Rajesh Suresh Bulbule

Scopus Publications

Security challenges in mobile ad hoc networks: Investigating vulnerabilities and proposing robust solutions for secure communication
Shilpa Choudhary, K. Shanti, Tanya Singh, Gurunath T. Chavan, Rajesh Suresh Bulbule, Amol Dattatray Sonawane
Journal of Discrete Mathematical Sciences and Cryptography, 2026
MANETs enable ad hoc wireless networking in unstructured and constantly changing environments. As a result, MANETs face a variety of security challenges associated with their lack of centralized control, unencrypted communication and constant changes in the network topology. The study explores the two major threats to MANETs: routing attacks and issues related to trust. A newly developed framework is introduced that integrates trust-based routing, lightweight encryption and artificial intelligence-powered anomaly detection to improve the security of mobile ad hoc networks. Evaluation of the proposed framework using the UCSD/mobility dataset shows that it achieves better packet delivery rates, lower latency, higher intrusion detection accuracy and less routing overhead compared to SAODV and ARAN. The results emphasize that the proposed framework effectively combats security threats without compromising the performance of the network.
Intelligent Islanding Detection Method Using Local Binary Pattern Feature Extraction from Contour Plot
Kumaresh Pal, Subhadip Goswami, Mukesh Ghogare, Rajesh Bulbule
Conference Proceedings 2025 IEEE Silchar Subsection Conference IEEE Silcon 2025, 2025
Islanding is a significant obstacle along with excessive penetration of DG sources. The islanding could cause harm to the customers and their equipments. The IEEE 1547 DG interconnection regulations stipulate that the DG must be disconnected, and the islanding must be observed within two seconds. This work presents a novel intelligent method for detecting islanding based upon image pattern recognition using machine learning (ML) classification technique. Images are utilised to derive the local binary pattern (LBP) characteristics so as to identify non-islanding as well as islanding events. Hence, the time series signal is obtained at point of common coupling and transformed into contour images. These images are utilised to extract the LBP features, which is then utilised as a feature vector input for the ML classifier for training, testing and validating. It utilises the three phase voltages to derive contour plot images. The ML classifier’s effectiveness is assessed with 5-fold cross-validations. The result from classification demonstrates that detection of islanding using ML classifier and LBP feature being used for image pattern recognition achieved excellent results with 98.19
Smart Hybrid Solar Dryer with Real-Time Monitoring and Adaptive Control for Enhanced Drying Efficiency
Sachin P Jadhav, Datta S Chavan, Digvijay Kanase, Rajesh Bulbule, Swapnil D. Patil
2025 International Conference on Future Technologies Icft 2025, 2025
Design & Implementation of Smart Elderly Assistant System for Blind Person
Rajesh Bulbule, Pawan Gawade, Sachin P Jadhav, Manali Pednekar, Snehal Rote, Gayatri Patil
2025 IEEE 4th International Conference for Advancement in Technology Iconat 2025, 2025
Conventional sun drying techniques widely employed in rural and semiurban regions are often constrained by weather dependency, contamination risks, and inconsistent product quality. As the global population ages, many elderly individuals especially those over 60-face distinct challenges in maintaining their independence and quality of life. Numerous older adults, particularly those living alone, lack consistent oversight and personal care, which can lead to significant health and safety issues. Without regular check-ins, problems like falls, medication mismanagement, and unreported emergencies can occur, often resulting in serious health complications or even tragic outcomes. These situations not only affect the individuals involved but also place emotional and financial burdens on their families and healthcare systems. To tackle these challenges, innovative e-health solutions are becoming increasingly vital. Advances in technology especially artificial intelligence (AI) and the Internet of Things (IoT) have the potential to transform elder care. Smart sensors, wearable devices, and intelligent monitoring systems can provide more than just fall prevention; they can deliver continuous, real-time support tailored to each individual's needs. Picture an AIpowered assistant that re- minds an elderly person to take their medications, tracks their overall health, monitors their emotional well-being, and even alerts caregivers in case of an emergency. These smart solutions create a safety net, enabling older adults to live more independently while ensuring they are never truly alone. By embracing these technologies, we can help our aging loved ones feel more secure, supported, and connected in their daily lives.
