SHAIK MUNAWAR

@kitsw.ac.in

Assistant Professor
kakatiya Institute of Technology and Science, warangal

SHAIK MUNAWAR


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https://researchid.co/shaikmunawar215

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary
12

Scopus Publications

31

Scholar Citations

3

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • IoT botnet attack detection using ensemble classifiers with optimal training
    Shankar Vuppu, V. Chandra Shekhar Rao, M. Sujatha, Shaik Munawar, S. Nagaraju, N. Gayatri
    Journal of Combinatorial Optimization, 2026
  • Elucidation of improved heuristic-assisted multi-dilated inception ResnetV2 with pyramidal attention for driver distraction detection
    Mohammed Sharfuddin Waseem, Shaik Munawar, Madugula Sujatha, Syed Abdul Moeed, Raghuram Bhukya
    International Journal of Signal and Imaging Systems Engineering, 2025
    Globally, driver distractions are required to be recognised since it becomes a major cause for traffic-related fatalities. In recent years, the models limit with examining the specific feature information of the input source. Hence the main purpose of this paper is to develop an automated model of detecting the driver behaviour. Further, the collected images are subjected as input to Adaptive Multi-dilated Inception ResnetV2 with Pyramidal Attention (AMIR-PA) for classifying the distracted behaviours. In order to further enhance the performance, the hyper-parameters are optimally selected using Renovated Position-based Crocodile Optimisation Algorithm (RP-COA). Finally, the proposed system is validated using different measures and compared among traditional approaches. After the validation, the proposed model acquires high accuracy value as 91.45% and 92.2% for ReLU and tanh activation function than existing models. Therefore, the findings reveal that the proposed system achieves higher detection results to evade the traffic accidents that occur in roadways.
  • Analysis on intelligent DevOps by using AI-Powered automation for cloud application management
    G Divya, Kasarla Priyanka, L Chandra Sekhar Reddy, Shaik Munawar, M.Mohammed Ibrahim, Balaji Kannan
    Icrteect 2025 2nd International Conference on Recent Trends in Electrical Electronics and Computing Technologies, 2025
    The combination of greater cloud computing pace and DevOps adoption allowed software deployment and monitoring as well as scaling to undergo automated enhancement. The current DevOps approaches using human intervention rules during automation process cause management inefficiencies in complex cloud-native systems. The research explores the integration of Machine Learning with Artificial Intelligence features into DevOps methodologies to develop autonomous healing capabilities through combining predictive diagnostic information with automated problem-fixing systems. When AI technologies are integrated under the AIOps framework companies acquire automatic anomaly detection tools and predictive resources management features and system failure assessment tools which improve their CI and CD deployment processes. The implementation of artificial intelligence-based monitoring systems enables organizations to receive genuine performance data and security protection functionality and cloud application scaling features. Automated correction systems initiated through programming helped identify sources of problems while reducing operational costs and system uptime losses.A DevOps platform created through AI automation enables deployment management and monitoring in addition to incident resolution as cloud-native functions. The analysis presents relevant research data and experimental evidence to prove how deployment operations benefit from AI and system reliability increases alongside the reduction of operational costs. Administrative roles experience substantial performance improvement through DevOps automation that employs AI because the system removes manual work and generates consistent reliable operations. The results indicate that cloud-native application control will utilize AI-driven DevOps that permits autonomous management along with self-adaptive operation of cloud platforms.
  • Enhancing healthcare accessibility: An AI-powered chatbot for symptom analysis and precautionary guidance
    P. Devika, Balingan Sangameshwar, V.T. Ram Pavan Kumar M., Ch. Lavanya Susanna, Shaik Munawar, Sudheer Nidamanuri
    Intelligent Computing Techniques and Applications, 2025
    This article introduces a healthcare chatbot powered by artificial intelligence, which aims to anticipate potential medical conditions based on symptoms reported by users and provide tailored precautionary measures for the predicted condition. The chatbot utilizes machine learning algorithms, including decision tree classifiers, to examine symptom patterns and forecast potential diagnoses. One distinctive feature of this system is its capability to offer users specific precautionary advice for managing their condition before seeking professional help, thereby enhancing the system’s usefulness by providing proactive health management support. The model was trained using a comprehensive dataset of symptoms and corresponding medical diagnoses, achieving a high level of accuracy in predicting and suggesting precautionary actions. We illustrate how integrating disease prediction with actionable recommendations can enhance healthcare outcomes, particularly for users in remote or underserved areas. This solution could be a vital tool in early diagnosis and preventive care.
