Computer Sciences Commons^™

Enhancing Motion Detection In Video Surveillance Systems Using The Three-Frame Difference Algorithm, Suhaib Qassem Yahya Al-Hashemi, Majid Salal Naghmash, Ahmad Ghandour

Mesopotamian Journal of Computer Science

This paper outlines a methodology for motion detection in video surveillance systems, leveraging advanced algorithms and TCP/IP networks for real-time data acquisition and analysis. The primary focus is on the implementation of the Three-Frame Difference Algorithm, which detects moving targets by analyzing the differences between three consecutive video frames. This method significantly reduces redundant data transmission and storage, addressing the challenges posed by limited wireless network capabilities. The surveillance model, designed in MATLAB using SIMULINK, integrates computer vision systems with embedded coders to facilitate effective communication and processing of video data. The results demonstrate the system's capability to detect motion …

Examining Ghana's National Health Insurance Act, 2003 (Act 650) To Improve Accessibility Of Artificial Intelligence Therapies And Address Compensation Issues In Cases Of Medical Negligence, George Benneh Mensah, Maad M. Mijwil, Mostafa Abotaleb

Mesopotamian Journal of Computer Science

Objective: Examine Ghana’s National Health Insurance Act (Act 650) to identify coverage gaps limiting artificial intelligence (AI) therapy access and address medical negligence liability issues surrounding automated healthcare systems. Methods: Legal and regulatory analysis of Act 650 were conducted, review of academic literature on global uptake of AI interventions and medical negligence principles were elucidated, examination of case studies implementing pilot AI therapy programs under insurance schemes were considered. Results & Conclusions: Act 650 lacks clear provisions for funding innovative AI treatments with proven efficacy and undefined negligence determination guidelines involving AI systems, contributing to accessibility and accountability issues. Proposed …

A Comparative Study Of Chest Radiographs And Detection Of The Covid 19 Virus Using Machine Learning Algorithm, Shaimaa Q. Sabri, Jahwar Y. Arif, Ghada A. Taqa, Ahmet Çınar

Mesopotamian Journal of Computer Science

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak that is causing coronavirus disease 2019 is being deemed a pandemic because of its quick spread around the globe.  Because chest X-ray pictures have shown to be beneficial in monitoring a variety of lung disorders, they have recently been utilized to monitor COVID-19 disease. It takes time to manually analyze a lot of chest X-ray pictures. Several previous studies have suggested machine-learning (ML)-based techniques for COVID-19 detection from chest X-ray pictures as a solution to this issue. Though little effort has been made to use traditional machine learning (ML) methods, the …

Credit Card Fraud Detection Based On Deep Learning Models, El-Sayed M. El-Kenawy, Ahmed Mohamed Zaki, Wei Hong Lim, Abdelhameed Ibrahim, Marwa M. Eid, Ahmed Osman Osman, Ahmed M. Elshewey

Mesopotamian Journal of Computer Science

Credit card fraud detection (FD) protects consumers and financial institutions by identifying suspicious or unauthorized transactions. To improve security and reduce false positives, fraud detection systems can analyze transaction data patterns in real time using advanced machine learning (ML) and deep learning (DL). This paper exploits DL models to detects transactional data which includes anomalies through Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) to verify data and mitigate fraud. The models used precision, recall, F1-score, and AUC on a balanced shared 559856-record Kaggle repository dataset. The RNN model detected anomalies with 99.39% accuracy, 0.9939 …

Data Mining Utilizing Various Leveled Clustering Procedures On The Position Of Workers In A Data Innovation Firm, Hussein Alkattan, Noor Razzaq Abbas, Oluwaseun A. Adelaja, Mostafa Abotaleb, Guma Ali

Mesopotamian Journal of Computer Science

The reason of this paper is to clarify dynamic clustering, the divisive and agglomerative dynamic clustering techniques. It fundamentally centers on the concept of the divisive different leveled shapes as well known as the top-down approach by creating a workflow appear, dendrograms, clustered data table which accumulated the clusters based the chosen property, and appear the isolated between each cluster with the assistance of an data mining device called Python. The DIANA dynamic approach utilized data tests of the list of laborers in a Data Advancement firm to induce clusters from the position column inside the data test table. In …

