Computer Engineering Commons^™

Identification Of Copy Number Variations (Cnvs) Of Epigenetic Factors Related To The Progression Of Pancreatic Ductal Adenocarcinoma (Pdac), Pavithra Raju

Master's Projects

Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge in oncology due to its aggressive form and late-stage detection. PDAC is known to be influenced by various epigenetic factors like DNA methylation and histone modifications. This study focuses on copy number variations (CNVs) within epigenetic factors which for their role in early diagnosis. Thus, paving the way for identification of potential biomarkers. The epigenetic pipeline was extended based on CNVs and the CNV modified sequences extracted were compared with the wild type sequences of epigenetic PDAC genes. The epigenetic gene KCNJ11 with copy number gain of CNV id 46771406 was used …

Gesture Recognition Of Sign Language Alphabet Using Machine Learning Techniques, Gursimran Singh

Master's Projects

With the rising incidence of hearing loss, effective sign language recognition has become crucial for enhancing communication for individuals with hearing impairments. Traditional sensor-based recognition systems have been challenged by the complexities of realworld settings, prompting a shift toward more adaptable vision-based recognition systems. Distinct from previous studies, this work pioneers the use of ensemble methods with advanced filtering techniques on the Sign Language MNIST dataset, offering a novel perspective on sign language recognition. This research delves into the intersection of machine learning and image processing to develop a robust framework for sign language recognition. A range of filters, including …

Temporal Dilation In Video Resnet For Sign Language Translation, Xiaoqian Yang

Master's Projects

Sign languages, vital for communication among the deaf and hard-of-hearing (DHH) people, face a significant linguistic diversity challenge with over 200 distinct sign languages worldwide. Bridging this communication gap is a priority. Traditional tools like interpreters and costly translation devices have limitations. This project aims to use deep learning techniques to develop a model capable of recognizing sign language from short videos. Our model not only recognizes the sign from a single video clip, but is also capable of making prediction of consecutive pairs of signs. To achieve zero-short gesture sequence recognition, we propose a novel temporal dilation strategy, converting …

Pygrapherconnect, Shubham Jain

Master's Projects

The evolving landscape of backend computational systems especially in biomedical research involving heavy data operations which have a gap of not being used properly. It is due to the lack of communication standard between the frontend and backend. This gap presents a problem to researchers who need to use the frontend for visualizing and manipulating their data but also want to do complex analysis. CAPRI a python-based backend system specializing in analyzing Evidential Reasoning data also has the same issue. This project offers a solution PyGrapherConnect module acting as a data conversion layer between CAPRI and PyGrapher, its frontend interface. …

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 …

Prediction Of 2024 Indian Pm Election Results Using Sentiment Analysis On Twitter Data, Surabhi Gupta

Master's Projects

This sentiments analysis study presents a methodical approach to predict the 2024 Indian Prime Minister Election. Data collected spanning from 2020 to 2023 from Twitter using hashtags such as IndianPMElection2024 and on topics such as the revocation of the special status of Jammu and Kashmir, the Farm Bill, and the Digital India initiative, form the core of this research. We utilized a combination of sentiment extraction tools-namely, the NLP Town's Bidirectional Encoder Representations from Transformers (BERT)-based multilingual uncased sentiment model, Valance Aware Dictionary for Sentiment Reasoning (VADER), and TextBlob. Additionally, we used a well-established machine learning model Naive Bayes, deep …

Multimap Implementation In Openjdk, Nishant Yadav

Master's Projects

A key-value pair is an elementary data model in which a unique key is associated with a given value. This association between the key and the value allows for a quick lookup of data based on the key and hence is extensively used in programming languages, NoSQL databases, caches, session management, etc. In Java OpenJDK, this elementary data model is implemented by the interface Map, which allows efficient storage and retrieval of data but can only store a single value against each key. In this project, we have implemented a MultiMap data structure in OpenJDK which allows associating multiple values …

