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Elizabeth Morgan, Piano, Department Of Music, University Of Richmond

Music Department Concert Programs

No abstract provided.

Diagnostic And Therapeutic Challenge: A Case Of Right Ventricular Thrombus, Angad Bedi, Muhammad Sabri, Prerana Sevella, Hussam Al Hennawi, Gregory Pirolli

Abington Jefferson Health Papers

Right ventricular thrombus is a rare finding found in 4% of people diagnosed with pulmonary embolism. Although right ventricular thrombi are usually associated with atrial fibrillation, deep venous vein thrombi, or intracardiac procedures, isolated right ventricular thrombi are rare. Right ventricular thrombus has also been reported in patients with right ventricular infarction, as hypokinesis of the right ventricle causes blood stasis and promotes thrombosis as per Virchow’s triad. However, we present a case of isolated RV thrombus in a patient without evidence of deep vein thrombosis or right ventricular hypokinesis who presented with dyspnea on exertion.

Identifying Critical Employability Skills For Employment Success Of Autistic Individuals: A Content Analysis Of Job Postings, Amy Jane Griffiths, Amy E. Hurley-Hanson, Cristina M. Giannantonio, Angel Miles Nash, Wallace Walrod, Petersen Walrod, Rachel Torres, Raquel Delgado

Education Faculty Articles and Research

This study aimed to examine the literature on the skill sets of autistic individuals and determine how these skills align with current and projected future labour market needs. Based on a literature review, researchers identified the following skill categories common to autistic individuals: visual skills, attention to detail and systemizing composite skills. Researchers then gathered aggregated data on occupations and industries from over 90 state and federal sources in the United States. Next, they collected data on the most in-demand jobs, their industries and relevant skills by analysing hundreds of millions of online job postings. The results indicate the most …

Cardozo Aelj’S Spring 2024 Symposium Explores The Implications Of The Warhol And Bad Spaniels Decisions On Copyright And Trademark Law, Cardozo Arts & Entertainment Law Journal

Cardozo Arts & Entertainment Law Journal (AELJ) Blog

Thank you to everyone who attended and participated in the Cardozo Arts & Entertainment Law Journal’s spring symposium, “Barking Up the Wrong Tree: An Exploration of Intellectual Property Law Protections Following Bad Spaniels and Andy Warhol.” AELJ is proud to have hosted such a successful event, highlighting a range of thoughtful and engaging scholarship.

This post was originally published on the Cardozo Arts & Entertainment Law Journal website on February 25, 2024. The original post can be accessed via the Archived Link button above.

Hybrid Framework To Exclude Similar And Faulty Test Cases In Regression Testing, Muhammad Asim Siddique, Wan M.N. Wan-Kadir, Johanna Ahmad, Noraini Ibrahim

Baghdad Science Journal

Regression testing is a crucial phase in the software development lifecycle that makes sure that new changes/updates in the software system don’t introduce defects or don’t affect adversely the existing functionalities. However, as the software systems grow in complexity, the number of test cases in regression suite can become large which results into more testing time and resource consumption. In addition, the presence of redundant & faulty test cases may affect the efficiency of the regression testing process. Therefore, this paper presents a new Hybrid Framework to Exclude Similar & Faulty Test Cases in Regression Testing (ETCPM) that utilizes automated …

Oil Spill Classification Based On Satellite Image Using Deep Learning Techniques, Abubakar Salihu Abba, Noorfa Haszlinna Mustaffa, Siti Zaiton Mohd Hashim, Razana Alwee

Baghdad Science Journal

An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using …

Research On Emotion Classification Based On Multi-Modal Fusion, Zhihua Xiang, Nor Haizan Mohamed Radzi, Haslina Hashim

Baghdad Science Journal

Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during …

Exploring Important Factors In Predicting Heart Disease Based On Ensemble- Extra Feature Selection Approach, Howida Abubaker, Farkhana Muchtar, Alif Ridzuan Khairuddin, Ahmad Najmi Amerhaider Nuar, Zuriahati Mohd Yunos, Carolyn Salimun

Baghdad Science Journal

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based …

Containerized Event-Driven Microservice Architecture, Siti Zulaikha Mohd Zuki, Radziah Mohamad, Nor Azizah Saadon