An Optimized Model For Liver Disease Classification Based On Bpso Using Machine Learning Models, El-Sayed M. El-Kenawy, Nima Khodadadi, Abdelhameed Ibrahim, Marwa M. Eid, Ahmed M. Osman, Ahmed M. Elshewey

Mesopotamian Journal of Computer Science

Liver disease (LD) is a world health concern that requires accurate diagnostic methods. This study proposes an optimized machine learning model (ML) based on BPSO for LD classification using a shared public dataset from kaggle Indicates to liver patients from India. The paper used six ML models such as Random Forest (RF), Support Vector Machine (SVM), Dummy Classifier (DC), Extra Trees Classifier (ET), K-Nearest Neighbors (KNN), and Logistic Regression (LT) to evaluate the performance. Through observations we detected that ET achieved an accuracy of 79.82%. The BPSO hyperparameter optimization optimized ET to enhance accuracy to reach 85%. The paper used …

Introduction To Wi-Fi 7: A Review Of History, Applications, Challenges, Economical Impact And Research Development, Sallar Salam Murad, Rozin Badeel, Banan Badeel Abdal, Tasmeea Rahman, Tahsien Al-Quraishi

Mesopotamian Journal of Computer Science

Wi-Fi 7, commonly referred to as IEEE 802.11be, is the most recent development in wireless communication technology. It provides significant improvements in terms of speed, capacity, and efficiency. The purpose of this study is to investigate Wi-Fi 7, a standard for wireless communication technology, with a particular focus on the technological advancements and security issues associated with it. In addition, it offers historical perspectives and investigates the institution's present capabilities as well as its potential for the future. The purpose of this study is to provide a complete examination of the development of Wi-Fi technology and its influence on a …

A Survey On Artificial Intelligence In Cybersecurity For Smart Agriculture: State-Of-The-Art, Cyber Threats, Artificial Intelligence Applications, And Ethical Concerns, Guma Ali, Maad M. Mijwil, Bosco Apparatus Buruga, Mostafa Abotaleb, Ioannis Adamopoulos

Mesopotamian Journal of Computer Science

Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review …

Computer Networking And Cloud-Based Learning/Teaching Environment Using Virtual Labs Tools: A Review And Future Aspirations, Mishall Al-Zubaidie, Raad A. Muhajjar, Lauy Abdulwahid Shihabe

Mesopotamian Journal of Computer Science

Physical laboratories for practical classrooms in computer networks can be prohibitively expensive, as well as requiring regular hardware/software upgrades. With Netkit and similar software, network laboratories can be set up in a computer lab, but the setup is complicated and each student still needs their own computer. Thanks to the Cloud-based infrastructure, Netkit is now available in pre-configured Amazon elastic compute Cloud (EC2) instances. This research reviews and introduces educational computer networks. Furthermore, a web interface is used to allow remote access to numerous lab scripts instantiated on the Cloud. This study also describes how to use the Cloud to …

An Image Processing Approach For Real-Time Safety Assessment Of Autonomous Drone Delivery, Assem A. Abdelhak, Dan Moss, Alan Hicks, Susan Mckeever

Articles

The aim of producing self-driving drones has driven many researchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic way to verify the safety of the delivery stage. One of the primary steps in the delivery operation is to ensure that the dropping zone is a safe area on arrival and during the dropping process. This paper proposes an image-processing-based classification approach for the delivery drone dropping process at a predefined destination. It employs live streaming …

Generalised Zero-Shot Learning For Action Recognition Fusing Text And Image Gans, Kaiqiang Huang, Susan Mckeever, Luis Miralles-Pechuán

Articles

Generalized Zero-Shot Action Recognition (GZSAR) is geared towards recognizing classes that the model has not been trained on, while still maintaining robust performance on the familiar, trained classes. This approach mitigates the need for an extensive amount of labeled training data and enhances the efficient utilization of available datasets. The main contribution of this paper is a novel approach for GZSAR that combines the power of two Generative Adversarial Networks (GANs). One GAN is responsible for generating embeddings from visual representations, while the other GAN focuses on generating embeddings from textual representations. These generated embeddings are fused, with the selection …

Designing Ris-Assisted Uav 3d Trajectory Using Deep Reinforcement Learning, Linsong Li

