Digital Commons Network^™

Provable Security In Idealised Models, Chandranan Dhar

Doctoral Theses

This thesis is a compilation of provable security analyses of various cryptographic constructions in idealised models. The first construction examined is the ABR hash. We revisit the existing proof of the ABR hash in the random oracle model and identify significant errors in the proof. Although we are unable to correct the original proof, we establish the security of the ABR tree of height 3 from scratch, addressing the first non-trivial case. As our second contribution, we conduct a tight and comprehensive security analysis of the Ascon AEAD mode in the random permutation model. We show that the efficiency of …

Stochastic Gradient Descent-Based Inference For Dynamic Network Models With Attractors, Hancong Pan, Xiaojing Zhu, Cantay Caliskan, Dino P. Christenson, Konstantinos Spiliopoulos, Dylan Walker, Eric D. Kolaczyk

Business Faculty Articles and Research

In Coevolving Latent Space Networks with Attractors (CLSNA) models, nodes in a latent space represent social actors, and edges indicate their dynamic interactions. Attractors are added at the latent level to capture the notion of attractive and repulsive forces between nodes, borrowing from dynamical systems theory. However, CLSNA reliance on MCMC estimation makes scaling difficult, and the requirement for nodes to be present throughout the study period limit practical applications. We address these issues by (i) introducing a Stochastic gradient descent (SGD) parameter estimation method, (ii) developing a novel approach for uncertainty quantification using SGD, and (iii) extending the model …

Human-Ai Collaboration In Writing: A Multidimensional Framework For Creative And Intellectual Authorship, James Hutson

Faculty Scholarship

The integration of AI technologies into the writing process has significantly altered traditional notions of authorship, creativity, and intellectual labor. Historically, writing was seen as a human-driven cognitive and creative exercise, but with the rise of generative AI tools such as ChatGPT and Claude, the line between human and AI contributions has become increasingly ambiguous. This paper addresses the limitations of the current sliding scale model, which views AI involvement as ranging from “none” to “complete”. In its place, we propose a new multidimensional framework that more accurately reflects the complexity of human-AI collaboration in writing. The model includes axes …

Beyond The Blue Skies: A Comprehensive Guide For Risk Assessment In Aviation, Leila Halawi, Mark Miller, Sam Holley

Publications

Risk assessment in aviation is a critical process that safeguards the safety and reliability of operations. Aviation operations encompass inherent risks, from mechanical failures to human errors and environmental factors. The significance of these risks may be severe, leading to accidents, injuries, and loss of life. Recognizing and mitigating risks is supreme in this dynamic environment, where emerging technologies and innovation constantly reshape this industry. This chapter includes an in-depth explanation of risk management and analysis, leading to the core elements of risk assessment specifically for aviation operations. We will describe the process and explore some of the applications and …

A Review Of Racial Differences And Disparities In Ecg, Jianwei Zheng, Chizobam Ani, Islam Abudayyeh, Yunfan Zheng, Cyril Rakovski, Ehsan Yaghmaei, Omolola Ogunyemi

Mathematics, Physics, and Computer Science Faculty Articles and Research

The electrocardiogram (ECG) is a widely used, non-invasive tool for diagnosing a range of cardiovascular conditions, including arrhythmia and heart disease-related structural changes. Despite its critical role in clinical care, racial and ethnic differences in ECG readings are often underexplored or inadequately addressed in research. Variations in key ECG parameters, such as PR interval, QRS duration, QT interval, and T-wave morphology, have been noted across different racial groups. However, the limited research in this area has hindered the development of diagnostic criteria that account for these differences, potentially contributing to healthcare disparities, as ECG interpretation algorithms largely developed from major …

A Novel Preprocessing Model For Multi Modal Brain Mri Image Classification For Stroke Prognosis, Alwin Joseph, Chandra J

Northeast Journal of Complex Systems (NEJCS)

Magnetic Resonance Imaging (MRI) is an imaging technique used for the diagnosis and observing the progression in various neurological disorders. Stroke is one of the prominent neurological disorders that creates significant impacts in the patients. It occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissues from getting oxygen and nutrients. Multimodal data from various modalities help clinicians in proper prognosis of stroke. Ischemic Stroke Lesion Segmentation Challenge (ISLES22) provides data of stroke data for various stroke patients, the dataset consists of three modalities of data – Fluid Attenuated Inversion Recovery (FLAIR), Apparent …

A Comprehensive Survey Of Data-Driven Solutions For Lorawan: Challenges And Future Directions, Poonam Maurya, Abhishek Hazra, Preti Kumari, Troels Bundgaard Sørensen, Sajal K. Das

