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2023

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Articles 961 - 990 of 1215

Full-Text Articles in Computer Engineering

Graphical User Interface For Evidential Reasoning Models, Rohin Gopalakrishnan Jan 2023

Graphical User Interface For Evidential Reasoning Models, Rohin Gopalakrishnan

Master's Projects

The Capri system is an evidential reasoning system based on the belief function calculus to support automated reasoning and decision making in uncertain environments. Example domains of application include, medical diagnosis, as well as identifying biological biomarkers. The purpose of this project is to build a Python web-based and app-based Graphical User Interface (GUI), called PyGrapher, that facilitates building graphical evidential reasoning models. The graphical models built using PyGrapher will then be converted to a form that is suitable for input to the Capri system. The PyGrapher system provides an intuitive means to build and manipulate evidential reasoning models as …

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Nuancenet: Comparative Analysis Of Ai In Complex Language Interpretation For Disaster Detection, Pavan Koushik Kommuri Jan 2023

Nuancenet: Comparative Analysis Of Ai In Complex Language Interpretation For Disaster Detection, Pavan Koushik Kommuri

Master's Projects

Disaster Detection using Twitter content is critical for emergency response, but accurately identifying relevant tweets remains challenging due to nuances, informal language, and emotional expressions. This paper presents a comparative analysis between traditional Machine Learning models, Deep Learning models and Large Language Models (LLM) for classifying disaster vs. non-disaster tweets. While existing works have applied pattern recognition and dataset-specific learning, LLMs with their deeper understanding of linguistics and semantics can potentially handle the complexities of tweets more effectively. This study leverages LLMs including Llama2, Mistral, and Falcon, Open AI GPT 3.5, hypothesizing their superior contextual comprehension will excel in tweets …

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Energy-Aware Ai-Driven Framework For Edge-Computing-Based Iot Applications, Muhammad Zawish, Nouman Ashraf, Rafay Iqbal Ansari, Steven Davy Jan 2023

Energy-Aware Ai-Driven Framework For Edge-Computing-Based Iot Applications, Muhammad Zawish, Nouman Ashraf, Rafay Iqbal Ansari, Steven Davy

Conference papers

The significant growth in the number of Internet of Things (IoT) devices has given impetus to the idea of edge computing for several applications. In addition, energy harvestable or wireless-powered wearable devices are envisioned to empower the edge intelligence in IoT applications. However, the intermittent energy supply and network connectivity of such devices in scenarios including remote areas and hard-to-reach regions such as in-body applications can limit the performance of edge computing-based IoT applications. Hence, deploying state-of-the-art convolutional neural networks (CNNs) on such energy-constrained devices is not feasible due to their computational cost. Existing model compression methods, such as network …

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Enhancing Zero‑Shot Action Recognition In Videos By Combining Gans With Text And Images, Kaiqiang Huang, Luis Miralles-Pechuán, Susan Mckeever Jan 2023

Enhancing Zero‑Shot Action Recognition In Videos By Combining Gans With Text And Images, Kaiqiang Huang, Luis Miralles-Pechuán, Susan Mckeever

Articles

Zero-shot action recognition (ZSAR) tackles the problem of recognising actions that have not been seen by the model during the training phase. Various techniques have been used to achieve ZSAR in the field of human action recognition (HAR) in videos. Techniques based on generative adversarial networks (GANs) are the most promising in terms of performance. GANs are trained to generate representations of unseen videos conditioned on information related to the unseen classes, such as class label embeddings. In this paper, we present an approach based on combining information from two different GANs, both of which generate a visual representation of …

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Cslinc - Development Of A National Outreach Vle, Keith Nolan, Keith Quille Jan 2023

Cslinc - Development Of A National Outreach Vle, Keith Nolan, Keith Quille

Conference Papers

Over the last year an online learning platform has been developed and piloted to the Irish second level education system allowing both students and teachers to participate in introductory computing modules. This poster will outline the development of the registration process of a system that is capable of managing potentially 728 schools, 1000+ classrooms and one million students (the entire Irish second level school system). CSLINC is an online student virtual learning environment for computing consisting of several modules built by academics and industry leaders and disseminated to schools through Moodle, our selected virtual learning environment. While Moodle has a …

