Ph. D. (Computer Engineering)
M.E. (Computer Engineering)
B.E. (Computer Engineering)
| From Date |
To Date |
Designation |
Organization |
Organization Address |
| 2023-06-12 |
2023-06-23 |
Associate Professor |
SIESGST |
Sri Chandrasekarendra Saraswati Vidyapuram Sector-V, Nerul, Navi Mumbai, Maharashtra 400706 |
| 2007-07-26 |
2012-06-25 |
Assistant Professor |
BVCOE |
Bharati Vidyapeet College of Engineering, Belapur, Navi Mumbai |
| 2004-06-24 |
2023-06-25 |
Assistant Professor |
RAIT |
Ramrao Adik Institute of Technology Sector 7, Phase I, Pad. Dr. D. Y. Patil Vidyapeeth, Nerul, Navi Mumbai – 400706. |
| Date |
Title |
Type |
Level |
Academic Year |
| 2023-04-19 |
Deep Learning and Binary Representational Image Approach for Malware Detection |
Conference |
International |
2022-23 |
| 2023-06-10 |
Visual Speech Recognition: A Deep Learning Approach |
Conference |
International |
2019-20 |
| 2023-06-16 |
Image hashing using AQ-CSLBP with double bit quantization |
Conference |
International |
2015-16 |
| 2016-09-09 |
Image hashing by SDQ-CSLBP |
Conference |
International |
2016-17 |
| 2016-12-19 |
Image hashing by CCQ-CSLBP |
Conference |
International |
2016-17 |
| 2016-11-26 |
Image hashing by LoG-QCSLBP |
Conference |
International |
2016-17 |
| 2019-08-01 |
Adaptive CSLBP compressed image hashing |
Journal |
International |
2019-20 |
| 2019-08-01 |
Modified CSLBP |
Journal |
International |
2019-20 |
| 2019-03-08 |
Image Hashing Using DWT-CSLBP |
Journal |
International |
2018-19 |
| 2018-04-06 |
Compressed Image Hashing using Minimum Magnitude CSLBP |
Journal |
International |
2018-19 |
| 2020-01-14 |
Image Mining by Multiple Features |
Conference |
International |
2019-20 |
| 2021-10-21 |
Attention Span Detection for Online Lectures |
Conference |
International |
2021-22 |
| 2022-04-07 |
Real-Time Convolution Neural Network for Emotion Classification |
Conference |
International |
2021-22 |
| Date |
Title |
Presented/Published |
Level |
Academic Year |
Organizer / Venue |
| 2016-06-09 |
IMAGE HASHING USING AQ-CSLBP WITH DOUBLE BIT QUANTIZATION |
Presented And Published |
International |
2016-2017 |
IEEE
Warsaw, Poland |
| This Paper Presents A Novel Image Hashing Method For Authentication And Tampering Detection. Creation, Modification And Transfer Of Multimedia Data Becomes An Easy Task Due To Digitization. Integrity Is Very Important For Crucial And Sensitive Matters Like Medical Records, Legal Matters, Scientific Research, Forensic Investigations And Government Documents. Image Hashing Is One Of The Popular Method To Maintain Integrity Of Image. We Proposed AQ-CSLBP (Average Compressed Center Symmetric Local Binary Pattern) As A Feature Descriptor For Image Hashing. First, Image Is Divided Into Sub Blocks, AQ-CSLBP Is Applied On Each Sub Block To Generate 8 Bin Histogram As A Feature. Then, We Used Double Bit Quantization To Generate Hash Code For The Image. The Proposed Method Is Compared With The Existing Methods By Way Of Quantitative Analysis, It Has Been Proved That The Proposed Method Works Well In Authentication Scenario With Less Hash Length And More Discrimination Power. |
| 2016-09-23 |
IMAGE HASHING BY SDQ-CSLBP |
Presented And Published |
International |
2016-2017 |
International Conference On Advances In Computing, Communications And Informatics (ICACCI)
Jaipur, India |