Computer Science Faculty Research & Creative Works

Long-range Wide-area Network (LoRaWAN) is an innovative and prominent communication protocol in the domain of Low-power Wide-area Networks (LPWAN), known for its ability to provide long-range communication with low energy consumption. However, the practical implementation of the LoRaWAN protocol, operating at the Medium Access Control layer and specially built to work upon the LoRa physical layer, presents numerous research challenges, including network congestion, interference, optimal resource allocation, collisions, scalability, and security. To mitigate these challenges effectively, the adoption of cutting-edge data-driven technologies such as Deep Learning (DL) and Machine Learning (ML) emerges as a promising approach. Interestingly, very few existing …

Clinician Experiences With Ambient Scribe Technology To Assist With Documentation Burden And Efficiency, Matthew J. Duggan, Julietta Gervase, Anna Schoenbaum, William Hanson, John T. Howell, Michael Sheinberg, Kevin B. Johnson

SKMC Student Presentations and Publications

IMPORTANCE: Timely evaluation of ambient scribing technology is warranted to assess whether this technology can lessen the burden of clinical documentation on clinicians.

OBJECTIVE: To investigate the association of ambient scribing technology with efficiency, quality, and perceived burden of clinical documentation in the outpatient setting.

DESIGN, SETTING, AND PARTICIPANTS: This prospective, single-group pre-post quality improvement study was conducted between April and June 2024 in the outpatient setting of an academic health system in Philadelphia, Pennsylvania. Participants included physicians, nurse practitioners, and physician assistants. Data were analyzed from July to August 2024.

EXPOSURE: Access to an artificial intelligence-driven ambient scribing tool …

Few-Shot Transfer Learning For Individualized Braking Intent Detection On Neuromorphic Hardware, Nathan A. Lutes, V. Sriram Siddhardth Nedendla, K. Krishnamurthy

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a Brain Chip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. The efficacy of the method is studied on an advanced driver assist system related task of predicting braking intention. Approach. Data are collected from participants operating an NVIDIA JetBot on a testbed simulating urban streets for three different scenarios. Participants receive a braking indicator in the form of: (1) an audio countdown in a nominal baseline, stress-free environment; (2) an …

Research On Task Planning Methods For Space Robot Assisted Operation, Guoqiang Fang, Haitao Chang, Xing Liu, Zhengxiong Liu, Panfeng Huang

Journal of System Simulation

Abstract: In view of the difficulties caused by the complicated task process and numerous task constraints during the space robot assisted operation, a task planning method combining fast forward search algorithm and hierarchical network algorithm is proposed, in which the task planning process is divided into task planning and replanning. Based on the fast forward search task planning method of operation cost, the execution sequence of actions with minimum operation cost is obtained. The task adaptive replanning method based on hierarchical network corrects and compensates the problems according to the priority of compensation for the movement, grab and release actions. …

An Algorithm For Cloud-Based Web Service Combination Optimization Through Plant Growth Simulation, Qiang Li, Huawei Qin, Bingqin Qiao, Ruifang Wu

Journal of System Simulation

Abstract: In order to improve the efficiency of cloud-based web services, an improved plant growth simulation algorithm scheduling model. This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources. Then, a lightinduced plant growth simulation algorithm was established. The performance of the algorithm was compared through several plant types, and the best plant model was selected as the setting for the system. Experimental results show that when the number of test cloud-based web services reaches 2 048, the model being 2.14 times faster than PSO, 2.8 times faster than the …

Quadrotor Uav Path Planning Based On Rapidly-Exploration Directional Tree Algorithm, Shijun Hu, Hailiang Liu, Binglei Wang, Wenke Su

Journal of System Simulation

Abstract: Aiming at the problems of low planning success rate, slow convergence speed, and suboptimal paths in the RRT algorithm for quadrotor UAV path planning of in complex environments, a directional exploration tree algorithm is proposed, which uses a directional sampling strategy to improve the directionality of the tree expansion, and by introducing an adaptive target adjustment strategy and a branch expansion strategy, the tree can expand quickly towards the target point while avoiding obstacles. The redundant points in the initial path are removed by the pruning process, and then the trajectory correction and smoothing process are performed on the …

Comprehensive Insights Into Smart Contracts: Architecture, Sectoral Applications, Security Analysis, And Legal Frameworks, Farah Mazlan, Nur Faizah Omar, Nik Nor Muhammad Saifudin Nik Mohd Kamal, Ahmad Anwar Zainuddin

