International Journal of Innovative Technology and Exploring Engineering(TM)
Exploring Innovation| ISSN:2278-3075(Online)| Reg. No.:65021/BPL/CE/12| Published by BEIESP| Impact Factor:4.66
Home
Articles
Conferences
Editor
Scopes
Author Guidelines
Publication Fee
Privacy Policy
Associated Journals
Frequently Asked Questions
Contact Us
Volume-5 Issue 5: Published on October 10, 2015
17
Volume-5 Issue 5: Published on October 10, 2015

  Download Abstract Book

S. No

Volume-5 Issue-5, October 2015, ISSN:  2278-3075 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Durgalakshmi S

Paper Title:

Experimental Study on Behavior of Partial Replacement in Concrete Materials

Abstract:  Infrastructure development across the world creates demand for construction material. The problem arising from continuous technological industrial development is the disposal of waste material, the raw material of concrete consists of cement, sand and crushed aggregate. Partial replacement or full replacement of this raw material by waste products may decrease the cost reduced the energy consumption and also reduce the environmental pollution. The main objective of the studies is to encourage the use of waste product as construction material in cost effective manner. A referral M-25 concrete mix was used in the present investigation. Totally 92 cubes have been casted, and tested their compressive strength. The physical and mechanical properties of the material used in concrete were investigated. In this study the replacement has been carried out for the cement by fly ash, sand by stone dust and coarse aggregate by coconut shell. An attempt was made to partially replace the cement by fly ash (10%, 20%, 30%), then fine aggregate by stone dust (10%, 20%, 30%), and coarse aggregate by coconut shell (10%, 20%, 30%).  for each replacement. 9 referral concrete cubes were casted for measuring 7, 14 and 28 days compressive strength. The result of replaced concrete is compared with the referral concrete. 

Keywords:
 Coarse Aggregate, Cement, Coconut Shell, Compressive Strength, Fine Aggregate, Fly ash, Stone Dust.


References:

1.           Aman Jatale, Kartikey Tiwari, Sahil Khandelwal,” Effects On Compressive Strength When Cement Is Partially Replaced By Fly-Ash”, IOSR Journal of Mechanical and Civil Engineering (IOSR-JM.CE), Jan-Feb 2013 Volume 5, Issue 4
2.           Baboo Rai, Sanjay Kumar and Kumar Satish,”Effect of Fly Ash on mortar mixes with Quarry Dust as Fine Aggregate, Hindawai Publishing Corporation Advances in Material Science and Engineering, 2014

3.           Damodhara Reddy B, S.Aruna Jyothy,Fawaz Shaik,”Experimental analysis of the use of Coconut Shell as Coarse Aggregate”, IOSR Journal of Mechsnical of Civil Engineering (IOSR-JMCE), 2014

4.           Himanshu Dahiya, Naveen Dharni, ” Concrete with crush coconut shell as aggregate”, International Journal of research and development organization, 2015.

5.           Jayeshkumar Pitroda1, Dr. L.B.Zala2, Dr.F.S.Umrigar, ” Experimental Investigation  on partial replacement of cement with fly ash in design”, International Journal of Advanced Engineering Technology, Vol.III/ Issue IV, Dec 2014

6.           Lakhan Nagpal Er, Arvind Dewangan, Er.Sandeep Dhian, Er.Sumit Kumar, “Evaluation of strength characteristic of concrete using crushed stone Dust as fine Aggregate”, International journal of Innovation  Technology and Exploring Engineering(IJITEE), Vol-3,Issue-6, May 2013

7.           Maninder Kaur & Manpreet Kaur, “Review On Utilization Of Coconut Shell As Coarse Aggregates in Mass Concrete”, International Journal of Applied Engineering Research, Vol.7, Issue 11, 2012.

8.           Md Jardar Anwer, Franklin Eric kujur, Anjelo F. Denis, Arpan Herbert and Ehsan Ali,” Optimum Replacement Level of Fine Aggregate with Stone Dust in Concrete with Reference to its Compressive Strength”, Journal of Academia and Industrial Research (JAIR),Volume 3, Issue 7, Dec 2014

9.           Olilade,I.O,” Use of saw dust ash as partial replacement for cement in concrete.” International journal of Engineering science Invention, Vol-3,Issue-8, Aug 2014

10.        Parag S. Kambli, Sandhya R, Mathapati,  “Application of coconut shell as coarse aggregate in concrete: Technical review”, International Journal of Engineering Research and Application.Volume- 4, Issue 3, 2014

11.        Radhikesh P. Nanda, Amiy K. Das, Moharana .N.C,” Stone Crusher Dust as a Fine Aggregate in Concrete for Paving Block”, International Journal of Civil and Structural Engineering, Volume 1, No. 3, 2013

12.        Sandeep Kumar Singh, Vikas Srivastava, V.C. Agarwal, Rakesh Kumar and P.K Mehta,”An Experimental Investigation On Stone Dust as Partial Replacement of Fine Aggregate in Concrete”, Journal of Academia and Industrial Research (JAIR), Volume 3, Issue 5, 2014


1-4

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

2.

