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

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Machine Learning Versus Deep Learning For Malware Detection

Machine Learning Versus Deep Learning For Malware Detection

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作者： Parth Jain San Jose State University

学位级别：硕士

It is often claimed that the primary advantage of deep learning is that such models can continue to learn as more data is available, provided that sufficient computing power is available for training. In contrast, for other forms of machine learning it is claimed that models ââsaturate,ââ in the sense that no additional learning can occur beyond some point, regardless of the amount of data or computing power available. In this research, we compare the accuracy of deep learning to other forms of machine learning for malware detection, as a function of the training dataset size. We experiment with a wide variety of hyperparameters for our deep learning models, and we compare these models to results obtained using ï¿½-nearest neighbors. In these experiments, we use a subset of a large and diverse malware dataset that was collected as part of a recent research project.

关键词： machine learning deep learning malware detection artificial intelligence and robotics Information Security

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Context-Based Multi-Stage Offline Handwritten Mathematical Symbol Recognition Using Deep Learning

Context-Based Multi-Stage Offline Handwritten Mathematical S...

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作者： Sui Kun Guan San Jose State University

学位级别：硕士

We propose a multi-stage machine learning (ML) architecture to improve the accuracy of offline handwritten mathematical symbol recognition. In the first stage, we train and assemble multiple deep convolutional neural networks to classify isolated mathematical symbols. However, certain ambiguous symbols are hard to classify without the context information of the mathematical expressions where the symbols belong. In the second stage, we train a deep convolutional neural network that further classifies the ambiguous symbols based on the context information of the symbols. To further improve the classification accuracy, in the third stage, we develop a set of rules to classify the ambiguity or otherwise the syntax of the mathematical expressions will be violated. We evaluate the proposed method by using the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) dataset. The proposed method results the state-of-the-art accuracy of 94.04%, which is 1.62% improvement compared with the previous single-stage approach.

关键词： Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME). Context-based Offline Handwritten Mathematical Recognition. Convolutional Neural Network (CNN). HAndwritten SYmbols (HASYv2). Machine Learning (ML). Multi-Column Deep Neural Network (MCDNN). artificial intelligence and robotics

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Learning For Free Â Object Detectors Trained On Synthetic Data

Learning For Free Â€“ Object Detectors Trained On Synthet...

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作者： Charles Thane MacKay San Jose State University

学位级别：硕士

A picture is worth a thousand words, or if you want it labeled, itâs worth about four cents per bounding box. Data is the fuel that powers modern technologies run by artificial intelligence engines which is increasingly valuable in todayâs industry. High quality labeled data is the most important factor in producing accurate machine learning models which can be used to make powerful predictions and identify patterns humans may not see. Acquiring high quality labeled data however, can be expensive and time consuming. For small companies, academic researchers, or machine learning hobbyists, gathering large datasets for a specific task that are not already publicly available is challenging. This research paper describes the techniques used to generate labeled image data synthetically which can be used in supervised learning for object detection. Technologies such as 3D modeling software in conjunction with Generative Adversarial Networks and image augmentation can create a realistic and diverse image dataset with bounding boxes and labels. The result of our effort is an accurate object detector in an environment of aerial surveillance with no cost to the end user. We achieved a best average precision score of 0.76 to classify and detect cars from an aerial perspective using a mix of GAN-refined data along with randomized synthetic data.

关键词： sythetic data generation image video data YOLO GAN artificial intelligence and robotics

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Data Set Generation Using Deep Learning Algorithms And Visual Feature Tracking

Data Set Generation Using Deep Learning Algorithms And Visua...

