The generalization and systematization of various scientific views of both domestic and foreign researchers on the problem of educational data mining (EDM) and their application to support decision-making on the educa...
ISBN:
(数字)9781728148106
ISBN:
(纸本)9781728148113
The generalization and systematization of various scientific views of both domestic and foreign researchers on the problem of educational data mining (EDM) and their application to support decision-making on the educational process management. Particular attention is paid to the problems of automatic extraction of interpreted knowledge from educational data in order to transfer it to users and facilitate the processes of interactive computing and communication among users. The proposed methods and models allow increasing the effectiveness of decision-making process at all stages of the educational process management. The novelty of the work results is a new approach to the extraction of interpretable knowledgebased on the use of a special class of neural network models, as well as the hybridization of several approaches developed as part of data mining into a single learning environment in order to implement an end-to-end learning strategy, including the steps of data collecting and analyzing, knowledge extracting and management. The paper presents the fragments of the developed software that provides EDM by individual methods.
Artificial intelligence and Machine learning advancements in stimulation of human knowledge to computers has led to the softwarebased application / program called chatbot. A chatbot acts as a conventional agent which...
Artificial intelligence and Machine learning advancements in stimulation of human knowledge to computers has led to the softwarebased application / program called chatbot. A chatbot acts as a conventional agent which uses natural languages for communicating with operators. The chatbots are aware of self learning algorithms, for example natural language processing (NLP). These software programs are made to help people and have a one-on-one interaction with them (either in text or speech format). The proposed chatbot provides a platform for crime registration and also getting information about various types of crimes. We can find chatbots in the field of entertainment, query clarification, social media, e-commerce sites etc. but rarely can we find chatbots serving the purpose of security and crime related functions. Our proposed system collects various information from the victims so that the authorities could cross-verify the information provided. It gives a stage for people to report about the crimes, getting data about different sorts of violations. It gathers different check archives from the casualties so the specialists could cross-confirm the data given. The chatbot works on the principle of natural language processing algorithm.
When computations consist of different processes or threads that may execute at the same time or in an interleaved manner, we say the computation is concurrent or that it exhibits concurrency. University students ofte...
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ISBN:
(数字)9781450371247
ISBN:
(纸本)9781728165257
When computations consist of different processes or threads that may execute at the same time or in an interleaved manner, we say the computation is concurrent or that it exhibits concurrency. University students often struggle to reason about concurrency. Researchers have presented students with natural language descriptions of scenarios involving concurrency and have evaluated students' natural language responses to explore their prior knowledge and to determine characteristics of student reasoning about concurrency. However, natural language responses are necessarily abstract and pertain to the design stage of software development, while studies in which students are asked to implement solutions provide a more concrete view of student reasoning and reveal difficulties that arise when putting design into practice. In this work we build upon prior work and ask: What are common and problematic features in student approaches to solving concurrency-related problems? How do these features differ from the design phase to the implementation phase of the software development life cycle? In this study we asked students enrolled in a jointly offered upper division undergraduate / MS level course on “Programming with Concurrency” to implement a solution to the same problem (a side-kick/superhero version of the party matching problem) using two different approaches: Threads-based in Java, and Actors-based in Scala. We performed qualitative feature analysis of their implementations and their written reflections to better understand student reasoning when programming concurrent solutions. The feature of additional complexity was found in our earlier natural language study in which students described their designs. Additional complexity was also represented in student implementations in this study and adversely impacted student success as their programs became cumbersome and hard to trace. Student reflections provided insight into their resistance to modeling prior to implementa
Nowadays, while increasing variety of online social networks (OSNs), content management has become harder. The paper suggests social media pages' managers to publish their contents, including the composition of im...
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Imagine that it is possible to learn effort estimation concepts and its application in an attractive manner, where complex and technical knowledge are presented through a playful experience. The serious game Back to P...
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Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Intelligent science(IS) is an interdisciplinary subject which dedicates to j...