Authors:

S.N. Surip, W.N.R. Wan Jaafar, M.A. Tarawneh, N.N Azmi

Paper Title:

Mechanical Properties of Polylactic Acid (PLA) Green Composites Reinforced by Kenaf Bast and Core Fibers

Abstract:   Polylactic acid (PLA) green composites were fabricated using melt compounding and compression moulding.  Kenaf bast and core fibres had undergone chemi-mechanical treatment before use. PLA and kenaf fibres were mixed at different fibre loadings (2%, 4% and 6%) and extruded with three different rotation speeds (60, 70 and 80 rpm). The mechanical properties of kenaf bast composites (KBC) and kenaf core composites (KCC) were studied by performing flexural and impact testing. KBC and KCC treated with 1.0M acid treatment at 60 rpm speed had higher flexural and impact strength. KBC at 6% fibre loading had a higher flexural modulus, which was caused by the stiffness of the fibre incorporated in the PLA. However, KBC with 4% fibre loading has higher flexural strength than 6% fibre loading. In contrast, KCC at 2% fibre loading had the highest flexural modulus and strength. Meanwhile, for impact properties, 4% fibre loading had the optimum strength for both KBC and KCC.

Keywords:
 Kenaf bast fibre, kenaf core fibre,  polylactic acid, green composites.


References:

1.     Jandas PJ, Mohanty S and Nayak SK. Renewable reseource-based biocomposites of various surface treated banana fibre and poly lactic acid: Characterization and biodegradability. Journal of polymer and environment. 2012; 20: 583-595.
2.     Goriparthi BK, Suman KNS and Rao NM. Effects of fiber surface treatments on mechanical and abrasive wear performance of polylactide/jute composites. Composites: Part A. 2012; 43: 1800-1808.

3.     Zhao YQ, Cheung HY, Lau KT, et al. Silkworm silk/poly(lactic acid) biocomposites: Dynamic mechanical, thermal and biodegradable properties. Polymer degradation and stability. 2010; 95: 1978-1987.

4.     Yu T, Ren J, Li S, et al. Effect of fibre surface-treatments on the properties of poly(lactic acid)/ramie composites. Composites: Part A. 2010; 41: 499-505.

5.     Shukor F, Hassan A, Islam MS, et al. Effect of ammonium polyphosphate on flame retardancy, thermal stability and mechanical properties of alkali treated kenaf
fibre filled PLA biocomposites. Materials and design. 2014; 5 (4): 425-429.

6.     Suprakas SR and Mosto B. Biodegradable polymers and their layered silicate nanocomposites: In greening the 21st century materials world. Progress in material science. 2005; 50: 962-1079.

7.     Pettersson l, Kvien K and Oksman K. Structure and thermal properties of poly(lactic acid)/cellulose whiskers nanocomposites materials. Composites science and technology. 2007; 67: 2535-2544.

8.     Huda MS, Mohanty AK, Drzal LT, et al.  “Green” composites from recycled cellulose and poly(lactic acid): Physico-mechanical and morphological properties evaluation. Journal of materials science. 2005; 40: 4421-4429.

9.     Islam MS, Pickering KL and Foreman NJ. Influenced of alkali treatment on the interfacial and physic-mechanical properties of industrial hemp fibre reinforced polylactic acid composites. Composites: Part A. 2010; 41: 596-603.

10.  Masud S, Huda A, Drzal LT, et al. Effect of fibre surface treatments on the properties of laminated biocomposites from poly(lactide acid) (PLA) and kenaf fibres. Composites science and technology. 2008; 68: 424-432.

11.  Mohanty AK, Mishra M and Drizal LT. Surface modification of natural fibre and performance of the resulting biocomposites: An overview. Composites Interf. 2001; 8-5: 313-343.

12.  Alemdar A and Sain M. Isolation and characterization of nanofibers from agricultural residues – Wheat straw and soy hulls. Bioresource technology. 2008; 99: 1664 – 1671.

13.  Li X, Tabil LG and Panigrahi S.  Chemical treatment of natural fibre for use in natural fibre-reinforced composites: A review. Polymer Environment. 2007; 15(1): 25-33.

14.  Mwaikambo L and Ansell M. Chemical modification of hemp, sisal, jute and kapok fibre by alkalization. Journal of applied polymer science. 2002; 84: 2222-2234.

15.  Yousif BF, Shalwan A, Chin CW, et al. Flexural properties of treated and untreated kenaf/epoxy composites. Materials and design. 2012; 40: 378-385.

16.  Chang VS and Holtzapple MT. Fundamental factors affecting biomass enzymatic reactivity. Applied Biochemistry and Biotechnology. 2000; 84–86:5–37.

17.  Teixeira LC, Linden JC and Schroeder HA. Simultaneous saccharification and cofermentation of peracetic acid-pretreated biomass. Applied Biochemistry and Biotechnology. 2000; 84–86:111–127.

18.  Yang B, Dai Z, Ding SY, et al. Enzymatic hydrolysis of cellulosic biomass. Biofuels. 2011; 2:421–450.

19.  Rajeev K. Kenaf-reinforced polypropylene composites. Department of Chemical Engineering. Michigan State University. 1996.

20.  Masud SH, Lawrence TD, Amar KM, et al. Chopped glass and recycled newspaper as reinforcement fibers in injection molded poly(lactic acid) (PLA) composites: A
comparative study. Composites science and technology. 2005; 66: 1813-1824.