  • Secure and Optimized Clustering Framework for Energy-Aware Wireless Sensor Networks
    Mohammad Sirajuddin, Ravikumar Thallapalli, Shaik Munawar, Sallauddin Mohmmad, Adicherla Ramesh, Rachoori Sagar
    Proceedings of the 6th International Conference on Electronics and Sustainable Communication Systems Icesc 2025, 2025
    Wireless sensor networks (WSNs) have been widely used in environmental surveillance, medical care, and military applications. However, their operation is limited by energy scarcity, inefficient cluster head (CH) selection and susceptibility to security risks. In this paper, we present an integrated solution for WSNs that addresses the improvement of efficiency and longevity of a WSN using adaptive clustering and intelligent optimization technologies. The models combine Adaptive GridBased LEACH (AG-LEACH), Modernized Pufferfish Optimization Algorithm (MPOA) with Multilevel K-Means, and secure routing scheme improved with a CNN-Fuzzy Logic (CNNFL) and Neuro-Genetic Optimizer (NGO). AG-LEACH adapts grid clustering and CH selection in real time by actual node energy and distribution to achieve the best performance of energy utilization and transmission delay. Meanwhile, MPOA improves the CH selection with bio-inspiration state-transition heuristic, and $C N N-F L$ detects the malicious nodes for secure routing. Simulation outcomes indicate that the proposed framework enhances lifetime of the network and power efficiency, improves the data delivery ratio and network security compared to LEACH and EE-OLEACH protocols. This work presents a customizable, scalable framework for deploying secure and energy efficient WSNs in dynamic situations.
  • Classification and Detection of Prostate Cancer Using Machine Learning Techniques
    D. Vetrithangam, Pramod Kumar, Shaik Munawar, Rituparna Biswas, Deependra Pandey, Amar Choudhary
    Natural Language Processing for Software Engineering, 2025
    Carcinoma is a significant contributor to the death rates of individuals. Reducing the amount of time it takes to diagnose a patient is very necessary to improve their prognosis. Diagnostic imaging and other traditional methods are used by highly trained medical professionals to identify any telltale indicators that may be present in the bodies of their patients. In spite of the abundance of medical imaging data, manual diagnosis may still be subjective and time-consuming due to the fact that people's perceptions differ so much from one another. One of the primary reasons for the variability is the collecting of data from medical imaging. A proper diagnosis may be more difficult to get as a result of this. When performing activities such as machine learning and the processing of complex pictures, it is important to make use of the most advanced computational power available. Ever since the 1980s, there has been a persistent effort to develop a computer-aided diagnostic system that has the potential to help in the early diagnosis of a wide variety of malignancies. According to the most recent estimates, around one- seventh of men will be diagnosed with prostate cancer at some point in their life. This illness claims the lives of so many men every year, and it is unbearable that the number of men who are diagnosed with prostate cancer continues to climb. It is a tragedy that this number continues to rise. A powerful diagnostic system that is capable of managing high-resolution, multi-dimensional MRI images is an absolute need, in addition to computer-aided design (CAD) software. In the present moment, I am focusing my attention on a project that will make it easier for us to achieve our shared goals. Scientists are now studying methods to improve the speed, accuracy, and precision of computer-aided design (CAD) technology since it has been shown to be valuable. CAD technology has been demonstrated to be effective, as shown by the evidence. The development of techniques for the diagnosis and classification of prostate cancer via the use of MRI image processing and machine learning is the fundamental objective of this study as well.
  • AI-Driven Privacy Preservation Using Homomorphic Encryption with AM-ResNet Based Classification in Gastrointestinal Diseases
    Syed Abdul Moeed, Shaik Munawar, G. Ashmitha
    Sustainable Development Using Private AI Security Models and Applications, 2024
