Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Artificial Intelligence and Robotics (248)
- Other Computer Sciences (125)
- Information Security (107)
- Databases and Information Systems (68)
- OS and Networks (38)
-
- Programming Languages and Compilers (22)
- Graphics and Human Computer Interfaces (20)
- Software Engineering (18)
- Systems Architecture (14)
- Theory and Algorithms (12)
- Data Science (7)
- Bioinformatics (4)
- Life Sciences (4)
- Arts and Humanities (1)
- Astrophysics and Astronomy (1)
- External Galaxies (1)
- Music (1)
- Numerical Analysis and Scientific Computing (1)
- Other Astrophysics and Astronomy (1)
- Other Music (1)
- Keyword
-
- Machine Learning (24)
- Machine learning (22)
- Deep Learning (16)
- Deep learning (13)
- CNNs (11)
-
- Natural Language Processing (9)
- Neural networks (8)
- SVM (8)
- CNN (7)
- Computer vision (6)
- Reinforcement Learning (6)
- BERT (5)
- Blockchain (5)
- Classification (5)
- Computer Vision (5)
- Twitter (5)
- Word2Vec (5)
- Community detection (4)
- Convolutional Neural Network (4)
- Convolutional Neural Networks (4)
- HMM (4)
- Logistic Regression (4)
- Malware (4)
- Malware classification (4)
- Malware detection (4)
- Reinforcement learning (4)
- Social networks (4)
- Visualization (4)
- Benchmarking (3)
- Bioinformatics (3)
Articles 361 - 390 of 859
Full-Text Articles in Computer Sciences
Ontology Based Technical Skill Similarity, Yeshwanth Bashyam Balachander
Ontology Based Technical Skill Similarity, Yeshwanth Bashyam Balachander
Master's Projects
Online job boards have become a major platform for technical talent procurement and job search. These job portals have given rise to challenging matching and search problems. The core matching or search happens between technical skills of the job requirements and the candidate's profile or keywords. The extensive list of technical skills and its polyonymous nature makes it less effective to perform a direct keyword matching. This results in substandard job matching or search results which misses out a closely matching candidate on account of it not having the exact skills. It is important to use a semantic similarity measure …
Feature Selection Using Genetic Algorithms, Vandana Kannan
Feature Selection Using Genetic Algorithms, Vandana Kannan
Master's Projects
With the large amount of data of different types that are available today, the number of features that can be extracted from it is huge. The ever-increasing popularity of multimedia applications, has been a major factor for this, especially in the case of image data. Image data is used for several applications such as classification, retrieval, object recognition, and annotation. Often, utilizing the entire feature set for each of these activities can be not only be time consuming but can also negatively impact the performance. Given the large number of features, it is difficult to find the subset of features …
Boosted Hidden Markov Models For Malware Detection, Aditya Raghavan
Boosted Hidden Markov Models For Malware Detection, Aditya Raghavan
Master's Projects
Digital security is an important issue today, and efficient malware detection is at the forefront of research into building secure digital systems. As with many other fields, malware detection research has seen a dramatic increase in the application of machine learning algorithms. One machine learning technique that has found widespread application in the field of pattern matching and malware detection is hidden Markov models (HMMs). Since HMM training is a hill climb technique, we can often significantly improve a model by training multiple times with different initial values. In this research, we compare boosted HMMs (using AdaBoost) to HMMs trained …
Using Sentiment Analysis And Pattern Matching To Signal User Review Abnormalities, Stefan S. Gloutnikov
Using Sentiment Analysis And Pattern Matching To Signal User Review Abnormalities, Stefan S. Gloutnikov
Master's Projects
User opinions on websites like Amazon, Yelp, and TripAdvisor are a key input for consumers when figuring out what to purchase, or where and what to eat. This means that in order for such websites to provide a better service to their customers, they must guard against fake and targeted reviews. Detecting such users and reviews automatically is a very complex multi-step process, and there is no direct mechanism for solving the problem reliably. Multiple AI and Machine Learning algorithms are coupled together when examining user reviews in determining if a review is fake or not. In this project we …
Anomaly Detection For Application Log Data, Aarish Grover
Anomaly Detection For Application Log Data, Aarish Grover
Master's Projects