Journal of Informatics and Web Engineering

Most conventional contract systems have issues with middlemen, drawn-out implementation procedures, fraud risk, and human error. Considering this, the project uses smart contract technology to provide a decentralized, automated, and safe solution in an effort to address such inefficiencies and the trust issues they raise. Smart contracts enable self-execution of contracts whose conditions are expressed explicitly in lines of code by presenting solutions using blockchain technology. The concept behind a smart contract is that each party may carry out their portion of the duties without depending on a third party and the contract will automatically execute in the meantime. This …

Predicting Short-Range Weather In Tropical Regions Using Random Forest Classifier, Sellappan Palaniappan, Rajasvaran Logeswaran, Anitha Velayutham, Bui Ngoc Dung

Journal of Informatics and Web Engineering

In this paper, we present a Random Forest classifier machine learning model for predicting short-range weather in in tropical regions like Malaysia. Our model uses environmental factors such as temperature, humidity, wind speed, and cloud cover to predict weather conditions like clear skies, rain, and thunderstorms. Tropical weather, influenced by high humidity, fluctuating temperatures, and frequent rainfall, present unique challenges for forecasting accurately. To address these challenges, we trained a Random Forest classifier on a synthetic (simulated) dataset comprising 1,500 samples, each representing a specific weather scenario. Our model achieved an accuracy of 98.66% in predicting short-term weather conditions, identifying …

Improved Accuracy For Heart Disease Diagnosis Using Machine Learning Techniques, Neeraja Joshi, Tejal Dave

Journal of Informatics and Web Engineering

This work primarily focuses on diagnosis of heart disease before explicit visit to the expert doctor. Machine learning based systems have been found useful in medical diagnosis applications because of their ability to learn human like expertise and to utilize acquired knowledge for diagnosis. This work is performs classification of heart disease utilizing subject’s vital parameters. Pathological laboratory results available after testing are not understood by common people and patients have to wait till they visit expert doctors for inference. In this paper, traditional methods like linear regression  to various machine learning based systems including back propagation neural network, support …

Implementing Identity-Based Signature Schemes For Secure Data Transfer In Cloud Computing Environments, Paul Osinuga, Ji-Jian Chin, Terry Shue Chien Lau

Journal of Informatics and Web Engineering

In this paper, we present the implementation of the Cha-Cheon Identity-Based Signature (IBS) scheme to enhance secure data transfer in cloud computing environments. Cloud computing rely on traditional Public Key Infrastructure (PKI) systems, which is burdened by certificate management infrastructure. The primary focus of this research to simplify key and certificate management by leveraging identity-based elliptic curve cryptography (ECC) within the Cha-Cheon IBS framework. We show that the proposed IBS solution integrates seamlessly with Amazon Web Services (AWS), utilizing services like S3 for secure data storage and KMS for key management. By applying ECC, the Cha-Cheon scheme achieves efficient cryptographic …

Lung Tumor Segmentation In Medical Imaging Using U-Net, J Jayapradha, Su-Cheng Haw, Naveen Palanichamy, Senthil Kumar Thillaigovindhan, Mutaz Al-Tarawneh

Journal of Informatics and Web Engineering

Tumors are a deadly condition often triggered by a range of abnormal modifications and genetic abnormalities. Early tumor diagnosis is essential due to the highly concerned nature of the disease. Early detection and treatment of tumors can significantly reduce mortality rates. This paper presents a model for tumor segmentation in medical imaging that uses the U-NET architecture to increase precision. The model’s encoding and decoding processes have been applied with skip connections to boost performance while simplifying model training. Images were cropped around the lower abdominal regions, and all images used in the study were then resized to 256*256 pixels …

A Hybrid Deep Learning Vgg-16 Based Svm Model For Vehicle Type Classification, Muhammad Imran, Jafar Usman, Muntazir Khan, Abdullah Khan

Journal of Informatics and Web Engineering

Car classification is important in daily life because there are many distinct types of automobiles made by various manufacturers. Although there are numerous methods for classifying autos, machine learning technologies have not been widely utilized, resulting in low accuracy levels. The goal of this paper is to create a machine learning system that is especially made to categories models of two Pakistan's top automakers, Toyota, and Honda. Ten Toyota models such as Avalon, Land Cruiser, Camry, Corolla, C-HR, Highlander, Prius, Tundra, RAV4, and Yaris and a dataset of Honda automobiles, which also includes 10 models (Accord, Civic, CR-V, Fit, HR-V, …

Early Identification Of Parkinson's Disease Using Time Frequency Analysis On Eeg Signals, Tanvir Hasib, V Vijayakumar, Ramakrishnan Kannan

