Grinrey Conference Proceedings

Editorial Preface: International Conference on Multidisciplinary Engineering and Scientific Research 2026 (ICMESR 2026)

Sanjay M. Gulhane — 2026-08-08

CFD Analysis of Sequential Dual-Layer PCM Thermal Management for a Cylindrical Li-Ion Battery Using RT-35 and RT-42

Sarag Motghare — 2026-08-08

Lithium-ion batteries are widely used in electric vehicles; however, their performance and safety are extremely sensitive to temperature increases during operation. Excessive heating can lead to thermal instability and thermal runaway, making effective thermal management critical. In this study, a transient Computational Fluid Dynamics (CFD) simulation of a dual-layer phase change material (PCM) cooling system for a cylindrical lithium-ion battery was performed using ANSYS Fluent 2024 R1. The model used the solidification and melting technique. RT-42 was employed as the inner PCM layer while RT-35 formed the outer layer in a concentric design. The battery core generated 726,000 W/m³ of heat during a continuous discharge of 12 W. Simulations were run for 1800 seconds. The creative element of this work is the sequential arrangement of dual layer PCMs with distinct melting ranges to achieve staged thermal regulation. The new aspect of this study is the unique dual-layer PCM arrangement, in which RT-42 is placed directly around the battery surface and RT-35 forms the outer annular layer. This arrangement allows sequential heat regulation during battery utilization. The temperature-time profile revealed two unique inflection points, representing the melting of RT-42 and RT-35, respectively. CFD-Post contour plots at t = 1800 s showed an active mushy zone near the battery-RT-42 interface, confirming latent heat absorption during phase change. A temperature drop of about 30 K was obtained between the battery core (435 K) and the outer RT-35 wall (405 K). This research demonstrated the effectiveness of the multi-stage passive thermal resistance concept for cylindrical lithium-ion cells under continuous discharge conditions.

Optimization of CNC Dry Milling Parameters for MSS 410 and MSS 420 Using Taguchi Method: A Comparative Study on Machining Temperature

Pradeep George — 2026-08-08

The experimental study focuses on the optimization of CNC dry milling parameters for AISI 410 stainless steel and AISI 420 stainless steel with respect to machining temperature using the Taguchi Method. An L9 orthogonal array was adopted with three levels of cutting parameters: spindle speed (1000–2000 rpm), depth of cut (0.3–0.9 mm) and constant Feed rate 60mm/min to systematically evaluate their influence under dry milling conditions. Taguchi response analysis based on mean values indicates that depth of cut is the most influential parameter affecting temperature (Rank 1), followed by spindle speed (Rank 2) and feed rate (Rank 3) for both materials. The results show a consistent increase in temperature with increasing levels of cutting parameters, with depth of cut contributing most significantly due to enhanced material removal rate and frictional heat generation at the tool–workpiece interface. A comparative evaluation reveals that both MSS 410 and MSS 420 exhibit similar thermal behavior trends; however, MSS 420 records slightly higher temperature values under identical machining conditions. This is attributed to its higher hardness, which results in increased cutting resistance and greater heat generation during machining. The optimal machining condition for minimizing temperature is identified as a spindle speed of 1000 rpm, depth of cut of 0.3 mm and feed rate of 60 mm/min, and for both materials. The close agreement between predicted and experimental results validates the effectiveness of the Taguchi approach in optimizing machining parameters.

Design and Testing of a Modular Autonomous Delivery Robot for Healthcare and Industrial Service

Aaryaman Kochar — 2026-08-08

The increased need for inexpensive intelligent robotic systems with autonomous capabilities in structured environments such as hospitals and warehouses has led to many innovations in the field of service robotics. This paper describes the design, development, and testing of a modular robot for autonomous delivery tasks featuring a four-wheeled differential drive chassis and ATmega328P microcontroller. The robot utilizes a combination of line-following using infrared sensors, object detection by ultrasonic sensors, smartphone Bluetooth control, and remote infrared controls on the same cost-effective platform. The robot has been successfully tested both in hardware and in Gazebo–ROS simulation, demonstrating the ability to track the defined path and detect and evade objects in a structured indoor environment. While the present version of the robot does not incorporate localization and SLAM capability, the modular chassis structure with three layers enables easy incorporation of LiDAR, IMU sensor, and wheel encoder to provide full autonomy in future versions of the robot.

