Digital Commons Network^™

Enhanced Key Generation Method Using Deep Q-Networks Algorithm With Chaotic Maps, Ali A. Mahdi, Mays M. Hoobi

Mesopotamian Journal of CyberSecurity

In contemporary digital environments, exponential cyber threat growth has made cryptographic key generation a critical security challenge. Traditional Pseudo-Random Number Generators (PRNGs) and existing chaos-based methods often exhibit insufficient entropy, limited randomness quality, and inadequate resistance to statistical attacks. Current implementations frequently produce suboptimal entropy values and fail to meet modern cryptographic security standards and rigorous randomness testing protocols. This paper aims to design and implement an advanced cryptographic key generation system that combines Deep Q-Networks (DQN) algorithms with multiple chaotic maps to produce cryptographically secure stream key bits with high randomness and strong resistance to cryptanalytic attacks. The proposed …

Enhancing Internet Of Things (Iot) Network Security: A Machine Learning-Driven Framework For Real-Time Intrusion Detection And Anomaly Classification, Loiy Alsbatin, Firas Zawaideh, Basem Mohamad Alrifai, Tareq A. Alawneh, Tareq A. Alawneh

Mesopotamian Journal of CyberSecurity

The rapid proliferation of IoT devices presents serious IoT network cybersecurity threats; hence, advanced IDSs are necessary. Signature and rule-based IDS mechanisms cannot address novel attacks, generate excessive alarms, and are computationally inefficient. Therefore, in response, in this paper, a machine learning IDS for IoT network real-time intrusion detection and anomaly categorization is proposed via black widow optimization (BWO) for optimal feature and hyperparameter selection. The IDS employs standard machine learning models, such as random forest and support vector machines (SVMs), and deep models, such as long short-term memory (LSTM), to address IoT environment nuances. The framework is evaluated on …

Priflex: A Secure Federated Learning Framework For Evaluating Privacy Leakage And Defense In Cross-Modal Medical Data, Mohammad Othman Nassar, Feras Fares Al-Mashagba

Mesopotamian Journal of CyberSecurity

Federated learning (FL) enables decentralized, privacy-preserving machine learning by training models across distributed data without sharing raw patient information. However, most FL frameworks focus on unimodal data and overlook critical challenges in multimodal healthcare settings, such as privacy risks, fairness disparities, and reduced model interpretability. We present PRIFLEX (Privacy-Resilient Integration Framework for Learning Exchange), a novel FL framework designed for secure integration of structured and unstructured medical data. PRIFLEX combines 12-lead electrocardiograms (ECG) from the PhysioNet PTB-XL dataset and clinical notes from the Medical Information Mart for Intensive Care IV (MIMIC-IV), supporting early, late, and hybrid data fusion strategies. To …

Comprehensive Study Of Integrating Clustering And Adversarial Learning For Enhanced Recommender Systems: A Systematic Review Of Hybrid Methodologies And Applications, M. E. Alqaysi, Murtadha M. Hamad, Ahmed Subhi Abdalkafor

Mesopotamian Journal of CyberSecurity

Recommender systems (RSs) have become critical elements in modern instances of information and decision-support systems, resulting in a transformation of user experiences through highly personalized suggestions for an undeniably vast range of items. Although RSs have become commonplace, they continue to evolve, and their challenges, including sparsity, cold-start, scalability, and vulnerability to adversarial challenges remain. The use of clustering methods has proven highly effective in resolving these issues through the discovery and exploitation of latent user behaviour patterns, segmenting user groups that contribute more towards personalized and adaptable RSs. Additionally, adversarial learning has become a growing focus of study as …

Blockchain-Based Accountability: A Systematic Literature Review, Nabeel Z. Tawfeeq, Dujan B. Taha