Journal of Informatics and Web Engineering

Parkinson's Disease (PD) is a progressive neurological disorder. It affects movement and can significantly impact quality of life. Early and accurate diagnosis is crucial for effective management and intervention. Traditional diagnostic methods can be time-consuming and less effective in the early stages of the disease. This study aims to develop an automated approach for identifying PD using time-frequency image analysis of electroencephalogram (EEG) signals. The goal is to enhance diagnostic accuracy and efficiency, facilitating early detection. EEG signals, often contaminated with artifacts such as eye blinks and muscle movements etc., were first cleaned. Time-frequency images were then plotted from the …

Debugprograde: Improving Automated Assessment Of Coding Assignments With A Focus On Debugging, Amit Patel, Hardik Joshi

Journal of Informatics and Web Engineering

In education, the evaluation of programming assignments is challenging, especially to do with the debugging aspect. Self-grading technologies are unable to capture the level of understanding of students and context-bound responses. In light of these, we created DebugProGrade to take what we normally know about grading and improve it with semantic analysis and keyword extraction. DebugProGrade identified 1000 first-year BCA students who in Google Forms provided their answers to evaluate error detection and solution proposals for a basic C programming assignment. For the explanations’ specificity and for the context-level evaluation, the system employs the SBERT embedding, namely, the sentence-transformer bidirectional …

Optimising Phishing Detection: A Comparative Analysis Of Machine Learning Methods With Feature Selection, Mohamad Asraf Daniel, Siew-Chin Chong, Lee-Ying Chong, Kuok-Kwee Wee

Journal of Informatics and Web Engineering

Phishing is an act of cybersecurity attack that tricks people into sharing sensitive data. Due to the inefficiency of the current security technologies, researchers have been paying much attention to employing machine learning methods for phishing detection lately. In our proposed solution, the effectiveness of machine learning techniques with feature selection techniques for phishing detection is investigated. To be specific, Random Forest (RF) and Artificial Neural Network (ANN) are integrated with feature selection techniques, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE). The goal was to identify and classify the model with the highest accuracy. The experiments were evaluated …

Integrating Moral Values In Ai: Addressing Ethical Challenges For Fair And Responsible Technology, Khushboo Shah, Hiren Joshi, Hardik Joshi

Journal of Informatics and Web Engineering

Today, Artificial Intelligence (AI) has become an integral part of our day-to-day life. From personal task to professional life everywhere we need AI. All the sectors like - transport, education, healthcare, agriculture, we find AI everywhere. Each coin has two side, similarly AI has its own pros and cons. Main challenge with AI is ethics. There is no doubt in the work efficiency of AI but ultimately, it’s a machine without emotions hence whatever the decision it takes it purely without ethical values. Sometimes, this type of decision may lead to disasters, especially in the industry where human life is …

Creating An Android-Based Calisthenics Application To Assist Students In Improving Their Physical Fitness, Restyandito, Narendra Poetra Wisnoewardhana, Danny Sebastian

Journal of Informatics and Web Engineering

College students, particularly those heavily involved in coursework, frequently do not prioritize physical fitness. Regular exercise is essential for preserving physical fitness and facilitating demanding academic tasks. Although there are other fitness programs available, such as GYM, jogging, CrossFit, and Yoga, users sometimes fail to make full use of them because the content is confined to exercise videos and descriptions. This matter highlights the progress of the Android application Kali Tech, which specifically concentrates on organizing and documenting calisthenics workouts. This program employs the concept of gamification by utilizing student achievement levels to encourage students to be diligent in doing …

Migraine Generative Artificial Intelligence Based On Mobile Personalized Healthcare, Michelle Ting-Ting Yong, Sin-Ban Ho, Chuie-Hong Tan

Journal of Informatics and Web Engineering

Migraine is a complicated genetic disorder characterized by episodes of moderate-to-severe headaches that are usually unilateral and are frequently accompanied by nausea and increased sensitivity to sound and light. A migraine attack induces intense pain, hindering an individual from engaging in daily activities and potentially persisting for hours or even days. By the growth of the Internet of Things, we have new opportunities to try to apply it to the medical field. To identify the origin of a migraine, specialists need access to a patient's medical history and a comprehensive understanding of migraine symptoms for effective treatment. Determining the true …

Exploration Of The Impact Of Cyber Situational Awareness On Small And Medium Enterprises (Smes) In Malaysia, Tan Chee Keong, Sofiah Kadar Khan, Umar Farooq Khattak