21.  Bledzki, AK, Mamun, AA, Lucka-Gabor, M, Gutowski, VS. The effects of acetylation on properties of flax fibre and its polypropylene composites. eXPRESS polymer letters. 2008: 6: 413-422.

22.  Mohd Edeerozey, AM, Akil, HM, Azhar, AB, Zainal Ariffin, MI. Chemical modification of kenaf fibers. Materials letters. 2007: 61: 2023-2025.

23.  Adel, AM, Abd El-Wahab, ZH, Ibrahim, AA, Al-Shemy, MT. Bioresource technology. 2010: 110: 4446-4455.

24.  Guo, J, and Catchmark, JM. Surface area porosity of acid hydrolyzed cellulose nanowhiskers and cellulose produced by Gluconacetobacter xylinus. Carbohydrate polymers. 2012: 87: 1026-1037.

25.  Samir, MASA, Alloin, F, Paillet, M, Dufresne, A. Tangling effect in fibrillated cellulose reinforced nanocomposites. Macromolecules. 2004: 37: 4313-4316.

26.  Corrales, F, Vilaseca, F, Llop, M, Gironos, J, Mandez, JA, Mutje, P. Chemical modification of jute fibres for the production of green composites. Journal of hazard materials. 2007: 144: 730-735.

27.  Jandas, PJ, Mohanty, S, Nayak, SK. Surface treated banana fiber reinforced poly (lactic acid) nanocomposites for disposable application. Journal of cleaner production. 2013: 52: 392-401.

28.  Sarasini, F, Puglia, D, Fortunaati, E, Kenny, JM, Santulli, C. Effect of fibre surface treatments on thermo-mechanical behaviour of poly (lactic acid)/Phormium Tenax composites. Journal of polymer environment. 2013: 21: 881-891.

29.  Gamon G, Evon P and Rigal L. Twin-screw extrusion impact on natural fibre morphology and material properties in poly(lactic acid) based biocomposites. Industrial crops and products. 2013; 46: 173-185.

30.  Villmow T, Pőtschke P, Pegel S, et al. Influence of twin-screw extrusion conditions on the dispersion of multi-walled carbon nanotubes in a poly(lactic acid) matrix. Polymer. 2008; 49: 3500-3509.

31.  Taubner, V, Shishoo, R. Influence of processing parameter on the degradation of poly(L-lactide) during extrusion. Journal of applied polymer science. 2001: 79: 2128-2135.

32.  Lim, LT, Auras, R, Rubino, M. Processing technologies for poly(lactic acid). Progress in polymer science. 2008: 33: 820-852.

33.  Yeh, SK and Gupta, RK. Improved wood-plastic composites through better processing. Composites: Part A. 2008: 39: 1694-1699.

34.  Wong, KJ, Nirmal, U, Lim, BK. Impact behaviour of short and continuous fibre-reinforced polyester composites. Journal of reinforced plastic and composites. 2010: 29 (23): 3462-3474.

35.  Zampaloni M, Pourboghrat F, Yankovich SA, et al. Kenaf natural fibre reinforced polypropylene composites: A discussion on manufacturing problems and solutions. Composites: Part A. 2007; 38: 1569-1580.

36.  Balakrishnan H, Hassan A, Wahit MU, et al. Novel toughened polylactic acid nanocomposites: Mechanical, thermal and morphological properties. Materials and design. 2010; 31: 3289-3298.

37.  Shibata, M, Ozawa, K, Teramoto, N, Yosomiya, R, Takeishi, H. Biocomposites made from short abaca fibre and biodegradable polyester. Macromolecule material engineering. 2003: 288: 35-43.

38.  Bledzki, AK, Jaszkiewicz, A, Scherzer, D. Mechanical properties of PLA composites with man-made cellulose and abaca fibers. Composites: Part A. 2009: 40: 404-412.

39.  Ismail H, Abdullah AH, and Abu Bakar A. Kenaf core reinforced high-density polyethylene/soya powder composites: The effect of filler loading and compatibilizer. Journal of reinforced plastics and composites. 2010; 29: 2489-2497.

40.  Van den Oever, M.J.A., Beck, B., Müssig, J., 2010. Agrofibre reinforced poly(lactic acid) composites: Effect of moisture on degradation and mechanical properties. Composites: Part A. 41: 1628-1635.

41.  Shyang CW and Kuen IS. Flexural, morphological and thermal properties of polylactic acid/organo-monmorillonite nanocomposites. Polymer composite. 2008; 16 (4): 263.

42.  Zhao Q, Tao J, Yam RCM, et al. Biodegradable behaviour of polycaprolactone/rice husk ecocomposites in simulated soil medium. Polymer degradation and stability. 2008; 93 (8): 1571-1576.

43.  Sawpan MA, Pickering KL and Fernyhough A. Improvement of mechanical performance of industrial hemp fibre reinforced polylactide biocomposites. Composites: Part A. 2011; 42: 310-319.


5-14

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

3.