Electronic Theses and Dissertations

Unmanned aerial vehicles (UAVs) are increasingly employed as temporary base stations or access points to facilitate data transfer between ground terminals (GTs). However, in urban environments, UAV-GT communication links often face challenges due to obstructions from buildings and other obstacles, resulting in reduced data transfer efficiency. Reconfigurable intelligent surfaces (RIS) provide a promising solution by reflecting signals to enhance communication quality between UAVs and GTs. This thesis addresses the critical challenge of responsive UAV trajectory optimization in RIS-assisted communication networks. A novel approach is proposed, integrating federated learning with reinforcement learning techniques, specifically Double Deep Q-Network (DDQN) and Deep Deterministic …

Effective Data Augmentation Techniques For Time Series Classification: An Empirical Evaluation, Pongpanod Sankosik

Chulalongkorn University Theses and Dissertations (Chula ETD)

Time series classification is crucial in fields such as healthcare, finance, and industrial processes, but it faces challenges like temporal data ordering, class im-balance, noise, and limited data. This research explores data augmentation techniques to improve classification performance, focusing on the MiniRocket classifier across 85 UCR datasets. The study identifies conditions under which augmentation techniques, like wDBA, enhance accuracy, though overall performance may vary. A dataset-specific approach is essential for effective augmentation. The research also examines the impact of augmentation on datasets with different characteristics, providing insights into when specific strategies are most benefi-cial. Future work includes optimizing augmentation methods …

Building A Human Digital Twin (Hdtwin) Using Large Language Models For Cognitive Diagnosis: Algorithm Development And Validation, Gina Sprint, Maureen Schmitter-Edgecombe, Diane Cook

Computer Science Faculty Scholarship

Background: Human digital twins have the potential to change the practice of personalizing cognitive health diagnosis because these systems can integrate multiple sources of health information and influence into a unified model. Cognitive health is multifaceted, yet researchers and clinical professionals struggle to align diverse sources of information into a single model. Objective: This study aims to introduce a method called HDTwin, for unifying heterogeneous data using large language models. HDTwin is designed to predict cognitive diagnoses and offer explanations for its inferences. Methods: HDTwin integrates cognitive health data from multiple sources, including demographic, behavioral, ecological momentary assessment, n-back test, …

Social Networks And Large Language Models For Division I Basketball Game Winner Prediction, Gina Sprint

Computer Science Faculty Scholarship

Sporting event outcome prediction is a well-established and actively researched domain, with a particular focus on college basketball’s March Madness tournament. Researchers, fans, and gamblers alike seek accurate game-level predictions using features such as tournament seeds, season performance, and expert opinions. While machine learning algorithms have been harnessed to build prediction models, no perfect model or human-created bracket has emerged. This paper explores a novel approach to basketball game outcome prediction by utilizing the power of social networks and large language models (LLMs). LLMs are trained to understand and generate text, often eliminating the need for a feature engineering step. …

Sales Forecasting For Retail Business Using Xgboost Algorithm And Timesfm, Prathana Dankorpho

Chulalongkorn University Theses and Dissertations (Chula ETD)

The retail industry is continuously evolving with the expansion of sales channels and the diversification of product assortments. However, current forecasting methods, relying on simplistic statistical models, frequently encounter difficulties in adjusting to the dynamic environment. This limitation leads to challenges in accurately predicting sales. Consequently, there is a critical need to improve the accuracy and frequency of sales predictions to enable timely decision-making for business strategies. Through a comprehensive analysis of datasets from 2019 to 2023, this study illustrates the advantages of integrating XGBoost and TimesFM to gain deeper insights into sales patterns. Results demonstrate a significant enhancement in …

Applications Of Ai/Ml In Maritime Cyber Supply Chains, Rafael Diaz, Ricardo Ungo, Katie Smith, Lida Haghnegahdar, Bikash Singh, Tran Phuong

School of Cybersecurity Faculty Publications

Digital transformation is a new trend that describes enterprise efforts in transitioning manual and likely outdated processes and activities to digital formats dominated by the extensive use of Industry 4.0 elements, including the pervasive use of cyber-physical systems to increase efficiency, reduce waste, and increase responsiveness. A new domain that intersects supply chain management and cybersecurity emerges as many processes as possible of the enterprise require the convergence and synchronizing of resources and information flows in data-driven environments to support planning and execution activities. Protecting the information becomes imperative as big data flows must be parsed and translated into actions …