In software development, there is an absolute requirement to ensure that a system once developed, functions at its best throughout its lifetime. Application log data is critical to maintaining application performance and thus techniques to parse, understand and detect anomalies in application log data are critical to ensuring efficiency in software development. While initially hampered by limited hardware and lack of quality datasets, anomaly detection techniques have recently received a surge of interest with advancements in machine learning technology and especially neural networks. In this paper, we explore anomaly detection, historical techniques to detect anomalies and recent advancements in neural …
Visual Question Answering, Pankti Kansara
Visual Question Answering, Pankti Kansara
Master's Projects
There has been immense progress in the fields of computer vision, object detection and natural language processing (NLP) in recent years. Artificial Intelligence (AI) systems, such as question answering models, use NLP to provide a "comprehensive" capability to the machine. Such a machine can answer natural language queries about any portion of an unstructured text. An extension of this system is to combine NLP with computer vision to accomplish the task of Visual Question Answering (VQA), which is to build a system that can answer natural language queries about images. A number of systems have been proposed for VQA that …
Resolving Cold Start Problem Using User Demographics And Machine Learning Techniques For Movie Recommender Systems, Sahil Motadoo
Resolving Cold Start Problem Using User Demographics And Machine Learning Techniques For Movie Recommender Systems, Sahil Motadoo
Master's Projects
There is a substantial increase in demand for recommender systems which have applications in a variety of domains. The goal of recommendations is to provide relevant choices to users. In practice, there are multiple methodologies in which recommendations take place like Collaborative Filtering (CF), Content-based filtering and Hybrid approach. For this paper, we will consider these approaches to be traditional approaches. The advantages of these approaches are in their design, functionality and efficiency. However, they do suffer from some major problems such as data sparsity, scalability and cold start to name a few. Among these problems, cold start is an …
Exploratory Data Analysis And Crime Prediction In San Francisco, Isha Pradhan
Exploratory Data Analysis And Crime Prediction In San Francisco, Isha Pradhan
Master's Projects
Crime has been prevalent in our society for a very long time and it continues to be so even today. The San Francisco Police Department has continued to register numerous such crime cases daily and has released this data to the public as a part of the open data initiative. In this paper, Big Data analysis is used on this dataset and a tool that predicts crime in San Francisco is provided. The focus of the project is to perform an in-depth analysis of the major types of crimes that occurred in the city, observe the trend over the years, …
Deep Learning Based Recommendation Systems, Nishanth Reddy Pinnapareddy
Deep Learning Based Recommendation Systems, Nishanth Reddy Pinnapareddy
Master's Projects
The usage of Internet applications, such as social networking and e-commerce is increasing exponentially, which leads to an increased offered content. Recommender systems help users filter out relevant content from a large pool of available content. The recommender systems play a vital role in today’s internet applications. Collaborative Filtering (CF) is one of the popular technique used to design recommendation systems. This technique recommends new content to users based on preferences that the user and similar users have. However, there are some shortcomings to current CF techniques, which affects negatively the performance of the recommendation models. In recent years, deep …
Aria A11y Analyzer: Helping Integrate Accessibility Into Websites, Jayashree Prabunathan
Aria A11y Analyzer: Helping Integrate Accessibility Into Websites, Jayashree Prabunathan
Master's Projects
Today, nearly 1 in 5 people have a disability that affects their daily life. These varied disabilities can include blindness, low vision or mobility impairments. When interacting with web content, users with such disabilities rely heavily on various assistive technologies, such as screen readers, keyboard, voice recognition software, etc. Here, assistive technologies are software applications or hardware devices that allows users with disabilities to interact with web and software applications. For instance, a screen reader is a software application that navigates through the page and speaks the content to users. Web accessibility is defined as the ability for assistive technology …
Metamorphic Code Generation Using Llvm, Michael Crawford
Metamorphic Code Generation Using Llvm, Michael Crawford
Master's Projects
