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Showing posts from September, 2024

🚀 From Static Models to Living Systems: How Agentic AI is Redefining Enterprise Workflows

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For years, AI has been treated like a calculator with a very advanced brain: you give it input, it gives you output. Useful? Yes. Transformative? Not quite. What’s shifting today is the rise of Agentic AI — AI that doesn’t just respond but acts , remembers , adapts , and coordinates . Think less about “getting an answer” and more about “delegating a process.” And here’s the real unlock: agentic systems don’t replace humans, they reshape how work gets done by connecting intelligence with action. 🏢 The Enterprise Pain Points Agentic AI Can Solve Decision Bottlenecks : Reports are generated, but decisions still stall in inboxes. Tool Fragmentation : Finance in Excel, sales in Salesforce, ops in Jira — nothing “talks.” Knowledge Drain : Institutional know-how gets lost when people leave. Process Rigidity : Static rules can’t flex when markets shift overnight. ⚡ Where Agentic AI Shines Instead of simply suggesting, agentic systems execute : Finance : An AI agent d...
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Enhancing Image Preprocessing with Canonical Renyi Correlation: A Refined Approach to Median Filtering for Fine Detail Preservation

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  Introduction Image preprocessing is a crucial step in image processing tasks, especially in applications like image captioning or object detection. Noises such as vignetting, reflection artifacts, scattered light, and Poisson noise often affect the quality of images, necessitating the use of denoising techniques. Among the popular methods, the Median Filter (MF) is widely used to remove such noise effectively. However, despite its utility, the MF can blur fine details in images. To address this, Canonical Renyi Correlation (CRC) is introduced as an enhancement to the MF, which helps preserve fine details by considering pixel correlations. Mathematical Formulation The denoising process starts by collecting multiple images from the image data source, denoted as: $$ h_{\text{images}} = \{ h^1, h^2, \dots, h^s \} $$ Here, s s  represents the total number of collected images. Preprocessing with Median Filter Median Filter (MF) is commonly applied to reduce noise. It works by rep...
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Advanced Object Segmentation: Bayesian YOLO (B-YOLO) vs YOLO – A Deep Dive into Precision and Speed

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  Introduction: Object detection and segmentation are essential tasks in computer vision. With models like YOLO (You Only Look Once) becoming widely popular for their real-time capabilities, new variations such as Bayesian YOLO (B-YOLO) have emerged to improve upon certain limitations. This article provides an in-depth explanation of the B-YOLO algorithm, focusing on the mathematical foundation behind its object segmentation process. We will also compare YOLO and B-YOLO, explaining their differences and advantages, and how B-YOLO's Bayesian framework addresses critical challenges like localization errors in small object detection. Mathematical Foundation of Object Segmentation in B-YOLO: B-YOLO refines the traditional YOLO approach by using a Bayesian factor-centric bounding box construction , aiming to resolve the issue of localization errors , especially when detecting small objects. The mathematical process for object segmentation in B-YOLO is as follows: Grid Division : The ...
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