Experienced Assistant Professor with over 14+ years of demonstrated experience in the higher education industry. Skilled in Computer Networking, Operating Systems, Network Security, Machine Learning, and Blockchain Technology. Strong education professional with a Ph.D. in Computer Science and Engineering from Koneru Lakshmaiah Education Foundation (KL Deemed to be University), Vaddeswaram, Mangalagiri, Andhra Pradesh, and M.Tech. in Computer Science from JNTU Hyderabad.
EDUCATION
Ph.D. in Computer Science and Engineering from Koneru Lakshmaiah Education Foundation (KL Deemed to be University), Vaddeswaram, Mangalagiri, Andhra Pradesh.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Networks and Communications, Multidisciplinary
20
Scopus Publications
138
Scholar Citations
7
Scholar h-index
6
Scholar i10-index
Scopus Publications
Energy-Efficient Clustering Algorithm-Based Routing Protocol by Optimized Machine Learning Algorithms in WSN Mohammed Ali Shaik, P. Praveen, P. Kumaraswamy, Mohan Kumar Chandol, Mulagundla Sridevi Journal of Circuits Systems and Computers, 2026 Pocket-friendly, small sensor nodes make up a wireless sensor network (WSN). The sensor nodes are designed to collect and transmit data from their environment to the base station (BS). The lifespan of the network is impacted by sensor nodes, which also consume more energy when sending data. Additional energy-related limitations on WSNs include restricted computation, high setup complexity, storage, clustering, and communication capability. The primary characteristics of WSNs are energy efficiency and lifetime extension, which are handled by clustering and routing strategies. Hence, an energy-efficient clustering-based routing (EECR) protocol for WSN is proposed in this study. Initially, the sensor nodes are clustered using the Fuzzy K-Medoids method. Then, the hybrid approach of mayfly and moth flame optimization (HMFMFO) is introduced for optimal cluster head (CH) selection. The hybrid algorithm improves the global search behavior of moth flames by MF and achieves optimal positioning of the CH. Finally, the optimized artificial neural network (Opt-ANN) is used for optimal route selection. The particle swarm optimization (PSO) is used to optimize ANN in order to find the quickest path while dynamically decreasing network overhead. The packet delivery ratio (PDR), efficiency, packet delay, power consumption and network lifespan are used to assess the effectiveness of the suggested strategy. The proposed method achieves with 22.49[Formula: see text]ms delay and energy consumption of 0.0416[Formula: see text]mJ, which is superior to the existing methods.
HHFHNet: Hybrid Deep Learning Network for Course Recommendation Using H-Matrix Nagarjuna Reddy Seelam, Chandra Sekhar Kolli, Mohan Kumar Chandol, R Ravi Kumar, Ravi Kumar Balleda, et al. Transactions on Emerging Telecommunications Technologies, 2025 BackgroundStudents often need help choosing the right courses to complete their degrees. Course recommender systems assist in selecting suitable academic courses. Recent attention‐based have been developed to distinguish the influence of past courses on recommendations. However, these models might not work well when users have diverse interests, because the effectiveness of the attention mechanism decreases with the variety of historical courses. To overcome these issues, this study introduces a new approach called Hierarchical Attention Network with Hierarchical Deep Learning for Text Forward Harmonic Net (HHFHNet) for course recommendations using H‐matrix.MethodsInitially, the input course data obtained from the dataset is processed into course overview and course genres. After that, the Term Frequency‐Inverse Document Frequency (TF‐IDF) method is applied to both the course overview and query, with the resulting output fed into the HHFHNet, which combines Hierarchical Deep Learning for Texts (HDLTex) and Hierarchical Attention Networks (HAN). This generates a Course Recommendation Probability Value (CRPV), which is used to retrieve recommended courses. Simultaneously, specific course genre features are selected using chord distance. Then, specific course genre features are selected using chord distance. These selected features and CRPV are then used with the H‐matrix to create ranking‐based recommendations. Finally, Explainable Artificial Intelligence (XAI) is utilized to generate course recommendation messages based on the ranking approach.ResultsThe effectiveness of the HHFHNet technique was evaluated using performance metrics such as precision, recall, and F‐measure, and it achieved values of 90.31%, 91.87%, and 91.08%, respectively.ConclusionsThe proposed HHFHNet technique significantly enhances course recommendation accuracy and offers a robust solution for guiding students in their academic course selection.
