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Full-Text Articles in Engineering

"When They Say Weed Causes Depression, But It's Your Fav Antidepressant": Knowledge-Aware Attention Framework For Relationship Extraction, Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth Mar 2021

"When They Say Weed Causes Depression, But It's Your Fav Antidepressant": Knowledge-Aware Attention Framework For Relationship Extraction, Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth

Publications

With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provide deeper insights about these associations to public health analysts. In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression. We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that leverages the pre-trained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology (DAO)) to jointly extract ...


On-Device Deep Learning Inference For System-On-Chip (Soc) Architectures, Tom Springer, Elia Eiroa-Lledo, Elizabeth Stevens, Erik Linstead Mar 2021

On-Device Deep Learning Inference For System-On-Chip (Soc) Architectures, Tom Springer, Elia Eiroa-Lledo, Elizabeth Stevens, Erik Linstead

Engineering Faculty Articles and Research

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can ...


On The Impact Of Gravity Compensation On Reinforcement Learning In Goal-Reaching Tasks For Robotic Manipulators, Jonathan Fugal, Hasan A. Poonawala, Jihye Bae Mar 2021

On The Impact Of Gravity Compensation On Reinforcement Learning In Goal-Reaching Tasks For Robotic Manipulators, Jonathan Fugal, Hasan A. Poonawala, Jihye Bae

Electrical and Computer Engineering Faculty Publications

Advances in machine learning technologies in recent years have facilitated developments in autonomous robotic systems. Designing these autonomous systems typically requires manually specified models of the robotic system and world when using classical control-based strategies, or time consuming and computationally expensive data-driven training when using learning-based strategies. Combination of classical control and learning-based strategies may mitigate both requirements. However, the performance of the combined control system is not obvious given that there are two separate controllers. This paper focuses on one such combination, which uses gravity-compensation together with reinforcement learning (RL). We present a study of the effects of gravity ...


Jack Voltaic 3.0 Cyber Research Report, Erica Mitchell, Douglas Fletcher, Erik Korn, Steven Whitham, Jason Hillman, Ron Yearwood, Clint Walker, Aryn Pyke, Gabriel Weaver, Brandon Pugh, Katherine Hutton, George Platsis, Timothy Klett, Ryan Hruska Mar 2021

Jack Voltaic 3.0 Cyber Research Report, Erica Mitchell, Douglas Fletcher, Erik Korn, Steven Whitham, Jason Hillman, Ron Yearwood, Clint Walker, Aryn Pyke, Gabriel Weaver, Brandon Pugh, Katherine Hutton, George Platsis, Timothy Klett, Ryan Hruska

ACI Technical Reports

The Jack Voltaic (JV) Cyber Research project is an innovative, bottom-up approach to critical infrastructure resilience that informs our understanding of existing cybersecurity capabilities and identifies gaps. JV 3.0 contributed to a repeatable framework cities and municipalities nationwide can use to prepare. This report on JV 3.0 provides findings and recommendations for the military, federal agencies, and policy makers.


Jack Voltaic 3.0 Cyber Research Report Executive Summary, Douglas Fletcher, Erica Mitchell, Erik Korn, Steven Whitham, Jason Hillman, Aryn Pyke Mar 2021

Jack Voltaic 3.0 Cyber Research Report Executive Summary, Douglas Fletcher, Erica Mitchell, Erik Korn, Steven Whitham, Jason Hillman, Aryn Pyke

ACI Technical Reports

This condensed Executive summary provides an overview of the information contained in the full JV 3.0 Cyber Research Report.


Knowledge Infused Policy Gradients For Adaptive Pandemic Control, Kaushik Roy, Qi Zhang, Manas Gaur, Amit P. Sheth Mar 2021

Knowledge Infused Policy Gradients For Adaptive Pandemic Control, Kaushik Roy, Qi Zhang, Manas Gaur, Amit P. Sheth

Publications

COVID-19 has impacted nations differently based on their policy implementations. The effective policy requires taking into account public information and adaptability to new knowledge. Epidemiological models built to understand COVID-19 seldom provide the policymaker with the capability for adaptive pandemic control (APC). Among the core challenges to be overcome include (a) inability to handle a high degree of non-homogeneity in different contributing features across the pandemic timeline, (b) lack of an approach that enables adaptive incorporation of public health expert knowledge, and (c) transparent models that enable understanding of the decision-making process in suggesting policy. In this work, we take ...