Journal of Informatics and Web Engineering

The objective of this study is to explore the cyber situational awareness (CSA) level among the employees of small and medium-sized enterprises (SME) in Malaysia, by extending Endsley's situation awareness (SA) theory. It is crucial to understand the level of cyber situational awareness among employees as it sheds light on how well the employees understand the cyber threats and if they can handle them effectively. Literature has reviewed that SMEs are subject to a greater danger of cyber-attacks. Therefore, employees' awareness of cyber situations is of the utmost significance in studying cyber security. A convenient non-probability sampling method was chosen …

Personalized Drug Recommendation System Using Wasserstein Auto-Encoders And Adverse Drug Reaction Detection With Weighted Feed Forward Neural Network (Waes-Adr) In Healthcare, J Omana, P. N Jeipratha, K Devi, S Benila, K Revathi

Journal of Informatics and Web Engineering

In recent years, the use of deep learning approaches in healthcare has yielded promising results in a variety of fields, most notably in the detection of adverse drug reactions (ADRs) and drug recommendations. This paper promises a breakthrough in this field by using Wasserstein autoencoders (WAEs) for personalized medicine recommendation and ADR detection. WAEs' capacity to manage complex data distributions and develop meaningful latent representations makes them ideal for modeling heterogeneous healthcare data. This study intends to improvise the precision and efficiency of drug recommendation systems while also improving patient safety by combining WAEs and early ADR detection strategies. Previous …

Multimodal Search On A Line, Jared Coleman, Dmitry Ivanov, Evangelos Kranakis, Danny Krizanc, Oscar Morales Ponce

Computer Science Faculty Works

Inspired by the diverse set of technologies used in underground object detection and imaging, we introduce a novel multimodal linear search problem whereby a single searcher starts at the origin and must find a target that can only be detected when the searcher moves through its location using the correct of p possible search modes. The target’s location, its distance d from the origin, and the correct search mode are all initially unknown to the searcher. We prove tight upper and lower bounds on the competitive ratio for this problem. Specifically, we show that when p is odd, the optimal …

Artificial Intelligence (Ai) In Pharmacy, Giang Nguyen, Elizabeth Sartschev, John Reyes, Allie Honigford, Marisa Petrunich, Kiley Devoll, Brianna Lu, Joshua Honaker, T'Bony M. Jewell

Pharmacy and Wellness Review

Artificial Intelligence (AI) has transformed the pharmaceutical field by enabling computer software systems to learn and perform human behavior. Specifically, AI has revolutionized chronic diabetes management through continuous glucose monitoring, showcasing its immense potential in healthcare. However, alongside its transformative impact, AI’s increasing role in healthcare has prompted concerns over privacy and its premature integration. Despite these challenges, AI offers limitless opportunities to improve medication management and treatment regimens, driving advancements across various domains. From improving CT imaging to enhancing adenoma detection in colonoscopies and facilitating medication adherence, AI’s impact on healthcare is profound. Furthermore, AI plays a pivotal role …

Accelerated Multiobjective Calibration Of Fused Deposition Modeling 3d Printers Using Multitask Bayesian Optimization And Computer Vision, Craig S. Ganitano, Benji Maruyama, Gilbert L. Peterson

Faculty Publications

Proper process parameter calibration is critical to the success of fused deposition modeling (FDM) three-dimensional (3D) printing, but is time-consuming and requires expertise. While existing systems for autonomous calibration have demonstrated success in calibrating for a single objective, users may need to balance multiple conflicting objectives. Herein, an easily deployable, camera-based system for autonomous calibration of FDM printers that optimizes for both part quality and completion time is presented. Autonomous calibration is achieved through a novel, multifaceted computer vision characterization and a multitask learning extension to Bayesian optimization. The system is demonstrated on four popular filament types using two distinct …

Plc-Controlled Intelligent Conveyor System With Ai-Enhanced Vision Of Efficient Waste Sorting, Natheer Almtireen, Nathir Rawashdeh, Viraj Reddy, Max Sutton, Alexander Nedvidek, Caden Karn, Et. Al.

Michigan Tech Publications

Current waste sorting mechanisms, particularly those relying on manual processes, semi-automated systems, or technologies without Artificial Intelligence (AI) integration, are hindered by inefficiencies, inaccuracies, and limited scalability, reducing their effectiveness in meeting growing waste management demands. This study introduces a prototype waste sorting machine that integrates an AI-driven vision system with a Programmable Logic Controller (PLC) for high-accuracy automated waste sorting. The system, powered by the YOLOv8 deep learning model, achieved sorting accuracies of 88% for metal cans, 75% for paper, and 91% for plastic bottles, with an overall precision of 90%, a recall of 80%, and a mean average …