Computer Engineering Commons^™

Process Knowledge-Infused Learning For Suicidality Assessment On Social Media, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential to reduce the burden on humans by providing quality assistance at scale. However, current methods rely on the traditional pipeline of predicting labels from data, thus completely ignoring the process and guidelines used to obtain the labels. Furthermore, post hoc explanations on the data to label prediction using explainable AI (XAI) models, while satisfactory to computer scientists, leave much to be desired to the end users due …

Comparing Symbolic And Connectionist Algorithms For Correlating The Age Of Healthy Children With Sigma-Lognormal Neuromuscular Parameters, Zigeng Zhang, Christian O'Reilly, Rejean Plamondon

Publications

It is important to accurately evaluate the motor control maturity to help physicians diagnose delayed or abnormal motor development in children. Traditionally, it has been challenging to design assessment methods that are practical and accurate at the same time. This study aims to develop an effective algorithm to predict motor control maturity based on the Kinematic Theory of rapid human movements. We used handwritten pen strokes made on an electronic tablet by 513 children (5.5 to 13 years of age). We considered two types of movements: a single stroke and a triangle drawing test. For the analysis, Sigma-Lognormal parameters were …

Ksat: Knowledge-Infused Self Attention Transformer - Integrating Multiple Domain-Specific Contexts, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit P. Sheth

Publications

Domain-specific language understanding requires integrating multiple pieces of relevant contextual information. For example, we see both suicide and depression related behavior (multiple contexts) in the text “I have a gun and feel pretty bad about my life, and it wouldn’t be the worst thing if I didn’t wake up tomorrow”. Domain specificity in self-attention architectures is handled by fine-tuning on excerpts from relevant domain specific resources (datasets and external knowledge - medical textbook chapters on mental health diagnosis related to suicide and depression). We propose a modified self-attention architecture Knowledge infused Self Attention Transformer (KSAT) that achieves the integration of …

Wise Causal Models: Wisdom Infused Semantics Enhanced Causal Models - A Study In Suicidality Diagnosis, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Sanjay Chandrasekar, Amit Sheth

Publications

The COVID-19 Pandemic has highlighted the gap between the number of mental health care seekers and care providers. Netizens have taken to internet-based platforms such as Reddit to express their experiences. Mental illness diagnosis processes have clinically accepted causal interpretations and semantics. Curiously, mental illness diagnosis accuracy is low relative to similar well-studied illnesses. Motivated by this discrepancy, we propose Wisdom Infused Semantics Enhanced (WISE) causal models, inspired by the wisdom of the crowd idea that learns from a collective agreement among causal models and their semantics for mental illness diagnoses. We use suicidality diagnosis task descriptions, datasets, and baseline …

Knowledge-Infused Reinforcement Learning, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Virtual health agents (VHAs) have received considerable attention, but the early focus has been on collecting data, helping patients follow generic health guidelines, and providing reminders for clinical appointments. While presenting the collected data and frequency of visits to the clinician is useful, further context and personalization are needed for a VHA to interpret and understand what the data means in clinical terms. This has made their use in managing health limited. Such understanding enables patient empowerment and self-appraisal – i.e., aiding the patient in interpreting the data to understand the changes in the patient’s health conditions, and self-management – …

Career Engine, Sai Akhil Gujja

All Capstone Projects

People's education is so widely available in this age of tough competition that openings for them in jobs are getting harder to find. Companies need people in their fields with a solid educational foundation and maximum years of job experience. Finding people who are talented, intelligent, and competent enough to be given a position at that time is challenging. Companies are working harder than ever to find people who can meet their needs.[1] Thousands of applicants are competing for one job opening. When considering these difficulties, one may come up with a strategy or approach that can help manage and …

R Stack Career, Hari Krishna Patti

All Capstone Projects

The Career Engine's main purpose is to create a platform for job searchers to find suitable and gratifying work based on their qualifications. It also connects job searchers with the recruiting process. This career engine recognizes when a job seeker applies for a job and when a company publishes a job and chooses a candidate. A career engine's principal objective is to aid job searchers in getting a quick career. In today's society, there are several employment portal platforms. In this case, we must create a web-based application utilizing the Career Engine. In this period of decline, everyone, experienced or …

Career Engine (Jobesy), Vijaya Lakshmi Patti

All Capstone Projects

In today's world, finding a job is like a rat race in every professional field. The scenario is such that finding jobs that fit the skill sets and interests takes much work for job seekers. On the other side, recruiters face similar challenges in finding the finest candidate to match their requirements.

