Dr. Suprava Ranjan Laha

@soa.ac.in

Asst. Professor and Department of Computer Science & Engineering
Siksha 'O' Anusandhan Deemed to be University

Dr. Suprava Ranjan Laha


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https://researchid.co/supravalaha

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Environmental Engineering, Biotechnology, Engineering
30

Scopus Publications

357

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • Boosted Query Expansion for Agricultural Decision Support: A Hybrid Framework Combining Case-Based Reasoning, Fuzzification, and Machine Learning
    Surabhi Solanki, Vaibhav Srivastav, Anirban Bhattacharya, Pulakesh Roy, Suprava Ranjan Laha, Sachin Kumar, Debasish Swapnesh Kumar Nayak
    Tikrit Journal of Engineering Sciences, 2025
    This framework, “BQ-CBRS,” Hybrid Bigger Query-Case Based Reasoning System model, is the first of its kind to unite contextual embedding-based query expansion (using BERT), IndRNN-based semantic similarity scoring, the fuzzification of uncertain parameters, and XGBoost classification within one application to support precision agriculture. Some of the steps include query preprocessing, generating contextual embeddings utilizing a pre-trained method (for example, BERT), semantic similarity scoring using IndRNN, and expanding the query by adding top-ranked search terms. Fuzzification will acknowledge any uncertainty present in the data, while XGBoost will enhance the predictive power and efficacy of the present work. The proposed methodology consists of query preprocessing, contextual representations using pre-trained models (like BERT), calculating a similarity score through IndRNN, and expanding the query according to the top-scoring terms. Fuzzification will address the uncertainty in the data, and XGBoost will enhance prediction accuracy and efficiency. The Crop Recommendation Dataset consists of parameters, such as nitrogen, phosphorus, pH, temperature, and rainfall. The present model has low accuracy and low mean square error (MSE). Also, it improves over traditional approaches. The model will utilize precision agriculture technology to link historical cases and improve approaches for more effective resource management and advancing sustainable farming. This combination of symbolic reasoning and deep learning in the agriculture domain is novel, establishing a generalizable framework for intelligent decision support in dynamic and uncertain situations.
  • Photovoltaic Stand-Alone Systems Using an Artificial Neural Network-Based Intelligent Control System
    Yaseen Al-Husban, Takialddin Al Smadi, Suprava Ranjan Laha, Khalid Al Smadi
    Tikrit Journal of Engineering Sciences, 2025
    This study introduces an adaptive artificial neural network (ANN)-based control system to enhance the efficiency of stand-alone photovoltaic (PV) systems under dynamic environmental conditions. Traditional maximum power point tracking (MPPT) methods, such as perturb and observe (P&O) and incremental conductance (INC), are hindered by slow convergence and oscillations. The proposed approach utilizes a hybrid ANN architecture with hyperbolic tangent (tanh) and rectified linear unit (ReLU) activation functions in a 6-3 neuron hidden layer structure, enabling real-time prediction of the optimal voltage (V_mpp). Integrated with a PID-controlled DC-DC boost converter, the system seamlessly transitions between the solar harvesting, battery charging, and load supply modes. Trained on 10,000 environmental samples (irradiance: 150–1000 W/m² and temperature: 25–50°C) using the Levenberg-Marquardt algorithm, the ANN achieved 99.2% tracking accuracy with a mean squared error (MSE) of 1.73×10⁻⁵ in 200 epochs. MATLAB/Simulink simulations demonstrated superior performance, surpassing P&O by 4.1% and INC by 3.2%, while maintaining a voltage ripple below 1.5%. Key innovations include the hybrid ANN design that mitigates saturation effects, adaptive PID tuning for minimal oscillations, and a three-mode converter that ensures a stable 24 V load voltage during irradiance fluctuations. This work underscores the potential of machine learning in advancing renewable energy systems, offering a computationally efficient and hardware-ready solution for off-grid applications with enhanced reliability and precision.
