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Personalized Medicine Using AI and Genomics

A conceptual change in healthcare, personalized medicine uses an individual's genetic profile to forecast disease risk, customize drug treatments, and increase patient outcomes. With its great genomic variety, the development of such systems is hampered in the Indian setting by the scarcity of region-specific, annotated clinical datasets. Using IndiGenome and PharmGKB as main references, this work presents a framework for combining genomic and pharmacogenomic data to enable personalized medicine. A manually built dataset with standardized notations was produced to replicate patient data due to integration difficulties between accessible datasets. This enabled the application of treatment logic and important gene mutations (BRCA1, BRCA2, TP53) based on working artificial intelligence. Future deployment with actual genomic data builds on this Streamlit-based application, which is able to predict treatments and provide health recommendations.

Published by: Khan NavidShaba

Author: Khan NavidShaba

Paper ID: V11I2-1257

Paper Status: published

Published: April 19, 2025

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Research Paper

The Development and Importance of Complex Numbers in Mathematics

The journey of complex numbers from mathematical heresy to fundamental scientific tool represents one of the most remarkable intellectual transformations in history. These numbers of the form a + bi (where i² = -1) have transcended their origins in algebra to become indispensable across physics, engineering, and technology. This comprehensive study examines their historical evolution, deep mathematical properties, and unparalleled applications that continue to shape modern science. Through detailed analysis of pivotal developments, we demonstrate how complex numbers provide the mathematical language for describing phenomena from quantum entanglement to wireless communication.

Published by: Aniruddha Kumar

Author: Aniruddha Kumar

Paper ID: V11I2-1256

Paper Status: published

Published: April 19, 2025

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Research Paper

Sign Language Translator

Sign language is a fundamental mode of communication for the deaf and hard-of-hearing yet there is often a gap in understanding between sign language Operators and the broader population. this search introduces amp real-time house speech Explainer mature exploitation tensorflow mediapipe and opencv specifically organized to Method arsenic associate in nursing informative drive that acquired immune deficiency syndrome non-signers inch acquisition house speech. The system translates sign language gestures into spoken language while providing interactive Characteristics that allow Operators to practise receive feedback and Improve their signing Precision. done Fancy motion credit and judgement Operators get increasingly arise their skills devising house speech acquisition available and visceral. This paper details the system's Structure Applyation and real-world Use emphasizing its potential to Improve communication and inclusivity across linguistic communities by fostering a broader understanding of sign language.

Published by: Khan Nausheen

Author: Khan Nausheen

Paper ID: V11I2-1246

Paper Status: published

Published: April 18, 2025

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Research Paper

Autonomous AI Agents for Real-Time Financial Transaction Monitoring and Anomaly Resolution Using Multi-Agent Reinforcement Learning and Explainable Causal Inference

Real-time financial fraud detection systems face significant challenges from adversaries' continually evolving attack strategies. Traditional static classifiers fail to adapt to these changes and often lack interpretability, leading to false positives and missed anomalies. This paper proposes a novel framework combining Multi-Agent Reinforcement Learning (MARL) with Explainable Causal Inference for transaction anomaly detection and resolution. A defender agent learns to identify and intercept fraud in an adversarial environment where an attacker agent simulates fraudulent behaviors. The agents interact within a stochastic game setting and are trained using a centralized critic and decentralized policies. A causal inference module constructs a directed acyclic graph over transaction features to enhance interpretability and applies do-calculus and counterfactual reasoning to explain flagged transactions. We implement a scalable, real-time deployment architecture and evaluate the system using simulated and real transaction data. Results demonstrate that our MARL-based agent outperforms static classifiers in adaptability and recall, while the causal module reduces false positives and provides transparent justifications for fraud decisions. This combination of adaptability and explainability makes the system highly suitable for practical deployment in financial institutions..

Published by: Akash Vijayrao Chaudhari, Pallavi Ashokrao Charate

Author: Akash Vijayrao Chaudhari

Paper ID: V11I2-1252

Paper Status: published

Published: April 18, 2025

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Research Paper

Utilization of Sugercane Bagasse Ash in Concert (SBA)

Sugarcane bagasse ash (SCBA) is an agro-industrial byproduct generated from sugarcane bagasse combustion in sugar mills. Due to its pozzolanic properties, SCBA can partially replace cement in concrete, enhancing sustainability and reducing environmental impact. This study explores the potential benefits of incorporating SCBA in concrete mixtures, focusing on its effects on mechanical properties, durability, and workability. Experimental results indicate that SCBA improves compressive strength, reduces permeability, and enhances resistance to chemical attacks when used in optimal proportions. Utilizing SCBA in concrete production minimizes industrial waste and contributes to cost-effective and eco-friendly construction practices.

Published by: Druv Ratndeep Aru, Karan Raju Ingle, Ganesh Gautam Gaikwad, Sujal Sunil Kamble, Chaitali Bagul

Author: Druv Ratndeep Aru

Paper ID: V11I2-1253

Paper Status: published

Published: April 18, 2025

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Research Paper

ARIA: An Intelligent Voice-Enabled Virtual Assistant for Desktop Automation and Conversation

To increase desktop productivity through natural language interaction, this research paper, Aria, introduces a Virtual Virtual Personal Assistant (VPA), built in Python. Application controls (memo blocks, calculators, etc.), webbrows, language typing, email, weather invocations, and natural conversations with DialOgpt conversation models are just some of the many tasks that ARIA can do. To interpret, create, and automate tasks on the user's computer, the assistant uses a variety of Python libraries, such as Speech_Recognition, Pyttsx3, Pyautogui, and Trans. Easy-to adjust modular architecture allows users to improve their skills according to their special needs. Integration of language-based automation into smartphones and intelligent devices has been passed considerably thanks to the efforts of established virtual assistants, providing Google Assistant, Siri, Siri, Alexa, Alexa and Cortana. The system and its capabilities rely primarily on a cloud-based ecosystem. Similarly, several open source initiatives, such as desktop-based Jarvis Clones and Mycroft AI, aim to introduce AI assistants to HR computers, but often only limited offline capabilities, are born on a specific platform and require conversational skills. Aria overcomes these limitations by providing a fully adjustable and conscious desktop assistant through privacy. Data security and user control are guaranteed by offline and online operations, support for customer-specific commands, and independence from local task cloud storage. These are open-source libraries, allowing developers and researchers to change or extend functionality at will. Aria is a convenient and clever way to improve interactions between humans and computers in everyday arithmetic environments. Thanks to the integration of conversation AI, you can respond directly to commands and have contextual conversations.

Published by: Aniket Supe, Amey Waikar, Aditya Tripathi, Dr. Shudhodhan Bokefode

Author: Aniket Supe

Paper ID: V11I2-1227

Paper Status: published

Published: April 17, 2025

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