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An Aggregation-Based Algebraic Multigrid Method With Deflation Techniques And Modified Generic Factored Approximate Sparse Inverses, Anastasia Natsiou, George A. Gravvanis, Christos K. Filelis-Papadopoulos, Konstantinos M. Giannoutakis Jan 2023

An Aggregation-Based Algebraic Multigrid Method With Deflation Techniques And Modified Generic Factored Approximate Sparse Inverses, Anastasia Natsiou, George A. Gravvanis, Christos K. Filelis-Papadopoulos, Konstantinos M. Giannoutakis

Articles

In this paper, we examine deflation-based algebraic multigrid methods for solving large systems of linear equations. Aggregation of the unknown terms is applied for coarsening, while deflation techniques are proposed for improving the rate of convergence. More specifically, the V-cycle strategy is adopted, in which, at each iteration, the solution is computed by initially decomposing it utilizing two complementary subspaces. The approximate solution is formed by combining the solution obtained using multigrids and deflation. In order to improve performance and convergence behavior, the proposed scheme was coupled with the Modified Generic Factored Approximate Sparse Inverse preconditioner. Furthermore, a parallel version …

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Survey Data On Dysfunctional Attitudes, Personality Traits, And Agreement With Persuasive Techniques, Annye Braca, Pierpaolo Dondio Jan 2023

Survey Data On Dysfunctional Attitudes, Personality Traits, And Agreement With Persuasive Techniques, Annye Braca, Pierpaolo Dondio

Articles

Persuasion techniques play a vital role in human commu- nication, influencing various aspects of our lives. With the increasing prevalence of digital platforms, these techniques have permeated online spaces such as websites, mobile apps, games, and social media. This article presents a dataset col- lected via a survey, designed to gather information about in- dividuals’ demographics, personality traits, dysfunctional at- titudes, and their responses to statements embedded with persuasion techniques. Core messages promoting paid news subscriptions, blood donations, and exercise serve as the fo- cus, while definitions and examples of persuasive techniques are provided. By analyzing this comprehensive dataset, re- …

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Setransformer: A Transformer-Based Code Semantic Parser For Code Comment Generation, Zheng Li, Yonghao Wu, Bin Peng, Xiang Chen, Zeyu Sun, Yong Liu, Paul Doyle Jan 2023

Setransformer: A Transformer-Based Code Semantic Parser For Code Comment Generation, Zheng Li, Yonghao Wu, Bin Peng, Xiang Chen, Zeyu Sun, Yong Liu, Paul Doyle

Conference Papers

Automated code comment generation technologies can help developers understand code intent, which can significantly reduce the cost of software maintenance and revision. The latest studies in this field mainly depend on deep neural networks, such as convolutional neural networks and recurrent neural network. However, these methods may not generate high-quality and readable code comments due to the long-term dependence problem, which means that the code blocks used to summarize information are far from each other. Owing to the long-term dependence problem, these methods forget the previous input data’s feature information during the training process. In this article, to solve the …

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A Type-2 Fuzzy Rule-Based Model For Diagnosis Of Covid-19, İhsan Şahi̇n, Erhan Akdoğan, Mehmet Emi̇n Aktan Jan 2023

A Type-2 Fuzzy Rule-Based Model For Diagnosis Of Covid-19, İhsan Şahi̇n, Erhan Akdoğan, Mehmet Emi̇n Aktan

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use in addition to existing methods in the diagnosis of COVID-19 has been developed. The developed system consists of three fuzzy units. The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, and the second fuzzy unit according to the C-reactive protein, lymphocyte, and D-dimer values obtained as a result of the blood tests. In the third fuzzy unit, the COVID-19 positivity risks according to the clinical …

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An Effective Hilbert-Huang Transform-Based Approach For Dynamic Eccentricity Fault Diagnosis In Double-Rotor Double-Sided Stator Structure Axial Flux Permanent Magnet Generator Under Various Load And Speed Conditions, Makan Torabi, Yousef Alinejad Beromi Jan 2023

An Effective Hilbert-Huang Transform-Based Approach For Dynamic Eccentricity Fault Diagnosis In Double-Rotor Double-Sided Stator Structure Axial Flux Permanent Magnet Generator Under Various Load And Speed Conditions, Makan Torabi, Yousef Alinejad Beromi