Mesopotamian Journal of CyberSecurity

The Blockchain algorithm has advanced the accountability and transparency of modern digital infrastructures. Enforcing responsible behavior and data integrity across distributed environments involves several key components, such as smart contracts, access control models, cryptographic techniques, and a decentralized identity framework. Because the blockchain ledger is immutable and transparent, once a transaction is recorded, it cannot be altered without detection, making fraudulent actions easily traceable and thereby ensuring accountability. However, the need for hybrid approaches that combine on-chain and off-chain solutions for an efficient reliability system introduces challenges, including privacy preservation, scalability, and regulatory compliance. This paper analyzes the effective features …

A New Framework Enhancing, Evaluation, And Benchmarking For Cybersecurity Ddos Attack Detection Models Through The Integration Of Bwm And Vikor Methods, Alaa Mohammed Mahmood, İsa Avci, Sahar Yousif Mohammed

Mesopotamian Journal of CyberSecurity

The proposed work aims to develop a DDoS attack detection model that targets web servers by selecting the appropriate classifier based on several criteria and enhancing the accuracy. The detection system has been constructed using machine-learning algorithms to train and test the CIC-DDoS2019 dataset, and it is integrated with a stacked classifier to enhance accuracy. The classifiers depend on multiple criteria. For that, we employed the BWM to calculate the criteria weights and VIKOR to rank the classifiers, which are part of the MCDM methods. We consulted three experts to establish weights for the categories. Also, we utilized two methods …

Blockchain-Integrated Edge-Cloud-Enabled Healthcare Data Analytics Based On Distributed Federated Learning And Deep Neural Networks, Mazin Abed Mohammed, Mohd Khanapi Abd Ghani, Israa Badr Al-Mashhadani, Sajida Memon, Sajida Memon, Haydar Abdulameer Marhoon, Marwan Ali Albahar

Mesopotamian Journal of CyberSecurity

The exponential growth of healthcare Internet of Things (IoT) data necessitates secure, low-latency analytics that extend beyond centralized architectures. This paper presents BDAFL DNN, a blockchain-integrated data analytics framework that combines Federated Learning (FL) and Deep Neural Networks (DNNs) for real-time, privacy-preserving healthcare analytics across edge and cloud resources. Local devices such as smartwatches and phones collect noninvasive time series sensor streams (heart rate, temperature, and abdomen sensors), perform on device DNN training, and send only model updates to healthcare edge nodes, where a blockchain ledger validates updates for integrity and traceability; validated updates are then aggregated in the cloud …

Improvement Of The Face Recognition Systems Security Against Morph Attacks Using The Developed Siamese Neural Network, Sura Abed Sarab Hussien, Thair Abed Sarab Hussien, Nada Hussein M. Ali

Mesopotamian Journal of CyberSecurity

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility …

Global Research Trends And Collaboration In Mitre Att&Ck Framework: A Bibliometric And Network Analysis In Cybersecurity, Asad Maqsood, Syifak Izhar Hisham, Ahmad Firdaus

Mesopotamian Journal of CyberSecurity

The MITRE ATT&CK framework has become a foundational tool for organizing adversarial behaviors and techniques in cybersecurity. While its adoption in practice and academia has grown significantly, no prior bibliometric review has comprehensively mapped the global research landscape surrounding this framework. This study addresses that gap by conducting a systematic bibliometric and network analysis of publications related to ATT&CK indexed in Scopus from 2017 to 2025. Using VOSviewer and quantitative bibliometric methods, we analyzed 391 publications to identify trends in research output, influential works, key contributors, thematic areas, and patterns of collaboration. Results show exponential growth in studies related to …

Mitigating Zero-Day Vulnerabilities In Iiot Systems: Challenges And Advances In Ai-Powered Intrusion Detection Systems, Khalid Asaad Hashim, Yusnani Binti Mohd Yussoff, Shahrani Binti - Binti Shahbudin

Mesopotamian Journal of CyberSecurity

Zero-day attacks are one of the great challenges that intrusion detection systems have been facing and keep on facing today, especially worsening within Industrial Internet of Things environments since their ability to take advantage of unknown vulnerabilities results in a high rate of false negatives. It is under this framework that this paper presents a set of experiments that have been carried out with the objective of analyzing the consequences of zero-day attacks with regard to performance degradation in Intrusion Detection System (IDS) and, secondly, and with greater emphasis, about those failings which have been pointed out up to now …