"Is Depression Related To Cannabis?": A Knowledge-Infused Model For Entity And Relation Extraction With Limited Supervision, Kaushik Roy, Usha Lokala, Vedant Khandelwal, Amit P. Sheth Mar 2021

"Is Depression Related To Cannabis?": A Knowledge-Infused Model For Entity And Relation Extraction With Limited Supervision, Kaushik Roy, Usha Lokala, Vedant Khandelwal, Amit P. Sheth

Publications

With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators. However, preliminary scientific research does not conclusively associate cannabis with improved mental health. In this study, we explore the relationship between depression and consumption of cannabis in a targeted social media corpus involving personal use of cannabis with the intent to derive its potential mental health benefit. We use tweets that contain an association among three categories annotated by domain experts - Reason, Effect, and Addiction. The state-of-the-art Natural Langauge Processing techniques fall short in extracting these relationships between cannabis ...


Enterprise Architecture Transformation Process From A Federal Government Perspective, Tonia Canada, Leila Halawi Mar 2021

Enterprise Architecture Transformation Process From A Federal Government Perspective, Tonia Canada, Leila Halawi

Publications

The need for information technology organizations to transform enterprise architecture is driven by federal government mandates and information technology budget constraints. This qualitative case study aimed to identify factors that hinder federal government agencies from driving enterprise architecture transformation processes from a compliancy to a flexible process. Common themes in interviewee responses were identified, coded, and summarized. Critical recommendations for future best practices, including further research, were also presented.


Bibliometric Analysis Of One-Stage And Two-Stage Object Detection, Aditya Lohia, Kalyani Dhananjay Kadam, Rahul Raghvendra Joshi, Dr. Anupkumar M. Bongale Feb 2021

Bibliometric Analysis Of One-Stage And Two-Stage Object Detection, Aditya Lohia, Kalyani Dhananjay Kadam, Rahul Raghvendra Joshi, Dr. Anupkumar M. Bongale

Library Philosophy and Practice (e-journal)

Object Detection using deep learning has seen a boom in the recent couple of years. Observing the trend and its research, it is important to summarize bibliometrics related to object detection which will help researchers contribute to this subject area. This paper details bibliometrics for one-stage object detection and two-stage object detection. This uses Scopus database for data analysis. This also uses tools like Sciencescape, Gephi, etc. It can be observed that the advancements to the field of object detection are seen in recent years and explored to its full extent. It is observed that Chinese universities and researchers are ...


Exploring The Efficiency Of Self-Organizing Software Teams With Game Theory, Clay Stevens, Jared Soundy, Hau Chan Feb 2021

Exploring The Efficiency Of Self-Organizing Software Teams With Game Theory, Clay Stevens, Jared Soundy, Hau Chan

CSE Conference and Workshop Papers

Over the last two decades, software development has moved away from centralized, plan-based management toward agile methodologies such as Scrum. Agile methodologies are founded on a shared set of core principles, including self-organizing software development teams. Such teams are promoted as a way to increase both developer productivity and team morale, which is echoed by academic research. However, recent works on agile neglect to consider strategic behavior among developers, particularly during task assignment–one of the primary functions of a self-organizing team. This paper argues that self-organizing software teams could be readily modeled using game theory, providing insight into how ...


Addressing Multiple Bit/Symbol Errors In Dram Subsystem, Ravikiran Yeleswarapu, Arun K. Somani Feb 2021

Addressing Multiple Bit/Symbol Errors In Dram Subsystem, Ravikiran Yeleswarapu, Arun K. Somani

Electrical and Computer Engineering Publications

As DRAM technology continues to evolve towards smaller feature sizes and increased densities, faults in DRAM subsystem are becoming more severe. Current servers mostly use CHIPKILL based schemes to tolerate up-to one/two symbol errors per DRAM beat. Such schemes may not detect multiple symbol errors arising due to faults in multiple devices and/or data-bus, address bus. In this article, we introduce Single Symbol Correction Multiple Symbol Detection (SSCMSD)—a novel error handling scheme to correct single-symbol errors and detect multi-symbol errors. Our scheme makes use of a hash in combination with Error Correcting Code (ECC) to avoid silent ...