Each instance of metamorphic software changes its internal structure, but the function remains essentially the same. Such metamorphism has been used primarily by malware writers as a means of evading signature-based detection. However, metamorphism also has potential beneficial uses in fields related to software protection. In this research, we develop a practical framework within the LLVM compiler that automatically generates metamorphic code, where the user has well-defined control over the degree of morphing applied to the code. We analyze the effectiveness of this metamorphic generator based on Hidden Markov Model (HMM) analysis, and discover that HMMs are effective at detection …
Implementation Of Faceted Values In Node.Js., Andrew Kalenda
Implementation Of Faceted Values In Node.Js., Andrew Kalenda
Master's Projects
Information flow analysis is the study of mechanisms by which developers may protect sensitive data within an ecosystem containing untrusted third-party code. Secure multi-execution is one such mechanism that reliably prevents undesirable information flows, but a programmer’s use of secure multi-execution is itself challenging and prone to error. Faceted values have been shown to provide an alternative to secure multi-execution which is, in theory, functionally equivalent. The purpose of this work is to show that the theory holds in practice by implementing usable faceted values in JavaScript via source code transformation. The primary contribution of this project is to provide …
Detecting Encrypted Malware Using Hidden Markov Models, Dhiviya Dhanasekar
Detecting Encrypted Malware Using Hidden Markov Models, Dhiviya Dhanasekar
Master's Projects
Encrypted code is often present in some types of advanced malware, while such code virtually never appears in legitimate applications. Hence, the presence of encrypted code within an executable file could serve as a strong heuristic for detecting malware. In this research, we consider the feasibility of detecting encrypted code using hidden Markov models.
Measuring The Effectiveness Of Generic Malware Models, Naman Bagga
Measuring The Effectiveness Of Generic Malware Models, Naman Bagga
Master's Projects
Malware detection based on machine learning techniques is often treated as a problem specific to a particular malware family. In such cases, detection involves training and testing models for each malware family. This approach can generally achieve high accuracy, but it requires many classification steps, resulting in a slow, inefficient, and impractical process. In contrast, classifying samples as malware or be- nign based on a single model would be far more efficient. However, such an approach is extremely challenging—extracting common features from a variety of malware fam- ilies might result in a model that is too generic to be useful. …
Word Sense Determination From Wikipedia Data Using Neural Networks, Qiao Liu
Word Sense Determination From Wikipedia Data Using Neural Networks, Qiao Liu
Master's Projects
Many words have multiple meanings. For example, “plant” can mean a type of living organism or a factory. Being able to determine the sense of such words is very useful in natural language processing tasks, such as speech synthesis, question answering, and machine translation. For the project described in this report, we used a modular model to classify the sense of words to be disambiguated. This model consisted of two parts: The first part was a neural-network-based language model to compute continuous vector representations of words from data sets created from Wikipedia pages. The second part classified the meaning of …
Multi Language Browser Support, Swapnil Mohan Patil
Multi Language Browser Support, Swapnil Mohan Patil
Master's Projects
Web browsers have become an increasingly appealing platform for application developers. Browsers make it relatively easy to deliver cross-platform applications. Web browsers have become a de facto universal operating system, and JavaScript its instruction set. Unfortunately, executing any other language than JavaScript in web browser is not usually possible. Previous approaches are either non-portable or demand extensive modifications for programs to work in the browser. Translation to JavaScript (JS) is one option but that can be challenging if the language is sufficiently different from JS. Also, debugging translated applications can be difficult. This paper presents how languages like Scheme and …
Virtualized Baseband Units Consolidation In Advanced Lte Networks Using Mobility- And Power-Aware Algorithms, Uladzimir Karneyenka
Virtualized Baseband Units Consolidation In Advanced Lte Networks Using Mobility- And Power-Aware Algorithms, Uladzimir Karneyenka
Master's Projects
Virtualization of baseband units in Advanced Long-Term Evolution networks and a rapid performance growth of general purpose processors naturally raise the interest in resource multiplexing. The concept of resource sharing and management between virtualized instances is not new and extensively used in data centers. We adopt some of the resource management techniques to organize virtualized baseband units on a pool of hosts and investigate the behavior of the system in order to identify features which are particularly relevant to mobile environment. Subsequently, we introduce our own resource management algorithm specifically targeted to address some of the peculiarities identified by experimental …
