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Articles 331 - 360 of 859
Full-Text Articles in Computer Sciences
Analyzing Android Adware, Supraja Suresh
Analyzing Android Adware, Supraja Suresh
Master's Projects
Most Android smartphone apps are free; in order to generate revenue, the app developers embed ad libraries so that advertisements are displayed when the app is being used. Billions of dollars are lost annually due to ad fraud. In this research, we propose a machine learning based scheme to detect Android adware based on static and dynamic features. We collect static features from the manifest file, while dynamic features are obtained from network traffic. Using these features, we initially classify Android applications into broad categories (e.g., adware and benign) and then further classify each application into a more specific family. …
A Convolutional Neural Network Based Approach For Visual Question Answering, Lavanya Abhinaya Koduri
A Convolutional Neural Network Based Approach For Visual Question Answering, Lavanya Abhinaya Koduri
Master's Projects
Computer Vision is a scientific discipline which involves the development of an algorithmic basis for the construction of intelligent systems that aim at analysis, understanding and extraction of useful information from visual data. This visual data can be plain images, video sequences, views from multiple cameras, etc. Natural Language Processing (NLP), is the ability of machines to read and understand human languages. Visual Question Answering (VQA), is a multi-discipline Artificial Intelligence (AI) research problem, which is a combination of Natural Language Processing (NLP), Computer Vision (CV), and Knowledge Reasoning (KR). Given an image and a question related to the image …
Image Robust Hashing For Malware Detection, Wei-Chung Huang
Image Robust Hashing For Malware Detection, Wei-Chung Huang
Master's Projects
This research is focused on a novel approach to detect malware based on static analysis of executable files. Specifically, we treat each executable file as a twodimensional image and use robust hashing techniques to identify whether a given executable belongs to a particular family or not. The hashing stage comprises two steps, namely, feature extraction, and compression. We compare our robust hashing approach to other machine learning-based techniques.
Pe Header Analysis For Malware Detection, Samuel Kim
Pe Header Analysis For Malware Detection, Samuel Kim
Master's Projects
Recent research indicates that effective malware detection can be implemented based on analyzing portable executable (PE) file headers. Such research typically relies on prior knowledge of the header to extract relevant features. However, it is also possible to consider the entire header as a whole, and use this directly to determine whether the file is malware. In this research, we collect a large and diverse malware data set. We then analyze the effectiveness of various machine learning techniques based on PE headers to classify the malware samples. We compare the accuracy and efficiency of each technique considered.
Automated Lyrical Narrative Writing, Divya Singh
Automated Lyrical Narrative Writing, Divya Singh
Master's Projects
Computational Creativity studies the potential of computers to act as autonomous creators and co-creators in addition to tools helping people. Creativity is evident in music, visual art, problem solving and languages. Significant work has been conducted in the area of linguistic creation mainly in the generation of stories, puns, rhymes, jokes, similes, and poetry. One of the major challenges of computational creativity is to generate lyrics that exhibit human-level creativity. On one hand, the lyrics generated should be meaningful and coherent, while on the other hand, they should satisfy poetry constraints such as rhyme scheme, rhyme type, and the number …
Modeling Human Migration Dynamics In Netlogo, Vikram Deshmukh
Modeling Human Migration Dynamics In Netlogo, Vikram Deshmukh
Master's Projects
Human Migration has often been the catalyst for the rise and fall of civilizations. It is imperative to study human migration dynamics if one is to gain insights into migratory behavior among human beings and how migration affects societies. There has been considerable research to study migration. This has given rise to some popular migration theories like the neoclassical approach, network migration, pull-push migration, etc. These theories shed light on some peculiar behaviors that influence the migration decision of an individual or a group, while also trying to predict the outcome of such actions. The goal of this project is …