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Intelligent science(IS) is an interdisciplinary subject which dedicates to joint research on informatics and technology of intelligence by brain science, artificial intelligence(AI), cognitive science, data science and others. IS is devoted to create intelligent computer software that models the human behavior. The main goal of IS is to make computers smarter by creating intelligent algorithms that will allow a computer to mimic some of the functions of the human brain in selected applications. All of these applications employ knowledge base and inferencing techniques to solve problems or help make decisions in specific domains. This talk discusses the potential role of intelligence science, AI methodologies, knowledgeengineering and intelligent computing paradigms, for smart detection and diagnosis of Corona virus, COVID-19. The talk presents the following issues; (a) detection intelligent software for people who have Corona virus, (b) super computing power resources to assist professors with research on the corona virus, (c) intelligent medical imaging evaluation systems, (d) facial recognition intelligent algorithms and thermal imaging temperature measurement technology, and (e) deep learning technique for detection of COVID-19 using X-Ray images. Moreover, the talk presents a proposal for case-based reasoning expert system for corona, COVID-19 diagnosis. In addition, the talk presents the research directions and some examples of the developed intelligent healthcare systems by the author and his colleagues at AIKER-Labs.
A Contradiction Matrix of TRIZ that classifies problems to solve as contradictions of features is an effective framework of knowledge management for problem solving. The features, however, may have a problem of comple...
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Nowadays, with the rapid development of industry, people's daily life is becoming more and more rich and diverse. However, while enjoying the pleasure brought by material life, a large number of garbage are produc...
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ISBN:
(纸本)9781665447034
Nowadays, with the rapid development of industry, people's daily life is becoming more and more rich and diverse. However, while enjoying the pleasure brought by material life, a large number of garbage are produced. They are various, and people lack the knowledge related to garbage classification, which leads to the difficulty of manual or robot classification. This paper studies a garbage classification algorithm model based on deep learning convolutional neural network Efficientnet to help identify garbage classification. In this research, data augmentation and normalization are carried out to solve the problem of small amount of data sets and different sizes of pictures. Efficientnet is used to extract the features of images. In order to solve the problem that BN has no obvious effect on small batches in the network, we replace BN with group normalization (GN). In order to prevent some irrelevant information in the image from affecting the training of the model, we add attention mechanism after the output of Efficientnet to emphasize or select the important information of the target processing object, and suppress some irrelevant details, so that the model can focus on the key features and better identify the image; according to the above process, we use softmax to classify the spam image and divide it into four categories (Recyclables, Kitchen garbage, Hazardous garbage, Other garbage) The results show that the model can effectively extract the features of the input garbage image, and get accurate judgment, and identify the types of garbage. The experimental results show that the average accuracy of the algorithm model is high, and has good classification performance and robustness. In the practical significance of the research, this reliable model can help people quickly know the type of garbage, or can be applied to robot sorting, to help detect the types of garbage for robot judgment and sorting, so it has very important application scenarios and significanc
Artificial neural networks have been increasingly used in many problems of data classification because of their learning capacity, robustness and extendibility. Training in the neural networks accomplished by identify...
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ISBN:
(纸本)9781728108728
Artificial neural networks have been increasingly used in many problems of data classification because of their learning capacity, robustness and extendibility. Training in the neural networks accomplished by identifying the weight of neurons which is one of the main issues addressed in this field. The process of network learning by back-propagation algorithm which is based on gradient, commonly fall into a local optimum. Due to the importance of weights and neural network structure, evolutionary neural networks have been emerged to obtain suitable weight set. This paper will concentrate on training a feed-forward networks by a modified evolutionary algorithm based on asexual reproduction optimization (ARO) in order to data classification problems. The idea is to use real representation (rather the binary) for adjusting weights of the network. Experimental results show a better result in terms of speed and accuracy compared with other evolutionary algorithms including genetic algorithms, simulated annealing and particle swarm optimization.
The paper presents an open source code-based module designed to help undergraduate students on higher education engineering programmes to learn about analogue-to-digital conversion (ADC). The developed Successive Appr...
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