Turkish Journal of Electrical Engineering and Computer Sciences

Eccentricity fault in double-sided axial flux permanent magnet generator is very difficult to be detected as the fault generated variations in terminal electrical parameters are very weak and chaotic, especially at the initial stages of the fault occurrence. In addition, one of the most important problems in any fault diagnosis approach is the investigation of load and speed variation on the proposed indices. To overcome the aforementioned difficulty and problems, this paper adopts a novelty detection algorithm based on Hilbert-Huang transform (HHT) which is a time-frequency signal analysis approach based on empirical mode decomposition and the Hilbert transform. It is …

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Early Diagnosis Of Pancreatic Cancer By Machine Learning Methods Using Urine Biomarker Combinations, İrem Acer, Firat Orhan Bulucu, Semra İçer, Fatma Lati̇foğlu Jan 2023

Early Diagnosis Of Pancreatic Cancer By Machine Learning Methods Using Urine Biomarker Combinations, İrem Acer, Firat Orhan Bulucu, Semra İçer, Fatma Lati̇foğlu

Turkish Journal of Electrical Engineering and Computer Sciences

The most common type of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancers. The five-year survival rate for PDAC due to late diagnosis is 9%. Early diagnosed PDAC patients survive longer than patients diagnosed at a more advanced stage. Biomarkers can play an essential role in the early detection of PDAC to assist the health professional. Machine learning and deep learning methods are used with biomarkers obtained in recent studies for diagnostic purposes. In order to increase the survival rates of PDAC patients, early diagnosis of the disease with a noninvasive test …

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A New Approach To Linear Displacement Measurements Based On Hall Effect Sensors, İsmai̇l Yari̇çi̇, Yavuz Öztürk Jan 2023

A New Approach To Linear Displacement Measurements Based On Hall Effect Sensors, İsmai̇l Yari̇çi̇, Yavuz Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

Since displacement is a vital variable to be considered in many industrial applications, displacement sensing devices have been extensively studied both theoretically and experimentally. There have been also many studies on Hall effect-based displacement measurement, but for many systems linearity still remains a problem. This paper discusses different approaches to calculate the magnetic field due to a cylindrical permanent magnet and proposes a new setup geometry with 2-Hall effect sensors and a permanent magnet between them to overcome the linearity problems. Furthermore, theoretical and experimental studies of the discussed displacement sensor were presented by focusing on the linear range and …

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Adversarial Training Of Deep Neural Networks, Anabetsy Termini Jan 2023

Adversarial Training Of Deep Neural Networks, Anabetsy Termini

CCAC Theses and Dissertations

Deep neural networks used for image classification are highly susceptible to adversarial attacks. The de facto method to increase adversarial robustness is to train neural networks with a mixture of adversarial images and unperturbed images. However, this method leads to robust overfitting, where the network primarily learns to recognize one specific type of attack used to generate the images while remaining vulnerable to others after training. In this dissertation, we performed a rigorous study to understand whether combinations of state of the art data augmentation methods with Stochastic Weight Averaging improve adversarial robustness and diminish adversarial overfitting across a wide …

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Improving The Performance, Energy Efficiency And Security Of Gpus, Xin Wang Jan 2023

Improving The Performance, Energy Efficiency And Security Of Gpus, Xin Wang

Theses and Dissertations

The work in this dissertation achieves to enhance the performance, energy-efficiency, and security of the GPUs. We noticed that, as the demand of hardware resources keeps rising in GPUs, the energy consumption becomes unaffordable and places barriers for further performance boost. To resolve this issue, we have proposed several novel GPU micro-architectures that are able to assist the GPUs to execute in an energy-efficient manner. They also provide the potential for further performance enhancement in GPUs. Firstly, we proposed a GPU register packing scheme that stores multiple narrow-width operands to a single register to save register file resources. The unoccupied …

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Improving The Flexibility And Robustness Of Machine Tending Mobile Robots, Richard Ethan Hollingsworth Jan 2023

Improving The Flexibility And Robustness Of Machine Tending Mobile Robots, Richard Ethan Hollingsworth

