Dense extreme inception network-based edge detection with deep reinforcement learning for object localization in an underwater environment S. Praveena, Ramesh NSVSC Sripada, E. Laxmi Lydia, Kalpana Gudikandhula, Bibhuti Bhusan Dash, Saroja Kumar Rout, Kanchan Bala Scientific Reports, 2026 The underwater environment is characterized by the vast expanses of water bodies such as seas, oceans, rivers, and lakes, together with their related ecosystems, that exist under the Earth's surface. This environment refers to unique physical properties such as pressure, buoyancy, light attenuation, and temperature, which bring about considerable adversity for observation and exploration. Object detection (OD) in the underwater environment includes the recognition and localization of different objects or entities submerged in the water. This object contains natural features like geological formations, marine life, and coral reefs, along with human-made artefacts such as debris, shipwrecks, and underwater infrastructure. Typically, object recognition in underwater environments relies on imaging technologies, namely optical cameras, SONAR, and LIDAR, which effectively operate in the complicated conditions of water. Edge detection algorithm, extracts crucial features from the surrounding aquatic landscape, fine-tuned to the unique challenges of underwater imagery, enhancing situational awareness and guiding navigation. Various techniques and algorithms are employed to detect objects in underwater imagery, as well as traditional image processing approaches, including deep learning (DL) and machine learning (ML) methods. These techniques analyze the data captured to detect distinct patterns and features related to the objects, enabling automated recognition and classification. Therefore, this study presents a new Dense Extreme Inception Network-based Edge Detection with Deep Reinforcement Learning for Object Localisation (DEINED-DRLOL) technique in an underwater environment. The primary focus of the DEINED-DRLOL technique is on the effective detection of edges and the classification of objects in the underwater environment. The Dense Extreme Inception Network for Edge Detection (DexiNed) method is employed to predict an edge map with a similar resolution. For OD, the DEINED-DRLOL technique employs the YOLOv5 method. Finally, the Q-Reinforcement Learning (QRL) method is implemented for classification. A wide range of experimentation with the DEINED-DRLOL approach is performed under the underwater OD dataset. The comparison study of the DEINED-DRLOL approach highlighted a superior accuracy value of 92.67% over existing models.
Multimodal deep feature fusion with transformer for brain tumor classification from magnetic resonance imaging M. Pajany, K. Boopalan, R. Rajesh, W. JaiSingh, Bibhuti Bhusan Dash, Saroja Kumar Rout, P. Pavan Kumar Scientific Reports, 2026 Brain tumors (BTs) arise due to abnormal cell growth, which has a high mortality rate globally. Millions of lives can be saved through the timely identification of BT. Precise identification and segmentation of BTs are essential to enhance the precision of analysis and the efficiency of therapeutic strategies. Magnetic resonance imaging (MRI) is a broadly utilized analytical tool. Furthermore, deep learning (DL) has recently shown efficiency in addressing several computer vision tasks. Several DL-driven methods are implemented for BT segmentation and attained impressive outcomes. This study presents a Multimodal Deep Feature Fusion Framework for Automated Brain Tumor Detection and Segmentation (MDFF-ABTDS) model. This objective is to develop a multimodal DL that integrates feature fusion and transformer networks for the precise detection and segmentation of BTs from medical images. Initially, image pre-processing is performed using Contrast Limited Adaptive Histogram Equalization (CLAHE) and image normalization. Feature extraction is carried out through fusion models such as CapsNet, ResNet-50, and AlexNet. These extracted features are then passed to a bi-directional convolutional long short-term memory combined with transformer (TBConvL-Net) models to classify tumors and non-tumors effectively. Finally, the tumor is classified to identify its location using the nnUNet model for a precise segmentation process. A series of experimental analyses of the MDFF-ABTDS method portrayed a superior accuracy value of 98.91% over existing models under the BT MRI dataset.