Authors:

J.C. Ochi, C.O. Ohaneme, A.C.O. Azubog

Paper Title:

Development of an Intelligent Fuzzy-Based Algorithm for Data Congestion Management Scheme in Wireless LAN

Abstract: Network congestion control remains a critical issue and a high priority, especially given the growing size, demand, and speed (bandwidth) of the increasing wireless services. Congestion control is the problem of managing network traffic or a network state where the total demand for resources such as bandwidth among the competing users exceeds the available capacity. This paper presents a fuzzy logic approach to congestion mitigation in TCP oriented network using University of Nigeria Nsukka (UNN) situated at the South-Eastern part of Nigeria as a case study. Using a deductive study mechanism, an intelligent fuzzy-based algorithm for the congestion management is developed while showing a validation analysis plot of the proposed scheme in relation to other TCP variants such as TCP Tahoe, TCP Reno, TCP-New Reno, TCP Vegas and TCP selective acknowledgments (SACKs), i.e.TCP-TRONVS. From the implementation of the proposed scheme, it was observed that a significant improvement in the Quality of service (QoS) metrics (such as latency, throughput, buffer utilization, and packet Loss Ratio) for users is practically feasible.

Keywords:
 Network congestion, latency, packet loss, buffer utilization, throughput.


References:

1.        S. Oueslati and J. Roberts, “A new direction for quality of service: Flow-aware Networking,” in NGI 2005, Rome, Italy, April 2005.
2.        K.C, Okafor, F.N. Ugwoke, O.U Oparaku, “Characterization of Distributed Cloud Computing Network (DCCN) Server Resource Pool Using Virtualization Dynamics”, In International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), India, Vol 4.No.2, Feb.2015, Pp.280-294. IF: 3.22, India

3.        U.S. Department of Energy. Office of Electricity Delivery and Energy Reliability, Recovery Act Financial Assistance Funding Opportunity Announcement, Smart Grid Investment Grant Program, DE-FOA-0000058, June 25, 2009.

4.        S. Floyd, T. Henderson, andA.Gurtov, The NewReno Modification to TCP’s Fast Recovery Algorithm, Request for Comments: 3782, Network Working Group, 2004.

5.        H. Singh and P. Singh, “Energy Consumption of TCP Reno, TCP NewReno, and SACK in Multihop Wireless Networks”,  Proceedings of ACM SIGMETRICS, Marina Del Rey, CA, pp.206-216, June 2002.

6.        U.U.A. Swathiga and C. Chandrasekar, “An Efficient Fuzzy based Congestion Control Technique for Wireless Sensor Networks International Journal of Computer Applications (0975 – 8887) Vol. 40– No.14, February 2012, Pp.47-55

7.        F.Xia, W.Zhao, Y.Sun and Yu-Chu Tian, “Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks” In Sensors 2007, 7(12), 3179-3191, www.mdpi.org/sensors.

8.        A.Chakraborty, S.Ganguly, M.K.Naskar, A.Karmakar, “A Trust Based Fuzzy Algorithm for Congestion Control in Wireless Multimedia Sensor Networks (TFCC)”,

9.        E.Natsheh, A.B. Jantan, S.Khatun, and S. Subramaniam, “Fuzzy Active Queue Management for Congestion Control in Wireless Ad-Hoc”, The International Arab Journal of Information Technology, Vol. 4, No. 1, Jan. 2007. Pp. 50-55.

10.     G.H.Ahn, A.T.Campbell, A.Veres, and L.H.Sun, “SWAN: Service Differentiation in Stateless Wireless Ad hoc Networks”, In the Proc.of IEEE Infocom, June, 2002

11.     S.B.Lee,G.S.Ahn,X.Zhang and A.T Campbell, “INSIGNAI: An IP-Based Quality of Service Framework for Mobile Ad Hoc networks,”, Journal of parallel and Distributed Computing, Special Issue on Wireless and Mobile computing and communication, vol.60,no.4,Aprial,2000,pp374-406

12.     H.Xiao,W.K.G,A.Lo and K.Chaing,”Flexible QoS Model for Mobile Ad hoc Networks, “In the Proc of IEEE vehicular technology Conf.Vol1.pp445-449,Tokyo,may 2000

13.     L.Khoukhi, S.Cherkaoui, “Intelligent Solution for Congestion Control in Wireless Ad hoc networks.

14.     Chrysostomos Chrysostomou, “Fuzzy Logic Based Aqm Congestion Control in Tcp/Ip Networks”, Department of Computer Science, University of Cyprus, September 2006

15.     Y.Bazaz, S. kumar and S.Anand, “ Congestion Control Mechanism using Fuzzy Logic, Volume 2, Issue 2, March – April 2013, Page 313- 318

16.     M.Kusumawardani, “Active Queue Management (AQM) and Adaptive Neuro Fuzzy Inference System (ANFIS) As Intranet Traffic Control, Academic Research International, Pp.129-141.

17.     Abdel-Jaber, H., Mahafzah, M., Thabtah, F. and Woodward, M. (2008).Fuzzy Logic Controller of Random Early Detection based on Average Queue Length and Packet Loss Rate,Proc. of SPECTS.


15-26

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

4.

