Dr Sreenivaslu Gogula

@vardhaman.org

Professor and Head of the Department
Vardhaman College of Engineering



           

https://researchid.co/gvsreenu

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science Applications, Computer Vision and Pattern Recognition, Information Systems and Management, Artificial Intelligence

17

Scopus Publications

Scopus Publications


  • Frequent Pattern Mining Using Artificial Intelligence and Machine Learning
    R. Deepika, Sreenivasulu Gogula, K. Kanagalakshmi, Anshu Mehta, S. J. Vivekanandan, and D. Vetrithangam
    Wiley

  • Machine Learning and Artificial Intelligence for Detecting Cyber Security Threats in IoT Environmment
    Ravindra Bhardwaj, Sreenivasulu Gogula, Bidisha Bhabani, K. Kanagalakshmi, Aparajita Mukherjee, and D. Vetrithangam
    Wiley

  • Internet of Things enabled open source assisted real-time blood glucose monitoring framework
    Abubeker K. M, Ramani. R, Raja Krishnamoorthy, Sreenivasulu Gogula, Baskar. S, Sathish Muthu, Girinivasan Chellamuthu, and Kamalraj Subramaniam
    Springer Science and Business Media LLC
    AbstractRegular monitoring of blood glucose levels is essential for the management of diabetes and the development of appropriate treatment protocols. The conventional blood glucose (BG) testing have an intrusive technique to prick the finger and it can be uncomfortable when it is a regular practice. Intrusive procedures, such as fingerstick testing has negatively influencing patient adherence. Diabetic patients now have an exceptional improvement in their quality of life with the development of cutting-edge sensors and healthcare technologies. intensive care unit (ICU) and pregnant women also have facing challenges including hyperglycemia and hypoglycemia. The worldwide diabetic rate has incited to develop a wearable and accurate non-invasive blood glucose monitoring system. This research developed an Internet of Things (IoT) - enabled wearable blood glucose monitoring (iGM) system to transform diabetes care and enhance the quality of life. The TTGOT-ESP32 IoT platform with a red and near-infrared (R-NIR) spectral range for blood glucose measurement has integrated into this wearable device. The primary objective of this gadget is to provide optimal comfort for the patients while delivering a smooth monitoring experience. The iGM gadget is 98.82 % accuracy when used after 10 hours of fasting and 98.04 % accuracy after 2 hours of breakfast. The primary objective points of the research were continuous monitoring, decreased risk of infection, and improved quality of life. This research contributes to the evolving field of IoT-based healthcare solutions by streaming real-time glucose values on AWS IoT Core to empower individuals with diabetes to manage their conditions effectively. The iGM Framework has a promising future with the potential to transform diabetes management and healthcare delivery.

  • Exploring AI and machine learning integration in medical assistive robotics: An experimental approach
    T. R. Saravanan, S. Suvitha, Dhanalaxmi Banavath, Sreenivasulu Gogula, Jyoti Upadhyay, and M. Sudhakar
    IGI Global
    This chapter investigates the integration of AI and machine learning (ML) techniques in medical assistive robotics, focusing on their potential in enhancing healthcare capabilities. It explores the synergy between AI, ML, and medical robotics; outlines the chosen methodology; and assesses AI applications in areas like image analysis, predictive modeling, real-time monitoring, surgical automation, and rehabilitation. The study compares results with existing literature, revealing insights into the contributions and limitations of AI-empowered medical robotics. The findings highlight the transformative possibilities of AI and ML in advancing patient care, diagnostics, and treatment planning. By bridging theoretical understanding with empirical validation, this chapter aims to advance the discourse on AI integration in medical assistive robotics.

  • Visual Recognized Attendance System
    N. Thulasi Chitra, G. Sreenivasulu, P. Subhashini, and Hrudaya Kumar Tripathy
    IEEE
    Usually in our regular class we face a situation where faculty or respective administrators take attendance which would consume time. So, this project aims to develop a visual recognized attendance register that will identify the people based on the video and mark as present in the register. The data set is created by taking pictures of the people that are in a particular class. Based on the pictures the model will develop a identification mark or some important patterns for each student, based on this when we start a web cam through our model it will identify people and post attendance. We have Local Binary pattern histogram and convolutional neural network to identify each person uniquely. And to develop this we use OpenCV library which contains which contains many features regarding face recognition.

  • E-Certificate Verification Using Block Chain
    N. Thulasi Chitra, G. Sreenivasulu, and P. Subhashini
    IEEE
    This universe shows mood through facial expressions. Every expression conveys emotions. AI allows us differentiate different emotions, even though everyone has a unique face expression and emotional repertoire. Deep learning builds face emotion-recognition AIs. Train a CNN on the dataset for high prediction accuracy. The goal is to recognise seven fundamental face emotions from hundreds. Happy, sad, neutral, anger, surprise, fear, and contempt are the most common expressions, followed by neutral. Deep face algorithms, machine learning, AI, TensorFlow, etc. can identify facial expressions. Our model is 70-80% correct. The easiest and most effective technique may be this.