    Digestive illnesses include several conditions that impact the gastrointestinal (GI) tract, sometimes referred to as the digestive system or gut. The digestive system comprises the abdominal region, the small and large intestines, the liver, the pancreas, and the gallbladder. Any disorder of the digestive system is considered to have its roots in gastrointestinal illness. Consequently, the research presents a novel and comprehensive framework for categorizing gastrointestinal (GI) disorders. The novel techniques have been widely used to guarantee precision and safeguard privacy. This study is dependent on the public dissemination of a KVASIR dataset, which includes many classifications, along with its accompanying data. After using an enhanced weighted non-local mean (IW-NLM) filtering technique, the noise is effectively reduced, allowing the data to pass the first quality control screening. In contrast, it is the feature extraction layer that largely depends on deep learning (DL) by employing DenseNet-100, a broad form of architecture that works incredibly well at extracting patterns from data. It utilizes this method to identify characteristics in the input picture and generate an additional storage container. The classifier utilizes a coordinated methodology by incorporating an attention mechanism (AM) into the pre-existing residual network architecture (ResNet), resulting in the creation of AM-ResNet. They blend the operations of both sections into a single composite model in an attempt to boost its sensitivity, whose objective is detection of characteristics that exhibit high precision and give reference on how to categorize illnesses. Encrypted patient data (PD) may assist secure privacy for patients by employing homomorphic encryption (HE) to re-accommodate the query while offering a safe tool, which has been introduced since this work was first produced. Also, it is now feasible to employ the Remora optimization algorithm (ROA) for key selection in homomorphic encryption protocols, and security might consequently be much greater. This model has a 99.4 % rate of accuracy in diagnosing GI illness and is proved more effective than other current models utilized for comparative analysis. That, in turn, emphasizes the healthcare profession’s lack of means to preserve its privacy and improvements in medical image analysis.
  • Arrhythmia Classification Based on Bi-Directional Long Short-Term Memory and Multi-Task Group Method
    Shaik Munawar, Geetha Angappan, Srinivas Konda
    International Journal of E Collaboration, 2023
    Early and accurate classification of arrhythmia helps the experts to select the treatment for the patient to increase the recovery rate. The deep learning method of convolution neural network (CNN) is used for classification, and this has an overfitting problem. In this research, the multi-task group bi-directional long short term memory (MTGBi-LSTM) method is proposed to increases the performance of arrhythmia classification. The multi-task learning technique learns two ECG signals in shared representation for effective learning. The global and intra LSTM method selects the relevant feature and easily escapes from local optima. The MTGBi-LSTM model learns the unique features in shared representation that helps to overcome overfitting problem and increases the learning rate of the model. The MTGBi-LSTM model in arrhythmia classification is evaluated on MIT-BIH dataset. The MTGBi-LSTM model has 96.48% accuracy, 97.73% sensitivity, existing AFibNet has 96.36% accuracy, and 93.65% sensitivity for arrhythmia classification in CPSC 2018 dataset.
  • Arrhythmia Classification Using BiLSTM with DTCWT and MFCC Features
    Shaik Munawar, A. Geetha, K. Srinivas
    Lecture Notes in Networks and Systems, 2023
  • Narrow Band Internet of Things (NB-IoT) for Wireless Communication and Network Optimization
    Smita Khond, Kanegonda Ravi Chythanya, Shaik Munawar, Manjusha Mandava, Kruthika C G
    2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques Easct 2023, 2023
    This research presents an all-encompassing method for improving Narrow Band Internet of Things (NB-IoT) network performance by including network design, interference management, power control techniques, and security measures. Planning your network ahead of time is essential, and will involve decisions like network design, base station placement, and channel allocation. The second stage, interference control, seeks to reduce background noise emanating from various sources. The third and last stage is to use transmission power management algorithms for NB-IoT devices. In real-time, these algorithms optimize power levels for the channel and the task at hand. In the last stage, we apply security measures including authentication, encryption, and other types of network access control. According to the data, the suggested approach would have cost a total of $500,000, but more conventional approaches would have cost between $700,000 and $900,000. Also, the suggested method had an average delay of 1.5 seconds.
  • SQUIRREL SEARCH-BASED OPTIMAL FEATURE EXTRACTION WITH BI-LSTM FOR THE ARRHYTHMIA CLASSIFICATION USING ECG
    Journal of Theoretical and Applied Information Technology, 2022
  • Data mining for cyber-physical systems
    M. Varaprasad Rao, D. Anji Reddy, Anusha Ampavathi, Shaik Munawar
    Data Mining and Machine Learning Applications, 2022