Advanced Machine Learning Based Detection of Islanding Using Scalogram and Histogram of Oriented Gradient
Appurva Rajendra Kapil, Kumaresh Pal, Md. Qaiser Reza, Rajesh Bulbule, Somila Hashunao, Subhadip Goswami
International Conference on Emerging Technologies in Electronics and Green Energy Iceteg 2025, 2025
A major challenge driven on by the increasing integration of dispersed power sources is islanding. Islanding may cause harm to consumers and their equipment. The IEEE 1547 DG connectivity standards state that the DG must be shut down if islanding is detected within two seconds. This paper introduces a novel deep learning classification algorithm for image pattern recognition for islanding detection. The proposed method employs the Gated Recurrent Unit (GRU) classification algorithm. Images are employed to derive the histogram of orientated gradient (HOG) features for identifying of both non-islanding and islanding events. The scalogram images are generated from the time series signal obtained at the point of common connection. The images are employed to derive HOG features, which are then utilised as feature vector inputs for training and testing the GRU classifiers. The scalogram image is generated utilising the rate of change of voltage parameter. The effectiveness of the GRU classifier is assessed using 10-fold cross-validation. The classification results demonstrate that islanding detection using a GRU classifier and HOG features for image pattern recognition achieved remarkable performance, with an accuracy of 97.43% and a detection time of 138 milliseconds.
IoT Based Smart Surveillance Bot for Night Patrolling Using Raspberry Pi
Pawan Gawade, Manali Pednekar, Bhagavat D Jadhav, Rajesh Bulbule, Vishal Vashikar, Armaan Suhel C. Shaikh
2025 2nd IEEE International Conference for Women in Computing Incowoco 2025, 2025
This paper discusses the use of a night patrol robot outfitted with a control and monitoring algorithm. The Night Patrolling Robot uses Internet of Things (IoT) technology and runs on a Raspberry Pi platform. This robot is used as a security solution in a variety of organisations, including hospitals and banks, and is an essential tool for national security. The Night Patrolling Robot system, also called the spy robot system is made up of a Raspberry Pi, which functions as a small computer, a night vision camera, and a sound sensor. While the Pi camera simultaneously records and sends video of moving objects to the server, users can access real-time information via the sound sensor's detection capabilities, which are enabled by a web server. A database built on the server allows control room staff to access the robot's data. To prevent collisions, the robots can also be programmed to move on their own by utilising obstacle-detecting sensors. The proposed system gives 92% accuracy which is better than other systems. This surveillance system provides customisable security solutions suited to particular needs and settings, making it suitable for deployment in a variety of settings, such as businesses, retail establishments, and airports.
Arduino-Based Dual-Axis Solar Tracker with Weather Condition Monitoring to Enhance Agricultural Energy Efficiency
Sachin P Jadhav, Vaiju N Kalkhambkar, Bansilal Bairwa, Digvijay Kanase, Rajesh Bulbule, Shankar D Chavan
2025 Global Conference in Emerging Technology Ginotech 2025, 2025
Efficient energy systems are vital for optimizing agricultural systems such as irrigation, greenhouse automation, and crop management. To meet these energy needs, an Arduino-Powered dual-axis solar tracking system is designed along with weather condition monitoring. The servo motors change the solar panel’s position to capture maximum sunlight, which is detected by light-dependent resistors (LDRs) placed around the solar panel. Moreover, sensors are added to monitor variables like temperature, humidity, and rainfall, aiding in agricultural management and providing real-time data on an LCD for monitoring. It tracks sunlight from several directions, maximizing energy production. Compared to fixed solar panels, this proposed dual-axis tracking system creates up to 30-40% more energy output, which performs better with a low-cost installation. The real-time data has been sent to the farmer’s mobile number from the GSM modem.