A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Based On Scopus And Wos, Shivali Amit Wagle, Harikrishnan R Feb 2021

A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Based On Scopus And Wos, Shivali Amit Wagle, Harikrishnan R

Library Philosophy and Practice (e-journal)

The maneuver of Artificial Intelligence (AI) techniques in the field of agriculture help in the classification of diseases. Early prediction of the disease benefits in taking relevant management steps. This is an important step towards controlling the disease growth that will yield good quality products to fulfill the global food demand. The main objective of this paper is to study the extent of research work done in this area of plant disease classification. The paper discusses the bibliometric analysis of plant disease classification with AI in Scopus and Web of Science core collection (WOS) database in analyzing the research by ...


A Bibliometric Analysis Of Distributed Incremental Clustering On Images, Ayushi Agrawal, Preeti Mulay, Krithika Iyer, Saloni Sarbhai Feb 2021

A Bibliometric Analysis Of Distributed Incremental Clustering On Images, Ayushi Agrawal, Preeti Mulay, Krithika Iyer, Saloni Sarbhai

Library Philosophy and Practice (e-journal)

Unstructured information is continuously irregular and streaming information from such a sequence is tedious because it lacks labels and accumulates with time. This is possible using Incremental Clustering algorithms that use previously learned information to accommodate new data and avoid retraining. This paper therefore seeks to understand the status of "Distributed Incremental Clustering" on images with text and numerical values, its limitations, scope, and other details to devise a better algorithm in future. To further enhance the analysis, we have also included methodology, which can be used to perform clustering on images or documents based on its content.


Plasmonic Waveguides To Enhance Quantum Electrodynamic Phenomena At The Nanoscale, Ying Li, Christos Argyropoulos Feb 2021

Plasmonic Waveguides To Enhance Quantum Electrodynamic Phenomena At The Nanoscale, Ying Li, Christos Argyropoulos

Faculty Publications from the Department of Electrical and Computer Engineering

The emerging field of plasmonics can lead to enhanced light-matter interactions at extremely nanoscale regions. Plasmonic (metallic) devices promise to efficiently control both classical and quantum properties of light. Plasmonic waveguides are usually used to excite confined electromagnetic modes at the nanoscale that can strongly interact with matter. The analysis of these nanowaveguides exhibits similarities with their low frequency microwave counterparts. In this article, we review ways to study plasmonic nanostructures coupled to quantum optical emitters from a classical electromagnetic perspective. These quantum emitters are mainly used to generate single-photon quantum light that can be employed as a quantum bit ...


Parking Recommender System Privacy Preservation Through Anonymization And Differential Privacy, Yasir Saleem Shaikh, Mubashir Husain Rehmani, Noel Crespi, Roberto Minerva Feb 2021

Parking Recommender System Privacy Preservation Through Anonymization And Differential Privacy, Yasir Saleem Shaikh, Mubashir Husain Rehmani, Noel Crespi, Roberto Minerva

Publications

Recent advancements in the Internet of Things (IoT) have enabled the development of smart parking systems that use services of third-party parking recommender system to provide recommendations of personalized parking spot to users based on their past experience. However, the indiscriminate sharing of users’ data with an untrusted (or semitrusted) parking recommender system may breach the privacy because users’ behavior and mobility patterns could be inferred by analyzing their past history. Therefore, in this article, we present two solutions that preserve privacy of users in parking recommender systems while analyzing the past parking history using k-anonymity (anonymization) and differential privacy ...


Bibliometric Survey On Zero-Knowledge Proof For Authentication, Adwait Pathak, Tejas Patil, Shubham Pawar, Piyush Raut, Smita Khairnar, Dr. Shilpa Gite Jan 2021

Bibliometric Survey On Zero-Knowledge Proof For Authentication, Adwait Pathak, Tejas Patil, Shubham Pawar, Piyush Raut, Smita Khairnar, Dr. Shilpa Gite

Library Philosophy and Practice (e-journal)

Background: Zero Knowledge Proof is a persuasive cryptographic protocol employed to provide data security by keeping the user's identity, using the services anonymously. Zero Knowledge Proof can be the preferred option to use in multiple circumstances. Instead of using the public key cryptographic protocols, the zero-knowledge proof usage does not expose or leak confidential data or information during the transmission. Zero Knowledge Proof protocols are comparatively lightweight; this results in making it efficient in terms of memory. Zero Knowledge Proof applications can reside in authentication, identity management, cryptocurrency transactions, and many more. Traditional authentication schemes are vulnerable to attacks ...