| Approach For Image Hashing Is To Use Powerful Feature Descriptor Which Captures Essence Of An Image. Applications Of Image Hashing Lies In The Area Of Content Authentication, Structural Tampering Detection, Retrieval And Recognition. Hashing Is A Compact Summarized Information Of An Image. Center Symmetric Local Binary Pattern (CSLBP) Is One Of The Powerful Texture Feature Descriptor Which Captures The Smallest Amount Of Change. Using CSLBP, Appressed Hash Code Can Be Obtained For An Image. If CSLBP Feature Is Weighted By A Boost Factor, It Will Enhance Success Rate Of An Image Hashing. The Proposed Method Of SDQ-CSLBP Extract Texture Feature Using CSLBP With Standard Deviation As Weight Factor. Standard Deviation Which Represents Local Contrast Is Also A Powerful Descriptor. Resultant Histogram Of CSLBP Is Of 16 Bin For Each Block Of An Image. Further It Can Be Compressed To 8 Bin By Using The Flipped Difference Concept. Without A Weight Factor, Compressed CSLBP Has Low Discrimination Power. Experimental Results Show That The Proposed Method Is Robust Against Content Preserving Manipulation And Sensitive To Content Changing And Structural Tampering. |
| 2016-01-19 |
IMAGE HASHING BY CCQ-CSLBP |
Presented And Published |
International |
2016-2017 |
IEEE International WIE Conference On Electrical And Computer Engineering (WIECON-ECE)
Pune, India |
| Recent Trends And Also Statistics Show That The Hashing Concept Has Been Gaining Importance Because Its Application Lies In The Area Of Authentication, Retrieval And Recognition. The Proposed Hashing Method CCQ-CSLBP (Compressed CSLBP With Correlation Coefficient) For Authentication Utilized Correlation Coefficient As A Weight Factor In CSLBP. CSLBP Is A Texture Operator, Whose Performance Is Better Than LBP, In Terms Of Rotation Invariance, Differentiation Power And Less Number Of Histogram Bin. In CSLBP, Histogram Of Each Sub Block Is Represented By 16 Bin. Reduction In Number Of Histogram Bin Is Achieved By The Flipped Difference Concept. Proposed Method Takes The Advantage Of Correlation Coefficient As A Boosting Factor During Histogram Construction, To Increase The Discrimination Power Of Compressed CSLBP. With The Use Of Correlation Coefficient, Results Of CSLBP Improved, In Terms TPR And FPR. CCQ-CSLBP Is Evaluated, Based On Normalized Hamming Distance And ROC Characteristics. |
| 2016-11-27 |
IMAGE HASHING BY LOG-QCSLBP |
Presented |
International |
2016-2017 |
ACM
Singapore |
| This Paper Presents An Image Hashing Algorithm For Authentication And Tampering Based On Texture Features. Center Symmetric Local Binary Pattern (CSLBP) Feature Is Computationally Simple, Rotation Invariant Which Works In Spatial Domain. In CSLBP, Number Of Histogram Bin For Each Sub Block Of An Image Is 16, Unlike 256 Bin In Local Binary Pattern (LBP). In Our Proposed Method, Flipped Difference Is Used To Generate A Histogram Of Only 8 Bin, For Each Sub Block. Resultant Method With 8 Bin Histogram Has Less Discrimination Power. To Enhance Discrimination Power, Laplacian Of Gaussian (LoG) Is Used As A Weight Factor During Histogram Construction. LoG Is Used To Find A Characteristic Scale For A Given Image Location. LoG Is A Second Order Derivative Edge Detection Operator Which Performs Well In Presence Of Noise. In Our Previous Papers, We Tried Various Local Descriptors Like Magnitude Of Difference, Standard Deviation, Coefficient Correlation As A Weight Factor, To Enhance The Success Rate Of Compressed CSLBP. Proposed LoG-QCSLBP Gives Good Results For JPEG, Salt & Pepper Noise, Brightness Plus, Increase/decrease Contrast. In The Results Section, We Compared All Variants Of Compressed CSLBP. Results Clearly Show That By Incorporating The Weight Of A Local Descriptor, Discrimination Power Of Compressed CSLBP Is Enhanced. |
| 2019-02-23 |