Theses and Dissertations

While traditional manufacturing production cells consist of a fixed base robot repetitively performing tasks, the Industry 5.0 flexible manufacturing cell (FMC) aims to bring Autonomous Industrial Mobile Manipulators (AIMMs) to the factory floor. Composed of a wheeled base and a robot arm, these collaborative robots (cobots) operate alongside people while autonomously performing tasks at different workstations. AIMMs have been tested in real production systems, but the development of the control algorithms necessary for automating a robot that is a combination of two cobots remains an open challenge before the large scale adoption of this technology occurs in industry. Currently popular …

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Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutua Mutiso, Ruairí De Fréin, Ali Malik, Eliel Kibanza, Marco Sahbane, Maxime Pantel Jan 2023

Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutua Mutiso, Ruairí De Fréin, Ali Malik, Eliel Kibanza, Marco Sahbane, Maxime Pantel

Conference papers

Range anxiety poses a hurdle to the adoption of Electric Vehicles (EVs), as drivers worry about running out of charge without timely access to a Charging Point (CP). We present novel methods for optimising the distribution of CPs, namely, EV portacharge and GEECharge. These solutions distribute CPs in Dublin, in this paper, by considering the population density and Points Of Interest (POIs) or road traffic. The object of this paper is to (1) develop and evaluate methods to distribute CPs in Dublin city; (2) optimise CP allocation; (3) visualise paths in the graph network to show the most used roads …

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An Assessment Of The Effectiveness Of Using Data Analytics To Predict Death Claim Seasonality And Protection Policy Review Lapses In A Life Insurance Company, Jennifer Loftus Jan 2023

An Assessment Of The Effectiveness Of Using Data Analytics To Predict Death Claim Seasonality And Protection Policy Review Lapses In A Life Insurance Company, Jennifer Loftus

ICT

Data analytics tools are becoming increasingly common in the life insurance industry. This research considers two use cases for predictive analytics in a life insurance company based in Ireland. The first case study relates to the use of time series models to forecast the seasonality of death claim notifications. The baseline model predicted no seasonal variation in death claim notifications over a calendar year. This reflects the life insurance company’s current approach, whereby it is assumed that claims are notified linearly over a calendar year. More accurate forecasting of death claims seasonality would enhance the life insurance company’s cashflow planning …

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The Role Of Data Analytics To Address Water Stress In Africa, Khalil Beladda Jan 2023

The Role Of Data Analytics To Address Water Stress In Africa, Khalil Beladda

ICT

Water stress, a global concern transcending geographical boundaries, significantly impacts the African continent. Affecting one in three people in Africa, sustainable water management is imperative for ecological and human welfare. This research emphasizes the pivotal role of data analytics in addressing water stress challenges in Africa and beyond.

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Recurrent Neural Networks For Flash Gdp Estimates In Ireland: A Comparison With Traditional Econometric Methods, Justin Flannery Jan 2023

Recurrent Neural Networks For Flash Gdp Estimates In Ireland: A Comparison With Traditional Econometric Methods, Justin Flannery

ICT

GDP is the single most important barometer for the health of an economy. It’s an important input into the decision making processes of government, industry and state institutions such as central banks. To be useful as an indicator, GDP estimates need to be both timely and accurate. To meet the needs of users, many national statistical institutes publish early or flash estimates of GDP which are produced within 30 days after the end of a quarter. Given the long lags involved in the data collection processes which feed into GDP estimates, these flash estimates are often largely model based. Within …

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Pronostic Of Colo-Rectal Cancer (Crc) Using Machine Learning Models On Organoids Derived Of Patient, Claudia Andrea Leiva Acevedo Jan 2023

Pronostic Of Colo-Rectal Cancer (Crc) Using Machine Learning Models On Organoids Derived Of Patient, Claudia Andrea Leiva Acevedo

ICT

Colorectal Cancer (CRC) is a globally prevalent and deadly carcinoma, necessitating advanced treatment approaches. Despite ongoing advancements, the mortality rate remains high. Various biological models, including animal studies, cell lines, and the emerging organoid model, contribute to understanding molecular mechanisms. Organoids, 3D cultures derived from tumor epithelial cells, offer advantages such as enhanced diversity, genetic modification, and extended culture capabilities. Recent applications of machine learning (ML) in predicting CRC treatment responses using organoids and tissue data indicate a promising avenue for advancing personalized therapies.