Bootbandit: A Macos Bootloader Attack, Armen Boursalian
Bootbandit: A Macos Bootloader Attack, Armen Boursalian
Master's Projects
Full disk encryption (FDE) is used to protect a computer system against data theft by physical access. If a laptop or hard disk drive protected with FDE is stolen or lost, the data remains unreadable without the encryption key. To foil this defense, an intruder can gain physical access to a computer system in a so-called “evil maid” attack, install malware in the boot (pre-operating system) environment, and use the malware to intercept the victim’s password. Such an attack relies on the fact that the system is in a vulnerable state before booting into the operating system. In this paper, …
Cache Management And Load Balancing For 5g Cloud Radio Access Networks, Chin Tsai
Cache Management And Load Balancing For 5g Cloud Radio Access Networks, Chin Tsai
Master's Projects
Cloud radio access network (CRAN) has been proposed for 5G mobile networks. The benefit of a CRAN includes better scalability, flexibility, and performance. The paper introduces a cache management algorithm for a baseband unit of CRAN and load balancing algorithms for virtual machines load within the CRAN. The proposed scheme, exponential decay (EXD) with analytical hierarchy process (AHP), increases hit rate and reduces network traffic. The scheme also provides preferential services for users with a higher service level agreement (SLA). Finally, the experiment shows the proposed load balancing algorithm can reduce the virtual machines’ (VM) queue size and wait time.
Cache Management Schemes For User Equipment Contexts In 5th Generation Cloud Radio Access Networks, Gurpreet Kaur
Cache Management Schemes For User Equipment Contexts In 5th Generation Cloud Radio Access Networks, Gurpreet Kaur
Master's Projects
Advances in cellular network technology continue to develop to address increasing demands from the growing number of devices resulting from the Internet of Things, or IoT. IoT has brought forth countless new equipment competing for service on cellular networks. The latest in cellular technology is 5th Generation Cloud Radio Access Networks, or 5G C-RAN, which consists of an architectural design created specifically to meet novel and necessary requirements for better performance, reduced latency of service, and scalability. As part of this design is the inclusion of a virtual cache, there is a necessity for useful cache management schemes and protocols, …
Time-Efficient Hybrid Approach For Facial Expression Recognition, Roshni Velluva Puthanidam
Time-Efficient Hybrid Approach For Facial Expression Recognition, Roshni Velluva Puthanidam
Master's Projects
Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of …
“Bluff” With Ai, Tina Philip
“Bluff” With Ai, Tina Philip
Master's Projects
The goal of this project is to build multiple agents for the game Bluff and to conduct experiments as to which performs better. Bluff is a multi-player, non-deterministic card game where players try to get rid of all the cards in their hand. The process of bluffing involves making a move such that it misleads the opponent and thus prove to be of advantage to the player. The strategic complexity in the game arises due to the imperfect or hidden information which means that certain relevant details about the game are unknown to the players. Multiple agents followed different strategies …
A Scrabble Artificial Intelligence Game, Priyatha Joji Abraham
A Scrabble Artificial Intelligence Game, Priyatha Joji Abraham
Master's Projects
Computer AI players have already surpassed human opponents in competitive Scrabble, however, defeating a Computer AI opponent is complex and demands efficient heuristics. The primary objective of this project is to build two intelligent AI players from scr atch for the Scrabble cross - board puzzle game having different move generation heuristics and endgame strategies to evaluate their performance based on various benchmarks like winning criteria, quality of moves, and time consumption. The first AI selected is the most popular Scrabble AI, Maven. It generates a three - ply look - ahead simulation to evaluate the most promising candidate move …
Improve And Implement An Open Source Question Answering System, Salil Shenoy
Improve And Implement An Open Source Question Answering System, Salil Shenoy
Master's Projects
A question answer system takes queries from the user in natural language and returns a short concise answer which best fits the response to the question. This report discusses the integration and implementation of question answer systems for English and Hindi as part of the open source search engine Yioop. We have implemented a question answer system for English and Hindi, keeping in mind users who use these languages as their primary language. The user should be able to query a set of documents and should get the answers in the same language. English and Hindi are very different when …
