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CMT 314 :MOBILE APPLICATIONS DEVELOPMENT
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Mobile Computing is a technology that allows transmission of data, voice and video via a computer or any other wireless enabled device without having to be connected to a fixed physical link.
57 Pages
3472 Views
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Uploaded: 2025-02-05
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Grade 9 Pre-technical studies
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Rationalized Pre-technical studies Notes
77 Pages
5589 Views
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19.87 MB
Uploaded: 2025-02-04
GRADE 9 AGRICULTURE AND NUTRITION MID TERM ASSESMENT TERM 1 2025
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Boost your Grade 9 students' agriculture and nutrition skills with the Mid Term 1 Exam 2025. This comprehensive exam paper is a valuable resource for both teachers and students. Enhance learning outcomes and assess knowledge effectively with this engaging math activity.
11 Pages
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5 Downloads
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Uploaded: 2025-02-04
GRADE 9 CREATIVE ARTS AND SPORTS ASSESMENT TERM 1 2025
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Boost your Grade 9 students' CAS skills with the Mid Term 1 Exam 2025. This comprehensive exam paper is a valuable resource for both teachers and students. Enhance learning outcomes and assess knowledge effectively with this engaging math activity.
17 Pages
9733 Views
7 Downloads
1006.49 KB
Uploaded: 2025-02-04
Decision tree problem
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A decision tree problem involves making a series of decisions based on a set of conditions, represented in a tree-like model. Each internal node of the tree represents a decision based on a feature, branches represent possible outcomes, and leaf nodes indicate the final decision or classification. Decision tree problems are common in machine learning, where they are used for classification and regression tasks by recursively splitting data based on feature values. They help in decision-making processes, such as diagnosing diseases, credit risk assessment, and customer segmentation, by providing a clear and interpretable decision structure.
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Uploaded: 2025-02-04
GRADE 7 AGRICULTURE AND NUTRITION SCHEME OF WORK TERM 1
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Enhance your Grade 7 teaching with our Rationalized Agriculture and Nutrition Schemes of Work for Term 1. Everything teachers need for effective planning.
13 Pages
4800 Views
1 Downloads
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Uploaded: 2025-02-04
GRADE 7 AGRICULTURE AND NUTRITION SCHEME OF WORK TERM 2
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Enhance your Grade 7 teaching with our Rationalized Agriculture and Nutrition Schemes of Work for Term 2. Everything teachers need for effective planning.
13 Pages
5782 Views
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Uploaded: 2025-02-04
GRADE 7 AGRICULTURE AND NUTRITION SCHEME OF WORK TERM 3
Enhance your Grade 7 teaching with our Rationalized Agriculture and Nutrition Schemes of Work for Term 3. Everything teachers need for effective planning.
11 Pages
379 Views
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121.68 KB
Uploaded: 2025-02-04
GRADE 7 AGRICULTURE AND NUTRITION SCHEME OF WORK TERM 3
Enhance your Grade 7 teaching with our Rationalized Agriculture and Nutrition Schemes of Work for Term 3. Everything teachers need for effective planning.
11 Pages
375 Views
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121.68 KB
Uploaded: 2025-02-04
AN INTRODUCTION TO THE THEORY AND PRACTICEOF TRANSLATION
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Fusing theory with advice and information about the practicalities of translating, Becoming a Translator is the essential resource for novice and practicing translators.
72 Pages
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Uploaded: 2025-02-04
Introduction to Expert Systems
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An Expert System is a computer-based system designed to simulate human expert decision-making in a specific domain. It uses a knowledge base containing facts and heuristics, along with an inference engine that applies logical rules to derive conclusions or solve complex problems. Expert systems assist in tasks such as medical diagnosis, engineering troubleshooting, and financial analysis by mimicking expert reasoning. They provide recommendations, explanations, and justifications for their decisions, making them valuable in environments where human expertise is limited or costly. Early expert systems, such as MYCIN for medical diagnosis, demonstrated the potential of artificial intelligence in specialized fields.
8 Pages
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Uploaded: 2025-02-04
Expert System 2
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An Expert System is a computer-based application that mimics human expertise to solve complex problems within a specific domain. It uses a knowledge base, containing facts and heuristics, and an inference engine that applies logical rules to analyze and interpret data. Expert systems are designed to assist decision-making by providing recommendations, diagnoses, or solutions, often used in fields like medicine, engineering, and business. They rely on techniques such as rule-based reasoning and machine learning to simulate human intelligence, improving efficiency and accuracy in specialized tasks.
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Genetic Algorithm
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A Genetic Algorithm (GA) is an optimization technique inspired by natural selection and evolution. It operates by encoding potential solutions as "chromosomes" and applying genetic operators such as selection, crossover, and mutation to evolve better solutions over generations. The fittest individuals, based on a predefined objective function, are more likely to pass their traits to the next generation, ensuring gradual improvement. GAs are widely used in complex problem-solving, including machine learning, engineering design, and artificial intelligence, where traditional optimization methods may be inefficient or impractical.
