krishnamohan

@rvrjcce.ac.in

Assistant Professor and Information Technology
RVR&JC College of engineering

krishnamohan


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https://researchid.co/krishnamohan

RESEARCH INTERESTS

Deep learning
11

Scopus Publications

279

Scholar Citations

7

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Sentiment Analysis Utilizing Artificial Intelligence for Effective Health Crisis Management in Diabetics with Smart Urban Environments
    Kris B, Mong-Fong Horng, Siva Shankar S, Chun-Chih Lo
    Journal of Applied Science and Technology Trends, 2026
    Sentiment analysis utilizing artificial intelligence offers a transformative approach to managing health crises among diabetics in smart urban environments. This research proposes a practical AI-based solution that can be integrated into existing smart urban infrastructure to support real-time health crisis interventions for diabetic patients. Challenges in sentiment analysis for health crisis management in diabetics using AI include the need for high-quality, diverse data to accurately capture sentiment and the potential for privacy issues with sensitive health information in smart urban environments. The objective of this study is to leverage sentiment analysis utilizing artificial intelligence to enhance health crisis management for diabetics within smart urban environments. Adaptive Median Filtering Technique (AMFT) is used in pre-processing to reduce noise in sentiment analysis, as textual data from sources often contains noise such as irrelevant information, spam, and outliers. The combination of AMFT for noise reduction, RNNs for temporal sentiment analysis, and AI-driven optimization introduces a novel, technologically advanced approach to health crisis prediction systems. Recurrent Neural Network (RNN) models are highly effective for sentiment analysis, especially in the health crisis management of diabetics within smart urban environments, due to their ability to process sequential data and capture temporal dependencies. AI-driven optimization (AIDO) can automatically tune hyperparameters of sentiment analysis models in RNNs to improve performance, ensuring the models are both accurate and efficient. The AI-driven sentiment analysis system outperforms traditional monitoring methods, such as rule-based lexicons and keyword frequency-based approaches implemented in Python, achieving an accuracy of 0.92, a precision of 0.90, and a recall of 0.93.The proposed system reflects the focus on applied science and technological innovations by demonstrating a scalable, intelligent health monitoring framework that can be deployed in smart cities and urban health systems. Future advancements in sentiment analysis using artificial intelligence could enhance real-time monitoring and prediction of health crises in diabetics, integrating more diverse data sources and adaptive learning algorithms.
  • An Enhanced Skin Cancer Detection Method Utilizing TriBlendNet and Deepdilated Focus U-Net
    D. Manju, K. Kishore Kumar, Movva Pavani, N. V. S. Pavan Kumar, V. S. N. Murthy, Rajesh Kumar Verma, Padmini Debbarma, M. Koteswara Rao, Anand Kumar Saraswathi Rathod, Bh. Krishna Mohan
    Engineering Technology and Applied Science Research, 2026
    Skin cancer remains a major global health concern, demanding advanced methods for accurate and early identification. This work presents an integrated framework that employs a Generative Adversarial Network (GAN) for effective data augmentation and a novel Deep Dilated-Focus U-Net enhanced with attention mechanisms for precise lesion segmentation. For classification, a hybrid model named TriBlendNet is proposed, combining the advantages of the SqueezeNet and DenseNet121 architectures. Using the SIIM-ISIC 2019 dataset, the proposed system outperforms existing models such as SqueezeNet, DenseNet121, ResNet50, and VGG16. The TriBlendNet model achieved an outstanding accuracy of 98.59%, along with high precision, recall, and specificity, showcasing its strong capability for reliable and efficient automated skin cancer detection.
  • Early Anomalus Action Detection in Surveillance Video Using MRCNN-LSTM Classification
    D. Manju, Kishore K. Kumar, Movva Pavani, Rajesh Kumar Verma, Anand Kumar Saraswathi Rathod, Pavan N. V. S. Kumar, V. S. N. Murthy, Bh. Krishna Mohan
    Engineering Technology and Applied Science Research, 2025
