International Journal of Emerging Science and Engineering (TM)
Exploring Innovation | ISSN:2319–6378(Online)| Reg. No.:68120/BPL/CE/12 | Published by BEIESP | Impact Factor:4.72
Home
Articles
Conferences
Editors
Scopes
Author Guidelines
Publication Fee
Privacy Policy
Associated Journals
Frequently Asked Questions
Contact Us
Volume-4 Issue-5: Published on June 25, 2016
07
Volume-4 Issue-5: Published on June 25, 2016

 Download Abstract Book

S. No

Volume-4 Issue-5, June 2016, ISSN:  2319–6378 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Ashish Kumar Sharma, Shiv Kumar Sahu, Amit Mishra

Paper Title:

Prototype Based Image Forgery Detection Based on Clustering and DWT Transform

Abstract:   In this paper proposed a image forgery detection method. The proposed method is a combination of prototype of clustering and transforms function. The prototype clustering technique gives the patch pattern and wavelet transform gives texture feature. For the texture extraction of image used wavelet transform function, these function is most promising texture analysis feature. For the selection of feature generation of pattern used clustering technique. Clustering technique is unsupervised learning technique process by iteration. The proposed method achieves 100% accuracy in just copy-move forgery (without any change in the size or characteristics of the object) forgery without post-processing and 98.43%, 86.58%, and 95.12% accuracies in copy-move forgery with rotation, scaling, and reflection, respectively.          

Keywords:
Image forgery detection, Digital images, Photography, Haar Transform, Wavelet, SBD.


References:

1.       Jian Li, Xiaolong Li, Bin Yang,  Xingming Sun “Segmentation-Based Image Copy-Move Forgery Detection Scheme” IEEE  2015 PP 507-518.
2.       Edoardo Ardizzone, Alessandro Bruno, Giuseppe Mazzola “Copy–Move Forgery Detection by Matching  Triangles of Keypoints”  IEEE 2015 PP 2084- 2093.

3.       Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva  “Efficient Dense-Field Copy–Move Forgery Detection” IEEE 2015 PP 2284-2296

4.       B.L.Shivakumar, Lt. Dr. S.Santhosh Baboo “Detecting Copy-Move Forgery in Digital Images: A Survey and Analysis of Current Methods” Global Journal of Computer Science and Technology 2010 PP 660-664.

5.       Raymond B. Wolfgang and Edward J. Delp “A Watermark For Digital Images”.

6.       Tanzeela Qazi, Khizar Hayat, Samee U. Khan, Sajjad A. Madani, Imran A. Khan, Joanna Kołodziej, Hongxiang Li, Weiyao Lin, Kin Choong Yow, Cheng-Zhong Xu “Survey on blind image forgery detection IET Image Processing” IET 2013 PP 660-669.

7.       Gajanan K. Birajdar , Vijay H. Mankar   “Digital image forgery detection using passive techniques: A survey”  ELESEVIER Digital Investigation 2013 PP 226–245.

8.       Archana V. Mire, Dr S. B. Dhok, Dr N. J. Mistry , Dr P. D. Porey “Catalogue of Digital Image Forgery Detection Techniques, an Overview”  Elsevier, 2013 502-508.

9.       Gung Polatkan, Sina Jafarpour, Andrei Brasoveanu, Shannon Hughes, Ingrid Daubechies “Detection Of Forgery In Paintings Using Supervised Learning”

10.    Yu-Feng Hsu ,Shih-Fu Chang “Detecting Image Splicing Using Geometry Invariants And Camera Characteristics Consistency”

11.    Gang Cao, Yao Zhao and Rongrong Ni “Edge-based Blur Metric for Tamper Detection” Journal of Information Hiding and Multimedia Signal Processing 2010.PP 20-27.

12.    Chih-Chung Hsu , Tzu-Yi Hung, Chia-Wen Lin , Chiou-Ting Hsu “Video Forgery Detection Using Correlation of Noise Residue” 2013.


1-5

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

























2.