RECENT SCHOLAR PUBLICATIONS

  • IoT botnet attack detection using ensemble classifiers with optimal training
    S Vuppu, VCS Rao, M Sujatha, S Munawar, S Nagaraju, N Gayatri
    Journal of Combinatorial Optimization 51 (4), 41 , 2026
    2026
  • Elucidation of improved heuristic-assisted multi-dilated inception ResnetV2 with pyramidal attention for driver distraction detection
    SAMRB 1. Mohammed Sharfuddin Waseem, Shaik Munawar, Madugula Sujatha
    International Journal of Signal and Imaging Systems Engineering 14 (2), 107-123 , 2026
    2026
  • Secure and Optimized Clustering Framework for Energy-Aware Wireless Sensor Networks
    M Sirajuddin, R Thallapalli, S Munawar, S Mohmmad, A Ramesh, ...
    2025 6th International Conference on Electronics and Sustainable … , 2025
    2025
  • Database Management System
    PK Dr. Shaik munawar, Ms. S. Abikayil Aarthi, Dr. Jaishree Jain
    2025
  • AI-Driven Framework For Smart Farming: Enhancing Crop Productivity Through Climate-Aware Decision Support
    DPS Dr. P. Niranjan, Dr. Syed Abdul Moeed, Dr. V.Chandra Shekhar Rao, Dr ...
    International Journal of Environmental Sciences 11 (6), 376- 385 , 2025
    2025
  • Enhancing healthcare accessibility: An AI-powered chatbot for symptom analysis and precautionary guidance
    SMSN 1. P. Devika, Balingan Sangameshwar, V. T. Ram Pavan Kumar M., Ch ...
    International Conference on Emerging Trends in Intelligent Computing … , 2025
    2025
  • Classification And Detection of Prostate Cancer using Machine Learning Techniques
    DPAC D. Vetrithangam, Pramod Kumar, Shaik Munawar, Rituparna Biswas
    Natural Language Processing For Software Engineering Methodology, 29-42 , 2025
    2025
  • Facial Expression Control System for VLC Media Player
    MS Shaik Munawar, Potte Mounika, Badise Manoj
    International Journal of Communication Networks and Information Security 16 … , 2024
    2024
  • BEYOND CLASSIFICATION AND REGRESSION: A COMPREHENSIVE GUIDE TO ADVANCED MACHINE LEARNING TECHNIQUES
    SM L.Sharmila, R.Aishwarya, G.Umadevi
    2024
  • Narrow Band Internet of Things (NB-IoT) for Wireless Communication and Network Optimization
    Smita Khond, Kanegonda Ravi Chythanya, Shaik Munawar, Manjusha Mandava ...
    IEEE Explore , 2024
    2024
  • AI-Driven Privacy Preservation Using Homomorphic Encryption with AM-ResNet Based Classification in Gastrointestinal Diseases
    SA Moeed, S Munawar, G Ashmitha
    Sustainable Development Using Private AI, 63-84 , 2024
    2024
    Citations: 1
  • ML BASED NURSERY RICE SEEDLING MACHINE
    DAS 1. Dr. Hajera Sana, Pradip Kumar Saini, P. Priyadharshini, Shaik Munawar ...
    IN Patent 0801/2024. , 2024
    2024
  • A METHOD AND SYSTEM OF ARTIFICIAL INTELLIGENCE BLOCK CHAIN ECOMMERCE SYSTEM
    DGNU Dr. Kakumani K C Deepthi, Dr. M S Saritha , Mr. Voodara Devender, Mr ...
    IN Patent 50/2,023 , 2023
    2023
  • Arrhythmia classification using BiLSTM with DTCWT and MFCC features
    S Munawar, A Geetha, K Srinivas
    Proceedings of Fourth International Conference on Computer and Communication … , 2023
    2023
    Citations: 11
  • ENHANCED ARRHYTHMIA CLASSIFICATION SYSTEM FROM ECG SIGNALS VIA HYBRID OPTIMIZATION-BASED IMPROVED 3DCNN-RESNET
    S Munawar, A Geetha, K Srinvas
    2023
    Citations: 1
  • Arrhythmia classification based on bi-directional long short-term memory and multi-task group method
    S Munawar, G Angappan, S Konda
    International Journal of e-Collaboration (IJeC) 19 (1), 1-18 , 2023
    2023
    Citations: 11
  • SQUIRREL SEARCH-BASED OPTIMAL FEATURE EXTRACTION WITH BI-LSTM FOR THE ARRHYTHMIA CLASSIFICATION USING ECG
    KS SHAIK MUNAWAR, A. GEETHA
    Journal of Theoretical and Applied Information Technology 100 (2), 6161-6172 , 2022
    2022
    Citations: 7
  • Data Mining for Cyber-Physical Systems
    SM M. Varaprasad Rao, D. Anji Reddy, Anusha Ampavathi
    Data Mining Technologies using Machine Learning Algorithms, 235-280 , 2021
    2021
  • AN EXPLORATION TO IDENTIFY THE MOST RELEVANT PARAMETERS FOR PREDICTION OF HEART DISEASE
    S Munawar, A Geetha, K Srinivas
    2020

