, 2017.
III. Brito, M. A. G. d., et al. “Evaluation of the Main MPPT Techniques for
Photovoltaic Applications.” IEEE Transactions on Industrial Electronics, 60(3):
1156-1167, 2013.
IV. Dash, S. K., et al. Comparative analysis of maximum power point (MPP)
tracking techniques for solar PV application using MATLAB simulink, 2016.
V. Hong, Y., et al. Efficient Maximum Power Point Tracking for a Distributed PV
System under Rapidly Changing Environmental Conditions, 2016.
VI. Khatib, T. E., Wilfried Mohamed,Azah “Simplified I-V characteristic tester for
photovoltaic modules using a DC-DC boost converter.” Sustainability
(Switzerland) 9(4): 66-77, 2017.
VII. Mohanty, P., et al. “MATLAB based modeling to study the performance of
different MPPT techniques used for solar PV system under various operating

conditions.” Renewable and Sustainable Energy Reviews 38(Supplement C):
581-593, 2014.
VIII. Murtaza, A. F., et al. “Comparative analysis of maximum power point tracking
techniques for PV applications.” INMIC: 83-88, 2013.
IX. Raj, J. S. C. M. and A. E. Jeyakumar A Novel Maximum Power Point Tracking
Technique for Photovoltaic Module Based on Power Plane Analysis of
Characteristics, 2015.
X. Reza Reisi, A., et al. “Classification and comparison of maximum power point
tracking techniques for photovoltaic system: A review.” Renewable and
Sustainable Energy Reviews, 19(Supplement C): 433-443, 2013.
XI. Rezk, H. and A. M. Eltamaly. “A comprehensive comparison of different
MPPT techniques for photovoltaic systems.” Solar Energy, 112(Supplement
C): 1-11, 2015.
XII. Romero-Cadaval, E., et al. “Grid-Connected Photovoltaic Generation Plants:
Components and Operation.” IEEE Industrial Electronics Magazine, 7(3): 6-20,
2013.
XIII. Sheraz, M. and M. A. Abido. “An efficient MPPT controller using differential
evolution and neural network.” 2012 IEEE International Conference on Power
and Energy (PECon), 6, 378-383, 2012.
XIV. Sreekanth, S. and I. J. Raglend. “A comparitive and analytical study of various
incremental algorithms applied in solar cell.” 2012 International Conference on
Computing, Electronics and Electrical Technologies (ICCEET): 452-456,
2012.

View Download

Abstract:

The pond ash (class F) as an individual material is unsuitable for utilization in pavement constructions due to few undesirable physico-mechanical properties. Treatment of pond ash by suitable additives like cement and lime would improve its usability. The present study is intended to determine the strength and stiffness properties such as Unconfined Compressive Strength (UCS), California Bearing Ratio (CBR) and Resilient modulus (MR) of both untreated and lime-treated pond ash for its pavement subbase application. The experimental investigation illustrates the enhancement in UCS, CBR, and MR properties of lime-treated pond ash compared to untreated/virgin pond ash specimens. Further, a significant improvement was observed at lime content about 8%, which can be considered as optimum addition to pond ash for pavement constructions.

Keywords:

Pond ash,Lime,UCS,CBR,Resilient Modulus,

Refference:

I. American Association of State Highway and Transportation Officials
AASHTO, 1993. AASHTO guide for design of pavement structures,
AASHTO, Washington, D.C.
https://habib00ugm.files.wordpress.com/2010/05/aashto1993.pdf
II. Arora, S., & Aydilek, A. H. (2005). Class F fly-ash-amended soils as
highway base materials. Journal of Materials in Civil Engineering, 17(6),
640-649. https://doi.org/10.1061/(ASCE)0899-1561(2005)17:6(640)
III. CEA “Fly ash generation at coal/lignite based thermal power stations and
its utilization in the country for the year 2016-2017”, New Delhi: Central
Electricity Authority, 2017 http://www.cea.nic.in/tcd.html
IV. Chand, S. K., & Subbarao, C. (2007). Strength and slake durability of lime
treated pond ash. Journal of materials in civil engineering, 19(7), 601-
608. https://doi.org/10.1061/(ASCE)0899-1561(2007)19:7(601)
V. Consoli, N. C., Prietto, P. D. M., Carraro, J. A. H., & Heineck, K. S.
(2001). Behavior of compacted soil-fly ash-carbide lime mixtures. Journal
of Geotechnical and Geoenvironmental Engineering, 127(9), 774-782.
VI. Dev, K. L., & Robinson, R. G. (2015). Pond ash based controlled low
strength flowable fills for geotechnical engineering
applications. International Journal of Geosynthetics and Ground
Engineering, 1(4), 32.https://doi.org/10.1007/s40891-015-0035-1
VII. Ghosh, A. (2009). Compaction characteristics and bearing ratio of pond
ash treated with lime and phosphogypsum. Journal of materials in civil
engineering, 22(4), 343-351. https://doi.org/10.1061/(ASCE)MT.1943-
5533.0000028
VIII. Ghosh, A., & Subbarao, C. (2007). Strength characteristics of class F fly
ash modified with lime and gypsum. Journal of geotechnical and
geoenvironmental engineering, 133(7), 757-766.
https://doi.org/10.1061/(ASCE)1090-0241(2007)133:7(757)
IX. Gupta, D., & Kumar, A. (2017). Performance evaluation of cement-treated
pond ash-rice husk ash-clay mixture as a highway construction
material. Journal of Rock Mechanics and Geotechnical Engineering, 9(1),
159-169. https://doi.org/10.1016/j.jrmge.2016.05.010
https://doi.org/10.1061/(ASCE)1090-0241(2001)127:9(774)
X. J. Groeger, G. Rada, and A. Lopez, “AASHTO T307 — Background and
Discussion,” in Resilient Modulus Testing for Pavement Components, ed.
G. Durham, W. DeGroff, and W. Marr (West Conshohocken, PA: ASTM
International, 2003), 16 29. https://doi.org/10.1520/STP12519S
XI. Kang, X., Ge, L., Kang, G. C., & Mathews, C. (2015). Laboratory
investigation of the strength, stiffness, and thermal conductivity of fly ash
and lime kiln dust stabilised clay subgrade materials. Road Materials and
Pavement Design, 16(4), 928-945.
https://doi.org/10.1080/14680629.2015.1028970

XII. Kumar Bera, A., Ghosh, A., & Ghosh, A. (2007). Compaction
characteristics of pond ash. Journal of materials in civil
engineering, 19(4), 349-357. https://doi.org/10.1061/(ASCE)0899-
1561(2007)19:4(349)
XIII. Lav, A. H., Lav, M. A., & Goktepe, A. B. (2006). Analysis and design of a
treated fly ash as pavement base material. Fuel, 85(16), 2359-2370.
https://doi.org/10.1016/j.fuel.2006.05.017
XIV. National Cooperative Highway Research Program (NCHRP). (2004).
Guide for mechanistic-empirical design of new and rehabilitated pavement
structures. National Cooperative Highway Research Program 1-37 A.
http://onlinepubs.trb.org/onlinepubs/archive/mepdg/2appendices_RR.pdf
XV. Nicholson, P., and Kashyap, V. (1993). “Fly ash stabilization of tropical
Hawaiian soils.” Geotechnical Special Publication, No. 36, ASCE, New
York, 15–29. http://worldcat.org/isbn/0872629864
XVI. Pani, A., & Singh, S. P. (2017). Influences of curing conditions on
strength and microstructure of lime-amended fly ash.
http://hdl.handle.net/2080/2749
XVII. Patel, S., & Shahu, J. T. (2016). Resilient response and permanent strain
of steel slag-fly ash-dolime mix. Journal of Materials in Civil
Engineering, 28(10), 04016106. https://doi.org/10.1061/(ASCE)MT.1943-
5533.0001619
XVIII. Patel, S., & Shahu, J. T. (2018). Comparison of Industrial Waste Mixtures
for Use in Subbase Course of Flexible Pavements. Journal of Materials in
Civil Engineering, 30(7), 04018124.
https://doi.org/10.1061/(ASCE)MT.1943-5533.0002320
XIX. Puppala, A. J., Ramakrishna, A. M., & Hoyos, L. R. (2003). Resilient
moduli of treated clays from repeated load triaxial test. Transportation
research record, 1821(1), 68-74. https://doi.org/10.3141/1821-08
XX. Rout, R. K., Ruttanapormakul, P., Valluru, S., & Puppala, A. J. (2012).
Resilient moduli behavior of lime-cement treated subgrade soils. In Geo
Congress, ASCE (pp. 1428-1437). https://doi.org/10.1061/9780784412121
XXI. Saghafi, B., Nageim, H. A., VisuliosMPhil, P., & Ghazireh, N. (2012).
Use of waste limestone dust and steel slag in UK highways type 1
unbound mixtures. Proceedings of the Institution of Civil Engineers-
Construction Materials, 166(2), 99-107.
https://doi.org/10.1680/coma.11.00029
XXII. Samanta, M. (2018). Investigation on geomechanical behaviour and
microstructure of cement-treated fly ash. International Journal of
Geotechnical Engineering, 12(5), 449-
461.https://doi.org/10.1080/19386362.2017.1295623
XXIII. Sarkar, R., & Dawson, A. R. (2017). Economic assessment of use of pond
ash in pavements. International Journal of Pavement Engineering, 18(7),
578-594. https://doi.org/10.1080/10298436.2015.1095915