  • Smart Waste Management Framework for Green Cities: Integrating IoT, LoRa, and Deep Learning for Efficient Waste Classification and Management
    Suprava Ranjan Laha, Khalid Al Smadi, Ahmad Khader Habboush, Binod Kumar Pattanayak, Saumendra Pattnaik, Bibhuprasad Mohanty
    Tikrit Journal of Engineering Sciences, 2025
    Waste management is recognized as a crucial issue in modern civilization, requiring substantial effort and resources while significantly impacting various societal aspects. In sustainable cities striving to eliminate carbon emissions, implementing effective waste management strategies is prioritized. Tackling the three interconnected challenges in trash management, including preventing overflow, tracking bin locations, and designing efficient garbage collection routes, is complex. Current methods often provide incomplete solutions for all three aspects simultaneously. To overcome these difficulties, a smart waste management framework was proposed for environmentally friendly cities, combining the Internet of Things (IoT), long-range (LoRa) technology, and Deep Learning techniques. The proposed system utilized ultrasonic sensors equipped with a LoRa connection to facilitate the real-time monitoring of bin status. Prompt intervention to prevent overflow scenarios was facilitated. Integrating the Floyd-Warshall algorithm enhanced the garbage collection route efficiency by considering the bin fill levels and their exact locations. Deployment was made affordable and straightforward using inexpensive IoT components, including LoRa modules, facilitating smooth data transfer. In addition, incorporating RecycleCnn, implemented using Python with TensorFlow and Keras frameworks, enhanced the proposed framework by enabling automatic garbage classification with a 98% accuracy rate. This classification system facilitated the categorization of garbage into specific groups, improved recycling initiatives, and advocated for sustainable waste management methods. The proposed system used Arduino UNO microcontrollers, ultrasonic sensors, and LoRaWAN technologies to provide a precise and effective method for assessing garbage levels and controlling waste distribution. This holistic approach to intelligent waste management seeks to provide cleaner, pollution-free urban environments by addressing problems arising from ineffective garbage collection methods. The proposed framework addressed trash management and recycling challenges while laying the foundation for sustainable development projects in smart towns like Khandagiri and Pokhariput. Also, it provided a comprehensive approach to garbage collection, categorization, and management.
  • IoT-Enabled Machine Learning for Comprehensive Water Quality Assessment in the Mahanadi River: A Multibelt Analysis of Seasonal Contamination and Predictive Modeling
    Suprava Ranjan Laha, Binod Kumar Pattanayak, Saurav Kumar, Mitrabinda Ray, Saumendra Pattnaik
    Journal of Engineering United Kingdom, 2025
    The increase in water contamination in the Mahanadi River, exacerbated by industrial discharges, domestic effluents, and agricultural runoff, requires urgent and advanced water quality monitoring. This research integrates IoT‐based monitoring systems with the powerful XGBoost machine learning model to address the limitations of traditional evaluation methods. The Mahanadi River, a vital resource amid rapid urbanization and industrialization, requires sustainable water quality management. Cutting‐edge technology facilitates real‐time data collection on pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total coliforms (TCs). The study delves into intricate relationships between variables, geographical regions, belts, and seasonal changes, providing a nuanced understanding of the dynamics of water pollution. Incorporating sophisticated data analysis and machine learning empowers precise predictions and comprehensive insights. A multibelt assessment across industrial, residential, and agricultural regions during various seasons offers a holistic perspective on water quality fluctuations. XGBoost demonstrates remarkable efficiency, achieving 95% accuracy in predicting water quality categories. Comparative evaluations highlight the superiority of the proposed method in seasonal patterns, the calculation of the water quality index (WQI), and belt‐wise comparisons. This research is crucial in developing effective management strategies and sustaining conservation efforts for the Mahanadi River ecosystem. It serves as a valuable resource for policymakers, conservationists, and concerned residents, offering insight into the future of the river and contributing to the broader discourse on environmental preservation.
  • Deep Networks and Internet of Medical Things for Tracking the Post Surgical Recovery Condition: A Comparative Approach
    Surendra Mohan Samal, Aparna Sibadutta Mishra, Moupiya Bose, Adyasha Swain, Suprava Ranjan Laha, Debasish Swapnesh Kumar Nayak
    2025 2nd IEEE International Conference for Women in Computing Incowoco 2025, 2025
    Proper post-operative recovery tracking is crucial for the early identification of complications and optimal patient outcomes. This paper introduces a computer-vision-based, sensor less technique fusing DL models with the Internet of Medical Things (IoMT) to predict a patient's recovery status Healthy or Unhealthy. Estimated vital signs such as heart rate (HR), blood pressure (BP), temperature, SpO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, and hemoglobin are obtained from facial photographs and video inputs by applying machine learning algorithms. A synthetic dataset of 10,000 features is reduced in dimension to less than 2,000 dimensions by using Principal Component Analysis (PCA) and autoencoders. Three models are compared: a Baseline Deep Neural Network (DNN), Autoencoder + Multi-Layer Perceptron (MLP), and Residual MLP. Among these, Residual MLP yields the best classification accuracy of 94.7%, beating others because of improved gradient flow in deep architectures. A Streamlit-enabled web interface makes real-time vitals estimation and prediction possible. The findings highlight the viability of contactless, AI-based post-operative monitoring and stress the need for both feature compression and sound model selection. Real-world dataset expansion and mobile health deployment remain future directions.
