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

共 112 条记录，以下是61-70 订阅

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Multifamily Malware Models

Multifamily Malware Models

引用

作者： Samanvitha Basole San Jose State University

学位级别：硕士

When training a machine learning model, there is likely to be a tradeoff between the accuracy of the model and the generality of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we obtain stronger results as compared to a case where we train a single model on multiple diverse families. During the detection phase, it would be more efficient to have a single model that could detect multiple families, rather than having to score each sample against multiple models. In this research, we conduct experiments to quantify the relationship between the generality of the training dataset and the accuracy of the resulting model within the context of the malware detection problem.

关键词： Multi-model malware detection machine learning artificial intelligence and robotics Information Security

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Toward On-Demand Profile Hidden Markov Models For Genetic Barcode Identification

Toward On-Demand Profile Hidden Markov Models For Genetic Ba...

引用

作者： Jessica Sheu San Jose State University

学位级别：硕士

Genetic identification aims to solve the shortcomings of morphological identification. By using the cytochrome c oxidase subunit 1 (COI) gene as the Eukaryotic âbarcode,â scientists hope to research species that may be morphologically ambiguous, elusive, or similarly difficult to visually identify. Current COI databases allow users to search only for existing database records. However, as the number of sequenced, potential COI genes increases, COI identification tools should ideally also be informative of novel, previously unreported sequences that may represent new species. If an unknown COI sequence does not represent a reported organism, an ideal identification tool would report taxonomic ranks to which the sequence is likely to belong. A potential solution is to dynamically create profile hidden Markov models (PHMMs): first at the genus level, then at the family level, traversing to higher taxonomic ranks until a significant score is found. This study experiments with creating PHMMs at the genus level, determining thresholds for classification, and assessing the general performance of this method and the requirements for future expansion to higher taxonomic groups. It ultimately determines that this model shows potential, but may require additional data pre-processing and may fall victim to current machine limitations.

关键词： Classification cytochrome c oxidase subunit 1 DNA barcoding genetic identification profile hidden Markov models taxonomy artificial intelligence and robotics Other Computer Sciences

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Classifying Classic Ciphers Using Machine Learning

Classifying Classic Ciphers Using Machine Learning

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作者： Nivedhitha Ramarathnam Krishna San Jose State University

学位级别：硕士

We consider the problem of identifying the classic cipher that was used to generate a given ciphertext message. We assume that the plaintext is English and we restrict our attention to ciphertext consisting only of alphabetic characters. Among the classic ciphers considered are the simple substitution, VigeneÌre cipher, playfair cipher, and column transposition cipher. The problem of classification is approached in two ways. The first method uses support vector machines (SVM) trained directly on ciphertext to classify the ciphers. In the second approach, we train hidden Markov models (HMM) on each ciphertext message, then use these trained HMMs as features for classifiers. Under this second approach, we compare two classification strategies, namely, convolutional neural networks (CNN) and SVMs. For the CNN classifier, we convert the trained HMMs into images. Extensive experimental results are provided for each of these classification techniques.

关键词： Classic Cryptography Machine Learning artificial intelligence and robotics Information Security

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Designing Single Guide Rnas For Crispr/Cas9

Designing Single Guide Rnas For Crispr/Cas9

引用

作者： Neha Atul Bhagwat San Jose State University

学位级别：硕士

Researchers have been working towards development of tools to facilitate regular use genome engineering techniques. In recent years, the focus of these efforts has been the Clustered Regularly Interspaced Short Palindromic Repeats(CRISPR)/CRISPR associated(Cas) systems. These systems, while found naturally in bacteria and archaea as an immunity mechanism, can be used for genome engineering in eukaryotes. There are three major computational challenges associated with the use of CRISPR/Cas9 in genome engineering for mammals - identification of CRISPR arrays, single guide RNA design and minimizing off-target effects. This project attempts to solve the problem of single guide RNA design using a novel approach. Researchers have been trying to solve the problem by using different machine learning classification algorithms. The algorithms have been trained to use the sequential and structural properties of single guide RNAs (sgRNAs). This project explores the use of a neural network based approach to solve the sgRNA design problem. A form of the Recurrent Neural Network (RNN) called the Long Short Term Memory (LSTM) model can be used as feature-less classification model to differentiate between functional and non-functional single guide RNAs. The project covers different experiments conducted using Support Vector Machine and Random Forest classifiers using sequential and structural features to identify the most potent sgRNAs in a given set of input sgRNAs. It also summarizes the implementation of the LSTM model and its results, along with the cross-validation results for each of these models. Through these results, it has been observed that LSTMs perform better than existing models such as Random Forest Classifiers and Support Vector Machines and give results comparable to existing tools.

关键词： CRISPR sgRNAs classification RNN artificial intelligence and robotics Other Computer Sciences

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Poriferal Vision

Poriferal Vision

引用

作者： Saketh Saxena San Jose State University

学位级别：硕士

Sponges provide nourishment as well as a habitat for various aquatic organisms. Anatomically, sponges are made up of soft tissue with a silica based exoskeleton which serves both as support and protection for the underlying tissue. The exoskeleton persists after the tissue decomposes, and microscopic parts of the exoskeleton break away to form spicules. Oceanographic studies have shown that the density of the sponge spicules is a good indicator of the sponge population in an area. This measure can be used to study sponge population dynamics over time. The spicule density is measured by imaging spicules from samples of water extracted from the oceans using an instrument called FlowCAM, which separates and photographs individual small items in a sample. It has a high processing rate, but is inefficient at computationally analyzing large numbers of photographs. Computer vision technologies, particularly deep learning using artificial Neural Networks, and Support Vector Machines have shown to be effective in handling large scale image classification problems and are the de-facto standard in image recognition problems. Typically, these models require a large amount of data to learn the underlying distribution in datasets effectively and avoid model overfitting, which is currently a challenge to procure a vast dataset of images. To mitigate this challenge and achieve the overarching purpose of developing a high- performance classifier, we demonstrate various geometrical image transformation techniques to enhance the size of the dataset. We also show initial experimental results for training Generative Adversarial Networks for artificial synthesis of spicule images. Finally, we develop a Convolutional Neural Network and compare its performance against a Support Vector Machine for classifying images of sponge spicules training both the models on the original set of images and the newly generated set of images and achieve a test accuracy of 95% with a CNN trained on the newl

关键词： artificial neural networks (ANN) sponge spicules bioinformatics computer vision deep convoluted neural networks (CNN) FlowCAM generative adversarial networks (GAN) global silica biogeochemical cycle image transformations artificial intelligence and robotics Other Computer Sciences

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Over Speed Detection Using artificial intelligence

Over Speed Detection Using Artificial Intelligence

引用

作者： Samkit Patira San Jose State University

学位级别：硕士

Over speeding is one of the most common traffic violations. Around 41 million people are issued speeding tickets each year in USA i.e one every second. Existing approaches to detect over- speeding are not scalable and require manual efforts. In this project, by the use of computer vision and artificial intelligence, I have tried to detect over speeding and report the violation to the law enforcement officer. It was observed that when predictions are done using YoloV3, we get the best results.

关键词： speeding computer vision YOLO artificial intelligence and robotics

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Yoda Â Your Only Design Assistant

Yoda Â€“ Your Only Design Assistant

引用

作者： Siddharth Kulkarni San Jose State University

学位级别：硕士

Converting user interface designs created by graphic designers into computer code is a typical job of a front end engineer in order to develop functional web and mobile applications. This conversion process can often be extremely tedious, slow and prone to human error. In this project, deep learning based object detection along with optical character recognition is used to generate platform ready prototypes directly from design sketches. Also, a new design language is introduced to facilitate expressive prototyping and allowing the creation of more expressive and functional designs. It is observed that the AI powered application along with modern web technology can significantly help streamline and automate the overall product development routine and eliminate hurdles from the product development process.

关键词： artificial intelligence (AI) machine learning (ML) user interface (UI) Convolutional Neural Networks (CNN) artificial intelligence and robotics Graphics and Human Computer Interfaces

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Deep Learning Based Real Time Devanagari Character Recognition

Deep Learning Based Real Time Devanagari Character Recogniti...

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作者： Aseem Chhabra San Jose State University

学位级别：硕士

The revolutionization of the technology behind optical character recognition (OCR) has helped it to become one of those technologies that have found plenty of uses in the entire industrial space. Today, the OCR is available for several languages and have the capability to recognize the characters in real time, but there are some languages for which this technology has not developed much. All these advancements have been possible because of the introduction of concepts like artificial intelligence and deep learning. Deep Neural Networks have proven to be the best choice when it comes to a task involving recognition. There are many algorithms and models that can be used for this purpose. This project tries to implement and optimize a deep learning-based model which will be able to recognize Devanagari scriptâs characters in real time by analyzing the hand movements.

关键词： Optical Character Recognition artificial intelligence Deep Neural Network Deep Learning Devanagari Script artificial intelligence and robotics

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Determining Political Inclination In Tweets Using Transfer Learning

Determining Political Inclination In Tweets Using Transfer L...

引用

作者： Mehtab Iqbal Georgia Southern University

学位级别：硕士

Last few years have seen tremendous development in neural language modeling for transfer learning and downstream applications. In this research, I used Howard and Ruderâs Universal Language Model Fine Tuning (ULMFiT) pipeline to develop a classifier that can determine whether a tweet is politically left leaning or right leaning by likening the content to tweets posted by @TheDemocrats or @GOP accounts on Twitter. We achieved 87.7% accuracy in predicting political ideological inclination.

关键词： Transfer learning Language Models Tweet Classification Stance Classification Computer medicated communication Deep learning artificial intelligence and robotics Social Influence and Political Communication Social Media

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Human Activity Recognition Based On Multimodal Body Sensing

Human Activity Recognition Based On Multimodal Body Sensing

引用

作者： Anish Hemant Narkhede San Jose State University

学位级别：硕士

In the recent years, human activity recognition has been widely popularized by a lot of smartphone manufacturers and fitness tracking companies. It has allowed us to gain a deeper insight into our physical health on a daily basis. However, with the evolution of fitness tracking devices and smartphones, the amount of data that is being captured by these devices is growing exponentially. This paper aims at understanding the process of dimensionality reduction such as PCA so that the data can be used to make meaningful predictions along with novel techniques using autoencoders with different activation functions. The paper also looks into how using autoencoders allows us to better capture the relations between features in the data. It also covers some of the classification techniques such as k-Nearest Neighbors, SVM and Random forest that are currently being used for activity recognition that have shown promising results.

关键词： Classification dimensionality reduction neural networks human activity recognition artificial intelligence and robotics

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