Rakesh Kalshetty

Working as Assistant Professor, AWS Educator, BoE, Placement & Social Media Coordinator in Department of CSE(Artificial Intelligence and Machine Learning), CSE(Cyber Security) and M.Tech., in AI at M S Ramaiah Institute of Technology , having 4+ years of Experience as Design Engineer at Shri Siddhi Vinayak Constructions and Builders. I am also a life time Member of IAENG and ISTE. Professional Member of AIER and IEEE. My area of interest are Wireless Sensor Networks, Machine Learning and Internet of Things.

EDUCATION

Ph.D. from Visvesvaraya Technological University in the research area of Wireless Sensor Networks, Machine Learning and Internet of Things. Completed my Bachelor of Engineering and Master of Technology from Poojya Doddappa Appa College of Engineering.

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Computer Science

Scopus Publications

A Critical Benchmark of Hybrid Quantum-Classical Graph Neural Networks for Molecular Property Prediction: Revealing Performance Bottlenecks in Current Architectures
Rakesh Kalshetty, Manish S M, Shashank Kotagi, and Vishwas Desai
IEEE



Polycystic Ovary Syndrome Detection Using Contextual Ensemble Network and ELMAN Neural Network with Green Anaconda Optimization
Rakesh Kalshetty, N. Vedavathi, M. Narender, C. I. Johnpaul, and Tojo Mathew
World Scientific Pub Co Pte Ltd
Polycystic Ovary Syndrome (PCOS) is a metabolic reproductive disorder characterized condition by an extended menstrual cycle. There are many methods currently in use, but they all have major limitations. The prediction rate, which takes longer due to factors like heterogeneity is one of the main aspects of PCOS that makes it difficult. Moreover, there was no correlation between the network’s generalization ability assessment and precise predictions. The ELMAN Neural Network has been used to identify PCOS in order to eliminate the aforementioned problems. The ovarian ultrasound image is pre-processed with Fast Local Laplacian Filter (FLLF) and Brightness Preserving Bi-Histogram Equalization. The Contextual Ensemble Network (CENET) is used in the segmentation process and the textural features are extracted using the Projective Integral (PI) and the color features are extracted using the Color Auto Correlogram (CAC). Finally, an Elman Network with a Green Anaconda Optimization (GAO) is employed for classification purposes to diagnose PCOS. According to the results of the experimental research, the proposed ELMAN network has an accuracy of 95%, 93% for precision, 92.5% for recall, 90% for specificity, F1-score is 91%. Thus, the CENET with ELMAN Neural Network for PCOS detection from ultrasound images was considerably simpler and more efficient.


Abnormal event detection model using an improved ResNet101 in context aware surveillance system
Rakesh Kalshetty and Asma Parveen
Institution of Engineering and Technology (IET)
AbstractSurveillance system plays a significant role for achieving security monitoring in the place of crowd areas. Offline monitoring of these crowd activity is quite challenging because it requires huge number of human resources for attaining efficient tracking. For shortcoming these issue automated and intelligent based system must be developed for efficiently monitor crowd and detect abnormal activity. However the existing methods faces issues like irrelevant features, high cost and process complexity. In this current research context aware surveillance‐system utilising hybrid ResNet101‐ANN is developed for effective abnormal activity detection. For this proposed approach video acquired from surveillance camera is considered as input. Then, acquired video is segmented into multiple frames. After that pre‐processing techniques such as denoising using mean filter, motion deblurring, contrast enhancement using Histogram Equalisation and canny edge detection is applied in this segmented frames. Further, the pre‐processed frame is fetched into hybrid ResNet101‐ANN classifier for abnormal event classification. Here, ResNet101 is used for extracting the features from the frames and Artificial neural network which replaces the fully connected layer of ResNet101 us used to detect the abnormal activity. If once abnormal‐events detected the context aware services generate alert to the user for preventing abnormal‐activities. Accuracy, precision, recall, and error values reached for the proposed‐model on simulation were 0.98, 0.98, 0.98 and 0.017 respectively. Using this proposed model effective crowd monitoring and abnormal activity detection can be achieved.


The various surveillance and detection techniques based on wireless sensor networks
Rakesh Kalshetty and Asma Parveen
IEEE
In this paper, we describe multiple methods used for surveillance and tracking of abnormalities instead of a commercial image processing system, we build an efficient surveillance system in areas of interest by utilizing internet of things and wireless sensors platform. The kinds of cameras and sensors considered that are to be deployed in the area of interest for monitoring suspicious behavior of intruders and conditions such as temperature, humidity, and fire accidents. Then the sensed data is collected and processed locally then transmitted wirelessly to alert an administrator or caretaker about mishappening in the area of interest.

RECENT SCHOLAR PUBLICATIONS