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作者： Kusuma Pallapotu Michigan Technological University

学位级别：硕士

Object detection and classification plays a major role in today's modern technology. The implementations of these concepts range from consumer products to self driving cars. These concepts largely reply on the data sets used for training these models. There is a considerable amount of effort in generating these data sets for every specific application of these algorithms. In this report, a method for generating image data sets with the use of visual feature tracking and deep learning algorithms for application in autonomous vehicles has been proposed. The aim is to reduce the time and effort dedicated towards the generation of these application specific data sets. For this purpose, a software has been developed in Python for a Linux based system using Tensorflow, Keras, Pygames and OpenCV libraries which is capable of tracking an object of interest in a given media input specified by the user along with detecting various similar objects using a pre-trained Classification neural network. This software then compiles a file containing all the annotations for the above specified objects.

关键词： Dataset generation Visual feature tracking object detection Training samples artificial intelligence and robotics Other Computer Sciences

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Brain Tumor Classification Using Hit-Or-Miss Capsule Layers

Brain Tumor Classification Using Hit-Or-Miss Capsule Layers

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作者： Chang, Spencer J California Polytechnic State University

学位级别：M.Sc., Master of Science

The job of classifying or annotating brain tumors from MRI images can be timeconsuming and difficult, even for radiologists. To increase the survival chances of a patient, medical practitioners desire a means for quick and accurate diagnosis. While datasets like CIFAR, ImageNet, and SVHN have tens of thousands, hundreds of thousands, or millions of samples, an MRI dataset may not have the same luxury of receiving accurate labels for each image containing a tumor. This work covers three models that classify brain tumors using a combination of convolutional neural networks and of the concept of capsule layers. Each network utilizes a hit-or-miss capsule layer to relate classes to capsule vectors in a one-to-one relationship. Additionally, this work proposes the use of deep active learning for picking the samples that can give the best model, PSP-HitNet, the most information when adding mini-batches of unlabeled data into the master, labeled training dataset. By using an uncertainty estimated querying strategy, PSP-HitNet approaches the best validation accuracy possible within the first 12-24% of added data from the unlabeled dataset, whereas random choosing takes until 30-50% of the unlabeled to reach the same performance.

关键词： artificial intelligence and robotics

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Improving Steering Ability Of An Autopilot In A Fully Autonomous Car

Improving Steering Ability Of An Autopilot In A Fully Autono...

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作者： Shivanku Mahna San Jose State University

学位级别：硕士

The world we live in is developing at a really rapid pace and along with it is developing the technology that we use. We have clearly come a long way from calling a car modern because it had a touch screen infotainment system to calling it modern because it drives on its own. The progress has been so rapid that it demands for us to analyze this and try to improvise a small part of this journey. With the same thought in mind, this project focuses on improvising the steering ability of an autonomous car. In order to make more sense of what is an autonomous car and what all goes on inside the working of a car that runs on its own, the thesis will be divided into a part of it explaining the theory and a part of it explaining the logic and results achieved as a part of the experiments performed on the data. It will show how an autonomous carâs CNN model can keep on becoming better as we keep on feeding more and more data into it. It will also show how a CNN model is a generalized model which can not only be used for steering a car but also to control and predict the speed of the car or the breaking ability of the car.

关键词： autonomous cars CNNs artificial intelligence and robotics

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Next Level: A Course Recommender System Based On Career Interests

Next Level: A Course Recommender System Based On Career Inte...

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作者： Shehba Shahab San Jose State University

学位级别：硕士

Skills-based hiring is a talent management approach that empowers employers to align recruitment around business results, rather than around credentials and title. It starts with employers identifying the particular skills required for a role, and then screening and evaluating candidatesâ competencies against those requirements. With the recent rise in employers adopting skills-based hiring practices, it has become integral for students to take courses that improve their marketability and support their long-term career success. A 2017 survey of over 32,000 students at 43 randomly selected institutions found that only 34% of students believe they will graduate with the skills and knowledge required to be successful in the job market. Furthermore, the study found that while 96% of chief academic officers believe that their institutions are very or somewhat effective at preparing students for the workforce, only 11% of business leaders strongly agree [11]. An implication of the misalignment is that college graduates lack the skills that companies need and value. Fortunately, the rise of skills-based hiring provides an opportunity for universities and students to establish and follow clearer classroom-to-career pathways. To this end, this paper presents a course recommender system that aims to improve studentsâ career readiness by suggesting relevant skills and courses based on their unique career interests.