Baghdad Science Journal

Microservice architecture offers many advantages, especially for business applications, due to its flexibility, expandability, and loosely coupled structure for ease of maintenance. However, there are several disadvantages that stem from the features of microservices, such as the fact that microservices are independent in nature can hinder meaningful communication and make data synchronization more challenging. This paper addresses the issues by proposing a containerized microservices in an asynchronous event-driven architecture. This architecture encloses microservices in containers and implements an event manager to keep track of all the events in an event log to reduce errors in the application. Experiment results show …

Early Diagnose Alzheimer's Disease By Convolution Neural Network-Based Histogram Features Extracting And Canny Edge, Karrar A. Kadhim, Farhan Mohamed, Fallah H Najjar, Ghalib Ahmed Salman

Baghdad Science Journal

Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop …

Hybrid Cnn-Based Recommendation System, Muhammad Alrashidi, Roliana Ibrahim, Ali Selamat

Baghdad Science Journal

Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model is a novel convolutional neural network architecture that combines the capabilities of deep learning for feature extraction with the effectiveness …

Qin Seal Script Character Recognition With Fuzzy And Incomplete Information, Yun Ou, Zhen-Jie Zhou, Di-Wen Kang, Pan Zhou, Xue-Wei Liu

Baghdad Science Journal

The dependable and efficient identification of Qin seal script characters is pivotal in the discovery, preservation, and inheritance of the distinctive cultural values embodied by these artifacts. This paper uses image histograms of oriented gradients (HOG) features and an SVM model to discuss a character recognition model for identifying partial and blurred Qin seal script characters. The model achieves accurate recognition on a small, imbalanced dataset. Firstly, a dataset of Qin seal script image samples is established, and Gaussian filtering is employed to remove image noise. Subsequently, the gamma transformation algorithm adjusts the image brightness and enhances the contrast between …

Simplified Novel Approach For Accurate Employee Churn Categorization Using Mcdm, De-Pareto Principle Approach, And Machine Learning, Faisal Bin Al Abid, Aryati Binti Bakri, Md. Golam Rabiul Alam, Jia Uddin, Shefayatuj Johara Chowdhury

Baghdad Science Journal

Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date. A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE …

Exploring The Actual Implementation Of E-Wallet Application In Malaysia, Jia Yee Yeh, Choon Sen Seah, Yin Xia Loh, Mei Peng Low, Ahmad Najmi Amerhaider Nuar, Farah Waheeda Jalaludin

Baghdad Science Journal

E-wallet, also referred to as digital wallet, is a software application designed to replace physical wallets, with the primary purpose of facilitating online transactions when users wish to make virtual payments. Nowadays, E-wallets are not limited to mobile applications, but they have also been extended to wearable devices, such as smartwatches, enabling users to make payments via their watches. This research study focuses on three main E-wallet service providers in Malaysia, namely TouchNGo E-wallet, Boost, and Grab pay, as they are the top three E-wallets in the country. The aim of this paper is to explore the real-world implementation of …

The Optimization Of A Natural Language Processing Approach For The Automatic Detection Of Alzheimer’S Disease Using Gpt Embeddings, Benjamin S. Runde, Ajit Alapati, Nicolas G. Bazan

School of Medicine Faculty Publications

The development of noninvasive and cost-effective methods of detecting Alzheimer’s disease (AD) is essential for its early prevention and mitigation. We optimize the detection of AD using natural language processing (NLP) of spontaneous speech through the use of audio enhancement techniques and novel transcription methodologies. Specifically, we utilized Boll Spectral Subtraction to improve audio fidelity and created transcriptions using state-of-the-art AI services—locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI—evaluating their performance against traditional manual transcription methods. Support Vector Machine (SVM) classifiers were then trained and tested using GPT-based embeddings of transcriptions. Our findings revealed that AI-based transcriptions …

Justice, Inclusion, And Incentives, Ghislain Herman Demeze-Jouatsa, Roland Pongou, Jean-Baptiste Tondji