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Unlocking The Pragmatics Of Emoji: Evaluation Of The Integration Of Pragmatic Markers For Sarcasm Detection, Niamh Farnham Jan 2023

Unlocking The Pragmatics Of Emoji: Evaluation Of The Integration Of Pragmatic Markers For Sarcasm Detection, Niamh Farnham

ICT

Emojis have become an integral element of online communications, serving as a powerful, under-utilised resource for enhancing pragmatic understanding in NLP. Previous works have highlighted their potential for improvement of more complex tasks such as the identification of figurative literary devices including sarcasm due to their role in conveying tone within text. However present state-of-the-art does not include the consideration of emoji or adequately address sarcastic markers such as sentiment incongruence. This work aims to integrate these concepts to generate more robust solutions for sarcasm detection leveraging enhanced pragmatic features from both emoji and text tokens. This was achieved by …

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Evaluating The Potential Of Ensemble Learning For One Day-Ahead Forecasting Of Power System Demand In Ireland, Karol Skowronski Jan 2023

Evaluating The Potential Of Ensemble Learning For One Day-Ahead Forecasting Of Power System Demand In Ireland, Karol Skowronski

ICT

Accurate One Day-Ahead Demand Forecasting (ODADF) is crucial for electrical network reliability, the environment, and trading markets. While individual models face challenges in achieving accurate predictions, ensemble learning models have emerged as potential solution. They have achieved success in ODADF in several countries; however, there has been no research conducted for the Irish power system. Therefore, research objectives were formed, to develop a framework of ensemble learning models, evaluate their performance, and examine their potential for ODADF in Ireland, to fill the gap. Experimentation, and CRISP-DM were selected as primary research methodology, and project management framework, respectively. The development of …

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Real-Time Polyp Analysis On Endoscopic Images Using Deep Learning Approach: Detection, Characterization And Size Estimation, Phanukorn Sunthornwetchapong Jan 2023

Real-Time Polyp Analysis On Endoscopic Images Using Deep Learning Approach: Detection, Characterization And Size Estimation, Phanukorn Sunthornwetchapong

Chulalongkorn University Theses and Dissertations (Chula ETD)

As medical devices advance, doctors adopt endoscopes to perform endoscopes for gastrointestinal disease screenings. For colonoscopy, skills such as polyp detection, polyp characterisation, and polyp size estimation needed to be practised while screening patients. This work aims to develop a deep learning model for performing polyp detection, characterisation, and size estimation to assist fellow doctors in these tasks. To maximise usability in assisting fellow doctors, the model must be able to perform in a real-time fashion. The work utilises existing object detection models for performing size estimation tasks. With the problems of data imbalances, we use depth information but without …

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Learned Compressive Representations For Single-Photon 3d Imaging, Felipe Gutierrez-Barragan, Fangzhou Mu, Andrei Ardelean, Atul Ingle, Claudio Bruschini, Edoardo Charbon, Yin Li, Mohit Gupta, Andreas Velten Jan 2023

Learned Compressive Representations For Single-Photon 3d Imaging, Felipe Gutierrez-Barragan, Fangzhou Mu, Andrei Ardelean, Atul Ingle, Claudio Bruschini, Edoardo Charbon, Yin Li, Mohit Gupta, Andreas Velten

Computer Science Faculty Publications and Presentations

Single-photon 3D cameras can record the time-of-arrival of billions of photons per second with picosecond accuracy. One common approach to summarize the photon data stream is to build a per-pixel timestamp histogram, resulting in a 3D histogram tensor that encodes distances along the time axis. As the spatio-temporal resolution of the histogram tensor increases, the in-pixel memory requirements and output data rates can quickly become impractical. To overcome this limitation, we propose a family of linear compressive representations of histogram tensors that can be computed efficiently, in an online fashion, as a matrix operation. We design practical lightweight compressive representations …

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Leveraging Signal Transfer Characteristics And Parasitics Of Spintronic Circuits For Area And Energy-Optimized Hybrid Digital And Analog Arithmetic, Adrian Tatulian Jan 2023

Leveraging Signal Transfer Characteristics And Parasitics Of Spintronic Circuits For Area And Energy-Optimized Hybrid Digital And Analog Arithmetic, Adrian Tatulian