Authors:

A. G. Mostafa, M. A. Sayed, M.Y. Hassaan, K. A. Aly, Y.B. Saddeek, A. El- Taher

Paper Title:

Physical Characterization of Glasses based on Blast Furnace Slag (BFS)

Abstract: Glasses based on Blast Furnace Slag (BFS) were prepared by conventional melt-quenching method. The ultrasonic velocities data of these glasses have been used to determine the elastic modulus. Densities of glass samples were measured by Archimedes’s principle using Toluene as an immersion liquid. The composition dependence of the elastic properties of these glasses was discussed. Furthermore, based on the measured transmittance and absorption spectra in the wavelength range 350-2000 nm, the optical constants (optical band gap (Eg) and index of refraction (n)) have been determined. The addition of BFS produced significant changes such as an increase in the glass density, refractive index, ultrasonic velocities and elastic moduli. On the other side, the BFS additions shifts the absorption edge toward long wavelength side i.e., leading to a decrease in the Eg values. The obtained results were well discussed in terms of the electronic polarizability and the change in the glass structure with the addition of BFS content.

Keywords:
 BFS, prepared glasses, XRF for raw materials, density and molar volume, Ultrasonic measurements.


References:

1. C. Fredericci , E.D. Zanotto, E.C. Ziemath "Crystallization mechanism and properties of a blast furnace slag glass", Journal of Non-Crystalline Solids, 273 (2000) pp. 64-75.
2. A.A. Francis, "Conversion of blast furnace slag into new glass-ceramic material", J. Eur. Ceram. Soc. , 24 (2004) pp. 2819.
3. Y. Yao, K. Yatsuda, T. Watanabe, F. Funabiki, T. Yano, "Investigation on in-flight melting behavior of granulated alkali-free glass raw material under different conditions with 12-phase AC arc", Chemical Engineering Journal, 144 (2008) pp. 317-323.
4. V. Soltan, Z. Taman, B. El-Kaliouby, "Recycling of Ornamental Stones Hazardous Wastes", J. Nat. Res., 2 (2011) pp. 244-249.
5. B. Bridge, A.A. Higazy, "Acoustic and optical Debye temperatures of the vitreous system CoO-Co2O3-P2O5", Journal of Materials Science, 21 (1986) pp. 2385-2390.
6. R. Laopaiboon, C. Bootjomchai, "Influence of CeO2 on structural properties of glasses by using ultrasonic technique: Comparison between the local sand and SiO2", J. Ultrasonics 53 (2013) pp. 907-912.

7. R. Laopaiboon, C. Bootjomchai, "Radiation effects on structural properties of glass by using ultrasonic techniques and FTIR spectroscopy: A comparison between local sand and SiO2", J. An. Nuc. Energy., 68 (2014) pp. 220-227.
8. R. Laopaiboon, C. Bootjomchai, M. Chanphet, J. Laopaiboon, "Elastic properties investigation of gamma-radiated barium lead borosilicate glass using ultrasonic technique", J. An. Nuc. Energy., 38 (2011) pp. 2333-2337.
9. R. Pappalardo, D.L. Wood, R.C. Linares Jr, "Optical absorption study of co-doped oxide systems.II", The Journal of Chemical Physics, 35 (1961) pp. 2041-2059.
10. K. Tanaka, N. Minamikawa, "Optical nonlinearity in PbO-SiO2 glass: Kramers-Kronig analyses", Applied Physics Letters, 86 (2005) pp. 1-3.
11. J. Singh, D. Singh, S.P. Singh, G.S. Mudahar, K.S. Thind, "Optical characterization of sodium borate glasses with different glass modifiers", Materials Physics and Mechanics, 19 (2014) pp. 9-15.
12. E.S. Moustafa, Y.B. Saddeek, E.R. Shaaban, "Structural and optical properties of lithium borobismuthate glasses", Journal of Physics and Chemistry of Solids, 69 (2008) pp. 2281-2287.
13. Y.B. Saddeek, K.A. Aly, S.A. Bashier, "Optical study of lead borosilicate glasses", Physica B: Condensed Matter, 405 (2010) pp. 2407-2412.
14. Y.B. Saddeek, I.S. Yahia, K.A. Aly, W. Dobrowolski, "Spectroscopic, mechanical and magnetic characterization of some bismuth borate glasses containing gadolinium ions", Solid State Sciences, 12 (2010) pp. 1426-1434.
15. H. Doweidar, G.M. El-Damrawi, Y.M. Moustafa, R.M. Ramadan, "Density of mixed alkali borate glasses: A structural analysis", Physica B: Condensed Matter, 362 (2005) pp. 123-132.
16. [16] K. Kodama, T. Matsushita, S. Kojima, "Velocity of sound and elastic properties of Li2O-B2O3 glasses", J. Jpn. Appl. Phys. , 34 (1995) pp. 2570-2574.
17. Y.B. Saddeek, A.M. Abousehly, S.I. Hussien, "Synthesis and several features of the Na2O-B2O3-Bi2O3-MoO3 glasses", Journal of Physics D: Applied Physics, 40 (2007) pp. 4674-4681.
18. K.A. Aly, "Optical band gap and refractive index dispersion parameters of AsxSe70Te30-x (0 ≤ x ≤ 30 at.%) amorphous films", Appl. Phys. A, 99 (2010) pp. 913-919.
19. K.A. Aly, "Optical properties of Ge-Se-Te wedge-shaped films by using only transmission spectra", Journal of Non-Crystalline Solids, 355 (2009) pp. 1489-1495.
20. K.A. Aly, A.M. Abousehly, M.A. Osman, A.A. Othman, "Structure, optical and electrical properties of Ge30Sb10Se60 thin films", Physica B: Condensed Matter, 403 (2008) pp. 1848-1853.
21. J. Tauc, "Electronic properties of amorphous materials", Science, 158 (1967) pp. 1543-1548.
22. Y.B. Saddeek, K.A. Aly, A. Dahshan, I.M.E. Kashef, "Optical properties of the Na2O-B2O3-Bi2O3-MoO3 glasses", Journal of Alloys and Compounds, 494 (2010) pp. 210-213.
23. K.A. Aly, A.M. Abd Elnaeim, M.A.M. Uosif, O. Abdel-Rahim, "Optical properties of Ge-As-Te thin films", Physica B: Condensed Matter, 406 (2011) pp. 4227-4232.
24. V. Dimitrov, T. Komatsu, "An interpretation of optical properties of oxides and oxide glasses in terms of the electronic ion polarizability and average single bond strength", Journal of the University of Chemical Technology and Metallurgy, 45 (2010) pp. 219-250.
25. K.A. Aly, N. Afify, A.M. Aboushly, "Incorporation of Bi, Cd and Zn on the optical properties of Ge20Se80 thin films", Physica B: Condensed Matter, 405 (2010) pp. 1846-1851.
26. https://www.webelements.com/ 
27. Y.B. Saddeek, M.A. Azooz, S.H. Kenawy, "Constants of elasticity of Li2O-B2O3-fly ash: Structural study by ultrasonic technique", Materials Chemistry and Physics, 94 (2005) pp. 213-220.
28. M.H.M. Zaid, K.A. Matori, L.C. Wah, H.A.A. Sidek, M.K. Halimah, Z.A. Wahab, B.Z. Azmi, "Elastic moduli prediction and correlation in soda lime silicate glasses containing ZnO", International Journal of Physical Sciences, 6 (2011) pp. 1404-1410.

