Category Archives: Uncategorized

AI-Driven Time Series Forecasting for Financial Markets: Leveraging Machine Learning for Smarter Predictions
Authors:-S.Likhita, S.Venkata Basavayya, Y.S.Santosh Kumar, P.Bhanu Divyasri, V.Sandeep, Mrs.V.Anantha Lakshmi

Abstract- Financial markets, including stock prices, exchange rates, and commodity prices, are inherently volatile and influenced by numerous factors, making their prediction a challenging yet essential task. Accurate forecasting of market trends is crucial for investors, financial analysts, and policymakers, as it helps in making informed decisions and mitigating risks. In this study, we explore the use of Support Vector Machine (SVM), a powerful machine learning algorithm, for time series forecasting of financial market trends. Traditional forecasting methods often struggle with financial data due to its non-linear and dynamic nature. However, SVM is well-known for its ability to handle high-dimensional data and capture complex patterns, making it a suitable choice for financial market prediction. Our approach leverages historical price and volume data to train the SVM model, enabling it to recognize patterns and predict future market movements. The study evaluates how effectively SVM adapts to changing market conditions, demonstrating its ability to model non-linear relationships within financial time series. Additionally, we consider external economic factors that may influence market behavior, further validating the robustness of the model. The findings highlight the potential of SVM in financial forecasting, offering a reliable alternative to traditional methods. Future work may involve integrating hybrid models combining SVM with deep learning techniques or incorporating macro-economic indicators to further enhance prediction accuracy. This research contributes to the growing field of AI-driven financial analysis, paving the way for more sophisticated and data-driven investment strategies.

DOI: 10.61137/ijsret.vol.11.issue2.329

Published by: Kajal Tripathi

AI-Powered Fraud Detection: Secure Online Transaction Monitoring Using Machine Learning
Authors:-G.Jashwitha, T.Sai Srinath, G.Naga Kastusi, V.Anshitha, G.Janitha Sree, Mrs.G.Tejasri Devi

Abstract-Fraud detection remains one of the most critical challenges in financial transactions, driving on going research and the adoption of advanced technologies such as machine learning. Financial transaction fraud detection aims to explore and compare various machine learning approaches to assess their effectiveness, challenges, and potential future developments comprehensively. This paper begins by highlighting the importance of fraud detection in financial transactions, emphasizing the widespread impact of fraudulent activities on individuals, businesses, and the overall economy. While traditional fraud detection methods have been valuable, they often struggle to counter increasingly sophisticated and evolving fraudulent schemes. As a result, more advanced techniques are required to enhance detection accuracy. Machine learning-based approaches have emerged as a promising solution, enabling algorithms to analyse vast amounts of transactional data and identify patterns indicative of potential fraud. In particular, supervised learning techniques—such as logistic regression, decision trees, and support vector machines—have gained significant popularity in fraud detection due to their ability to classify transactions as legitimate or fraudulent based on historical data.

DOI: 10.61137/ijsret.vol.11.issue2.326

Published by: Kajal Tripathi

Application of First Order Linear Ordinary Differential Equations in Mechanics and Thermodynamics
Authors:- Jyotika Sa, Tejaswini Pradhan

Abstract- This comprehensive study explores the profound applications of first-order linear ordinary differential equations (ODEs) in the domains of classical mechanics and thermodynamics. These mathematical tools serve as vital instruments in modeling and analyzing real-world physical phenomena. In particular, this research focuses on Newton’s Second Law of Motion and Newton’s Law of Cooling, both of which are quintessential examples of how first-order linear ODEs can effectively describe dynamic systems. The paper provides an in-depth explanation of the formulation, derivation, and solution of these equations, supported by descriptive illustrations and analytical interpretations. Emphasis is placed on demonstrating the solution techniques such as the integrating factor method, and the separation of variables method, while linking their mathematical elegance to practical engineering, environmental, and forensic applications. The ultimate objective is to illuminate how first-order linear ODEs not only simplify complex physical laws but also enable predictions that are essential in technological and scientific advancements.

DOI: 10.61137/ijsret.vol.11.issue2.332

Published by: Kajal Tripathi

Material Optimization of Bracket For Maximum Stiffness Conditon to Withstand Higher Loading Conditions
Authors:-Mrs. K. Tulasi, Gundu Jaswanth Narayana, Karumanchi Karthik, Anusuri Taraka Sai Surya Venkata Siva, Maddi Chandra Sekhar