Autonomous Delivery Robots for Last-Mile Logistics: A Comprehensive Review of Navigation, Optimization, and Human Acceptance

Aditya M. Warat — 2026-08-08

Last-mile delivery is one of the most expensive and challenging stages of the supply chain due to increasing e-commerce demand, urban congestion, and rising labour costs. Autonomous Delivery Robots (ADRs) have emerged as a promising solution by enabling efficient, contactless, and sustainable package delivery. This review examines recent advancements in ADR technologies, focusing on navigation and localization methods, routing and optimization techniques, operational delivery models, regulatory challenges, sustainability, and human acceptance. A systematic review of 41 publications published between 2018 and 2025 was conducted using major scientific databases. The reviewed studies indicate that technologies such as SLAM, reinforcement learning, and optimization algorithms significantly improve navigation accuracy and delivery efficiency. However, large-scale deployment remains constrained by battery limitations, infrastructure requirements, regulatory issues, and public acceptance. The review also identifies current research gaps and highlights future directions, including multi-robot coordination, artificial intelligence-driven decision-making, smart city integration, and energy-efficient delivery systems. Overall, ADRs have strong potential to transform last-mile logistics while supporting sustainable and intelligent transportation systems.

Retinal Vessel Analysis for Early Detection of Retinopathy of Prematurity Using Image Processing

Prakruthi P N — 2026-08-08

Retinopathy of Prematurity is an eye disease that occurs in premature babies[1] where the major risk factors include birth before thirty-four weeks of gestation, low birth weight (less than 2 kg), and high exposure to oxygen. Early detection of these abnormalities can reduce the rate of blindness worldwide. The main objective of this work is to develop an algorithm using image processing techniques to analyse important retinal blood vessel features such as vessel width and tortuosity for the detection of Retinopathy of Prematurity (ROP). The process begins by converting retinal fundus images into grayscale and resizing them to a fixed size so that all images can be processed in a consistent manner. After that, a Gaussian filter is applied to reduce noise, and adaptive histogram equalization is used to improve the image contrast and make the blood vessels more visible. Once the pre-processing stage is completed, the optic disc is removed because it can affect the vessel analysis. The blood vessels are then enhanced using a bottom-hat transform, and global thresholding is applied to obtain a binary image of the vessel network. Small unwanted branches and noise are removed so that only the major vessel structures remain. Skeletonization and geodesic path analysis are then carried out to segment the vessels and extract the required features for further analysis. The vessel width is estimated by measuring the distance between each vessel centreline pixel and the vessel boundary using a distance transform technique. Tortuosity is calculated by comparing the actual length of a vessel with the straight-line distance between its starting and ending points.it just display the values of vessel width and tortuosity it does not directly classify whether ROP is present or not. Based on this, it assists doctors in making quicker decisions and may help reduce the chances of vision-related complications in premature infants through early detection.

A CDR - Based Image Processing Approach for Glaucoma Detection: Validation on DRISHTI-GS and ACRIMA Datasets

Pavan M — 2026-08-08

Glaucoma is among the most frequent causes of vision loss in patients who suffer from retinal diseases. This disease is highly dangerous since in its initial phase it manifests itself asymptotically and leads to irreversible vision loss. Thus, early detection of glaucoma is an essential step that will help detect and cure the disease. In this work, an algorithmic method based on computer-aided technology has been proposed for glaucoma diagnosis using retinal fundus images. The main idea is to design a simple glaucoma screening method. Firstly, pre-processing is applied to the retinal image in order to extract regions of interest, remove noises, enhance the contrast and smooth the image using Gaussian filter. Image processing techniques are applied for segmentation of optic disc and optic cup. The Cup-to-Disc Ratio (CDR) is then calculated, which is an important clinical parameter in diagnosing glaucoma. The fundus image is classified as normal, borderline, or glaucoma on the basis of calculated CDR value. The outcome clearly indicates that the proposed method is accurate, efficient, and useful in detecting glaucoma using computer-aided approach.

Intelligent Deep Learning Framework for Accurate Crack Detection and Width Measurement

Kanchan Dhapekar — 2026-08-08

The automated crack detection and width measurements in concrete structures are important to structural health monitoring, but the manual inspection of crack is inconsistent, and the traditional image processing methods are difficult to adapt under different illumination environments and conditions. In this study, a novel hybrid deep learning network is designed using the convolutional neural network and transformer networks to effectively identify and segment cracks on complex concrete surfaces under different scenarios. For segmented images, the procedure of skeletonization returns the center of the crack for the whole image allowing measurements of the crack width at multiple positions across the width of the crack that are perpendicular to the centerline. An FOV based calibration step follows, which translates the measurements as obtained from the pixels to accurate real-world metric measurements. Its combination of deep-learning, skeleton extraction and calibration allows for fully-automated quantitative crack characterization. Experimental results are shown with high accuracy and reliability in crack detection, segmentation and width estimation compared with classic techniques which suggest the capability for real-time and automatic inspection of infrastructures through automation provided by CSE.