XXIV. Singh, S. P., & Ramaswamy, S. V. (2006). Utilisation Potential of Cement
Stabilised Flyash-GBFS Mixes in Highway Construction. Water and
Energy Abstracts, 16(1).
http://www.indianjournals.com/ijor.aspx?target=ijor:wea&volume=16&is
sue=1&article=143
XXV. Sivapullaiah, P. V., Prashanth, J. P., & Sridharan, A. (2000). Optimum
lime content for fly ashes and the role of the curing period. Journal of
testing and evaluation, 28(6), 499-506. https://doi.org/10.1520/JTE12141J
XXVI. Sivapullaiah, P., & Moghal, A. (2011). CBR and strength behavior of
class F fly ashes treated with lime and gypsum. International Journal of
Geotechnical Engineering, 5(2), 121-130.
https://doi.org/10.3328/IJGE.2011.05.02.121-130
XXVII. Suthar, M., & Aggarwal, P. (2018). Bearing ratio and leachate analysis of
pond ash treated with lime and lime sludge. Journal of Rock Mechanics
and Geotechnical Engineering, 10(4), 769-
777.https://doi.org/10.1016/j.jrmge.2017.12.008
XXVIII. Titli, H., Coenen, A., & Elias, M. (2012). Resilient Characteristics of
Bottom Ash and Bottom Ash-Soil Mixtures. In Testing and Specification
of Recycled Materials for Sustainable Geotechnical Construction. ASTM
International.https://doi.org/10.1520/JAI103700
XXIX. Viswanathan, R., Saylak, D., Estakhri, C. K., Tauferner, D &
Chimakurthy, H. (1996). Evaluation of the Use of Coal Combustion By-
Products in Highway and Airfield Pavement Construction (No. TX-
97/2969-1F). https://trid.trb.org/view/572404
XXX. Wong, C., & Ho, M. K. (1989). Experimental Fly Ash Base. Farm-To-
Market Road 1093, Fulshear, TEXAS (No. DHT-
17).https://trid.trb.org/view/317525

View Download

Abstract:

As indicated by Breast Cancer Research, Breast malignancy is the disease most unmistakable in the female populace of the world. According to the clinical specialists, identifying this malignant growth in its beginning time helps in sparing lives. The site cancer.net offers individualized aides for more than 120 sorts of malignancy and related innate disorders. For visualization of bosom malignant growth through innovation, AI strategies are, for the most part, favored. In this structure, an adaptable group AI calculation by surveying among different strategies is proposed for the conclusion of bosom disease. Reports utilizing the Wisconsin Breast Cancer database is utilized. The point of this system is to analyze and clarify how ANN and calculated relapse calculation together gives a superior answer to identify Breast malignancy even though the factors are diminished. This procedure demonstrates that the neural system is additionally compelling for necessary human information. We can do pre-finding with no uncommon therapeutic learning.

