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检索条件"主题词=Artificial intelligence and robotics"

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Chatbots With Personality Using Deep Learning

Chatbots With Personality Using Deep Learning

引用

作者： Susmit Gaikwad San Jose State University

学位级别：硕士

Natural Language Processing (NLP) requires the computational modelling of the complex relationships of the syntax and semantics of a language. While traditional machine learning methods are used to solve NLP problems, they cannot imitate the human ability for language comprehension. With the growth in deep learning, these complexities within NLP are easier to model, and be used to build many computer applications. A particular example of this is a chatbot, where a human user has a conversation with a computer program, that generates responses based on the userâs input. In this project, we study the methods used in building chatbots, what they lack and what can be improved.

关键词： Chatbots deep learning natural language processing neural networks artificial intelligence and robotics Other Computer Sciences

来源：评论

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Deep Learning For Image Spam Detection

Deep Learning For Image Spam Detection

引用

作者： Tazmina Sharmin San Jose State University

学位级别：硕士

Spam can be defined as unsolicited bulk email. In an effort to evade text-based spam filters, spammers can embed their spam text in an image, which is referred to as image spam. In this research, we consider the problem of image spam detection, based on image analysis. We apply various machine learning and deep learning techniques to real-world image spam datasets, and to a challenge image spam-like dataset. We obtain results comparable to previous work for the real-world datasets, while our deep learning approach yields the best results to date for the challenge dataset.

关键词： image spam deep learning artificial intelligence and robotics Information Security

来源：评论

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Intelligent Log Analysis For Anomaly Detection

Intelligent Log Analysis For Anomaly Detection

引用

作者： Steven Yen San Jose State University

学位级别：硕士

Computer logs are a rich source of information that can be analyzed to detect various issues. The large volumes of logs limit the effectiveness of manual approaches to log analysis. The earliest automated log analysis tools take a rule-based approach, which can only detect known issues with existing rules. On the other hand, anomaly detection approaches can detect new or unknown issues. This is achieved by looking for unusual behavior different from the norm, often utilizing machine learning (ML) or deep learning (DL) models. In this project, we evaluated various ML and DL techniques used for log anomaly detection. We propose a hybrid neural network (NN) we call "CausalConvLSTM" for modeling log sequences, which takes advantage of both Convolutional Neural Network and Long Short-Term Memory Network's strengths. Furthermore, we evaluated and proposed a concrete strategy for retraining NN anomaly detection models to maintain a low false-positive rate in a drifting environment.

关键词： computer log mining anamoly detection machine learning deep learning artificial intelligence and robotics Information Security

来源：评论

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An Empirical Comparison Of Different Machine

An Empirical Comparison Of Different Machine

引用

作者： Piyush Bajaj San Jose State University

学位级别：硕士

Sketching has been used by humans to visualize and narrate the aesthetics of the world for a long time. With the onset of touch devices and augmented technologies, it has attracted more and more attention in recent years. Recognition of free-hand sketches is an extremely cumbersome and challenging task due to its abstract qualities and lack of visual cues. Most of the previous work has been done to identify objects in real pictorial images using neural networks instead of a more abstract depiction of the same objects in sketch. This research aims at comparing the performance of different machine learning algorithms and their learned inner representations. This research studies some of the famous machine learning models in classifying sketch images. It also does a study of legacy and the new datasets to classify a new sketch through various classifiers like support vector machines and the use of deep neural networks. It achieved remarkable results but still lacking behind the accuracy in the classification of the sketch images.

关键词： Sketches drawing datasets machine learning algorithms neural networks. artificial intelligence and robotics

来源：评论

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Dish: Democracy In State Houses

Dish: Democracy In State Houses

引用

作者： Nicholas A Russo Cal Poly

学位级别：硕士

In our current political climate, state level legislators have become increasingly impor- tant. Due to cuts in funding and growing focus at the national level, public oversight for these legislators has drastically decreased. This makes it difficult for citizens and activists to understand the relationships and commonalities between legislators. This thesis provides three contributions to address this issue. First, we created a data set containing over 1200 features focused on a legislatorâs activity on bills. Second, we created embeddings that represented a legislatorâs level of activity and engagement for a given bill using a custom model called Democracy2Vec. Third, we provided a case study focused on the 2015-2016 California State Legislator and had our results verified by a political expert. Our results show that our embeddings can explain relationships between legislator and how they will likely act during the legislative process.