Reverse-Engineering Of Disinformation Campaigns During The War In Ukraine, Lora Pitman, Ava Baratz, Kelly Morgan, Marcy Alvarado

School of Cybersecurity Faculty Publications

Information operations have long been a part of warfare. Disinformation campaigns, in particular, are usually launched by states in order to mislead and confuse populations in adversarial countries, but also to obtain support for their actions from domestic audiences. These campaigns threaten human security, at the individual level, but also state- and even international security. The invasion of Ukraine by Russia came with a new wave of disinformation not only in Ukraine itself, but also in countries from various other continents. This paper studies the characteristics of the spread of disinformation from the first day of the war in February …

Age Of Sensing Empowered Holographic Isac Framework For Nextg Wireless Networks: A Vae And Drl Approach, Apurba Adhikary, Avi Deb Raha, Yu Qiao, Md. Shirajum Munir, Monishanker Halder, Choong Seon Hong

School of Cybersecurity Faculty Publications

This paper proposes an artificial intelligence (AI) framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)- enabled wireless network. The AI-driven framework guarantees optimal power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO base station. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of the received signal, beam-pattern gains to improve the sensing SINR of reflected echo signals and maximizing the evidence lower bound …

Optimal Network Analysis Through Vertex Order Coloring Of Intuitionistic Fuzzy Graph Operations, A. Meenakshi, S. Dhanushiya, Hong Qin, Maniyandy Elangovan

Data Science Faculty Publications

Intuitionistic fuzzy graphs IFGs are a powerful tool for modeling uncertainty and complex relationships. They offer versatile frameworks for addressing real-world challenges. In this research, we have introduced intuitionistic fuzzy vertex order coloring IFVOC and analyzed the alpha-strong (alpha str), beta-strong (beta str), and gamma-strong (gamma str) vertices through their degree. We explored important theorems based on the types of strong vertices, broadening the scope of our study. We analyzed multiple IFG products to determine the most optimal network based on some important metrics, including the weight and total number of alpha str vertices, the chromatic number, and the weight …

Heuristic Machine Learning Approaches For Identifying Phishing Threats Across Web And Email Platforms, Ramprasath Jayaprakash, Krishnaraj Natarajan, J. Alfred Daniel, Chandru Vignesh Chinnappan, Jayant Giri, Hong Qin, Saurav Mallik

Data Science Faculty Publications

Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques. In addition, phishing attacks include delivering false messages that originate from a trusted source. Moreover, a phishing attack aims to get the victim to run malicious programs and reveal confidential data, such as bank credentials, one-time passwords, and user login credentials. …

Anonymous Attribute-Based Broadcast Encryption With Hidden Multiple Access Structures, Tran Viet Xuan Phuong

School of Cybersecurity Faculty Publications

Due to the high demands of data communication, the broadcasting system streams the data daily. This service not only sends out the message to the correct participant but also respects the security of the identity user. In addition, when delivered, all the information must be protected for the party who employs the broadcasting service. Currently, Attribute-Based Broadcast Encryption (ABBE) is useful to apply for the broadcasting service. (ABBE) is a combination of Attribute-Based Encryption (ABE) and Broadcast Encryption (BE), which allows a broadcaster (or encrypter) to broadcast an encrypted message, including a predefined user set and specified access policy to …

Deep Transfer Learning-Based Bird Species Classification Using Mel Spectrogram Images, Mrinal Kanti Baowaly, Bisnu Chandra Sarkar, Md.Abul Ala Walid, Md. Martuza Ahamad, Bikash Chandra Singh, Eduardo Silva Alvarado, Imran Ashraf, Md. Abdus Samad

School of Cybersecurity Faculty Publications

The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification. This study aims to establish an audio-based bird species classification system for 264 Eastern African bird species employing modified deep transfer learning. In particular, the pre-trained EfficientNet …

Robustsentembed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning, Javad Rafiei Asl, Prajwal Panzade, Eduardo Blanco, Daniel Takabi, Zhipeng Cai