    Public space monitoring systems are critical for observing typical human behavior and detecting abnormal activities, especially in high-security environments. With the rise in public space thefts, there is a growing need for intelligent systems capable of detecting suspicious movements early enough to prevent criminal acts. Although Convolutional Neural Networks (CNNs) are widely used in image classification, they are inadequate to differentiate between abnormal and normal behavior and identify criminal activity in its early stage. To overcome these limitations, this study proposes a new hybrid model that combines Mask R-CNN (MRCNN) with Long Short-Term Memory (LSTM) networks for accurate object detection, tracking, and sequential behavior analysis. The main contribution of this study is a multistage anomaly detection pipeline that involves frame conversion, contrast enhancement, background removal, object tracking, and feature extraction. The MRCNN-LSTM framework can extract both spatial and temporal characteristics to allow precise early-stage anomaly detection. Thorough testing on three benchmarking datasets, UCF Crime, Snatch1.0, and CUHK, exhibited excellent performance, with a 93.6% accuracy for the UCF Crime dataset. Performance metrics such as observation ratio and time duration were used to assess the responsiveness and effectiveness of the system in real-time surveillance scenarios. This research advances the field of intelligent surveillance by enabling proactive threat mitigation through the early and precise detection of anomalous behavior.
  • ENHANCING FAULT DIAGNOSIS AND IMPROVING PRODUCTIVITY IN INDUSTRIAL MANUFACTURING USING DEEP LEARNING TECHNIQUES
    Journal of Theoretical and Applied Information Technology, 2025
  • Android malware detection using GIST based machine learning and deep learning techniques
    Ponnuswamy Udayakumar, Srilatha Yalamati, Lavadiya Mohan, Mohd Junedul Haque, Gaurav Narkhede, Krishna Mohan Bhashyam
    Indonesian Journal of Electrical Engineering and Computer Science, 2024
    In today’s digital world, Android phones play a vital part in a variety of facets of both professionals and individuals’ personal and professional lives. Android phones are great for getting things done faster and more organized. The proportionate increase in the number of malicious applications has also been seen to be expanding. Since the play store offers millions of apps, detection of malware apps is challenging task. In this paper, a methodology is introduced for detecting malware in Android applications through the utilization of global image shape transform (GIST) features extracted from grayscale images of the applications. The dataset comprises samples of both malware and benign apps collected from the virus share website. After converting the apps into grayscale images, GIST features are extracted to capture their global spatial layout. Various machine learning (ML) algorithms, such as logistic regression (LR), k-nearest neighbour (KNN), AdaBoost, decision tree (DT), Naïve Bayes (NB), random forest (RF), support vector machine (SVM), extra tree classifier (ETC), and gradient boosting (GB), are employed to classify the applications according to their GIST features. Furthermore, a feed forward neural network (FFNN) is utilized as a deep learning (DL) technique to further improve the accuracy of classification. The performance of each algorithm is evaluated using metrics such as accuracy, precision and recall. The results demonstrated that the FFNN achieves superior accuracy compared to traditional ML classifiers, indicating its effectiveness in detecting malware in Android apps.
  • QUANTUM MACHINE LEARNING: BRIDGING THE GAP BETWEEN CLASSICAL AND QUANTUM COMPUTING
    Krishna Mohan B.H, Padmaja Pulicherla, M Purnachandrarao, P Nagamalleswararao
    Journal of Engineering and Technology for Industrial Applications, 2024
    This research examines the revolutionary potential of Quantum Machine Learning (QML), which combines machine instruction and quantum computer technology. The work carefully compares QML methods to their traditional counterparts throughout real-world datasets using an interpretive approach as well as a deductive approach. The results show that in some areas, QML algorithms, such as Quantum Support Vector Machines (QSVM) and overall Variational Quantum Eigen solvers (VQE), provide substantial advantages in terms of accuracy and efficiency. Nevertheless, context-dependent factors such as dataset qualities and problem complexity have an impact on the practical consequences. This research underlines the necessity of further research on quantum simulation software and hardware in order to fully utilize QML. Additionally, it highlights the significance of quantum-resistant encryption and promotes cooperation between the various areas of quantum computers and machine learning. Future research ought to concentrate on improving QML programs' scalability and investigating QML's function in developing quantum technology.
  • Financial time series prediction using deep computing approaches
    M. Durairaj, Ch. Suneetha, BH. Krishna Mohan
    Journal of Autonomous Intelligence, 2023