Cloud based IoT System for Plant health Monitoring
Pawan Gawade, Rajesh Bulbule, Pavankumar Paikrao, Rutuja Kadam, Amruta Rathod, Gauri Marwadkar
2025 Global Conference in Emerging Technology Ginotech 2025, 2025
This paper presents the Internet of Things (IoT)-enabled plant monitoring system with a nutrient management addon is intended to automate and maximize plant care through the integration of environmental monitoring and nutrient delivery. The system employs sensors to gather real-time information on soil moisture, temperature, humidity, and light intensity to determine plant health and environmental conditions. It also has nutrient sensors that track major soil nutrients like nitrogen, phosphorus, and potassium. The information gathered is processed and presented through a easy-to-use interface on a mobile or web platform, enabling users to track plant health remotely. The system can dynamically control irrigation and nutrient levels according to pre-set parameters or user entry, providing ideal growing conditions. This solution minimizes human interaction, saves resources, and facilitates accurate nutrient delivery, leading to healthier plants and enhanced yields, particularly valuable for smart farming and urban gardening environments. This work demonstrates an IoT-based plant monitoring system with 90% accelerated updates, 100% enhanced storage, 50-70% improved automation, 50-60% better accuracy, reduced power consumption, improved UI and scalability compared to existing methods.
Raspberry Pi-powered door lock with facial recognition
Anjali Jha, Rajesh Bulbule, Nilesh Nagrale, Tanavi Belambe
2024 IEEE International Students Conference on Electrical Electronics and Computer Science Sceecs 2024, 2024
Facial recognition is a widely-used process that aims to detect and verify an individual's identity. This technique is employed in various applications, such as image and video analysis, surveillance, and security systems. OpenCV is a computer vision technique that enables users to comprehend the storage, modification and feature extraction of images and videos. The advancement of artificial intelligence is heavily reliant on computer vision techniques, particularly in self-driving cars, robotics, and image-editing software. The OpenCV Library is a valuable tool for processing and displaying camera images. This research utilizes the Haar Cascade Classifier, a machine learning-based algorithm, to process a user's face image and detect faces accurately.
IoT-based System to Integrate with Wheelchair for Disable Person
Akanskha Ingale, Rutuja Jagdhane, Kartiki Purohit, Rajesh Bulbule, Pramodkumar H. Kulkarni, Nikita Maralbhavi
2024 IEEE International Conference on Blockchain and Distributed Systems Security Icbds 2024, 2024
This paper presents the design and implementation of an Internet of Things (IoT)-enabled wheelchair system using the ESP32 microcontroller platform. The project aims to enhance the mobility and quality of life for individuals with limited mobility by integrating various sensors, connectivity options like Bluetooth And wifi, and a user-friendly mobile application. The ESP32 microcontroller is employed as the central processing unit to gather data from sensors, communicate wirelessly with a mobile app, and control the wheelchair's movement. The wheelchair is equipped with ultrasonic sensors to detect obstacles and ensure safe navigation.
Design and Optimization of Suspended RF Stripline Lowpass Filter
Akshit Khare, Nilesh Nagrale, Pramod Kulkarni, Shivani Kumari, Vishakha Gorad, Rajesh Bulbule
2024 IEEE International Students Conference on Electrical Electronics and Computer Science Sceecs 2024, 2024
Design and Optimization of Suspended RF Stripline Low Pass Filter for Communication Network
Jitendra Tahalyani, Khushbu Katre, Vishaka Gorad, Akshit Khare, Shivani Kumari, Rajesh Bulbule
2024 International Conference on Intelligent Systems and Advanced Applications Icisaa 2024, 2024
Design of Miniaturized and Lightweight PIF Antenna for Mobile LIDS System to Detect Unwanted Aerial Objects
Aayush Pardeshi, Sahil Auti, Aditya Jagtap, Jitendra Tahalyani, Khushbu Katre, Rajesh Bulbule
2024 4th Asian Conference on Innovation in Technology Asiancon 2024, 2024

Rajesh Suresh Bulbule

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