Authors:

Kushal G. Dhawad

Paper Title:

Sensor less BLDC Motor Control with Back EMF Zero Crossing Detection using PIC 18F2431 MCU

Abstract: From Mid 19th Century Brushless Direct Current (BLDC) motors are one of the motor types that are rapidly gaining popularity. These motors are being utilized in many of the industrial, commercial appliances such as in Automotive, Aerospace, Medical Instruments, Industrial automation equipment's & Instrumentation. These motors do not use brushes for commutation; instead, they are electronically commuted which is having complex control circuitry. For this electronic sensor circuits like Hall Effect sensor & Rotary encoders were used to directly measure the rotor’s position. But with Sensorless control a BLDC motor call for commutation based on the Back-EMF which is produced in the stator windings. Sensorless control has two distinct advantages: 1. Lower system cost & 2. Increased reliability. Hall Effect sensors are not required for this type of control of BLDC motors.  

Keywords:
 Sensorless BLDC motor control, BLDC motor, 3-phase bridge inverter circuit, IGBT driver.      


References:

1.       http://www.microchip.com/design-centers/motor-control-and-drive/motor-types/bldc
2.       http://www.digikey.com/en/articles/techzone/2013/jun/controlling-sensorless-bldc-motors-via-back-emf H. Poor, An Introduction to Signal Detection and Estimation.   New York: Springer-Verlag, 1985, ch. 4.

3.       http://www.epd-ee.eu/print.php?id=8118

4.       Sensorless BLDC Motor Control Using MC9S08AW60, DRM086, Freescale 2007.

5.       3-phase BLDC/PMSM Low Voltage Motor Control Drive User’s Manual, LVMCDBLDCPMSMUG, Freescale 2008.

6.       MC9S08AC16 Controller Daughter Board User’s Manual, CDBBLDCPMSMUG, Freescale 2008.

7.       MC9S08AC16: Technical Data Sheet for MC9S08AC8/AC16/AW8A/AW16A MCU’s, Freescale 2008.

8.       3-phase BLDC Motor Control with Sensorless Back EMF Zero Crossing Detection Using 56F80x, AN1914, Freescale 2005.

9.       FreeMaster for Embedded Apllications User Manual, Freescale 2004

10.    https://en.wikipedia.org


6-9

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

3.

Authors:

Harish Patidar, Amit Mishra, Shiv K. Sahu

Paper Title:

Detection of Software Cloning by using Visual Detection Technique with Result Analysis

Abstract:  Code duplication or copying a code fragment and then reuse by pasting with or without any modifications is a well known code smell in software maintenance. Many record show that about 5% to 20% of software systems can contain duplicated code, which is basically the result of copying existing code fragments and using then by pasting with or without minor modifications. Researchers think clones lead to additional changes during maintenance phase, in later stage increase the overall maintenance effort because of to find modified cloned code in same or another code file within time period with higher accuracy for any kind of modification in it. This project use visual detection technique to find the clone code. Visual detection technique uses near-miss clones detection method to find clones in a program file with higher accuracy and give results better than current Clone detection techniques.

Keywords:
  Code clone, Web server application, Visual detection, Token and flag.          


References:

1.       D.Gayathri Devi and Dr. M Punithavalli “DETECTING     SOFTWARE CLONES USING ASSOCIATION RULE MINING “Volume 3, Issue 1, Jan. 2013.
2.       Shaheen Khatoon and Azhar Mahmood “An Evaluation of Source Code Mining Techniques” Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD),  2011, Page No.234-246.

3.       Chanchal Kumar Roy and James R. Cordy ,“ClonesA Survey on Software Clone Detection Research”, September 26, 2007, PageNo.1-115.

4.       Christopher Forbes, Iman Keivanloo, Juergen Rilling “Doppel-Code: A Clone Visualization Tool for Prioritizing Global and Local Clone”, IEEE 36th International Conference on Computer Software and Applications, 2012, PageNo.366-368.

5.       Rainer Koschke, “Large-Scale Inter-System Clone Detection Using Suffix Trees”, 16th European Conference on Software Maintenance and Reengineering, 2012, pp. 309-318.

6.       Yuehua Zhang, Ying Liu, Lingling Zhang and Yong Shi,“ A Data Mining Based Method Detecting Software Defects in Source Code” in ICSM ’98: Proceedings of the International Conference on Software Maintenance, 1998, Page No. 368–377.

7.       Alexander Breckel “Error Mining: Bug Detection through Comparison with Large Code Databases,” MSR 2012, Zurich, Switzerland, page No.175-178.

8.       Salwa K. Abd-El-Hafiz “A Metrics-Based Data Mining Approach for Software Clone Detection”,  36 th International Conference on Computer Software and Applications, 2012, PageNo.35-42.