Performance Of Single Board Computers For Vision Processing, Curtis Manore Cdt'21, Pratheek Manjunath, Dominic Larkin Jan 2021

Performance Of Single Board Computers For Vision Processing, Curtis Manore Cdt'21, Pratheek Manjunath, Dominic Larkin

West Point Research Papers

With the increasing complexity of machine vision algorithms and growing applications of image processing, how do computers without a dedicated graphics processor perform? This research discusses the computational abilities of two lowcost single board computers (SBCs) by subjecting them to various Visual Inertial Odometry (VIO) algorithms. The end goal of this research is to identify a SBC which meets the requirements of being employed on an Unmanned Aerial System for autonomous navigation.


Estimating Remaining Useful Life In Machines Using Artificial Intelligence: A Scoping Review, Sameer Sayyad, Satish Kumar, Arunkumar Bongale, Anupkumar Bongale, Shruti Patil Jan 2021

Estimating Remaining Useful Life In Machines Using Artificial Intelligence: A Scoping Review, Sameer Sayyad, Satish Kumar, Arunkumar Bongale, Anupkumar Bongale, Shruti Patil

Library Philosophy and Practice (e-journal)

The remaining useful life (RUL) estimations become one of the most essential aspects of predictive maintenance (PdM) in the era of industry 4.0. Predictive maintenance aims to minimize the downtime of machines or process, decreases maintenance costs, and increases the productivity of industries. The primary objective of this bibliometric paper is to understand the scope of literature available related to RUL prediction. Scopus database is used to perform the analysis of 1673 extracted scientific literature from the year 1985 to 2020. Based on available published documents, analysis is done on the year-wise publication data, document types, language-wise distribution of ...


Time Series Data Analysis Using Machine Learning-(Ml) Approach, Mvv Prasad Kantipudi Dr., Pradeep Kumar N.S Dr., S.Sreenath Kashyap Dr., Ss Anusha Vemuri Ms Jan 2021

Time Series Data Analysis Using Machine Learning-(Ml) Approach, Mvv Prasad Kantipudi Dr., Pradeep Kumar N.S Dr., S.Sreenath Kashyap Dr., Ss Anusha Vemuri Ms

Library Philosophy and Practice (e-journal)

Healthcare benefits related to continuous monitoring of human movement and physical activity can potentially reduce the risk of accidents associated with elderly living alone at home. Based on the literature review, it is found that many studies focus on human activity recognition and are still active towards achieving practical solutions to support the elderly care system. The proposed system has introduced a joint approach of machine learning and signal processing technology for the recognition of human's physical movements using signal data generated by accelerometer sensors. The framework adopts the concept of DSP to select very descriptive feature sets and ...


Bibliometric Of Feature Selection Using Optimization Techniques In Healthcare Using Scopus And Web Of Science Databases, Rahul Joshi, Harita Gadikta, Saneeka Kharat, Soumi Mandal, Kalyani Kadam, Anupkumar M. Bongale Dr., Siddhant Pandit Jan 2021

Bibliometric Of Feature Selection Using Optimization Techniques In Healthcare Using Scopus And Web Of Science Databases, Rahul Joshi, Harita Gadikta, Saneeka Kharat, Soumi Mandal, Kalyani Kadam, Anupkumar M. Bongale Dr., Siddhant Pandit

Library Philosophy and Practice (e-journal)

Feature selection technique is an important step in the prediction and classification process, primarily in data mining related aspects or related to medical field. Feature selection is immersive with the errand of choosing a subset of applicable features that could be utilized in developing a prototype. Medical datasets are huge in size; hence some effective optimization techniques are required to produce accurate results. Optimization algorithms are a critical function in medical data mining particularly in identifying diseases since it offers excellent effectiveness in minimum computational expense and time. The classification algorithms also produce superior outcomes when an objective function is ...


