Intrusion Detection in AWS Cloud Environments Using Machine Learning on Network Flow Data
AWS Cloud Environments support core workloads and services, but are exposed to malicious actions and unauthorized activities in the transmission of network flow data. The threats subject cloud infrastructures to different types of attacks, thus Intrusion Detection in AWS Cloud Environments ensures privacy, reliability, and availability. This study explores the use of Machine Learning for intrusion detection by analyzing traffic patterns in cloud systems. The CSE-CIC-IDS2018 dataset, containing realistic benign and attack traffic, was employed for model training and evaluation. After comprehensive preprocessing and analysis, five Machine Learning algorithms were implemented: Random Forest, Decision Tree, Ridge Classifier, Logistic Regression, and Linear Support Vector Classifier. Their performance was measured using accuracy, precision, recall, F1 score, ROC-AUC, and detection time. Results showed that Random Forest and Decision Tree achieved the highest accuracy at 100%, with the Decision Tree demonstrating superior efficiency by classifying all instances in 0.056 seconds. Ridge Classifier followed with an accuracy of 99.2%, while Logistic Regression achieved 98.8%. The Linear Support Vector Classifier recorded the lowest performance with 96.2% accuracy. This research confirms the effectiveness of Machine Learning for cloud security. The Decision Tree Classifier, combining flawless accuracy with the fastest detection speed, emerges as the most practical model for real-time intrusion detection in AWS environments.
Published by: Oduwunmi Odukoya, Mariam Adetoun Sanusi, Samuel Adenekan
Author: Oduwunmi Odukoya
Paper ID: V11I5-1145
Paper Status: published
Published: October 8, 2025
Ancient Indian Scripture Based Retrieval-Augmented Systems: A Comprehensive Analysis
This paper focuses on the development and systematic comparison of Retrieval-Augmented Generation (RAG) systems, retrieval-only systems and LLM models all trained on ancient Sanskrit Scriptures. This was done in order to analyse whether RAG systems improved faithfulness in answers to reflective questions, by storing two pertinent Sanskrit scriptures: the Itihasa (including the Mahabharata and Ramayana) and the Bhagavad Gita in a FAISS index, I developed the following: a basic retrieval system from the FAISS index, a prebuilt LLM model (Qwen 2.5-3B-Instruct), an RAG system with the LLM model Qwen 2.5-3B-Instruct and an RAG system with Gemini 2.5 Flash. After development, I evaluated the four models on a list of twenty questions pertaining to philosophy, interpersonal and intrapersonal understanding, and emotional well-being. I ranked each answer on a scale from 1 to 5 on relevance, helpfulness, clarity and faithfulness. All retrieval and RAG models scored a perfect 5 in the ‘faithfulness’ metric in contrast to the base LLM model, which scored a 4.3. Moreover, I discovered that the use of a weaker LLM model in an RAG system can lead to worse results in the ‘helpfulness’ and ‘clarity’ metrics when compared to a regular LLM model when the retrieved verses are low. Through the methods and results of my research, I showed that RAG systems are necessary to provide specific and faithful answers from ancient Sanskrit philosophy.
Published by: Pradhyumna Prakash
Author: Pradhyumna Prakash
Paper ID: V11I5-1176
Paper Status: published
Published: October 6, 2025
IPO Timing and Market Readiness: An Interdisciplinary Review of Strategic Entry Points
This study provides an interdisciplinary examination of the determinants of Initial Public Offering (IPO) timing and market readiness. Integrating perspectives from economics, financial consultancy, trading practice, and entrepreneurial decision-making, the analysis demonstrates that IPO performance is contingent upon both external market conditions and internal organisational preparedness. Economists underscore the influence of macroeconomic cycles, venture capital flows, and volatility indices in shaping IPO windows. Financial consultants emphasise the critical role of governance structures, financial integrity, and operational discipline in sustaining post-listing stability. Traders, in contrast, interpret IPOs primarily as events of liquidity and sentiment-driven volatility, privileging short-term momentum indicators over fundamentals. Business leaders conceptualise IPOs as transformative junctures, motivated by capital sufficiency, investor dynamics, and founder psychology. The study concludes that successful IPOs emerge not from opportunistic timing alone but from the alignment of external conditions with institutional resilience, governance capacity, and long-term strategic vision.