Speech Emotion Detection Using Machine Learning Techniques, Neethu Sundarprasad
Speech Emotion Detection Using Machine Learning Techniques, Neethu Sundarprasad
Master's Projects
Communication is the key to express one’s thoughts and ideas clearly. Amongst all forms of communication, speech is the most preferred and powerful form of communications in human. The era of the Internet of Things (IoT) is rapidly advancing in bringing more intelligent systems available for everyday use. These applications range from simple wearables and widgets to complex self-driving vehicles and automated systems employed in various fields. Intelligent applications are interactive and require minimum user effort to function, and mostly function on voice-based input. This creates the necessity for these computer applications to completely comprehend human speech. A speech percept …
Deep Learning For Chatbots, Vyas Ajay Bhagwat
Deep Learning For Chatbots, Vyas Ajay Bhagwat
Master's Projects
Natural Language Processing (NLP) requires modelling complex relationships between the semantics of the language. While traditional machine learning techniques are used for NLP, the models built for conversations, called chatbots, are unable to be truly generic. While chatbots have been made with traditional machine learning techniques, deep learning has allowed the complexities within NLP to be easier to model and can be leveraged to build a chatbot which has a real conversation with a human. In this project, we explore the problems and techniques used to build chatbots and where improvements can be made. We analyze different architectures to build …
Validating Key-Value Based Implementations Of The Raft Consensus Algorithm For Distributed Systems, Deepthi Vishwanath
Validating Key-Value Based Implementations Of The Raft Consensus Algorithm For Distributed Systems, Deepthi Vishwanath
Master's Projects
Distributed systems are a group of systems connected via a network, all working towards achieving a common goal. To achieve fault tolerance and reliability, all the systems should work towards achieving consensus. Paxos is the most widely used consensus algorithm since 2 or 3 decades, but the shift is now happening towards a new algorithm known as Raft. Raft is a consensus algorithm (paper published in the year 2014) which is easier to understand and works like Paxos in terms of fault tolerance and performance. Since Raft is new, there is a need for a tool that verifies systems built …
Facial Emotion Recognition Using Machine Learning, Nitisha Raut
Facial Emotion Recognition Using Machine Learning, Nitisha Raut
Master's Projects
Face detection has been around for ages. Taking a step forward, human emotion displayed by face and felt by brain, captured in either video, electric signal (EEG) or image form can be approximated. Human emotion detection is the need of the hour so that modern artificial intelligent systems can emulate and gauge reactions from face. This can be helpful to make informed decisions be it regarding identification of intent, promotion of offers or security related threats. Recognizing emotions from images or video is a trivial task for human eye, but proves to be very challenging for machines and requires many …
Load Balancing And Virtual Machine Allocation In Cloud-Based Data Centers, Saily Satish Ghodke
Load Balancing And Virtual Machine Allocation In Cloud-Based Data Centers, Saily Satish Ghodke
Master's Projects
As cloud services see an exponential increase in consumers, the demand for faster processing of data and a reliable delivery of services becomes a pressing concern. This puts a lot of pressure on the cloud-based data centers, where the consumers’ data is stored, processed and serviced. The rising demand for high quality services and the constrained environment, make load balancing within the cloud data centers a vital concern. This project aims to achieve load balancing within the data centers by means of implementing a Virtual Machine allocation policy, based on consensus algorithm technique. The cloud-based data center system, consisting of …
Stock Price Prediction Using Deep Learning, Abhinav Tipirisetty
Stock Price Prediction Using Deep Learning, Abhinav Tipirisetty
Master's Projects