  • Facial Emotion Recognition for University Students using CNN: Transforming Learning Environment
    Adyasha Swain, Suprava Ranjan Laha, Sasmita Sahoo, Ankit Dalei, Vaibhav Srivastav, et al.
    Icoicc 2025 3rd International Conference on Intelligent and Cloud Computing, 2025
    The well-being and academic performance of university students are significantly influenced by their emotional experiences within learning environments. This research presents a novel application of Facial Emotion Recognition (FER) using web cameras and Convolutional Neural Networks (CNN) to enhance classroom and laboratory settings. The system is designed to monitor students’ emotional states in real time, enabling educators to adapt learning environments for improved engagement and well-being. Initially, an image dataset was collected through web cameras placed in classrooms, capturing students' facial expressions during lectures and activities. The dataset was preprocessed to account for variations in lighting, facial orientations, and occlusions. A CNN-based model was developed and trained to classify emotions such as happiness, sadness, anger, and neutrality. Hyperparameter tuning and data augmentation techniques were employed to optimize model performance. The model was evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure its effectiveness across diverse conditions. Additionally, the system was optimized to handle real-time emotion detection in varying classroom environments. This real-time FER system allows continuous monitoring of students' emotional states, providing actionable insights for educators to create more supportive and adaptive learning environments. The results demonstrate the potential of FER technologies to transform educational environments by fostering a deeper understanding of student emotions and promoting well-being. This research offers a foundation for the integration of AI-driven tools in modern education to enhance learning experiences.
  • Optimized Deep Learning Architecture for Maize Disease Detection Using Efficient Channel Attention and Transfer Learning
    Lucy Dash, Suprava Ranjan Laha, Binod Kumar Pattanayak, Debasish Swapnesh Kumar Nayak, Pranashi Chakraborty, Salankara Sarkar
    2025 Global Conference on Information Technology and Communication Networks Gitcon 2025, 2025
    Maize is a major staple food crop worldwide. However, its yield is threatened by foliar diseases, such as northern corn leaf blight, common rust, and gray leaf spot, due to which timely disease detection is essential to ensure food safety and minimize pesticide usage. We propose a hybrid deep-learning model by combining a CNN trunk (7 × 7 convolution, batch normalization, ReLU) with EfficientNet inverted-bottleneck blocks, along with Efficient Channel Attention (ECA) to improve cross-channel feature calibration in a lightweight model with a competitive approach for 4.5M parameters. PlantVillage maize recall images were resized to 224×224 (Maize-leaf-PV) and augmentation (rotations, flips, zooms, brightness, and channel shifts) to mimic field diversity. The model was trained using the Adam optimizer with a cosine-annealing schedule for 350 epochs on TPU pods, batch size 64, dropout rate 0.5, early stopping, and model checkpointing to avoid overfitting. The model achieved an accuracy of 98.85 with F<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf>-scores of 93.37 (Blight), 99.45 (Common Rust), 89.57 (Gray Leaf Spot), and 99.90 (Healthy). These are significantly better than state-of-the-art baselines, including ResNet-50 (95.58%), Inception-v3 (95.99%), and EfficientNetB2 (94.00%), while decreasing the computational cost and model memory footprint. The lightweight structure is conducive to real-time application in a precision-agriculture system and allows targeted response to reduce crop yield losses and environmental harm. Towards implementation: robustness validation under diverse field conditions with UAV and smartphone imaging, transfer learning for additional crops, ensemble and severity-estimation modules, optimization for mobile-device implementation to democratize access to automated disease monitoring, collaborative farmer feedback loops, and cloud-based deployment pipelines for continuous model improvements.