9.       YOSHIHITO HIGO AND SHINJI KUSUMOTO, “HOW OFTEN DO UNINTENDED INCONSISTENCIES HAPPEN? DERIVING MODIFICATION PATTERNS AND DETECTING OVERLOOKED CODE FRAGMENTS,” 2012 28TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE (ICSM)

10.    S. BELLON, R. KOSCHKE, G. ANTONIOL, J. KRINKE AND E. MERLO, “COMPARISON AND EVALUATION OF CLONE DETECTION TOOLS,” IEEE TSE, VOL. 33, NO. 9, 2007, PP. 577-591.

11.    FILIP VAN RYSSELBERGHE, SERGE DEMEYER “EVALUATING CLONE DETECTION TECHNIQUES”, 2010, PAGENO.1-12.

12.    Chanchal Roy “A Mutation / Injection-based Automatic Framework for Evaluating Code Clone Detection Tools”,The 9th CREST Open Workshop.

13.    Aaron Bloomfield “Scanning”, 2005,  ppt 1-32.

14.    Wu Zhifei ,Wang Tie, Zhang Qinghua, GaoTingyu, Li Hongfang,”Research on Generating Detector  Algorithm in Fault Detection” page 7-8.


10-15

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




























































4.

Authors:

Devyani Jivani, Aadish Agrawal, Abhishek Mukherjee, Heli Shah

Paper Title:

The Amalgamated Algorithm

Abstract:   In today’s world, the main communication form on the Internet is human to human. But it is foreseeable that in the near future every object will have a unique way of identification and will be addressable so that it can be connected and can communicate interchangeably. The Internet will soon become the Internet of Things (IoT). In this paper we have discussed the applications of IoT and then further have proposed an algorithm which we have called as the ‘Amalgamated Algorithm’ since it uses an IoT based device as a source of input and using the Map Reduce Framework, monitors the heart beat of patients continuously for abnormalities.

Keywords:
IoT; Map Reduce; medical application; heartbeat;              


References:

1.    J. Daniel Garcia, Jesus Carrretero, “The Internet of things: Connecting the world”.
2.    Feng Xial, Lawrence T. Yang, Lizhe Wanng, Alexey Vine[2012, p. 1].
3.    http://www.youtube.com/watch?v=Q3ur8wzzhBU.

4.    http://www.libelium.com.

5.    R. Javier Cubo *, Adrián Nieto and Ernesto Pimentel, “A Cloud-Based Internet of Things Platform for Ambient Assisted Living”.

6.    Nabeena Ameen, “AnExtensive Review of Significant Researches on Cloud Based Elderly Patient Monitoring During Emergency Using Wireless Sensor Networks”.


16-18

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



















5.

Authors:

Gabriella Leggio

Paper Title:

Acute Lymphoblastic Leukemia

Abstract: Leukemia is a cancer of the blood and bone marrow due to increased amounts of abnormal or immature white blood cells. A type of leukemia that occurs in the early forms of lymphocytes is called acute lymphoblastic leukemia (ALL). Diagnosis is made by the confirmation of different tests that are preformed. Once diagnosed, ALL is aggressively treated following an oncologist’s treatment plan. There are a few different options for treatment of ALL. However, the prognosis for acute lymphoblastic leukemia is very poor. There is current research being conducted for new treatments and agents in order to cure patients with acute lymphoblastic leukemia (ALL).

Keywords:
 Leukemia, ALL, leukemia, Once diagnosed, confirmation


References:

1.       Abbott, J. (2013) Blood–brain barrier structure and function and the challenges for CNS drug delivery. Journal of Inherited Metabolic Disease, 36(3), 437-449.
2.       Appelmann, I., Rillahan, C. D., et al. (2015). Janus kinase inhibition by ruxolitinib extends dasatinib- and dexamethasone-induced remissions in a mouse model of Ph+ ALL. Blood, 125(9), 1444–1451.

3.       Blazar, B. R., Murphy, W. J., & Abedi, M. (2012). Advances in graft-versus-host disease biology and therapy. Nature Reviews Immunology, 12(6), 443-458.

4.       Chan A, Tetzlaff J, Altman D, et al. (2013) SPIRIT 2013 Statement: Defining Standard Protocol Items for Clinical Trials. Annals of Internal Medicine, 158(3), 200-207.

5.       Gastier-Foster, J. (Ed.). (2010). Molecular Pathology of Hematolymphoid Diseases.  Molecular Pathology Library 4. Columbus, OH. pp. 287.