Question Type Recognition Using Natural Language Input, Aishwarya Soni
Question Type Recognition Using Natural Language Input, Aishwarya Soni
Master's Projects
Recently, numerous specialists are concentrating on the utilization of Natural Language Processing (NLP) systems in various domains, for example, data extraction and content mining. One of the difficulties with these innovations is building up a precise Question and Answering (QA) System. Question type recognition is the most significant task in a QA system, for example, chat bots. Organization such as National Institute of Standards (NIST) hosts a conference series called as Text REtrieval Conference (TREC) series which keeps a competition every year to encourage and improve the technique of information retrieval from a large corpus of text. When a user …
Improving Text Classification With Word Embedding, Lihao Ge
Improving Text Classification With Word Embedding, Lihao Ge
Master's Projects
One challenge in text classification is that it is hard to make feature reduction basing upon the meaning of the features. An improper feature reduction may even worsen the classification accuracy. Word2Vec, a word embedding method, has recently been gaining popularity due to its high precision rate of analyzing the semantic similarity between words at relatively low computational cost. However, there are only a limited number of researchers focusing on feature reduction using Word2Vec. In this project, we developed a Word2Vec based method to reduce the feature size while increasing the classification accuracy. The feature reduction is achieved by loosely …
Housing Price Prediction Using Support Vector Regression, Jiao Yang Wu
Housing Price Prediction Using Support Vector Regression, Jiao Yang Wu
Master's Projects
The relationship between house prices and the economy is an important motivating factor for predicting house prices. Housing price trends are not only the concern of buyers and sellers, but it also indicates the current economic situation. Therefore, it is important to predict housing prices without bias to help both the buyers and sellers make their decisions. This project uses an open source dataset, which include 20 explanatory features and 21,613 entries of housing sales in King County, USA. We compare different feature selection methods and feature extraction algorithm with Support Vector Regression (SVR) to predict the house prices in …
An Open Source Discussion Group Recommendation System, Sarika Padmashali
An Open Source Discussion Group Recommendation System, Sarika Padmashali
Master's Projects
A recommendation system analyzes user behavior on a website to make suggestions about what a user should do in the future on the website. It basically tries to predict the “rating” or “preference” a user would have for an action. Yioop is an open source search engine, wiki system, and user discussion group system managed by Dr. Christopher Pollett at SJSU. In this project, we have developed a recommendation system for Yioop where users are given suggestions about the threads and groups they could join based on their user history. We have used collaborative filtering techniques to make recommendations and …
Neural Net Stock Trend Predictor, Sonal Kabra
Neural Net Stock Trend Predictor, Sonal Kabra
Master's Projects
This report analyzes new and existing stock market prediction techniques. Traditional technical analysis was combined with various machine-learning approaches such as artificial neural networks, k-nearest neighbors, and decision trees. Experiments we conducted show that technical analysis together with machine learning can be used to profitably direct an investor’s trading decisions. We are measuring the profitability of experiments by calculating the percentage weekly return for each stock entity under study. Our algorithms and simulations are developed using Python. The technical analysis methodology combined with machine learning algorithms show promising results which we discuss in this report.
Predicting Pancreatic Cancer Using Support Vector Machine, Akshay Bodkhe
Predicting Pancreatic Cancer Using Support Vector Machine, Akshay Bodkhe
Master's Projects
This report presents an approach to predict pancreatic cancer using Support Vector Machine Classification algorithm. The research objective of this project it to predict pancreatic cancer on just genomic, just clinical and combination of genomic and clinical data. We have used real genomic data having 22,763 samples and 154 features per sample. We have also created Synthetic Clinical data having 400 samples and 7 features per sample in order to predict accuracy of just clinical data. To validate the hypothesis, we have combined synthetic clinical data with subset of features from real genomic data. In our results, we observed that …