  • Efficient Trajectory Clustering of Movements of Moving Objects
    Y. Subba Reddy, V. Thanuja, G. Sreenivasulu, N. Thulasi Chitra, and K. Venu Madhav
    Springer Nature Switzerland

  • An Effective approach for preparation of News Summary using web scraping techniques
    N.Thulasi Chitra, G. Sreenivasulu, P. Subhashini, Ganti Krishna Sharma, and N. Sandhya
    IEEE
    Due to the Internet’s quick development and far reaching use, network media has turned into another window for people to grasp the rest of the world. News is a medium through which people might find out about their environmental factors, yet a huge number of reports are distributed consistently on the Internet. A major need in individuals’ lives is to sort out some way to get the news things we want from the web rapidly and definitively. This strategy tries to assemble news from specific sites and give it to guests in a reasonable and compact way. Clients might utilize specific watchwords to find news that they are keen on, which takes into account personalization. The viable advantage of this technique is that it gives quick, proficient, and simple admittance to news that individuals care about, need, and are keen on.

  • Deep Learning-Based Smart Surveillance System
    G. Sreenivasulu, N. Thulasi Chitra, S. Viswanadha Raju, and Venu Madhav Kuthadi
    Springer Nature Singapore

  • Modernized Wildlife Surveillance and Behaviour Detection using a Novel Machine Learning Algorithm
    Sreenivasulu Gogula, M. Rajesh Khanna, Neelappa Neelappa, Ajith Sundaram, E. Rajesh Kumar, and Sravanth Kumar R.
    Auricle Technologies, Pvt., Ltd.
    In a natural ecosystem, understanding the difficulties of the wildlife surveillance is helpful to protect and manage animals also gain knowledge around animals count, behaviour and location. Moreover, camera trap images allow the picture of wildlife as unobtrusively, inexpensively and high volume it can identify animals, and behaviour but  it has the issues of high expensive, time consuming, error, and low accuracy. So, in this research work, designed a novel wildlife surveillance framework using DCNN for accurate prediction of animals and enhance the performance of detection accuracy. The executed research work is implemented in the python tool and 2700 sample input frame datasets are tested and trained to the system. Furthermore, analyze whether animals are present or not using background subtraction and features extracted is performed to extract the significant features. Finally, classification is executed to predict the animal using the fitness of seagull. Additionally, attained results of the developed framework are compared with other state-of-the-art techniques in terms of detection accuracy, sensitivity, F-measure and error.


  • Text Summarization Using Natural Language Processing
    G. Sreenivasulu, N. Thulasi Chitra, B. Sujatha, and K. Venu Madhav
    Springer Singapore


  • Review of clustering techniques
    G. Sreenivasulu, S. Viswanadha Raju, and N. Sambasiva Rao
    Springer Singapore

  • A proficient approach for clustering of large categorical data cataloguing
    G. Sreenivasulu, S. Viswanadha Raju, and N. Sambasiva Rao
    IEEE
    To extract knowledge from Data bases Data mining is being used. Data mining is associated with various techniques. In those Clustering is considered to be one of the best approaches. Clustering a huge data set specifically categorical data is difficult and tedious procedure. In this context a proficient method is proposed that is focused on Rough purity for humanizing accuracy of grouping and keeping the unlabeled objects into proper clusters. Data cataloging is a general method in numerical and mixed domain but it is a key setback in categorical domain. To address this problem we proposed an proficient approach for clustering and cataloguing of categorical data. This system is mainly divided into three parts. Initially data will be divided into various groups, secondly clustering algorithm will be applied and finally Drifting is going to be done when ever complete list of objects are new in a group. This is proven with a large data set in detailed manner. This method improves efficiency for cluster cataloguing.

  • Data Labeling method based on rough entropy for categorical data clustering
    G. Sreenivasulu, S. Viswanadha Raju, and N. Sambasiva Rao
    IEEE
    Clustering is one of the most important method in data mining. Clustering a huge data set is difficult and time taking process. In this scenario a new method proposed that is based on Rough Entropy for improving efficiency of clustering and labeling the unlabeled data points in clusters. Data Labeling is a simple process in numerical domain but not in categorical domain. Why because distance is a major parameter in numerical whereas not in categorical attributes. So, In this paper proposed a new method for data labeling using Rough Entropy for clustering categorical data attributes. This method is mainly divided into two phases. Phase-1 is aimed to find the partition with respect to attributes and phase-II is to find the Rough Entropy to know the node importance for data labeling.