School of Economics and Finance Faculty Publications

How does justice affect individual incentives and efficiency in a political economy? We show that elementary principles of distributive justice guarantee the existence of a self-enforcing contract whereby agents non-cooperatively choose their inputs and derive utility from their pay. Chief among these principles is that your pay should not depend on your name, and a more productive individual should not earn less. We generalize our analysis to incorporate inclusivity, ensuring basic pay to unproductive agents, implemented through progressive taxation and redistribution. Our findings show that without redistribution, any self-enforcing agreement may be inefficient, but a minimal level of redistribution guarantees …

An Exploratory Study Of History-Based Test Case Prioritization Techniques On Different Datasets, Syed Muhammad Junaid Hassan, Dayang N. A. Jawawi, Johanna Ahmad

Baghdad Science Journal

In regression testing, Test case prioritization (TCP) is a technique to arrange all the available test cases. TCP techniques can improve fault detection performance which is measured by the average percentage of fault detection (APFD). History-based TCP is one of the TCP techniques that consider the history of past data to prioritize test cases. The issue of equal priority allocation to test cases is a common problem for most TCP techniques. However, this problem has not been explored in history-based TCP techniques. To solve this problem in regression testing, most of the researchers resort to random sorting of test cases. …

Self-Localization Of Guide Robots Through Image Classification, Muhammad S. Alam, Farhan B. Mohamed, Akm B. Hossain

Baghdad Science Journal

The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots. To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. …

Natural Language Processing For Requirement Elicitation In University Using Kmeans And Meanshift Algorithm, Devi Yurisca Bernanda, Dayang N.A. Jawawi, Shahliza Abd Halim, Fransiskus Adikara

Baghdad Science Journal

Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in …

An Adaptive Harmony Search Part-Of-Speech Tagger For Square Hmong Corpus, Di-Wen Kang, Shao-Qiang Ye, Sharifah Zarith Rahmah Syed Ahmad, Li-Ping Mo, Feng Qin, Pan Zhou

Baghdad Science Journal

Data-driven models perform poorly on part-of-speech tagging problems with the square Hmong language, a low-resource corpus. This paper designs a weight evaluation function to reduce the influence of unknown words. It proposes an improved harmony search algorithm utilizing the roulette and local evaluation strategies for handling the square Hmong part-of-speech tagging problem. The experiment shows that the average accuracy of the proposed model is 6%, 8% more than HMM and BiLSTM-CRF models, respectively. Meanwhile, the average F1 of the proposed model is also 6%, 3% more than HMM and BiLSTM-CRF models, respectively.

Facial Emotion Images Recognition Based On Binarized Genetic Algorithm-Random Forest, Murad Ibrahim Husin Alzawali, Yusliza Yusoff, Razana Alwee, Zuriahati Mohd Yunos, Mohamad Shukor Talib, Haswadi Hassan, Fahad Taha Al-Dhief, Musatafa Abbas Abbood Albadr, Majid Razaq Mohamed Alsemawi, Sharifah Zarith Rahmah Syed Ahmad

Baghdad Science Journal

Most recognition system of human facial emotions are assessed solely on accuracy, even if other performance criteria are also thought to be important in the evaluation process such as sensitivity, precision, F-measure, and G-mean. Moreover, the most common problem that must be resolved in face emotion recognition systems is the feature extraction methods, which is comparable to traditional manual feature extraction methods. This traditional method is not able to extract features efficiently. In other words, there are redundant amount of features which are considered not significant, which affect the classification performance. In this work, a new system to recognize human …

An Improved Cuckoo Search Algorithm For Maximizing The Coverage Range Of Wireless Sensor Networks, Sen-Yu Yang, Yin-Hong Xiang, Di-Wen Kang, Kai-Qing Zhou

Baghdad Science Journal

The issue of increasing the range covered by a wireless sensor network with restricted sensors is addressed utilizing improved CS employing the PSO algorithm and opposition-based learning (ICS-PSO-OBL). At first, the iteration is carried out by updating the old solution dimension by dimension to achieve independent updating across the dimensions in the high-dimensional optimization problem. The PSO operator is then incorporated to lessen the preference random walk stage's imbalance between exploration and exploitation ability. Exceptional individuals are selected from the population using OBL to boost the chance of finding the optimal solution based on the fitness value. The ICS-PSO-OBL is …