Deep learning-based privacy-preserving recommendations in federated learning Chandra Sekhar Kolli, V. V. Krishna Reddy, Tatireddy Subba Reddy, Mohan Kumar Chandol, Durga Bhavani Dasari, et al. International Journal of General Systems, 2024 Privacy preservation in recommendations has been increasingly garnering huge interest from the research community owing to the rapid rise in data security and privacy concerns among users. The computation overhead and attaining high recommendation accuracy remain the key issues in the existing methods. In this research, a course recommendation method using Federated Learning (FL) based on Deep Learning is presented. The course recommendation technique is carried out in the local nodes using multiple phases, like agglomerative matrix generation, course grouping, bi-level matching, retrieval of learner-preferred courses, and course recommendation. Here, course grouping is accomplished using Deep Fuzzy Clustering (DFC), and Deep Convolutional Neural Networks (DCNN) performs recommendation. The DFC-DCNN-FL is examined based on accuracy, False Positive Rate (FPR), loss function, Mean Square Error (MSE), Root MSE (RMSE), and Mean Average Precision (MAP) and is found to have attained values of 0.909, 0.116, 0.126, 0.291, 0.539, and 0.925.
Real-Time Water Quality Tracking and Alert System with IoT Integration Mohan Kumar Ch, Masthan Siva Krishna Munaga, Chandra Sekhar Kolli, Suresh Kumar Maddila Proceedings 2023 3rd International Conference on Pervasive Computing and Social Networking Icpcsn 2023, 2023 Water is an essential resource for all living beings, and given the limited availability of natural resources, it is crucial to use water efficiently. Monitoring and analysing water quality parameters have become imperative in today's scenario to ensure good health and prevent excessive usage and wastage of water. The traditional approach of manually collecting water samples from different sources and sending them to research labs for analysis is neither feasible nor cost-effective. Moreover,frequent manual sampling and analysis could be more practical. IoT technology can be leveraged to monitor and alert people in real time about water quality in specific areas. The proposed system comprises sensors to measure essential water characteristics in real time, enabling decision-making without external intervention. The system can control all the critical parameters to ensure accurate measurements, and the readings can be stored in a cloud environment for future analysis. This proposed system can be used for real-time water quality analysis.
Border Collie Cat Optimization for Intrusion Detection System in Healthcare IoT Network Using Deep Recurrent Neural Network Mohan Kumar Chandol, M Kameswara Rao Computer Journal, 2022 Attacks are the major problems in the Internet of Things (IoT) applications and communication networks. The undetected intruders affect the availability of the system for end-users, increase identity theft and data breaches. Hence, it is required to detect the attacks in the IoT systems to ensure effective defense and security. In this research, the Border Collie Cat Optimization-based Deep Recurrent Neural Network is proposed to detect intrusion in the IoT networks. Here, the proposed Border Collie Cat Optimization algorithm is derived by the integration of Border Collie Optimization and Cat Swarm Optimization. At first, the messages are authenticated at the authentication phase using the hashing and encryption function. After authenticating the device, the communication between the server and user is carried out at the communication phase to make the IoT device eligible for data transfer within the network. Then, the Deep Recurrent Neural Network classifier is employed to detect the intruders in the IoT network in such a way that the training process is carried out using the proposed Border Collie Optimization algorithm. The proposed approach obtained higher performance with the metrics, like detection rate, sensitivity, specificity and accuracy with the values of 0.9375, 0.9539, 0.8791 and 0.9263, respectively.
