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Research Paper

Analysing Recursive Artificial Intelligence: A Multidomain Case-Based Study of Risks, Concerns, and Oversight Mechanisms

Recursive Artificial Intelligence (AI), where systems can design, optimize, or evolve other AI systems, represents a significant turning point in the development of autonomous technologies. As recursive mechanisms become increasingly integrated into machine learning workflows, the potential for rapid innovation also comes with substantial technical and ethical risks. This paper critically examines the development and use of recursive AI systems through real-world examples and theoretical insights. It highlights key challenges, including model collapse, error amplification, alignment drift, recursive deception, and the loss of human interpretability and oversight. By examining explainability tools such as LIME and SHAP, case studies like AlphaGo, and potential paths into cognitive and multi-agent recursion, the work highlights the urgent need for responsible research and regulation. The paper aims to reveal overlooked dangers and spark discussion about the fragility, unpredictability, and governance challenges in recursively self-improving AI systems.

Published by: Henil Diwan, Debopam Bera

Author: Henil Diwan

Paper ID: V11I3-1400

Paper Status: published

Published: July 1, 2025

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Research Paper

Design of Peptide Inhibitors Targeting MYC Oncogenic Protein Complexes

Cancer remains one of the leading causes of death worldwide, with the MYC oncogene being a key driver of tumor progression through its role in promoting uncontrolled cell growth. This study aims to design and evaluate peptide inhibitors targeting the interaction between MYC and DNA, which is essential for MYC’s oncogenic function. Utilizing advanced computational methods, including RF diffusion, AlphaFold, and PyMOL, 30 potential peptide candidates were identified. These peptides were assessed based on their IPAE values, which ranged from 6.151 to 9.981, reflecting their effectiveness in disrupting MYC-DNA interactions. The use of AlphaFold enabled accurate prediction of the 3D structures of the MYC-MAX-DNA complex, while PyMOL provided visualization and structural analysis to confirm binding sites and key hotspots where the peptides interact. This detailed analysis confirmed that the peptides effectively target critical regions within the complex. Our findings underscore the potential of these peptides as novel inhibitors of MYC-driven cancer progression. The promising results suggest that these peptides could serve as the basis for new targeted cancer therapies. Moving forward, experimental validation of the peptide candidates will be conducted to confirm their binding affinity and biological activity. Additionally, structural refinement and optimization of the peptide designs will be pursued to enhance their therapeutic potential. Preclinical studies will be essential to evaluate the efficacy and safety of these peptide inhibitors in vivo. This research provides a foundation for developing innovative treatments aimed at targeting MYC-driven cancers.

Published by: Varshika Ram Prakash

Author: Varshika Ram Prakash

Paper ID: V11I3-1379

Paper Status: published

Published: June 28, 2025

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Research Paper

A Comparative Review of Nanomaterials for Neuroprotection in Neurodegenerative Diseases

Neurodegenerative illnesses like Alzheimer's, Parkinson's, and ALS cause progressive injury to the brain, causing loss of memory, movement, and cognitive functions over time. The problem with these diseases is that most drugs cannot pass through the brain's protective barrier—the blood-brain barrier (BBB). In this review, the new use of nanomaterials—extremely tiny particles measured in nanometers—is described to transport drugs across the BBB without harming brain cells. We analyzed eight of the most well-researched types of nanomaterials: fat-based, plastic-like, dendrimers, carbon-based, gold, cerium oxide, iron oxide nanoparticles, and quantum dots. Each was assessed on its ability to deliver drugs to the brain, safety, stability, and performance in laboratory tests. Fat-based and plastic-like nanoparticles outperformed all others based on biocompatibility and drug delivery ability. Gold nanoparticles were highly multifunctional and versatile for therapy and imaging. Cerium oxide proved to be a great antioxidant and could protect neurons from injury. However, some nanomaterials, like carbon nanotubes and quantum dots, were of concern due to toxicity. The review concludes that just as there is no single nanomaterial that is perfect, their benefits can be leveraged in hybrid systems to enable more powerful, targeted, and safer treatment. Nanotechnology has tremendous potential for future advances in the fight against brain disease by enabling precise and protective drug delivery to the brain.