Serverless Architecture For Machine Learning, Ikshaku Goswami

Master's Projects

Serverless computing is an area under cloud computing which does not require individual management of cloud infrastructure and services. It is the groundwork behind Function as a Service or FaaS cloud computing technique. FaaS provides a stateless event-driven orchestration of functions and services for applications deployed in the cloud, without having to manage the servers and other infrastructure resources. This event driven architecture is being well utilized to manage different web-applications and services. Machine learning can bring a unique challenge to serverless computing, as it involves high-intensive tasks which requires voluminous data. In such a scenario it becomes essential to …

Uncertainty-Aware And Explainable Artificial Intelligence For Identification Of Human Errors In Nuclear Power Plants, Bhavya Reddy Kotla

Master's Projects

Nuclear Power Plants (NPPs) can face challenges in maintaining standard operations due to a range of issues, including human mistakes, mechanical breakdowns, electrical problems, measurement errors, and external influences. Swift and precise detection of these issues is crucial for stabilizing the NPPs. Identifying such operational anomalies is complex due to the numerous potential scenarios. Additionally, operators need to promptly discern the nature of an incident by tracking various indicators, a process that can be mentally taxing and increase the likelihood of human errors. Inaccurate identification of problems leads to inappropriate corrective actions, adversely affecting the safety and efficiency of NPPs. …

Image-Based Malware Classification On Noise Extraction, Venkata Sai Sathwik Nadella

Master's Projects

Any malicious software designed to cause harm or damage to a computer system can be termed as malware. One common form of malware is as executable files. Such files are often used as a delivery mechanism for malware since they can be easily disguised as legitimate software and can be executed without raising suspicion. They are often used to exploit vulnerabilities in software, allowing malware to bypass security measures and gain access to sensitive information.

There are several methods used to detect malware in executable files, including Signaturebased detection, Behavioral-based detection, Heuristic-based detection, Sandboxing, Machine Learning and Artificial Intelligence (AI). …

Governance-Aware Cloud Architectures For Enterprise Information Systems, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Enterprise cloud adoption has accelerated dramatically, yet governance frameworks struggle to keep pace with evolving architectural complexity. This research examines how organizations can embed governance principles directly into cloud architecture designs rather than treating compliance as an afterthought. We investigate the integration of regulatory requirements, risk management protocols, and organizational policies into cloud infrastructure patterns that enforce governance automatically. The study addresses critical gaps where traditional governance approaches fail in dynamic cloud environments, particularly around data sovereignty, access control, audit requirements, and regulatory compliance. Through analysis of existing cloud governance challenges and architectural patterns, we propose a comprehensive framework that …

Post-Implementation Erp Value Realization: A Decision Intelligence Framework, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Enterprise Resource Planning systems represent substantial organizational investments, yet many organizations struggle to realize expected benefits after implementation. This research develops a decision intelligence framework specifically designed to maximize ERP value realization during the critical post-implementation phase. While extensive literature addresses ERP implementation challenges, significantly less attention focuses on extracting value after systems go live. Our framework integrates data analytics, organizational learning, and strategic decision-making into a cohesive approach that transforms ERP systems from operational tools into strategic assets. Through analysis of post-implementation patterns across multiple organizations, we identify key decision points where intelligent interventions dramatically improve value capture. The …

Erp As A Digital Backbone: Redefining Enterprise Systems For Continuous Value Creation, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Enterprise Resource Planning systems have evolved from transactional processing tools into strategic digital backbones that orchestrate organizational value creation. This research examines how modern ERP implementations transcend traditional operational efficiency goals to enable continuous innovation, real-time decision-making, and ecosystem integration. Through analysis of contemporary ERP architectures and their impact on organizational capabilities, we demonstrate that successful digital transformation requires reconceptualizing ERP not as a software package but as an adaptive infrastructure supporting diverse business models. Our findings reveal that organizations treating ERP as a digital backbone achieve 35% higher agility scores and 42% faster time-to-market for new capabilities compared to …

A Semantic Web Approach To Fault Tolerant Autonomous Manufacturing, Fadi El Kalach, Ruwan Wickramarachchi, Ramy Harik, Amit Sheth