    <p class="Abstracttitle">A financial time series is chaotic and non-stationary in nature, and predicting it outcomes is a very complex and challenging task. In this research, the theory of chaos, Long Short-Term Memory (LSTM), and Polynomial Regression (PR) are used in tandem to create a novel financial time series prediction hybrid, Chaos+LSTM+PR. The first step in this hybrid will determine whether or not a financial time series contains chaos. Following that, the chaos in the time series is modeled using Chaos Theory. The modeled time series is fed into the LSTM to obtain initial predictions. The error series obtained from LSTM predictions is fitted by PR to obtain error predictions. The error predictions and initial predictions from LSTM are combined to obtain final predictions. The effectiveness of this hybrid is examined by three types of financial time series (Chaos+LSTM+PR), including stock market indices (S&P 500, Nifty 50, Shanghai Composite), commodity prices (gold, crude oil, soya beans), and foreign exchange rates (INR/USD, JPY/USD, SGD/USD). The results show that the proposed hybrid outperforms ARIMA (autoregressive integrated moving average), Prophet, CART (Classification and Regression Tree), RF (Random Forest), LSTM, Chaos+CART, Chaos+CART, and Chaos+LSTM. The results are also checked for statistical significance.</p>
  • A convolutional neural network based approach to financial time series prediction
    Dr. M. Durairaj, B. H. Krishna Mohan
    Neural Computing and Applications, 2022
  • An Efficient Deep Learning Model with Interrelated Tagging Prototype with Segmentation for Telugu Optical Character Recognition
    Srinivasa Rao Dhanikonda, Ponnuru Sowjanya, M. Laxmidevi Ramanaiah, Rahul Joshi, B. H. Krishna Mohan, Dharmesh Dhabliya, N. Kannaiya Raja
    Scientific Programming, 2022
    More than 66 million people in India speak Telugu, a language that dates back thousands of years and is widely spoken in South India. There has not been much progress reported on the advancement of Telugu text Optical Character Recognition (OCR) systems. Telugu characters can be composed of many symbols joined together. OCR is the process of turning a document image into a text-editable one that may be used in other applications. It saves a great deal of time and effort by not having to start from scratch each time. There are hundreds of thousands of different combinations of modifiers and consonants when writing compound letters. Symbols joined to one another form a compound character. Since there are so many output classes in Telugu, there’s a lot of interclass variation. Additionally, there are not any Telugu OCR systems that take use of recent breakthroughs in deep learning, which prompted us to create our own. When used in conjunction with a word processor, an OCR system has a significant impact on real-world applications. In a Telugu OCR system, we offer two ways to improve symbol or glyph segmentation. When it comes to Telugu OCR, the ability to recognise that Telugu text is crucial. In a picture, connected components are collections of identical pixels that are connected to one another by either 4- or 8-pixel connectivity. These connected components are known as glyphs in Telugu. In the proposed research, an efficient deep learning model with Interrelated Tagging Prototype with Segmentation for Telugu Text Recognition (ITP-STTR) is introduced. The proposed model is compared with the existing model and the results exhibit that the proposed model’s performance in text recognition is high.
  • Statistical Evaluation and Prediction of Financial Time Series Using Hybrid Regression Prediction Models
    M. Durairaj, B. H. Krishna Mohan
    International Journal of Intelligent Systems and Applications in Engineering, 2021
    : Financial time series are chaotic by nature, which makes prediction difficult and complicated. This research employs the new hybrid model for the prediction of FTS which comprises Long Short-Term Memory (LSTM), Polynomial Regression (PR), and Chaos Theory. First of all, FTS is tested for the presence of chaos, in this hybrid model. Later, using Chaos Theory, the time series is modelled with the chaos existence. The model time series will be entered in LSTM for initial forecasts. The sequence of errors derived from LSTM forecasts is PR appropriate for error predictions. Error forecasts and original model forecasts are applied to produce the final hybrid model forecasts. Performance testing of the hybrid model (Chaos+LSTM+PR) is conducted using three categories namely foreign exchange, commodity price and stock-market indices. The hybrid model proposed in this study, in compliance with MSE, Dstat and Theil’s U, is proved superior to the individual models like ARIMA, Prophet, LSTM and Chaos+LSTM. The execution of these various hybrid proposed methods is done mainly using Python, additionally, the authors used Gretl® and R for some methods respectively. Ultimately, the final result of this hybrid model describes with a better result than the existing prediction models and it is proved using various types of FTS like Foreign exchange rates, commodity prices, and stock market indices respectively. Hence, the result shows that the proposed hybrid models of Chaos+LSTM+PR achieved with better prediction rate than the existing models on the nine datasets executed.
  • A review of two decades of deep learning hybrids for financial time series prediction
    International Journal on Emerging Technologies, 2019

