Edge-Computing Frameworks for Real-Time Big Data Analytics in Smart Cities Waleed Noori Hussein, Bidoor Noori Ishaq, Sabah Adnan Chaseb, Khawlah Hashim Hussain Proceedings Icses 2026 5th International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems, 2026
Rabbit-256 Optimisation for Secure Blockchain Hashing in IoT-Healthcare Data Khalid Jadaa, Aymen Badr, Waleed Hussein, Latifah Kamarudin Al Khwarizmi Engineering Journal, 2025 The recent trend towards the use of a blockchain as a means to guarantee the security of health data has raised concerns with regard to its applicability in Internet of Things (IoT) scenarios due to computationally heavy primitives (e.g. hashing functions) and lack of scalability. As a solution to this problem, this article introduces Rabbit-256: an addition–rotation–XOR-based sponge construction derived from the Rabbit stream cipher that is twisted and adapted to a lightweight hash function, suitably adapted for distributed solutions in healthcare systems with a blockchain nature. Rabbit-256 is a lightweight encryption cipher that wears the mask of a hash function but with better diffusion and avalanche through an official buildup in Merkle trees. The presented system is evaluated using common cryptographic measures against SHA-256, i.e. grid operators of 100, 500, and 1000 inputs for the avalanche effect, Hamming distance, and mean standard deviation. We observe that Rabbit-256 exhibits a higher security margin and lower computational overhead, and thus, it is an optimal alternative to resource-constrained IoT systems given its resistance against attacks. Although the current work is developed in simulation, Rabbit-256 can be utilised for actual deployment to ensure the privacy of e-health records and medical sensor data in IoT and clinical services over a blockchain. In the future, we will focus on hardware design, energy efficiency, and integration (i.e. to be compliant with the Health Insurance Portability and Accountability Act in the U.S. and the General Data Protection Regulation in Europe).
Driving sustainable construction performance through green-oriented digital transformation and stakeholder-responsive innovation: the mediating role of organisational agility Hisham Noori Hussain Al-Hashimy, Jinfang Yao, Anwar Allah Pitchay, Waleed Noori Hussein Construction Innovation, 2025 Purpose This study aims to explore how green-oriented digital transformation (GDT) and stakeholder-responsive innovation (SRI) jointly influence sustainable construction performance (SCP), emphasising the dual role of organisational agility in innovation (OAI) as both a mediator and a moderator. It addresses the increasing environmental and digitalisation challenges facing China’s construction sector. Design/methodology/approach A quantitative, cross-sectional survey was administered to 173 senior managers, innovation specialists and project leaders from construction firms located in five of the most economically developed provinces in China. Structural equation modelling (SEM) was used to evaluate both the direct and indirect relationships among the key variables. Findings The findings reveal that OAI fully mediates the relationship between GDT and SCP and partially mediates the link between SRI and SCP. SRI also has a substantial direct impact on SCP, underscoring its importance. However, the moderating effect of OAI is nuanced and context-dependent, suggesting that OAI does not universally enhance SCP. Practical implications For construction firms, achieving sustainability goals requires more than isolated adoption of GDT or SRI. Embedding these within an agile organisational framework is essential. A nuanced, context-sensitive approach to OAI can better align internal innovation capabilities with external sustainability demands. Originality/value This study presents a novel empirical model that integrates GDT, SRI and OAI, highlighting OAI’s dual role in innovation processes. It provides actionable insights for firms in emerging markets aiming to align GDT and SRI to drive SCP strategically.
An overview of the diagnostic and prognostic values of biochemical markers in patients with COVID-19 Zainab A. Almnaseer, Amani Naama Mohammed, Ihsan Mardan Al-Badran, Hamid Jaddoa, Waleed Noori Hussein, et al. European Journal of Clinical and Experimental Medicine, 2025 Introduction and aim. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global pandemic that disrupts both public health operations and financial structures throughout the world. This article aims to evaluate the relationship between the severity of coronavirus disease 2019 (COVID-19) and its alterations in specific biomarkers such as D-dimer and C-reactive protein (CRP) together with alanine transaminase (ALT), aspartate aminotransferase (AST) and lactate dehydrogenase (LDH). Material and methods. Participants in this study were split into two groups consisting of 200 COVID-19 patients and 200 healthy controls ranging from 18 to 80 years old. Polymerase chain reaction and chest radiograph examinations were used to officially verify the participant’s diagnosis. This study used Mann-Whitney U tests combined with logistic regression and receiver operating characteristic (ROC) curves to identify and determine their value as diagnostic tools and prognostic indicators. Results. D-dimer and CRP along with ALT, AST, and LDH demonstrated significant and elevated levels in COVID-19 infected participants when analyzed against control participants (p<0.0001). D-dimer emerged as a diagnostic biomarker according to ROC analysis with an AUC value of 0.96 and a p-value<0.001 which signified its quality for the evaluation of the severity of the disease. The additional biomarkers AST and LDH received AUC scores of 0.79 and 0.76, respectively,,with ALT reaching an AUC value of 0.74. Conclusion. The combination of the biochemical markers D-dimer, AST, and LDH significantly improves risk assessment while enhancing predictions about disease outcomes. These biomarkers provide vital data for early disease detection in combination with disease progression through tracking patient outcomes and therapeutic planning assessments.
Exploring Technological Success Factors of Big Data in E-Learning Systems Waleed Noori Hussein, Khalid Jamal Jadaa, Haider Noori Hussain Aiccit 2023 Al Sadiq International Conference on Communication and Information Technology, 2023 Big data plays an important role in the development of e-learning systems. There are many factors affecting its implementation and success with e-learning systems. This study aims to identify the implementation success factors related to big data within e-learning systems. To identify these elements, a comprehensive examination of the literature was done by using exploratory and single-case research approaches, the study used Basrah University as the case study to assess the success factors of big data in their e-learning system. Additionally, survey and expert interviews were conducted to validate the literature review’s conclusions and identify further factors. The collected data were analyzed using NVivo software to identify themes and sub-themes. To assess the quantitative survey data, machine learning methods are combined with qualitative analysis using a Random Forest Classifier. The finding showed that five factors should be considered which can aid in the creation of more efficient e-learning systems namely: Positive Impact on Students, Faculty/Staff Support and Training, Effective Content Design, System Functionality and Usability, and Assessment and Feedback. The results of this study can be used to improve learning outcomes by developing more efficient big data analytics-integrated e-learning systems.