  • A Prioritized-Recommendation System with Association Rule Mining and Random Forest for Retailing
    Debasish Swapnesh Kumar Nayak, Pragyan Nanda, Payal Agasti, Roseleen Anjum, Amit Kumar Harichandan, Suprava Ranjan Laha
    Proceedings of the 2025 International Conference on Artificial Intelligence and Emerging Technology Global AI Summit 2025, 2025
    This work proposes a Machine Learning-based Prioritized-Recommendation System for the retail industry. Traditional recommendation systems often face challenges in ranking items effectively, which can lead to lower engagement and sales. This research addresses this gap by integrating Association Rule Mining (ARM) with a Random Forest classifier to predict the likelihood of customer purchases based on transaction data. The system first extracts item relationships using ARM and creates feature vectors from these rules and customer data. A Random Forest model is then used to predict purchase probabilities, and prioritization ranks recommendations accordingly. The proposed system archives model accuracy of 94.96%, Precision 0.85, Recall 0.893, F1-Score 0.88, and AUC-ROC 0.93, demonstrating superior performance in recommending the most relevant products. Furthermore, the use of Incremental ARM ensures the model can dynamically adapt the changes in customer behavior, providing an efficient and scalable solution for personalized retail marketing.
  • Efficient DDoS Detection in IoT Networks Using a CNN-GRU Hybrid Model
    Mohammad Osama Addas, Suprava Ranjan Laha, Susmita Panda, Binod Kumar Pattanayak, Bibhuti Bhusan Dash, Utpal Chandra De, Sudhansu Shekhar Patra
    2025 5th Asian Conference on Innovation in Technology Asiancon 2025, 2025
    The Internet of Things (IoT) is changing how we live and work by connecting devices worldwide, yet as it becomes more common, so do the security risks. Among them, DDoS attacks are now considered a significant threat to the seamless operations of IoT networks, significantly contributing to the demand for efficient IDS. This paper proposes a new method to combine CNN with gated recurrent units (GRU) for DDoS attack detection in IoT networks. Using influence from both CNN and GRU, our model can effectively catch complex attack patterns of network data. We assess this CNN+GRU hybrid on the CICIDS2017 dataset, including realistic and regular attack traffic. Our model achieves the best accuracy of 98.76% compared to CNN, LSTM, and other conventional machine learning based models. The hybrid CNN+GRU model achieves a 98.76% detection rate for the CICIDS2017 dataset with a CNN+LSTM baseline improvement of 2.87% with lower inference latency and false positive rate, which shows our model is promising for edge real-time IoT intrusion detection tasks. This hybrid architecture effectively identifies cyberattacks in the IoT domain as the spatial features input to the CNN are combined with its spatial extraction capability and GRU's time-sensitive mean features. However, it also enjoys a significantly simplified model compared to RNNs, especially compared to LSTM, and it has good training efficiency. We will extend the model in future work for the distributed fog-to-node architectures and improve the model's performance for unbalanced datasets.
  • Energy Efficient Localization Technique Using Multilateration for Reduction of Spatially and Temporally Correlated Data in RFID System
    Lucy Dash, Binod Kumar Pattanayak, Suprava Ranjan Laha, Saumendra Pattnaik, Bibhuprasad Mohanty, Ahmad Khader Habboush, Takialddin Al Smadi
    Tikrit Journal of Engineering Sciences, 2024
    RFID plays a vital role in data communication in multidimensional WSNs as it collects vast amounts of redundant data. The physical phenomena constitute the correlated observations in the space domain and generate spatial correlation. Periodic observations of sensor nodes result in a temporal correlation in the data. Reducing these spatio-temporal correlations in RFID surveillance data is necessary for the smooth functioning of the network. This paper proposes a Voronoi diagram-based spatio-temporal data redundancy elimination approach for RFID systems having multiple readers so only one reader will read every RFID tag depending on the distance between the tag and the center of the Minimum Enclosing Circle of the Voronoi cell to which the reader belongs. This approach eliminates spatial redundancy in the gathered data. Reading the RFID tags at regular time intervals larger than a chosen threshold value minimized temporal redundancy. In contrast to existing methods, the proposed technique is free from any false positive and false negative errors, with no loss of data and every tag being read by only one reader. Simulation of the proposed approach also established its superiority to the existing techniques in terms of these performance parameters.
