A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Using Convolutional Neural Network, Sumit Kumar, Rutuja Rajendra Patil, Vasu Kumawat, Yashovardhan Rai, Navaneeth Krishnan, Shubham Kumar Singh

Library Philosophy and Practice (e-journal)

In 2021 and the modern future which everyone is going to be a part of, Artificial intelligence is going to be the biggest part of our livelihood. In the future there is going to be a huge expansion of population especially at the rate right now which we are moving but the biggest problem which everyone should be concerned about is the food supply as many of the nations would not be able to feed and make survive their population as even now, there is scarcity of it. Currently in the world the people revolving around the artificial intelligence are …

Design And Implementation Of A Microservices Web-Based Architecture For Code Deployment And Testing, Soin Abdoul Kassif Traore

Symposium of Student Scholars

Design and Implementation of a Microservices Web-based Architecture for CodeDeployment and Testing

Many tech stars like Netflix, Amazon, PayPal, eBay, and Twitter are evolving from monolithic to a microservice architecture due to the benefits for Agile and DevOps teams. Microservices architecture can be applied to multiple industries, like IoT, using containerization. Virtual containers give an ideal environment for developing and testing of IoT technologies. Since the IoT industry has an exponential growth, it is the responsibility of universities to teach IoT with hands-on labs to minimize the gap between what the students learn and what is on-demand in the job …

An Investigation On Non-Invasive Brain-Computer Interfaces: Emotiv Epoc+ Neuroheadset And Its Effectiveness, Md Jobair Hossain Faruk

Symposium of Student Scholars

Neurotechnology describes as one of the focal points of today’s research around the domain of Brain-Computer Interfaces (BCI). The primary attempts of BCI research are to decoding human speech from brain signals and controlling neuro-psychological patterns that would benefit people suffering from neurological disorders. In this study, we illustrate the progress of BCI research and present scores of unveiled contemporary approaches. First, we explore a decoding natural speech approach that is designed to decode human speech directly from the human brain onto a digital screen introduced by Facebook Reality Laband University of California San Francisco. Then, we study a recently …

Framework For Collecting Data From Iot Device, Md Saiful Islam

Symposium of Student Scholars

The Internet of Things (IoT) is the most significant and blooming technology in the 21st century. IoT has rapidly developed by covering hundreds of applications in the civil, health, military, and agriculture areas. IoT is based on the collection of sensor data through an embedded system, and this embedded system uploads the data on the internet. Devices and sensor technologies connected over a network can monitor and measure data in real-time. The main challenge is to collect data from IoT devices, transmit them to store in the Cloud, and later retrieve them at any time for visualization and data analysis. …

Data Analysis Methods For Health Monitoring Sensors, Shahriar Sobhan

Symposium of Student Scholars

Innovations in health monitoring systems are fundamental for the continuous improvement of remote healthcare. With the current presence of SARS-CoV-2, better known as COVID-19, in people’s daily lives, solutions for monitoring heart and especially respiration and pulmonary functions are more needed than ever. Besides, health monitoring systems are widely used for patients who need isolated care, unconscious patients who cannot get medical attention for themselves. As it is well-known, monitoring systems rely on sensor technologies. Currently, there are multiple research studies for remote monitoring using different types of sensors. In this effort, we survey the current approaches that utilize the …

A Bibliometric Survey On The Use Of Long Short-Term Memory Networks For Multivariate Time Series Forecasting, Vidur Sood Mr., Manobhav Mehta Mr., Vedansh Mishra Mr., Akash Upadhyay Mr., Shilpa Hudnurkar, Shilpa Gite Dr., Neela Rayavarapu Dr.