Publications

The next phase of manufacturing is centered on making the switch from traditional automated to autonomous systems. Future factories are required to be agile, allowing for more customized production, and resistance to disturbances. Such production lines would be able to reallocate resources as needed and minimize downtime while keeping up with market demands. These systems must be capable of complex decision-making based on parameters such as machine status, sensory/IoT data, and inspection results. Current manufacturing lines lack this complex capability and instead focus on low-level decision-making on the machine level without utilizing the generated data to its full extent. This …

The Troubling Emergence Of Hallucination In Large Language Models--An Extensive Definition, Quantification, And Prescriptive Remediations, Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, Amitava Das

Publications

The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a finegrained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) …

Persuasive Communication Systems: A Machine Learning Approach To Predict The Effect Of Linguistic Styles And Persuasion Techniques, Annye Braca, Pierpaolo Dondio

Articles

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

Performance Evaluation Of Ingenious Crow Search Optimization Algorithm For Protein Structure Prediction, Ahmad M. Alshamrani, Akash Saxena, Shalini Shekhawat, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious Crow Search Algorithm (ICSA). First, the algorithm is tested on standard mathematical functions with known properties. Then, the application …

Blockchain-Enabled Authenticated Key Agreement Scheme For Mobile Vehicles-Assisted Precision Agricultural Iot Networks, Anusha Vangala, Ashok Kumar Das, Ankush Mitra, Sajal K. Das, Youngho Park

Computer Science Faculty Research & Creative Works

Precision Farming Has a Positive Potential in the Agricultural Industry Regarding Water Conservation, Increased Productivity, Better Development of Rural Areas, and Increased Income. Blockchain Technology is a Better Alternative for Storing and Sharing Farm Data as It is Reliable, Transparent, Immutable, and Decentralized. Remote Monitoring of an Agricultural Field Requires Security Systems to Ensure that Any Sensitive Information is Exchanged Only among Authenticated Entities in the Network. to This End, We Design an Efficient Blockchain-Enabled Authenticated Key Agreement Scheme for Mobile Vehicles-Assisted Precision Agricultural Internet of Things (IoT) Networks Called AgroMobiBlock. the Limited Existing Work on Authentication in Agricultural Networks …

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 …

Past Challenges And The Future Of Discrete Event Simulation, Andrew J. Collins, Farinaz Sabz Ali Pour, Craig A. Jordan

Engineering Management & Systems Engineering Faculty Publications

The American scientist Carl Sagan once said: “You have to know the past to understand the present.” We argue that having a meaningful dialogue on the future of simulation requires a baseline understanding of previous discussions on its future. For this paper, we conduct a review of the discrete event simulation (DES) literature that focuses on its future to understand better the path that DES has been following, both in terms of who is using simulation and what directions they think DES should take. Our review involves a qualitative literature review of DES and a quantitative bibliometric analysis of the …

Modeling Wealth Distribution In A Society, Dylan Berns, Peihsien Sun, Adrian V. Gheorghe

Engineering Management & Systems Engineering Faculty Publications

The interconnectedness of social mood, changing dynamics, income inequality, and wealth distribution underscores the complexity of understanding and addressing these issues. This complexity inspires researchers to develop models and conduct further research to gain insights into the mechanisms driving income inequality and wealth distribution. By studying these phenomena more comprehensively, one can aim to develop strategies and policies that promote a more equitable distribution of wealth and opportunities, thereby fostering social stability and economic prosperity. In the present paper, there was build a model on wealth distribution and income inequality to help people understand the complexities of wealth inequality and …

A Novel Approach To Detecting And Mitigating Keyloggers, Damilola Osedumbi Elelegwu