Abstract- Topology optimisation has been essential for the design of lightweight mechanical components since their inception in the aerospace industry. Its potential and thorough research yielded computationally viable methods, resulting in its incorporation into numerous computer-aided design applications. Concurrent with the advancement of topology optimisation, additive manufacturing technologies have significantly improved, leading to dependable additive manufacturing processes. The integration of these two technologies enables the fabrication of optimised components more efficiently than conventional production methods. This study offers a concise comparison between topology optimization and other forms of structural optimization. Furthermore, it succinctly contrasts additive production with other production techniques. Solidworks uses the SIMP approach to address topology optimisation issues, and this article presents an overview of this approach, along with additional theoretical considerations of the subject. e primary objective of the research is to illustrate the topology optimization of a jet engine bracket via a SolidWorks simulation. We achieved this by following a structured methodology and meticulously documenting each phase of the optimisation process. We then exported the generated topology in a graphical body format and used it to reconstruct the bracket. The rebuilt bracket is 50% lighter than the original, and it worked well enough in finite element analysis simulations under all the different loading conditions that were given. This article also discusses the collected data, focusing on identifying the sources of mistakes in the research and evaluating their impact on the performance of the optimized bracket. Finally, the research gives a full assessment of possible production methods based on how they affect the finished bracket’s mechanical properties.

DOI: 10.61137/ijsret.vol.11.issue2.330

Published by: Kajal Tripathi

Explainable AI for Event and Anomaly Detection and Classification in Healthcare Monitoring Systems
Authors:-J.Sree Varenya, D.Sahithi, S.Sri Divya, A.Chakri, S.Asritha, Mrs.L.Yamuna

Abstract-Artificial intelligence (AI) is transforming healthcare by automating the detection and classification of events and anomalies, enhancing patient monitoring and intervention. In this context, events refer to abnormalities caused by medical conditions such as seizures or falls, while anomalies are erroneous data resulting from sensor faults or malicious attacks. AI-based event and anomaly detection (EAD) enables early identification of critical issues, reducing false alarms and improving patient outcomes. The advancement of Medical Internet of Things (MIoT) devices has further facilitated real-time data collection, AI-driven processing, and transmission, enabling remote monitoring and personalized healthcare. However, ensuring the transparency and explainability of AI systems is crucial in medical applications to foster trust and understanding among healthcare professionals. This work presents an online EAD approach utilizing a lightweight autoencoder (AE) on MIoT devices to detect abnormalities in real time. The detected abnormalities are then explained using Kernel SHAP, a technique from explainable AI (XAI), and subsequently classified as either events or anomalies using an artificial neural network (ANN). Extensive simulations conducted on the Medical Information Mart for Intensive Care (MIMIC) dataset demonstrate the robustness of the proposed approach in accurately detecting and classifying events, regardless of the proportion of anomalies present.

DOI: 10.61137/ijsret.vol.11.issue2.327

Published by: Kajal Tripathi

Enhancing The Discretization Method by Implementing The DOE Method to Optimize The Discretization Value Using Workbench and Ansys
Authors:-Mr. M. Vinil, Meddisetti Ramesh, Polisetti Chaitanya Anil, Bharthala Subrhamanyaswamy, Kantareddy Siva Sai

Abstract-Discretization, commonly known as meshing, plays a pivotal role in finite element analysis (FEA) as it divides a component into smaller elements for numerical simulations. The quality and size of the mesh significantly influence the accuracy, convergence, and computational cost of the simulation. This project focuses on enhancing the discretization process by implementing the Design of Experiments (DOE) method to optimize the mesh size for achieving an optimal balance between computational efficiency and result accuracy. ANSYS was utilized to perform FEA simulations on a selected component with varying mesh sizes to observe their influence on key output parameters such as stress, strain, deformation, and factor of safety. Coarser meshes lead to faster computation but may compromise accuracy, while finer meshes provide more precise results but at a higher computational expense. The DOE method was applied using Minitab software to design a set of systematic experiments, enabling the identification of the most influential factors and their interactions affecting the output.

DOI: 10.61137/ijsret.vol.11.issue2.325

Published by: Kajal Tripathi

Orientation Optimization for Reducing The Support Material Wastage in Material Extrusion Process
Authors:-Mr. M. Rambabu, Akula Hemanth Raja, Bobboli Vijay Durga Rao, Namu Karthik, Bandi Ajay Shankar

Abstract-Additive Manufacturing (AM) using material extrusion processes, such as Fused Deposition Modeling (FDM), often requires support structures to ensure the stability of overhanging or complex geometries during fabrication. However, the use of support material increases material consumption, printing time, and post-processing effort, ultimately raising production costs. This project focuses on optimizing the orientation of parts during the manufacturing process to minimize the use of support material while maintaining the structural integrity and quality of the final product .Fusion 360 was used to simulate the manufacturing process by evaluating various part orientations. By analyzing the overhang angles, build directions, and contact areas requiring supports, optimal orientations were identified. The optimization process considered parameters such as material usage, print time, and surface finish quality. Additionally, simulations were conducted to evaluate the impact of orientation changes on part strength and dimensional accuracy. The study revealed that strategic orientation adjustments could significantly reduce support material wastage by minimizing the number and size of overhangs. For instance, aligning the part’s geometry with the build platform or leveraging self-supporting angles helped achieve material efficiency. The outcomes demonstrated that reducing support material by optimizing orientation not only improves material utilization but also enhances sustainability in AM processes. This research highlights the importance of orientation optimization in material extrusion processes and showcases the potential of simulation tools like Fusion 360 to refine the manufacturing process, enabling cost-effective and resource-efficient production.