Library Philosophy and Practice (e-journal)

In this paper, we aim to review and analyze the publications related to the utilization of Long Short-Term Memory (LSTM) networks for multivariate time series forecasting. The purpose of this bibliometric survey was to study how technology in the field of LSTM has evolved over the years. There were 242 research papers published, by over 50 researchers, over 6 years, on the topic of “Multivariate time series forecasting using LSTM”. The majority of these papers were published between the years 2018 and 2020. The Scopus database was utilized for analyzing recent trends in this area and to determine the …

Performance Analysis Of Whale Optimization Based Data Clustering, Ahamed Shafeeq B M, Zahid Ahmed Ansari, Shyam Karanth

Future Computing and Informatics Journal

Data clustering is the method of gathering of data points so that the more similar points will be in the same group. It is a key role in exploratory data mining and a popular technique used in many fields to analyze statistical data. Quality clusters are the key requirement of the cluster analysis result. There will be tradeoffs between the speed of the clustering algorithm and the quality of clusters it produces. Both the quality and speed criteria must be considered for the state-of-the-art clustering algorithm for applications. The Bio-inspired technique has ensured that the process is not trapped in …

Multilayer Perceptron With Auto Encoder Enabled Deep Learning Model For Recommender Systems, Subhashini Narayan

Future Computing and Informatics Journal

In this modern world of ever-increasing one-click purchases, movie bookings, music, health- care, fashion, the need for recommendations have increased the more. Google, Netflix, Spotify, Amazon and other tech giants use recommendations to customize and tailor their search engines to suit the user’s interests. Many of the existing systems are based on older algorithms which although have decent accuracies, require large training and testing datasets and with the emergence of deep learning, the accuracy of algorithms has further improved, and error rates have reduced due to the use of multiple layers. The need for large datasets has declined as well. …

Deep Feature Learning For Fog Episodes Prediction In Patients With Pd, Hadeer Elziaat, Nashwa El-Bendary, Ramdan Mowad

Future Computing and Informatics Journal

A common symptom of Parkinson's Disease is Freezing of Gait (FoG) that causes an interrupt of the forward progression of the patient’s feet while walking. Therefore, Freezing of Gait episodes is always engaged to the patient's falls. This paper proposes a model for Freezing of Gait episodes detection and prediction in patients with Parkinson's Disease. Predicting Freezing of Gait in this paper considers as a multi-class classification problem with 3 classes namely, FoG, pre-FoG, and walking episodes. In this paper, the extracted feature scheme applied for the detection and the prediction of FoG is Convolutional Neural Network (CNN) spectrogram time-frequency …

A Literature Review For Contributing Mining Approaches For Business Process Reengineering, Noha Ahmed Bayomy Nab, Ayman E. Khedr Aek, Laila A. Abd-Elmegid Laa, Amira M. Idrees Ami

Future Computing and Informatics Journal

Due to the changing dynamics of the business environment, organizations need to redesign or reengineer their business processes in order to provide services with the lowest cost and shortest response time while increasing quality. Thence, Business Process Re-engineering (BPR) provides a roadmap to achieve operational goals that leads to enhance flexibility and productivity, cost reduction, and quality of service/product. In this paper, we propose a literature review for the different proposed models for Business Process Reengineering. The models specify where the breakdowns occur in BPR implementation, justifies why such breakdowns occur, and propose techniques to prevent their occurrence again. The …

Bibliometric Survey On Flood Prediction Using Machine Learning, Seema Patil Prof., Daksh Khurana Mr., Kartik Rao Mr, Priyanshu Meena Mr, Shivendra Singh Mr

Library Philosophy and Practice (e-journal)

Floods are one of the most devastating natural hazards, and modelling them is extremely difficult. Flood prediction model advancement study led to factors such as loss of human and animal life, property damage, and risk mitigation. The focus of this bibliometric survey is to recognise the few studies which have upheld on the factors affecting the floods. The analysis is done based on 254 documents such as articles, conference papers, article reviews and some reviews and notes. India contributes to the maximum number of documents followed by China and the United States of America. This bibliometric survey is conducted using …

Decentralized Aggregation Design And Study Of Federated Learning, Venkata Naga Surya Sameeraja Malladi