  • Cybersecurity Challenges in IoT-Based Healthcare Systems A Survey
    Suprava Ranjan Laha, Debasish Swapnesh Kumar Nayak
    Intelligent Security Solutions for Cyber Physical Systems, 2024
  • Challenges Associated with Cybersecurity for Smart Grids Based on IoT
    Suprava Ranjan Laha, Binod Kumar Pattanayak, Saumendra Pattnaik, Mohammad Reza Hosenkhan
    Intelligent Security Solutions for Cyber Physical Systems, 2024
  • A Novel Hybrid Smart Appliances Control Framework for Specially Challenged Persons
    Suprava Ranjan Laha, Saumendra Pattnaik, Sushil Kumar Mahapatra, BBinod Kumar Pattanayak
    Smart Sensors for Industry 4 0 Fundamentals Fabrication and Iiot Applications, 2024
  • Dynamic Load Balancing with Task Migration: A Genetic Algorithm Approach for Optimizing Cloud Computing Infrastructure
    Aliva Priyadarshini, Sateesh Kumar Pradhan, Suprava Ranjan Laha, Soumen Nayak, Bikash Chandra Pattanaik
    Proceedings of 2nd International Conference on Advancements in Smart Secure and Intelligent Computing Assic 2024, 2024
  • A Robust Deep Learning-Based Speaker Identification System Using Hybrid Model on KUI Dataset
    Subrat Kumar Nayak, Ajit Kumar Nayak, Suprava Ranjan Laha, Nrusingha Tripathy, Takialddin AI Smadi
    International Journal of Electrical and Electronics Research, 2024
  • Advancements in Precision Agriculture: A Machine Learning Based Approach for Crop Management Optimization
    Chinmayee Senapati, Swagatika Senapati, Satyaprakash Swain, Kumar Janardan Patra, Binod Kumar Pattanayak, Suprava Ranjan Laha
    Sustainable Farming Through Machine Learning Enhancing Productivity and Efficiency, 2024
  • Securing Industrial IoT Environments through Machine Learning-Based Anomaly Detection in the Age of Pervasive Connectivity
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Enhanced Network Lifetime with EPMS: An Energy-Aware PSO Based Routing Algorithm with Mobile Sink Support for Hot Spot Mitigation in WSNs
    International Journal of Intelligent Systems and Applications in Engineering, 2023
  • Dynamic Fault Tolerance Management Algorithm for VM Migration in Cloud Data Centers
    International Journal of Intelligent Systems and Applications in Engineering, 2023
  • An IoT Based Novel Hybrid-Gamified Educational Approach to Enhance Student’s Learning Ability
    International Journal of Intelligent Systems and Applications in Engineering, 2023
  • Enhancing Fault Tolerance and Load Balancing in Cloud computing for improved e-healthcare Systems Performance
    Aliva Priyadarshini, Sateesh Kumar Pradhan, Suprava Ranjan Laha, Debasish Swapnesh Kumar Nayak
    2023 2nd International Conference on Ambient Intelligence in Health Care Icaihc 2023, 2023
  • Dynamic Task Migration for Enhanced Load Balancing in Cloud Computing using K-means Clustering and Ant Colony Optimization
    Aliva Priyadarshini, Sateesh Kumar Pradhan, Saumendra Pattnaik, Suprava Ranjan Laha, Binod Kumar Pattanayak
    International Journal on Recent and Innovation Trends in Computing and Communication, 2023
  • An IOT-Based Soil Moisture Management System for Precision Agriculture: Real-Time Monitoring and Automated Irrigation Control
    Suprava Ranjan Laha, Binod Kumar Pattanayak, Saumendra Pattnaik, Debashree Mishra, Debasish Swapnesh Kumar Nayak, Bibhuti Bhusan Dash
    Proceedings of the 4th International Conference on Smart Electronics and Communication Icosec 2023, 2023
  • A Novel Technique for Handwritten Text Recognition Using Easy OCR
    Binod Kumar Pattanayak, Anil Kumar Biswal, Suprava Ranjan Laha, Saumendra Pattnaik, Bibhuti Bhusan Dash, Sudhansu Shekhar Patra
    International Conference on Self Sustainable Artificial Intelligence Systems Icssas 2023 Proceedings, 2023
  • Effect of Student's Stress Level on Academic Resilience: A Mediation Role of Mental Health
    Suprava Ranjan Laha, Smruti Rekha Sahoo, Aum Atman Behera, Bidush Kumar Sahoo, Sk. Zahir Hossain, Binod Kumar Pattanayak
