Homework_EXERCISE

This's my homework of Expert System subject for my own studies!

01 Homework​ ( Expert System )

Expert Systems are AI programs designed to mimic the decision-making abilities of human experts in specific domains by using a knowledge base of facts and a set of logical rules to draw conclusions, mainly using if-then rules. They were created in the 1970s and were some of the first successful forms of AI software. Unlike modern machine learning systems that learn from data, Expert Systems rely on explicitly programmed knowledge from domain experts, making them highly interpretable but limited to narrow, well-defined problem areas.

UP intake 2 Assessment II

This project presents the design and implementation of a desktop-based Restaurant Management System developed using Python 3.11+, PyQt6, and SQLite3. Traditional manual ordering, paper-based billing, and unmonitored ingredient tracking in food service operations often lead to calculation discrepancies, order delays, and inventory shortages. To address these challenges, an event-driven architecture separates presentation layouts, business services, and database persistence layers. The software incorporates core modules: food and beverage catalog CRUD operations, real-time dining table status tracking, order line-item calculation (subtotal, VAT, and discounts), and transactional stock adjustments. System evaluation verified data persistence across relational tables and accurate execution of input validation safeguards and business calculations. The final deliverable provides small-to-medium restaurant establishments with an offline-capable, structured solution for operational workflows.

Building a Production Data Pipeline for On Demand Super Apps: An Engineering Blueprint

On-demand super apps platforms integrating food delivery, grocery marts, express courier logistics, and digital banking into a single interface present unique architectural challenges for data teams. Unlike standard e-commerce models where orders follow a linear, static checkout funnel, an on-demand ecosystem like Cambodia's Nham24 runs on hyper-local geographic boundaries, dynamic dispatching latencies, multi-party settlements, and dual-currency transactions. To transform high-velocity, deeply nested operational payloads into boardroom-ready analytics, data engineers must move beyond basic CRUD scripts toward resilient, dimensional data warehousing. Here is an architectural walkthrough of designing, transforming, and operationalizing an end-to-end data pipeline for an on demand delivery super app.

Expert System Development Homework 01

This study looks at how Rule-Based Expert Systems work and how they can help find and fix Python programming errors. By looking at Syntax, Runtime, and Logic errors, it shows why we need clear evidence, exercise rules, and careful handling when information is missing.

មេរៀនទី២៖ ប្រព័ន្ធអ្នកជំនាញ និងការវិភាគកំហុសភាសាកម្មវិធី (Expert Systems and Programming Error Analysis)

ការអភិវឌ្ឍប្រព័ន្ធអ្នកជំនាញ (Expert System) ដែលអាចផ្ដល់ការវិនិច្ឆ័យត្រឹមត្រូវ និងអាចទុកចិត្តបាន មិនមែនចាប់ផ្ដើមពីការសរសេរកូដ ឬការបង្កើតច្បាប់ IF–THEN ច្រើនបំផុតនោះទេ។ វាត្រូវចាប់ផ្ដើមពីការកំណត់បញ្ហា ការសិក្សាអ្នកប្រើប្រាស់ ការប្រមូលភស្តុតាង ការកំណត់វិសាលភាព និងការបញ្ជាក់លទ្ធផលដែលប្រព័ន្ធត្រូវសម្រេចឱ្យបានច្បាស់លាស់។ មេរៀនទី២ នៃមុខវិជ្ជា Expert System Development ផ្ដោតលើការវិភាគបញ្ហាក្នុងការរៀនភាសាកម្មវិធី Python ដើម្បីបម្លែងបញ្ហាដែលអ្នករៀនជួបប្រទះទៅជាតម្រូវការសម្រាប់អភិវឌ្ឍប្រព័ន្ធអ្នកជំនាញ។ មេរៀនពន្យល់ពីភាពខុសគ្នារវាង Syntax Error, Runtime Error និង Logic Error ព្រមទាំងបែងចែកគោលគំនិត Problem, Symptom, Error Message, Evidence, Root Cause និង Diagnosis។ លើសពីនេះ មេរៀនណែនាំពីការរៀបចំ Problem Statement, Stakeholder Analysis, Functional Requirements, Non-Functional Constraints, Project Scope និង Expected Results ដែលអាចត្រួតពិនិត្យបាន។ តាមរយៈគម្រោងគំរូ Python Programming Learning Expert System និស្សិតនឹងរៀនរៀបចំករណីកំហុស កំណត់ភស្តុតាងចាំបាច់ និងបង្កើត Case Worksheet សម្រាប់ប្រមូលព័ត៌មានដែលមានប្រភពច្បាស់លាស់។ នៅចុងមេរៀន និស្សិតត្រូវអាចរៀបចំ Problem Statement និង Exercise Scope សម្រាប់គម្រោងក្រុមរបស់ខ្លួន ដើម្បីប្រើជាមូលដ្ឋានសម្រាប់មេរៀនទី៣ ស្ដីពី Knowledge Acquisition and Scientific Sources។

My Journey as a Young Developer: Learning, Growing, and Building My Future

Technology is more than just writing code. It is a journey of learning, solving problems, and growing through experience. As a young developer, I am exploring web development, building real-world projects, and improving my skills step by step. This article shares my perspective on the challenges, lessons, and mindset needed to grow as a developer in the digital age.

Blockchain Technology: Overview and History of Blockchain

Blockchain is a digital technology used to record and share information across many computers. It connects records in blocks and uses cryptography to help protect information from changes. This report explains the meaning and history of Blockchain Technology, its main components, and the differences between blockchain and traditional databases. It also compares Public Blockchain, Private Blockchain, and Consortium Blockchain. Finally, it describes real-world blockchain applications, including Bitcoin, IBM Food Trust, Decentralized Finance (DeFi), Digital Identity, and Healthcare. The purpose of this report is to explain blockchain in simple words and show how it can be used in real life.

Homework

Overview and History of Blockchain

មេរៀនទី៣៖ ការប្រមូលចំណេះដឹង និងប្រភពវិទ្យាសាស្ត្រ (Knowledge Acquisition and Scientific Sources)

ប្រព័ន្ធអ្នកជំនាញត្រូវការចំណេះដឹងដែលមានប្រភពច្បាស់លាស់ និងបានពិនិត្យ មុននឹងអាចផ្ដល់ការវិនិច្ឆ័យ ឬការណែនាំដល់អ្នកប្រើ។ មេរៀននេះណែនាំអំពីការប្រមូលចំណេះដឹងពីគ្រូបង្រៀន ឯកសារបច្ចេកទេស និងករណីកំហុសក្នុងការរៀន Python។ និស្សិតនឹងរៀនកត់ត្រាភស្តុតាង បង្កើតសម្មតិកម្ម ផ្ទៀងផ្ទាត់ប្រភព និងរៀបចំចំណេះដឹងសម្រាប់បម្លែងទៅជា Facts និង IF–THEN Rules នៅមេរៀនបន្ទាប់។

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