6.       Hahn, T., McCarthy, P., Hassebroek L., et al. (2013). Significant Improvement in Survival After Allogeneic Hematopoietic Cell Transplantation During a Period of Significantly Increased Use, Older Recipient Age, and Use of Unrelated Donors. Journal of Clinical Oncology, 31(19), 2437–2449.

7.       Henig, I., & Zuckerman, T. (2014). Hematopoietic Stem Cell Transplantation—50 Years of Evolution and Future Perspectives. Rambam Maimonides Medical Journal, 5(4)

8.       Hjortholm, N., Jaddini, E., Hałaburda, K., et al. (2013). Strategies of pain reduction during the bone marrow biopsy. Annals of Hematology, 92(2), 145–149.

9.       Inaba H, Pui CH. (2013). Glucocorticoid use in acute lymphoblastic leukemia. Lancet Oncology, 11: 1096–106.

10.    Jain, N., Lamb, A., et al. (2016) Early T-cell precursor acute lymphoblastic leukemia/lymphoma (ETP-ALL/LBL) in adolescents and adults: a high-risk subtype. Blood. 127(15), 1863-1869.

11.    Kondo, M. (2010) Lymphoid and myeloid lineage commitment in multipotent hematopoietic progenitors. Immunological Reviews, 238(1), 37–46.

12.    Leukemia & Lymphoma Society. (2016) Acute Lymphoblastic Leukemia. http://www.lls.org/leukemia/acute-lymphoblastic-leukemia (Mar 19, 2016).

13.    Majhail, N., Fernia, S., et al. (2015). Indications for Autologous and Allogeneic Hematopoietic Cell Transplantation: Guidelines from the American Society for Blood and Marrow Transplantation. Biology of Blood and Marrow Transplantation, 21(11), 1863–1869.

14.    Øbro, N., Ryder, L., et al. (2012) Identification of residual leukemic cells by flow cytometry in childhood B-cell precursor acute lymphoblastic leukemia: verification of leukemic state by flow-sorting and molecular/cytogenetic methods. Haematologica, 97(1), 137-141.

15.    Peters, C. Schrappe, M. Von Stackelberg, A, et al. (2015). Stem-cell transplantation in children with acute lymphoblastic leukemia: a prospective international multicenter trial comparing sibling donors with matched unrelated donors-The ALL-SCT-BFM-2003 trial. Journal of Clinical Oncology, 33, 1265–1274.

16.    Pocock, S. J. (ed.) (2013) Clinical trials: a practical approach.  John Wiley & Sons.  New York, NY. pp. 1.

17.    Rawlinson, N., Baker, P., Kahwash, B. (2011) Burkitt’s Leukemia with an atypical immunophenotype: report of a case and review of literature. Lab Hematol. 17(4), 27-31.

18.    Soverini, S., De Benedittis, C., Papayannidis, C., et al. (2014) Drug resistance and BCR-ABL kinase domain mutations in Philadelphia chromosome-positive lymphoblastic leukemia from the imatinib to the second-generation tyrosine kinase inhibitor era: the main challenge are in the type of mutations, but not in the frequency of mutation
involvement. Cancer, 120: 1002–1009.

19.    Tang, G., Zuo, Z., et al. (2012). Precursor B-acute lymphoblastic leukemia occurring in patients with a history of prior malignancies: is it therapy-related? Haematologica, 97(6), 919–925.

20.    Talamo G, Liao J, Bayerl M, et al. (2010) Oral administration of analgesia and anxiolysis for pain associated with bone marrow biopsy. Support Care Cancer, 18(3), 301–305.

21.    Texas Children's Hospital. (2016) Health Conditions, Leukemia. <http://www.texaschildrens.org/health/leukemia> (Mar 22, 2016).

22.    U.S. National Library of Medicine. (March, 2016) Translocation. <https://ghr.nlm.nih.gov/glossary=translocation> (April 2, 2016)

23.    Wood, J. H. (Ed.). (2013). Neurobiology of cerebrospinal fluid 2.  Springer Science & Business Media. New York, NY. pp.1.

24.    Yu, H., Dong, T., et al. (2013) Individualized leukemia cell-population profiles in common B-cell acute lymphoblastic leukemia patients. Chinese Journal of Cancer, 32(4), 213–223. [24]

25.    Zhang, J., Ding L., et al. (2011) The genetic basis of early T-cell precursor acute lymphoblastic leukemia. Nature, 481(1), 157-163.


19-25

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