A Proposed Algorithm For Avoiding Jammer In Structure-Free Wireless Sensor Networks, Zainab Shaker Matar Al-Husseini, Hussain K. Chaiel, Amel Meddeb, Ahmed Fakhfakh, Ahmed Fakhfakh

Mesopotamian Journal of CyberSecurity

Wireless Sensor Networks (WSNs) have emerged as a transformative technology with applications in critical and often inaccessible environments, including military and security domains. These networks comprise cooperative sensor nodes that gather and relay data to a base station. However, their inherent resource constraints—particularly the non-rechargeable nature of energy—pose a major challenge to network longevity and reliability. Conventional fixed-path transmission protocols exacerbate this issue, as energy-depleted or jammed nodes can disrupt communication, leading to partial or complete data loss. The primary objective of this study is to design and evaluate a structure-free transmission protocol that dynamically adapts data routing in order …

Associations Of Biomarkers Of Systemic Inflammation, Angiogenesis, And Cell-To-Cell Adhesion With Tumor Budding Among Early-Onset And Later-Onset Colorectal Cancer Patients, Oda Hausmann, Adetunji T Toriola, Et Al.

2020-Current year OA Pubs

BACKGROUND: High tumor budding and elevated systemic inflammation are adverse prognostic indicators in colorectal cancer. Its underlying mechanisms remain poorly understood. It is unclear whether systemic inflammation, angiogenesis, and cell-to-cell adhesion influence tumor budding.

METHODS: We investigated n = 132 stage I-III colorectal cancer patients recruited at Huntsman Cancer Institute enrolled in the ColoCare Study. Tumor budding was evaluated using an evidence-based scoring system, and patient sera were analyzed for nine circulating biomarkers using the Meso Scale Discovery platform. We examined associations between biomarkers and tumor budding using multivariable linear regression models adjusted for age, sex, neoadjuvant treatment, stage, and …

Minimally Invasive, Bioadaptive Multimodal Sensor Probe With Safe Deployment For Real-Time Acute Compartment Syndrome Diagnosis, Seung Gi Seo, Amanda M Westman, William Moritz, Joseph Ribaudo, Mitchell A Pet, Et Al.

2020-Current year OA Pubs

Acute Compartment Syndrome (ACS) is a serious medical condition that arises from increased pressure within osteofascial compartments, leading to impaired blood flow and potential tissue damage. Early and accurate diagnosis is critical for preventing permanent damage. Current methods rely largely on qualitative assessments with limited accuracy, and those that exploit invasive pressure measurements often prove inadequate. Herein, a soft materials-based multimodal sensor probe is introduced, as well as the mechanical and thermal influences to monitor intra-compartmental pressure, tissue oxygen saturation (StO

The Effects Of Covid-19 On Mice Tourism, Gülay Cakmak, Gurel Cetin, Cemil Kilic, Erol Sozen

Journal of Global Hospitality and Tourism

The sector of Meetings, Incentives, Conferences, and Exhibitions (MICE) tourism play an integral role in the tourism industry not only because of its volume but also per capita spending. MICE tourism is predominantly characterized by its international reach and the significant number of participants it attracts. However, Covid-19 pandemic precipitated a marked, leading to far-reaching ramifications and transformations. This study is focused on evaluating the impact of the Covid-19 pandemic on the MICE tourism sector. It aims to explore both the anticipated and the actual shifts within the industry amidst the pandemic. Adopting a qualitative research methodology, this investigation utilized …

Bridging The Digital Skills Gap: A Comparative Study Of Employability Readiness Among Tourism Students And Professionals In Malaysia, Asif Khurshid Mian, Fraz Arshed Butt, Omar Sajid

