Nkongolo mike (47 risultati)

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Paperback or Softback. Condizione: New. Une expressivit� s�mantique de la complexit� d'alternance logique avec des variables continues. Book.

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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Research Paper (postgraduate) from the year 2018 in the subject Computer Science - Internet, New Technologies, , course: Machine Learning, language: English, abstract: Human Action Recognition is the task of recognizing a set of actions being performed in a video sequence. Reliably and efficiently detecting and identifying actions in video could have vast impacts in the surveillance, security, healthcare and entertainment spaces.The problem addressed in this paper is to explore different engineered spatial and temporal image and video features (and combinations thereof) for the purposes of Human Action Recognition, as well as explore different Deep Learning architectures for non-engineered features (and classification) that may be used in tandem with the handcrafted features. Further, comparisons between the different combinations of features will be made and the best, most discriminative feature set will be identified.In the paper, the development and implementation of a robust framework for Human Action Recognition was proposed. The motivation behind the proposed research is, firstly, the high effectiveness of gradient-based features as descriptors - such as HOG, HOF, and N-Jets - for video-based human action recognition. They are capable of capturing both the salient spatialand temporal information in the video sequences, while removing much of the redundant information that is not pertinent to the action. Combining these features in a hierarchical fashion further increases performance.…

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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Academic Paper from the year 2018 in the subject Computer Science - Applied, University of the Witwatersrand, course: Machine learning - Artificial Intelligence - Big Data - Natural Language Processing, language: English, abstract: Cloud computing makes it possible to build scalable machine learning systems for processing massive amounts of complex data, be them structured or unstructured, real-time or historical, the so-called Big Data. Publicly available cloud computing platforms have been made available, for instance, Amazon EC2, EMR, and Google Compute Engine. More importantly, open source APIs and libraries have also been developed for ease of programming on the cloud, for instance, Cascading, Storm, Scalding, Apache Spark and Trackur. Meanwhile, computational intelligence approaches, examples of which include evolutionary computation, immune-inspired approaches, and swarm intelligence, are also employed to develop scalable machine learning and data analytics tools.In this project, we presented the sentiment-focused web crawling problem and designed a sentiment-focused web crawler frame-work for faster discovery and retrieval of sentimental context on the Web. We have developed a computational framework to perform automated reputation analysis on the Web using Natural Language Processing and Machine Learning. This paper introduces such framework and tests its performance on automated sentiment analysis for brand reputation. In addition, we proposed different strategies for predicting the polarity scores of web pages.Experiments have shown that the performance of our proposed framework is more efficient than existing frameworks. Reputation analysis is a useful application for organizations that are looking for people's opinions about their products and services.Our approach consists of 4 parts: in the first part, the framework performed Web crawling based on the query specified by the user. In the second part, the framework locates relevant information within textual data using Entity Recognition. In the third part, relevant information was recorded in the database for feature extraction/engineering and classification. Lastly, the framework displayed the data for reputation analysis. In the training phase, we used data provided by the marketing team of the University of the Witwatersrand, Emoticons, a subset of the SentiStrength lexicon and ClueWeb09 dataset. Each domain was labelled accordingly (positive/negative and neutral) with equal numbers of polarity in plain text. In the test phase, the classifier predicted the polarity of real-time data. We used accuracy as evaluation metric to measure how much our classifier acted precisely. …

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Project Report from the year 2017 in the subject Computer Science - Miscellaneous, grade: BSc Honours in Computer Science, , course: Honors research project, language: English, abstract: Most universities offer a wide range of courses in which students can enrol. As a result, students may feel overwhelmed with the many possibilities and large amount of information, resulting in having a difficult time deciding what to sign up for. To this end, there is a need for a system that can assist students in this crucial process. Thus, we set out to develop a web-based recommender application that could generate a list of valuable, accurate course recommendations, taking into account a student's likelihood of succeeding academically.Choosing an effective and stimulating set of courses is not an easy task for a student. There are several factors at play when it comes to choosing courses that one must study. One of these factors may be the assumed difficulty of a course that a student is considering to take. Of course, if the course is compulsory, then the student has no choice but to enrol in it. However, in the situation in where there are many different subjects to choose from, the student may shy away from taking optional courses that might pose a significant challenge, in terms of workload or being unable to fully understand the course content. These courses would clearly have a negative effect on academic performance. However, there may also be some students who are looking to be challenged, and for whom choosing more difficult courses would be an exciting challenge. In terms of academic performance, effective course selection is of utmost importance in ensuring a student is able to succeed in her or his studies and obtain their qualification(s).…