RECENT SCHOLAR PUBLICATIONS

  • An Enhanced Skin Cancer Detection Method Utilizing TriBlendNet and Deepdilated Focus U-Net
    D Manju, KK Kumar, M Pavani, NVSP Kumar, VSN Murthy, RK Verma, ...
    Engineering, Technology & Applied Science Research 16 (1), 31371-31378 , 2026
    2026
  • An Early Breast Cancer Detection Using Deep Learning Technique
    M Saravanapandian, KP Shivani, PR Kumar, B Mohan, R Jenitha, ...
    2025 IEEE International Conference on Blockchain and Distributed Systems … , 2025
    2025
  • Early Anomalus Action Detection in Surveillance Video Using MRCNN-LSTM Classification
    D Manju, KK Kumar, M Pavani, RK Verma, AKS Rathod, PNVS Kumar, ...
    Engineering, Technology & Applied Science Research 15 (4), 25668-25676 , 2025
    2025
    Citations: 2
  • HYBRID OPTIMIZATION TECHNIQUES FOR MOBILITY-AWARE, ENERGY-EFFICIENT SMALL CELL DEPLOYMENT IN 5G NETWORKS
    SV KUMAR, A VIJAYALAKSHMI, A PACKIALATHA, BE ABISHEK, ...
    Journal of Theoretical and Applied Information Technology 103 (14) , 2025
    2025
    Citations: 2
  • ENHANCING FAULT DIAGNOSIS AND IMPROVING PRODUCTIVITY IN INDUSTRIAL MANUFACTURING USING DEEP LEARNING TECHNIQUES
    RK VERMA, KSN PRASAD, KS RANJITH, VSN MURTHY, IN PADMAJA, ...
    Journal of Theoretical and Applied Information Technology 103 (14) , 2025
    2025
  • The impact of artificial intelligence on the global competitiveness of labour markets
    Y SIKORSKYI, R ZABLOTSKA, Z Mariia, H BRATUS, K DADASHOVA, ...
    Journal of Theoretical and Applied Information Technology 103 (4), 1145-1155 , 2025
    2025
    Citations: 7
  • Design and implementation of electric vehicle charging station integrated with different energy sources using ANFIS controller
    B Mohan, MV Ramesh, M Seshu, PM Kumar, M Valavala
    Journal of Theoretical and Applied Information Technology 103 (4), 1245-1255 , 2025
    2025
    Citations: 7
  • Muthu Kumar, and Madhu Valavala,“Design and Implementation of Electric Vehicle Charging Station Integrated With Different Energy Sources Using Anfis Controller,”
    B Mohan, MV Ramesh, P Moturu Seshu
    Journal of Theoretical and Applied Information Technology 103 (4), 1245-1255 , 2025
    2025
    Citations: 5
  • A Review of Reinforcement Learning Approaches for Autonomous Systems in Industry 4.0
    BHK Mohan, P Padmaja, M Durairaj, P Nagamalleswrarao, ...
    Industry 4.0 Key Technological Advances and Design Principles in Engineering … , 2024
    2024
  • Designing of a wireless charging system for electric vehicles
    B Mohan, MV Ramesh, VR Rajan, D Rene Dev
    J Theor Appl Inf Technol 102, 426-439 , 2024
    2024
    Citations: 3
  • Charging station for electric vehicle using hybrid sources
    B Mohan, MV Ramesh, PM Kumar, R Vr
    Journal of Theoretical and Applied Information Technology 101 (7) , 2023
    2023
    Citations: 5
  • Financial time series prediction using deep computing approaches
    M Durairaj, C Suneetha, M Krishna
    Journal of Autonomous Intelligence 6 (1), 558 , 2023
    2023
    Citations: 6
  • A convolutional neural network based approach to financial time series prediction
    DM Durairaj, BHK Mohan
    Neural Computing and Applications 34 (16), 13319-13337 , 2022
    2022
    Citations: 175
  • ENHANCEMENT OF VOLTAGE REGULATION AND LOAD SHARING IN DC MICROGRID USING PSO AND FUZZY LOGIC
    B MOHAN, CD VARAPRASAD, TN PRASAD
    Journal of Theoretical and Applied Information Technology 100 (8) , 2022
    2022
  • Statistical evaluation and prediction of financial time series using hybrid regression prediction models
    M Durairaj, KBH Mohan
    International Journal of Intelligent Systems and Applications in Engineering … , 2021
    2021
    Citations: 13
  • A Review of Two Decades of Deep Learning Hybrids for Financial Time Series Prediction
    MDBHK Mohan
    International Journal on Emerging Technologies 10 (3), 324-331 , 2019
    2019
    Citations: 26
  • Malarial acute kidney injury
    M Ramesh, B Mohan, P Murlidharan, S Balan
    Med Sci Int Med J 6 (2), 355-356 , 2016
    2016
    Citations: 1
  • Probe Packet Security for Wireless and Mobile Ad-hoc Networks
    BSDTSR B. Krishna Mohan#1
    2014
  • Optimal DG placement under standard market design using GA
    B Mohan, MV Ramesh
    2012 International conference on emerging trends in electrical engineering … , 2012
    2012
    Citations: 12
  • Comparative efficacy of two different disinfection methods against coccidian oocysts.
    P Kumar, VR Saravanakumar, PNR Jagatheesan, B Mohan
    2006