    2023 2nd International Conference on Ambient Intelligence in Health Care Icaihc 2023, 2023
  • Advancement of Environmental Monitoring System Using IoT and Sensor: A Comprehensive Analysis
    Suprava Ranjan Laha, Binod Kumar Pattanayak, Saumendra Pattnaik
    Aims Environmental Science, 2022
  • A Novel Intelligent Street Light Control System Using IoT
    Saumendra Pattnaik, Sayan Banerjee, Suprava Ranjan Laha, Binod Kumar Pattanayak, Gouri Prasad Sahu
    Smart Innovation Systems and Technologies, 2022
  • Software Quality Prediction Using Machine Learning
    Aparna Mohapatra, Saumendra Pattnaik, Binod Kumar Pattanayak, Srikanta Patnaik, Suprava Ranjan Laha
    Lecture Notes on Data Engineering and Communications Technologies, 2022
  • A framework to detect digital text using android based smartphone
    Saumendra Pattnaik, Suprava Ranjan Laha, Binod Kumar Pattanayak, Bikash Chandra Pattanaik
    1st Odisha International Conference on Electrical Power Engineering Communication and Computing Technology Odicon 2021, 2021
  • Issues, Challenges and Techniques for Resource Provisioning in Computing Environment
    Suprava Ranjan Laha, Manoranjan Parhi, Saumendra Pattnaik, Binod Kumar Pattanayak, Srikanta Patnaik
    Proceedings 2020 2nd International Conference on Applied Machine Learning Icaml 2020, 2020

RECENT SCHOLAR PUBLICATIONS

  • Development and Validation of a Novel Bayesian Belief Network: A Reliable Fuzzy Weighted Diabetes Predictive Model
    S Kharya, S Soni, P Nanda, G Urkudee, ASS Ojha, DSK Nayak, SR Laha, ...
    Tikrit Journal of Engineering Sciences 32 (SP1), 1-12 , 2025
    2025
  • Boosted Query Expansion for Agricultural Decision Support: A Hybrid Framework Combining Case-Based Reasoning, Fuzzification, and Machine Learning
    S Solanki, V Srivastav, A Bhattacharya, P Roy, SR Laha, S Kumar, ...
    Tikrit Journal of Engineering Sciences 32 (4), 1-11 , 2025
    2025
  • Efficient DDoS Detection in IoT Networks Using a CNN–GRU Hybrid Model
    MO Addas, SR Laha, S Panda, BK Pattanayak, BB Dash, UC De, ...
    2025 5th Asian Conference on Innovation in Technology (ASIANCON), 1-6 , 2025
    2025
  • A Prioritized-Recommendation System with Association Rule Mining and Random Forest for Retailing
    DSK Nayak, P Nanda, P Agasti, R Anjum, AK Harichandan, SR Laha
    2025 2nd Global AI Summit-International Conference on Artificial … , 2025
    2025
  • Deep Networks and Internet of Medical Things for Tracking the Post Surgical Recovery Condition: A Comparative Approach
    SM Samal, AS Mishra, M Bose, A Swain, SR Laha, DSK Nayak
    2025 IEEE 2nd International Conference for Women in Computing (InCoWoCo), 1-5 , 2025
    2025
  • Photovoltaic Stand-Alone Systems Using Artificial Neural Network-Based Intelligent Control System
    Y Al-Husban, TA Smadi, SR Laha, KA Smadi
    Tikrit Journal of Engineering Sciences 32 (Sp1) , 2025
    2025
    Citations: 2
  • Optimized Deep Learning Architecture for Maize Disease Detection Using Efficient Channel Attention and Transfer Learning
    L Dash, SR Laha, BK Pattanayak, DSK Nayak, P Chakraborty, S Sarkar
    2025 Global Conference on Information Technology and Communication Networks … , 2025
    2025
  • Smart Waste Management Framework for Green Cities: Integrating IoT, LoRa, and Deep Learning for Efficient Waste Classification and Management
    SR Laha, K Al Smadi, AK Habboush, BK Pattanayak, S Pattnaik, ...
    Tikrit Journal of Engineering Sciences 32 (SP1), 1-14 , 2025
    2025
    Citations: 1
  • Facial Emotion Recognition for University Students using CNN: Transforming Learning Environment
    A Swain, SR Laha, S Sahoo, A Dalei, V Srivastav, DSK Nayak
    2025 International Conference on Intelligent and Cloud Computing (ICoICC), 1-6 , 2025
    2025
  • Enhancing Environmental Impact Assessments for Sustainable Development: A Machine Learning Approach
    SR Laha, DSK Nayak, C Senapati, S Swain, A Samal, BK Pattanayak, ...
