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Chế tạo ghế văn phòng có chức năng cảnh báo tình trạng sức khỏe bất thường dùng công nghệ IoT
Nghiên cứu này đề xuất một giải pháp tích hợp công nghệ IoT (Internet of Things) lên ghế văn phòng, gọi tắt là ghế IoT. Mô-đun IoT ESP8266 sẽ thu thập các cử động của người ngồi và gửi dữ liệu về máy tính qua kết nối WiFi.
4/8/2023 12:20:48 PM +00:00
Lecture note Artificial Intelligence - Chapter 22: Communication and Language
Chapter 22: Communication and Language. The main contents of this chapter include all of the following: Recall the principles of effective communication, identify the responsibilities of women and health care providers in the consultation process, discuss and list the principles of informed consent.
4/8/2023 10:57:36 AM +00:00
Lecture note Artificial Intelligence - Chapter 20b: Neural networks
Chapter 20b: Neural networks. The main contents of this chapter include all of the following: Brains, neural networks, perceptrons, multilayer perceptrons, applications of neural networks.
4/8/2023 10:57:23 AM +00:00
Lecture note Artificial Intelligence - Chapter 20a: Statistical learning
Chapter 20a: Statistical learning. The main contents of this chapter include all of the following: Bayesian learning, maximum a posteriori and maximum likelihood learning, bayes net learning.
4/8/2023 10:57:10 AM +00:00
Lecture note Artificial Intelligence - Chapter 16: Rational decisions
Chapter 16: Rational decisions. The main contents of this chapter include all of the following: Rational preferences, utilities, money, multiattribute utilities, decision networks, value of information.
4/8/2023 10:56:51 AM +00:00
Lecture note Artificial Intelligence - Chapter 15b: Speech recognition (briefly)
Chapter 15b: Speech recognition (briefly). The main contents of this chapter include all of the following: Speech as probabilistic inference, Speech sounds, Word pronunciation, Word sequences.
4/8/2023 10:56:38 AM +00:00
Lecture note Artificial Intelligence - Chapter 14b: Inference in Bayesian networks
Chapter 14b: Inference in Bayesian networks. The main contents of this chapter include all of the following: Exact inference by enumeration, exact inference by variable elimination, approximate inference by stochastic simulation, approximate inference by Markov chain Monte Carlo.
4/8/2023 10:56:12 AM +00:00
Lecture note Artificial Intelligence - Chapter 13: Uncertainty
Chapter 13: Uncertainty. The main contents of this chapter include all of the following: Probability is a rigorous formalism for uncertain knowledge; joint probability distribution specifies probability of every atomic event; queries can be answered by summing over atomic events; for nontrivial domains; we must find a way to reduce the joint size; independence and conditional independence provide the tools.
4/8/2023 10:55:52 AM +00:00
Lecture note Artificial Intelligence - Chapter 9: Inference in first-order logic
Chapter 9: Inference in first-order logic. The main contents of this chapter include all of the following: Reducing first-order inference to propositional inference, unification, generalized modus ponens, forward and backward chaining, logic programming, resolution.
4/8/2023 10:55:39 AM +00:00
Lecture note Artificial Intelligence - Chapter 8: First-order logic
Chapter 8: First-order logic. The main contents of this chapter include all of the following: First-order logic: objects and relations are semantic primitives; syntax: constants, functions, predicates, equality, quantifiers; Increased expressive power: sufficient to define wumpus world; Situation calculus: conventions for describing actions and change in FOL; situation calculus: can formulate planning as inference on a situation calculus KB.
4/8/2023 10:55:21 AM +00:00
Lecture note Artificial Intelligence - Chapter 7: Logical agents
Chapter 7: Logical agents. The main contents of this chapter include all of the following: Knowledge-based agents, wumpus world, logic in general—models and entailment, propositional (boolean) logic; equivalence, validity, satisfiability; inference rules and theorem proving.