Talent jet is a website with unique ingredients to solve all these challenges. Talent jet provides an easy and convenient search application for job seekers to find their desired job and recruiters to find suitable candidates. Talent jet will provide a platform where job seekers and recruiters can …

Career Engine, Anoop Vivek Sagar Mallavarapu

All Capstone Projects

People's education is so widely available in this age of tough competition that openings for them in jobs are getting harder to find. Companies need people in their fields with a solid educational foundation and maximum years of job experience. Finding people who are talented, intelligent, and competent enough to be given a position at that time is challenging. Companies are working harder than ever to find people who can meet their needs.[1] Thousands of applicants are competing for one job opening. When considering these difficulties, one may come up with a strategy or approach that can help manage and …

Study On Performance Of Pruned Cnn-Based Classification Models, Mengling Deng

Electronic Theses and Dissertations

Convolutional Neural Network (CNN) is a neural network developed for processing image data. CNNs have been studied extensively and have been used in numerous computer vision tasks such as image classification and segmentation, object detection and recognition, etc. [1] Although, the CNNs-based approaches showed humanlevel performances in these tasks [2], they require heavy computation in both training and inference stages, and the models consist of millions of parameters. This hinders the development and deployment of CNN-based models for real world applications. Neural Network Pruning and Compression techniques have been proposed [3, 4] to reduce the computation complexity of trained CNNs …

Real-Time Image Classification For Malignant Biliary Strictures On Cholangioscopy Images Based On Deep Learning Approach, Passakron Phuangthongkham

Chulalongkorn University Theses and Dissertations (Chula ETD)

It is challenging to determine if the cause of bile duct strictures is benign or malignant. Currently, endoscopists may more precisely inspect the bile duct thanks to computerized single-operator cholangioscopy. As a result, lesions in the bile duct can be seen with the naked eye. However, endoscopists continue to diagnose patients differently. Consequently, a biopsy is typically regarded as the gold standard. The necessity to repeat operations results from a biopsy sample mistake that results in a false-negative cancer diagnosis. In this study, we suggest a convolutional neural network developed particularly for real-time malignant biliary stricture classification. Our approach, which …

Real-Time Gastric Intestinal Metaplasia Semantic Segmentation With Multiple Abnormalities Using Deep Learning Approach, Passin Pornvoraphat

Chulalongkorn University Theses and Dissertations (Chula ETD)

This thesis declares the segmentation of gastric intestinal metaplasia (GIM) in real-time. Recently, GIM segmentation of endoscopic images has been conducted to distinguish GIM from a healthy stomach. However, achieving real-time detection is difficult. Challenging conditions include multiple color modes (white light endoscopy and narrow-band imaging), other abnormal lesions (erosion and ulcer), noisy labels, etc. Herein, our model is based on BiSeNet and can overcome the many issues regarding GIM. Applying auxiliary head and loss boosts the performance on multiple color modes. In addition, pre-processing techniques, including location-wise negative sampling, jigsaw augmentation, and label smoothing, are utilized to improve detection …

Formal Models For Consent Management In Healthcare Software System Development, Neda Peyrone

Chulalongkorn University Theses and Dissertations (Chula ETD)

In the era of data-driven opportunities, many businesses are missing the data-privacy challenge, which leads to risks in safeguarding their customers’ data. To empower individuals (data subjects) to control their data, the General Data Protection Regulation (GDPR) mandated businesses or organizations (data controllers) to protect individuals’ data (personal data) within data protection law. Nevertheless, many businesses still struggle to enhance and develop their software systems to comply with the GDPR because it is difficult to interpret and apply to software development practices. Besides, the processing of personal data begins when the data subject provides explicit consent to the data controller, …

Deep Reinforcement Learning For Electricity Energy Trading, Manassakan Sanayha

Chulalongkorn University Theses and Dissertations (Chula ETD)

The deregulation and liberalization of the energy market in the 1990s prompted short-term electricity trading, allowing energy markets to produce net output over a range of time periods as a result of this decentralized system, most commonly minutes to days ahead of time. The energy industry urgently requires a system that has undergone substantial modernization in place to handle a variety of issues, including the current climate, renewable resources, and the energy framework. In this dissertation, we investigate a deep reinforcement learning framework for both wholesale and local energy trading, which probes the challenge of RL to optimize the real-world …