Electronic Theses and Dissertations, 2020-2023

While Internet of Things (IoT) sensors offer numerous benefits in diverse applications, they are limited by stringent constraints in energy, processing area and memory. These constraints are especially challenging within applications such as Compressive Sensing (CS) and Machine Learning (ML) via Deep Neural Networks (DNNs), which require dot product computations on large data sets. A solution to these challenges has been offered by the development of crossbar array architectures, enabled by recent advances in spintronic devices such as Magnetic Tunnel Junctions (MTJs). Crossbar arrays offer a compact, low-energy and in-memory approach to dot product computation in the analog domain by …

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Analyzing Ground Motion Records With Cvi Fuzzy Art, Dustin Tanksley, Xinzhe Yuan, Genda Chen, Donald C. Wunsch Jan 2023

Analyzing Ground Motion Records With Cvi Fuzzy Art, Dustin Tanksley, Xinzhe Yuan, Genda Chen, Donald C. Wunsch

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This paper explores using Cluster Validity Indices Fuzzy Adaptative Resonance Theory (CVI Fuzzy ART) to cluster ground motion records (GMRs). Clustering the features extracted from a supervised network trained for predicting the structure damage results in less overfitting from the trained network. Using Cluster Validity Indices (CVIs) to evaluate the clustering gives feedback to how well the data is being classified, allowing further separation of the data. By using CVI Fuzzy ART in combination with features extracted from a trained Convolutional Neural Network (CNN), we were able to form additional clusters in the data. Within the primary clusters, accuracy was …

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Optimized Deep Learning Audio Tagging Approach, Fatma S. El-Metwally, Ali I. Eldesouky, Sally M. Elghamrawy Jan 2023

Optimized Deep Learning Audio Tagging Approach, Fatma S. El-Metwally, Ali I. Eldesouky, Sally M. Elghamrawy

Mansoura Engineering Journal

Audio signal processing is a method for applying powerful algorithms and techniques to record, improve, save and transmit audio content signals. Audio Tagging (AT) is a challenge that requires predicting the tags of audio clips. Developments in deep learning and audio signal processing have resulted in a significant improvement in audio tagging. Many techniques have been used. Several studies have introduced different audio tagging techniques, but the performance of the results obtained from these studies is insufficient. This study proposes an Optimized Deep Learning Audio Tagging (ODLAT] approach to classify and analyze audio tagging. Each input signal is used to …

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Parallel Real Time Rrt*: An Rrt* Based Path Planning Process, David Yackzan Jan 2023

Parallel Real Time Rrt*: An Rrt* Based Path Planning Process, David Yackzan

Theses and Dissertations--Mechanical and Aerospace Engineering

This thesis presents a new parallelized real-time path planning process. This process is an extension of the Real-Time Rapidly Exploring Random Trees* (RT-RRT*) algorithm developed by Naderi et al in 2015 [1]. The RT-RRT* algorithm was demonstrated on a simulated two-dimensional dynamic environment while finding paths to a varying target state. We demonstrate that the original algorithm is incapable of running at a sufficient rate for control of a 7-degree-of-freedom (7-DoF) robotic arm while maintaining a path planning tree in 7 dimensions. This limitation is due to the complexity of maintaining a tree in a high-dimensional space and the network …

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Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat Jan 2023

Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat

Graduate Theses/Dissertations

Social media has become a domain that involves a lot of hate speech. Some users feel entitled to engage in abusive conversations by sending abusive messages, tweets, or photos to other users. It is critical to detect hate speech and prevent innocent users from becoming victims. In this study, I explore the effectiveness and performance of various machine learning methods employing text processing techniques to create a robust system for hate speech identification. I assess the performance of Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and K Nearest Neighbors using three distinct datasets sourced from social …

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Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance For Telehealth: The Mental Health Case, Kaushik Roy, Vedant Khandelwal, Raxit Goswami, Nathan Dolbir, Jinendra Malekar, Amit Sheth Jan 2023

Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance For Telehealth: The Mental Health Case, Kaushik Roy, Vedant Khandelwal, Raxit Goswami, Nathan Dolbir, Jinendra Malekar, Amit Sheth

Publications

After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient understanding, (b) Safety-constrained and medically validated chatbot patient interactions, and (c) Support for continued feedback-based refinements in design using chatbot-patient interactions. We propose Alleviate, a chatbot designed to assist patients suffering from mental health challenges with personalized care and assist clinicians with understanding their patients better. Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed …

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