4/8/2023 10:55:08 AM +00:00
Lecture note Artificial Intelligence - Chapter 6: Game playing
Chapter 6: Game playing. The main contents of this chapter include all of the following: Games, Perfect play, Resource limits and approximate evaluation, Games of chance, Games of imperfect information.
4/8/2023 10:54:55 AM +00:00
Lecture note Artificial Intelligence - Chapter 5: Constraint Satisfaction Problems
Chapter 5: Constraint Satisfaction Problems. The main contents of this chapter include all of the following: CSP examples, backtracking search for CSPs, problem structure and problem decomposition, local search for CSPs.
4/8/2023 10:54:42 AM +00:00
Lecture note Artificial Intelligence - Chapter 4b: Local search algorithms
The main contents of Lecture note Artificial Intelligence - Chapter 4b: Local search algorithms include all of the following: Hill-climbing, simulated annealing, genetic algorithms (briefly), local search in continuous spaces (very briefly).
4/8/2023 10:54:30 AM +00:00
Lecture note Artificial Intelligence - Chapter 4a: Informed search algorithms
Chapter 4a: Informed search algorithms. The main contents of this chapter include all of the following: Heuristic functions estimate costs of shortest paths, good heuristics can dramatically reduce search cost, greedy best-first search expands lowest h – incomplete and not always optimal, admissible heuristics can be derived from exact solution of relaxed problems.
4/8/2023 10:54:15 AM +00:00
Lecture note Artificial Intelligence - Chapter 3: Problem solving and search
The main contents of Lecture note Artificial Intelligence - Chapter 3: Problem solving and search include all of the following: Problem-solving agents, problem types, problem formulation, example problems, basic search algorithms, informed search algorithms.
4/8/2023 10:54:02 AM +00:00
Lecture note Artificial Intelligence - Chapter 2: Intelligent agents
Lecture note Artificial Intelligence - Chapter 2: Intelligent agents include all of the following: Agents interact with environments through actuators and sensors, the agent function describes what the agent does in all circumstances, the performance measure evaluates the environment sequence, a perfectly rational agent maximizes expected performance, agent programs implement (some) agent functions, PEAS descriptions define task environments Environments are categorized along several dimensions.
4/8/2023 10:53:49 AM +00:00
Lecture note Artificial Intelligence - Chapter 1: Introduce
Lecture note Artificial Intelligence - Chapter 1: Introduce presents the following content: What is AI? A brief history, the state of the art, thinking humanly: Cognitive Science,...
4/8/2023 10:53:33 AM +00:00
Overview of university management information system
The paper presents an overview of the university information management system (UMIS). Management information system (MIS) provides necessary information for the management and administration of an organization/enterprise. A university is an educational organization that needs a UMIS to increase efficiency in implementation and management of activities.
4/8/2023 10:11:08 AM +00:00
Cách tạo sách điện tử lật trang (Flipbook) và triển khai lên hệ thống học tập LMS
Trường Cao đẳng Sư phạm Trung ương (Trường CĐSPTƯ) đang xây dựng kho học liệu phục vụ đào tạo trực tuyến trên hệ thống LMS của nhà trường như: Học liệu dạng bài giảng điện tử, bài giảng elearning, học liệu dạng video tương tác, học liệu dạng sách lật,… trong đó phần mềm iSpring Suite 10 hiện là công cụ hữu ích để xây dựng học liệu điện tử. Bài viết này tác giả sẽ trình bày cách tạo sách lật bằng phần mềm iSpring Suite 10 và đưa lên hệ thống đào tạo trực tuyến LMS.
4/8/2023 9:47:57 AM +00:00
Mô hình trưởng thành về quản trị dữ liệu đám mây cloud data governance maturity
Nghiên cứu xây dựng một bộ hệ thống lý thuyết và hướng dẫn thực hành về quản trị dữ liệu đám mây dựa trên đặc điểm và nhu cầu quản trị của dữ liệu trong môi trường điện toán đám mây là rất cần thiết.