College of Graduate Studies: Theses & Dissertations

As the digital world gets increasingly ingrained in our daily lives, cyberattacks—especially those involving malware—are growing more complex and common, which calls for developing innovative safeguards. Keylogger spyware, which combines keylogging and spyware functionalities, is one of the most insidious types of cyberattacks. This malicious software stealthily monitors and records user keystrokes, amassing sensitive data, such as passwords and confidential personal information, which can then be exploited. This research work introduces a novel browser extension designed to thwart keylogger spyware attacks effectively. The extension is underpinned by a cutting-edge algorithm that meticulously analyzes input-related processes, promptly identifying and flagging any …

An Optimized Deep Learning-Based Framework For Predicting Diabetes Mellitus Using Ffnn, Norhan S. Elmongy, Sally M. Elghamrawy, Amr M. T. Ali-Eldin, Ali I. Eldesouky

Mansoura Engineering Journal

Diabetes mellitus (DM) is a major public health problem in Egypt, and the illness is regarded as a contemporary epidemic across the world. Diabetes is becoming more common, which is a cause for serious concern. As a result, precise and timely identification of the illness is critical. Health and research institutions have also recently expressed a serious interest in developing and implementing cutting-edge healthcare systems. Therefore, it is necessary to accurately and quickly identify the condition. To solve this issue, scientific research has been carried out, but the outcomes have fallen short. Four layers make up the proposed Diabetes mellitus …

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 …

Enhanced Load Balancing Based On Hybrid Artificial Bee Colony With Enhanced Β-Hill Climbing In Cloud, Maha Zeedan, Gamal Attiya, Nawal El-Fishawy

Mansoura Engineering Journal

This paper proposes enhanced load balancer based artificial bee colony and β-Hill climbing for improving the performance metrics such as response time, processing cost, and utilization to avoid overloaded or under loaded situations of virtual machines. In this study, the suggested load balancer is called enhanced load balancing based on hybrid artificial bee colony with enhanced β-Hill climbing (ELBABCEβHC) to improve the response time, processing cost and the resource utilization. Our proposed approach starts by ranking the task then the greedy randomized adaptive search procedure (GRASP) is used in initializing populations. Further, the binary artificial bee colony (BABC) enhanced with …

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 …

Does Self-View Mode Generate Video Conferencing Fatigue? An Experiment Using Eeg Signals, Jin Xu, Eoin Whelan, Ann O'Brien, Denis O’Hora

Conference papers

The ability to see or hide one’s own image is a typical feature of video conferencing platforms. This study will conduct an EEG-based neurobiological experiment to determine if the self-view mode generates video conference fatigue and if this differs between males and females. 40 volunteers will participate in a simulated video conference meeting with the self-view mode on and off at different times. In addition, an EEG-based fatigue monitor will be proposed to demonstrate the level of human mental fatigue. The experimental insights will provide direct biological evidence of the impact of video conferencing features on the user experience and …

Medical Concept Mention Identification In Social Media Posts Using A Small Number Of Sample References, Vasudevan Nedumpozhimana, Sneha Rautmare, Meegan Gower, Maja Popovic, Nishtha Jain, Patricia Buffini, John Kelleher

Conference papers

Identification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model on Twitter, Reddit, and News/Media datasets.

Co-Design Of An Interactive Wellness Park: Exploring Design Requirements For A Multimodal Outdoor Physical Web Installation With Older Adults, Fatima Badmos

Academic Posters Collection

The global demographic landscape is experiencing a notable shift, characterised by a growing proportion of adults over 60. According to projections, the proportion of individuals aged 60 and above is expected to reach one-sixth of the global population by 2030. Furthermore, by 2050, this demographic is projected to exceed a staggering two billion people. Amidst this shift, there is an urgent need to develop interactive and innovative solutions to address older adults' unique challenges, particularly in outdoor physical activity.

A co-design methodology involving older adults’ participation from the idea generation to the application development process will be adopted to address …

Automation, Ai, And Future Skills Needs: An Irish Perspective, Raimunda Bukartaite, Daire Hooper

Articles

This study explores insights from key stakeholders into the skills they believe will be necessary for the future of work as we become more reliant on artificial intelligence (AI) and technology. The study also seeks to understand what human resource policies and educational interventions are needed to support and take advantage of these changes.