Optimizing Blockchain Consensus: Incorporating Trust Value In The Practical Byzantine Fault Tolerance Algorithm With Boneh-Lynn-Shacham Aggregate Signature, Dayong Zhang, Nur Haliza Abdul Wahab, Adi Wira Mohd Zin

Baghdad Science Journal

The fundamental component of Blockchain technology, the consensus algorithm, is employed to guarantee data consistency among Blockchain nodes. Because it is resistant to Byzantine errors, consortium chains frequently use the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism. Nevertheless, the current PBFT still has issues with high node communication complexity and random master node selection. This study proposes the IBFT consensus mechanism, which is based on the aggregate signature of Boneh-Lynn-Shacham (BLS) and node trust value. Multi-level indications in IBFT are made to determine each node's trust value. A few very reliable nodes are chosen to serve as consensus nodes. Whichever …

The Effect Of Optimizers On The Generalizability Additive Neural Attention For Customer Support Twitter Dataset In Chatbot Application, Sinarwati Mohamad Suhaili, Naomie Salim, Mohamad Nazim Jambli

Baghdad Science Journal

When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST …

Prioritized Text Detergent: Comparing Two Judgment Scales Of Analytic Hierarchy Process On Prioritizing Pre-Processing Techniques On Social Media Sentiment Analysis, Ummu Hani’ Hair Zaki, Roliana Ibrahim, Shahliza Abd Halim, Izyan Izzati Kamsani

Baghdad Science Journal

Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most …

Sboa: A Novel Heuristic Optimization Algorithm, Qi Diao, Apri Junaidi, Wenghowe Chan, Azland Mohd Zain, Hao Long Yang

Baghdad Science Journal

A new human-based heuristic optimization method, named the Snooker-Based Optimization Algorithm (SBOA), is introduced in this study. The inspiration for this method is drawn from the traits of sales elites—those qualities every salesperson aspires to possess. Typically, salespersons strive to enhance their skills through autonomous learning or by seeking guidance from others. Furthermore, they engage in regular communication with customers to gain approval for their products or services. Building upon this concept, SBOA aims to find the optimal solution within a given search space, traversing all positions to obtain all possible values. To assesses the feasibility and effectiveness of SBOA …

Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network, Shengqing Lin, Nor Azizah Ali, Azlan Bin Mohd Zain, Muhalim Mohamed Amin

Baghdad Science Journal

Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the …

Sentiment Analysis On Roman Urdu Students’ Feedback Using Enhanced Word Embedding Technique, Noureen, Sharin Hazlin Huspi Huspi, Zafar Ali

Baghdad Science Journal

Students’ feedback is crucial for educational institutions to assess the performance of their teachers, most opinions are expressed in their native language, especially for people in south Asian regions. In Pakistan, people use Roman Urdu to express their reviews, and this applied in the education domain where students used Roman Urdu to express their feedback. It is very time-consuming and labor-intensive process to handle qualitative opinions manually. Additionally, it can be difficult to determine sentence semantics in a text that is written in a colloquial style like Roman Urdu. This study proposes an enhanced word embedding technique and investigates the …

Efficient Task Scheduling Approach In Edge-Cloud Continuum Based On Flower Pollination And Improved Shuffled Frog Leaping Algorithm, Nasiru Muhammad Dankolo, Nor Haizan Mohamed Radzi, Noorfa Haszlinna Mustaffa, Mohd Shukor Talib, Zuriahati Mohd Yunos, Danlami Gabi

Baghdad Science Journal

The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements …

Does Chinese Investment Into Europe Facilitate Strategic Asset Growth In The Chinese Parent Company? The Role Of Entry Mode, John Anderson, Dylan Sutherland, Sean Severe

Faculty Publications

Strategic asset seeking foreign direct investment has undergone tremendous growth over the past decade. This paper first attempts to evaluate the location choice of such investments in Europe. We find that Chinese companies target strategic assets in Europe. The paper then moves to understand the efficacy of these investments in terms of the creation of strategic assets in the Chinese parent company. Our results show the intangible assets of Chinese domestic parent firms significantly increase in the wake of their investments. For greenfield investments, there is a longer time-lag in creation of intangible strategic assets than for acquisitions. However, greenfield …