IMAGE MINING USING MULTIPLE FEATURES |
Presented And Published |
International |
2014-2015 |
Springer
Bhubaneswar, Odisha |
| This Paper Discusses An Image Mining Algorithm For Efficient And Accurate Image Retrieval. The Proposed Method Uses Multiple Image Features Combination. Color Feature In The Form Of 1st Order, 2nd Order And 3rd Order Moment Is Calculated On A Sub-block. These Moments Calculates Statistics Features Over A Local Region. The Computed First Order Moment Is Considered As Mean. Standard Deviation And Skewness Is Considered As Second Order And Third Order Moment Respectively. These Features Are Calculated For Images And Stored In Database. For Texture Extraction, Center Symmetric Local Binary Pattern (CSLBP) Feature Is Used. CSLBP Is Straightforward In Computation And It Is Also Rotation Consistent. CSLBP Generates Histogram Of 16 Bin For One Sub-block. In Order To Capture Minute Details Around Local Region, Laplacian Of Gaussian (LoG) Is Used As A Weight Factor During CSLBP Histogram Construction. This Weight Factor Makes Texture Feature More Powerful And Increases Its Discrimination Power. Resultant Weighted Histogram Is Quantized And Binary Result Is Stored In Database. For Test Image, Its Texture And Color Features Compared With Stored Texture And Color Features. Based On Color And Texture Feature’s Correlation With Test Image, Results Are Retrieved. Our Results Clearly Shows That, By Incorporating The Multiple Features As Well As Local Weight Factor, The Proposed Image Mining Gives Desirable Retrieval Results. |
| Date |
Title |
Type |
Level |
Role |
Venue |
| 2016-06-10 |
Presented Paper: Image Hashing Using AQ-CSLBP With Double Bit Quantization |
CONFERENCE |
INTERNATIONAL |
PRESENTER |
Warsaw, Poland |
| 2016-09-24 |
Presented Paper: Image Hashing By SDQ-CSLBP |
CONFERENCE |
INTERNATIONAL |
PARTICIPANT |
Jaipur, India |
| 2019-02-22 |
Paper Presented: Image Mining By Multiple Features |
CONFERENCE |
INTERNATIONAL |
PARTICIPANT |
Bhubaneswar, Odisha |
| 2016-11-22 |
Paper Presented: Image Hashing By CCQ-CSLBP |
CONFERENCE |
INTERNATIONAL |
PARTICIPANT |
Pune, India |
| Year |
Award Title |
Awarded By |
| 2021-22 |
Eye Disease Classification using Deep Neural Networks |
Applied Sciences and Management (IC-TEAM International Conference on Trends in Engineering, 2022) |
| Date |
Title |
Details |
| 2017-08-07 |
Image Processing 2012-16 Batch |
SIES GRADUATE SCHOOL OF TECHNOLOGY NERUL, NAVI MUMBAI DEPARTMENT OF COMPUTER ENGG SEM:- VII BRANCH:-CE UNIT I: INTRODUCTION For Odd Roll No: Odd Questions And Vice Versa No. Question 1. Applications Of Image Processing 2. Define: I) Spatial Resolution (ii) Intensity Resolution (iii) PSF (Point Spread Function) Iv) Dynamic Range 3. Uniform And Non-uniform Sampling. 4. Quality Of Picture Depends On The Number Of Pixels And Grey Levels That Represents In The Picture. 5. Various Color Models 6. Explain Checkboard Pattern? When Does It Happen In Digital Image 7. What Is False Contouring? When Does It Happen In Digital Images? 9. State Fidelity Objective And Subjective Criteria Of Image Evaluation 10 Isopreference Curve 11. Chroma Subsampling |
| Academic Year |
Type |
Details |
| 2019-2020 |
Co-curricular |
NPTEL- Introduction To Artificial Intelligence. Topper [88%] |
| 2018-2019 |
Co-curricular |
Introduction To R Software Gold Medal [94%] |
| 2018-2019 |
Co-curricular |
Introduction To Fuzzy Logic's & Neural Networks Silver Medal [81%] |
| 2018-2019 |
Co-curricular |
Introduction To Soft Computing Gold Medal [91%] |
| 2018-2019 |
Co-curricular |
NPTEL: Introduction To Machine Learning Topper 5% |