 

27-31

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

5.

Authors:

Samkeet Shah, Dakshil Shah, Lakshmi Kurup

Paper Title:

Survey on Detection of Malicious Web Pages and URLs Using Machine Learning

Abstract:  Web based security threat is rising every day. Web pages serve as one of the primary ways for interaction with and for the users. However, certain web application or websites are directed to mislead the user and try to gain access to the user’s system in order to steal sensitive personal information. The old legacy based approaches on malicious web pages or URLs detection consist of using blacklist that check the URL against an existing database of flagged and suspicious links. The World Wide Web has progressed significantly, with the active use of JavaScript, ActiveX, Flash Player and related technologies. The heavy use of these technologies has improved the user experience and available services on web pages. Attackers tend to find security loopholes into these technologies and use them to their advantage. This method however fails to detect ever evolving attack methods. Thus there is a need to use methods that can adopt to and evolve simultaneously with the advancing threats. Hence, in this paper we have reviewed various types of web based attacks and machine learning techniques to detect malicious web pages and URLs.

Keywords:
 Machine Learning, Malicious Webpages, Web Security


References:

1.     Ramana, Bendi Venkata, M. Surendra Prasad Babu, and N. B. Venkateswarlu. "A critical comparative study of liver patients from usa and india: An exploratory analysis." International Journal of Computer Science Issues 9.2 (2012): 506-516.
2.     Chong, Christophe, Daniel Liu, and Wonhong Lee. "Malicious URL Detection."

3.     Sehun Yoo, Sehun Kim “Two-Phase Malicious Web Page Detection Scheme Using Misuse and Anomaly Detection” International Journal of Reliable Information and Assurance Vol.2, No.1, 2014

4.     M. Cova, C. Kuregel, and G. Vigna. Detection and analysis of drive-by-download attacks and malicious JavaScript code. In Proc. of the International World Wide Web Conference (WWW’10), Releigh, North Carolina, USA, pages 281–290. ACM, 2010.

5.     Ma, Justin, et al. "Identifying suspicious URLs: an application of large-scale online learning." Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 2009.

6.     Da Huang · Kai Xu · Jian Pei "Malicious URL detection by dynamically mining patterns without pre-defined elements". Springer, WorldWideWeb (2014)

7.     R. B. Basnet and A. H. Sung, “Learning to Detect Phishing Webpages”, Journal of Internet Services and Information Security (JISIS), vol. 4, no. 3, (2014), pp. 21-39.

8.     WANG, Wei-Hong, et al. "A Static Malicious Javascript Detection Using SVM." Proceedings of the International Conference on Computer Science and Electronics Engineering. Vol. 40. 2013.

9.     L. Rokach and O. Maimon, "Decision trees," in, O. Maimon andL. Rokach, Eds. Springer US, 2005, pp. 165-192.

10.  J. Dukart, "Support Vector Machine Classification Basic Principles and Application," pp. 19-19, 2012.

11.  Hyusang Choi, Bin B. Zhu, Heejo Lee “Detecting Malicious Web Links and Identifying Their Attack Types”

12.  DMOZ. Netscape open directory project. http://www.dmoz.org.

32-35

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

6.

