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Unleashing the Power of Pre-existing Knowledge in Machine Learning : K-Based Classifiers

Knowledge-Based Classification Algorithms Knowledge-Based Classification Algorithms Knowledge-based classification algorithms refer to a category of machine learning algorithms that employ pre-existing knowledge or rules to classify novel instances or data points. The algorithms in question are dependent on a knowledge base, which is a compilation of established patterns, rules, or associations among attributes, for the purpose of predicting outcomes or assigning class designations to unobserved data. In contrast to conventional classification algorithms that acquire patterns or rules directly from the training data, knowledge-based classification algorithms utilise pre-existing knowledge or domain expertise to steer the classification procedure. Pre-existing knowledge can be sourced from human experts or obtained from pre-existing resources such as databases, ontologies, or expe...

Demystifying Ground Truth, Gold Standard, and Benchmark Terms in Image and Machine Learning

 Building a ground truth image is an important task in many image classification projects, especially in medical imaging or in projects that require expertise in labeling objects. However, for many other tasks, such as classifying vegetation in an image, building a labeled image can be easy and simple with the help of unsupervised learning models.                                                                       Ground truth refers to a set of measurements or conditions that serve as a benchmark or target for a classification system or machine learning algorithm. Here are some examples of what ground truth can mean in different contexts: In object recognition, ground truth can refer to a set of labeled images that have been manually annotated by human experts. These label...

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The Two Sides of Wokeism: A Complex Social Phenomenon

Wokeism, a term that gained prominence in the 2010s, refers to a heightened awareness of social, cultural, and political issues, with a particular focus on combating racism and promoting social justice. This concept has become a focal point of both praise and criticism, dividing public opinion along ideological lines. In this article, we explore the two sides of wokeism and delve into the complexities of this social phenomenon. The Positive Side: Wokeism has undoubtedly played a crucial role in raising awareness about systemic inequalities and injustices that have long plagued society. By alerting people to racial prejudice, discrimination, and other forms of social disparity, it has fostered a deeper understanding of marginalized communities' struggles. Many proponents argue that being woke is a step towards progress, as it encourages individuals to empathize, listen, and take meaningful action to address societal imbalances. One of the significant achievements of Wokeism is its a...

Inequities in India's Taxation: Unfolding the GST Council's Ironies .

 Introduction: The GST Council in India convenes periodically to discuss and refine tax structures. Each meeting brings forth a mix of positive and negative developments, often revealing new ironies and fallacies within the economy.  This article sheds light on certain inconsistencies, focusing on the taxation of non-branded atta or wheat, the exemption of cricket games like IPL from GST, and the associated revenue generation. Taxing Essentials, Exempting Entertainment: One of the glaring ironies in the Indian tax system is the imposition of GST on non-branded atta or wheat, an essential commodity consumed by the poor. Despite the burden on those struggling to make ends meet, cricket, a form of entertainment, remains exempt from GST. The Board of Control for Cricket in India (BCCI), the governing body of cricket in India, enjoys tax-free status despite being the wealthiest cricket governing body globally. Example: A daily wage laborer, s...

How to Measure the Quality of Images When You Don't Have Gold Standard Images

What to do When You Don't Have Gold Standard Images to Compare Your Processed Images How to Measure the Quality of Images When You Don't Have Gold Standard Images to Compare In the realm of image analysis and processing, quantifying image quality forms a crucial cornerstone of various methodologies. This is especially true when there's a lack of gold standard images or ground truth to set the benchmark. In such scenarios, we're often left asking: how can we measure image quality objectively or subjectively? Let's delve into this topic with an emphasis on establishing robust and reliable metrics for image quality analysis. Objective Measurement of Image Quality Without Ground Truth Objective quality metrics quantify the difference between two images based on numerical methods. But, how do we assess image quality objectivel...