Recasnet: Reducing Mismatch Within The Two-Stage Mitosis Detection Framework, Chawan Piansaddhayanon

Chulalongkorn University Theses and Dissertations (Chula ETD)

Mitotic count (MC) is an important histological parameter for cancer diagnosis and grading, but the manual process to obtain this metric is tedious and not fully reproducible across different pathologists. To mitigate this problem, several deep learning models have been utilized to speed up the process. Typically, the problem is formulated as a two-stage deep learning pipeline: the detection stage for proposing the potential candidates for mitotic cells and the classification stage for refining prediction confidences from the former stage. However, this paradigm can lead to inconsistencies in the classification stage due to the poor prediction quality of the detection …

Leverage Graph Neural Network For Molecular Properties Prediction, Kamol Punnachaiya

Chulalongkorn University Theses and Dissertations (Chula ETD)

During the age of deep learning technologies, which have exhibited significant potential in reducing costs and expediting medical development, predicting molecular properties has become a prevalent task that capitalizes on the capabilities of deep learning. This thesis proposed a multimodal Graph Neural Network (GNN) model that utilizes the topology information obtained from molecular graphs through a baseline GNN, facilitating precise property predictions. The thesis improves the baseline CMPNN model by exploring various methods to address potential missing gaps. These methods include incorporating the multimodal module, such as a Bidirectional LSTM module capable of processing text sequences in SMILES format or …

Removing Physical Presence Requirements For A Remote And Automated World - Api Controlled Patch Panel For Conformance Testing, Hunter George Wells

Honors Theses and Capstones

Quality assurance test engineers at the UNH-InterOperability Lab must run tests that require driving and monitoring a selection of DC signals. While the number of signals is numerous, there are limited ports on the test equipment, and only a few signals need patching for any given test. The selection of signals may vary between the 209 different tests and must be re-routed frequently. Currently, testers must leave their desk to manually modify the test setup in another room. This posed a considerable issue at the onset of the COVID-19 Pandemic when physical access was not possible. In order to enable …

A Comparison Of Two Generalizations To The Linear Sampling Method For Inverse Scattering, Yeasmin Sultana, James E. Richie

Electrical and Computer Engineering Faculty Research and Publications

The linear sampling method (LSM) is a very popular method for determining the boundary of an object from the scattered field. However, there are instances where LSM provides the convex hull of the boundary rather than the true boundary. There are two common generalizations to LSM: the Generalized Linear Sampling Method (GLSM) and the Multipoles-based Linear Sampling Method (MLSM). In this paper, the ability of GLSM and MLSM to overcome some of the deficiencies of LSM are investigated. It is found that GLSM may be ideal for imaging thin features of scatterers and that MLSM can provide an improvement over …

Spectrum Sensing With Energy Detection In Multiple Alternating Time Slots, Călin Vlădeanu, Alexandru Marţian, Dimitrie C. Popescu

Electrical & Computer Engineering Faculty Publications

Energy detection (ED) represents a low complexity approach used by secondary users (SU) to sense spectrum occupancy by primary users (PU) in cognitive radio (CR) systems. In this paper, we present a new algorithm that senses the spectrum occupancy by performing ED in K consecutive sensing time slots starting from the current slot and continuing by alternating before and after the current slot. We consider a PU traffic model specified in terms of an average duty cycle value, and derive analytical expressions for the false alarm probability (FAP) and correct detection probability (CDP) for any value of K . Our …

The Implementation Of Advanced Digitalization In The Oil And Gas Industry, Ensieh Rezafar

Walden Dissertations and Doctoral Studies

AbstractThe immature advanced digitalization in the oil and gas industry can limit access to the potential value of mature progressive digitalization. Oil and gas leaders must expedite the implementation of advanced digitalization to increase organizational proficiencies and reduce costs and losses. Grounded in Pettigrew and Whipp’s dimensions of strategic change theory, the purpose of this qualitative multiple case study was to explore strategies some leaders in the oil and gas industry used to implement advanced digitalization. The participants were seven leaders in two oil service companies in North America involved in the successful implementation of progressive digitalization. Data were collected …