Master of Science in Software Engineering Theses

The advent of machine learning techniques has given rise to modern devices with built-in models for decision making and providing rich content to users. This typically involves processing huge volumes of data in central servers and sending updated models to end-user devices. There are two main concerns on this server architecture, one is the privacy of data that is being transferred to a central server and the other is volumes of data sent over the network for the model update. Federated Learning helps solve these problems by training models on local data within the device and aggregating the model with …

Blockchain-Based Healthcare Portal – A Bibliometric Analysis, Aditi Goyal, Aditya Banerjee, Aniket Mulik, Yashika Chhabaria, Sonali Kothari Tidke Dr, Vijayshri Khedkar

Library Philosophy and Practice (e-journal)

User privacy has been a topmost priority and one such domain where this is neglected is the area of healthcare. Our solution focuses on the idea of using blockchain to provide a platform for healthcare experts and patients, giving patients full control over the data that will be shared. This paper focuses on identifying current research that has been conducted in this area in the form of a bibliometric analysis. A bibliometric study on a research area involves a detailed analysis of citations and papers across a domain of study. The purpose of this study is a statistical analysis of …

Redai: A Machine Learning Approach To Cyber Threat Intelligence, Luke Noel

Masters Theses, 2020-current

The world is continually demanding more effective and intelligent solutions and strategies to combat adversary groups across the cyber defense landscape. Cyber Threat Intelligence (CTI) is a field within the domain of cyber security that allows for organizations to utilize threat intelligence and serves as a tool for organizations to proactively harden their defense posture. However, there is a large volume of CTI and it is often a daunting task for organizations to effectively consume, utilize, and apply it to their defense strategies. In this thesis we develop a machine learning solution, named RedAI, to investigate whether open-source intelligence (OSINT) …

Artificial Intelligence And The Ethics Behind It, Isaac Johnston

Senior Honors Theses

Artificial intelligence (AI) has been a widely used buzzword for the past couple of years. If there is a technology that works without human interaction, it is labeled as AI. But what is AI, and should individuals be concerned? The following research aims to define what artificial intelligence is, specifically machine learning (ML) and neural networks. It is important to understand how AI is used today in cars, image recognition, ad marketing, and other areas. Although AI has many benefits, there are areas of ethical concerns such as autonomous cars, military applications, social media marketing, and others. This paper helps …

Heterogeneous Graph-Based User-Specific Review Helpfulness Prediction, Dongkai Chen

Dartmouth College Master’s Theses

With the popularity of e-commerce and review websites, it is becoming increasingly important to identify the helpfulness of reviews. However, existing works on predicting reviews’ helpfulness have three major issues: (i) the correlation between helpfulness and features from review text is not clear yet, although many standard features are proposed, (ii) the relations between users, reviews and products have not been considered, (iii) the effectiveness of the existing approaches have not been systematically compared. To address these challenges, we first analyze the correlation between standard features and review helpfulness that are widely used in other work. Based on this analysis, …

Machine Learning-Based Recognition On Crowdsourced Food Images, Aditya Kulkarni

Honors Scholar Theses

With nearly a third of the world’s population suffering from food-induced chronic diseases such as obesity, the role of food in community health is required now more than ever. While current research underscores food proximity and density, there is a dearth in regard to its nutrition and quality. However, recent research in geospatial data collection and analysis as well as intelligent deep learning will help us study this further.

Employing the efficiency and interconnection of computer vision and geospatial technology, we want to study whether healthy food in the community is attainable. Specifically, with the help of deep learning in …

Synthesizer Parameter Approximation By Deep Learning, Daniel Faronbi, Alisa Gilmore

Theses/Capstones/Creative Projects

Synthesizers have been an essential tool for composers of any style of music including computer generated sound. They allow for an expansion in timbral variety to the orchestration of a piece of music or sound scape. Sound designers are trained to be able to recreate a timbre in their head using a synthesizer. This works well for simple sounds but becomes more difficult as the number of parameters required to produce a specific timbre increase. The goal of this research project is to formulate a method for synthesizers to approximate a timbre given an input audio sample using deep learning. …

A Deep Learning-Based Automatic Object Detection Method For Autonomous Driving Ships, Ojonoka Erika Atawodi

Master's Theses

An important feature of an Autonomous Surface Vehicles (ASV) is its capability of automatic object detection to avoid collisions, obstacles and navigate on their own.