Journal of Global Hospitality and Tourism

The increasing digitalisation of the tourism industry has introduced new expectations for workforce competencies, particularly in developing economies like Malaysia. The present study investigates how three core digital competencies, i.e. digital literacy, platform/tool awareness, and technology readiness, influence employability readiness among two key groups including final-year tourism students and in-service professionals. Drawing on Human Capital Theory and the Technology Readiness Index, the research applies Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine 350 responses collected through purposive sampling. Results confirm that all three competencies positively influence employability readiness, with platform/tool awareness exerting the strongest effect. Multi-group analysis reveals that …

The Double-Edged Role Of Cognitive Reappraisal: A Moderated Mediation Model Of Manager Support And Emotional Exhaustion Among Hotel Supervisors, Han Chen, Yvette Green

Journal of Global Hospitality and Tourism

Applying the Conservation of Resources Theory and the Affective Event Theory, this study explores the interplay between perceived manager support (PMS), negative emotions, and emotional exhaustion among hotel supervisors. The study also examines the moderating role of cognitive reappraisal strategy on the above relationships. Data were collected from a sample of U.S. hotel supervisors. Results showed that negative emotions play a mediating role in the relationship between PMS and supervisors’ emotional exhaustion. Cognitive reappraisal amplified the negative effect of PMS on negative emotions, the positive relationship between negative emotions and emotional exhaustion, and the indirect effect of PMS on emotional …

Customer Food Intake: An Empirical Analysis Of Gender, Restaurant Use And Attitude Toward Fast Food Among Adolescents And Young Adults, In Nairobi, Kenya, Gerishom G. Madivoli, Rosemarie Ayuma Khayiya, Beatrice Kamau

Journal of Global Hospitality and Tourism

Fast food is the fastest- growing food category worldwide, providing young people and adolescents with a quick, cost- effective, and easily accessible alternative to home- cooked meals. There has been considerably less research into how gender, restaurant usage, and attitudes influence patterns of fast- food consumption. Moreover, the growth of the fast- food industry in every nation creates a significant environmental impetus for increasing fast food consumption and promoting unhealthy eating behaviours. If this issue is not addressed, the prevalence of lifestyle diseases such as obesity, along with the costs of managing these conditions, will escalate, particularly in Kenya. This …

Reframing Service Quality In Ai-Powered Hospitality Environments, Olena Ciftci

Journal of Global Hospitality and Tourism

The hospitality and tourism industry is experiencing a technological shift through the integration of artificial intelligence (AI), transforming both guest experiences and service operations. While AI offers hyper-personalization, efficiency, and scalability, it challenges traditional notions of service quality rooted in human interaction. This viewpoint explores the emerging concept of tech-augmented hospitality, where AI and human employees co-create guest experiences. The paper highlights key research directions for reconceptualizing service quality in the era of AI-enhanced hospitality. It argues for the development of new evaluative frameworks that encompass hybrid service models, cultural adaptability, inclusivity, and AI literacy.

Multi-Generational Biases Perceived By Women To Senior Leadership Positions In Non-Profit Higher Education, Regina Watkins, Deanna R. Davis

Journal of Research Initiatives

Women remain underrepresented in senior leadership in both public and private industry and in non-profit postsecondary education. One reason that has been identified for the gender disparity in senior leadership positions is second-generation or implicit biases. The problem addressed in this study was the existence and persistence of second-generation biases that contribute to the lack of active senior women mentors, potentially inhibiting junior women from Generation X and millennials from achieving senior leadership status in non-profit higher education. The purpose of this qualitative descriptive case study was to gain a deeper understanding of the significance of perceptions of second-generation biases, …

Like Playing Plinko: A Conceptual Framework For Student Financial Aid Communication, Alyssa Takatori, Karla A. Weber-Wandel, Z.W. Taylor

Journal of Research Initiatives

The process of applying for and receiving student financial aid is an incredibly complex process involving multiple stakeholders (students, institutions, parents, counselors), levels of information (federal, state, institutional, personal), and modes of communication (print, email, text message, social media, phone calls). Moreover, first-generation students, low-income students, and students of color are most likely to struggle with the financial aid application and awarding process, further marginalizing these students. To date, research has examined how complicated the financial aid process is, how to identify financial aid communication issues, and how to remediate financial aid communication to render it simpler and more intuitive …