Detection of intruders in iot networks using interloper software based on authentication International Journal of Innovative Technology and Exploring Engineering, 2019
Toxic gas detection and monitoring utilizing internet of things International Journal of Civil Engineering and Technology, 2017
Comparative study on security threats in mobiles and iot devices International Journal of Mechanical Engineering and Technology, 2017
Comparative study on security threats in mobiles and iot devices International Journal of Mechanical Engineering and Technology, 2017
General survey on implementation of security in IOT International Journal of Mechanical Engineering and Technology, 2017
RECENT SCHOLAR PUBLICATIONS
Energy-Efficient Clustering Algorithm-Based Routing Protocol by Optimized Machine Learning Algorithms in WSN MA Shaik, P Praveen, P Kumaraswamy, MK Chandol, M Sridevi Journal of Circuits, Systems and Computers 35 (06), 2550469 , 2026 2026 Citations: 1
Smart Water Quality Monitoring and Prediction using IoT with Improved Local Binary Pattern Shallow Deep Convolutional Neural Networks and Starling Murmuration Optimization NK Balaraman, H Pandey, MK Chandol, K Sutaria, SMU Iqbal, R Maranan 2025 6th International Conference on Electronics and Sustainable … , 2025 2025
HHFHNet: Hybrid Deep Learning Network for Course Recommendation Using H‐Matrix NR Seelam, CS Kolli, MK Chandol, RR Kumar, RK Balleda, MSK Munaga Transactions on Emerging Telecommunications Technologies 36 (4), e70090 , 2025 2025 Citations: 2
Blockchain-based cryptographic approach for privacy enabled data integrity model for IoT healthcare MK Chandol, M Kameswara Rao Journal of Experimental & Theoretical Artificial Intelligence 37 (1), 53-74 , 2025 2025 Citations: 15
Deep learning-based privacy-preserving recommendations in federated learning CS Kolli, VV Krishna Reddy, TS Reddy, MK Chandol, DB Dasari, ... International Journal of General Systems 53 (6), 651-677 , 2024 2024 Citations: 12
Classification of defective product for smart factory through deep learning method R Raffik, PK Misra, CS Kolli, VVK Reddy, MK Chandol, SK Shukla AIP Conference Proceedings 2937 (1), 020029 , 2024 2024 Citations: 3
Real-time water quality tracking and alert system with IoT integration MK Ch, MSK Munaga, CS Kolli, SK Maddila 2023 3rd International Conference on Pervasive Computing and Social … , 2023 2023 Citations: 3
Prediction of chronic kidney disease from patient record using ensemble ranking SVM M Chandrababu, VVK Reddy, CS Kolli, K Chokkanathan, MK Chandol, ... AIP Publishing LLC 2603 (1), 020014 , 2023 2023 Citations: 3
Fr-ROA: trust-aware routing using fractional remora optimisation algorithm for secure communication in IoT MK Chandol, MK Rao International Journal of Bio-Inspired Computation 22 (4), 237-249 , 2023 2023 Citations: 3
Border collie cat optimization for intrusion detection system in healthcare IoT network using deep recurrent neural network MK Chandol, MK Rao The Computer Journal 65 (12), 3181-3198 , 2022 2022 Citations: 15
Modelling a Dense N Model for Anomaly Prediction in IoT Environment MK Chandol, MK Rao, CS Kolli 2022 4th International Conference on Inventive Research in Computing … , 2022 2022
Intelligent Routing Protocol for Energy Efficient B Ashok, B Chandini, MK Chandole, N Jyothi Innovations in Electrical and Electronic Engineering: Proceedings of ICEEE … , 2022 2022
Detecting Payment Fraud Using Automatic Feature Engineering with Harris Grey Wolf Deep Neural Network CS Kolli, MK Ch, G Ramasamy, GK Chaitanya Internet of Things, 71-80 , 2022 2022
Intelligent Routing Protocol for Energy Efficient Wireless Sensor Networks N Srikanth, B Ashok, B Chandini, MK Chandole, N Jyothi International Conference on Electrical and Electronics Engineering, 387-396 , 2022 2022 Citations: 4
Enhancement of agriculture based crop yield prediction using R tool and machine learning MK Chandol, M Elangovan, U Muthusamy, K Sankar Turkish Online Journal of Qualitative Inquiry (TOJQI) 12 (7), 5155-5165 , 2021 2021 Citations: 1
A systematic review on anomaly based intrusion detection system R Ganeshan, C kolli, CM kumar, T Daniya IOP Conference Series: Materials Science and Engineering 981 (2), 022010 , 2020 2020 Citations: 14
Detection of intruders in iot networks using interloper software based on authentication S Kumar, C.M., Rahul Ratna, T., Geethika, S., Uday Kiran International Journal of Innovative Technology and Exploring Engineering 8 … , 2019 2019
Critical review attacks and countermeasures in internet of things enabled environments C Mohan Kumar, M Kameswara Rao Int J Eng Technol (UAE) 7 (2), 163-167 , 2018 2018 Citations: 8