Deep learning has made some significant headway in solving fundamental challenges associated with object detection and computer vision. With tremendous demand and advancement in the technologies associated with ASVs, a growing interest in applying deep learning techniques in handling challenges pertaining to autonomous ship driving has substantially increased over the years.

In this thesis, we study, design, and implement an object recognition framework that detects and recognizes objects found in the sea. We first curated …

Wearables And Wearable Data In Tele-Health Applications, Jack Mazza

Honors Theses

With the sudden emergence of Covid-19, Tele-Health has been forced into the forefront of healthcare. With no human contact, regular in-person doctor or clinic visits could not be made. Unfortunately, there is a gap in patient data for healthcare professionals when making diagnoses remotely. Fortunately, many users are constantly collecting some primary health data through wearables that have become commonplace in users' homes. Tapping into this unused data could provide healthcare professionals with a better picture of patients' health remotely. In this thesis, I will determine whether this wearable data can be a viable addition to Tele-Health applications, providing additional …

Multi-Style Explainable Matrix Factorization Techniques For Recommender Systems., Olurotimi Nugbepo Seton

Electronic Theses and Dissertations

Black-box recommender system models are machine learning models that generate personalized recommendations without explaining how the recommendations were generated to the user or giving them a way to correct wrong assumptions made about them by the model. However, compared to white-box models, which are transparent and scrutable, black-box models are generally more accurate. Recent research has shown that accuracy alone is not sufficient for user satisfaction. One such black-box model is Matrix Factorization, a State of the Art recommendation technique that is widely used due to its ability to deal with sparse data sets and to produce accurate recommendations. Recent …

A Fully-Automated, Deep Learning-Based Framework For Ct-Based Localization, Segmentation, Verification And Planning Of Metastatic Vertebrae, Tucker Netherton, Tucker James Netherton

Dissertations and Theses (Open Access)

Palliative radiotherapy is an effective treatment for the palliation of symptoms caused by vertebral metastases. Visible evidence of disease is localized on medical images as part of the treatment planning process. However, complicating factors such as time pressures, anatomic variants in the spine, and similarities in adjacent vertebrae are associated with wrong level treatments of the spine. In addition, erroneous manual contouring of anatomic structures is a major failure mode in radiotherapy treatment planning.

The purpose of this study is to mitigate the challenges associated with treatment planning of the spine by automating the treatment planning process for three-dimensional conformal …

Bibliometric Analysis Of Emerging Technologies In The Field Of Computer Science Helping In Ovarian Cancer Research, Sonali Kothari Dr., Anvita Gupta, Muskaan Agrawal Agrawal, Kajal Jaggi, Adhiraj Dev Goswami, Ketan Kotecha, M. Karthikeyan Dr., Vijayshri Khedkar

Library Philosophy and Practice (e-journal)

This study is carried out to provide an analysis of the literature available at the intersection of ovarian cancer and computing. A comprehensive search was conducted using Scopus database for English-language peer-reviewed articles. The study administers chronological, domain clustering and text analysis of the articles under consideration to provide high-level concept map composed of specific words and the connections between them.

Mapping Renewal: How An Unexpected Interdisciplinary Collaboration Transformed A Digital Humanities Project, Elise Tanner, Geoffrey Joseph

Digital Initiatives Symposium

Funded by a National Endowment for Humanities (NEH) Humanities Collections and Reference Resources Foundations Grant, the UA Little Rock Center for Arkansas History and Culture’s “Mapping Renewal” pilot project focused on creating access to and providing spatial context to archival materials related to racial segregation and urban renewal in the city of Little Rock, Arkansas, from 1954-1989. An unplanned interdisciplinary collaboration with the UA Little Rock Arkansas Economic Development Institute (AEDI) has proven to be an invaluable partnership. One team member from each department will demonstrate the Mapping Renewal website and discuss how the collaborative process has changed and shaped …

