N SREEKANTH

@mallareddyecw.com

PROFESSOR & HOD / ELECTRONICS AND COMMUNICATION ENGINEERING (ECE)
Malla Reddy Engineering College for Women



           

https://researchid.co/sreekanth

RESEARCH INTERESTS

MICROWAVE ANTENNAS, COMMUNICATIONS, IMAGE & SIGNAL PROCESSING

6

Scopus Publications

Scopus Publications

  • Predictive Analytics for Crime Prevention in Smart Cities Using Machine Learning
    Ponugoti Kalpana, Sarangam Kodati, Nara Sreekanth, Hassan Mohamed Ali, and Ramachandra A C
    IEEE
    Crime prevention in smart city infrastructure significantly impacts improving quality of human life. However, the substantial increase in the urban population in recent years affects accuracy, safety, and security. This paper presents a solution using the Hyperplane to improve the Radial Basis Function with Support Vector Machine (SVM) based on Machine Learning techniques to enhance accuracy, safety, and security. The SVM method aims to reduce crime rates through proactive measures based on predictive insights. The RBF kernel handles nonlinear relationships by mapping input features into a higher-dimensional space, allowing the model to capture complex patterns. Initially, data is obtained from the Chicago crime dataset and pre-processed by checking for missing values and performing feature scaling to ensure consistency. Feature selection is conducted using Principal Component Analysis (PCA) to identify relevant features from the data. Crime prediction using RBF with SVM is performed efficiently to enhance accuracy. The proposed RBF-SVM model is evaluated using the Chicago crime dataset, achieving a high accuracy of 0.89, a precision of 0.80, a recall of 0.82, and an F1-score of 0.85. The performance is compared to existing techniques such as Decision Tree (DT) and Long Short-Term Memory (LSTM).

  • Ensemble Framework of Artificial immune system based on Network Intrusion Detection System for Network Security Sustainability
    Sarangam Kodati, Nara Sreekanth, K.S.R.K. Sarma, P. Chandra Sekhar Reddy, Archana Saxena, and Boya Palajonna Narasaiah
    EDP Sciences
    The popularity and rapid growth of the internet have reemphasized the importance of intrusion detection systems (IDS) significance in the network security. IDS decreases hacking, data theft risk, privacy intrusion, and others. To save the system from external and internal intruders, the primary approaches of IDS are used. Many techniques[13], like genetic algorithms, artificial neural networks, and artificial immune systems, have been applied to IDS. This paper describes an Ensemble Framework of Artificial Immune System (AIS) based on Network Intrusion Detection System. Without placing a significant additional load on networks and monitoring systems, the large volume of data is analysed by a network-based Intrusion Detection System (NIDS). For determining the connection type, data from KDD Cup 99 competitions is utilized. To differentiate between attacks and valid connections, IDS can be utilized. Optimized feature selection is used to speed up the time-consuming rough set. The results obtained from the IDS system indicate that it can effectively identify the attacking connections with a high success rate.

  • Fusion-Based Celebrity Profiling Using Deep Learning
    K. Adi Narayana Reddy, Naveen Kumar Laskari, G. Shyam Chandra Prasad, and N. Sreekanth
    Springer Nature Singapore

  • Analysis of Heart Disease Data Using K-Means Clustering Algorithm in Orange Tool
    Sarangam Kodati, Kumbala Pradeep Reddy, G. Ravi, and Nara Sreekanth
    Springer Singapore

  • Region based age classification using cross diagonal centre symmetric motif matrix (CD-CS-MM)


  • Age classification based on appearance model using local ternary direction pattern approach