Obstacle Detection for UAV & Drone using YOLO V8 and HC-SR04 Ultrasonic Sensor Chittaranjan Pradhan, Gourab Biswas, Jugul Kishor Gupta, Bibhuti Bhusan Dash, Utpal Chandra De, Sudhansu Shekhar Patra Proceedings of the 2026 International Conference on AI Driven Smart Systems and Ubiquitous Computing Icauc 2026, 2026 The article dives deep into the complications and inspects the development of a real-time obstacle detection system designed for UAVs and drones with the integration of advanced computer vision techniques and ultrasonic sensor technology. The combination of object detection, location, bounding box, and motion analysis conveys the exact state and shape of the existing object in the environment so that the drone or the remote pilot could locate and differentiate between stationary and moving objects. The YOLOv8 model is chosen due to its high precision for detection with added segmentation masks that outline the shape of obstacles, giving better situational awareness. By introducing the HCSR-04 ultrasonic sensor also complements this by measuring distance from the drone to detected obstacles. The HCSR-04 works on the principle of transmitting ultrasonic waves and then later using the time that reflected waves take to come back (SONAR) the sensor measures the accurate distance in real time. It uses an Arduino Uno micro controller so that it can control and process the outputs of the HCSR-04 in order to amplify the flexibility and responsiveness of the system. The proposed system demonstrates experiments which assures that it works perfectly in different circumstances, accurately segmenting and localizing obstacles. The autonomous UAVs or drones get feedback in correct time which help them avoid obstacles and make their way through safely in complex surroundings by using the high-performance, user-friendly and affordable solution of integrated object detection capability of YOLOv8 with Arduino-controlled ultrasonic sensing.
Mitigating data exfiltration from side-channel attacks on graphics processing units Nelson Lungu, Bibhuti Bhusan Dash, Binod Kumar Pattanayak, Rajen Bose, Utpal Chandra De, Sudhansu Shekhar Patra Futuristic Information and Communication A Multimodal Multidisciplinary Signal Analysis, 2026 Graphics Processing Units (GPUs) are progressively used to expedite compute-intensive applications. Adversaries may use the data parallelism intrinsic to GPUs to extract sensitive information via timing, power, and cache assaults. This study introduces a Secure Shader Execution Framework that addresses these vulnerabilities via the integration of randomized execution, power balancing, and cache partitioning. The proposed vendor-agnostic method aligns with current GPU programming paradigms, as shown by the GPUOwl benchmarks using OpenCL. Experimental findings indicate that the framework successfully conceals side-channel information leakage in security-sensitive data with a minimal performance cost. Randomized execution lowers the success rate of timing assaults by as much as 75%, while power balancing decreases leakage in power traces by over 60%.
Multi-Model Feature Fusion-based Ensemble of Pre-trained CNNs for Chest X-ray Classification Soumyarashmi Panigrahi, Dibya Ranjan Das Adhikary, Binod Kumar Pattanayak, Bibhuti Bhusan Dash, Utpal Chandra De, Sudhansu Shekhar Patra Proceeding of International Conference on Computing Communication Control and Cyber Physical Systems I5cps 2026, 2026 A correct diagnosis is necessary to confirm prompt and effective therapy for the proliferation of aberrant cells in the thoracic area and pulmonary nodules. Accurate classification of chest X-rays (CXR) is utmost necessary to generate effective treatment and improve patient condition. CXR are widely and fundamentally available diagnostic tool for initial findings. However, assessment of CXR is very challenging and leads to human error, which evolves the need for automatic and accurate pathology classification. The latest developments in deep learning (DL), specially convolutional neural networks (CNNs), have transformed medical image analysis. In this study, framework ensembling multiple pre-trained CNN models such as EfficientNetB7, InceptionV3, ResNet50V2, and VGG19 are proposed for image classification. The feature-fused model, including individual models are trained and evaluated on a particular dataset with variety of metrics such as accuracy, AUC, Precision, Specificity, and F1-Score. Our results demonstrate that the ensemble of these pre-trained models achieves an accuracy 98.88%. This shows the efficacy of advanced CNN architectures in giving reliable decision support for CXR classification.