A Brief Bibliometric Survey Of Explainable Ai In Medical Field, Nilkanth Mukund Deshpande, Shilpa Shailesh Gite

Library Philosophy and Practice (e-journal)

_{^{Background: This study aims to analyze the work done in the field of explainability related to artificial intelligence, especially in the medical field from 2004 onwards using the bibliometric methods.}}

_{^{Methods: different articles based on the topic leukemia detection were retrieved using one of the most popular database- Scopus. The articles are considered from 2004 onwards. Scopus analyzer is used for different types of analysis including documents by year, source, county and so on. There are other different analysis tools such as VOSviewer Version 1.6.15. This is used for the analysis of different units such as co-authorship, co-occurrences, citation analysis …}}

Owsnet: Towards Real-Time Offensive Words Spotting Network For Consumer Iot Devices, Bharath Sudharsan, Sweta Malik, Peter Corcoran, Pankesh Patel, John G. Breslin, Muhammad Intizar Ali

Publications

Every modern household owns at least a dozen of IoT devices like smart speakers, video doorbells, smartwatches, where most of them are equipped with a Keyword spotting(KWS) system-based digital voice assistant like Alexa. The state-of-the-art KWS systems require a large number of operations, higher computation, memory resources to show top performance. In this paper, in contrast to existing resource-demanding KWS systems, we propose a light-weight temporal convolution based KWS system named OWSNet, that can comfortably execute on a variety of IoT devices around us and can accurately spot multiple keywords in real-time without disturbing the device's routine functionalities.

When OWSNet …

Evaluation Of State-Of-The-Art Nlp Deep Learning Architectures On Commonsense Reasoning Task, Guo Rui (Justin) Lee

Honors Theses

The goal of this project was to explore modern neural network technology in the application of discerning and generating statements that are ‘reasonable’, in what is known as commonsense reasoning. We built off of the work of Saeedi et al. In their work on the 2020 SemEval task, Commonsense Validation and Explanation (ComVE). SemEval is a workshop that creates a variety of semantic evaluation tasks to examine the state of the art in the practical application of natural language processing. This particular task involved three sections: task A, Validation, in which a program tries to select which of two statements …

Cognitive Digital Twins For Smart Manufacturing, Muhammad Intizar Ali, Pankesh Patel, John G. Breslin, Ramy Harik, Amit Sheth

Publications

Smart manufacturing or Industry 4.0, a trend initiated a decade ago, aims to revolutionize traditional manufacturing using technology-driven approaches. Modern digital technologies such as the Industrial Internet of Things (IIoT), Big Data Analytics, Augmented/Virtual Reality, and Artificial Intelligence (AI) are the key enablers of new smart manufacturing approaches. The digital twin is an emerging concept whereby a digital replica can be built of any physical object. Digital twins are becoming mainstream; many organizations have started to rely on digital twins to monitor, analyze, and simulate physical assets and processes. The current use of digital twins for smart manufacturing is largely …

Human-Machine Communication: Complete Volume. Volume 2

Human-Machine Communication

This is the complete volume of HMC Volume 2.

Side Channel Attack Counter Measure Using A Moving Target Architecture, Jithin Joseph

Electrical and Computer Engineering ETDs

A novel countermeasure to side-channel power analysis attacks called Side-channel Power analysis Resistance for Encryption Algorithms using DPR or SPREAD is investigated in this thesis. The countermeasure leverages a strategy that is best characterized as a moving target architecture. Modern field programmable gate arrays (FPGA) architectures provide support for dynamic partial reconfiguration (DPR), a feature that allows real-time reconfiguration of the programmable logic (PL). The moving target architecture proposed in this work leverages DPR to implement a power analysis countermeasure to side-channel attacks, the most common of which are referred to as differential power analysis (DPA) and correlation power analysis …