    Computing, Communication and Intelligence, 213-216 , 2025
    2025
    Citations: 1
  • BCViT: A Vision Transformer Enabled Deep Learning Model for Brest Cancer Identification
    DSK Nayak, T Das, K Rout, SP Mohapatra, SR Laha, S Swain, ...
    Computing, Communication and Intelligence, 252-256 , 2025
    2025
    Citations: 1
  • A Novel Hybrid Smart Appliances Control Framework for Specially Challenged Persons
    SR Laha, S Pattnaik, SK Mahapatra, BBK Pattanayak
    Smart Sensors for Industry 4.0: Fundamentals, Fabrication and IIoT … , 2025
    2025
    Citations: 1
  • IoT‐Enabled Machine Learning for Comprehensive Water Quality Assessment in the Mahanadi River: A Multibelt Analysis of Seasonal Contamination and Predictive Modeling
    SR Laha, BK Pattanayak, S Kumar, M Ray, S Pattnaik
    Journal of Engineering 2025 (1), 5549990 , 2025
    2025
    Citations: 6
  • A Robust Deep Learning-Based Speaker Identification System Using Hybrid Model on KUI Dataset
    SK Nayak, AK Nayak, SR Laha, N Tripathy, TAI Smadi
    International Journal of Electrical and Electronics Research 12 (4), 1502-1507 , 2024
    2024
    Citations: 16
  • Advancements in Precision Agriculture: A Machine Learning-Based Approach for Crop Management Optimization
    C Senapati, S Senapati, S Swain, KJ Patra, BK Pattanayak, SR Laha
    Sustainable Farming through Machine Learning, 162-173 , 2024
    2024
    Citations: 2
  • 13 AdvancementsPrecision in
    C Senapati, S Senapati, S Swain, KJ Patra, BK Pattanayak, SR Laha
    Sustainable Farming through Machine Learning: Enhancing Productivity and … , 2024
    2024
  • Comparative Analysis of AI models for Cardiovascular Disease Prediction
    SSSM ,Manoranjan Dash, Saumendra Pattnaik, Suprava Ranjan Laha
    Journal of Emerging Technologies and Innovative Research 11 (9) , 2024
    2024
  • Edge computing and advanced data analytics in monitoring chemical pollution effects on marine life
    B Gaddala, SR Laha
    Zoology (Animal Science) 43 (2S), 1067-1079 , 2024
    2024
    Citations: 1
  • 12 Challenges Associated with Cybersecurity for Smart Grids
    SR Laha, BK Pattanayak, S Pattnaik, MR Hosenkhan
    Intelligent Security Solutions for Cyber-Physical Systems, 191 , 2024
    2024
  • Cybersecurity Challenges in IoT-Based Healthcare Systems: A Survey
    SR Laha, DSK Nayak
    Intelligent Security Solutions for Cyber-Physical Systems, 203-215 , 2024
    2024
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Advancement of Environmental Monitoring System Using IoT and Sensor: A Comprehensive Analysis
    BKPSP Suprava Ranjan Laha*
    AIMS Environmental Science 9 (6), 771-800 , 2022
    2022
    Citations: 105
  • An IOT-based soil moisture management system for precision agriculture: real-time monitoring and automated irrigation control
    SR Laha, BK Pattanayak, S Pattnaik, D Mishra, DSK Nayak, BB Dash
    2023 4th International Conference on Smart Electronics and Communication … , 2023
    2023
    Citations: 36
  • Energy Efficient Localization Technique Using Multilateration for Reduction of Spatially and Temporally Correlated Data in RFID System
    L Dash, BK Pattanayak, SR Laha, S Pattnaik, B Mohanty, AK Habboush, ...