Stock price prediction is one among the complex machine learning problems. It depends on a large number of factors which contribute to changes in the supply and demand. This paper presents the technical analysis of the various strategies proposed in the past, for predicting the price of a stock, and evaluation of a novel approach for the same. Stock prices are represented as time series data and neural networks are trained to learn the patterns from trends. Along with the numerical analysis of the stock trend, this research also considers the textual analysis of it by analyzing the public sentiment …
Intrusion Detection In Containerized Environments, Shyam Sundar Durairaju
Intrusion Detection In Containerized Environments, Shyam Sundar Durairaju
Master's Projects
In this paper, we present the results of using Hidden Markov Models for learning the behavior of Docker containers. This is for use in anomaly-detection based intrusion detection system. Containers provide isolation between the host system and the containerized environment by efficiently packaging applications along with their dependencies. This way, containers become a portable software environment for applications to run and scale. Unlike virtual machines, containers share the same kernel as the host operating system. This is leveraged to monitor the system calls of the container from the host system for anomaly detection. Thus, the monitoring system is not required …
Subtopics In Yelp Reviews, Riya Suchdev
Subtopics In Yelp Reviews, Riya Suchdev
Master's Projects
Yelp is a review platform that connects people to local businesses. It is a very popular platform that helps customers decide which business to choose. It relies on crowd sourced plain text reviews. From the business’s description some facts can be determined, such as category and location. However, more detailed description can be extracted from the reviews. Discovering latent topics and subtopics in Yelp reviews, can help summarize the reviews to gain knowledge. For example, we can deduce that reviews related to the Restaurant category tend to emphasize on service, food, order etc. Additionally, one can deduce positive or negative …
Bitcoin Transaction Fee Estimation Using Mempool State And Linear Perceptron Machine Learning Algorithm, Abdullah Al-Shehabi
Bitcoin Transaction Fee Estimation Using Mempool State And Linear Perceptron Machine Learning Algorithm, Abdullah Al-Shehabi
Master's Projects
Bitcoin, the world’s most valued cryptocurrency, uses a network of computers across the globe to create an immutable transaction record on a public ledger known as the blockchain. The blockchain consists of a series of timestamped blocks, where each block contains a series of transactions selected for inclusion in the block, generally based on how high of a fee the transaction allocates to the party responsible for confirming the transaction. Estimating an appropriate fee for Bitcoin transactions is a challenge for many transacting parties using Bitcoin as a digital currency. This work aims to help Bitcoin users save funds in …
Image Spam Classification Using Deep Learning, Ajay Pal Singh
Image Spam Classification Using Deep Learning, Ajay Pal Singh
Master's Projects
Image classification is a fundamental problem of computer vision and pattern recognition. Spam is unwanted bulk content and image spam is unwanted content embedded inside the images. Image spam creates threat to the email based communication systems. Nowadays, a lot of unsolicited content is circulated over the internet. While a lot of machine learning techniques are successful in detecting textual based spam, this is not the case for image spams, which can easily evade these textual-spam detection systems. In this project, we explore and evaluate four deep learning techniques that detect image spams. First, we study neural networks and the …
Deep Learning Algorithm Recommender, Rajat Kabra
Deep Learning Algorithm Recommender, Rajat Kabra
Master's Projects
Deep learning contains a set of algorithms that are based on the functioning of human brain i.e. neural networks. These algorithms require a lot of computation power and time along with complex setup to get good results. The project contains several artificial neural network implementation for a variety of tasks like data classification, image classification, natural language processing and more. The project contains an exploratory analysis of hyperparameters of deep learning algorithms in domain of deep learning applications to prove that it is possible to achieve a good accuracy with less resources.