COMPARATIVE STUDY ON SECURITY THREATS IN MOBILES AND IOT DEVICES MK Ch, N Shikha, SL Sowndarya, NA Ramarao 2017
Toxic Gas Detection and Monitoring Utilizing Internet of Things S Chalasani, M Kumar International Journal of Civil Engineering and Technology (IJCIET) 8 (12 … , 2017 2017 Citations: 37
MOST CITED SCHOLAR PUBLICATIONS
Toxic Gas Detection and Monitoring Utilizing Internet of Things S Chalasani, M Kumar International Journal of Civil Engineering and Technology (IJCIET) 8 (12 … , 2017 2017 Citations: 37
Spatial data mining using cluster analysis CNS Kumar, VS Ramulu, KS Reddy, S Kotha, CM Kumar International Journal of Computer Science & Information Technology 4 (4), 71 , 2012 2012 Citations: 17
Blockchain-based cryptographic approach for privacy enabled data integrity model for IoT healthcare MK Chandol, M Kameswara Rao Journal of Experimental & Theoretical Artificial Intelligence 37 (1), 53-74 , 2025 2025 Citations: 15
Border collie cat optimization for intrusion detection system in healthcare IoT network using deep recurrent neural network MK Chandol, MK Rao The Computer Journal 65 (12), 3181-3198 , 2022 2022 Citations: 15
A systematic review on anomaly based intrusion detection system R Ganeshan, C kolli, CM kumar, T Daniya IOP Conference Series: Materials Science and Engineering 981 (2), 022010 , 2020 2020 Citations: 14
Deep learning-based privacy-preserving recommendations in federated learning CS Kolli, VV Krishna Reddy, TS Reddy, MK Chandol, DB Dasari, ... International Journal of General Systems 53 (6), 651-677 , 2024 2024 Citations: 12
Critical review attacks and countermeasures in internet of things enabled environments C Mohan Kumar, M Kameswara Rao Int J Eng Technol (UAE) 7 (2), 163-167 , 2018 2018 Citations: 8
Intelligent Routing Protocol for Energy Efficient Wireless Sensor Networks N Srikanth, B Ashok, B Chandini, MK Chandole, N Jyothi International Conference on Electrical and Electronics Engineering, 387-396 , 2022 2022 Citations: 4
Classification of defective product for smart factory through deep learning method R Raffik, PK Misra, CS Kolli, VVK Reddy, MK Chandol, SK Shukla AIP Conference Proceedings 2937 (1), 020029 , 2024 2024 Citations: 3
Real-time water quality tracking and alert system with IoT integration MK Ch, MSK Munaga, CS Kolli, SK Maddila 2023 3rd International Conference on Pervasive Computing and Social … , 2023 2023 Citations: 3
Prediction of chronic kidney disease from patient record using ensemble ranking SVM M Chandrababu, VVK Reddy, CS Kolli, K Chokkanathan, MK Chandol, ... AIP Publishing LLC 2603 (1), 020014 , 2023 2023 Citations: 3
Fr-ROA: trust-aware routing using fractional remora optimisation algorithm for secure communication in IoT MK Chandol, MK Rao International Journal of Bio-Inspired Computation 22 (4), 237-249 , 2023 2023 Citations: 3
HHFHNet: Hybrid Deep Learning Network for Course Recommendation Using H‐Matrix NR Seelam, CS Kolli, MK Chandol, RR Kumar, RK Balleda, MSK Munaga Transactions on Emerging Telecommunications Technologies 36 (4), e70090 , 2025 2025 Citations: 2
Energy-Efficient Clustering Algorithm-Based Routing Protocol by Optimized Machine Learning Algorithms in WSN MA Shaik, P Praveen, P Kumaraswamy, MK Chandol, M Sridevi Journal of Circuits, Systems and Computers 35 (06), 2550469 , 2026 2026 Citations: 1
Enhancement of agriculture based crop yield prediction using R tool and machine learning MK Chandol, M Elangovan, U Muthusamy, K Sankar Turkish Online Journal of Qualitative Inquiry (TOJQI) 12 (7), 5155-5165 , 2021 2021 Citations: 1
Smart Water Quality Monitoring and Prediction using IoT with Improved Local Binary Pattern Shallow Deep Convolutional Neural Networks and Starling Murmuration Optimization NK Balaraman, H Pandey, MK Chandol, K Sutaria, SMU Iqbal, R Maranan 2025 6th International Conference on Electronics and Sustainable … , 2025 2025
Modelling a Dense N Model for Anomaly Prediction in IoT Environment MK Chandol, MK Rao, CS Kolli 2022 4th International Conference on Inventive Research in Computing … , 2022 2022
Intelligent Routing Protocol for Energy Efficient B Ashok, B Chandini, MK Chandole, N Jyothi Innovations in Electrical and Electronic Engineering: Proceedings of ICEEE … , 2022 2022
Detecting Payment Fraud Using Automatic Feature Engineering with Harris Grey Wolf Deep Neural Network CS Kolli, MK Ch, G Ramasamy, GK Chaitanya Internet of Things, 71-80 , 2022 2022
Detection of intruders in iot networks using interloper software based on authentication S Kumar, C.M., Rahul Ratna, T., Geethika, S., Uday Kiran International Journal of Innovative Technology and Exploring Engineering 8 … , 2019 2019