MOST CITED SCHOLAR PUBLICATIONS

  • A convolutional neural network based approach to financial time series prediction
    DM Durairaj, BHK Mohan
    Neural Computing and Applications 34 (16), 13319-13337 , 2022
    2022.0
    Citations: 175
  • A Review of Two Decades of Deep Learning Hybrids for Financial Time Series Prediction
    MDBHK Mohan
    International Journal on Emerging Technologies 10 (3), 324-331 , 2019
    2019.0
    Citations: 26
  • Statistical evaluation and prediction of financial time series using hybrid regression prediction models
    M Durairaj, KBH Mohan
    International Journal of Intelligent Systems and Applications in Engineering … , 2021
    2021.0
    Citations: 13
  • Optimal DG placement under standard market design using GA
    B Mohan, MV Ramesh
    2012 International conference on emerging trends in electrical engineering … , 2012
    2012.0
    Citations: 12
  • Sparfloxacin induced toxic epidermal necrolysis
    M Ramesh, G Parthasarathi, B Mohan, AB Harugeri
    Indian Journal of Dermatology, Venereology and Leprology 69, 235 , 2003
    2003.0
    Citations: 8
  • The impact of artificial intelligence on the global competitiveness of labour markets
    Y SIKORSKYI, R ZABLOTSKA, Z Mariia, H BRATUS, K DADASHOVA, ...
    Journal of Theoretical and Applied Information Technology 103 (4), 1145-1155 , 2025
    2025.0
    Citations: 7
  • Design and implementation of electric vehicle charging station integrated with different energy sources using ANFIS controller
    B Mohan, MV Ramesh, M Seshu, PM Kumar, M Valavala
    Journal of Theoretical and Applied Information Technology 103 (4), 1245-1255 , 2025
    2025.0
    Citations: 7
  • K, & Preetha, M.(2024),“
    S Srinivasan, DD Hema, B Singaram, D Praveena, KB Mohan
    Decision Support System based on Industry 5 , 0
    Citations: 7
  • Financial time series prediction using deep computing approaches
    M Durairaj, C Suneetha, M Krishna
    Journal of Autonomous Intelligence 6 (1), 558 , 2023
    2023.0
    Citations: 6
  • Muthu Kumar, and Madhu Valavala,“Design and Implementation of Electric Vehicle Charging Station Integrated With Different Energy Sources Using Anfis Controller,”
    B Mohan, MV Ramesh, P Moturu Seshu
    Journal of Theoretical and Applied Information Technology 103 (4), 1245-1255 , 2025
    2025.0