Sql Injection Detection Using Machine Learning Techniques And Multiple Data Sources, Kevin Ross
Sql Injection Detection Using Machine Learning Techniques And Multiple Data Sources, Kevin Ross
Master's Projects
SQL Injection continues to be one of the most damaging security exploits in terms of personal information exposure as well as monetary loss. Injection attacks are the number one vulnerability in the most recent OWASP Top 10 report, and the number of these attacks continues to increase. Traditional defense strategies often involve static, signature-based IDS (Intrusion Detection System) rules which are mostly effective only against previously observed attacks but not unknown, or zero-day, attacks. Much current research involves the use of machine learning techniques, which are able to detect unknown attacks, but depending on the algorithm can be costly in …
A Neural Network Classifier For The Coi Barcode Gene, Saurabh Marathe
A Neural Network Classifier For The Coi Barcode Gene, Saurabh Marathe
Master's Projects
Mitochondrial Cytochrome C Oxidase subunit I (CO I – to be read as “see – oh one”) is a 658 base pair region in the gene encoding that is proposed as standard barcode for animals. Meaning, the CO I is a special region found in animal DNA that is studied to identify the species of the animal. Currently, there is an implementation of an algorithm called ARBitrator which identifies and extracts these CO I sequences from enormous genes database called GenBank. The ARBitrator is good at extracting the CO I sequences that have better specificity and accuracy as compared to …
Joint Computation Offloading And Prioritized Scheduling In Mobile Edge Computing, Lingfang Gao
Joint Computation Offloading And Prioritized Scheduling In Mobile Edge Computing, Lingfang Gao
Master's Projects
With the rapid development of smart phones, enormous amounts of data are generated and usually require intensive and real-time computation. Nevertheless, quality of service (QoS) is hardly to be met due to the tension between resourcelimited (battery, CPU power) devices and computation-intensive applications. Mobileedge computing (MEC) emerging as a promising technique can be used to copy with stringent requirements from mobile applications. By offloading computationally intensive workloads to edge server and applying efficient task scheduling, energy cost of mobiles could be significantly reduced and therefore greatly improve QoS, e.g., latency. This paper proposes a joint computation offloading and prioritized task …
Machine Learning Playground, Adil Khan
Machine Learning Playground, Adil Khan
Master's Projects
Machine learning is a science that “learns” about the data by finding unique patterns and relations in the data. There are a lot of libraries or tools available for processing machine learning datasets. You can upload your dataset in seconds and quickly start using these tools to get prediction results in a few minutes. However, generating an optimal model is a time consuming and tedious task. The tunable parameters (hyper-parameters) of any machine learning model may greatly affect the accuracy metrics. While most of the tools have models with default parameter setting to provide good results, they can often fail …
Mandala Generation From Brainwave With Feedforward, Kumari Anamika Sharaf
Mandala Generation From Brainwave With Feedforward, Kumari Anamika Sharaf
Master's Projects
Most experiments conducted in the early 1900s with Electroencephalography (EEG) [10] devices explored mental illness of the participants. Historically, EEG has had specific applications to diagnose sleep disorder, epilepsy, coma and brain death. Today, EEG devices are used extensively for research purposes [10], especially in the field of neuroscience. Traditionally, most experiments included a human participant wherein an EEG device was connected to the subject’s forehead to detect electrical impulses indicating different brainwaves. Each brainwave implied a different emotional state of mind. Past experiments [2] [3] [4] then used the brainwave signals as input to build audio/visual art to aid …
Image To Latex Via Neural Networks, Avinash More
Image To Latex Via Neural Networks, Avinash More
Master's Projects
Many research papers in mathematics, computer science, and physics are written in LaTeX. Technical papers and articles in these areas often involve mathematical equations. Writing such equations in LaTeX takes longer than handwriting the same equations on paper. In this report, we want to show that the time-consuming process of typesetting LaTeX equations from images of these equations can be automated and optimized. Neural networks are good at solving related problems such as handwritten digit recognition, so we adapted these well-studied approaches to the LaTeX problem. Neural network model training involves large amounts of good quality data. So, for our …
Genetic Barcode Identification With Profile Hidden Markov Models, Vishrut Sharma
Genetic Barcode Identification With Profile Hidden Markov Models, Vishrut Sharma
Master's Projects
