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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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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.
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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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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.
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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.
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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.
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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.
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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.
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HTML Beginner Guide
This HTML Beginner's Guide assumes that you have no previous
knowledge of HTML or CSS.
It should be quite easy to follow if you work through each step, which are all
brought together at the end, before moving on to the CSS Beginner's Guide.
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cdaac notes level 5 business management
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This curriculum has been designed for competency-based training and has independent units of learning that allow the trainee flexibility in entry and exit. In developing the curriculum, significant involvement and support was received from various organizations.
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INTRODUCTION TO WEATHER
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WEATHER
-Daily atmospheric conditions of a place at a particular time and changes from place to place and time to time. Condition of the atmosphere is determined by the elements of weather.
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INTRODUCTION TO CRIMINOLOGY
The topic seeks to discuss Cesari Beccaria's support on the use of deterrence as a crime preventative measure citing relevant examples
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INTRODUCTION TO CRIMINOLOGY
The notes describe the sources of criminal law in Kenya
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Grade 9 Mathematics CAT Exam
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Mid term 1 2025 grade 9 Mathematics exam
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Pharmacology
Pharmacology is a medical science that forms a backbone of the medical profession as drugs form the corner stone of therapy in human diseases. Therefore, it is of utmost importance to describe the pharmacological basis of therapeutics in order to maximize the benefits and minimize the risks of drugs to recipients. This lecture note on pharmacology is primarily a note for undergraduate health science students such as health officer, nursing, midwifery and laboratory technology students. However, other health professionals whose career involves drug therapy or related aspects should also find much of the material relevant.
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PRINCIPLES OF HUMAN NUTRITION
Definitions of terms: food, health, nutrition, nutrients, nutritional status, nutritional care,
malnutrition, catabolism, anabolism, metabolism, good nutrition and food choices.
Food
What does food means to you? Food-menu -diet-hunger-nutrition-malnutrition. What
images do these words bring to your mind? Are they oriented to your senses? To your
social enjoyment? To your concerns about your own well-being? To your emotions? Do
they raise questions about the quality of life for your fellow human beings? When you sit
down to your next meal you will have definite ideas-positive or negative--about that meal
and the specific foods that are served to you.
By your eyes you will delight in the texture variations and colour combinations of the
food, the artistic touch of a garnish, and the beautiful table arrangements; or perhaps
you may be repelled because the food lacks colour and is carelessly served.
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INTRODUCTION TO FOOD MICROBIOLOGY AND PARASITOLOGY
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Microbiology- It is the study of microorganisms
Microorganisms- They is living entities of microscopic size and include bacteria, viruses, yeast, algae & protozoa, mound (fungi)
Parasitology- It is the study of parasites, their host and relationship between them
Prokaryote- they are unicellular organisms that lack a membrane bound nucleus mitochondria or any other membrane bound organelle.
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Principles of Human Nutrition
Nutrition as defined as the science of food sand nutrients ,their action ,interaction and
balance in relationship to health and disease, the processes by which the organism ingests ,digests ,absorbs ,transports and utilizes nutrients and disposes off their end product. It is a multi disciplinary science that embraces agriculture ,food technology ,psychology ,sociology ,economics ,religion ,culture and communication.
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Introduction to nutrition surveillance
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Surveillance: It refers to the act of carefully watching someone or something
especially in order to prevent or detect an occurrence of a given situation. It can
also be defined as a continuous observation of a place, person, group, or ongoing
activity in order to gather information:
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Nutrition Care Process
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Nutrition care process module is designed to equip the learner with knowledge, skills and attitude to enable the learner implement nutrition care process in every nutrition intervention. The prerequisite modules include; Principles of human nutrition, Nutrition
intervention in HIV/AIDS and Communicable & non communicable diseases, Management of malnutrition, Nutrition surveillance, Diet therapy 1 and Nutrition in the Lifespan
The module takes 30 contact hours: 13 hours for theory and 17 hours for practicals. Learners undertaking this module will have both theory and practical assessments. The formative assessment will be in the form of
continuous assessment tests, assignments, clinical and field assessments and
end of semester examination whereas summative assessment will be done in
form of final qualifying examination.
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