Privacy-Preserving Ridge Regression Over Encrypted Data Under Multiple Keys, Yanling Li, Junzuo Lai, Meng Sun, Beibei Song, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the increase of private data being collected by data owners, it has been a trend for data owners to store the data on cloud computing platforms. The huge amounts of data in cloud servers bring fresh development opportunities to machine learning, which is applied to build a high-quality machine learning model based on a large training dataset. However, to ensure the privacy of data and facilitate retrieval, data owners often upload encrypted data under their public keys. But it creates new challenges for machine learning to learn a predictive model over these encrypted data under different keys. Most existing …

Map As A By-Product: Collective Landmark Mapping From Imu Data And User-Provided Texts In Situated Tasks, Ryo Yonetani, Kotaro Hara

Research Collection School Of Computing and Information Systems

This paper presents Collective Landmark Mapper, a novel map-as-a-by-product system for generating semantic landmark maps of indoor environments. Consider users engaged in situated tasks that require them to navigate these environments and regularly take notes on their smartphones. Collective Landmark Mapper exploits the smartphone's IMU data and the user's free text input during these tasks to identify a set of landmarks encountered by the user. The identified landmarks are then aggregated across multiple users to generate a unified map representing the positions and semantic information of all landmarks. In developing the proposed system, we focused specifically on retail applications and …

Guiding Multiple Remote Users In Physical Tasks With Language-Driven Robotic Telepresence, Ruyi Li, Jingfei Guo, Xinyi Zhang, Xuji Zhang, Zeqing Li, Jiannan Li, Jiangtao Gong

Research Collection School Of Computing and Information Systems

Remote assistance through robotic telepresence could involve both control and memory challenges, particularly in one expert to multiple workers situation. In this work, we proposed a novelty language-driven interface to facilitate remote collaboration through telepresence robots. Through operations and maintenance expert interviews and a scenario simulation study, we identified key pain points in executing one-expert-multiple-workers remote guidance using the telepresence robot and proposed two design goals, which together consist of five sub-design goals with corresponding features. These features were integrated into a standard telepresence robot, resulting in the development of a Collaborative LLM-based Embodied Assistant Robot, named CLEAR Robot. A …

Routine Antenatal Ultrasound By Nurse-Midwives In Rural Kenya: A Pragmatic Trial Assessing Feasibility And Effects Of The Mimba Yangu (My Pregnancy) Project, Claudia Hanson, Lucy Nyaga, Nidhi Leekha, Michaela Mantel, Sarah Kedenge, Caroline Gitonga, Violet Naanyu, Jasmit Shah, Marleen Temmerman

Centre of Excellence in Women and Child Health

Introduction: Task-sharing of obstetric ultrasound between nurse-midwives and doctors has the potential to operationalize the World Health Organization’s recommendation of at least one ultrasound before 24 weeks of gestational age for every pregnant woman. Here, we report on the feasibility, acceptability, and effects of the Mimba Yangu (My Pregnancy) task-sharing approach in rural Kenya.

Methods: We conducted a pragmatic trial including 28 primary care facilities between April 2021 and March 2022, selected based on feasibility criteria. Fourteen facilities received the ultrasound intervention composed of (i) task-sharing with nurse-midwives, (ii) the use of portable ultrasound devices (Lumify™) connected to a tablet, …

Reimagining U.S. Tort Law For Deepfake Harms: Comparative Insights From China And Singapore, Huijuan Peng, Pey Woan Lee

Research Collection Yong Pung How School Of Law

This Article explores how U.S. tort law can respond more effectively to the distinct harms posed by deepfakes, including reputational injury, identity appropriation, and emotional distress. Traditional tort doctrines, such as defamation, the right of publicity, and intentional infliction of emotional distress (IIED), remain fragmented and ill-suited to the speed, scale, and anonymity of deepfake dissemination. Using a comparative functionalist approach, the Article analyzes how China and Singapore respond to deepfake harms through structurally divergent but functionally instructive frameworks. China’s model combines codified personality rights with intermediary obligations under a civil law regime, while Singapore adopts a hybrid approach that …