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Texte Universitaire de l'année 2019 dans le domaine Informatique - Divers, note: Msc, University of the Witwatersrand (University), cours: Symbolic Logics, langue: Français, résumé: Nous montrons que les logiques temporelles de temps de branchement, ainsi que les logiques temporelles a temps alternatif, sont expressif dans la langue avec une seule variable propositionnelle ou avec une offre limite de variables.…

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Research Paper (postgraduate) from the year 2018 in the subject Computer Science - Internet, New Technologies, , course: Machine Learning, language: English, abstract: Human Action Recognition is the task of recognizing a set of actions being performed in a video sequence. Reliably and efficiently detecting and identifying actions in video could have vast impacts in the surveillance, security, healthcare and entertainment spaces.The problem addressed in this paper is to explore different engineered spatial and temporal image and video features (and combinations thereof) for the purposes of Human Action Recognition, as well as explore different Deep Learning architectures for non-engineered features (and classification) that may be used in tandem with the handcrafted features. Further, comparisons between the different combinations of features will be made and the best, most discriminative feature set will be identified.In the paper, the development and implementation of a robust framework for Human Action Recognition was proposed. The motivation behind the proposed research is, firstly, the high effectiveness of gradient-based features as descriptors - such as HOG, HOF, and N-Jets - for video-based human action recognition. They are capable of capturing both the salient spatialand temporal information in the video sequences, while removing much of the redundant information that is not pertinent to the action. Combining these features in a hierarchical fashion further increases performance. 40 pp. Englisch.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Academic Paper from the year 2018 in the subject Computer Science - Applied, University of the Witwatersrand, course: Machine learning - Artificial Intelligence - Big Data - Natural Language Processing, language: English, abstract: Cloud computing makes it possible to build scalable machine learning systems for processing massive amounts of complex data, be them structured or unstructured, real-time or historical, the so-called Big Data. Publicly available cloud computing platforms have been made available, for instance, Amazon EC2, EMR, and Google Compute Engine. More importantly, open source APIs and libraries have also been developed for ease of programming on the cloud, for instance, Cascading, Storm, Scalding, Apache Spark and Trackur. Meanwhile, computational intelligence approaches, examples of which include evolutionary computation, immune-inspired approaches, and swarm intelligence, are also employed to develop scalable machine learning and data analytics tools.In this project, we presented the sentiment-focused web crawling problem and designed a sentiment-focused web crawler frame-work for faster discovery and retrieval of sentimental context on the Web. We have developed a computational framework to perform automated reputation analysis on the Web using Natural Language Processing and Machine Learning. This paper introduces such framework and tests its performance on automated sentiment analysis for brand reputation. In addition, we proposed different strategies for predicting the polarity scores of web pages.Experiments have shown that the performance of our proposed framework is more efficient than existing frameworks. Reputation analysis is a useful application for organizations that are looking for people's opinions about their products and services.Our approach consists of 4 parts: in the first part, the framework performed Web crawling based on the query specified by the user. In the second part, the framework locates relevant information within textual data using Entity Recognition. In the third part, relevant information was recorded in the database for feature extraction/engineering and classification. Lastly, the framework displayed the data for reputation analysis. In the training phase, we used data provided by the marketing team of the University of the Witwatersrand, Emoticons, a subset of the SentiStrength lexicon and ClueWeb09 dataset. Each domain was labelled accordingly (positive/negative and neutral) with equal numbers of polarity in plain text. In the test phase, the classifier predicted the polarity of real-time data. We used accuracy as evaluation metric to measure how much our classifier acted precisely. 60 pp. Englisch.…

Condizione: New. Print on Demand pp. 30.

Condizione: New. Print on Demand pp. 30.