Published by: Aaryan Rahul Sethi
Author: Aaryan Rahul Sethi
Paper ID: V11I5-1169
Paper Status: published
Published: October 1, 2025
Analysing Gender Bias in Job Descriptions Using Machine Learning and NLP Techniques
The growing use of automated recruitment systems has raised concerns about gender bias in job descriptions. Subtle linguistic cues can discourage qualified candidates from underrepresented groups, reinforcing workplace inequality. This study presents a computational framework using Natural Language Processing (NLP) and Machine Learning (ML) to detect and analyse such bias. The methodology involves text preprocessing, gender-coded word scoring, topic modelling with Latent Dirichlet Allocation (LDA), clustering via KMeans, and visualisation through t-SNE. A curated lexicon of masculine- and feminine-coded words assigns bias scores, while topic modelling uncovers latent themes in postings. Clustering groups of semantically similar descriptions enables analysis of bias distributions across occupational categories. Findings show that bias varies by job type: technical and managerial roles tend to use more masculine-coded language, while service and support roles favour feminine-coded terms. Semantic cluster visualisations confirm systemic patterns in word usage. This research underscores the need for fairness-aware audits in recruitment, offering both theoretical and practical insights into bias detection. The framework provides organisations with a scalable tool to identify and mitigate hidden biases, promoting inclusive hiring practices and supporting compliance with ethical and regulatory standards.
Published by: Sanvi Choukhani
Author: Sanvi Choukhani
Paper ID: V11I5-1163
Paper Status: published
Published: September 27, 2025
What are the Economic Implications of Congestion Pricing on Urban Traffic Management?
Urban congestion poses significant economic, social, and environmental challenges, from wasted fuel and time losses to deteriorating air quality. Congestion pricing has emerged as a policy tool to address these negative externalities by charging vehicles for road use in high-demand areas. This paper examines the economic implications of congestion pricing through both theoretical foundations and case studies from London, Stockholm, and Singapore, while also considering its potential in India. Findings show that congestion pricing reduces traffic volumes, increases travel speeds, and generates substantial revenues that can be reinvested into public transportation and sustainable infrastructure. However, its effectiveness depends heavily on equitable policy design, with exemptions, subsidies, and transparent reinvestment strategies playing a key role in public acceptance. The analysis concludes that while congestion pricing is not a standalone solution, it can serve as a cornerstone of sustainable urban mobility when integrated with broader strategies for equity, technological innovation, and inclusive growth.
Published by: Shaurya Vikas Agarwal
Author: Shaurya Vikas Agarwal
Paper ID: V11I5-1157
Paper Status: published
Published: September 25, 2025
From Concept to Creation: Prototyping A Centrifugal Projectile Launcher and Analyzing Its Performance
This study presents the design, prototype, and performance analysis of a centrifugal projectile launcher. The research encompasses the conceptual design, key components, and manufacturing processes involved in creating the launcher. The design considerations include material selection, structural integrity, aerodynamics, and precision. The manufacturing process details the fabrication of the rotating arm, integration of the motor and power source, development of the projectile release mechanism, and implementation of safety features. Extensive testing was conducted to evaluate the launcher's performance, analysing parameters such as rotation speed, projectile shape and other characteristics. The results provide insights into its launch velocity, revolutions per minute, precision, and energy efficiency. The study also explores potential future applications and improvements, including advanced materials, automated systems, and scaling possibilities. This research contributes to the understanding of centrifugal projectile launchers and their potential applications in scientific research, sports, and industry.
Published by: Aradhya Sharma
Author: Aradhya Sharma
Paper ID: V11I5-1159
Paper Status: published
Published: September 25, 2025