Keywords:

Artifical Neural Network,Convolutional Networks,Machine Learning,Support Vector Machine,

Refference:

I. AlirezaOsarech, BitaShadgar,”A Computer Aided Diagnosis System for Breast
Cancer”,International Journal of Computer Science Issues, Vol. 8, Issue 2, March
2011.
II. B. Krawczyk, M. Galar, L.Jelen, F.Herrera (2016), “Evolutionary undersampling
boosting for imbalanced classification of breast cancer malignancy”, Article in
Applied soft computing, Elsevier B.V., pp 1-14.
III. Breast Cancer Detection Using K-Nearest Neighbor Machine Learning Algorithm
by M.R.Al- Hadidi, A. Alarabeyyat and M. Alhanahnah.
IV. Breast cancer mass localization based on machine learning by A. Qasem et al
V. Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method.
Author : Tongan Cai, Hongliang He , Wenyu Zhang
VI. Biopsy-guided learning with deep convolutional neural networks for Prostate
Cancer detection on multiparametric MRI by Yohannes Tsehaya, Nathan Laya,
Xiaosong Wanga, Jin Tae Kwaka, Baris Turkbeyb, Peter Choykeb, Peter Pintob,
Brad Woodc, and Ronald M. Summersa.
VII. Cao, D.S., Xu, Q. S., Liang, Y.Z., Zhang, L.X., Li, H.-D. (2010), “The boosting:
A new idea of building models”, Chemometrics and Intelligent Laboratory
Systems, Vol.4, pp 1-11.
VIII. Comparative Study of Machine Learning Algorithms for Breast Cancer Detection
and Diagnosis by Dana Bazazeh and Raed Shubair

IX. Dongdong Sun , Minghui Wang and Huanqing Feng, “Prognosis prediction of
human breast cancer by integrating deep neural network and support vector
machine: Supervised feature extraction and classification for breast cancer
prognosis prediction”, 978-1-5386-1937- 7/17/$31.00 ©2017 IEEE.
X. Ebrahim Edriss Ebrahim Ali1 , Wu Zhi Feng2- “Breast Cancer Classification
using Support Vector Machine and Neural Network”– International Journal of
Scienceand Research(IJSR) Volume 5 Issue 3, March 2016.
XI. García, F. Herrera (2009), “Evolutionary undersampling for classification with
imbalanced datasets: proposals and taxonomy”, Evol. Computing, Vol.17, pp
275–306.
XII. Haifeng Wang and Sang Won Yoon – Breast Cancer Prediction using Data
Mining Method, IEEE Conference paper.
XIII. Ismail Saritas, “Prediction of Breast Cancer Using Artificial Neural Networks”,
Springer Science+Business Media, LLC 2011.
XIV. J. Huang, C.X. Ling (2005), “Using AUC and accuracy in evaluating learning
algorithms”, IEEE Trans. Knowl. Data Eng., Vol. 17(3), pp 299–310.
XV. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, F. Herrera (2012), “A
review on ensembles for the class imbalance problem: bagging-, boosting-, and
hybrid based approaches”, IEEE Trans. Systems Man Cybern. C: Appl. Rev., Vol.
42 (4), pp 463–484.
XVI. Rejani YIA, Selvi ST (2009) Early detection of breast cancer using SVM
classifier technique. International Journal on Computer Science and Engineering
1(3):127-130.
XVII. R.W. Scarff, H. Torloni (1968), “Histological typing of breast tumors”,
International histological classification of tumours, World Health Organization,
Vol. 2 (2), pp 13–20.

View Download