    Tikrit Journal of Engineering Sciences 31 (1), 101-112 , 2024
    2024
    Citations: 32
  • A novel technique for handwritten text recognition using easy OCR
    BK Pattanayak, AK Biswal, SR Laha, S Pattnaik, BB Dash, SS Patra
    2023 International Conference on Self Sustainable Artificial Intelligence … , 2023
    2023
    Citations: 18
  • A Smart Waste Management System Framework Using IoT and LoRa for Green City Project
    SK Suprava Ranjan Laha, Binod Kumar Pattanayak, Saumendra Pattnaik
    International Journal on Recent and Innovation Trends in Computing and … , 2023
    2023
    Citations: 18
  • A Robust Deep Learning-Based Speaker Identification System Using Hybrid Model on KUI Dataset
    SK Nayak, AK Nayak, SR Laha, N Tripathy, TAI Smadi
    International Journal of Electrical and Electronics Research 12 (4), 1502-1507 , 2024
    2024
    Citations: 16
  • A novel intelligent street light control system using IoT
    S Pattnaik, S Banerjee, SR Laha, BK Pattanayak, GP Sahu
    Intelligent and Cloud Computing: Proceedings of ICICC 2021, 145-156 , 2022
    2022
    Citations: 16
  • Cognitive Informatics and Soft Computing: Proceeding of CISC 2019
    PK Mallick, VE Balas, AK Bhoi, GS Chae
    Springer Nature , 2020
    2020
    Citations: 16
  • An IoT based novel Hybrid-Gamified educational approach to enhance student’s learning ability
    SK Mahapatra, BK Pattanayak, B Pati, SR Laha, S Pattnaik, B Mohanty
    International Journal of Intelligent Systems and Applications in Engineering … , 2023
    2023
    Citations: 10
  • Dynamic load balancing with task migration: a genetic algorithm approach for optimizing cloud computing infrastructure
    A Priyadarshini, SK Pradhan, SR Laha, S Nayak, BC Pattanaik
    2024 International Conference on Advancements in Smart, Secure and … , 2024
    2024
    Citations: 9
  • Issues, Challenges and Techniques for Resource Provisioning in Computing Environment
    SR Laha, M Parhi, S Pattnaik, BK Pattanayak, S Patnaik
    2020 2nd International Conference on Applied Machine Learning (ICAML), 157-161 , 2020
    2020
    Citations: 9
  • Challenges associated with cybersecurity for smart grids based on IoT
    SR Laha, BK Pattanayak, S Pattnaik, MR Hosenkhan
    Intelligent Security Solutions for Cyber-Physical Systems, 191-202 , 2024
    2024
    Citations: 8
  • Dynamic fault tolerance management algorithm for VM migration in cloud data centers
    BC Pattanaik, BK Sahoo, B Pati, SR Laha
    Int. J. Intell. Syst. Appl. Eng 11 (3), 85-96 , 2023
    2023
    Citations: 8
  • U-INS: an android-based navigation system
    SR Laha, SK Mahapatra, S Pattnaik, BK Pattanayak, B Pati
    Cognitive Informatics and Soft Computing: Proceeding of CISC 2020, 125-132 , 2021
    2021
    Citations: 8
  • Securing Industrial IoT Environments through Machine Learning-Based Anomaly Detection in the Age of Pervasive Connectivity
    BM Bassam Mohammad Elzaghmouri,Ahmad Khader Habboush, Marwan Abu-Zanona ...
    International Journal of Intelligent Systems and Applications in Engineering … , 2023
    2023
    Citations: 7
  • IoT‐Enabled Machine Learning for Comprehensive Water Quality Assessment in the Mahanadi River: A Multibelt Analysis of Seasonal Contamination and Predictive Modeling
    SR Laha, BK Pattanayak, S Kumar, M Ray, S Pattnaik
    Journal of Engineering 2025 (1), 5549990 , 2025
    2025
    Citations: 6
  • Software quality prediction using machine learning
    A Mohapatra, S Pattnaik, BK Pattanayak, S Patnaik, SR Laha
    Advances in Data Science and Management: Proceedings of ICDSM 2021, 137-146 , 2022
    2022
    Citations: 5
  • Enhancing Fault Tolerance and Load Balancing in Cloud computing for improved e-healthcare Systems Performance
    A Priyadarshini, SK Pradhan, SR Laha, DSK Nayak
    2023 2nd International Conference on Ambient Intelligence in Health Care … , 2024
    2024
    Citations: 4
  • Enhanced Network Lifetime with EPMS: An Energy-Aware PSO Based Routing Algorithm with Mobile Sink Support for Hot Spot Mitigation in WSNs
    L Dash, BK Pattanayak, SR Laha, S Pattnaik
    International Journal of Intelligent Systems and Applications in Engineering … , 2023
    2023
    Citations: 4
  • Software reliability reckoning by applying neural network algorithm
    S Pattnaik, SR Laha, BK Pattanayak, R Mohanty, M Alnabhan, ...
    Journal of Information and Optimization Sciences 43 (5), 1061-1071 , 2022
    2022
    Citations: 4