School of Cybersecurity Faculty Publications

Pre-trained language models (PLMs) have consistently demonstrated outstanding performance across a diverse spectrum of natural language processing tasks. Nevertheless, despite their success with unseen data, current PLM-based representations often exhibit poor robustness in adversarial settings. In this paper, we introduce RobustSentEmbed, a self-supervised sentence embedding framework designed to improve both generalization and robustness in diverse text representation tasks and against a diverse set of adversarial attacks. Through the generation of high-risk adversarial perturbations and their utilization in a novel objective function, RobustSentEmbed adeptly learns high-quality and robust sentence embeddings. Our experiments confirm the superiority of RobustSentEmbed over state-of-the-art representations. Specifically, …

A Systemic Mapping Study On Intrusion Response Systems, Adel Rezapour, Mohammad Ghasemigol, Daniel Takabi

School of Cybersecurity Faculty Publications

With the increasing frequency and sophistication of network attacks, network administrators are facing tremendous challenges in making fast and optimum decisions during critical situations. The ability to effectively respond to intrusions requires solving a multi-objective decision-making problem. While several research studies have been conducted to address this issue, the development of a reliable and automated Intrusion Response System (IRS) remains unattainable. This paper provides a Systematic Mapping Study (SMS) for IRS, aiming to investigate the existing studies, their limitations, and future directions in this field. A novel semi-automated research methodology is developed to identify and summarize related works. The innovative …

Examining Information Systems Use To Facilitate The Workplace Accommodation Process, Shiya Cao

Statistical and Data Sciences: Faculty Publications

BACKGROUND: The workplace accommodation process is often affected by ineffective and inefficient communications and information exchanges among disabled employees and other stakeholders. Information systems (IS) can play a key role in facilitating a more effective and efficient accommodation process since IS has been shown to facilitate business processes and effect positive organizational changes.

OBJECTIVE: Since there is little to no research that exists on IS use to facilitate the workplace accommodation process, this paper, as a critical first step, examines how IS have been used in the accommodation process.

METHODS: Thirty-six interviews were conducted with disabled employees from various organizations. …

Questions (And Answers) For Incorporating Nontraditional Grading In Your Statistics Courses, Brenna Curley

Statistical and Data Sciences: Faculty Publications

Nontraditional grading methods have recently become more common, and as with any large pedagogical shift, there are a number of questions to consider when applying a new grading scheme to a course. This article summarizes four types of nontraditional grading and shares experiences from the authors who have applied them to a variety of courses in statistics. This article is structured as a set of questions and answers, seeking to address many of the concerns and considerations that one may face as they transition a course’s grading structure. Supplementary materials for this article are available online.

Identifying Fact-Checks Helpful For Vetting Factual Claims, Theodora Toutountzi

Computer Science and Engineering Dissertations - Archive

Recent efforts to combat misinformation have increasingly focused on automating the fact-checking process. For fact-checking systems to be automated, they need to recognize claims worth checking, match them with previously fact-checked claims, and determine their truthfulness. We refer to the task of matching unvetted claims with previously fact-checked claims as claim-matching. To be precise, the task is defined as follows: given a factual claim, identifying the fact-checks from a repository that could be helpful, or partially helpful, for vetting the given claim. A solution to this task is useful in practice since claimants often repeat the same claims even if …

Enhancing The Efficiency And Scalability Of Cloud Networking Systems, Jiaxin Lei

Computer Science and Engineering Dissertations - Archive

Overlay networks are the de facto network virtualization technique for providing flexible and customized connectivity among distributed containers in the cloud. Despite their widespread adoption, overlay networks incur significant overhead due to their complexity, resulting in notable performance degradation compared to physical networks.

In this dissertation, I present our three-stage solutions aimed at addressing the challenges of efficiency and scalability in cloud-based container overlay networks: Firstly, we conduct a comprehensive empirical performance study of container overlay networks, identifying crucial parallelization bottlenecks within the kernel network stack. Our observations and root cause analysis uncover that these inefficiencies primarily arise from the …

Detection Of Tooth Position By Yolov4 And Various Dental Problems Based On Cnn With Bitewing Radiograph, Kuo Chen Li, Yi-Cheng Mao, Mu-Feng Lin, Yi-Qian Li, Chiung-An Chen, Tsung-Yi Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding …