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

共 112 条记录，以下是71-80 订阅

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Classification Of Stars From Redshifted Stellar Spectra Utilizing Machine Learning

Classification Of Stars From Redshifted Stellar Spectra Util...

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作者： Michael J. Brice Central Washington University

学位级别：硕士

The classification of stellar spectra is a fundamental task in stellar astrophysics. There have been many explorations into the automated classification of stellar spectra but few that involve the Sloan Digital Sky Survey (SDSS). Stellar spectra from the SDSS are applied to standard classification methods such as K-Nearest Neighbors, Random Forest, and Support Vector Machine to automatically classify the spectra. Stellar spectra are high dimensional data and the dimensionality is reduced using standard Feature Selection methods such as Chi-Squared and Fisher score and with domain-specific astronomical knowledge because classifiers work in low dimensional space. These methods are utilized to classify the stellar spectra into the two standard star classification schemes, the Harvard Spectral Classification and the Morgan Keenan Luminosity Classes. If a star is classified into both of these schemes, many stellar properties can be approximated with ease, whereas the direct approaches can take up to months of observations. A physical phenomenon known as redshift causes machine learning complications through the feature matrix when classifying stellar spectra. However, classifiers utilizing redshifted stellar spectra performed with high accuracy. An approach for extracting redshift using predictions from the classification models is explored.

关键词： Stellar Spectra Classification Star Classification Redshift Machine Learning Feature Selection Sloan Digital Sky Survey Random Forest Support Vector Machine K-Nearest Neighbors artificial intelligence and robotics Numerical Analysis and Scientific Computing Stars, Interstellar Medium and the Galaxy

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Speaker Recognition Using Machine Learning Techniques

Speaker Recognition Using Machine Learning Techniques

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作者： Abhishek Manoj Sharma San Jose State University

学位级别：硕士

Speaker recognition is a technique of identifying the person talking to a machine using the voice features and acoustics. It has multiple applications ranging in the fields of Human Computer Interaction (HCI), biometrics, security, and Internet of Things (IoT). With the advancements in technology, hardware is getting powerful and software is becoming smarter. Subsequently, the utilization of devices to interact effectively with humans and performing complex calculations is also increasing. This is where speaker recognition is important as it facilitates a seamless communication between humans and computers. Additionally, the field of security has seen a rise in biometrics. At present, multiple biometric techniques co-exist with each other, for instance, iris, fingerprint, voice, facial, and more. Voice is one metric which apart from being natural to the users, provides comparable and sometimes even higher levels of security when compared to some traditional biometric approaches. Hence, it is a widely accepted form of biometric technique and is constantly being studied by scientists for further improvements. This study aims to evaluate different pre-processing, feature extraction, and machine learning techniques on audios recorded in unconstrained and natural environments to determine which combination of these works well for speaker recognition and classification. Thus, the report presents several methods of audio pre- processing like trimming, split and merge, noise reduction, and vocal enhancements to enhance the audios obtained from real-world situations. Additionally, a text-independent approach is used in this research which makes the model flexible to multiple languages. Mel Frequency Cepstral Coefficients (MFCC) are extracted for each audio, along with their differentials and accelerations to evaluate machine learning classification techniques such as kNN, Support Vector Machines, and Random Forest Classifiers. Lastly, the approaches are evaluated against exi

关键词： Speaker recognition human computer interaction biometrics internet of things mel frequency cepstral coefficients artificial intelligence and robotics

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Benchmarking Optimization Algorithms For Capacitated Vehicle Routing Problems

Benchmarking Optimization Algorithms For Capacitated Vehicle...

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作者： Pratik Surana San Jose State University

学位级别：硕士

The Vehicle Routing Problem (VRP) originated in the 1950s when algorithms and mathematical approaches were applied to find solutions for routing vehicles. Since then, there has been extensive research in the field of VRPs to solve real-life problems. The process of generating an optimal routing schedule for a VRP is complex due to two reasons. First, VRP is considered to be an NP-Hard problem. Second, there are several constraints involved, such as the number of available vehicles, the vehicle capacities, time-windows for pickup or delivery etc. The main goal for this project was to compare different optimization algorithms for solving Capacitated Vehicle Routing Problems (CVRP). The three specific aims for this project were to (1) survey existing research and identify suitable optimization algorithms for CVRP and (2) implement a work-flow in the Python programming lan- guage, to evaluate their performance, (3) perform different computational experiments on existing CVRP benchmarks. Experiments were conducted by leveraging Googleâs OR-Tools library on the well-known benchmarks. Different strategies were evaluated to see if there exists a solution or a better solution than the best-known solutions for these benchmarks. The results show that almost 60% of the problems in the benchmarks have a better solution than the current best-known solution. The second finding of this project is that there is not one strategy which can provide the best solution for all types of CVRPs.

关键词： Capacitated Vehicle Routing Problem Benchmarking OR-Tools artificial intelligence and robotics Other Computer Sciences

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Smartphone Gesture-Based Authentication

Smartphone Gesture-Based Authentication

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作者： Preethi Sundaravaradhan San Jose State University

学位级别：硕士

In this research, we consider the problem of authentication on a smartphone based on gestures, that is, movements of the phone. Accelerometer data from a number of subjects was collected and we analyze this data using a variety of machine learning techniques, including support vector machines (SVM) and convolutional neural networks (CNN). We analyze both the fraud rate (or false accept rate) and insult rate (or false reject rate) in each case.

关键词： Gesture Based Authentication CNNs SVMs artificial intelligence and robotics Graphics and Human Computer Interfaces Information Security

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Sql Injection Detection Using Machine Learning

Sql Injection Detection Using Machine Learning

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作者： Sonali Mishra San Jose State University

学位级别：硕士

Sharing information over the Internet over multiple platforms and web-applications has become a quite common phenomenon in the recent times. The web-based applications that accept critical information from users store this information in databases. These applications and the databases connected to them are susceptible to all kinds of information security threats due to being accessible through the Internet. The threats include attacks such as Cross Side Scripting (CSS), Denial of Service Attack (DoS0, and Structured Query Language (SQL) Injection attacks. SQL Injection attacks fall under the top ten vulnerabilities when we talk about web-based applications. Through this kind of attack, the attacker can steal critical and confidential information and hence it could have damaging effects on a business or organization. The effects could range from monetary loss, leaking confidential business information, decrease in companyâs stock market value or any combination of these. In this paper we have used an algorithm called Gradient Boosting Classifier from ensemble machine learning approaches to classify and detect SQL Injection attacks.

关键词： SQL Injection Attack Machine Learning Classifier artificial intelligence and robotics Information Security

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Masquerade Detection In Automotive Security

Masquerade Detection In Automotive Security

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作者： Ashraf Saber San Jose State University

学位级别：硕士

In this paper, we consider intrusion detection systems (IDS) in the context of a controller area network (CAN), which is also known as the CAN bus. We provide a discussion of various IDS topics, including masquerade detection, and we include a selective survey of previous research involving IDS in a CAN network. We also discuss background topics and relevant practical issues, such as data collection on the CAN bus. Finally, we present experimental results where we have applied a variety of machine learning techniques to CAN data. We use both actual and simulated data in order to detect the status of a vehicle from its network packets as well as detect masquerade behavior on a vehicle network.

关键词： Intrusion Detection CAN bus masquerade detection artificial intelligence and robotics Other Computer Sciences

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Robust Lightweight Object Detection

Robust Lightweight Object Detection

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作者： Siddharth Kumar San Jose State University

学位级别：硕士

Object detection is a very challenging problem in computer vision and has been a prominent subject of research for nearly three decades. There has been a promising in- crease in the accuracy and performance of object detectors ever since deep convolutional networks (CNN) were introduced. CNNs can be trained on large datasets made of high resolution images without flattening them, thereby using the spatial information. Their superior learning ability also makes them ideal for image classification and object de- tection tasks. Unfortunately, this power comes at the big cost of compute and memory. For instance, the Faster R-CNN detector required 180 billion FLOPs for training, and has over 100 million parameters. In this project, we explore the popular state-of-the-art object detectors and present their contributions and shortcomings. Then we explore the recent lightweight detectors which try to address the issue of high resource requirements by building leaner models. Building upon the contributions of the state-of-the-art object detectors, and recent de- velopments in CNN training, we propose our own lightweight detector. We proposed a novel CNN block, to improve the inter-channel dependency in feature maps, called the inter-channel dependency block (ICDB). Through experiments on benchmark datasets we demonstrated our model attains better accuracy compared to the previous methods. Three benchmarking datasets PASCAL VOC 2007, KITTI and COCO have been used to demonstrate that our model scales well to different scenarios.