4/8/2023 9:36:11 AM +00:00
Lecture Design and Analysis of Algorithms: Lecture 45 - Dr. Sohail Aslam
It is well known that planar graphs can be colored (maps) with four colors. There exists a polynomial time algorithm for this. But determining whether this can be done with 3 colors is hard and there is no polynomial time algorithm for it. In this lecture, you find clear explanations of Clique Cover.
4/8/2023 6:38:20 AM +00:00
Lecture Design and Analysis of Algorithms: Lecture 44 - Dr. Sohail Aslam
The class NP-complete (NPC) problems consists of a set of decision problems (a subset of class NP) that no one knows how to solve efficiently. But if there were a polynomial solution for even a single NP-complete problem, then ever problem in NPC will be solvable in polynomial time. For this, we need the concept of reductions. In this lecture, you find clear explanations of Reductions.
4/8/2023 6:38:13 AM +00:00
Lecture Design and Analysis of Algorithms: Lecture 43 - Dr. Sohail Aslam
The following will be discussed in this chapter: Complexity Theory, Decision Problems, Complexity Classes, Polynomial Time Verification, The Class NP, Reductions, Polynomial Time Reduction, NP-Completeness, Boolean Satisfiability Problem: Cook’s Theorem.
4/8/2023 6:38:05 AM +00:00
Lecture Design and Analysis of Algorithms: Lecture 42 - Dr. Sohail Aslam
We consider the generalization of the shortest path problem: to compute the shortest paths between all pairs of vertices. This is called the all-pairs shortest paths problem. In this lecture, you find clear explanations of Floyd-Warshall Algorithm.
4/8/2023 6:37:59 AM +00:00
Lecture Design and Analysis of Algorithms: Lecture 41 - Dr. Sohail Aslam
Dijkstra’s single-source shortest path algorithm works if all edges weights are non-negative and there are no negative cost cycles. Bellman-Ford allows negative weights edges and no negative cost cycles. The algorithm is slower than Dijkstra’s, running in Θ(VE) time. In this lecture, you find clear explanations of Bellman-Ford Algorithm.
4/8/2023 6:37:52 AM +00:00
Lecture Design and Analysis of Algorithms: Lecture 40 - Dr. Sohail Aslam
Negative edges weights maybe counter to intuition but this can occur in real life problems. However, we will not allow negative cycles because then there is no shortest path. If there is a negative cycle between, say, s and t, then we can always find a shorter path by going around the cycle one more time. In this lecture, you find clear explanations of Dijkstra’s Algorithm (con't).
4/8/2023 6:37:45 AM +00:00
Lecture Design and Analysis of Algorithms: Lecture 39 - Dr. Sohail Aslam
Dijkstra’s algorithm is a simple greedy algorithm for computing the single-source shortest-paths to all other vertices. Dijkstra’s algorithm works on a weighted directed graph G = (V, E) in which all edge weights are non-negative, i.e., w(u, v) ≥ 0 for each edge (u, v) ∈ E. In this lecture, you find clear explanations of Dijkstra’s Algorithm.
4/8/2023 6:37:39 AM +00:00
Lecture Design and Analysis of Algorithms: Lecture 38 - Dr. Sohail Aslam
A motorist wishes to find the shortest possible route between Peshawar and Karachi. Given a road map of Pakistan on which the distance between each pair of adjacent cities is marked Can the motorist determine the shortest route? In this lecture, you find clear explanations of Shortest Path.
4/8/2023 6:37:31 AM +00:00
Lecture Design and Analysis of Algorithms: Lecture 37 - Dr. Sohail Aslam
Kruskal’s algorithm worked by ordering the edges, and inserting them one by one into the spanning tree, taking care never to introduce a cycle. Intuitively Kruskal’s works by merging or splicing two trees together, until all the vertices are in the same tree. In contrast, Prim’s algorithm builds the MST by adding leaves one at a time to the current tree. In this lecture, you find clear explanations of Prim’s Algorithm.
4/8/2023 6:37:24 AM +00:00