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Uploaded: 2025-02-04
What is Artificial Intelligence
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Artificial Intelligence (AI) is a branch of computer science that focuses on creating machines capable of performing tasks that typically require human intelligence. These tasks include problem-solving, learning, reasoning, perception, and language understanding. AI can be categorized into narrow AI, which is designed for specific tasks like speech recognition, and general AI, which aims to replicate human cognitive abilities. It incorporates techniques such as machine learning, neural networks, and natural language processing to enhance automation and decision-making. AI is transforming industries like healthcare, finance, and robotics, driving innovation and efficiency in various domains.
5 Pages
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Uploaded: 2025-02-04
Applications of Artificial intelligence
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Artificial Intelligence (AI) is widely applied across various industries, revolutionizing how tasks are performed. In healthcare, AI aids in disease diagnosis, drug discovery, and personalized treatment plans. In finance, it enhances fraud detection, risk assessment, and algorithmic trading. AI-driven automation boosts efficiency in manufacturing and supply chains. In customer service, AI chatbots provide instant support, while in marketing, AI optimizes ad targeting and consumer insights. Autonomous vehicles, smart assistants, and robotics showcase AI's impact on daily life. Additionally, AI is crucial in cybersecurity, climate modeling, and scientific research, making it an essential tool for innovation and problem-solving.
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Uploaded: 2025-02-04
History of Artificial Intelligence
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The history of Artificial Intelligence (AI) dates back to ancient times, with myths of mechanical beings. However, modern AI began in the 1950s when Alan Turing proposed the concept of machine intelligence and developed the Turing Test. In 1956, the Dartmouth Conference marked AI's formal birth. Early AI research focused on symbolic reasoning and problem-solving but faced challenges due to limited computing power. The 1980s saw the rise of expert systems, and in the 1990s, machine learning gained traction. The 21st century brought deep learning, big data, and powerful neural networks, leading to breakthroughs in natural language processing, computer vision, and autonomous systems. Today, AI continues to evolve, transforming industries and daily life.
4 Pages
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Uploaded: 2025-02-04
Knowledge-based Agent in Artificial Intelligence
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A knowledge-based agent in Artificial Intelligence (AI) is a system that uses a structured knowledge base to make decisions, solve problems, and interact with its environment intelligently. It consists of a knowledge base, which stores facts and rules, and an inference engine that applies logical reasoning to derive conclusions. These agents can learn from past experiences, update their knowledge, and make informed decisions. They are widely used in expert systems, medical diagnosis, robotics, and automated decision-making. By combining symbolic reasoning with machine learning, knowledge-based agents enhance AI's ability to handle complex tasks requiring logic, inference, and domain expertise.
5 Pages
3551 Views
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Uploaded: 2025-02-04
Knowledge Representation
Knowledge representation in Artificial Intelligence (AI) refers to the methods used to encode information about the world in a format that a computer can process and reason with. It enables AI systems to store, retrieve, and manipulate knowledge to make informed decisions. Common techniques include semantic networks, frames, production rules, and ontologies, each suited for different problem domains. Effective knowledge representation allows AI to perform reasoning, understand natural language, and interact intelligently with humans. It is fundamental to expert systems, natural language processing, and automated decision-making, making AI systems more efficient and capable of solving complex problems.
8 Pages
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Uploaded: 2025-02-04
Techniques of knowledge representation
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Techniques of knowledge representation in Artificial Intelligence (AI) define how information is structured and processed for reasoning and decision-making. The main techniques include logical representation, which uses formal logic to express facts and rules; semantic networks, which represent knowledge as interconnected nodes and relationships; frames, which organize knowledge into structured templates with attributes and values; and production rules, which use "if-then" statements for decision-making. Ontologies provide a hierarchical structure of concepts and their relationships, enabling AI to understand context. These techniques help AI systems efficiently store, retrieve, and apply knowledge, making them crucial for expert systems, natural language processing, and intelligent decision-making.
7 Pages
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Uploaded: 2025-02-04
Propositional Logic in Artificial Intelligence
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Propositional Logic in Artificial Intelligence (AI) is a formal system used to represent and reason about facts and relationships in a structured and unambiguous way. It consists of propositions, which are statements that can be either true or false, and logical connectives such as AND, OR, NOT, IMPLICATION, and BICONDITIONAL. In AI, propositional logic is used for knowledge representation, automated reasoning, and decision-making. It enables inference through rules of deduction, such as Modus Ponens and Resolution, allowing AI systems to derive new knowledge from existing facts. However, while propositional logic is useful for simple reasoning tasks, it lacks expressiveness for handling complex domains involving variables, quantifiers, or uncertainty, which are addressed by more advanced logical systems like First-Order Logic and Probabilistic Logic.
7 Pages
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Uploaded: 2025-02-04