AI-Enabled Framework for Continuous Cybersecurity Awareness in Healthcare SMEs: A Behavior-Centered Approach

Rahul Azmeera — 2026-08-08

Cybersecurity awareness programs in healthcare small and medium-sized enterprises (SMEs) continue to rely on static, periodic training that offers minimal evaluation of long-term behavioral change. Research indicates that over 90% of security training content is forgotten shortly after one-time sessions, yet existing approaches rarely employ artificial intelligence for real-time assessment or behavioral reinforcement. This paper proposes an AI-enabled framework for continuous evaluation of security awareness programs in healthcare SMEs. Through a systematic review of 20 peer-reviewed studies published between 2018 and 2025, we synthesize how machine learning is applied to monitor employee actions, identify risk behaviors such as phishing susceptibility and policy violations, and deliver real-time feedback through nudges and personalized alerts. Grounded in Kirkpatrick’s Four Levels of Evaluation and Nudge Theory, our framework categorizes AI tools by their ability to measure and influence security outcomes across knowledge, behavior, and incident reduction. Findings indicate that AI-based feedback loops significantly enhance training effectiveness by reinforcing lessons in context, identifying vulnerable users, and enabling curriculum adaptation. For resource-constrained healthcare SMEs, the proposed framework provides scalable, behavior-informed interventions that sustain organizational cybersecurity readiness and foster a human-centric security culture.

Game Theory Approaches to Sustainable Resource Allocation in Cloud and AI Systems: A Review

Shivam Gupta — 2026-08-08

Algorithmic game theory is a popular concept that has numerous applications like designing mathematical frameworks for analyzing interactions between rational agents which has become increasingly important in artificial intelligence and cloud computing for efficient resource allocation and optimization that helps users in saving money and resources. This study examines the applications of classical and modern game theory concepts to these kinds of energy efficient workloads and cloud infrastructure management in general using various algorithms. The main objective of this study is to review key concepts like normal form games, Nash equilibrium, mixed strategies and mechanism design to evaluate their scope in scalable and systems that are based on incentives with an analysis of auction mechanisms like Vickrey Clarke Groves model and congestion games which helps us formulate a unified modeling framework using utility functions, congestion analysis and mechanism design. The results also provide a look into tradeoffs between system efficiency, scalability and energy consumption in these systems where the findings indicate that game theory mechanisms improve allocation efficiency, fairness and energy utilization in cloud systems that are shared and helps in reducing substantial computational overhead. However, challenges related to scalability, dynamic pricing and live decision making are still prevalent. Overall, algorithmic game theory offers a strong foundation for developing sustainable, intelligent and energy efficient artificial intelligence in cloud computing systems and is expected to support future green computing architectures.

Student Transit Portal: A Web–Based Platform for Managing Seasonal Native-Place Travel for Hostel Residents

Sivaprakash Sriram — 2026-08-08

In many educational institutions such as colleges, they are hiring the government vehicles during the weekends for the hostellers to travel to their native places. Before journey, hostellers need to register their details manually. Students fill their details in forms which may lead to wrong entry and make offline payments. This may lead to data entry errors such as students’ details mismatch which may take more time for both students and staff to complete the task. To overcome these problems a dedicated web application has been developed to automate the registration and allocation process. The web application consists of two modules - students and administrator. The newly joined students can use their hostel admission number for signup and after that they can book their bus pass through their login credentials. In the administrator login, the students’ details and the travelling details can be managed. Admin can allocate the buses according to the places where the students need to travel. The backend is developed using python language whereas, for the frontend HTML and CSS is used with the database MySQL. This makes the people easy and pleasant to use. Updates carried out in the admin login will be reflected in the student’s login also. If there is any issue in the bus pass it can be easily rectified by the admin and also students can easy to track their data. Future enhancements for this platform include the integration of secure digital payment gateways and live GPS tracking to ensure a cashless, transparent, and safe journey for all residents.

EMOSIC: Emotion-Based Music Player Using Haar Cascade and Deep CNN

Aswin Animon — 2026-08-08

This paper is aimed at enhancing the user experience of listening to music by incorporating emotion detection through facial recognition technology. Facial expressions are used to detect the user’s emotional state and the system recommends a playlist matching their mood. After each song, the emotion detection process is iterated to make sure that the next song played serves the user’s current emotional state. This is made possible by coining emotion detection algorithms and machine learning techniques. The user’s facial expressions are captured using a camera which is connected to the system, and the image is processed to identify the emotion using deep learning models. The system then matches the detected emotion with a pre-defined playlist that matches the user’s mood and plays a random song from that playlist. This creates a personalized music experience for the user. The proposed paper aims to provide users a unique and interactive music-listening experience. The system can accurately determine the user’s emotional state and provide them with music that matches their mood. The repetition of the emotion detection process after each song ensures that the playlist remains relevant to the user’s emotional state, creating a more personalized music experience.