Multiscale Modeling of Microbial Interactions in Nanostructured Environments
Authors:-Harish L

Abstract-:Microbial interactions with nanostructured materials are a topic of great significance in various scientific and industrial fields, particularly in medicine, biotechnology, and environmental science. Nanostructures, due to their unique properties such as small size, high surface area, and reactivity, can significantly influence microbial behavior. Understanding the complex interactions between microorganisms and nanostructured environments is essential for advancing the use of nanomaterials in drug delivery, biofilm control, and microbial bioremediation. This article explores the role of multiscale modeling in studying microbial interactions with nanostructures, emphasizing how the integration of various models at different spatial and temporal scales offers a more comprehensive understanding of these interactions and their implications for future applications.

DOI: 10.61137/ijsret.vol.11.issue2.438

Published by: Kajal Tripathi

Simulating Immune-Nano Interactions Using AI-Enhanced Agent-Based Models
Authors:-Gunashekar D

Abstract-:The interface between nanomaterials and the immune system is a critical aspect of the success of nanomedicine, particularly for applications like drug delivery and diagnostic imaging. The complexity of immune-nano interactions, which are influenced by the physicochemical properties of nanoparticles and the dynamic responses of immune cells, necessitates sophisticated modeling techniques. Agent-based models (ABMs) have been widely employed to simulate such interactions due to their ability to represent individual agents, such as immune cells and nanoparticles, and track their interactions over time. However, traditional ABMs often struggle with accurately simulating the nonlinear, high-dimensional relationships that govern these complex biological processes. By integrating artificial intelligence (AI) into ABMs, it becomes possible to enhance the predictive accuracy of these models, enabling more efficient designs for nanomedicine applications. This article explores the integration of AI with agent-based models to simulate immune-nano interactions, discussing the methodology, advantages, and challenges associated with this approach.

DOI: 10.61137/ijsret.vol.11.issue2.437

Published by: Kajal Tripathi

Machine Learning Approaches to Engineer Nanoantibiotics for Treating Infections in Immunocompromised Patients
Authors:-Bhagya S

Abstract-:Microorganisms interact with their environment across multiple scales, ranging from molecular interactions to population dynamics. In the presence of nanostructures, the complexity of these interactions increases, as nanomaterials possess the unique ability to alter microbial behavior at both the cellular and molecular levels. Their applications are vast, spanning antimicrobial coatings, medical devices, biosensors, and bioremediation technologies. Despite these advancements, the exact mechanisms by which nanostructures influence microbial behavior, including biofilm formation, antibiotic resistance, and metabolic activity, are not fully understood. The integration of multiscale modeling, which combines molecular dynamics simulations with population-level models, holds significant promise in unraveling these complexities. This paper explores the interaction of microorganisms with nanomaterials, the role of multiscale modeling, and the potential applications in healthcare, biotechnology, and environmental science.

DOI: 10.61137/ijsret.vol.11.issue2.436

Published by: Kajal Tripathi

Reinforcement Learning Applications in Autonomous Systems: From Traffic Optimization to Robotics
Authors:-Asha Devi

Abstract-:Reinforcement Learning (RL), a dynamic branch of machine learning, has emerged as a powerful tool for enabling autonomous decision-making in complex and uncertain environments. By learning through interaction, trial, and reward-based feedback, RL equips agents to optimize their actions without requiring explicit programming. This review explores the expanding role of RL across diverse autonomous systems, including traffic management, autonomous vehicles, industrial robotics, unmanned aerial vehicles (UAVs), and healthcare robotics. In traffic optimization, RL adapts to real-time flow patterns, significantly reducing congestion. For autonomous vehicles, RL facilitates safe and efficient navigation, leveraging deep learning for real-time perception and control. Industrial robotics benefit from RL by enhancing adaptability in tasks such as assembly and material handling, while UAVs gain from RL’s ability to support complex aerial maneuvers and cooperative missions. In healthcare, RL contributes to the development of intelligent surgical and rehabilitation robots that learn from both simulation and human interaction. The integration of RL with technologies like deep learning, computer vision, and sensor fusion continues to enhance autonomy across domains. While challenges such as safety, sample efficiency, and sim-to-real transfer remain, ongoing research promises scalable, robust RL solutions. This article presents a comprehensive analysis of current applications and the future trajectory of reinforcement learning in autonomous systems.

DOI: 10.61137/ijsret.vol.11.issue2.435

Published by: Kajal Tripathi

AI-Powered Control Systems for Nanobots in Microbial Infection Zones
Authors:-Amruth P

Abstract-:The use of nanobots in treating microbial infections has emerged as a promising strategy, particularly given the growing concerns about antibiotic resistance. These nanobots are small-scale machines capable of performing highly specific tasks within the human body, including pathogen detection, drug delivery, and biofilm disruption. However, to be truly effective in microbial infection zones, where conditions are often dynamic and unpredictable, nanobots require advanced control systems. Artificial intelligence (AI) has the potential to provide these control systems with the necessary intelligence to navigate these challenging environments autonomously. This article explores the integration of AI-powered control systems in nanobots for microbial infection zones, focusing on their ability to enhance precision, adaptability, and efficiency in medical applications.

DOI: 10.61137/ijsret.vol.11.issue2.434

Published by: Kajal Tripathi

Stock Predator: ML-driven Stock Prediction
Authors:-Anushka Sakure, Shrishti Mishra, Riya Das, Reetika Roy

Abstract-:Stock price prediction remains a challenging task due to the inherent volatility and non-linear nature of financial markets. This study proposes a deep learning approach using Long Short-Term Memory (LSTM) networks to forecast stock prices, leveraging their ability to model temporal dependencies. Historical data from the S&P 500 index (2010–2023) was pre-processed, normalized, and used to train an LSTM model. The model’s performance was evaluated against ARIMA and SVM using RMSE, MAE, and directional accuracy. Results indicate that the LSTM model outperforms traditional methods, achieving an RMSE of 1.82 and 87% directional accuracy. This work highlights the potential of LSTM in financial forecasting and algorithmic trading strategies.