Authors:

Shyamily Kuriakose, Rosna P. Haroon

Paper Title:

Predicting The Efficiency of Difficult Queries over Databases using SRC

Abstract:   Keyword queries on databases provide easy access to data, but often suffer from low ranking quality, i.e., low precision and/or recall, as shown in recent benchmarks. It would be useful to identify queries that are likely to have low ranking quality to improve the user satisfaction. For instance, the system may suggest to the user alternative queries for such hard queries. In this paper, we analyze the characteristics of hard queries and propose a novel framework to measure the degree of difficulty for a keyword query over a database, considering both the structure and the content of the database and the query results. We evaluate our query difficulty prediction model against two effectiveness benchmarks for popular keyword search ranking methods. Our empirical results show that our model predicts the hard queries with high accuracy. The proposed method use two level corruption module compare to structured robustness algorithm

Keywords:
 keyword query, query effectiveness , robustness.


References:

1.     Hristidis, L. Gravano, and Y. Papakonstantinou, “Efficient IRstyle keyword search over relational databases,” in Proc. 29th VLDB Conf., Berlin, Germany, 2003, pp. 850–861.
2.     Y. Luo, X. Lin, W. Wang, and X. Zhou, “SPARK: Top-k keyword query in relational databases,” in Proc. 2007 ACM SIGMOD,Beijing, China, pp. 115–126.

3.     V. Ganti, Y. He, and D. Xin, “Keyword++: A framework to improve keyword search over entity databases,” in Proc. VLDB Endowment, Singapore, Sept. 2010, vol. 3, no. 1–2, pp. 711–722.

4.     J. Kim, X. Xue, and B. Croft, “A probabilistic retrieval model for semistructured data,” in Proc. ECIR, Tolouse, France, 2009, pp. 228–239.

5.     N. Sarkas, S. Paparizos, and P. Tsaparas, “Structured annotations of web queries,” in Proc. 2010 ACM SIGMOD Int. Conf. Manage. Data, Indianapolis, IN, USA, pp. 771–782.

6.     G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan, “Keyword searching and browsing in database using BANKS,” in Proc. 18th ICDE, San Jose, CA, USA, 2002, pp. 431–440.

7.     Manning, P. Raghavan, and H. Schütze, An Introduction to Information Retrieval. New York, NY: Cambridge University Press, 2008.

8.     Trotman and Q. Wang, “Overview of the INEX 2010 data centric track,” in 9th Int. Workshop INEX 2010, Vugh, The Netherlands, pp. 1–32,

9.     T. Tran, P. Mika, H. Wang, and M. Grobelnik, “Semsearch ´S10,”in Proc. 3rd Int. WWW Conf., Raleigh, NC, USA, 2010.

36-38

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

7.

Authors:

Chinnu C. George, Abdul Ali

Paper Title:

Information Filtering Model Based on Topic Pattern for Document Modeling

Abstract:  In the field of machine learning and text mining topic modelling is widely used. Topic modelling generates models to discover the hidden topics in a collection of documents and each of these topics are represented by the distribution- of words. Many term-based and pattern-based approaches are there in the field of information filtering. Patterns are more discriminative than the single words. In many pattern-based methods only the presence or absence of the patterns in the documents are considered. Even if the pattern occurs multiple times in the documents to be filtered equal importance is considered. Another problem with the existing pattern-based methods is that the semantics of the terms in the patterns are not considered. Another limitation is that the distribution of the patterns is not given any importance. To deal with the above limitations and problems this paper includes a new ranking method that considers the frequency of the patterns, pattern distribution and semantic based pattern representation to estimate the relevance of the documents based on the user information needs. This helps to filter out the irrelevant documents effectively. Extensive experiments are conducted using the TREC data collection Reuters Corpus Volume 1 to evaluate the effectiveness of the proposed method .The result shows that the proposed model outperforms the pattern based topic for document modeling in information filtering.

Keywords:
 Topic modelling, information filtering, user interest modeling, semantic based relevance ranking.


References:
1.        S. Robertson, H. Zaragoza, and M. Taylor, “Simple BM25 extension to multiple weighted fields,” in Proc. 13th ACM Int. Conf. Inform. Knowl. Manag., 2004, pp. 42–49.
2.        F. Beil, M. Ester, and X. Xu, “Frequent term-based text clustering,” in Proc. 8th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2002, pp. 436–442.

3.        X. Wei and W. B. Croft, “LDA-based document models for ad-hoc retrieval,” in Proc. 29th Annu. Int. ACM SIGIR Conf. Res. Develop. Inform. Retrieval, 2006, pp. 178–185.

4.        D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003.

5.        T. Hofmann, “Probabilistic latent semantic indexing,” in Proc. 22nd Annu. Int. ACM SIGIR Conf. on Res. Develop. Inform. Retrieval, 1999, pp. 50–57.R.

6.        Hanani, U., Shapira, B., and Shoval, P. (2001). Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction, 11(3):203–259.

7.        S.-T. Wu, Y. Li, and Y. Xu, “Deploying approaches for pattern refinement in text mining,” in Proc. 6th Int. Conf. Data Min., 2006, pp. 1157–1161.