Deepsea: A Deep Learning-Based Secure and Energy-Aware Adaptive Protocol for Underwater Sensor Networks Smita Patra, Manoj Ranjan Mishra, Sudhansu Shekhar Patra, AparnaRajesh Atmakuri, Bibhuti Bhusan Dash, Utpal Chandra De Proceedings of the 5th International Conference on Sentiment Analysis and Deep Learning Icsadl 2026, 2026 Underwater Sensor Networks (UWSNs) are essential for marine exploration, environmental monitoring, and naval operations, but their performance is constrained by limited energy, dynamic communication conditions, and vulnerability to cyber attacks. This study introduces DeepSEA (Deep-learning Secured Energy-Aware Adaptive protocol), communication protocol designed to enhance energy efficiency, reliability, and security in UWSNs. The framework integrates deep learning and cyber security mechanisms to enable autonomous and resilient underwater networking.. The proposed framework incorporates deep learning and cyber security strategies to facilitate autonomous and robust underwater networking. Specifically, An LSTM-based energy management model predicts energy consumption and harvesting trends, while a Deep Q-Network (DQN) optimizes routing decisions dynamically based on residual energy, link quality, and trust scores. A lightweight cryptographic layer employing Elliptic Curve Cryptography (ECC) ensures data confidentiality with minimal overhead. Furthermore, an AIbased Intrusion Detection System (IDS) trained on the UNSWNB15 dataset detects attacks such as Sybil, replay, and wormhole using Random Forest, XGBoost, and SVM classifiers. Experimental simulations using NS-3 with AquaSim demonstrate that DeepSEA improves energy efficiency by 29.7 %, extends network lifetime by 43.1 %, and achieves an intrusion detection accuracy of 93.8 %, while maintaining low latency and high packet delivery. The results highlight that the integration of AI-driven adaptivity and lightweight cryptography makes DeepSEA a scalable and robust framework for secure autonomous underwater communications.
Advanced Deep Learning OCR Algorithm for Deciphering and Digitizing Chola Period Tamil Inscriptions M Rajalakshmi, Shanthi Jeyabal, Manoj Ranjan Mishra, Bibhuti Bhusan Dash, Utpal Chandra De, Sudhansu Shekhar Patra 7th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2026, 2026 The Tamil inscriptions of the Chola are important in terms of conserving historical, cultural and linguistic history of the area. It could be tedious and time-consuming to read these inscriptions with the hand though. This paper discusses a customized Optical Character Recognition (OCR) system that facilitates easier digitization and transcription of old Tamil inscriptions. The solution is considered to be a combination of the YOLOv8, Tensorflow, and PyTorch systems that will promote the effectiveness of character recognition, segmentation, and detection. Our experiment with the method on a number of the inscriptions of the Chola-period was 94.7 percent, 93.5 percent, and 95.2 percent correct, precise, and recalled. The average time of processing each picture was 2.8 seconds. This model is more effective in dealing with broken inscriptions, complex backgrounds and mixed character styles than those that preceded it. The proposed OCR approach is appropriate to facilitate easier study and preservation of the Tamil cultural heritage as the epigraphers, archaeologists and researchers can scan and save these ancient manuscripts.