DNA barcoding is a method that uses an organism’s DNA to identify its species. The gene cytochrome c oxidase I (COI) has been used effectively as a DNA barcode to identify organisms and elucidate relationships among species [1]. There also exists a database BOLD (Barcode Of Life Database) that contains COI sequences used for DNA barcoding for more than 1 million different species. Using BOLD to identify samples that have a match in the database is an uncomplicated process. However, this method fails to determine samples that are absent from the database. Given a sample that is not represented in …
Outfit Recommender System, Nikita Ramesh
Outfit Recommender System, Nikita Ramesh
Master's Projects
The online apparel retail market size in the United States is worth about seventy-two billion US dollars. Recommendation systems on retail websites generate a lot of this revenue. Thus, improving recommendation systems can increase their revenue. Traditional recommendations for clothes consisted of lexical methods. However, visual-based recommendations have gained popularity over the past few years. This involves processing a multitude of images using different image processing techniques. In order to handle such a vast quantity of images, deep neural networks have been used extensively. With the help of fast Graphics Processing Units, these networks provide results which are extremely accurate, …
Optimal Constrained Wireless Emergency Network Antennae Placement, Swapnil Mohan Gaikwad
Optimal Constrained Wireless Emergency Network Antennae Placement, Swapnil Mohan Gaikwad
Master's Projects
With increasing number of mobile devices, newly introduced smart devices, and the Internet of things (IoT) sensors, the current microwave frequency spectrum is getting rapidly congested. The obvious solution to this frequency spectrum congestion is to use millimeter wave spectrum ranging from 6 GHz to 300 GHz. With the use of millimeter waves, we can enjoy very high communication speeds and very low latency. But, this technology also introduces some challenges that we hardly faced before. The most important one among these challenges is the Line of Sight (LOS) requirement. In the emergent concept of smart cities, the wireless emergency …
Analysis Of Encrypted Malicious Traffic, Anish Singh Shekhawat
Analysis Of Encrypted Malicious Traffic, Anish Singh Shekhawat
Master's Projects
In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation and communication. Due to the volume of legitimate encrypted data, encrypted malicious traffic resembles benign traffic. As the malicious traffic is similar to benign traffic, it poses a challenge for antivirus software and firewalls. Since antivirus software and firewalls will not typically have access to encryption keys, detection techniques are needed that do not require decrypting the traffic. In this research, we apply a variety of machine learning techniques to the problem of distinguishing malicious encrypted HTTP traffic …
A Study On Effects Of Data Poisoning On Hmms, Rachel Gonsalves
A Study On Effects Of Data Poisoning On Hmms, Rachel Gonsalves
Master's Projects
With the ever increasing use of burgeoning volumes of data, machine learning systems involving minimal human oversight are crucial for classification and analysis tasks. Machine learning algorithms used for such purposes have revolutionized the way we sort, classify, and analyze data. The accuracy of any machine learning algorithm depends heavily on the data it is trained on. In some circumstances, an attacker can attempt to poison the training data to subvert a machine learning system. In this research, we analyze the effects of training data poisoning attacks on hidden Markov models (HMMs), in the context of malware classification. With the …
Sentiment Analysis Using An Ensemble Of Feature Selection Algorithms, Manankumar Bhagat
Sentiment Analysis Using An Ensemble Of Feature Selection Algorithms, Manankumar Bhagat
Master's Projects
To determine the opinion of any person experiencing any services or buying any product, the usage of Sentiment Analysis, a continuous research in the field of text mining, is a common practice. It is a process of using computation to identify and categorize opinions expressed in a piece of text. Individuals post their opinion via reviews, tweets, comments or discussions which is our unstructured information. Sentiment analysis gives a general conclusion of audits which benefit clients, individuals or organizations for decision making. The primary point of this paper is to perform an ensemble approach on feature reduction methods identified with …
Multi-Objective Path Finding Using Reinforcement Learning, Prashant Thombre
Multi-Objective Path Finding Using Reinforcement Learning, Prashant Thombre
Master's Projects
Path Finding is a vastly studied subject in the field of Computer Science. The problem of path-finding is defined as the discovery and plotting of an optimal route between two points on a plane. The existing algorithms that solve this problem are mostly static and rely heavily on the prior knowledge of the environment. They also require the environment to be deterministic. However, in real-world applications of the path-finding problem, often the environment is priorly unknown and stochastic, and with several conflicting objectives. In such cases, the aforementioned algorithms fail to produce effective results. In this project, we study and …