关键词： Convolutional neural networks deep learning Faster R-CNN neural networks object detection SSD YOLO artificial intelligence and robotics

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Ai Dining Suggestion App

Ai Dining Suggestion App

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作者： Bao Pham San Jose State University

学位级别：硕士

Trying to decide what to eat can sometimes be challenging and time-consuming for people. Google and Yelp have large scale data sets of restaurant information as well as Application Program Interfaces (APIs) for using them. This restaurant data includes time, price range, traffic, temperature, etc. The goal of this project is to build an app that eases the process of finding a restaurant to eat. This app has a Tinder-like user friendly User Interface (UI) design to change the common way that lists of restaurants are presented to users on mobile apps. It also uses the help of artificial intelligence (AI) with neural networks to train both supervised and unsupervised learning models that can learn from one's dining pattern over time to make better suggestions at any time.

关键词： Food Recommendation System Reinforcement Learning artificial intelligence and robotics Other Computer Sciences

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Using Computer Vision To Quantify Coral Reef Biodiversity

Using Computer Vision To Quantify Coral Reef Biodiversity

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作者： Niket Bhodia San Jose State University

学位级别：硕士

The preservation of the worldâs oceans is crucial to human survival on this planet, yet we know too little to begin to understand anthropogenic impacts on marine life. This is especially true for coral reefs, which are the most diverse marine habitat per unit area (if not overall) as well as the most sensitive. To address this gap in knowledge, simple field devices called autonomous reef monitoring structures (ARMS) have been developed, which provide standardized samples of life from these complex ecosystems. ARMS have now become successful to the point that the amount of data collected through them has outstripped the capacity of research organizations to analyze through molecular methods. To facilitate these efforts, the present study explores the use of computer vision techniques to analyze the complex image data of these samples in order to extract useful information based on morphological (visual) characteristics of the collected organisms. Various techniques at varying levels of sophistry are surveyed for their suitability to the present problem. In the end, the more complex techniques are ruled out in the favor of basic image processing ones, of which three are tested: canny edge detection, color space transformations, and histogram equalization. While the first one does not directly yield useful results, the latter two turn out to be surprisingly effective, showing great promise as means to prepare data that more sophisticated techniques can be subsequently trained on. Future directions of investigation are recorded in detail, along with suggestions and relevant references, towards ultimately realizing an online analysis tool and repository for marine life that would accelerate related research and conservation efforts.

关键词： computer vision OpenCV deep learning machine learning unsupervised learning artificial neural networks image processing autonomous reef monitoring structures coral reef biodiversity Census of Marine Life artificial intelligence and robotics Other Computer Sciences

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Graph Classification Using Machine Learning Algorithms

Graph Classification Using Machine Learning Algorithms

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作者： Monica Golahalli Seenappa San Jose State University

学位级别：硕士

In the Graph classification problem, given is a family of graphs and a group of different categories, and we aim to classify all the graphs (of the family) into the given categories. Earlier approaches, such as graph kernels and graph embedding techniques have focused on extracting certain features by processing the entire graph. However, real world graphs are complex and noisy and these traditional approaches are computationally intensive. With the introduction of the deep learning framework, there have been numerous attempts to create more efficient classification approaches. For this project, we will be focusing on modifying an existing kernel graph convo- lutional neural network approach. Moreover, subgraphs (patches) are extracted from the graph using a community detection algorithm. These patches are provided as input to a graph kernel and max pooling is applied. We will be experimenting with different commu- nity detection algorithms and graph kernels and compare their efficiency and performance. For the experiments, we use eight publicly available real world datasets, ranging from bi- ological to social networks. Additionally, for these datasets we provide results using a baseline algorithm and a spectral decomposition of Laplacian graph for comparison pur- poses.

关键词： Graph Kernels Convolutional Neural Network Community detec- tion Spectral decomposition artificial intelligence and robotics

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