DOI: 10.61137/ijsret.vol.11.issue2.433

Published by: Kajal Tripathi

Adventure of Artemis (2D Game for PC using Unity Engine)
Authors:-Vaibhav Singh, Lucky Yadav, Shivam Dewangan, Shubham Singh, Professor Ravikant Soni

Abstract-:This project documents the collaborative work of four individuals in the creation of a 2D game, using the Unity Engine, C# and Visual Studios. Merging tech skills with natural creative talent, the team is on a mission to build an unforgettable and enjoyable gaming adventure. The project unfolds as a testament to the quality of game development, exploring the combined contributions of programming, artistry, and design. By leveraging modern game development tools and technologies, the team moves through all the complexities of game mechanics, level design, background score integration and wonderful sound effects. The culmination of their efforts is a polished arcade styled game that exemplifies their collective dedication, innovation, and expertise. Through this project, the team presents a comprehensive narrative of their collaborative journey, offering insights into the challenges, triumphs, and lessons learned in the pursuit of gaming excellence.

DOI: 10.61137/ijsret.vol.11.issue2.432

Published by: Kajal Tripathi

Crafting Worlds: 3d Animation
Authors:-Agarwal Sneha, Anampaka Sneha, Repalle Deepika Persis, Assistant Professor Mrs. Nandita Manvar

Abstract-:The process of rebuilding 3D high-resolution (HR) [1]models from 2D photographs has grown very important in multiple applications, such as augmented reality (AR), virtual reality (VR) [22], games[6] , medical imaging [12], and digital content creation. Conventional methods of 3D scanning may need costly equipment, making access difficult. The current study demonstrates an AI-based system that fully automates 3D model generation from 2D photographs based on computer vision and deep learning methods. The system utilizes Neural Radiance Fields (NeRF) [11], Open3D[8], OpenCV [18], and Blender API to generate high-quality 3D reconstructions. Images are uploaded through a web interface by the users, which are then processed through a pipeline of sparse structure generation, structured latent generation, and multi-image optimization. Exporting the resulting models in different formats like Gaussian Splats and GLB is supported by the system, which allows them to be used in different applications. Furthermore, a 10-second animated visualization is created based on OpenCV[18] and FFmpeg[16] to increase user interaction with the model. The suggested method presents an improved multi-image processing algorithm that enhances depth reconstruction and estimation accuracy. In contrast to conventional photogrammetry techniques that are plagued by perspective changes and uneven lighting, this technique corrects 3D models through structured latent learning. Future developments are real-time rendering via WebGL or Three.js [14], cloud processing for scalability, greater file format compatibility, and AI-based texture augmentation. Further functionalities like AR/VR integration, automatic animation synthesis, and a marketplace with community support will make the platform even more usable. By offering a scalable and user-friendly solution, this work closes the gap between sophisticated 3D modelling technologies and practical applications, enabling wider use in fields that need precise 3D reconstructions.

DOI: 10.61137/ijsret.vol.11.issue2.431

Published by: Kajal Tripathi

Advanced Collision Prevention and Iot- Based Vehicle Safety System
Authors:-Mrs. Shaik. Vaheedha(Assistant Professor), Kalava Reshma, Battula Maha Lakshmi Thanuja, Chevula Geetha Manogna, Jetti Sathwika

Abstract-:Railway accidents involving humans and animals are a serious safety concern. To address this, we propose a Railway Track Guardian System using an ESP32 microcontroller and a LiDAR sensor to continuously monitor railway tracks for any obstruction caused by living beings. A LiDAR sensor mounted along the track scans for the presence of humans, animals, or other living beings. Simultaneously, a sensor installed on the train detects its motion and activates the monitoring system. When an obstruction is detected, the ESP32 processes the sensor data and immediately sends an alert to the train driver via the Blynk IoT app. Additionally, an onboard alarm is triggered to draw the driver’s attention quickly, enabling them to take timely action to prevent accidents. This system ensures real-time monitoring, fast communication, and enhances safety by providing early warnings to the train driver, thus significantly reducing the risk of collisions with living beings on the tracks.

DOI: 10.61137/ijsret.vol.11.issue2.430

Published by: Kajal Tripathi

Advanced Collision Prevention and Iot- Based Vehicle Safety System
Authors:-Assistant Professor Rajani Veluvolu, Shaik Shareef, Nidamanuri Prasad, Upputuri Phanindra Kumar, Uppala Hemanth, Shaik Jony Basha

Abstract-:This project introduces an Accident-Avoidance System based on Arduino Uno, utilizing Ultrasonic Sensors and Long-Range Distance Measurement Sensors for collision prevention. When the long-range sensor identifies an obstacle within a set distance, the system reduces the engine speed via PWM control using an L298N motor driver and triggers a buzzer alert. If an obstacle is detected within a critical range by the ultrasonic sensor, the system halts the engine completely and activates a continuous buzzer warning. Moreover, real-time system status updates, including obstacle detection and motor control actions, are displayed on a 16×2 LCD module and transmitted to the ThingSpeak IoT cloud through a NodeMCU ESP8266 for remote monitoring. This system can be effectively implemented in autonomous vehicles, industrial automation, and safety-critical transportation applications.

DOI: 10.61137/ijsret.vol.11.issue2.429

Published by: Kajal Tripathi