    Citations: 5
  • Charging station for electric vehicle using hybrid sources
    B Mohan, MV Ramesh, PM Kumar, R Vr
    Journal of Theoretical and Applied Information Technology 101 (7) , 2023
    2023.0
    Citations: 5
  • Designing of a wireless charging system for electric vehicles
    B Mohan, MV Ramesh, VR Rajan, D Rene Dev
    J Theor Appl Inf Technol 102, 426-439 , 2024
    2024.0
    Citations: 3
  • Early Anomalus Action Detection in Surveillance Video Using MRCNN-LSTM Classification
    D Manju, KK Kumar, M Pavani, RK Verma, AKS Rathod, PNVS Kumar, ...
    Engineering, Technology & Applied Science Research 15 (4), 25668-25676 , 2025
    2025.0
    Citations: 2
  • HYBRID OPTIMIZATION TECHNIQUES FOR MOBILITY-AWARE, ENERGY-EFFICIENT SMALL CELL DEPLOYMENT IN 5G NETWORKS
    SV KUMAR, A VIJAYALAKSHMI, A PACKIALATHA, BE ABISHEK, ...
    Journal of Theoretical and Applied Information Technology 103 (14) , 2025
    2025.0
    Citations: 2
  • Malarial acute kidney injury
    M Ramesh, B Mohan, P Murlidharan, S Balan
    Med Sci Int Med J 6 (2), 355-356 , 2016
    2016.0
    Citations: 1
  • An Enhanced Skin Cancer Detection Method Utilizing TriBlendNet and Deepdilated Focus U-Net
    D Manju, KK Kumar, M Pavani, NVSP Kumar, VSN Murthy, RK Verma, ...
    Engineering, Technology & Applied Science Research 16 (1), 31371-31378 , 2026
    2026.0
  • An Early Breast Cancer Detection Using Deep Learning Technique
    M Saravanapandian, KP Shivani, PR Kumar, B Mohan, R Jenitha, ...
    2025 IEEE International Conference on Blockchain and Distributed Systems … , 2025
    2025.0
  • ENHANCING FAULT DIAGNOSIS AND IMPROVING PRODUCTIVITY IN INDUSTRIAL MANUFACTURING USING DEEP LEARNING TECHNIQUES
    RK VERMA, KSN PRASAD, KS RANJITH, VSN MURTHY, IN PADMAJA, ...
    Journal of Theoretical and Applied Information Technology 103 (14) , 2025
    2025.0
  • A Review of Reinforcement Learning Approaches for Autonomous Systems in Industry 4.0
    BHK Mohan, P Padmaja, M Durairaj, P Nagamalleswrarao, ...
    Industry 4.0 Key Technological Advances and Design Principles in Engineering … , 2024
    2024.0
  • ENHANCEMENT OF VOLTAGE REGULATION AND LOAD SHARING IN DC MICROGRID USING PSO AND FUZZY LOGIC
    B MOHAN, CD VARAPRASAD, TN PRASAD
    Journal of Theoretical and Applied Information Technology 100 (8) , 2022
    2022.0