关键词： Recommender Systems Collaborative Filtering Hybrid Approach Job search artificial intelligence and robotics Other Computer Sciences

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Predicting Off-Target Potential Of Crispr-Cas9 Single Guide Rna

Predicting Off-Target Potential Of Crispr-Cas9 Single Guide ...

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作者： Ishita Mathur San Jose State University

学位级别：硕士

With advancements in the field of genome engineering, researchers have come up with potential ways for site-specific gene editing. One of the methods uses the Clustered Regularly Interspaced Short Palindromic Repeats - CRISPR-Cas technology. It consists of a Cas9 nuclease and a single guide RNA (sgRNA) that cleaves the DNA at the intended target site. However, the target genome could contain multiple potential off-target sites and cleaving an off-target site can have deleterious effects in case of gene editing in humans. Lab based assays have been developed to test the off-target effects of guide RNAs. However, it is not feasible to scale these assays for reasons related to cost and labor. The use of Machine Learning models to compute the off-target potential makes these calculations cheaper and scalable. Both, classification as well as regression, can be used to solve this problem. In this project, we explore three classification models - Support Vector Machines (SVM), Logistic Regression and Convolutional Neural Networks (CNN).

关键词： CRISPR CNNs SVMs Logistic Regression artificial intelligence and robotics Other Computer Sciences

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Classification Of Humans Into Ayurvedic Prakruti Types Using Computer Vision

Classification Of Humans Into Ayurvedic Prakruti Types Using...

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作者： Gayatri Gadre San Jose State University

学位级别：硕士

Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a personâs âPrakrutiâ. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the palm, body frame, strength of digestion and many more as well as the psychological features like their nature (introverted, extroverted, calm, excitable, intense, laidback), and their reaction to stress and diseases. All these features are coded in the constituents at the time of a personâs creation and do not change throughout their lifetime. Ayurvedic doctors analyze the Prakruti of a person either by assessing the physical features manually and/or by examining the nature of their heartbeat (pulse). Based on this analysis, they diagnose, prevent and cure the disease in patients by prescribing precision medicine. This project focuses on identifying Prakruti of a person by analysing his facial features like hair, eyes, nose, lips and skin colour using facial recognition techniques in computer vision. This is the first of its kind research in this problem area that attempts to bring image processing into the domain of Ayurveda.

关键词： indian medicine image processing computer vision artificial intelligence and robotics Other Computer Sciences

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Machine Learning In Crop Classification Of Temporal Multispectral Satellite Image

Machine Learning In Crop Classification Of Temporal Multispe...

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作者： Ravali Koppaka San Jose State University

学位级别：硕士

Recently, there has been a remarkable growth in artificial intelligence (AI) with the development of efficient AI models and high-power computational resources for processing complex datasets. There has been a growing number of applications of machine learning in satellite remote sensing image data processing. In this work, machine learning methods were applied for crop classification of temporal multi- spectral satellite image to achieve better prediction of crop-wise area statistics. In India, agriculture has a huge impact on the national economy and most of the critical decisions are dependent on agricultural statistics. Sentinel-2 satellite image data for the Guntur district region of Andhra Pradesh, India has been used as the study area. The main reason for selecting this region is the diversity of agricultural crops and the availability of ground truth. As a baseline, the performance of machine learning models like Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbor (KNN) on crop classification was evaluated on 2016 single time multi-spectral satellite image. SVM performed well with an F1 score of 0.94. Further, the performance of SVM, RF, 1D and 2D Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) and RNN with Gated Recurrent Unit (GRU) on 2018 Kharif season temporal multi-spectral satellite image has been evaluated for estimation of crop areas. The results show that SVM has the best F1 score of 0.99 and achieved a 95.9% agreement with the ground surveyed crop areas.

关键词： crop classification india machine learning models satellite images artificial intelligence and robotics Other Computer Sciences

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