ROUGH SET BASED PRIVACY PRESERVATION OF HEALTHCARE DATA USING ASSOCIATION RULE MINING AND GENETIC ALGORITHM Journal of Theoretical and Applied Information Technology, 2025
Attention-Infused Densenet for Brain Tumor Classification: A Novel Approach Soumyarashmi Panigrahi, Dibya Ranjan Das Adhikary, Binod Kumar Pattanayak, Rajen Bose, Bibhuti Bhusan Dash, Sudhansu Shekhar Patra 2025 International Conference on Information Implementation and Innovation in Technology I2itcon 2025, 2025
Adaptive Gamified Cybersecurity Training for Enhanced User Engagement Nelson Lungu, Bibhuti Bhusan Dash, Utpal Chandra De, Suchismita Rout, Tanvir Habib Sardar, Sudhansu Shekhar Patra Proceedings of 5th International Conference on Soft Computing for Security Applications Icscsa 2025, 2025
Feature Evaluation of Nvidia R555-R560 Open-Source GPU Driver Transition Nelson Lungu, Bibhuti Bhusan Dash, Utpal Chandra De, Simon Tembo, Satyendr Singh, Sudhansu Shekhar Patra Proceedings of 2025 International Conference on Signal Processing Computation Electronics Power and Telecommunication Iconscept 2025, 2025
Intelligent Air Quality Control Through Continuous Policy Learning Jayashri Deb Sinha, Subir Gupta, Sayanti Samanta, Bibhuti Bhusan Dash, Utpal Chandra De, Sudhansu Shekhar Patra Proceedings of the 9th International Conference on Inventive Systems and Control Icisc 2025, 2025
An Integrated Model for Face Recognition Using HOG LTP and CNN Kamaluddin Mandal, Rahul Banerjee, Subir Gupta, Bibhuti Bhusan Dash, Utpal Chandra De, Sudhansu Shekhar Patra 2025 IEEE International Conference on Emerging Trends in Computing and Communication Etcom 2025, 2025
Auto Text Correction using NLP Techniques Nalla Srivarsha, Gummadi Nithin, Shanmugasundaram Hariharan, Bibhuti Bhusan Dash, Subrata Chowdhury, Sudhansu Shekhar Patra 6th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2025 Proceedings, 2025
Evaluating Human AI Collaboration Through Survey Based Random Forest Approach Subir Gupta, Priyanka Roy, Sudipta Hazra, Bibhuti Bhusan Dash, Utpal Chandra De, Sudhansu Shekhar Patra Proceedings of 2025 International Conference on Signal Processing Computation Electronics Power and Telecommunication Iconscept 2025, 2025
Hybrid Binary SGO-GA for solving MAX-SAT problem Rhiddhi Prasad Das, Anuruddha Paul, Junali Jasmine Jena, Bibhuti Bhusan Dash, Utpal Chandra De, Mahendra Kumar Gourisaria Procedia Computer Science, 2025
Edge Computing Resource Allocation Using Game-Theoretic Voting System Anjan Bandyopadhyay, Abtsega Tesfaye Chufare, Bealu Girma Gebresilasse, Bibhuti Bhusan Dash, Sudhansu Shekhar Patra, Mahendra Kumar Gourisaria Proceedings 2025 7th International Conference on Computational Intelligence and Communication Technologies Ccict 2025, 2025
Classifying Crisis Types and Urgency Levels in Tweets using TF-IDF and LSTM Basudev Nath, Suchismita Rout, Lalbihari Barik, Bibhuti Bhusan Dash, Utpal Chandra De, Sudhansu Shekhar Patra Proceedings of 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2025, 2025
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
Blockchain-Based Secure Architecture for Distributed Healthcare Data in IoMT Rajen Bose, Kadim A. Jabbar, Hassan Abozibid, Rasha Abed, Abbas Jumaah Jayed, Sudeshna Chakraborty, Bibhuti Bhusan Dash, Utpal Chandra De, Sudhansu Shekhar Patra 2025 International Conference on Next Generation of Green Information and Emerging Technologies Giet 2025, 2025
Blockchain-Based Framework for Enhanced Healthcare Data Accessibility Bibhuti Bhusan Dash, Hassan Abozibid, Hameed Hassan, Yaser Ahmad Ibrahim, Ali Ihsan Alanssari, Kadim A. Jabbar, Satyendr Singh, Utpal Chandra De, Sudhansu Shekhar Patra 2025 International Conference on Next Generation of Green Information and Emerging Technologies Giet 2025, 2025
DEW COMPUTING WITH EDGE INTELLIGENCE FOR INDUSTRIAL AUTOMATION AND PREDICTIVE MAINTENANCE REAL-TIME ANOMALY DETECTION Journal of Theoretical and Applied Information Technology, 2025
Enhancing Breast Cancer Detection Using SVM and Explainable AI J. Anushree, GR. Ashisha, Utpal Chandra De, Bibhuti Bhusan Dash, X. Anitha Mary, C.Karthik, Matam Mohan Babu, P. Jyotheeswari, Sudhansu Shekhar Patra 2025 5th International Conference on Emerging Research in Electronics Computer Science and Technology Icerect 2025, 2025
Analyzing Security Implications for Artificial Intelligence Driven Medical Training Simulations Bibek Bikram Dash, Hameed Hassan Khalaf, Ahmed Read Al-Tameemi, Hiba ganem Hussain, Kadhim Abbas Jabbar, Amran Mezher Lawas, Bibhuti Bhusan Dash, Sudhansu Shekhar Patra, Utpal Chandra De 2025 International Conference on Next Generation of Green Information and Emerging Technologies Giet 2025, 2025
Privacy Preserving Strategies in Artificial Intelligence Enhanced Healthcare Interactions Bibek Bikram Dash, Hassan Abozibid, Kadhim Abbas Jabbar, Zainab Failh Al Lami, Ahmed Read Al-Tameemi, Saoud Chayid Mashkoor, Bibhuti Bhusan Dash, Sudhansu Shekhar Patra, Manoj Ranjan Mishra 2025 International Conference on Next Generation of Green Information and Emerging Technologies Giet 2025, 2025
Blockchain-Assisted Serverless Framework for AI-Driven Healthcare Applications Akash Ghosh, Abhraneel Dalui, Lalbihari Barik, Jatinderkumar R. Saini, Sunil Kumar Sharma, Bibhuti Bhusan Dash, Satyendr Singh, Namita Dash, Susmita Patra, Sudhansu Shekhar Patra International Journal of Advanced Computer Science and Applications, 2025
THE ROLE OF CITIZEN ENGAGEMENT IN DEMOCRATIC GOVERNANCE ENHANCEMENT THROUGH E-GOVERNANCE: A CASE STUDY OF LUSAKA CITY COUNCIL, ZAMBIA Journal of Theoretical and Applied Information Technology, 2024
NIST CSF-2.0 Compliant GPU Shader Execution Nelson Lungu, Ahmad Abdulqadir Al Rababah, Bibhuti Bhusan Dash, Asif Hassan Syed, Lalbihari Barik, Suchismita Rout, Simon Tembo, Charles Lubobya, Sudhansu Shekhar Patra Engineering Technology and Applied Science Research, 2024
Detection of Eye Disease Using Deep Learning Algorithms Omkumar J. Patel, Kshitij Verma, Chirayu Vyas, Trilok Nath Pandey, Bibhuti Bhusan Dash, Sudhansu Shekhar Patra 4th International Conference on Sustainable Expert Systems Icses 2024 Proceedings, 2024
RISC-V Power Analysis Attack Mitigation in a GPU Execution Framework Nelson Lungu, Bibhuti Bhusan Dash, Manoj Ranjan Mishra, Parthasarathi Pattnayak, Mahendra Kumar Gourisaria, Sudhansu Shekhar Patra 2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2024 Proceedings, 2024
Energy Optimization in WSN Through Vacation Policy Smita Patra, Manoj Ranjan Mishra, Sudhansu Shekhar Patra, Parthasarathi Pattnayak, Mahendra Kumar Gourisaria, Bibhuti Bhusan Dash Proceedings 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks Icicv 2024, 2024
Performance Analysis of Classifiers in Predicting Car Insurance Claim Spoorthi Bhoji, Trilok Nath Pandey, Bibhuti Bhusan Dash, Rabinarayan Satpathy, Utpal Chandra De, Sudhansu Shekhar Patra Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
Advance Chess Engine: an use of ML Approach Kushagra Srivastava, Trilok Nath Pandey, Bibhuti Bhusan Dash, Sudhansu Shekhar Patra, Manoj Ranjan Mishra, Utpal Chandra De 2024 3rd International Conference for Innovation in Technology Inocon 2024, 2024