关键词： Digital Democracy Machine Learning Embeddings Neural Networks Clusterings American Politics artificial intelligence and robotics Databases and Information Systems Software Engineering Theory and Algorithms

来源：评论

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Sentiment Analysis For Search Engine

Sentiment Analysis For Search Engine

引用

作者： Saravana Gunaseelan San Jose State University

学位级别：硕士

The chief purpose of this study is to detect and eliminate the sentiment bias in a search engine. Sentiment bias means a bias induced in the search results based on the sentiment of the userâs search query. As people increasing depend on search engines for information, it is important to understand the quality of results produced by the search engines. This study does not try to build a search engine but leverage the existing search engines to provide better results to the user. In this study, only the queries that have high sentiment polarity are analyzed and the machine learning models are used to predict the sentiment polarity of the input query, sentiment polarity of the documents produced by the search engine for the given query and also to change the sentiment polarity of the input query to its opposite sentiment. This project proposes an end-to-end system that eliminates the search engine bias by producing results that align with the query sentiment as well as the opposite sentiment. The system comprising of three models for document level sentiment analysis, aspect level sentiment analysis and sentiment style transfer. The document level sentiment analyzer is an LSTM based model that uses GloVe word embeddings to analyze the sentiment of the documents produced by the search engine. The aspect level sentiment analyzer uses deep memory network with attention and auxiliary memory to analyze the sentiment of each search query. In order to obtain the iv documents of the opposite polarity, the sentiment of the search query is reversed using the sentiment style transfer model that uses a bi-directional LSTM. The results are analyzed to determine the sentiment bias of the search engine based on the input query. In our experiments, we observed that positive sentiment queries yielded 67% documents with positive sentiment and negative sentiment queries yielded 70% documents with negative sentiment. The proposed system eliminates this bias by providing the users with

关键词： sentiment bias search results LSTM model word embeddings artificial intelligence and robotics Databases and Information Systems

来源：评论

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Detection Of Hate Speech In Videos Using Machine Learning

Detection Of Hate Speech In Videos Using Machine Learning

引用

作者： Unnathi Bhandary San Jose State University

学位级别：硕士

With the progression of the internet and social media, people are given multiple platforms to share their thoughts and opinions about various subject matters freely. However, this freedom of speech is misused to direct hate towards individuals or group of people due to their race, religion, gender etc. The rise of hate speech has led to conflicts and cases of cyber bullying, causing many organizations to look for optimal solutions to solve this problem. Developments in the field of machine learning and deep learning have piqued the interest of researchers, leading them to research and implement solutions to solve the problem of hate speech. Currently, machine learning techniques are applied to textual data to detect hate speech. With the ample use of video sharing sites, there is a need to find a way to detect hate speech in videos. This project deals with classification of videos into normal or hateful categories based on the spoken content of the videos. The video dataset is built using a crawler to search and download videos based on offensive words that are specified as keywords. The audio is extracted from the videos and is converted into textual format using a speech-to-text converter to obtain a transcript of the videos. Experiments are conducted by training four models with three different feature sets extracted from the dataset. The models are evaluated by computing the specified evaluation metrics. The evaluated metrics indicate that random forest classifier model delivers the best results in classifying videos.