Speculative Bubbles In The Recent Ai Boom: Nasdaq And The Magnificent Seven, Rerotlhe B. Basele, Peter C. B. Phillips, Shuping Shi

Research Collection School Of Economics

The recent artificial intelligence (AI) boom covers a period of rapid innovation and wide adoption of AI intelligence technologies across diverse industries. These developments have fueled an unprecedented frenzy in the Nasdaq, with AI-focused companies experiencing soaring stock prices that raise concerns about speculative bubbles and real-economy consequences. Against this background, this study investigates the formation of speculative bubbles in the Nasdaq stock market with a specific focus on the so-called Magnificent Seven (Mag-7) individual stocks during the AI boom, spanning the period from January 2017 to January 2025. We apply the real-time PSY bubble detection methodology of Phillips et …

Learning Camp, Language Of Instruction, And Education Outcomes: Evidence From A Field Experiment In Malawi, Hyuncheol Bryant. Kim, Kim

Research Collection School Of Economics

We conducted a randomized experiment to study the impacts of a summer learning camp and the language of instruction on education outcomes. The program, covering social studies and mathematics, provided additional high-quality learning time for 4th and 5th graders in Malawi. The program significantly increased test scores in social studies and mathematics (0.24–0.36 standard deviations). We find suggestive evidence that the impact on test scores in social studies was greater for students instructed in Chichewa, the local language, than for those instructed in English. However, we find no such evidence of differential impacts on test scores in mathematics by the …

Kegamangan Sistem Perlindungan Dan Pengelolaan Lingkungan: Melihat Uu Ciptaker Dari Perspektif Prinsip Non-Regresi, Andri G. Wibisana

Jurnal Hukum & Pembangunan

This article evaluates the Environmental Protection and Management Law (UUPPLH) as amended by the Omnibus Law on Job Creation (UUPPLH-Ciptaker) and its implementing regulations. The evaluation uses the normative framework of the principle of non-regression to assess whether the provisions of the UUPPLH-Ciptaker and its implementing regulations represent a setback compared to the legal conditions prior to the enactment of the Omnibus Law. This paper argues that the changes introduced by the UUPPLH-Ciptaker indicate several regressions in Indonesia’s environmental law, particularly in the following areas: a) the weakening of provisions on Environmental Impact Assessment (AMDAL) and Environmental Management and Monitoring …

Preparing The Next Generation Of Health Education Teachers For The Ethical Use Of Ai, Melissa J. Haithcox-Dennis, Reeshemah Johnson

Journal of Research Initiatives

This article examines how health education teacher preparation programs can prepare future educators to use artificial intelligence (AI) in responsible and effective ways. It begins with a brief overview of the use of AI in education, considering the growing impact of generative AI technologies on teaching and learning. The article outlines international and national standards that govern the application of AI in education, addressing key ethical concerns, including the dissemination of inaccurate health information, violations of student privacy, bias, and inequitable access to AI tools. The article emphasizes the importance of ethical decision-making and professional responsibility when integrating AI into …

Stand For Support, Shatter The Silence: Administrative Approaches To Eradicate Bullying In Higher Education, Elaine M. Mora, Shawn P. Saladin

Journal of Research Initiatives

Bullying in higher education has become a widespread and complex issue that affects staff, faculty, and students worldwide, causing a detrimental impact on both psychological health and academic achievement. This paper provides a comprehensive examination of the phenomenon, encompassing all its manifestations, underlying causes, and significant consequences. The study, which draws on academic literature, outlines the various forms of bullying, including cyberbullying, mobbing, microaggressions, and social exclusion, and highlights the adverse effects these behaviors have on both individuals and academic communities. The research emphasizes how critical it is to comprehend the power relationships that exist in higher education settings and …