Diabetes Prediction Using Machine Learning : A Bibliometric Analysis, Vijayshri Nitin Khedkar, Sina Patel Jan 2021

Diabetes Prediction Using Machine Learning : A Bibliometric Analysis, Vijayshri Nitin Khedkar, Sina Patel

Library Philosophy and Practice (e-journal)

Diabetes Mellitus is a chronic disease which can be deadly if undetected for longer time. Artificial intelligence is helping in healthcare industry to a great extent by helping professionals to derive useful information and patterns from data available in various formats: Survey data, electronic health records, laboratory data.. Diabetes, if predicted at an early stage can help many people to save lives and cost for healthcare. Decision-making, diagnosing and predicting diabetes have become an increasing trend in recent years. There are numerous publications in diabetes prediction and yet it’s an ongoing research topic with availability of new data and ...


A Bibliometric Analysis Of The Tea Quality Evaluation Using Artificial Intelligence, Amruta Bajirao Patil Research Scholar, Mrinal Rahul Bachute Ph.D Guide And Associate Professor Jan 2021

A Bibliometric Analysis Of The Tea Quality Evaluation Using Artificial Intelligence, Amruta Bajirao Patil Research Scholar, Mrinal Rahul Bachute Ph.D Guide And Associate Professor

Library Philosophy and Practice (e-journal)

ABSTRACT: In this study, we have carried the bibliometric review of the “Tea quality evaluation using artificial intelligence”. Only the Scopus database is under consideration for this analysis. To coat all possible research approaches here we have generated the valid search queries which excludes irrelevant literature. The result analysis shows overall 602 useful papers are available on the tea quality evaluation out of which 12 papers are specifically on artificial taste perception of tea. This survey illustrates the emerging trend of quality evaluation and assurance (QEA) in tea industry and its importance. As the production of tea is huge, storage ...


Effect Of Information Technology Capital: Technology Infrastructure, Database, Software, And Brainware Toward Optimize The Use Of Information Technology (Case Study : Uin Sunan Ampel Of Surabaya), Rismawati Br Sitepu, Ilham M.Said, Tanti Handriana, Praptini Yulianti Jan 2021

Effect Of Information Technology Capital: Technology Infrastructure, Database, Software, And Brainware Toward Optimize The Use Of Information Technology (Case Study : Uin Sunan Ampel Of Surabaya), Rismawati Br Sitepu, Ilham M.Said, Tanti Handriana, Praptini Yulianti

Library Philosophy and Practice (e-journal)

This research was conducted to determine the extent of the influence of technology infrastructure costs, software costs, database costs and brainware costs to increase the information technology budget of the Sunan Ampel State Islamic University in Surabaya and efficient use of the budget. The purpose of this study is to prove that there is a positive and significant influence of technology infrastructure costs, software costs, database costs and brainware costs to increase information technology budgets by using validity and reliability tests and classic tests such as the Normality test, Multicollinearity test, autocorrelation test, Heteroskedasticity test , and Linearity test. This study ...


A Literature Survey And Bibliometric Analysis Of Application Of Artificial Intelligence Techniques On Wireless Mesh Networks, Smita R. Mahajan Mrs., Harikrishnan R Dr., Ketan Kotecha Dr. Jan 2021

A Literature Survey And Bibliometric Analysis Of Application Of Artificial Intelligence Techniques On Wireless Mesh Networks, Smita R. Mahajan Mrs., Harikrishnan R Dr., Ketan Kotecha Dr.

Library Philosophy and Practice (e-journal)

Recent years have seen a surge in the use of technology for executing transactions in both online and offline modes. Various industries like banking, e-commerce, and private organizations use networks for the exchange of confidential information and resources. Network security is thus of utmost importance, with the expectation of effective and efficient analysis of the network traffic. Wireless Mesh Networks are effective in communicating information over a vast span with minimal costs. A network is evaluated based on its security, accessibility, and extent of interoperability. Artificial Intelligence techniques like machine learning and deep learning have found widespread use to solve ...


Transfer Learning By Similarity Centred Architecture Evolution For Multiple Residential Load Forecasting, Santiago Gomez-Rosero, Miriam A M Capretz, Syed Mir Jan 2021

Transfer Learning By Similarity Centred Architecture Evolution For Multiple Residential Load Forecasting, Santiago Gomez-Rosero, Miriam A M Capretz, Syed Mir

Electrical and Computer Engineering Publications

The development from traditional low voltage grids to smart systems has become extensive and adopted worldwide. Expanding the demand response program to cover the residential sector raises a wide range of challenges. Short term load forecasting for residential consumers in a neighbourhood could lead to a better understanding of low voltage consumption behaviour. Nevertheless, users with similar characteristics can present diversity in consumption patterns. Consequently, transfer learning methods have become a useful tool to tackle differences among residential time series. This paper proposes a method combining evolutionary algorithms for neural architecture search with transfer learning to perform short term load ...