关键词： hate speech detector cyber bullying random forest classifier artificial intelligence and robotics

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Pose Estimation And Action Recognition In Sports And Fitness

Pose Estimation And Action Recognition In Sports And Fitness

引用

作者： Parth Vyas San Jose State University

学位级别：硕士

The emergence of large datasets and major improvements in Deep Learning has lead to many real-world applications. These applications have been focused on automotive markets, mobile markets, stock markets, and the healthcare market. Although Deep Learning has strong foundations across many areas, the few applications in Sports, Fitness, or even Injury Rehabilitation could benefit greatly from it. For example, if you are performing a workout and you need to evaluate your form, but do not have access or resources for an instructor to evaluate your form, it would be great to have an artificial Intelligent agent provide real time feedback through your laptop or phone. Therefore our goal in this research study is to find a foundation for an exercise feedback application by comparing two computer vision models. The two approaches we will be comparing will be pose estimation and action recognition. The latter will be covered in more depth, as we will provide an end to end approach, while the former will be used as a benchmark to compare with. Action recognition will cover the collection, labeling, and organization of the data, training and integrating with real-time data to provide the user with feedback. The exercises we will focus on during our testing and analysis will be squats and push-ups. We were able to achieve an accuracy score of 79% with our best model, given a validation set of 391 squatting images from the PennAction dataset for squat exercise action recognition.

关键词： Pose Estimatation Action Recognition Deep Learning artificial intelligence and robotics Other Computer Sciences

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Glovenor - Global Vectors For Node Representations

Glovenor - Global Vectors For Node Representations

引用

作者： Shishir Kulkarni San Jose State University

学位级别：硕士

A graph is a very powerful abstract data type that can be used to model entities (nodes) and relationships (edges). Many real world networks like biological, computer and friendship networks can be represented as graphs. Graphs can be mined to extract interesting patterns and interactions between the participating entities. Recently, various artificial intelligence (AI) and Machine Learning (ML) techniques are used for this purpose. In order to do that, the nodes of a graph have to be represented as low dimensional feature vectors. Node embedding is the process of generating a ï¿½-dimensional feature vector corresponding to each node of a graph, such that the structurally similar nodes remain close in the ï¿½-dimensional space. There are many state-of-the-art methods, like node2vec and DeepWalk to com- pute node embeddings. These techniques borrow methods like the Skip-Gram model, used in the domain of Natural Language Processing (NLP) to compute word embed- dings. This project explores the idea of porting the GloVe (Global Vectors for Word Representation) model, a popular technique for word embeddings, to a new method called GloVeNoR to compute node embeddings in a graph. We evaluate the modelâs quality by comparing it with node2vec and DeepWalk on the problem of community detection on five different data sets. We observe that GloVeNoR discovers similar or better communities than the other existing models on all the datasets.

关键词： GloVE NLP DeepWalk node2vec GloVeNoR artificial intelligence and robotics Other Computer Sciences

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Image Compression Using Neural Networks

Image Compression Using Neural Networks

引用

作者： Kunal Rajan Deshmukh San Jose State University

学位级别：硕士

Image compression is a well-studied field of Computer Vision. Recently, many neural network based architectures have been proposed for image compression as well as enhancement. These networks are also put to use by frameworks such as end-to-end image compression. In this project, we have explored the improvements that can be made over this framework to achieve better benchmarks in compressing images. Generative Adversarial Networks are used to generate new fake images which are very similar to original images. Single Image Super-Resolution Generative Adversarial Networks (SI-SRGAN) can be employed to improve image quality. Our proposed architecture can be divided into four parts : image compression module, arithmetic encoder, arithmetic decoder, image reconstruction module. This ar- chitecture is evaluated based on compression rate and the closeness of the reconstructed image to the original image. Structural similarity metrics and peak signal to noise ratio are used to evaluate the image quality. We have also measured the net reduction in file size after compression and compared it with other lossy image compression techniques. We have achieved better results in terms of these metrics compared to legacy and newly proposed image compression algorithms. In particular, an average PSNR of 28.48 and SSIM value of 0.86 is achieved as compared to 28.45 PSNR and 0.81 SSIM value in end to end image compression framework [1]

关键词： Convolutional Neural Networks Generative Adversarial Networks artificial intelligence and robotics

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