DOI: 10.61137/ijsret.vol.11.issue2.324

Published by: Kajal Tripathi

Optimization of Support Patterns to Reduce Material Wastage Using Doe Method
Authors:-Mr. K. Simon Rupas1, Dondapati Vijaya Sukruthi, Chevvakula Sai Vamsi, Appana Mahesh Aditya, Bondapalli Abhiram

Abstract-In Fused Deposition Modeling (FDM), support structures are essential for printing overhanging and complex geometries. However, excessive support material increases printing costs, material wastage, and post-processing time. This study focuses on optimizing support patterns and part orientation using Design of Experiments (DOE) methods to minimize material usage while maintaining structural stability during printing. The study evaluates two commonly used support structures: normal (grid-based) and tree-like supports, along with different part orientations to determine their impact on material consumption and overhang stability. The Taguchi DOE method is implemented to systematically analyze the influence of orientation and support type on key parameters such as printing time, support volume, and ease of removal.

DOI: 10.61137/ijsret.vol.11.issue2.323

Published by: Kajal Tripathi

Design and Fluent Simulation of Draft Tube to Increase Exist Pressure
Authors:-Mr. Ch. Sai Mohan Reddy, Punnam Yesu, Yedla Tejaswi, Adabala Srirama Surya Prakash, Mangam Ajay Kumar

Abstract-The draft tube is a critical component of hydropower plants, especially for reaction and mixed-flow turbines, as it plays a vital role in ensuring efficient energy conversion and system stability. Its design significantly impacts the overall performance of the turbine by reducing velocity at the outlet, converting kinetic energy into pressure energy, and minimizing energy losses. However, the design of an efficient draft tube comes with numerous challenges, including addressing problems such as cavitation, backflow, surging, swirl flow, and erosion of metal components due to high-velocity water flow. A well-designed draft tube should effectively mitigate these issues while maintaining optimal performance. The primary objective of this project was to design a draft tube and analyze its performance under real working conditions using advanced computational tools. The draft tube design was created using a CAD software tool, providing an accurate and detailed 3D model for further analysis. Simulation of the design was then performed using ANSYS software, with Computational Fluid Dynamics (CFD) analysis carried out in ANSYS Fluent. The CFD analysis included defining key parameters such as inlet velocity, flow patterns, outlet pressure, velocity distribution, and turbulence behaviour to replicate realistic operating conditions.

DOI: 10.61137/ijsret.vol.11.issue2.322

Published by: Kajal Tripathi

Multi Objective Optimization of Print Settings For Nominal Print Time Using Frontier Analyse Method
Authors:-Mrs. S. Hemani, Vasamsetti Mallika, Ayithireddy Bhavani Raja, Annamdevula Naga Venkata Ramana, Pujari Rupesh

Abstract-In Fused Deposition Modeling (FDM), optimizing print settings is crucial to balance print time, material consumption, and part quality. Achieving an optimal combination of parameters ensures efficient production without compromising mechanical integrity. This study employs the Frontier Analysis Method for multi-objective optimization of infill pattern, wall count, and print speed to achieve a nominal print time while minimizing material consumption and maintaining print quality .The study investigates how different infill patterns (grid, gyroid, honeycomb, and line), wall counts (single, double, and multiple), and print speeds affect the total print time, material usage, and surface finish. The Frontier Analysis Method, a data-driven optimization approach, is implemented to determine the most efficient print settings that provide the best trade-off among speed, strength, and material efficiency. The results indicate that higher print speeds reduce print time but may lead to defects such as layer misalignment and poor adhesion. Increasing wall count improves strength but leads to higher material consumption and longer print times. Similarly, infill pattern selection significantly impacts part strength and material usage, with honeycomb and gyroid infills showing better strength-to-material ratios compared to grid-based structures. Through multi-objective optimization, the study identifies optimal print settings that reduce excess material use and printing time while maintaining dimensional accuracy and mechanical properties. The findings help improve FDM printing efficiency, providing a systematic approach for selecting ideal print parameters based on specific manufacturing needs.

DOI: 10.61137/ijsret.vol.11.issue2.321

Published by: Kajal Tripathi