8.        N. Zhong, Y. Li, and S.-T. Wu, “Effective pattern discovery for text mining,” IEEE Trans. Knowl. Data Eng., vol. 24, no. 1, pp. 30–44, Jan. 2012.

9.        S. Robertson, H. Zaragoza, and M. Taylor, “Simple BM25 extension to multiple weighted fields,” in Proc. 13th ACM Int. Conf. Inform. Knowl. Manag., 2004, pp. 42–49.G.H.

10.     J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: Current status and future directions,” Data Min. Knowl. Discov., vol. 15, no. 1, pp. 55–86, 2007.

11.     L. Azzopardi, M. Girolami, and C. Van Rijsbergen, “Topic based language models for ad hoc information retrieval,” in Proc. Neural Netw. IEEE Int. Joint Conf., 2004, vol. 4, pp. 3281–3286

12.     C. Wang and D. M. Blei, “Collaborative topic modeling for recommending scientific articles,” in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2011, pp. 448–456.

13.     Y. Zhang, J. Callan, and T. Minka, “Novelty and redundancy detection in adaptive filtering,” in Proc. 25th Annu. Int. ACM SIGIR Conf. Res. Develop. Inform. Retrieval, 2002, pp. 81–88.

14.     Gao, Y., Xu, Y., and Li, Y. (2013a). Pattern-based topic models for information filtering. In Proceedings of International Conference on Data Mining Workshop SENTIRE, ICDM’2013. IEEE.J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufman, 1993.

39-43

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

8.

Authors:

Mitty Abraham, Safiya K.M

Paper Title:

Rule-Based Entity Resolution using Distinct Tree

Abstract:   Entity resolution identifies object referring to the same entity .Entity resolution is performed by generating rules from training set and applies them on records. Traditional ER considered each attribute value as the rule in a random fashion and performs conjunction with other rules according to length threshold .This method is very complex and tedious. Our proposed method generated rules from a distinct tree using RL method, which consider the length criteria and RNN methods which does not. Distinct tree is formed by arranging attribute and its value of records in the training set in a particular fashion .These generated rules are applied to the dataset for entity identification .Our experimental results show that the proposed method is more accurate.

Keywords:
  Entity resolution, Length criteria


References:

1.     Lingli Li, Jianzhong Li, and Hong Gao, “Rule-Based Method for Entity Resolution,” IEEE Transactions On Knowledge And Data Engineering, vol. 27, no. 1, january 2015.
2.     S. Chaudhuri, V. Ganti, and R. Motwani, “Robust identification of fuzzy duplicates,” in Proc. 21st Int. Conf. Data Eng., 2005, pp. 865–876.

3.     R. Baxter, P. Christen, and T. Churches,” A comparison of fast blocking methods for record linkage”. In Proceedings of the ACM SIGKDD workshop on data cleaning, record linkage, and object identification, August 2003. 

4.     Sheila Tejada, Craig A. Knoblock, and Steven Minton,“Learning Object Identification Rules For Information Integration” Information Systems    vol. 26, no. 8, pp. 607-633, 2001.

5.     M. Ganesh,Jaideep Srivastava and Travis Richardson”Mining Entity-Identification Rules For Database    Integration”, KDD-96 Proceedings,1996.

6.     Monge and C. Elkan.,” An Efficient Domain Independent Algorithm For Detecting Approximately Duplicate Database Records”. In Proceedings of the SIGMOD Workshop on Data Mining and Knowledge Discovery, Arizona, May 1997.

7.     Jeremy A. Hylton,” Identifying and Merging Related Bibliographic Records”  M.I.T. Laboratory for Computer Science Technical , June       1996

8.     Alvaro E. Monge and Charles P. Elkan ,”The field matching problem: Algorithms and applications” KDD-96 Proceedings,1996     


44-47

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

9.

Authors:

Adrian Titi Pascu, Daniel Băcescu

Paper Title:

Computation Specifics for a Family of Monochromators with a Plane Diffraction Grid

Abstract:  The use of a family of monochromators offers the possibility of obtaining high performance measurements for multiple parameters. The Czerny – Turner version is the one studied in this paper. The choice is justified by the fact that this schematic allows the obtaining of very low levels of dispersed light. In addition, the technological execution of a mirror of the type employed by the Ebert schematic is difficult to construct and expensive. The spectral interval proposed for the study is 280 nm – 750 nm

Keywords:
   Czerny – Turner monochromator, diffraction grid, mirror, spectrometer


References:

1.        Dumitrescu N, Optică tehnică, Curs I.P.B,1980
2.        Curatu E, Calitatea sistemelor optice. Funcția de transfer optic, Ed. Academiei Române,1992

3.        Dumitrescu N., Ionescu S., Opticã instrumentală, Ed. Politehnica, Bucureşti, 1991.

4.        D.Băcescu, Optică Aplicată. Analiza şi sinteza componentelor,  Ed. Medro 2004.          

5.        Dodoc, P. - Calculul şi construcţia aparatelor optice, E.D.P., 1983

6.        Dodoc, P. - Teoria şi construcţia aparatelor optice, Ed. Tehnică, 1989
7.        Dodoc, P. - Teoria şi construcţia sistemelor optice,  


48-52

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html