On The Mandelbrot Set For I**2 = ±1 And Imaginary Higgs Fields, Jonathan Blackledge Jan 2021

On The Mandelbrot Set For I**2 = ±1 And Imaginary Higgs Fields, Jonathan Blackledge

Articles

We consider the consequence of breaking with a fundamental result in complex analysisby lettingi2=±1wherei=√−1is the basic unit of all imaginary numbers. An analysis of theMandelbrot set for this case shows that a demarcation between a Fractal and a Euclidean object ispossible based oni2=−1andi2= +1, respectively. Further, we consider the transient behaviourassociated with the two cases to produce a range of non-standard sets in which a Fractal geometricstructure is transformed into a Euclidean object. In the case of the Mandelbrot set, the Euclideanobject is a square whose properties are investigate. Coupled with the associated Julia sets and othercomplex ...


Learning Adl Daily Routines With Spatiotemporal Neural Networks, Shan Gao, Ah-Hwee Tan, Rossi Setchi Jan 2021

Learning Adl Daily Routines With Spatiotemporal Neural Networks, Shan Gao, Ah-Hwee Tan, Rossi Setchi

Research Collection School Of Computing and Information Systems

The activities of daily living (ADLs) refer to the activities performed by individuals on a daily basis and are the indicators of a person’s habits, lifestyle, and wellbeing. Learning an individual’s ADL daily routines has significant value in the healthcare domain. Specifically, ADL recognition and inter-ADL pattern learning problems have been studied extensively in the past couple of decades. However, discovering the patterns performed in a day and clustering them into ADL daily routines has been a relatively unexplored research area. In this paper, a self-organizing neural network model, called the Spatiotemporal ADL Adaptive Resonance Theory (STADLART), is ...


Exploiting Bert And Roberta To Improve Performance For Aspect Based Sentiment Analysis, Gagan Reddy Narayanaswamy Jan 2021

Exploiting Bert And Roberta To Improve Performance For Aspect Based Sentiment Analysis, Gagan Reddy Narayanaswamy

Dissertations

Sentiment Analysis also known as opinion mining is a type of text research that analyses people’s opinions expressed in written language. Sentiment analysis brings together various research areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is fast becoming of major importance to companies and organizations as it is started to incorporate online commerce data for analysis. Often the data on which sentiment analysis is performed will be reviews. The data can range from reviews of a small product to a big multinational corporation. The goal of performing sentiment analysis is to extract information from ...


Adequately Generating Captions For An Image Using Adaptive And Global Attention Mechanisms., Shravan Kumar Talanki Venkatarathanaiahsetty Jan 2021

Adequately Generating Captions For An Image Using Adaptive And Global Attention Mechanisms., Shravan Kumar Talanki Venkatarathanaiahsetty

Dissertations

Generating description to images is a recent surge and with latest developments in the field of Artificial Intelligence, it can be one of the prominent applications to bridge the gap between Computer vision and Natural language processing fields. In terms of the learning curve, Deep learning has become the main backbone in driving many new applications. Image Captioning is one such application where the usage of Deep learning methods enhanced the performance of the captioning accuracy. The introduction of the Encoder-Decoder framework was a breakthrough in Image captioning. But as the sequences got longer the performance of captions was affected ...


Feature Augmentation For Improved Topic Modeling Of Youtube Lecture Videos Using Latent Dirichlet Allocation, Nakul Srikumar Jan 2021

Feature Augmentation For Improved Topic Modeling Of Youtube Lecture Videos Using Latent Dirichlet Allocation, Nakul Srikumar

Dissertations

Application of Topic Models in text mining of educational data and more specifically, the text data obtained from lecture videos, is an area of research which is largely unexplored yet holds great potential. This work seeks to find empirical evidence for an improvement in Topic Modeling by pre- extracting bigram tokens and adding them as additional features in the Latent Dirichlet Allocation (LDA) algorithm, a widely-recognized topic modeling technique. The dataset considered for analysis is a collection of transcripts of video lectures on Machine Learning scraped from YouTube. Using the cosine similarity distance measure as a metric, the experiment showed ...