MOST CITED SCHOLAR PUBLICATIONS

  • Arrhythmia classification using BiLSTM with DTCWT and MFCC features
    S Munawar, A Geetha, K Srinivas
    Proceedings of Fourth International Conference on Computer and Communication … , 2023
    2023
    Citations: 11
  • Arrhythmia classification based on bi-directional long short-term memory and multi-task group method
    S Munawar, G Angappan, S Konda
    International Journal of e-Collaboration (IJeC) 19 (1), 1-18 , 2023
    2023
    Citations: 11
  • SQUIRREL SEARCH-BASED OPTIMAL FEATURE EXTRACTION WITH BI-LSTM FOR THE ARRHYTHMIA CLASSIFICATION USING ECG
    KS SHAIK MUNAWAR, A. GEETHA
    Journal of Theoretical and Applied Information Technology 100 (2), 6161-6172 , 2022
    2022
    Citations: 7
  • AI-Driven Privacy Preservation Using Homomorphic Encryption with AM-ResNet Based Classification in Gastrointestinal Diseases
    SA Moeed, S Munawar, G Ashmitha
    Sustainable Development Using Private AI, 63-84 , 2024
    2024
    Citations: 1
  • ENHANCED ARRHYTHMIA CLASSIFICATION SYSTEM FROM ECG SIGNALS VIA HYBRID OPTIMIZATION-BASED IMPROVED 3DCNN-RESNET
    S Munawar, A Geetha, K Srinvas
    2023
    Citations: 1
  • IoT botnet attack detection using ensemble classifiers with optimal training
    S Vuppu, VCS Rao, M Sujatha, S Munawar, S Nagaraju, N Gayatri
    Journal of Combinatorial Optimization 51 (4), 41 , 2026
    2026
  • Elucidation of improved heuristic-assisted multi-dilated inception ResnetV2 with pyramidal attention for driver distraction detection
    SAMRB 1. Mohammed Sharfuddin Waseem, Shaik Munawar, Madugula Sujatha
    International Journal of Signal and Imaging Systems Engineering 14 (2), 107-123 , 2026
    2026
  • Secure and Optimized Clustering Framework for Energy-Aware Wireless Sensor Networks
    M Sirajuddin, R Thallapalli, S Munawar, S Mohmmad, A Ramesh, ...
    2025 6th International Conference on Electronics and Sustainable … , 2025
    2025
  • Database Management System
    PK Dr. Shaik munawar, Ms. S. Abikayil Aarthi, Dr. Jaishree Jain
    2025
  • AI-Driven Framework For Smart Farming: Enhancing Crop Productivity Through Climate-Aware Decision Support
    DPS Dr. P. Niranjan, Dr. Syed Abdul Moeed, Dr. V.Chandra Shekhar Rao, Dr ...
    International Journal of Environmental Sciences 11 (6), 376- 385 , 2025
    2025
  • Enhancing healthcare accessibility: An AI-powered chatbot for symptom analysis and precautionary guidance
    SMSN 1. P. Devika, Balingan Sangameshwar, V. T. Ram Pavan Kumar M., Ch ...
    International Conference on Emerging Trends in Intelligent Computing … , 2025
    2025
  • Classification And Detection of Prostate Cancer using Machine Learning Techniques
    DPAC D. Vetrithangam, Pramod Kumar, Shaik Munawar, Rituparna Biswas
    Natural Language Processing For Software Engineering Methodology, 29-42 , 2025
    2025
  • Facial Expression Control System for VLC Media Player
    MS Shaik Munawar, Potte Mounika, Badise Manoj
    International Journal of Communication Networks and Information Security 16 … , 2024
    2024
  • BEYOND CLASSIFICATION AND REGRESSION: A COMPREHENSIVE GUIDE TO ADVANCED MACHINE LEARNING TECHNIQUES
    SM L.Sharmila, R.Aishwarya, G.Umadevi
    2024
  • Narrow Band Internet of Things (NB-IoT) for Wireless Communication and Network Optimization
    Smita Khond, Kanegonda Ravi Chythanya, Shaik Munawar, Manjusha Mandava ...
    IEEE Explore , 2024
    2024
  • ML BASED NURSERY RICE SEEDLING MACHINE
    DAS 1. Dr. Hajera Sana, Pradip Kumar Saini, P. Priyadharshini, Shaik Munawar ...
    IN Patent 0801/2024. , 2024
    2024
  • A METHOD AND SYSTEM OF ARTIFICIAL INTELLIGENCE BLOCK CHAIN ECOMMERCE SYSTEM
    DGNU Dr. Kakumani K C Deepthi, Dr. M S Saritha , Mr. Voodara Devender, Mr ...
    IN Patent 50/2,023 , 2023
    2023
  • Data Mining for Cyber-Physical Systems
    SM M. Varaprasad Rao, D. Anji Reddy, Anusha Ampavathi
    Data Mining Technologies using Machine Learning Algorithms, 235-280 , 2021
    2021
  • AN EXPLORATION TO IDENTIFY THE MOST RELEVANT PARAMETERS FOR PREDICTION OF HEART DISEASE
    S Munawar, A Geetha, K Srinivas
    2020