Detection of Leading CNN Models for AI Image Accuracy and Efficiency Meshal Nayim, Vishnu Mohan, Trilok Nath Pandey, Bibhuti Bhusan Dash, Bibek Bikram Dash, Sudhansu Shekhar Patra International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2024, 2024
Probing Vulnerabilities in GPU Shader Execution Nelson Lungu, Simon Tembo, Sudhansu Shekhar Patra, Ngula Walubita, Bibhuti Bhusan Dash, Utpal Chandra De Proceedings 2nd IEEE International Conference on Device Intelligence Computing and Communication Technologies Dicct 2024, 2024
Performance of VM in SDN-Assisted Cloud Data Center with Working Vacations Lalbihari Barik, Bibhuti Bhusan Dash, Manoj Ranjan Mishra, Suchismita Rout, Utpal Chandra De, Sudhansu Shekhar Patra 7th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2023 Proceedings, 2023
Energy Efficient SDN-assisted Routing Scheme in Cloud Data Center Bibhuti Bhusan Dash, Rabinarayan Satapathy, Sudhansu Shekhar Patra Vitecon 2023 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies Proceedings, 2023
Performance Assessment of Multi-Controller in Software Defined Network Ankita Jaiswal, Sudhansu Shekhar Patra, Bibhuti Bhusan Dash, Lalbihari Barik, Trilok Nath Pandey, Manoj Ranjan Mishra Proceedings 4th IEEE 2023 International Conference on Computing Communication and Intelligent Systems Icccis 2023, 2023
A task offloading scheme with Queue Dependent VM in fog Center Sibananda Behera, Namita Panda, Utpal Chandra De, Bibhuti Bhusan Dash, Binita Dash, Sudhansu Shekhar Patra 2023 6th International Conference on Information Systems and Computer Networks Iscon 2023, 2023
Sentiment analysis of reviews using Nature Inspired PSO and ANN Abinash Tripathy, Utpal Chandra De, Bibhuti Bhusan Dash, Sudhansu Shekhar Patra, Ch. Chakradhara Rao, Trilok Nath Pandey 2023 Global Conference on Information Technologies and Communications Gcitc 2023, 2023
Performance Evaluation of Drones in FANETs using Queueing Model Akash Ghosh, Bibhuti Bhusan Dash, Sudhansu Shekhar Patra, Trilok Nath Pandey, Binod Kumar Pattanayak, Utpal Chandra De International Conference on Sustainable Communication Networks and Application Icscna 2023 Proceedings, 2023
Leasing in IaaS Cloud Using Queuing Model Bibhuti Bhusan Dash, Utpal Chandra De, Manoj Ranjan Mishra, Rabinarayan Satapathy, Sibananda Behera, Namita Panda, Sudhansu Shekhar Patra Lecture Notes in Networks and Systems, 2023
Large Scale Follower Recommendation in Instagram Bibhuti Bhusan Dash, Shobhan Banerjee, Tapaswini Samant, Tanmaya Swain, Manas Kumar Rath 2022 3rd International Conference for Emerging Technology Incet 2022, 2022
SFA4SDI: A Secure Fog Architecture for Spatial Data Infrastructure Bibhuti Bhusan Dash, Sudhansu Shekhar Patra, Sandeep Nanda, Jyotsna Rani Jena, Suchismita Rout, Rabindra Kumar Barik Proceedings 2022 IEEE 2nd International Symposium on Sustainable Energy Signal Processing and Cyber Security Isssc 2022, 2022
Improving VM Placement in fog Center by Multi-objective optimization Sudhansu Shekhar Patra, Bibhuti Bhusan Dash, Lalbihari Barik, Jyotsna Rani Jena, Sandeep Nanda, Rabindra Kumar Barik Proceedings 2022 IEEE 2nd International Symposium on Sustainable Energy Signal Processing and Cyber Security Isssc 2022, 2022
Rice Quality Prediction using Computer Vision Jyotiprakash Panigrahi, Priyanka Pattnaik, Bibhuti Bhushan Dash, Satya Ranjan Dash 2020 International Conference on Computer Science Engineering and Applications Iccsea 2020, 2020