Published by: Ayaan Bansal

Author: Ayaan Bansal

Paper ID: V11I3-1369

Paper Status: published

Published: June 28, 2025

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Research Paper

Autonomous Threat Detection and Response in Cloud Environments Using AI and Machine Learning: Focus on Real-Time AI-Driven Anomaly Detection and Self-Healing Cloud Security Architectures

Cloud threat detection and response technologies are at the forefront of maintaining cybersecurity amid increasingly dynamic and complex infrastructures. The technologies are programmed to identify, assess, and respond to likely threats in real time to provide cloud service integrity and availability. Static rule-based mechanisms and manual control mechanisms find it challenging to keep pace with evolving attack patterns, and this has necessitated the use of automated, intelligent solutions. This paper explores the employment of autonomous threat detection and response systems using artificial intelligence (AI) and machine learning (ML). The binary classification model was used to identify harmless and threat-related network traffic from a Kaggle-based DDoS dataset. The data underwent rigorous preprocessing, exploratory data analysis, and feature engineering. Five machine learning (ML) models were trained and evaluated against performance measures like accuracy, precision, F1-score, and detection time. The Decision Tree model gave better performance, with a high accuracy of 98.0% and real-time capability. Its integration into cloud infrastructures allows for self-healing, adaptive cybersecurity defenses.

Published by: Mariam Sanusi, Tolulope Onasanya, Oduwunmi Odukoya, Moyinoluwa Senjobi

Author: Mariam Sanusi

Paper ID: V11I3-1375

Paper Status: published

Published: June 28, 2025

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Case Study

La Corruption Et La Mediocrite Dans L’Administration : Deux Facteurs Cles De La Decadence Socio-Economique D’Un éTat

La présente étude scientifique s'intéresse à l’impact conjugué de deux fléaux structurels au sein de l’administration publique : la corruption et la médiocrité. Ces phénomènes, souvent analysés séparément, forment en réalité un système de réciprocité qui nourrit la déchéance progressive de l’État. À travers une approche pluridisciplinaire intégrant les sciences politiques, la sociologie et l’économie du développement, cet article met en lumière les mécanismes par lesquels ces deux facteurs se renforcent mutuellement, engendrant une gouvernance inefficace, un affaiblissement des institutions et un ralentissement notable de la croissance socio-économique. Le cas de Madagascar constitue le centre d’attention de cette étude, illustrant comment une culture administrative tolérant l’incompétence et la corruption a conduit à une perte de confiance généralisée envers les structures étatiques. En recourant à une méthodologie combinant revue documentaire, analyse statistique des données disponibles (indices de gouvernance, classements internationaux, etc.) et entretiens semi-directifs avec des experts locaux, l’étude met en exergue l’urgence d’une réforme structurelle. Les résultats révèlent que la mauvaise qualité du capital humain dans l’administration malgache découle souvent de pratiques de nomination non méritocratiques, alimentées par le clientélisme politique. L’article plaide pour une refondation de la gouvernance basée sur l’éthique, la compétence et la transparence. Il met également en évidence des comparaisons utiles avec d’autres pays africains ayant entrepris des réformes réussies en matière de gouvernance publique.

Published by: Dr. Georges Solofoson, Zoelison Andriandratoarivo, Georges Emma Rakotomalala, Julieph Ranaivo

Author: Dr. Georges Solofoson

Paper ID: V11I3-1380

Paper Status: published

Published: June 28, 2025

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Research Paper

Quantum-Inspired Deep Learning for High-Dimensional Data Processing in Finance

This paper explores the application of quantum-inspired deep learning techniques for processing high-dimensional financial data, specifically focusing on predicting stock price movement using Apple Inc. (AAPL) stock data. We investigate the effectiveness of a quantum-inspired model in comparison to a standard Long Short-Term Memory (LSTM) network. The study incorporates various technical indicators as features and evaluates model performance using standard classification metrics and visualizations. The challenges of high-dimensional data in finance are discussed, and the potential benefits of quantum-inspired approaches in this domain are explored.

Published by: R. Vasuki, Manupati Ramana Kumar, Kokkiligadda Nischal varma, Kamireddy Yaswanth Reddy

Author: R. Vasuki

Paper ID: V11I3-1184

Paper Status: published

Published: June 28, 2025

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